For tissue sample, Click Here for the product page.
For cell sample by plate reader, see information down below.
This product is a simple detection kit by plate assay for senescence-associated β-galactosidase (SA-β-gal) activity which is used as a marker for senescent cells. By simply adding SPiDER-βGal, a reagent for detection of β-galactosidase, to 96 well plates, this kit allows you to quantify SA-β-gal activity and makes it possible to evaluate multiple specimens.
When normalization is done by the results obtained by counting cells, quantifying nucleic acids (the relevant product), or quantifying proteins, the measured values obtained using this kit become available for evaluating SA-β-gal activity according to cell number.
Cells prepared in advance are lysed in the buffer supplied with this kit.Fluorescence intensity is obtainable according to SA-β-gal activity simply by adding the fluorescent substrate SPiDER-βGal to the cell lysate.
Even when you prepare cells in 100 mm dishes or others, fluorescence intensity can be measured by transferring cell lysate in 96 well plates after cell lysis.
As a result, it was confirmed that in both kits, SA-β-gal staining increased in the high-passage WI-38 cells.
Bear in mind that although initial cell seeding densities are the same, cell densities at the time of plate assay differ due to low proliferation rate of senescent cells at higher passage levels. Therefore, in this experiment, we used SA-β-Gal activity values normalized by the results obtained using the Cell Count Normalization Kit (coming soon) in which cell number is determined by a nuclear marker.
We performed plate assays using this kit with WI-38 cells after adding doxorubicin which increases the production of mitochondrial reactive oxygen species (ROS). As a result, it was confirmed that the addition of doxorubicin to WI-38 cells caused increased staining intensity of SA-β-gal due to DNA damage-induced senescence.
Cell counts may need to be normalized. When cells are analyzed in a microplate, the results obtained may sometimes differ depending on cell numbers per well.In such cases, normalization of the measured values obtained from cell counting and total protein will be necessary. In this kit, cell numbers can be easily measured by the fluorescence intensity induced by a reagent added to cell culture medium for staining nuclei.


text:
<?php

namespace League\OAuth2\Client\Grant;

class IgRefreshToken extends AbstractGrant
{
    /**
     * @inheritdoc
     */
    public function __toString()
    {
        return 'ig_refresh_token';
    }

    /**
     * @inheritdoc
     */
    protected function getRequiredRequestParameters()
    {
        return [
            'access_token',
        ];
    }

    /**
     * @inheritdoc
     */
    protected function getName()
    {
        return 'ig_refresh_token';
    }
}

redpajama_set_name: RedPajamaGithub


text:
You are here: Home > Recreation > What's Happening
See All Explore Marin
See All Parks in Marin
See All Arts and Theaters
See All What's Happening
All Recreation Services
Calendar of events happening in Marin County, California, including fairs, meetings, art exhibits, and more.
The Marin County Farmers Markets allow local producers to sell their certified commodities directly to the public. The following farmers markets have been certified by the Agricultural Commissioner to market local produce in Marin County.
Calendar of events happening at the Marin County free libraries.
Calendar of events happening at the Marin Center, including fairs, meetings, art exhibits, and more.
Join us and celebrate the Marin County Fair with friends and family!
Parks & Open Space Calendar
Marin County Parks and Open Space calendar of events

redpajama_set_name: RedPajamaCommonCrawl


If you search for Personal Training Revere MA you will discover Assembly Sports Club a high end fitness gym without the high cost, located near Revere MA. Assembly Sports Club is a spacious, state-of-the-art fitness center that offers affordable gym memberships. It's a place where you can clear your mind, de-stress, and focus on improving your health and wellness.


text:
Ana Kalandadze (gruz. ანა კალანდაძე, ur. 15 grudnia 1924 we wsi Chidistawi k. Czochatauri, zm. 11 marca 2008 w Tbilisi) – gruzińska poetka.

Życiorys
W 1946 ukończyła studia na Wydziale Filologicznym Tbiliskiego Uniwersytetu Państwowego im. Iwane Dżawachiszwilego, w tym samym roku opublikowała swoje pierwsze wiersze, a w 1953 wydała swój pierwszy zbiór wierszy pt. Leksebi. Kolejne zbiory wierszy wydała w 1957, 1960 i 1967. Uprawiała lirykę intelektualno-filozoficzną, w swojej twórczości podejmowała m.in. tematykę polską. Przekłady jej poezji na język polski ukazały się w Antologii poezji gruzińskiej (1961), Antologii poezji radzieckiej (1979) i w antologii Poezja gruzińska (1985). Została pochowana w Panteonie Mtacminda. Jej śmierć ówczesny prezydent Gruzji Micheil Saakaszwili skomentował słowami, że gruzińska literatura nie poniosła takiej straty od śmierci Galaktiona Tabidze.

Przypisy

Bibliografia

Gruzińscy poeci
Urodzeni w 1924
Zmarli w 2008

redpajama_set_name: RedPajamaWikipedia


Home Flow Business Artificial Intelligence in Fractal Economy; Skynet Bots Takes Over World of Finance?
The significant advancements in technology and the connectedness of the world have made the financial environment more complicated than it already is. There are billions of transactions everyday from payments, investments and other enterprises all over the world and these convoluted schemes are the core of the fractal economy. With more money being invested in developing efficient Artificial Intelligence, it seems as if the world of finance is shifting to Skynet type bots to assist in the ever increasing data volumes required in the fractal ecosystem.
According to reports, there are currently 2.5 exabytes worth of data produced daily. That number will increase to 44 zettabytes daily come 2020, Tech Crunch reported. These ever rising umbers makes the foundation for an AI-powered future.
Since 2010, $2.1 billion has already been invested in startup companies focused on AI infrastructure. $1.2 billion of that investment were recorded in 2015 alone. Meanwhile, $6.9 billion has been invested in startup companies focused on AI applications since 2010.
These investments show that there is a movement towards AI development to help address the problem in data volumes and the dynamic technical landscape in the Fractal economy.
This shift into the AI has some doomsday sayers that it could lead to a Skynet-like system that might wipe out all of humanity. Skynet is the tech company in the Terminator franchsie that uses AI. Of course, these are all just doomsday sayers paranoid beliefs.
There are about 500 million people leveraging bots and other digital assistants to help them in their everyday activities. This number is expected to increase to 2.2 billion come 2020. This will minimize human-tohuman transactions, especially in the banking industry.
Master Card recently revealed their Decision Intelligence, which is an app that uses AI to avoid embarrassing false declines when swiping credit cards in stores, Investopedia reported. Apparently Master Card has been using an old method in denying credit cards. According to Ernst & Young's Todd Marlin, these rules-based systems on credit cards increase the probability of false positives.
This is why a lot of institutions are shifting to AI to help them solve this issue. With Artificial Intelligence taking over the Fractal Economics, its seems a gold rush is happening into the AI, according to Marlin.


Dr. Lindsey May began her career in dentistry in 2004 as an assistant at Langworthy Family Dental.
She then attended the University of Iowa College of Dentistry from 2005-2009 earning her DDS with focus on pediatric dentistry.
While Dr. Lindsey has considered a pediatric specialty, it is her love for adult dentistry that makes her the general family dentist she is.
Dr. Lindsey also takes special interest in those suffering from dental anxiety. Her undergraduate studies of psychology lend itself very useful in this particular field. Dr. Lindsey is looking forward to serving the tri-states for years to come!


