# US 2025/0095802 A1 · Systems and methods to generate personal documentation from audio data > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0095802 A1 - Document ID: US20250095802A1 - Status: Published application - Kind: A1 - Application number: 18/829,651 - Filed: 2024-09-10 - Published: 2025-03-20 - Earliest claimed: 2023-09-15 - Assignees: Laboratoire Coeurway Inc. - Applicants: Laboratoire Coeurway Inc. - Inventors: Bastien GADOURY; Jean-Phillipe GAGNON; Mariane VAIL; Pierre-Olivier GRAVEL - Web page: https://patentgrep.com/patent/US20250095802A1 ## CPC - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G06F 40/186: Templates - G06F 40/197: Version control (for software G06F8/71) - G06F 40/40: Processing or translation of natural language (natural language analysis G06F40/20; semantic analysis G06F40/30) - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G06F 40/174: Form filling; Merging - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Systems and methods for generating personal documentation from audio data are disclosed. Meeting audio of a meeting between a person and a professional is received and transcribed, generating a transcription. A text string corresponding to a prompt is generated from the transcription, and is used to generate a document by a generative language model trained to take a string corresponding to the prompt as input and generate a string corresponding to the document as output. When the professional is a health professional and the person is a patient, the generated document can be a medical document such as a clinical note. Therefore, the systems and methods can be used as part of an electronic health records system. ## Claims ### Claim 1 A system for generating personal documentation from audio data, the system comprising at least one processing device and at least one memory for storing instructions executable by the at least one processing device, the instructions implementing modules, the modules comprising: an audio acquisition module configured to receive meeting audio of a meeting between a person and a professional and to store the meeting audio in the memory; a transcription module configured to transcribe the meeting audio, generating a transcription and to store the transcription in the memory; a text generation module configured to generate a document from at least the transcription, the text generation module comprising: a prompt engineering submodule configured to generate a text string corresponding to a prompt from at least the transcription, and a generative language model trained to take the text string as input and generate as output a continuation text string, wherein the continuation text string corresponds to a continuation of the text string predicted by the generative language model, and wherein the continuation text string further corresponds to the document; and a storage module configured to securely store at least one of the meeting audio, the transcription and the document in a persistent storage device. ### Claim 2 (depends on 1) The system of claim 1, wherein the system is part of a electronic health records system, wherein the document is a medical document, the professional is a health professional and the person is a patient. ### Claim 3 (depends on 1) The system of claim 1, wherein the document is one of: a meeting summary; a meeting note; a personal recommendation; a filled form, wherein the prompt engineering submodule is configured to generate at least one field prompt from at least an unfilled form and the transcription; and a case history. ### Claim 4 (depends on 1) The system of claim 1, wherein the text generation module is configured to: generate an intermediary document, by the generative language model, from a first prompt based on at least the transcription, wherein the intermediary document corresponds to a compressed version of the first prompt; and generate the document, by the same generative language model, from a second prompt based at least on the intermediary document and on an additional document. ### Claim 5 (depends on 1) The system of claim 1, wherein the text generation module is configured to: generate a plurality of intermediary documents, by the generative language model, from a corresponding plurality of prompts, each prompt of the plurality based on at least the transcription, an indication of a document section and an indication of at least one example generated by an examples specifier, the indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example section, each of the at least one example being selected from a set comprising predetermined examples, the plurality of intermediary documents corresponding to a plurality of document sections; and generate the document by concatenating the plurality of intermediary documents. ### Claim 6 (depends on 1) The system of claim 1, wherein the prompt engineering submodule comprises a system instruction preparer configured to prepare system instructions for aggregation within the prompt, the system instructions comprising at least one of: an indication of a persona generated by a persona specifier, the indication of a persona comprising textual instructions for the generative language model to adopt a persona corresponding to a scribe, to use a style based on a style of the professional, to use a professional tone, and to address an audience comprising peers of the professional; an indication of text structure generated by a structure specifier, the indication of text structure comprising textual instructions for the generative language model to prepare a desired document type and to observe a desired text structure comprising a plurality of document sections, the indication of text structure selected based on the desired document type from a set comprising predetermined indications of text structure; and an indication of at least one example generated by an examples specifier, the indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example document, each of the at least one example being selected based on the desired document type from a set comprising predetermined examples. ### Claim 7 (depends on 6) The system of claim 6, further comprising a template builder configured to allow the professional to personalize the plurality of document sections, wherein the prompt engineering submodule is configured to select each of the at least one example based on the personalized document sections. ### Claim 8 (depends on 1) The system of claim 1, comprising a first, second, third and fourth remote cloud infrastructures, wherein: the text generation module is implemented by the first remote cloud infrastructure; the transcription module is implemented by the second remote cloud infrastructure; the storage module implemented by the third remote cloud infrastructure; and the first remote cloud infrastructure is in communication over a network with the second and third remote cloud infrastructures and with an application server, and is configured to: receive the meeting audio from the application server, transmit the meeting audio to the second remote cloud infrastructure for transcription and receive the transcription, transmit the meeting audio and the transcription to the third remote cloud infrastructure for storage, and generate and transmit the document to the application server. ### Claim 