US 12,596,739 B2 · Post-work summary generation, refinement, and update for field service calls

Abstract

Disclosed herein are system, method, and computer program product aspects for textual post-work summary generation, refinement, and/or update. An example implementation operates by generating a first natural-language textual prompt based on a textual transcription of a first speech of a user and a first prompt template selected based on a work order. The implementation then queries a LLM with the first natural-language textual prompt to generate a textual post-work summary. The implementation then generates a second natural-language textual prompt based on a textual transcription of a second speech of the user and a second prompt template. The implementation then queries the LLM with the second natural-language textual prompt that directs the LLM to refine the textual post-work summary. In addition, the implementation updates the work order based on the refined textual post-work summary. In some aspects, the implementation may generate an updated textual post-work summary based on incorporating a textual transcription of a third speech of the user into the generated and/or the refined textual post-work summary, and then update the work order based on the updated textual post-work summary.

Claims20

Dependent claims

A method, comprising:

generating, by one or more computing devices, a first natural-language textual prompt based on a textual transcription of a first speech of a user and a first prompt template selected based on a work order;
querying, by the one or more computing devices, a large language model (LLM) with the first natural-language textual prompt to generate a first textual post-work summary that summarizes work performed by the user;
generating, by the one or more computing devices, a second natural-language textual prompt based on a textual transcription of a second speech of the user and a second prompt template;
querying, by the one or more computing devices, the LLM with the second natural-language textual prompt that directs the LLM to refine the first textual post-work summary to generate a refined first textual post-work summary;
in response to the refined first textual post-work summary not being confirmed by the user to update the work order, querying, by the one or more computing devices, the LLM with a third natural-language textual prompt that directs the LLM, in a composite manner, to generate a second textual post-work summary and refine the second textual post-work summary, wherein the third natural-language textual prompt combines the first natural-language textual prompt and the second natural-language textual prompt;
and
updating, by the one or more computing devices, the work order based on a refined second textual post-work summary.

4 more elements

A system, comprising:

a memory configured to store operations;
and
one or more processors configured to perform the operations, the operations comprising:
generating a first natural-language textual prompt based on a textual transcription of a first speech of a user and a first prompt template selected based on a work order;
querying a large language model (LLM) with the first natural-language textual prompt to generate a first textual post-work summary that summarizes work performed by the user;
generating a second natural-language textual prompt based on a textual transcription of a second speech of the user and a second prompt template;
querying the LLM with the second natural-language textual prompt that directs the LLM to refine the first textual post-work summary to generate a refined first textual post-work summary;
in response to the refined first textual post-work summary not being confirmed by the user to update the work order, querying the LLM with a third natural-language textual prompt that directs the LLM, in a composite manner, to generate a second textual post-work summary and refine the second textual post-work summary, wherein the third natural-language textual prompt combines the first natural-language textual prompt and the second natural-language textual prompt;
and
updating the work order based on a refined second textual post-work summary.

7 more elements

A non-transitory computer-readable storage device having instructions stored thereon, execution of which, by one or more processing devices, causes one or more processors to perform operations comprising:

generating a first natural-language textual prompt based on a textual transcription of a first speech of a user and a first prompt template selected based on a work order;
querying a large language model (LLM) with the first natural-language textual prompt to generate a first textual post-work summary that summarizes work performed by the user;
generating a second natural-language textual prompt based on a textual transcription of a second speech of the user and a second prompt template;
querying the LLM with the second natural-language textual prompt that directs the LLM to refine the first textual post-work summary to generate a refined first textual post-work summary;
in response to the refined first textual post-work summary not being confirmed by the user to update the work order, querying the LLM with a third natural-language textual prompt that directs the LLM, in a composite manner, to generate a second textual post-work summary and refine the second textual post-work summary, wherein the third natural-language textual prompt combines the first natural-language textual prompt and the second natural-language textual prompt;
and
updating the work order based on a refined second textual post-work summary.

4 more elements

Dependencies are inferred from the claim text.

Post-work summary generation, refinement, and update for field service calls

Life of this patent

Post-work summary generation, refinement, and update for field service calls

Life of this patent

Abstract

Claims20