The International Centre for Human Development (IC4HD) has announced human development fellowships for young professionals for 2014. The fellowships offered by the Centre support innovative analytical work on major human development issues. The theme of this year's fellowships is social security in the Global South. IC4HD is a joint initiative of the Government of India and UNDP.
Fellowships are open to young professionals including academics, civil servants and civil society practitioners. The selected fellows will receive INR 50,000 per month, and will be hosted by the Indian Institute of Advanced Studies in Shimla, India, for three months.
The deadline for submitting applications is 15 June 2014. As the Indian Institute of Advanced Study organizes several national-level seminars every year, on themes of pressing contemporary relevance as well as of fundamental theoretical significance, the fellowship provides the opportunity to interact with several scholars.
Project proposal, not exceeding 1,200 words (which does not involve fieldwork with data collection) that highlights the main contours of the research to be undertaken during the tenure of the Fellowship which is residential. The questions that the research aims to address, in line with the theme of the Fellow-ship, and the reasons behind their selection should be clearly stated.


text:
Vikings Defense Frustrated Despite Valiant Performance in Seattle
SEATTLE — Don't let the score fool you, the Vikings defense did all it could to get Minnesota a win Monday night.
The unit shut down Seahawks quarterback Russell Wilson through the air and were stout on third down and in the red zone, but the Vikings fell 21-7 on the road to the Seahawks.
"Yeah, I thought we played our rear ends off on defense tonight," said Vikings Head Coach Mike Zimmer. "So yeah, it was discouraging."
Added Vikings defensive end Danielle Hunter, who recorded a key sack of Wilson early: "It's very frustrating. We lost, but there's nothing we can do about it now but move into the next game."
Minnesota's defense allowed just 14 of the 21 points on the scoreboard, as Seattle had a defensive touchdown.
And with three minutes left in regulation, the Vikings had held Seattle to six measly points.
Vikings quarterback Kirk Cousins was adamant that Minnesota's defense played well enough for Minnesota to get a win in a tough environment on the road.
"I think the added piece of the disappointment is when your defense is playing so well," Cousins said. "Our defense, I feel, the last couple weeks or the last several weeks, really, has played playoff football, high-level football, and put us in a position where we really should and can be in the playoff conversation as a result of the way they're playing defense.
"You know that if our offense would bring it the way we brought it in some of the earlier weeks of the season, we can be pretty dangerous," Cousins added. "Unfortunately we haven't really put the whole complementary football together enough this season to be really be the team we know we're capable of. That'll be the challenge these last three weeks."
Minnesota's offense had 276 yards of offense but was held to 110 yards through three quarters and didn't score until there were 70 seconds remaining.
The Vikings defense limited Seattle to just two conversions on 10 third-down tries, and Seattle was just one of four in red-zone efficiency.
Minnesota allowed 274 total yards of offense to Seahawks, which was Seattle's lowest output of the season.
The Vikings surrendered 214 of those yards on the ground on 42 attempts, which made it appear that Seattle dominated on the ground.
But Wilson had a 40-yard scramble late in the game, and Seahawks running back Rashaad Penny had a 17-yard run on a broken play in the second quarter that ballooned Seattle's stats on the ground.
While the numbers may look inflated, the Vikings defense felt they did well against the NFL's top rushing attack.
"I know it was a lot of yards, but the quarterback had 60 or something," Zimmer said. "The one runner we had bottled up, and he ran back the other way, and that was probably a 20- or 30-yard run.
"Early in the game, I didn't think we, you know, they had a lot of second-and-5s. After halftime, it was second-and-8s, second-and-9s. So honestly, I didn't think we played the run that bad," Zimmer added. "They're a good running football team, and [Chris Carson is] a good, hard-running back. So if you don't get in there and get physical with him, they're going to gain some yards."
Added Weatherly: "I mean, on paper that's what it is. But if you look at two key run plays … when (Penny) reversed the ball on that toss on the outside stretch, and then Russell got out of the pocket and ran … subtract those yards, and I thought those were your true [yards] when they wanted to run the ball. Regardless, we can't give up that many rushing yards."
The Vikings trailed 3-0 at the half but secured their lone turnover when linebacker Eric Kendricks intercepted Wilson on the penultimate play of the second quarter.
But with a clear field in front of him, Kendricks caught the ball but couldn't keep his balance.
Monday Night Football: Vikings-Seahawks
View game action images as the Vikings take on the Seahawks under the lights for Monday Night Football at CenturyLink Field.
Dane Kuhn // Minnesota Vikings
Travis Ellison / Minnesota Vikings/Travis Ellison / Minnesota Vikings
Travis Ellison/Travis Ellison
Did he think he should have scored on an 86-yard interception return?
"Absolutely. I feel like I should have scored," Kendricks said. "I want to be great, and if I want to be one of the great linebackers in this league, then I have to score there."
"I just bobbled it. Maybe I was running before I caught it," Kendricks added. "I was happy we stopped that play. But I feel like if I score on that play then we get a little more momentum going into the half. That's my fault."
Wilson completed just 10 of 20 passes for 72 yards and had a passer rating of 37.9. He had 61 yards rushing.
The Vikings now sit at 6-6-1 on the season as they head home to face Miami in Week 15 at U.S. Bank Stadium.
Minnesota would still be the No. 6 seed in the Wild Card race if the season ended today, but the Vikings know there is still plenty of work to be done over the final three weeks of the 2018 season.
"Looking at where we are now, we still have a chance to accomplish the things we want to accomplish," said safety Anthony Harris. "It's just all about coming together and pushing through and finding a way to come out with a W.
"We're all one team and all under one roof," Harris added. "There might be frustration sometimes, but at the end of the day, we all look at each other as brothers, and we're all trying to accomplish one goal. The only way we can do that is to help each other get better each week."
Added Weatherly: "It's time to learn from it and then forget about it as quickly as possible so you can go into the next week. We need to have two wins in these last three games to control our destiny."


text:
If You've Ever Had Lyme Disease, Blame the Anti-Vaxxers
Lyme disease has been spreading for years, and thanks to global warming it's poised to explode over the next few years. This map is from New Scientist:
That's bad. But it turns out there's a vaccine for Lyme disease. Or I guess I should say, there used to be a vaccine for Lyme disease. In 1998 the FDA approved a a drug called Lymerix, and it was pretty effective until the chronic Lyme crowd and the anti-vaxxers started ranting:
Influenced by now-discredited research purporting to show a link between the MMR vaccine and autism, activists raised the question of whether the Lyme disease vaccine could cause arthritis. Media coverage and the anti-Lyme-vaccination groups gave a voice to those who believed their pain was due to the vaccine, and public support for the vaccine declined.
"The chronic arthritis was not associated with Lyme," says Stanley Plotkin, an adviser to pharmaceutical company Sanofi Pasteur. "When you're dealing with adults, all kinds of things happen to them. They get arthritis, they get strokes, heart attacks. So unless you have a control group, you're in la-la land."
But there was a control group – the rest of the US population. And when the FDA reviewed the vaccine's adverse event reports in a retrospective study, they found only 905 reports for 1.4 million doses. Still, the damage was done, and the vaccine was benched.
All of you who have had Lyme disease should know this. You could have avoided it if not for the ravings of the anti-vax nitwits and the gullibility of the mainstream TV talkers who give them a platform. It's long past time to put an end to this idiocy.
But I won't leave you without some good news. First, although you can't vaccinate yourself, you can vaccinate your dog. So there's that. Second, a French company has developed an even better Lyme vaccination, and it should be ready in 2023. That's a mere six years. Just be patient, OK?


text:
More Support for Northwood
Government and the Nova Scotia Health Authority (NSHA) are working to help Northwood implement an emergency plan to respond to the COVID-19 outbreak at its Halifax Campus.
Starting today, April 19, recovered patients are being moved offsite, additional staff are coming in from across the province, and residents are being tested and treated onsite at the facility.
"Residents and families with loved ones at Northwood can be assured that we are on this," said Premier Stephen McNeil. "I want to thank all of the health-care workers and everyone volunteering to support Northwood during this critical time. Our health-care unions have been very supportive and are working hard to ensure their workers are also protected. Thank you is not enough for what you, the front-line workers, are doing."
The emergency plan includes:
NSHA and Department of Health and Wellness onsite at Northwood to support its response
establishing a Northwood COVID-19 Recovery Unit at a local hotel to care for recovered residents; first resident moved today, April 19
equipment to support recovery unit donated by Shannex, including electric beds, tables and other supplies
support from many organizations to ensure sufficient staff to care for residents, manage operations and relieve current staff. Includes NSHA, college and university students in related programs, Victorian Order of Nurses, Red Cross, Emergency Health Services, government staff and staff from other long-term care facilities
redeploying the Halifax Infirmary COVID-19 unit to Northwood to treat COVID-19 positive residents onsite
establishing a team onsite to conduct testing for COVID-19
"I want residents, families and staff to know that we're here to support them and work with them to bring the virus under control at this facility," said Dr. Robert Strang, Nova Scotia's chief medical officer of health. "Northwood is an excellent operator and I have confidence in their work. But this is a very large facility, the virus is circulating broadly, many staff and residents have tested positive and they need our help."
Nova Scotia has recorded nine deaths related to COVID-19 and a total of 675 positive test results. Confirmed cases range in age from under 10 to over 90. Eleven individuals are currently in hospital, four of those in ICU. Two hundred individuals have now recovered and their cases of COVID-19 are considered resolved. Cases have been identified in all parts of the province. A map and graphic presentation of the case data is available at https://novascotia.ca/coronavirus/data .
If you have two or more of the following symptoms, visit https://811.novascotia.ca/ to determine if you should call 811 for further assessment:
We are so grateful for the support we've received from our health-care colleagues, so we can continue to focus on the care of our residents. With the emergency measures that are being put in place, these additional resources will help our team focus on what is most important – providing quality care for our residents. We remain committed to ensuring those in our care continue to receive the best care possible. Janet Simm, CEO, Northwood
the Halifax Campus of Northwood has 485 residents and 600 staff
Northwood: https://nwood.ns.ca/


