Patent Issued for Cross Selling Recommendation Engine (USPTO 10,699,319)

2020 JUL 14 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- State Farm Mutual Automobile Insurance Company (Bloomington, Illinois, United States) has been issued patent number 10,699,319, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Flowers, Elizabeth (Bloomington, IL); Dua, Puneit (Bloomington, IL); Balota, Eric (Bloomington, IL); Phillips, Shanna L. (Bloomington, IL).

This patent was filed on April 24, 2017 and was published online on July 13, 2020.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Organizations involved in customer service activities often process large amounts of unstructured data to make decisions while interacting with a customer in real-time. For example, in the case of a customer service representative speaking on the telephone with a customer experiencing an issue with a product or service, appropriate solutions may include a combination of timeliness of response and accuracy in content.

“Such unstructured data may include voluminous transaction records spanning decades, unstructured customer service data, or real-time transcripts of customer service interactions with scattered contextual indicators. To reasonably expect a customer service representative to effectively leverage such large data sets in real-time places an unreasonable burden on a customer service representative. However, failing to do so robs the customer service representative of vital context not readily apparent, and the wealth of knowledge gained throughout the history of an organization that would otherwise need to be distilled to briefing materials and expensively trained over time. Thus, organizations may value tools to rapidly process large data sets, to infer context, suggest lessons learned based upon transaction data, while learning through successive process iterations. Furthermore, appropriate application of such tools may provide a competitive advantage in a crowded and competitive customer service industry.

“In an effort to automate and provide better predictability of customer service experiences, many organizations develop customer relationship management (CRM) software packages. Organizations that develop these software packages often develop custom solutions, at great expense, to best meet the needs of their customers in unique industries. Such tools while providing a great level of detail for the customer service representative, lack the flexibility to react to changing business conditions or fully exploit the underlying technology, driving additional cost into an already expensive solution.

“Some organizations where able to make concessions on customized solutions turn to off-the-shelf or commercially available software solutions that reduce the overall cost of implementation. Such solutions may provide customer service representative prompting tools with question and answer formats that allow for consistency of customer experience, however, at the expense of a less personalized experience required in many industries. While more flexible than fully-custom solutions, the impersonal question-answer format of customer interaction may not improve without costly software revisions, rarely performed by original equipment manufacturers (OEMs) of off-the-shelf solutions.

“The ability for a customer service experience to learn and improve over successive iterations remains paramount for organizations to offer discriminating customer service experiences. Often the burden of continual improvement falls to the customer service representative, as a human being able to adapt and learn to changing conditions more rapidly even within the confines of a rigid customer service software application. However, with the advent of outsourcing prevalent in the customer service industry, the customer service representative may lack much of the necessary context required to provide high levels of relevant customer service. This lack of context in an interconnected company is less an issue of distance and more an issue of data access and the ability to contextually process data to present relevant solutions in a timely manner.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “One exemplary embodiment includes a computer-implemented method, executed with a computer processor, that generates a credit score. This method may include retrieving an un-structured data set including an aggregated transaction set that includes a plurality of users and at least one correlation of a user to a credit score. This method may include receiving a plurality of financial transactions, accessing and executing a heuristic algorithm to generate a credit score using the aggregated transaction set, the correlation, and/or the plurality of financial transactions. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“Yet another alternative embodiment includes a computer-implemented method, executed with a computer processor, that generates cross-selling recommendations using an aggregated customer transaction list. The method may include retrieving an aggregated transaction list from a plurality of customers and receiving a natural language input in a customer service environment. The method may also include accessing and executing a heuristic algorithm to generate at least one product recommendation using the language input and the transaction list. A product category of the recommendation, for example, may correlate with a predicted need. The method may include receiving an indication of interest in the recommendation and/or updating the algorithm using a calculated correlation between the recommendation and the indication. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“Still another embodiment includes a computer-implemented method, executed with a computer processor, that predicts an impact on a book of business by a change in an offered credit interest rate. The method may include retrieving an aggregated behavior list from a plurality of customers including offered credit interest rate data, and/or receiving the offered credit interest rate. The method may also include accessing and executing a heuristic algorithm to generate a predicted impact on a book of business, including the number of customers using the offered credit interest rate and the behavior list. Further, the method may include receiving an actual behavior with a human machine interface and/or updating the algorithm using a calculated correlation between the offered rate and the behavior. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“In yet another embodiment, a computer-implemented method, executed in a computer processor, includes targeting a portion of a business process for modification. The method may include retrieving an un-structured transaction set from a plurality of customers including a time associated with a plurality of portions of the business process. Furthermore, the process may include accessing and executing a heuristic algorithm to generate an indication associated with the portion of the business process that exceeds a threshold required for modification, using the un-structured transaction set. Still further, the method may include receiving a quantified impact on the portion of the business process and/or updating the algorithm using the quantified impact. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“An exemplary embodiment includes a computer-implemented method, executed with a computer processor, that generates a financial literacy suggestion using a transaction history. The method may include retrieving an un-structured transaction set, associated with a customer, accessing and executing a heuristic algorithm to generate the financial literacy suggestion using the transaction history. Furthermore, the method may include receiving an indication of relevance from the customer and updating, the algorithm using a calculated correlation between the suggestion and the relevance. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

“Exemplary embodiments may include computer-implemented methods that may in other embodiments include apparatus configured to implement the method, and/or non-transitory computer readable mediums comprising computer-executable instructions that cause a processor to perform the method.

“Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, 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 claims supplied by the inventors are:

“What is claimed is:

“1. A computer-implemented method, executed with a computer processor, that generates cross-selling recommendations using an aggregated customer transaction list, comprising: retrieving, with the computer processor, an aggregated transaction list from a plurality of customers, stored in a first memory; receiving, with the computer processor, a natural language input in a customer service environment from a human machine interface; accessing, with the computer processor, a heuristic algorithm stored in a second memory; executing the heuristic algorithm, with the computer processor, to generate at least one product recommendation using the natural language input and the aggregated transaction list, wherein a product category of the at least one product recommendation correlates with a predicted need; receiving, with the computer processor, an indication of interest in the at least one product recommendation with the human machine interface; and updating, with the computer processor, the heuristic algorithm in the second memory using a calculated correlation between the at least one product recommendation and the indication of interest.

“2. The computer-implemented method of claim 1, wherein the natural language input comprises data related to a product support issue.

“3. The computer-implemented method of claim 1, wherein the natural language input comprises data related to a new product purchase.

“4. The computer-implemented method of claim 1, wherein the indication of interest includes the predicted need.

“5. The computer-implemented method of claim 1, wherein the first memory comprises an external transaction server.

“6. The computer-implemented method of claim 1, wherein the second memory comprises an external heuristic server.

“7. The computer-implemented method of claim 1, wherein the natural language input in the customer service environment comprises spoken words received from the human machine interface.

“8. A computer system configured to generate cross-selling recommendations using an aggregated customer transaction list, the computer system comprising one or more processors and/or transceivers: retrieve an aggregated transaction list from a plurality of customers, stored in a first memory; receive a natural language input in a customer service environment from a human machine interface; access a heuristic algorithm stored in a second memory; execute the heuristic algorithm to generate at least one product recommendation using the natural language input and the aggregated transaction list, wherein a product category of the at least one recommendation correlates with a predicted need; receive an indication of interest in the at least one product recommendation with the human machine interface; and update the heuristic algorithm in the second memory using a calculated correlation between the at least one product recommendation and the indication of interest.

“9. The computer system of claim 8, wherein the natural language input comprises data related to a product support issue.

“10. The computer system of claim 8, wherein the natural language input comprises data related to a new product purchase.

“11. The computer system of claim 8, wherein the indication of interest includes the predicted need.

“12. The computer system of claim 8, wherein the first memory comprises an external transaction server.

“13. The computer system of claim 8, wherein the second memory comprises an external heuristic server.

“14. A non-transitory computer readable medium, comprising computer readable instructions that when executed by a computer processor cause the computer processor to: retrieve an aggregated transaction list from a plurality of customers, stored in a first memory; receive a natural language input in a customer service environment from a human machine interface; access a heuristic algorithm stored in a second memory; execute the heuristic algorithm to generate at least one product recommendation using the natural language input and the aggregated transaction list, wherein a product category of the at least one product recommendation correlates with a predicted need; receive an indication of interest in the at least one product recommendation with the human machine interface; and update the heuristic algorithm in the second memory using a calculated correlation between the at least one product recommendation and the indication of interest.

“15. The non-transitory computer readable medium of claim 14, wherein the natural language input comprises data related to a product support issue.

“16. The non-transitory computer readable medium of claim 14, wherein the natural language input comprises data related to a new product purchase.

“17. The non-transitory computer readable medium of claim 14, wherein the indication of interest includes the predicted need.

“18. The non-transitory computer readable medium of claim 14, wherein the first memory comprises an external transaction server.

“19. The non-transitory computer readable medium of claim 14, wherein the second memory comprises an external heuristic server.

“20. The non-transitory computer readable medium of claim 14, wherein the natural language input in the customer service environment comprises spoken words received from the human machine interface.”

For the URL and additional information on this patent, see: Flowers, Elizabeth; Dua, Puneit; Balota, Eric; Phillips, Shanna L. Cross Selling Recommendation Engine. U.S. Patent Number 10,699,319, filed April 24, 2017, and published online on July 13, 2020. 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=10,699,319.PN.&OS=PN/10,699,319RS=PN/10,699,319

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