Researchers Submit Patent Application, “Customized Risk Relationship User Interface Workflow”, for Approval (USPTO 20230017739): Patent Application
2023 FEB 07 (NewsRx) -- By a
No assignee for this patent application has been made.
News editors obtained the following quote from the background information supplied by the inventors: “An enterprise may want to collect information about a new potential risk relationship customer. For example, an insurer might want to collect information about a user in connection with a new automobile or homeowners insurance policy (e.g., a make, model, and year of an automobile or the address, square footage, and roof type of a residential property). Typically, a customer service representative might talk about these details via a telephone call center to collect this information. Increasingly, however, customers may prefer to interact with an enterprise digitally, such as via a smartphone application or web interface. In such cases, user interface workflows may guide the user through the digital collection of risk relationship information (e.g., which questions are asked, in what particular order, and the exact wording that is used to communicate with the user). Note, however, that a user interface workflow that is appropriate for one user might not be optimal with respect to another user (e.g., a younger user, a user who is already an existing customer, etc.).
“It would be desirable to provide improved systems and methods to accurately and/or automatically customize a risk relationship user interface workflow. Moreover, the results should be easy to access, understand, interpret, update, etc.”
As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “According to some embodiments, systems, methods, apparatus, computer program code and means are provided to accurately and/or automatically customize a risk relationship user interface workflow in a way that provides fast and useful results and that allows for flexibility and effectiveness when responding to those results.
“Some embodiments are directed to a user interface workflow customization system implemented via a back-end application computer server. A computer server receives, from a remote user device, information about a new potential risk relationship customer (including at least one new user parameter). Based on the new user parameter, the computer server accesses third-party data and utilizes a stored procedure to read data about the new potential risk relationship customer from an internal table of cloud data. The data read from the internal table is processed to dynamically evolve a schema and create an incremental view of cloud data. The computer server uses the incremental view to read and output a current batch of cloud data. A user interface workflow is then customized via a machine learning algorithm that processes the information about the new potential risk relationship customer, the third-party data, and the current batch of cloud data. A user information data store can then be updated based on information collected via user interface displays.
“Some embodiments comprise: means for receiving, at a back-end application computer server from a remote user device, information about a new potential risk relationship customer of an enterprise, including at least one new user parameter; based on the new user parameter, means for accessing third-party data about the new potential risk relationship customer; means for utilizing a stored procedure of a cloud computing environment curation engine to read data about the new potential risk relationship customer from an internal table of cloud data; means for processing the data read from the internal table to dynamically evolve a schema and create an incremental view of cloud data; means for using the created incremental view to read and output a current batch of cloud data about the new potential risk relationship customer; means for customizing a user interface workflow via a machine learning algorithm that processes the information about the new potential risk relationship customer, the third-party data, and the current batch of cloud data; and means for collecting information, including user parameters, via interactive user interface displays, to be stored in a user information data store in connection with a potential risk relationship, wherein the user information data store contains electronic records associated with users, each electronic record including an electronic record identifier and user parameters.
“In some embodiments, a communication device associated with a back-end application computer server exchanges information with remote devices in connection with interactive graphical user interfaces. The information may be exchanged, for example, via public and/or proprietary communication networks.
“A technical effect of some embodiments of the invention is an improved and computerized way to accurately and/or customize workflows in a way that provides fast and useful results. With these and other advantages and features that will become hereinafter apparent, a more complete understanding of the nature of the invention can be obtained by referring to the following detailed description and to the drawings appended hereto.”
The claims supplied by the inventors are:
“1. A user interface workflow customization system implemented via a back-end application computer server, comprising: (a) a user information data store that contains electronic records associated with users, each electronic record including an electronic record identifier and user parameters; (b) the back-end application computer server, associated with the enterprise and coupled to the user information data store, to: receive, from a remote user device, information about a new potential risk relationship customer of an enterprise, including at least one new user parameter, based on the new user parameter, access third-party data about the new potential risk relationship customer, utilize a stored procedure of a cloud computing environment curation engine to read data about the new potential risk relationship customer from an internal table of cloud data, process the data read from the internal table to dynamically evolve a schema and create an incremental view of cloud data, use the created incremental view to read and output a current batch of cloud data about the new potential risk relationship customer, and customize a user interface workflow via a machine learning algorithm that processes the information about the new potential risk relationship customer, the third-party data, and the current batch of cloud data; and © a communication port coupled to the back-end application computer server to facilitate an exchange of data with the remote user device to support interactive user interface displays that collect information, including user parameters, to be stored in the user information data store in connection with a potential risk relationship.
“2. The system of claim 1, wherein the user interface workflow is associated with at least one of: (i) an order of questions on the interface, (ii) a wording of questions on the interface, (iii) a selection of questions on the interface, (iv) a graphical presentation of the interface, and (v) an online-to-offline handoff process.
“3. The system of claim 1, wherein the enterprise is an insurer, the risk relationship is a potential insurance policy, and the user interface workflow is associated with at least one of: (i) automobile insurance, (ii) homeowners insurance, and (iii) an insurance bundle.