text:
Systems and methods for managing accounts
A system and method is provided for determining whether to contact a party associated with an account. The disclosed system places a value on an account, estimates a cost in contacting the account holder or customer, and attempts contact if the account value exceeds the estimated cost. The account value is an amount the customer likely would pay if he were contacted or agreed to make a purchase. It is based on the customer's financial situation and credit history. The cost in contacting a customer is determined from a probability of contacting the customer. The probability is based largely on the customer's demographic information, including the mobility of the population in his demographic area and the number of people having telephone service in that area. The system determines that a customer will be hard to contact if he lives in a highly mobile area or in an area wherein most of the population lacks telephone service. The system further determines the number of times it may attempt to contact the customer before the cost of contacting the customer exceeds the value of the account.
Lambiotte, Kenneth G. (Richmond, VA, US)
Arnett, David L. (Boise, ID, US)
Degeorge, Alan G. (Boise, ID, US)
LAMBIOTTE KENNETH G.
ARNETT DAVID L.
DEGEORGE ALAN G.
G06Q10/04; (IPC1-7): G06F17/60
20040249747 Signature verification apparatus and method December, 2004 Ramian
20050251459 System and method for buying goods using number based domain and mobile internet November, 2005 Kim et al.
20090018918 Influence-based Social Network Advertising January, 2009 Moneypenny et al.
20020190862 Methods of managing the transfer, use, and importation of data December, 2002 Berquist et al.
20080027805 Mutually beneficial management system for website advertisements January, 2008 Chang
20050137927 System and method for multi-site workforce deployment June, 2005 Jura
20030195845 Method of conducting business among entities participating in a system for distributed network authentication, access and aggregation October, 2003 Francis Jr. et al.
20040153359 Integrated supply chain management August, 2004 Ho et al.
20060282356 System and method for structured put auction rate combination structure December, 2006 Andres et al.
20040236688 Universal positive pay database method, system, and computer useable medium November, 2004 Bozeman
20080228668 SYSTEM FOR SETTING SPECIAL WHOLESALE PRICING FOR RESELLERS September, 2008 Melzer
DASS, HARISH T
FINNEGAN, HENDERSON, FARABOW, GARRETT & DUNNER (WASHINGTON, DC, US)
What is claimed is:
1. A method for managing an account to determine whether to contact a party associated with the account, said method comprising: determining an account value of the account based on a likelihood of receiving a payment on the account and an amount likely to be received from the associated party when the associated party is contacted; determining an account cost based on a likelihood of contacting the associated party, wherein the account cost represents the cost of obtaining payment from the associated party; and determining whether to contact the party associated with the account based on a comparison of the determined account value and the determined account cost.
2. The method of claim 1, wherein determining an account value includes: determining the likelihood of receiving the payment on the account from the associated party; determining the amount that the associated party is likely to pay on the account; and combining the likelihood of receiving payment and the amount that the associated party is likely to pay on the account into an overall account score reflecting the account value.
3. The method of claim 2, wherein determining the likelihood of receiving payment is based on financial information of the associated party.
4. The method of claim 2, wherein determining the likelihood of receiving payment further includes: analyzing whether payment was received on other accounts from parties associated with those other accounts; and determining the likelihood of receiving payment from the associated party based on the analysis of the other accounts.
5. The method of claim 2, wherein determining the likelihood of receiving payment from the associated party further includes: using a logistic regression model to describe the likelihood of receiving payment based upon predefined account criteria; applying information about the account to the logistic regression model to determine a likelihood score; and determining the likelihood of receiving payment based upon the determined likelihood score.
6. The method of claim 2, wherein determining the amount likely to be paid is based on financial information of the associated party.
7. The method of claim 2, wherein determining the amount likely to be paid further includes: analyzing whether amounts paid on other accounts from parties associated with those other accounts; and determining the amount likely to be paid based on the analysis of the other accounts.
8. The method of claim 2, wherein determining the amount likely to be paid further includes: using a linear regression model to describe the amounts likely to be paid on particular accounts based upon predefined account criteria; applying information about the account to the linear regression model to determine a likelihood score; and determining the amount likely to be paid based upon the determined likelihood score.
9. The method of claim 1, wherein determining the account cost includes: determining the likelihood of contacting the associated party based on a demographic area in which the associated is located.
10. The method of claim 9, wherein determining the likelihood of contacting the associated party further includes: using a logistic regression model to describe the likelihood of contacting a party located in a particular demographic area; applying information about the demographic area of the associated party to the logistic regression model to determine a likelihood score; and determining the likelihood of contacting the associated party based upon the determined likelihood score.
11. The method of claim 1, further including: decreasing the likelihood of contacting the associated party with each attempt at contacting the associated party.
12. The method of claim 1, wherein determining an account cost includes: finding a higher account cost when the likelihood of contact is low and a lower account cost when the likelihood of contact is high.
13. The method of claim 1, wherein determining whether to attempt contacting the party includes: determining whether the determined account value exceeds the determined account cost; and attempting to contact the associated party when the determined account value exceeds the account cost.
14. The method of claim 13, further including: determining a number of contact attempts that can be made before the account cost exceeds the account value.
15. A computer program product for managing an account to determine whether to contact a party associated with the account, the computer program product comprising computer-readable media having computer-readable code, the computer program product comprising the following computer-readable program code for effecting actions in a computing platform: program code for determining an account value of the account based on a likelihood of receiving a payment on the account and an amount likely to be received from the associated party when the associated party is contacted; program code for determining an account cost based on a likelihood of contacting the associated party, wherein the account cost represents the cost of obtaining payment from the associated party; and program code for determining whether to contact the party associated with the account based on a comparison of the determined account value and the determined account cost.
16. The computer program product of claim 15, wherein the program code for determining an account value includes: program code for determining the likelihood of receiving the payment on the account from the associated party; program code for determining the amount that the associated party is likely to pay on the account; and program code for combining the likelihood of receiving payment and the amount that the associated party is likely to pay on the account into an overall account score reflecting the account value.
17. The computer program product of claim 16, wherein the program code for determining the likelihood of receiving payment is based on financial information of the associated party.
18. The computer program product of claim 16, wherein the program code for determining the likelihood of receiving payment further includes: program code for analyzing whether payment was received on other accounts from parties associated with those other accounts; and program code for determining the likelihood of receiving payment from the associated party based on the analysis of the other accounts.
19. The computer program product of claim 16, wherein the program code for determining the likelihood of receiving payment from the associated party further includes: program code for a logistic regression model describing the likelihood of receiving payment based upon predefined account criteria; program code for applying information about the account to the logistic regression model to determine a likelihood score; and program code for determining the likelihood of receiving payment based upon the determined likelihood score.
20. The computer program product of claim 16, wherein the program code for determining the amount likely to be paid is based on financial information of the associated party.
21. The computer program product of claim 16, wherein the program code for determining the amount likely to be paid further includes: program code for analyzing whether amounts paid on other accounts from parties associated with those other accounts; and program code for determining the amount likely to be paid based on the analysis of the other accounts.
22. The computer program product of claim 16, wherein the program code for determining the amount likely to be paid further includes: program code for a linear regression model describing the amounts likely to be paid on particular accounts based upon predefined account criteria; program code for applying information about the account to the linear regression model to determine a likelihood score; and program code for determining the amount likely to be paid based upon the determined likelihood score.
23. The computer program product of claim 15, wherein the program code for determining the account cost includes: program code for determining the likelihood of contacting the associated party based on a demographic area in which the associated is located.
24. The computer program product of claim 23, wherein the program code for determining the likelihood of contacting the associated party further includes: program code for a logistic regression model describing the likelihood of contacting a party located in a particular demographic area; program code for applying information about the demographic area of the associated party to the logistic regression model to determine a likelihood score; and program code for determining the likelihood of contacting the associated party based upon the determined likelihood score.
25. The computer program product of claim 15, further including: program code for decreasing the likelihood of contacting the associated party with each attempt at contacting the associated party.
26. The computer program product of claim 15, wherein the program code for determining an account cost includes: program code for finding a higher account cost when the likelihood of contact is low and a lower account cost when the likelihood of contact is high.
27. The computer program product of claim 15, wherein the program code for determining whether to attempt contacting the party includes: program code for determining whether the determined account value exceeds the determined account cost; and program code for attempting to contact the associated party when the determined account value exceeds the account cost.
28. The computer program product of claim 27, further including: program code for determining a number of contact attempts that can be made before the account cost exceeds the account value.
29. A system for managing an account to determine whether to contact a party associated with the account, said system comprising: means for determining an account value of the account based on a likelihood of receiving a payment on the account and an amount likely to be received from the associated party when the associated party is contacted; means for determining an account cost based on a likelihood of contacting the associated party, wherein the account cost represents the cost of obtaining payment from the associated party; and means for determining whether to contact the party associated with the account based on a comparison of the determined account value and the determined account cost.
30. The system of claim 29, wherein the means for determining an account value includes: means for determining the likelihood of receiving the payment on the account from the associated party; means for determining the amount that the associated party is likely to pay on the account; and means for combining the likelihood of receiving payment and the amount that the associated party is likely to pay on the account into an overall account score reflecting the account value.
