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US 12,731,582 B2Granted
Natural language processing model for task-based system
AssigneeInternational Business Machines Corporation
InventorsMichelle Brachman, James Johnson, Qian Pan, Casey Dugan
Life of this patent
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Abstract
A present invention embodiment analyzes user input via natural language processing. A natural language utterance from a user is analyzed to determine one or more computing tasks. the natural language utterance is analyzed using a knowledge base to identify one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance. An indication that the user accepted at least one modification of the one or more modifications is received, wherein the at least one modification modifies the one or more computing tasks. The modified one or more computing tasks are executed.
Claims20
A computer-implemented method of analyzing a user input via natural language processing comprising:
- training a machine learning based natural language processing (NLP) model, using sequences of tasks, to determine one or more computing tasks from a constrained set of computing tasks based at least on a knowledge base and user utterance data;
- analyzing, using the machine learning based NLP model, a natural language utterance from a user to determine the one or more computing tasks;
- analyzing the natural language utterance to identify one or more common n-grams, from prior attempt records of the knowledge base, that are similar to the natural language utterance;
- identifying, based on the one or more common n-grams, one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance;
- receiving an indication that the user accepted at least one modification of the one or more modifications, wherein the at least one modification modifies the one or more computing tasks and the one or more modifications are selected based on a score of each of a plurality of previous user utterances, and wherein the score is determined, using the prior attempt records, based on how many times a previous user accepted the at least one modification;
- executing, using the machine learning based NLP model, the modified one or more computing tasks;
- updating the knowledge base based at least in part on executing the modified one or more computing tasks;
- and
- retraining the machine learning based NLP model using the updated knowledge base.
6 more elements
A computer system for analyzing user input via natural language processing comprising:
- one or more processors;
- and
- one or more memory devices coupled to the one or more processors, wherein the one or more processors are configured to:
- train a machine learning based natural language processing (NLP) model, using sequences of tasks, to determine one or more computing tasks from a constrained set of computing tasks based at least on a knowledge base and user utterance data;
- analyze, using the machine learning based NLP model, a natural language utterance from a user to determine the one or more computing tasks;
- analyze the natural language utterance to identify one or more common n-grams, from prior attempt records of the knowledge base, that are similar to the natural language utterance;
- identify, based on the one or more common n-grams, one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance;
- receive an indication that the user accepted at least one modification of the one or more modifications, wherein the at least one modification modifies the one or more computing tasks and the one or more modifications are selected based on a score of each of a plurality of previous user utterances, and wherein the score is determined, using the prior attempt records, based on how many times a previous user accepted the at least one modification;
- execute, using the machine learning based NLP model, the modified one or more computing tasks;
- update the knowledge base based at least in part on executing the modified one or more computing tasks;
- and
- retrain the machine learning based NLP model using the updated knowledge base.
9 more elements
A non-transitory computer-readable medium storing a set of instructions for analyzing user input via natural language processing, the set of instructions comprising:
- one or more instructions that, when executed by one or more processors of a device, cause the device to:
- train a machine learning based natural language processing (NLP) model, using sequences of tasks, to determine one or more computing tasks from a constrained set of computing tasks based at least on a knowledge base and user utterance data;
- analyze, using the machine learning based NLP model, a natural language utterance from a user to determine the one or more computing tasks;
- analyze the natural language utterance to identify one or more common n-grams, from prior attempt records of the knowledge base, that are similar to the natural language utterance;
- identify, based on the one or more common n-grams, one or more modifications to the natural language utterance that are based on previous user modifications to a previous user utterance;
- receive an indication that the user accepted at least one modification of the one or more modifications, wherein the at least one modification modifies the one or more computing tasks and the one or more modifications are selected based on a score of each of a plurality of previous user utterances, and wherein the score is determined, using the prior attempt records, based on how many times a previous user accepted the at least one modification;
- and
- execute, using the machine learning based NLP model, the modified one or more computing tasks;
- update the knowledge base based at least in part on executing the modified one or more computing tasks;
- and
- retrain the machine learning based NLP model using the updated knowledge base.
8 more elements
Dependencies are inferred from the claim text.