Patent Issued for Using cognitive computing for presenting targeted loan offers (USPTO 11004116)

2021 JUN 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Attig, Melissa (Bloomington, IL, US), Batra, Reena (Alpharetta, GA, US), Dua, Puneit (Bloomington, IL, US), Flowers, Elizabeth (Bloomington, IL, US), Mattingly, Adam (Normal, IL, US), Zwilling, Alan (Downs, IL, US), filed on April 27, 2017, was published online on May 11, 2021, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11004116 is assigned to State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Online access to financial accounts has become commonplace, with users now enjoying the convenience of paying bills, transferring funds, and checking account balances from the comfort of their own homes. Many people typically have more than one bank account, such as a checking account and a savings account, for example, and may move money between these accounts as needed. Similarly, many people are accustomed to receiving offers for financial products, such as loans, on their mobile devices.

“Since many people have multiple financial accounts to their name, they may not always be aware of how often they use each account. It can be difficult for individuals to know when is the best time to use one particular account over another account. Similarly, companies that provide financing may wish to increase the frequency with which their customers use their products and services. Offering products or services to customers at the right time is one way in which companies attempt to entice the customers to use their products or services.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “In one aspect, a computer-implemented method for presenting targeted loan offers to customers may be provided. The method may include, via one or more local or remote processors, sensors, servers, and/or transceivers: (1) generating a financial profile associated with a customer; (2) receiving one or more locations of a customer mobile device over a period of time; (3) determining that at least one of the one or more locations of the customer mobile device is within a predetermined distance of an asset listed for sale; (4) determining a loan offer for the customer based upon the financial profile; and/or (5) transmitting the loan offer to the customer for their review to facilitate providing loan offers to customers currently shopping for assets. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In another aspect, a system to present targeted loan offers to customers via their mobile device may be provided. The system may include one or more local or remote processors, sensors, servers, and transceivers configured to: (1) build a financial profile associated with a customer; (2) receive one or more locations of a customer mobile device in a given day; (3) determine that at least one of the one or more locations of the customer mobile devices is within a predetermined distance of an asset listed for sale with a vendor; (4) determine a loan offer for the customer based upon the financial profile; and/or (5) transmit the loan offer to the customer for his or her review to facilitate providing loan offers to customers shopping for assets. The system may include additional, less, or alternate components, including those discussed elsewhere herein.

“Advantages will become more apparent to those of ordinary skill in the art from the following description of the preferred aspects, which have been shown and described by way of illustration. As will be realized, the present aspects may be capable of other and different aspects, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

“The Figures depict aspects of the present invention for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternate aspects of the structures and methods illustrated herein may be employed without departing from the principles of the invention described herein.”

The claims supplied by the inventors are:

“1. A computer-implemented method, comprising: receiving, via one or more processors, location data corresponding to a mobile device associated with a first customer, the location data indicating: a first location of the mobile device at a first time; and a second location of the mobile device at a second time; determining, via the one or more processors, that the first location of the mobile device is greater than a predetermined distance from an asset listed for sale by a vendor; based at least in part on determining that the first location is greater than the predetermined distance from the asset, refraining, by the one or more processors, from generating a loan offer for purchase of the asset and from transmitting the loan offer to the mobile device; determining, via the one or more processors, that the second location of the mobile device is less than or equal to the predetermined distance from the asset; and based at least in part on determining that the second location is less than or equal to the predetermined distance from the asset: generating, via the one or more processors, a first financial profile associated with the first customer, based at least in part on a balance of an account associated with the first customer; generating, via the one or more processors and using at least one machine learning model, the loan offer for purchase of the asset listed for sale, wherein the loan offer is generated by the at least one machine learning model based at least in part on a comparison of the first financial profile associated with the first customer and second financial profiles associated with second customers of the vendor; and transmitting, via the one or more processors, the loan offer to the mobile device.

“2. The computer-implemented method of claim 1, wherein receiving the location data corresponding to the mobile device comprises receiving indications of locations of the mobile device over a day, a week, or a month.