31. The system of claim 30, wherein the means for determining the likelihood of receiving payment is based on financial information of the associated party.
32. The system of claim 30, wherein the means for determining the likelihood of receiving payment further includes: means for analyzing whether payment was received on other accounts from parties associated with those other accounts; and means for determining the likelihood of receiving payment from the associated party based on the analysis of the other accounts.
33. The system of claim 30, wherein the means for determining the likelihood of receiving payment from the associated party further includes: means for using a logistic regression model to describe the likelihood of receiving payment based upon predefined account criteria; means for applying information about the account to the logistic regression model to determine a likelihood score; and means for determining the likelihood of receiving payment based upon the determined likelihood score.
34. The system of claim 30, wherein the means for determining the amount likely to be paid is based on financial information of the associated party.
35. The system of claim 30, wherein the means for determining the amount likely to be paid further includes: means for analyzing whether amounts paid on other accounts from parties associated with those other accounts; and means for determining the amount likely to be paid based on the analysis of the other accounts.
36. The system of claim 30, wherein the means for determining the amount likely to be paid further includes: means for using a linear regression model to describe the amounts likely to be paid on particular accounts based upon predefined account criteria; means for applying information about the account to the linear regression model to determine a likelihood score; and means for determining the amount likely to be paid based upon the determined likelihood score.
37. The system of claim 29, wherein the means for determining the account cost includes: means for determining the likelihood of contacting the associated party based on a demographic area in which the associated is located.
38. The system of claim 37, wherein the means for determining the likelihood of contacting the associated party further includes: means for using a logistic regression model to describe the likelihood of contacting a party located in a particular demographic area; means for applying information about the demographic area of the associated party to the logistic regression model to determine a likelihood score; and means for determining the likelihood of contacting the associated party based upon the determined likelihood score.
39. The system of claim 29, further including: means for decreasing the likelihood of contacting the associated party with each attempt at contacting the associated party.
40. The system of claim 29, wherein the means for determining an account cost includes: means for finding a higher account cost when the likelihood of contact is low and a lower account cost when the likelihood of contact is high.
41. The system of claim 29, wherein the means for determining whether to attempt contacting the party includes: means for determining whether the determined account value exceeds the determined account cost; and means for attempting to contact the associated party when the determined account value exceeds the account cost.
42. The system of claim 41, further including: means for determining a number of contact attempts that can be made before the account cost exceeds the account value.
DESCRIPTION OF THE INVENTION
[0001] The present invention relates generally to systems and methods for managing accounts, and more particularly to systems and methods for prioritizing accounts for contacting customers associated with those accounts.
[0002] Whether trying to contact a customer to sell a product or service, or to recover on a debt owed, a problem occurs when the money spent contacting the customer exceeds the money received. For example, a telemarketer may spend $10 trying to sell a product for $3, thus incurring a $7 loss even if the customer buys the product. Similarly, a customer may agree to purchase an offered product, only to deny payment at a later date. This action may create a loss if the sum of the money spent contacting the customer and the cost of the unpaid portion of the product exceeds any money eventually received.
[0003] Although telemarketers attempt to maximize profits by targeting customers who are likely to accept an offer, their attempt to predict an acceptance is simplistic and they do not consider the cost of contacting a customer or the amount a customer will likely pay for the offered product when determining whether to contact that customer. For example, a telemarketer may purchase lists of potential customers from a vendor and then purchase information from a credit bureau, for example, for each customer included in the list. The telemarketer may use a variable, such as income, to determine whether a customer is likely to accept an offer. For example, if a customer's income is above a certain level, the telemarketer may determine that a customer is likely to accept an offer. This type of model does not accurately predict whether a customer will accept an offer, because it is overly simplistic. Further, because the telemarketer does not consider the cost of contacting the client or the likelihood of the customer making payment, the telemarketer may incur a loss when, for example, selling to a customer with a pattern of delinquent payments or trying to sell to a customer who is difficult to contact.
[0004] Similarly, recovery services (i.e., services that attempts to collect debt which has been overdue for a lengthy period of time) do not weigh the amount of debt they are likely to collect against the cost of obtaining payment when deciding whether to contact a customer for payment. In fact, recovery services do not determine the probability of a customer making a payment. As described herein, a recovery service may also include a collection service or any other service used to collect debt on overdue payments.
[0005] These recovery services often fail to collect much of the debt from the accounts it seeks repayment. Although certain accounts are likely to pay, current recovery services do not adequately target those accounts for the reasons given above. Accordingly, such a service may spend more than the value of the debt it collects when attempting debt recovery.
[0006] Currently, many recovery services employ an age-value method to exclude certain accounts from recovery attempts. Before seeking repayment, the recovery service may obtain information about the account from a financial institution. This information may include the balance owed on the account, the age of the debt, and the address and telephone number of the account holder. The age-value method employed by the recovery service sets an age threshold for the debt and a value threshold for the account balance using this information. The method makes the following assumptions: (1) an older debt is less likely to be re-payed; and (2) small balances are not worth the expense of collection.
[0007] However, this method has several drawbacks. First, the method typically excludes only about 5% of accounts. Therefore, the recovery service still must attempt to collect from nearly every account, even though only a small percentage of accounts are likely to pay. Accordingly, the recovery service does not effectively reduce its expenses in seeking recovery. Second, the method uses factors (e.g., age of the debt and account balance) that do not accurately predict which accounts are likely to pay. Therefore, even the modest reduction of 5% does not adequately focus the recovery service on the optimal accounts. Finally, the method does not consider the difficulties, and hence the cost, in contacting specific account holders. For example, an account holder may be likely to pay, but difficult to reach because of outdated contact information. In such cases, the recovery service may spend more money in trying to locate the individual than it may eventually recover.
[0008] In view of the foregoing, there is presently a need for an improved system and method for determining whether to contact a party of the account. Specifically, there is a need to target only those customers who are likely to pay when contacted (e.g., sign up for a product or service or pay a debt) and who are not too costly to contact, thereby reducing costs and maximizing profits of the managing system.
[0009] Systems and methods consistent with the principles of the present invention address the need to determine whether to contact a party of the account. Specifically, a method for managing an account to determine whether to contact a party associated with the account includes determining an account value for the account based on a likelihood of receiving payment on the account from the party, determining an account cost of obtaining payment from the account holder, and determining whether to contact the party of the account based on a comparison of the determined account value and account cost.
[0010] According to another aspect of the invention, a computer program is provided for determining whether to contact a party associated with an account. The computer program product comprises computer-readable media having computer-readable code. The computer-readable program code determines an account value for the account based on a likelihood of receiving payment on the account from the party, determines an account cost of obtaining payment from the party, and determines whether to contact the party of the account based on a comparison of the determined account value and account cost.
[0011] Further, a system determining whether to contact a party associated with an account, consistent with the present invention may include means for determining an account value for the account based on a likelihood of receiving payment on the account from the party, means for determining an account cost of obtaining payment from the party; and means for determining whether to contact the party of the account based on a comparison of the determined account value and account cost.
[0012] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
[0013] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate one embodiment of the invention and together with the description, serve to explain the principles of the invention. In the drawings:
[0014] FIG. 1 illustrates an exemplary system environment in which the features of the present invention may be implemented;
[0015] FIG. 2 is an exemplary flowchart of a method, consistent with the principles of the present invention, for prioritizing accounts for debt collection;
[0016] FIGS. 3A and 3B are exemplary flowcharts of a method, consistent with the principles of the present invention, for determining a potential account value; and
[0017] FIG. 4 is an exemplary graph illustrating the number of contacts that can profitably be made on a given account consistent with the principles of the present invention.
[0018] Systems and methods consistent with the present invention, determine whether to contact a party associated with an account. To this end, the system may determine which account holders or customers to contact and how often. As described below, the system first determines an account value and a cost of contacting the customer. With this information, the system determines how many times the customer should be contacted before the cost of attempting contact exceeds the value of the account. The managing system will stop contact attempts when the cost of contacting the customer exceeds the value of the account. Although the example described below is for a recovery service, one skilled in the art can readily appreciate that the features of the present invention may be applied in any system that may decide whether to contact a customer based on the potential value of the customer and the likelihood of contact. For example, the present invention also relates to telemarketing systems as well as debt collection services.
[0019] By way of a non-limiting example, FIG. 1 illustrates a system environment 50 for implementing the features and principles of the present invention. As illustrated in the block diagram of FIG. 1, system environment 50 includes an input module 100, an output module 110, a computing platform 120, a database 130, a network 140, a financial institution 150, a financial clearinghouse 160, a demographic vendor 170, and a dialer 190. Computing platform 120 provides the necessary functionality and computing capabilities to analyze each customer's credit history or financial data received over network 140 or from input module 100. Platform 120 may receive purchased account debt (e.g., for a recoveries service) or customer lists (e.g., for a telemarketing service) from a financial institute 150 over network 140. Network 140 may comprise, alone or in any suitable combination, a telephony-based network (such as a PBX or POTS), a local area network (LAN), a wide area network (WAN), a dedicated Internet, and/or the Internet. Information regarding these accounts may be stored in database 130.
