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US 12,755,169 B2Granted
Workflow-specific recommendation framework
AssigneeSap SE
InventorsSumaiya P K, Prateek Bajaj
Life of this patent
Estimating the term…
Abstract
Systems and methods include acquisition of data representing one or more user interactions with a user interface of an application, determination of a user workflow from a plurality of user workflows based on the acquired data, determination of one of a plurality of trained models based on the determined user workflow, each of the plurality of trained models associated with a respective one of the plurality of user workflows, and generation of an inference based on the data using the determined trained model.
Claims9
A system comprising:
- at least one processing unit;
- and
- a non-transitory machine-readable medium storing instructions that, when executed by the at least one processing unit, cause the at least one processing unit to perform operations comprising:
- acquiring data identifying one or more user interactions executed by an application user on a user interface of an application;
- inputting a first subset of the acquired data to a first trained model to predict, from a plurality of user workflows, a first user workflow in which the application user is engaged;
- identifying one of a plurality of trained models which has been trained based on historical data identifying one or more user interactions executed by prior application users engaged in the first user workflow, each of the plurality of trained models having been trained based on respective different historical data identifying one or more user interactions executed by prior application users engaged in a respective different one of the plurality of user workflows, and each of the plurality of trained models having been trained to output a different respective inference target;
- and
- generating an inference by inputting a second subset of the acquired data to the identified trained model, the second subset and the first subset being mutually exclusive.
5 more elements
A method comprising:
- acquiring data identifying one or more user interactions executed by an application user on a user interface of an application;
- inputting a first subset of the acquired data to a first trained model to predict, from a plurality of user workflows, a first user workflow in which the application user is engaged;
- identifying one of a plurality of trained models which has been trained based on historical data identifying one or more activities executed by prior application users engaged in the first user workflow, each of the plurality of trained models having been trained based on respective different historical data identifying one or more activities executed by prior application users engaged in a respective different one of the plurality of user workflows, and each of the plurality of trained models trained to output a different respective inference target;
- and
- generating an inference by inputting a second subset of the acquired data to the identified trained model, the second subset and the first subset being mutually exclusive.
2 more elements
One or more non-transitory computer-readable media storing processor-executable program code executable by a processing unit of a computing system to cause the computing system to:
- acquire data identifying one or more user interactions executed by an application user on a user interface of an application;
- inputting a first subset of the acquired data to a first trained model to predict, from a plurality of user workflows, a first user workflow in which the application user is engaged;
- identify one of a plurality of trained models which has been trained based on historical data identifying one or more user interactions executed by prior application users engaged in the first user workflow, each of the plurality of trained models having been trained based on respective different historical data identifying one or more user interactions executed by prior application users engaged in a respective different one of the plurality of user workflows, and each of the plurality of trained models having been trained to output a different respective inference target;
- and
- generate an inference by inputting a second subset of the acquired data to the identified trained model, the second subset and the first subset being mutually exclusive.
2 more elements
Dependencies are inferred from the claim text.