“3. The computer-implemented method of claim 1, wherein generating the loan offer further comprises: adjusting at least one of a loan length or a loan interest rate for the loan offer based upon the first financial profile.

“4. The computer-implemented method of claim 1, wherein the first financial profile indicates at least one of a net worth, a net income, a credit rating, or a credit score for the first customer.

“5. The computer-implemented method of claim 1, wherein the first financial profile indicates at least one of a customer longevity or a customer loyalty.

“6. The computer-implemented method of claim 1, wherein the first financial profile indicates at least one predicted life event associated with the first customer, and the loan offer is generated based at least in part upon the at least one predicted life event.

“7. The computer-implemented method of claim 6, wherein the at least one predicted life event includes at least one of: a graduation from high school or college by the first customer or a child of the first customer, a birth, a marriage, an increase in income, or a move to a new residence.

“8. The computer-implemented method of claim 1, wherein the loan offer is an offer for a vehicle loan, and the asset is a vehicle.

“9. The computer-implemented method of claim 1, further comprising training, via the one or more processors, the machine learning model to identify one or more metrics within the second financial profiles that are predictive of failures to pay back loans.

“10. The computer-implemented method of claim 9, wherein the comparison of the first financial profile and the second financial profiles includes determining whether the first financial profile includes at least one of the one or more metrics that are predictive of failures to pay back loans.

“11. A computer system, comprising: one or more processors; memory storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to: receive first data indicative of a first location of a first mobile device associated with a first customer; determine that the first location is less than or equal to a predetermined distance from an asset listed for sale by a vendor; based at least in part on determining that the first location is less than or equal to the predetermined distance from the asset: generate a first financial profile associated with the first customer, based at least in part on a balance of an account associated with the first customer; generate, using at least one machine learning model, a first loan offer to facilitate purchase of the asset by the first customer, wherein the first loan offer is generated by the at least one machine learning model based at least in part on a comparison of the first financial profile and other financial profiles; and transmit the first loan offer to the first mobile device; receive second data indicative of a second location of a second mobile device associated with a second customer; determine that the second location is greater than the predetermined distance from the asset; and based at least in part on determining that the second location is greater than of the predetermined distance of the asset, refrain from generating a second financial profile associated with the second customer, from generating a second loan offer to facilitate purchase of the asset by the second customer using the at least one machine learning model, and from transmitting the second loan offer to the second mobile device.

“12. The computer system of claim 11, wherein at least one of the first data and the second data is received over a day, a week, or a month.

“13. The computer system of claim 11, wherein generating the first loan offer further comprises: adjusting a loan length or a loan interest rate for the first loan offer based upon the first financial profile.

“14. The computer system of claim 11, wherein the first financial profile indicates at least one of a net worth, a net income, a credit rating, or a credit score for the first customer.

“15. The computer system of claim 11, wherein the first financial profile indicates at least one of a customer longevity or a customer loyalty.

“16. The computer system of claim 11, wherein the first financial profile for the first customer indicates at least one predicted life event associated with the first customer, and the first loan offer is generated based at least in part upon the at least one predicted life event.

“17. The computer system of claim 16, wherein the at least one predicted life event includes at least one of: a graduation from high school or college by the first customer or a child of the first customer, a birth, a marriage, an increase in income, or a move to a new residence.

“18. The computer system of claim 11, wherein the first loan offer is an offer for a vehicle loan, and the asset is a vehicle.

“19. The computer system of claim 11, wherein the one or more processors are further configured to train the machine learning model to identify one or more metrics within the other financial profiles that are predictive of failures to pay back loans.

“20. The computer system of claim 19, wherein the comparison of the first financial profile and the other financial profiles includes determining whether the first financial profile includes at least one of the one or more metrics that are predictive of failures to pay back loans.”

URL and more information on this patent, see: Attig, Melissa. Using cognitive computing for presenting targeted loan offers. U.S. Patent Number 11004116, filed April 27, 2017, and published online on May 11, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11004116.PN.&OS=PN/11004116RS=PN/11004116

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