[0020] Platform 120 may analyze each customer's credit history information accessed through a commercially available source (such as the FICO model from Fair, Isaac and Company, Inc.) and/or through a financial clearinghouse 160, such as a major credit bureau like TRW/Experian, Equifax, or TransUnion. With this account information, platform 120 may determine a potential value for each customer's account.
[0021] Platform 120 may also consider information received at the time of purchase, such as the age of the debt and the account balance, when determining the potential account value. Platform 120 may also analyze information received from a demographic vendor 170 to predict the cost of contacting each customer. Platform 120 may compare the potential value and predicted cost to determine a potential profitability level, including a determination of whether and how often to contact each customer for debt collection or to make a sale. Platform 120 then outputs the results of analyzed data to output module 110, which prints or displays the results, or outputs it to other system devices, and to dialer 190, which attempts to contact the customer. Output from computing platform 120 can also be provided to database 130, which may be utilized as a persistent storage device for storing, for example, the number of times dialer 190 has attempted to contact the customer, the maximum number of attempts that should be made for contacting the customer, and the results of each contact attempt (i.e., whether the customer was reached).
[0022] In the embodiment of FIG. 1, computing platform 120 preferably comprises a PC or mainframe computer for performing various functions and operations of the invention. Computing platform 120 may be implemented, for example, by a general purpose computer selectively activated or reconfigured by a computer program stored in the computer, or may be a specially constructed computing platform for carrying out the features and operations of the present invention. Computing platform 120 may also be implemented or provided with a wide variety of components or subsystems including, for example, one or more of the following: a central processing unit, a co-processor, memory, registers, and other data processing devices and subsystems. Computing platform 120 also communicates or transfers customer and credit data to and from input module 100 and output module 110 through the use of direct connections or other types of communication links, as illustrated in FIG. 1.
[0023] Alternatively, communication between computing platform 120 and modules 100, 110 can be achieved through the use of a network architecture (not shown). In the alternative embodiment (not shown), the network architecture may comprise, alone or in any suitable combination, a telephony-based network (such as a PBX or POTS), a local area network (LAN), a wide area network (WAN), a dedicated Internet, and/or the Internet. Further, it may comprise any suitable combination of wired and/or wireless components and systems. By using dedicated communication links or a shared network architecture, computing platform 120 may be located in the same location or at a geographically distant location from input module 100 and/or output module 110.
[0024] Input module 100 of system environment 50 may be implemented with a wide variety of devices to receive and/or provide the account data as input to computing platform 120. As illustrated in FIG. 1, input module 100 includes an input device 102, a storage device 104, and/or a network interface 106. Input device 102 may comprise a keyboard, a mouse, a disk drive or any other suitable input device for providing customer or credit data to computing platform 120. Memory device 104 may be implemented with various forms of memory or storage devices, such as read-only memory (ROM) devices and random access memory (RAM) devices, and may include a memory tape or disk drive for reading and providing the customer's financial information and credit history as input to computing platform 120 for the identification and review processes. Input module 100 may also include network interface 106, as illustrated in FIG. 1, to receive data over a network (such as a LAN, WAN, Internet or the Internet) and to provide the same as input to computing platform 120. For example, network interface 106 may be connected to a credit bureau (such as TRW/Experian, Equifax, or TransUnion) or a private database over a network for the purpose of receiving and transferring customer financial or credit data to computing platform 120.
[0025] As illustrated in FIG. 1, output module 110 includes a display 112, a printer device 114, and/or a network interface 116 for receiving the results provided as output from computing module 110. As indicated above, the output from computing platform may include a potential value of each customer's account, a prediction of the cost of contacting each customer, and/or a potential profitability level including a determination of whether and how often to contact each customer. The output from computing platform 120 may be displayed or viewed through display 112 (such as a CRT or LCD) and printer device 114. If needed, network interface 116 may also be provided to facilitate the communication of the results from computing platform 120 over a network (such as a LAN, WAN, Internet or the Internet) to remote or distant locations for further analysis or viewing. In either case, the output from output module 110 can be used by the credit card issuer to generate, for example, a chart indicating the maximum number of attempts that should be made for contacting each customer for debt recovery, debt collection, or to make a sale. The output from output module 110 can also be used for other purposes, such as internal reports or monitoring.
[0026] In accordance with the principles of the present invention, an exemplary process for determining whether to contact a party associated with an account will now be described with reference to FIGS. 2 and 3. FIG. 2 is an exemplary flowchart of a process for prioritizing accounts consistent with the principles of the present invention. As shown in FIG. 2, the method first determines a value of the account (step S.200). That is, computing platform 120 predicts an amount the customer would likely pay on the account if the customer was contacted. As part of this step, the method of the present invention may consider a number of variables reflecting the customer's current financial situation and credit history.
[0027] FIGS. 3A and 3B are exemplary flowcharts of a method for determining a potential account value consistent with the principles of the present invention. FIG. 3A is an exemplary flowchart of a method for generating a processing model, which is then used to determine an account value for each particular customer's account. As shown in FIG. 3A, the method creates a first formula for determining a customer's likelihood of making a payment (step S.300). In a telemarketing application, this step involves determining the probability that a customer will pay for the offered product or service (i.e., that the customer will accept the offer).
[0028] The first formula may be created from historical account data using a multivariate logistic regression model, which is well known in the art. The historical account data includes information from financial clearinghouse 160 and information regarding accounts for all customers. The account information includes information on all customers that have made previous payments and the amount of those payments. The account information may be obtained from the archived records in database 130 or another storage medium of system environment 50. Alternatively, the account information may be purchased from an outside vendor. The historical information from financial clearinghouse 160 (such as a credit bureau like Equifax) includes many statistics (such as those listed below) describing the financial situation of each customer listed in the account information.
[0029] The multivariate logistic regression model analyzes the historical account data to identify the combination of financial statistics, or variables, that best predicts the probability that a particular customer will make a payment. Most of the statistics or variables analyzed (e.g., a recovery score, debt-to-income ratio, a number of charged-off accounts with a balance greater than zero, FICO score, income, a number of satisfactory bankcard ratings, a number of bankcards opened in the past 24 months, a number of credit inquiries made in the past 6 months, and a number of accounts with delinquent payments of at least 90 days) are obtained through financial clearinghouse 160. However, some variables, such as the age of the debt and account balance (e.g., for a recovery service application), are received at the time the account was purchased from financial institute 150. Other variables may also be determined in-house by computing platform 120. Although the regression model initially identifies the variables, the variables used may be changed at a later time to comply with experimental data, for example. One skilled in the art will readily appreciate the flexibility of the present invention in selecting both the type and amount of variables identified.
[0030] After the multivariate logistic regression model identifies the most predictive variables (such as the top 10-15 predictive financial statistics) for determining whether a client will make a payment, it creates the first formula including only the identified variables. The first formula weights each identified variable to minimize the error in predicting whether a customer will pay. For example, the multivariate logistic regression model may, by using regression techniques well known in the art, weigh the most predictive of the identified variables more heavily than the least predictive of the identified variables. In a recovery service application consistent with the present invention, the three most heavily weighted financial statistics or variables may include a customer's debt-to-income ratio, the total number of charged-off accounts with a balance greater than zero, and the recovery score, for example.
[0031] Also, the weights may account for differences in the types of data analyzed. For example, the first formula allows percentages, probabilities, numbers, and dollar amounts to be entered simultaneously. A small number, such as a probability (ranging from 0-1), may be weighted more heavily than a large number, such as income, to account for the different data types. As described above, formulas with weighted variables created by a logistic regression model are well known in the art. Although the multivariate logistic regression model described herein initially determines the weights, one skilled in the art can appreciate that the weights may be modified later to comply with experimental results or other personal experience, for example.
[0032] After generating the first formula, computing platform 120 creates a first scoring grid using the historical account data (step S.310). As a part of this step, computing platform 120 uses the determined weighted combination of variables to further determine a probability of receiving payment, ranging from 0-1, for each account included in the entered historical account data. Computing platform 120 then generates a scoring table by dividing these probabilities into predetermined groups. For example, the probabilities may be divided into five groups, each group receiving a score ranging from 1 (low) to 5 (high). In a preferred embodiment, computing platform 120 determines the range of probabilities for each group according to the percentage of accounts that fall within that range. For example, the range of probabilities containing the highest 20% of the probability scores receives a score of 5. The range containing the next highest 20% of the probability scores receives a score of 4, etc. In this manner, the population is equally distributed among the groups. One skilled in the art can recognize that other scoring methods are possible. For example, in another embodiment the computing platform may determine the groups by dividing the probabilities into equal ranges. For example, a probability range from 1 to 0.8 may receive a score of 5 and a range from 0.8 to 0.6 may receive a score of 4.
[0033] Computing platform 120 then generates a second formula for determining an amount a customer will likely pay (step S.320). The second formula assumes the customer will make a payment. Therefore, the historical credit and account data analyzed to generate the second formula includes only data from accounts which have made a payment (e.g., for a recovery service or collection service application) or have agreed to purchase a service or product (e.g., for a telemarketing service application). As described above with respect to step S.300, the information analyzed by the second formula may be obtained from archived records stored in database 130 or from financial clearinghouse 160.
