Patent Issued for Apparatus and method for relativistic event perception prediction and content creation (USPTO 11310175): Assurant Inc.

2022 MAY 10 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Brusky, Ron (Miami, FL, US), Matta, Rajiv (Atlanta, GA, US), filed on August 13, 2020, was published online on April 19, 2022, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11310175 is assigned to Assurant Inc. (New York, New York, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “The effective preparation for potentially catastrophic events, such as storms and other natural disasters, infrastructure failure, and other casualties has challenged individuals and the collective population for centuries. Some of the technical challenges that hinder effective preparation efforts involve the difficulties in predicting how individuals within a given population will perceive and react to relatively rare events and the difficulty in communicating targeted information and other content to multiple individuals who are likely to respond to such events and information in widely divergent ways. The inventors of the invention disclosed herein have identified these and other technical challenges, and developed the solutions described and otherwise referenced herein.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “An apparatus, computer program product, and method are therefore provided in accordance with an example embodiment in order permit the efficient prediction of a user-specific, relativistic perception of one or more events and the generation of digital content item sets based at least in part on the predicted perception. In this regard, the method, apparatus and computer program product of an example embodiment provide for the creation of renderable digital content item sets through the collection of user-related data objects and the application of such data objects to a machine learning model to determine a predicted relativistic perception. Moreover, the method, apparatus, and computer program product of an example embodiment provide for use of the machine learning model in connection with the selection and/or generation of user-specific digital content item sets that may be presented as renderable objects in response to received queries from a user.

“In an example embodiment, an apparatus is provided, the apparatus comprising a processor and a memory, the memory comprising instructions that configure the apparatus to: receive a message request data object from a client device associated with a user; extract, from the message request data object, a user identification data set and a request data set; receive a user context data object, wherein the user context data object is associated with the user identification data set; receive an event probability data object, wherein the user context data object is associated with the user identification data set; receive an event perception data object, wherein the event perception data object is associated with the user identification data set; retrieve a user-specific digital content item set, wherein retrieving the user-specific digital content item set comprises applying the user context data object, the event probability data object, and the event perception data object to a first model; and generate a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of the client device associated with the user.

“In some example implementations of such an apparatus, the user identification data set comprises an authenticated indication of the identity of the user. In some such example implementations, and in other example implementations, the user context data object comprises a geographic parameter set, wherein the geographic parameter set comprises an indication of a user-specific geographic location; a user history parameter set, wherein the user history parameter set comprises a set of data associated with prior interactions between the user and an entity associated with the apparatus; and a user biographical parameter set, wherein the user biographical parameter set comprises a set of traits that are each associated with the user. In some such example implementations, and in other example implementations, the event probability data object comprises a set of indications of a plurality of potential events and a set of probabilities of the occurrence of each potential event within the plurality of potential events. In some such example implementations, and in other example implementations, the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set and a user behavior set.

“In some example implementations of an apparatus in accordance with this embodiment, the user-specific digital content item set comprises a first user-specific message and a first user-selectable option set. In some such example implementations, and in other example implementations, retrieving a user-specific digital content item set comprises generating, by the first model, a learned user profile; applying the learned user profile to a plurality of potential messages; selecting the first user-specific message from the plurality of potential messages; applying the learned user profile to a plurality of user-selectable options; selecting the first user-selectable option set from among a plurality of user-selectable options; and assigning the first user-specific message and the first user-selectable option set to the user-specific digital content item set. In some such example implementations, and in other example implementations, generating the learned user profile comprises a user-specific expected response set based at least in part on the user context data object, the event probability data object, and the event perception data object. In some such example implementations, and in other example implementations, the first model is a machine learning model.

“In another example embodiment, a computer program product is provided, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions configured to: receive a message request data object from a client device associated with a user; extract, from the message request data object, a user identification data set and a request data set; receive a user context data object, wherein the user context data object is associated with the user identification data set; receive an event probability data object, wherein the user context data object is associated with the user identification data set; receive an event perception data object, wherein the event perception data object is associated with the user identification data set; retrieve a user-specific digital content item set, wherein retrieving the user-specific digital content item set comprises applying the user context data object, the event probability data object, and the event perception data object to a first model; and generate a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of the client device associated with the user.”

The claims supplied by the inventors are:

“1. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to: retrieve a user-specific digital content item set for a user comprising a first user-specific message, wherein retrieving the user-specific digital content item set comprises applying a user context data object, an event probability data object, and an event perception data object to a first model, wherein retrieving the user-specific digital content item set comprises: generating, by the first model, a learned user profile; applying the learned user profile to a plurality of potential messages; selecting the first user-specific message from the plurality of potential messages; and assigning the first user-specific message to the user-specific digital content item set; and generate a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of a client device associated with the user.

“2. The apparatus of claim 1, wherein the user-specific digital content item set further comprises a first user-selectable option set, and wherein retrieving the user-specific digital content item set further comprises: applying the learned user profile to a plurality of user-selectable options; selecting the first user-selectable option set from among a plurality of user-selectable options; and assigning the first user-selectable option set to the user-specific digital content item set.

