Patent Issued for Dynamic ranking of recommendation pairings (USPTO 11373232): salesforce.com inc.

2022 JUL 18 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventors Casalaina, Marco (San Francisco, CA, US), Sanghavi, Tejas (San Francisco, CA, US), filed on May 24, 2019, was published online on June 28, 2022.

The patent’s assignee for patent number 11373232 is salesforce.com inc. (San Francisco, California, United States).

News editors obtained the following quote from the background information supplied by the inventors: “A challenge for businesses is figuring out which product, service, or actions are appropriate fits for new and existing customers. Consumers are bombarded with advertisements, offers and promotions and they are getting immune to these, especially because most of it used to be based on what the company wants to promote, rather than what the individual wants or needs. Broad brush rules, such as heuristic or intuitive rules, or customer segments, are not able to achieve the same performance as a personalized recommendation that is unique to each individual customer. Providing such irrelevant offers may frustrate customers leading to dissatisfaction or even attrition.

“Thus, there is a need for an improved systems and methods for determining appropriate products and services that are relevant for particular customers to drive sales of products or services.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventors’ summary information for this patent: “Some implementations of the disclosed systems, apparatus, methods and computer program products are configured for integrating a machine learning prediction platform with business rules to sort recommendations in near real-time based on propensity of the target object to accept. The predictive model is continuously updated with recommendation responses received by the system (closed-loop feedback). The described systems and methods also provide a solution to the “cold start” problem to provide recommendations for building a prediction model when data is lacking.

“As described in further detail below, such prediction platform may be seamlessly integrated with any type of application or service such as a Customer Relationship Management (CRM) Platform, a social networking system, or any other consumer or business software. While CRM platforms are discussed herein as an example of an application or service, the examples of applications or services described herein may be substituted for any suitable application or service such as those described above.

“Existing methods use heuristic and intuitive rules to established product fit recommendations. However, these broad-brush rules do not achieve the same performance as a personalized recommendation that is unique to each individual customer. Machine learning approaches for personalized recommendations work when substantial amounts of data are available. However, businesses with large catalogues of products increase the chances that they will offer customers irrelevant services/products because the machine learning (ML) models are poorly trained.

“By way of illustration, Raymond works as Inside Sales for a Sunrise Insurance, a major insurance provider that serves high-touch B2C (business-to-consumer) and smaller B2B (business-to-business) clients. He primarily focuses on existing accounts and increasing their share of wallet across their entire product line, for instance by offering Home or Renter’s insurance products to existing Auto insurance customers. However, simply offering additional insurance products to existing customers, who do not need such products or already have purchased similar products, may frustrate customers who may feel pressured by unwanted solicitations or annoyed by receiving irrelevant information.

“Using conventional techniques, offers may be provided in accordance with static rules. For example, the in-house marketing team at Sunrise Insurance may define account outreach strategies each quarter based on ad-hoc analyses about which types of customers are most likely to purchase specific products. These strategies also include various state specific rules based on which products can be sold in each state. However, the application of static rules ignores customer-specific characteristics.”

The claims supplied by the inventors are:

“1. A method comprising: transmitting via a communications interface one or more instructions for providing a graphical user interface (GUI) for presentation at a client device, the graphical user interface identifying a plurality of object definitions; processing an indication of a selection of a designated object definition of the plurality of object definitions; configuring a prediction model based, at least in part, on the designated object definition that has been selected; generating, using the prediction model, for each of a plurality of recommendations, a corresponding one of a plurality of propensity scores in association with a target object instance of the designated object definition based, at least in part, on a set of data field values associated with the target object instance of the designated object definition; selecting at least one recommendation of the plurality of recommendations based, at least in part, on the corresponding propensity score; and dynamically updating the prediction model based, at least in part, on a response to the selected recommendation.

“2. The method recited in claim 1, wherein each propensity score indicates a respective probability that the selected one of the plurality of recommendations will be accepted.

“3. The method recited in claim 1, wherein selecting at least one recommendation is performed according to a filter rule.

“4. The method recited in claim 1, further comprising: ordering the plurality of recommendations based, at least in part, on a predetermined hierarchy.

“5. The method recited in claim 1, the method further comprising: processing an indication of a selection of one or more object fields associated with the designated object definition; wherein the prediction model is further configured based, on the one or more object fields that have been selected.

“6. The method recited in claim 5, the method further comprising: selecting the target object instance based, at least in part, on a prediction group, the prediction group being based on a designated one of the object fields.

“7. The method recited claim 1, wherein the designated object definition is a contact object associated with an account object.

“8. The method recited in claim 1, wherein the prediction model is dynamically updated based on a plurality of responses, each response corresponding to a respective one of the recommendations.

“9. A database system implemented using a server system, the database system configurable to cause: transmitting via a communications interface one or more instructions for providing a graphical user interface (GUI) for presentation at a client device, the graphical user interface identifying a plurality of object definitions; processing an indication of a selection of a designated object definition of the plurality of object definitions; configuring a prediction model based, at least in part, on the designated object definition that has been selected; generating, using the prediction model, for each of a plurality of recommendations, a corresponding one of a plurality of propensity scores in association with a target object instance of the designated object definition based, at least in part, on a set of data field values associated with the target object instance of the designated object definition; selecting at least one recommendation of the plurality of recommendations based, at least in part, on the corresponding propensity score; and dynamically updating the prediction model based, at least in part, on a response to the selected recommendation.

“10. The computing system recited in claim 9, wherein each propensity score indicates a respective probability that the selected one of the plurality of recommendations will be accepted.

“11. The database system as recited in claim 9, the database system further configurable to cause: processing an indication of a selection of one or more object fields associated with the designated object definition; wherein the prediction model is further configured based, on the one or more object fields that have been selected.

“12. One or more non-transitory computer readable media having instructions stored thereon for performing a method, the method comprising: transmitting via a communications interface one or more instructions for providing a graphical user interface (GUI) for presentation at a client device, the graphical user interface identifying a plurality of object definitions; processing an indication of a selection of a designated object definition of the plurality of object definitions; configuring a prediction model based, at least in part, on the designated object definition that has been selected; generating, using the prediction model, for each of a plurality of recommendations, a corresponding one of a plurality of propensity scores in association with a target object instance of the designated object definition based, at least in part, on a set of data field values associated with the target object instance of the designated object definition; selecting at least one recommendation of the plurality of recommendations based, at least in part, on the corresponding propensity score; and dynamically updating the prediction model based, at least in part, on a response to the selected recommendation.

“13. The database system as recited in claim 11, the database system further configurable to cause: selecting the target object instance based, at least in part, on a prediction group, the prediction group being based on a designated one of the object fields.

“14. The database system as recited in claim 11, wherein the designated object definition is a contact object associated with an account object.

“15. The non-transitory computer-readable media as recited in claim 12, the method further comprising: processing an indication of a selection of one or more object fields associated with the designated object definition; wherein the prediction model is further configured based, on the one or more object fields that have been selected.

“16. The non-transitory computer-readable media as recited in claim 12, the method further comprising: selecting the target object instance based, at least in part, on a prediction group, the prediction group being based on a designated one of the object fields.

“17. The non-transitory computer-readable media as recited in claim 12, wherein the designated object definition is a contact object associated with an account object.”

For additional information on this patent, see: Casalaina, Marco. Dynamic ranking of recommendation pairings. U.S. Patent Number 11373232, filed May 24, 2019, and published online on June 28, 2022. 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=11373232.PN.&OS=PN/11373232RS=PN/11373232

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