[0034] The second formula may be created from the historical data using a multivariate linear regression model, which is also well known in the art. The techniques for generating the second formula with the multivariate linear regression model are the same as those used to generate the first formula. In particular, the multivariate linear regression model identifies the combination of variables that minimizes the error in predicting the amount a customer will pay. The variables analyzed are the same variables analyzed in the first model described above. However, the variables identified as the most predictive may differ.
[0035] After the most predictive variables are identified (such as the top 10-15 variables), the multivariate linear regression model generates the second formula including the identified variables. The second formula weights each variable to minimize error in predicting the amount a customer will pay on the account, as described above for the first formula. The multivariate linear regression model is well known in the prior art for analyzing data to generate a formula defining a "best fit" line (i.e., a line that minimizes the error, or distance squared to the variables). However, one skilled in the art will recognize the flexibility of using other variables or predictive models to generate a formula for predicting an amount a customer will likely pay.
[0036] After generating the second formula, computing platform 120 creates a second scoring grid using the relevant historical data (i.e., accounts that have made a payment) (step S.330). Namely, for each historical account having a payment made, computing platform 120 enters the financial statistics or variables into the second formula. After all the historical data has been used to generate a predicted amount, the second scoring grid is generated by dividing these amounts into predetermined groups. For example, the predicted amounts may be divided into five groups, each group receiving a score ranging from 1 (low) to 5 (high). In a preferred embodiment, computing platform 120 determines the range of amounts for each group according to the percentage of accounts that fall within that range. For example, the range corresponding to the highest 20% of the generated payment amounts receives a score of 5. The range corresponding to the next highest 20% receives a score of 4, etc.
[0037] Computing platform 120 may then use the historical data to combine the first and second scoring grids into a matrix for predicting the overall account value, such as that shown in Table 1 below (step S.340). Each account value in Table 1 corresponds to a particular score generated from the first formula and first scoring grid and a particular score generated from the second formula and second scoring grid. Each value in the table is within a predetermined range, such as from 1 (low) to 8 (high). Each value in the table may then be associated with a particular range of dollar account values.
[0038] Computing platform 120 generates a numerical overall account value for each score combination by analyzing historical account and credit information. Particularly, computing platform may generate a logistic regression score and a linear regression score for each archived account in database 130, using the first formula and first scoring grid and the second formula and second scoring grid, respectively. After generating the logistic regression and linear regression scores, computing platform 120 analyzes the archived account data to determine the actual overall account values for each possible combination of linear regression and logistic regression scores, so that the error in predicting the account value is minimized.
[0039] In general, high scores from both the linear regression model and the logistic regression model yield a high account value, and vice versa. If one model outputs a high score and the other a low score, the resulting account value lies somewhere between the scores, with the logistic regression model (i.e., the first formula) being weighted slightly more heavily, however. For example, Table 1 shows that a linear regression score of 5 and a logistic regression score of 1 yields an account value of 2. However, a linear regression score of 1 and a logistic regression score of 5 yields a score of 6, since the logistic regression model score is weighted more heavily. 1
Account Value
Linear Regression Model Score
Logistic Regression 5 4 3 2 1
Model Score
[0040] The first and second formulas, the first and second scoring grids, and the account value table for combining the scores, may then be used to predict an account value for a particular customer's account. FIG. 3B is an exemplary flowchart of a method for using the determined processing model to determine an account value for a particular customer's account. As shown in FIG. 3B, the method scores an account using the first formula and the first scoring grid (step S.350). More particularly, computer platform 120 enters the required variables from the particular customer's credit history and financial information into the first formula to obtain a probability that the customer will make a payment. Computing platform 120 then uses the first scoring grid to determine a score (e.g., 1-5) for the customer based on the determined
[0041] Computing platform 120 also enters the required variables from a customer's credit history and financial information into the second formula to obtain an amount the customer will likely pay if he makes a payment (step S.360). Computing platform 120 uses the second scoring grid to determine a linear regression model score for the customer based on the calculated amount. Computing platform 120 then applies the determined scores to a matrix, such as that shown in Table 1, to obtain an overall account value (step S.370). Each determined overall account value relates to a predefined dollar range of account values.
[0042] Returning to FIG. 2, after determining the value of the account, computing platform 120 determines an estimated cost of contacting the account holder (step S.210). Computing platform 120 generates a formula and scoring grid for determining the likelihood of contacting a customer. Computing platform 120 generates the cost formula using a multivariate logistic regression model, such as that described above. The data analyzed by the regression model in determining the likelihood of contacting a customer, however, differs from the data analyzed to determine the likelihood of a customer making a payment, as described with respect to step S.300. Specifically, the data analyzed for determining the probability of contact includes historical demographic information, accessed from demographic vendor 170, if available, and account information, such as whether the customer was previously contacted and the age of the debt.
[0043] The multivariate logistic model separately analyzes the data from accounts not having demographic information, thereby generating a separate cost formula with the multivariate logistic regression model. This separate cost formula only has financial statistics or variables obtained from the account information, such as the age of the debt and whether the customer was previously contacted. Because the separate cost formula is used to analyze financial statistics or variables for customers without available demographic information, and not the majority of customers with available demographic information, it will be referred to hereinafter as the "back-up cost formula."
[0044] Although the account managing system may contact a customer based on information originally provided by financial institute 150 (e.g., when it sold the account debt to the recovery service), the present invention may also send a list of names and addresses to a demographic vendor 170 to obtain updated customer contact information. For example, demographic vendor 170 provides to computing platform 120 the name and address of each customer it locates based upon original contact information provided to computing platform 120. If demographic vendor 170 can locate the name and address of a customer, it provides information regarding the characteristics of a population in the customer's geographic area, which is then used by the multivariate logistic regression model to generate the cost formula predicting a likelihood of contacting a customer in that geographic area. Otherwise, computing platform 120 separately analyzes the historical account information of the accounts without demographic information using the multivariate logistic regression model to generate the back-up cost formula for calculating the likelihood of contacting the customer. The demographic information may be stored in database 130 with the other account information. Alternatively, the historical demographic and account data may be purchased from an outside vendor.
[0045] The variables analyzed from the demographic information include, for example, the average and median length of residence, the number of people owning homes, the number renting apartments, the percentage of people who were born in the state, the percentage of people who are enrolled in college, the percentage of people employed in a professional capacity, the average number of adults in a home, the median household income, and the average car retail value for people living within a particular geographic area. Other financial statistics or variables analyzed by the multivariable logistic regression model includes account information, such as the age of the debt and whether the customer was contacted, and variables generated by computing platform 120. For example, computing platform 120 may generate a variable corresponding to whether demographic vendor 170 could find the name and address of the customer. The multivariable logistic regression model identifies which of these variables best predicts whether a customer will be successfully contacted and weights the identified variables in a formula for determining this probability. The variables are weighted, using the historical demographic and account data, to minimize error in calculating the probability of contacting a particular customer.
[0046] Systems and methods consistent with the present invention may assign a lower probability of contacting customers who live in a transient population or an area that lacks telephone service. More particularly, the multivariate logistic model may rate a customer located in a predominately "young" or urban area with a low probability of contact. Although people in urban areas tend to have telephone service, they also tend to relocate often, making them harder to track despite them having telephone service. Similarly, a person located in the suburbs may have a higher probability of contact, because they tend to stay in the same location and have telephone service. Further, the multivariate logistic model may rate a customer located in a remote area that tends to lack telephone service with a lower probability of contact.
[0047] Computing platform 120 then creates a scoring grid for scoring the range of probabilities, as discussed above, for example, with respect to FIG. 3. More specifically, the probabilities generated for each of the accounts is divided into predetermined groups. Each group receives a score within a predetermined range. In a preferred embodiment, the generated probabilities are divided into ten groups, with the value of each group ranging from 1 (low) to 10 (high). In this case, the range of probabilities generated with the historical data and containing the highest 10% of the probability scores, preferably receives a score of 10. The range containing the next highest 10% of the probability scores receives a score of 9, etc.
[0048] After generating the cost formula and scoring grid, computing platform 120 may adjust the scoring grid, for example, into seven groups ranging in value from 1-7. The scoring grid may be adjusted by determining whether the demographic vendor 170 found the customer's name and address information. This adjustment serves to weight this statistic or variable more heavily, thereby recognizing that dialer 190 will not likely contact the customer if the customer cannot be found. Alternatively, the formula for predicting the probability of contact may be optimized by weighing this statistic or variable more heavily, so that the scores do not need further adjustment, for example.
[0049] Each score (e.g., 1-7) in the adjusted scoring grid corresponds to a predefined range of costs associated with contacting the customer. The relationship between the probability of contact and the cost of contact is inverse. For example, if the probability of contacting the customer is low, then dialer 190 will likely need to attempt contacting the customer several times before successfully doing so, causing the cost to be high. Similarly if the probability of contacting the customer is high, then dialer 190 will likely attempt to contact the customer only a few times before successfully doing so, causing the cost to be low. One skilled in the art will recognize that various factors, weights, and values may be used to determine the formula for determining the likelihood of contacting the customer.