“3. The apparatus of claim 1, wherein the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set, the user-generated content set comprising data associated with one or more interactions between the user and one or more social media platforms.

“4. The apparatus of claim 1, wherein the event probability data object comprises real-time event data based on one or more of contemporaneous weather reports, contemporaneous social media posts, and contemporaneous news records.

“5. The apparatus of claim 4, wherein the event probability data object further comprises historical event data based on one or more of historical event records, historical footprint data, and historical event frequency data.

“6. The apparatus of claim 5, wherein at least one of the real-time event data or the historical event data further comprises data associated with emergency service telephonic records.

“7. The apparatus of claim 1, wherein applying the learned user profile to the plurality of potential messages comprises: determining a score for each potential message of the plurality of potential messages based on an application of profile weights associated with the learned user profile; wherein the first user-specific message is selected from the plurality of potential messages based the first user-specific message having a score greater than each other potential message of the plurality of potential messages.

“8. The apparatus of claim 1, wherein the user-specific digital content item set is retrieved in response to receiving a message request data object from the client device associated with the user.

“9. A non-transitory computer-readable medium storing computer-executable instructions for: retrieving a user-specific digital content item set for a user comprising a first user-specific message, wherein retrieving the user-specific digital content item set comprises applying a user context data object, an event probability data object, and an event perception data object to a first model, wherein retrieving the user-specific digital content item set comprises: generating, by the first model, a learned user profile; applying the learned user profile to a plurality of potential messages; selecting the first user-specific message from the plurality of potential messages; and assigning the first user-specific message to the user-specific digital content item set; and generating a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of a client device associated with the user.

“10. The non-transitory computer-readable medium of claim 9, wherein the user-specific digital content item set further comprises a first user-selectable option set, and wherein retrieving the user-specific digital content item set further comprises: applying the learned user profile to a plurality of user-selectable options; selecting the first user-selectable option set from among a plurality of user-selectable options; and assigning the first user-selectable option set to the user-specific digital content item set.

“11. The non-transitory computer-readable medium of claim 9, wherein the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set, the user-generated content set comprising data associated with one or more interactions between the user and one or more social media platforms.

“12. The non-transitory computer-readable medium of claim 9, wherein applying the learned user profile to the plurality of potential messages comprises: determining a score for each potential message of the plurality of potential messages based on an application of profile weights associated with the learned user profile; wherein the first user-specific message is selected from the plurality of potential messages based the first user-specific message having a score greater than each other potential message of the plurality of potential messages.

“13. A method comprising: retrieving a user-specific digital content item set for a user comprising a first user-specific message, wherein retrieving the user-specific digital content item set comprises applying a user context data object, an event probability data object, and an event perception data object to a first model, wherein retrieving the user-specific digital content item set comprises: generating, by the first model, a learned user profile; applying the learned user profile to a plurality of potential messages; selecting the first user-specific message from the plurality of potential messages; and assigning the first user-specific message to the user-specific digital content item set; and generating a control signal causing a renderable object comprising the user-specific digital content item set to be displayed on a user interface of a client device associated with the user.

“14. The method of claim 13, wherein the user-specific digital content item set further comprises a first user-selectable option set, and wherein retrieving the user-specific digital content item set further comprises: applying the learned user profile to a plurality of user-selectable options; selecting the first user-selectable option set from among a plurality of user-selectable options; and assigning the first user-selectable option set to the user-specific digital content item set.

“15. The method of claim 13, wherein the event perception data object comprises a user-specific event perception profile based at least in apart on a user-generated content set, the user-generated content set comprising data associated with one or more interactions between the user and one or more social media platforms.

“16. The method of claim 13, wherein the event probability data object comprises real-time event data based on one or more of contemporaneous weather reports, contemporaneous social media posts, and contemporaneous news records.

“17. The method of claim 16, wherein the event probability data object further comprises historical event data based on one or more of historical event records, historical footprint data, and historical event frequency data.

“18. The method of claim 17, wherein at least one of the real-time event data or the historical event data further comprises data associated with emergency service telephonic records.

“19. The method of claim 13, wherein applying the learned user profile to the plurality of potential messages comprises: determining a score for each potential message of the plurality of potential messages based on an application of profile weights associated with the learned user profile; wherein the first user-specific message is selected from the plurality of potential messages based the first user-specific message having a score greater than each other potential message of the plurality of potential messages.

“20. The method of claim 13, wherein the user-specific digital content item set is retrieved in response to receiving a message request data object from the client device associated with the user.”

URL and more information on this patent, see: Brusky, Ron. Apparatus and method for relativistic event perception prediction and content creation. U.S. Patent Number 11310175, filed August 13, 2020, and published online on April 19, 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=11310175.PN.&OS=PN/11310175RS=PN/11310175

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