Patent Issued for Next generation digitized modeling system and methods (USPTO 11544041): Aetna Inc.

2023 JAN 20 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Aetna Inc. (Hartford, Connecticut, United States) has been issued patent number 11544041, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Bliss, Michelle (Venetia, PA, US), Jensen, Claus T. (Pawling, NY, US).

This patent was filed on November 22, 2019 and was published online on January 3, 2023.

From the background information supplied by the inventors, news correspondents obtained the following quote: “In developing new applications for business, there is an increasing complexity in projects that seek to integrate multiple existing applications and/or data sources to generate results. When architects sit down to design a new application, they may take into account overall company strategies, architectural principles, vendor and hosted infrastructure, among other factors. As industries grow and the list of resources available to leverage increase, the job of defining a list of design elements that a project may require will continue to increase in complexity. As such, an organization may be required to streamline a design blueprint quickly and efficiently.

“Systems and techniques available for determining a solution design blueprint are highly dependent on a knowledge base and skillset possessed by a particular individual (e.g., the architect). Such existing techniques lack the application of a holistic approach that is integrated into existing architectural resources. Furthermore, the existing techniques fail to apply leading edge artificial intelligence solutions to assist the architects in creating sufficient solution design blueprints that meet the goals for specific projects. There is a need to increase the efficiency and quality of the design process and to support the process across a breadth of different design goals.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “A system for generating solution design blueprints is disclosed herein. In some embodiments, the system includes a user interface configured to receive a textual request from a user and an ingestion engine in communication with the user interface. The ingestion engine is configured to process the textual request by one or more machine learning algorithms to generate a solution design blueprint. In an embodiment, at least one machine learning algorithm is configured to parse the textual request based on semantic data models. In an embodiment, at least one additional machine learning algorithm is configured to map parsed components of the textual request to components in a design library. In an embodiment, the design library includes at least one of relational tables and unstructured data.

“In some embodiments, the one or more machine learning algorithms includes at least one of a recurrent neural network (RNN), a convolutional neural network (CNN), or a self-organizing feature map (SOFM).

“In some embodiments, the system further includes an intelligent design generator. The intelligent design generated is configured to reject or accept the solution design blueprint and, if the solution design blueprint is rejected, then select a second solution design blueprint generated by the ingestion engine in response to the textual request, or, if the solution design blueprint is accepted, then present the solution design blueprint to the user in the user interface. In an embodiment, the user interface is configured to receive feedback from the user for the solution design blueprint. Furthermore, at least one machine learning algorithm can be trained based on the feedback.

“In some embodiments, the intelligent design generator is further configured to consume the solution design blueprint to generate an application framework. The application framework can include source code in a particular programming language that is readable by a compiler.

“In some embodiments, the system can also include at least one user queue that monitors a work flow of a user in generating an application from the application framework. At least one machine learning algorithm can be adjusted based on an analysis of the work flow.”

The claims supplied by the inventors are:

“1. A system for generating solution design blueprints, the system comprising: a memory; and a processor coupled to the memory and configured to implement: a user interface configured to receive a textual request from a user; and an ingestion engine in communication with the user interface and configured to: process the textual request by two or more machine learning algorithms to generate a solution design blueprint, wherein the solution design blueprint comprises a machine-readable representation of a description for a software framework, and wherein a format of the solution design blueprint is a structured document in an eXtended Markup Language (XML) or a JavaScript Object Notation (JSON) format, wherein at least one machine learning algorithm of the two or more machine learning algorithms is configured to parse the textual request based on semantic data models, and at least one additional machine learning algorithm of the two or more machine learning algorithms is configured to map parsed components of the textual request to components in a design library.

“2. The system of claim 1, wherein the design library includes at least one of relational tables and unstructured data.

“3. The system of claim 1, wherein the two or more machine learning algorithms includes at least one of a recurrent neural network (RNN), a convolutional neural network (CNN), or a self-organizing feature map (SOFM).

“4. The system of claim 1, wherein the processor is further configured to implement: an intelligent design generator configured to: reject or accept the solution design blueprint, and responsive to determining that the solution design blueprint is rejected, select a second solution design blueprint generated by the ingestion engine in response to the textual request, or responsive to determining that the solution design blueprint is accepted, present the solution design blueprint to the user in the user interface.

“5. The system of claim 4, wherein the user interface is configured to receive feedback from the user for the solution design blueprint, and wherein at least one machine learning algorithm is trained based on the feedback.

“6. The system of claim 4, wherein the intelligent design generator is further configured to consume the solution design blueprint to generate an application framework.

“7. The system of claim 6, wherein the processor is further configured to implement: at least one user queue that monitors a work flow of the user in generating an application from the application framework, wherein at least one machine learning algorithm is adjusted based on an analysis of the work flow.

“8. The system of claim 1, wherein the user interface utilizes predictive analytics to suggest completions for the textual request while the user is typing.

“9. A method for generating solution design blueprints, the method comprising: receiving a textual request from a user interface; parsing, via at least one machine learning algorithm, the textual request based on semantic data models to generate a parsed request; generating, via at least one additional machine learning algorithm, a solution design blueprint based on a design library, wherein the solution design blueprint comprises a machine-readable representation of a description for a software framework, and wherein a format of the solution design blueprint is a structured document in an eXtended Markup Language (XML) or a JavaScript Object Notation (JSON) format; and presenting the solution design blueprint to a user in the user interface.

“10. The method of claim 9, wherein the design library includes at least one of relational tables and unstructured data.

“11. The method of claim 9, wherein the at least one machine learning algorithm and the at least one additional machine learning algorithm comprise at least one of a recurrent neural network (RNN), a convolutional neural network (CNN), or a self-organizing feature map (SOFM).

“12. The method of claim 9, further comprising: receiving a plurality of solution design blueprints for the textual request; ranking each of the plurality of solution design blueprints; selecting a particular solution design blueprint based on the ranking; and presenting the particular solution design blueprint to the user in the user interface.

“13. The method of claim 9, further comprising: receiving feedback from the user for the solution design blueprint, and training at least one machine learning algorithm based on the feedback.

“14. The method of claim 9, further comprising: consuming the solution design blueprint to generate an application framework.

“15. The method of claim 14 further comprising: collecting, in a user queue, a work flow of the user in developing an application using the application framework; and adjusting at least one machine learning algorithm based on an analysis of the work flow.

“16. A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to generate solution design blueprints by performing steps comprising: receiving a textual request from a user interface; parsing, via at least one machine learning algorithm, the textual request based on semantic data models to generate a parsed request; generating, via at least one additional machine learning algorithm, a solution design blueprint based on a design library, wherein the solution design blueprint comprises a machine-readable representation of a description for a software framework, and wherein a format of the solution design blueprint is a structured document in an eXtended Markup Language (XML) or a JavaScript Object Notation (JSON) format; and presenting the solution design blueprint to a user in the user interface.

“17. The non-transitory computer readable medium of claim 16, the steps further comprising: receiving a plurality of solution design blueprints for the textual request; ranking each of the plurality of solution design blueprints; selecting a particular solution design blueprint based on the ranking; and presenting the particular solution design blueprint to the user in the user interface.

“18. The non-transitory computer readable medium of claim 16, the steps further comprising: consuming the solution design blueprint to generate an application framework; collecting, in a user queue, a work flow of the user in developing an application using the application framework; and adjusting at least one machine learning algorithm based on an analysis of the work flow.”

For the URL and additional information on this patent, see: Bliss, Michelle. Next generation digitized modeling system and methods. U.S. Patent Number 11544041, filed November 22, 2019, and published online on January 3, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11544041)&db=USPAT&type=ids

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