[0050] Returning to FIG. 2, after determining the estimated value of the account and the expected cost of contacting the customer, computing platform 120 determines whether to contact the customer. Computer platform 120 bases this decision on the potential profitability of the account, by comparing account value to the cost of contacting the customer (step S.220). If the account value is greater than the cost of the contacting the customer, dialer 190 attempts contacting the customer to collect a debt or to make a sale (step S.230). Otherwise, dialer 190 does not attempt to contact the account holder (step S.240). By focusing on customers who are more likely to pay their debt or an product they agreed to purchase and are easier to contact, a managing system may maximize its profits. In this respect, systems and methods consistent with the present invention may reduce the number of accounts that are pursued for recoveries, for example, by about 50%.
[0051] Further, once dialer 190 decides to contact the customer, it may continue attempting to contact the customer until the cost of contact exceeds or equals the account value. Particularly, by determining the initial cost of attempting to contact a customer, computing platform 120 also may determine how often dialer 190 may profitably attempt to contact a customer (i.e., how many additional calls, for example, can be made before the estimated cost of contacting the customer exceeds the estimated account value.) More specifically, computing platform 120 may update the adjusted cost scores (e.g., 1-7) to determine the probability of contacting the customer as the number of attempted contacts increase. Particularly, an adjusted cost score may be lower to reflect that the probability of contacting the customer decreases as the number of contacts increase. The lower probability, which indicates a higher cost for attempting contact, may be used to determine the number of times dialer 190 should attempt to contact the customer before the expected costs exceed the account value. Alternatively, the formula generated with a logistic regression model and historical demographic and account data may be modified to include the number of contacts attempted as a variable. In this embodiment, a new probability score may be calculated for each attempted contact to determine how often dialer 190 may attempt to contact a customer profitably.
[0052] FIG. 4 is an exemplary graph illustrating the number of contacts that can profitably be made on a given account consistent with the principles of the present invention. In a preferred embodiment, the system contacts the customer by telephone. However, other methods of contact, such as mail, electronic mail, or facsimile may be implemented.
[0053] FIG. 4 illustrates two accounts, A and B. Both accounts have an estimated account value 400 of $60, which may be determined as described above with respect to step S.200. As shown in FIG. 4, the estimated cost of initially contacting customers A and B (e.g., approximately $52 and $25, respectively) is less than the account value. Therefore, as described above with respect to step S.230, dialer 190 may attempt contacting customers A and B. This contact may be repeated until the cost of attempting contact exceeds the account value.
[0054] Although both accounts have an account value of $60, a recovery service may attempt contacting customer B more often than A. As shown in FIG. 4, customer A has a higher cost per contact attempt 410 than customer B. Specifically, the cost per contact attempt 410 shows that account value 400 equals the estimate cost at 10 attempts. Therefore, to maintain a possible profit for account A, dialer 190 should make less than 10 attempts to contact customer A. Similarly, the cost per contact attempt 420 shows that 34 contact attempts may be made to customer B before the costs exceed the account value 400.
[0055] Using this information, dialer 190 determines when to stop attempting to contact the customer. For example, if no contact has been made with the customer during previous calls, computing platform 120 may check database 130 to determine whether to attempt another call. If the number of attempted calls is less than or equal to the maximum permitted calls, computing platform 120 allows the recovery service to make the call. However, if the number is greater than the maximum permitted, computing platform 120 indicates a call cannot be made.
[0056] If database 130 indicates that contact has been made with the customer, the method may differ depending on the application. For example, dialer 190 may continue contacting the customer once contact is made, without regard to the maximum number of attempted contacts, if the method is applied in a recoveries service or a collection service. In these applications, the customer may be contacted until the debt is collected or until dialer 190 is informed that the debt cannot be collected due to bankruptcy or death of the debtor, for example. However, dialer 190 may stop contacting the customer once the maximum number of attempted contacts is reached, regardless of whether the customer was contacted, if the method is applied in a telemarketing service.
[0057] Systems consistent with the present invention overcome the shortcomings of conventional apparatus and methods for determining whether to contact a party of the account. By prioritizing accounts, as described herein, systems and methods consistent with the present invention minimize the expense incurred during any outbound calling service, thereby maximizing profits. Applications for the present invention include telemarketing, debt collection, and debt recovery service. For example, in a telemarketing system consistent with the present invention predicts, the likelihood of the customer accepting the offer, the amount the customer will likely pay for the product or service if an offer is accepted, and the cost of attempting to sell the product or service are calculated and combined to determine whether to contact the customer. Similarly, in a collecting service or recovery service consistent with the present invention predicts, the likelihood of the customer paying debt owed, the amount the customer will likely pay if a payment is made and the cost of attempting to contact the customer are calculated and combined to determine whether to contact the customer.
[0058] The above-noted features and other aspects and principles of the present invention may be implemented in various system or network environments to provide automated computational tools to facilitate data collection and risk analysis of accounts. Such environments and applications may be specially constructed for performing the various processes and operations of the invention or they may include a general-purpose computer or computing platform selectively activated or reconfigured by program code to provide the necessary functionality. The processes disclosed herein are not inherently related to any particular computer or other apparatus, and may be implemented by a suitable combination of hardware, software, and/or firmware. For example, various general purpose machines may be used with programs written in accordance with the teachings of the invention, or it may be more convenient to construct a specialized apparatus or system to perform the required methods and techniques. The present invention also relates to computer readable media that include program instruction or program code for performing various computer-implemented operations based on the methods and processes of the invention. The media and program instructions may be those specially designed and constructed for the purposes of the invention, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of program instructions include both machine code, such as produced by compiler, and files containing a high level code that can be executed by the computer using an interpreter.
[0059] Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.
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Make sense to use it only if your pipeline is used say 10% of the time during particular month or less.
Not suitable for continues day to day computations: too high cost.
Microsoft Azure is running on a customized, stripped-down, and hardened version of Hyper-V, also known as the Azure Hypervisor.
Relatively easy to deploy virtual machines and use these virtual machines to develop, test, or even host your applications.
Documentation, SDKs, and examples are all Open Source and available on GitHub: https://github.com/Azure .
Personalized sysadmin help with the possibility of direct communication by email and phone. Which avoids helpdesk as additional level of indirection.
In Azure you are granted access to root on your VM which provides you with the maximum degree of flexibility. Important for qualified researchers with good understanding of Linux and application developers. Such privileges are impossible (or very difficult to obtain) on production servers in enterprise datacenter environment.
Access to root gives the ability to create VM images or containers with necessary software installed, while accessing Internet directly.
Those images can later be imported and run in enterprise environment.
A way of avoiding the problems caused by excessive regimentation of access, or too tight security. As well as negative affects of excessive outsourcing and overcentralization.
Better, more flexible, environment for installing and experimenting with complex software then researcher's workstation due to direct access to internet (if you are working in proxy environment). That includes cluster software such as schedulers.
You can create as many VMs as you need to the short term computationally intensive tasks. Azure is optimal for non-uniform computation workloads with high but rare peaks load. In other words when high peak loads period is followed by a long period of minimal or no activity ("inaction/hibernation" period). This is typical when batched of data are arrived infrequently or for testing various complex applications.
The cost of computational resources is the cost per hour. That makes Azure not suitable for continuous high CPU demand computations typical for Quantum chemistry and several other research areas. But it make it perfectly suitable for genomic research.
You need to master additional pretty complex environment. And additional complexities of integration of Azure and your corporate environment (but the latter needs to be done only once; as soon as your first VM is run as you want, you are done).
Enterprise, typically obtained via the enterprise portal such as "Software defined datacenter" portal.
For individual account you can free evaluation period of one month with $200 credit from Microsoft. You need to provide your credit card. If you have proxy in enterprise environment you can access VMs from it iether via socks or via your phone (used as hotspot) or from your home PC.
Suitable for your own computational needs as the cost can minimized to less then $30 a month. But please note that a good used server such as Dell R620 (2x8-Core Xeon E5-2690 2.90Ghz 96GB without harddrives costs around $450 on eBay (plus $60 shipping). A couple of 900GB 10K RPM SAS harddrives is $110 so for $600 you can have a server that will last probably three years or more.
To cut costs you can use containers that you can store locally and deallocate VMs for the period if inactivity.
Enterprise accounts usually enjoy substantial discount from Microsoft (often 50%). Typically need typically need to apply for a resource group, providing your cost center. Often login to Azure is done via CyberArk Pass or similar two factor authentication software.
In any case the net result of those efforts should be the ability to login to Azure portal.
Shell is more powerful and more complex.
Initially it makes sense to limit yourself to portal.
A key component in the storage services is the storage account.
It's needed to access the storage. It also defines the type of storage that is behind the service.
Hot storage tier: Optimized for storing data that is accessed frequently.
Cool storage tier: Optimized for storing data that is infrequently accessed and stored for at least 30 days.
Archive storage tier: Optimized for storing data that is rarely accessed and stored for at least 180 days with flexible latency requirements. It's only available for Blob storage.
Cold storage tier can be used for storing not frequently used data and as a backup. Prices are moderate.
When you create a virtual machine, this machine will have at least two disks that are stored in an (not in the one you created) Azure Storage account: an operating system disk, created based on an image, and a temporary disk.
For enterprise environment accounts creation of resource group is automatic and it is linked to your cost center.
Microsoft provides a gallery of virtual machines, from which you can chose the most suitable for your needs.
For individual account you need to specify port 22 (SSH) so that you are able to connect to it.
You can also access VM via portal is you use individual account.
you can execute command on VM via portal. That allows you to correct errors.
'Deallocated' is the only state where the VM won't cost you money. Stopped VM still consume money although the amount is small, line a dollar a day or less.
Idle VM (CPU 0.5%) configured as Standard D2s v3 (2 vcpus, 8 GB memory, 32GB of storage) with the CentOs 7 consume approximately $2.3 a day or $70 a month (or around $.10 per hour). That's more expensive than buying your own used server on EBay and paying for electricity, but less if you need to pay for internet connectivity as well.
Shutdown deallocates VM public address. You typically need to use DNS name to access your VM after restart.


