Systems and methods for selecting a treatment schema based on user willingness
US-2021098099-A1 · Apr 1, 2021 · US
US12436959B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-12436959-B2 |
| Application number | US-202418816376-A |
| Country | US |
| Kind code | B2 |
| Filing date | Aug 27, 2024 |
| Priority date | Apr 30, 2023 |
| Publication date | Oct 7, 2025 |
| Grant date | Oct 7, 2025 |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
An apparatus and method for generating an instruction set for a user is provided. The apparatus includes at least a processor and a memory connected to the processor. The memory contains instructions configuring the at least a processor to receive a client datum from a client, where the client datum describes resources of the client, and to receive a user datum from the user, where user datum includes a target datum that describes resource transfer data from the client to the user. Initiation of resource transfer described by the target datum is triggered by the pattern exceeding a threshold. In addition, the memory contains instructions configuring the at least a processor to generate an interface query data structure including an input field and to display the first transfer datum and the second transfer datum hierarchically based on a user-input datum input to the input field.
Opening claim text (preview).
What is claimed is: 1. An apparatus for generating an instruction set for a user, the apparatus comprising: at least a processor; a memory connected to the processor, the memory containing instructions configuring the at least a processor to: receive a client datum from a client device, wherein the client datum describes at least a resource of the client device; receive a user datum from the user; classify the client datum and the user datum to a category of a plurality of categories; calculate a target datum wherein the target datum identifies an optimum confidence level; generate a transfer datum as a function of the optimum confidence level; wherein generating the transfer datum further comprises generating an interface query data structure including an input field based on ranking the first transfer datum and the at least a second transfer datum; receive at least a user-input datum into an input field, wherein the user-input datum describes data for selecting a preferred attribute of resource transfer data associated the transfer datum; and display an instruction set including displaying the transfer datum based on the user-input datum wherein the instruction set is further configured to: generate a strategy recommendation as a function of at least the target datum wherein the strategy recommendation identifies a client relationship recommendation. 2. The apparatus of claim 1 , wherein classifying the client datum further comprises a client characteristic classification model wherein the client characteristic classification model classifies the client datum into at least a client character group. 3. The apparatus of claim 1 , wherein generating the transfer datum further comprises identifying a first transfer datum and at least a second transfer datum from transfer data. 4. The apparatus of claim 3 , wherein identifying the first transfer datum comprises classifying the target datum to the first transfer datum. 5. The apparatus of claim 1 , wherein the interface query data structure configures a remote display device to display the input field to the user. 6. The apparatus of claim 1 , wherein generating the instruction set further comprises: classifying the client datum to one or more of the plurality of categories based on a pattern that is representative of client interaction with the user. 7. The apparatus of claim 1 , wherein the client datum further comprises a pattern that is representative of client interactions. 8. The apparatus of claim 1 , wherein generating the strategy recommendation further comprises a machine learning model. 9. The apparatus of claim 1 , wherein generating the strategy recommendation further comprises a client relationship recommendation. 10. A method for generating an instruction set for a user, the method comprising: receiving, by a computing device, a client datum from a client device, wherein the client datum describes resources of the client device and a pattern that is representative of client interactions; receiving, by the computing device, a user datum from the user; classifying, by the computing device, the client datum and the user datum to a category of a plurality of categories; calculating, by the computing device, a target datum wherein the target datum identifies an optimum confidence level; generating, by the computing device, a transfer datum as a function of the optimum confidence level; wherein generating the transfer datum further comprises generating an interface query data structure including an input field based on ranking the first transfer datum and the at least a second transfer datum; receiving, by the computing device, at least a user-input datum into an input field, wherein the user-input datum describes data for selecting a preferred attribute of resource transfer data associated the transfer datum displaying, by the computing device, an instruction set including displaying the first transfer datum based on the user-input datum wherein the instruction set is further configured to: generate a strategy recommendation as a function of at least the target datum wherein the strategy recommendation identifies a client relationship recommendation. 11. The method of claim 10 , wherein classifying the client datum further comprises a client characteristic classification model wherein the client classification model classifies the client datum into at least a client character group. 12. The method of claim 10 , wherein generating the transfer datum further comprises identifying a first transfer datum and at least a second transfer datum from transfer data. 13. The method of claim 12 , wherein identifying the first transfer datum comprises classifying the target datum to the first transfer datum. 14. The method of claim 10 , wherein the interface query data structure configures a remote display device to display the input field to the user. 15. The method of claim 10 , wherein generating the instruction set further comprises: classifying the client datum to one or more of the plurality of categories based on the pattern that is representative of client interaction with the user. 16. The method of claim 10 , wherein the client datum further comprises a pattern that is representative of client interactions. 17. The method of claim 10 , wherein generating the strategy recommendation further comprises a machine learning model. 18. The method of claim 10 , wherein generating the strategy recommendation further comprises a client relationship recommendation.
Clustering or classification · CPC title
Fuzzy inferencing · CPC title
Services · CPC title
Marketing; Price estimation or determination; Fundraising · CPC title
Resource planning, allocation, distributing or scheduling for enterprises or organisations · CPC title
Related publications grouped by family.
Answers are generated from the same data shown on this page.