“4. The system of claim 3, wherein the user interface workflow is associated with at least one of: (i) a policy renewal, (ii) a potential insurance claim event, and (iii) insurance claims processing.
“5. The system of claim 3, wherein the user interface workflow leads to an insurance premium quote for the new potential insurance policy customer of the insurer.
“6. The system of claim 1, wherein the cloud computing environment curation engine receives data from a cloud computing environment ingestion engine.
“7. The system of claim 6, wherein the cloud computing environment ingestion engine receives data from a cloud management portal.
“8. The system of claim 1, wherein the cloud computing environment curation engine transmits data to a metadata framework.
“9. The system of claim 8, wherein the metadata framework transmits data to a data cloud platform publication engine that publishes entities for business consumption.
“10. The system of claim 9, wherein the data cloud platform publication engine creates analytical reporting.
“11. The system of claim 1, wherein the machine learning algorithm is associated with at least one of: (i) artificial intelligence, (ii) data mining, (iii) optimization, (iv) generalization, (v) supervised learning, (vi) unsupervised learning, (vii) semi-supervised learning, (viii) reinforcement learning, and (ix) dimensionality reduction.
“12. A computerized workflow customization method implemented via a back-end application computer server, comprising: receiving, at the back-end application computer server from a remote user device, information about a new potential risk relationship customer of an enterprise, including at least one new user parameter; based on the new user parameter, accessing third-party data about the new potential risk relationship customer; utilizing a stored procedure of a cloud computing environment curation engine to read data about the new potential risk relationship customer from an internal table of cloud data; processing the data read from the internal table to dynamically evolve a schema and create an incremental view of cloud data; using the created incremental view to read and output a current batch of cloud data about the new potential risk relationship customer; customizing a user interface workflow via a machine learning algorithm that processes the information about the new potential risk relationship customer, the third-party data, and the current batch of cloud data; and collecting information, including user parameters, via interactive user interface displays, to be stored in a user information data store in connection with a potential risk relationship, wherein the user information data store contains electronic records associated with users, each electronic record including an electronic record identifier and user parameters.
“13. The method of claim 12, wherein the user interface workflow is associated with at least one of: (i) an order of questions on the interface, (ii) a wording of questions on the interface, (iii) a selection of questions on the interface, (iv) a graphical presentation of the interface, and (v) an online-to-offline handoff process.
“14. The method of claim 12, wherein the enterprise is an insurer, the risk relationship is a potential insurance policy, and the user interface workflow is associated with at least one of: (i) automobile insurance, (ii) homeowners insurance, and (iii) an insurance bundle.
“15. The method of claim 14, wherein the user interface workflow is associated with at least one of: (i) a policy renewal, (ii) a potential insurance claim event, and (iii) insurance claims processing.
“16. The method of claim 14, wherein the user interface workflow leads to an insurance premium quote for the new potential insurance policy customer of the insurer.
“17. The method of claim 12, wherein the machine learning algorithm is associated with at least one of: (i) artificial intelligence, (ii) data mining, (iii) optimization, (iv) generalization, (v) supervised learning, (vi) unsupervised learning, (vii) semi-supervised learning, (viii) reinforcement learning, and (ix) dimensionality reduction.
“18. A non-transitory, computer-readable medium storing instructions, that, when executed by a processor, cause the processor to perform a workflow customization method implemented via a back-end application computer server, the method comprising: receiving, at the back-end application computer server from a remote user device, information about a new potential risk relationship customer of an enterprise, including at least one new user parameter; based on the new user parameter, accessing third-party data about the new potential risk relationship customer; utilizing a stored procedure of a cloud computing environment curation engine to read data about the new potential risk relationship customer from an internal table of cloud data; processing the data read from the internal table to dynamically evolve a schema and create an incremental view of cloud data; using the created incremental view to read and output a current batch of cloud data about the new potential risk relationship customer; customizing a user interface workflow via a machine learning algorithm that processes the information about the new potential risk relationship customer, the third-party data, and the current batch of cloud data; and collecting information, including user parameters, via interactive user interface displays, to be stored in a user information data store in connection with a potential risk relationship, wherein the user information data store contains electronic records associated with users, each electronic record including an electronic record identifier and user parameters.
“19. The medium of claim 18, wherein the user interface workflow is associated with at least one of: (i) an order of questions on the interface, (ii) a wording of questions on the interface, (iii) a selection of questions on the interface, (iv) a graphical presentation of the interface, and (v) an online to offline handoff process.
“20. The medium of claim 18, wherein the enterprise is an insurer, the risk relationship is a potential insurance policy, and the user interface workflow is associated with at least one of: (i) automobile insurance, (ii) homeowners insurance, and (iii) an insurance bundle.
“21. The method of claim 20, wherein the user interface workflow is associated with at least one of: (i) a policy renewal, (ii) a potential insurance claim event, and (iii) insurance claims processing.”
For additional information on this patent application, see: Feldman, Julia M.; Huber, Sara M.; Kolanji, Gopinath; M’Sadoques,
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