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A rising high school junior, Alicia Weaver strives to create a platform for students to explore STEM topics that are not covered in school. Her science background consists of physical computing and video production. She is dedicated to raising awareness of the lack of opportunity for students in underrepresented communities to ensure all students have equal resources to learn.

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Five powerful insights from William S. Plumer's "Devotional Thoughts" on Psalm 1
William S. Plumer's commentary on the Psalms is a massive volume of practical divinity and an overlooked treasure. Here are five gems from his "Devotional Thoughts" on Psalm 1.
All preaching and writing, which uniformly fail to draw a vigorous line between the friends and foes of God, cannot much profit men's souls. A discriminating statement of the truths of God's word is eminently scriptural. So we learn from the first Psalm and from all the sacred writers. Let the difference between sin and holiness, saints and sinners never be denied, never be forgotten. Eternity alone will show how great it is.
It is a great thing to have a heart for religion, and for spiritual truth, v. 2. To delight in divine things is as necessary as to see their importance, or to believe their reality. We must love as well as know. If we have spiritual discernment we will have our affections engaged. No man can really perceive beauty without being affected by it.
He, who would be truly blessed, must become a student of Scripture. There is no substitute for this. God's word is able to make men wise unto salvation. It is quick and powerful. Nothing so penetrates the heart of man. With a good man it has authority. Even devils know and to some extent feel its power. Matt. 4:11.
Any religion which sets aside God's law, is spurious. It is not the religion of the Psalmist, v. 2. It is not the religion of Jesus Christ. Matt. 5:17, 18. It is not the religion of his apostles. Rom. 3:31. Antinomianism is one of the worst forms of error. It makes Christ the minister of sin.
In all our study of God's word we must have faith. Heb. 4:2. This grace of the Spirit is of the greatest importance. Without it we always go astray, live in darkness, and are made miserable by the stings inflicted by our own minds. "Nothing greater can be said of faith, than that it is the only thing, which can bid defiance to the accusations of conscience." This it does by beholding the Lamb of God, that taketh away the sin of the world. Christ Jesus is the only hope of perishing sinners.
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iBike Dash CC Deluxe
It's no secret that the iPhone has changed the way we work, play and communicate. The plethora of apps is staggering and this smartphone can now do anything from find your car to perform surgery (not quite).
By Jason Heaton
It's no secret that the iPhone has changed the way we work, play and communicate. The plethora of apps is staggering and this smartphone can now do anything from find your car to perform surgery (not quite). Given its capabilities, it was only a matter of time before someone figured out how to turn an iPhone 3 or 4 (and iPod Touch) into a cycling computer. Behold the iBike Dash CC Deluxe ($329).
Cycling computers are not only fun accessories for tracking your time and distance on weekend rides, they're serious training tools for club racers and pros alike. The iBike Dash CC is a combination iPhone app, hard case and bike sensors that pull together a formidable amount of data in an easy-to-read format. Through easy-to-install wheel and crank sensors and a stem-mounted case for the iPhone, you get speed, distance, cadence, heart rate, power output (with an optional module), wind speed, hill gradient, elevation and real-time location information all at your fingertips. Sound complicated? It isn't. The makers of the iBike have managed to continue the highly intuitive iPhone interface in their app. Change views on the fly by tapping the screen and customize settings based on your preferences. You can even do a live fitness test to set up thresholds and workouts for yourself.
The iBike does have some downsides. The "Phone Booth" case, which mounts to your bike's stem, is a bit rattly and oversized. We're also not sure we'd want to be caught in a downpour (or, God forbid, a crash) with our iPhone so exposed. Even with the auxiliary battery iBike supplies, your phone battery drops quickly with the display brightness cranked up. So don't plan on an all-day century ride. For weight weenies, the setup is a bit heavy too. But most of that weight is in the iPhone which you'd probably carry with you anyway.
All in all, we're impressed with the iBike Dash CC It capitalizes on the iPhone's legendary capabilities and ease of use and takes it a step further. Now if it would only pedal for you. Maybe that's coming in version 2.0
Buy Now: $329
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