Patent Issued for Processing system performing dynamic training response output generation control (USPTO 11601384): Allstate Insurance Company

2023 MAR 29 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- A patent by the inventors Schreier, Elizabeth (Glenview, IL, US), Tillotson, Tiffany (Algonquin, IL, US), filed on September 10, 2020, was published online on March 7, 2023, according to news reporting originating from Alexandria, Virginia, by NewsRx correspondents.

Patent number 11601384 is assigned to Allstate Insurance Company (Northbrook, Illinois, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “Aspects of the disclosure relate to enhanced processing systems for providing a dynamic training response output with improved dynamic training response output determination capabilities. In particular, one or more aspects of the disclosure relate to dynamic training response output generation control platforms that utilize natural language processing and understanding to facilitate dynamic training interactions.

“Because many organizations and individuals rely on portals as a method for receiving information related to products and concepts, improving the quality of such portals for their managing organizations is important. In many instances, however, it may be difficult to effectively generate dynamic training response outputs tailored to a user.”

In addition to the background information obtained for this patent, NewsRx journalists also obtained the inventors’ summary information for this patent: “Aspects of the disclosure provide effective, efficient, scalable, and convenient technical solutions that address and overcome the technical problems associated with optimizing the performance of dynamic training response output generation control platforms, along with the information that such systems may provide in response to training input requests, using dynamic training response output generation techniques.

“In accordance with one or more arrangements discussed herein, a computing platform having at least one processor, a communication interface, and memory may receive, from a user device, a request for a dynamic training interface. The computing platform may generate, in response to receiving the request for the dynamic training interface, initial dynamic training interface information. The computing platform may generate one or more commands directing the user device to generate an initial dynamic training interface using the initial dynamic training interface information. The computing platform may send, via the communication interface, the initial dynamic training interface information and the one or more commands directing the user device to generate the initial dynamic training interface using the initial dynamic training interface information. The computing platform may receive, from the user device and in response to the initial dynamic training interface, a training request input. The computing platform may generate one or more commands directing a natural language understanding (NLU) engine to perform natural language understanding and processing on the training request input to determine a natural language result output. The computing platform may send, to the NLU engine, the training request input and the one or more commands directing the NLU engine to perform natural language understanding and processing on the training request input to determine the natural language result output. The computing platform may receive, from the NLU engine, the natural language result output. The computing platform may determine one or more third party data sources that correspond to the natural language result output. The computing platform may generate one or more commands directing the one or more third party data sources to send source data corresponding to the natural language result output. The computing platform may send, to the one or more third party data sources, the one or more commands directing the one or more third party data sources to send source data corresponding to the natural language result output. The computing platform may receive, from the one or more third party data sources, the source data corresponding to the natural language result output. The computing platform may generate a dynamic training response output based on the natural language result output and the source data. The computing platform may generate one or more commands directing the user device to cause display of the dynamic training response output. The computing platform may send, to the user device, the dynamic training response output and the one or more commands directing the user device to cause display of the dynamic training response output.

“In some arrangements, the computing platform may establish a wireless data connection with a profile correlation computing platform. The computing platform may generate one or more commands directing the profile correlation computing platform to update based on the natural language result output. The computing platform may send, using the wireless data connection with the profile correlation computing platform, the one or more commands directing the profile correlation computing platform to update based on the natural language result output.

“In some examples, the computing platform may determine that a final dynamic training response output has been sent. The computing platform may determine, after determining that the final dynamic training response output has been sent, that an agent is requested. The computing platform may generate one or more commands to identify an agent corresponding to a user of the user device. The computing platform may send, to the profile correlation computing platform, the one or more commands to identify the agent corresponding to the user, wherein sending the one or more commands causes the profile correlation computing platform to determine the agent using one or more machine learning algorithms and one or more machine learning datasets.

“In some arrangements, the computing platform may determine that the final dynamic training response output has been sent based on an indication from the user.”

The claims supplied by the inventors are:

“1. A system comprising: a dynamic training response output generation control platform configured to send one or more commands directing a user device to generate an initial dynamic training interface using initial dynamic training interface information, the dynamic training response output generation control platform receiving a training request input captured using the initial dynamic training interface; and a natural language understanding (NLU) engine configured to perform natural language understanding and processing on the training request input to determine a natural language result output, a dynamic training response output being generated based on the natural language result output and sent to the user device for display, wherein an agent is identified based on the training request input, wherein the agent is determined to correspond with a user corresponding to the user device based on at least one of a topic, an agent matching output representing a strength of a match between the user and the agent, or a location, and wherein the location corresponds to a predetermined distance from a residence associated with the user when the agent is determined.

“2. A method comprising: sending one or more commands directing a user device to generate an initial dynamic training interface using initial dynamic training interface information; receiving training request input captured using the initial dynamic training interface; obtaining a natural language result output corresponding to the training request input; generating a dynamic training response output based on the natural language result output; sending one or more commands directing the user device to cause display of the dynamic training response output; and determining that an agent has been requested; and identifying the agent based on at least one of a topic, an agent matching output representing a strength of a match between a user corresponding to the user device and the agent, or a location, and wherein the location corresponds to a predetermined distance from a residence associated with the user when the agent is identified.

“3. The method of claim 2, further comprising: determining, based on previously received training request inputs, that granularity of the dynamic training response output should be increased; and determining a more specific dynamic training response output in comparison to previously sent dynamic training response outputs.

“4. One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor cause the computing platform to: send one or more commands directing a user device to generate an initial dynamic training interface using initial dynamic training interface information; receive a training request input from the user device; obtaining a natural language result output generated based on the training request input; generate a dynamic training response output based on the natural language result output; send one or more commands directing the user device to cause display of the dynamic training response output; identify an agent based on the training request input and in response to a request for the agent; and select the agent based on at least one of a topic, an agent matching output representing a strength of a match between a user corresponding to the user device and the agent, or a location, and wherein the location corresponds to a predetermined distance from a residence associated with the user when the agent is selected.

“5. The system of claim 1, wherein the one or more commands are sent to the user device in response to a request from the user device.

“6. The system of claim 1, wherein the initial dynamic training interface prompts the user to select either a guided dynamic training experience or an unguided dynamic training experience.

“7. The system of claim 1, wherein the dynamic training response output is further generated based on source data corresponding to the natural language result output.

“8. The system of claim 7, wherein the source data is obtained from one or more third party data sources.

“9. The system of claim 1, wherein a profile correlation platform is updated based on the natural language result output.

“10. The method of claim 2, further comprising: identifying the agent using a profile correlation platform updated using the natural language result output.

“11. The one or more non-transitory computer-readable media of claim 4, wherein the initial dynamic training interface prompts the user to select either a guided dynamic training experience or an unguided dynamic training experience.

“12. The one or more non-transitory computer-readable media of claim 4, wherein the dynamic training response output is further generated based on source data corresponding to the natural language result output.”

URL and more information on this patent, see: Schreier, Elizabeth. Processing system performing dynamic training response output generation control. U.S. Patent Number 11601384, filed September 10, 2020, and published online on March 7, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11601384)&db=USPAT&type=ids

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