9 (depends on 1) The system of claim 1, further comprising a user interface, the user interface comprising: an authentication module configured to authenticate a user of the system using credentials stored by the storage module; and a control centre configured to allow an authenticated user to generate a document. ### Claim 10 (depends on 1) The system of claim 1, further comprising an anonymization module configured to generate an anonymized meeting audio and a pseudonymized transcription, wherein the transcription module is further configured to generate a timestamped transcription, the anonymization module comprising: a named entity recognizer configured to recognize in the transcription named entities corresponding to private information; a textual pseudonymization service configured to generate the pseudonymized transcription by replacing each of the recognized named entities with a distinct named entity of a same class; and an audio anonymization service configured to generate the anonymized meeting audio by, for each named entity of the recognized named entities: finding the named entity in the timestamped transcription, retrieving an initial timestamp and a terminal timestamp corresponding to the named entity in the timestamped transcription, and cropping an audio segment between the initial timestamp and the terminal timestamp out of the meeting audio. ### Claim 11 A method for generating personal documentation from audio data, the method comprising: receiving meeting audio of a meeting between a person and a professional; transcribing the meeting audio, generating a transcription; generating a text string corresponding to a prompt from at least the transcription; generating a document from the prompt, by a generative language model trained to take the text string as input and generate as output a continuation text string, wherein the continuation text string corresponds to a continuation of the text string predicted by the generative language model, and wherein the continuation text string further corresponds to the document; and securely storing at least one of the meeting audio, the transcription and the document in a distant persistent storage device. ### Claim 12 (depends on 11) The method of claim 11, implemented as part of a electronic health records system, wherein the document is a medical document, the professional is a health professional and the person is a patient. ### Claim 13 (depends on 11) The method of claim 11, wherein the document is one of: a meeting summary; a meeting note; a personal recommendation; a filled form, the method further comprising a step of generating at least one field prompt from at least an unfilled form and the transcription; and a case history. ### Claim 14 (depends on 11) The method of claim 11, wherein generating the document comprises: generating an intermediary document, by the generative language model, from a first prompt based on at least the transcription, wherein the intermediary document corresponds to a compressed version of the first prompt; and generating the document, by the same generative language model, from a second prompt based at least on the intermediary document and on an additional document. ### Claim 15 (depends on 11) The method of claim 11, wherein generating the prompt comprises: generate a plurality of intermediary documents, by the generative language model, from a corresponding plurality of prompts, each prompt of the plurality based on at least the transcription, an indication of a document section and an indication of at least one example generated by an examples specifier, the indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example section, each of the at least one example being selected from a set comprising predetermined examples, the plurality of intermediary documents corresponding to a plurality of document sections; and generate the document by concatenating the plurality of intermediary documents. ### Claim 16 (depends on 11) The method of claim 11, wherein generating the prompt comprises preparing system instructions for aggregation within the prompt, the system instructions comprising at least one of: an indication of a persona comprising textual instructions for the generative language model to adopt a persona corresponding to a scribe, to use a style based on a style of the professional, to use a professional tone, and to address an audience comprising peers of the professional; an indication of text structure comprising textual instructions for the generative language model to prepare a desired document type and to observe a desired text structure comprising a plurality of document sections, the indication of text structure selected based on the desired document type from a set comprising predetermined indications of text structure; and an indication of at least one example comprising textual instructions for the generative language model to rely on the at least one example in generating the document, each of the at least one example comprising at least an example transcription and an example document, each of the at least one example being selected based on the desired document type from a set comprising predetermined examples. ### Claim 17 (depends on 16) The method of claim 16, further comprising allowing the professional to personalize the plurality of document sections, wherein generating the prompt comprises selecting each of the at least one example based on the personalized document sections. ### Claim 18 (depends on 11) The method of claim 11, further comprising: authenticating a user of the system using stored credentials; and allowing an authenticated user to generate a document. ### Claim 19 (depends on 11) The method of claim 11, wherein generating the transcription comprises generating a timestamped transcription, the method further comprising comprising: recognizing in the transcription named entities corresponding to private information; generating a pseudonymized transcription by replacing each of the recognized named entities with a distinct named entity of a same class; and for each named entity of the recognized named entities: finding the named entity in the timestamped transcription, retrieving an initial timestamp and a terminal timestamp corresponding to the named entity in the timestamped transcription, and generating an anonymized meeting audio by cropping an audio segment between the initial timestamp and the terminal timestamp out of the meeting audio. ### Claim 20 A computer readable memory having recorded thereon statements and instructions for execution by a computer, said statements and instructions comprising: receiving meeting audio of a meeting between a person and a professional; transcribing the meeting audio, generating a corresponding transcription; generating a text string corresponding to a prompt from at least the transcription; generating a document from the prompt, by a generative language model trained to take the text string as input and generate as output a continuation text string, wherein the continuation text string corresponds to a continuation of the text string predicted by the generative language model, and wherein the continuation text string further corresponds to the document; and securely storing at least one of the meeting audio, the transcription and the document in a distant persistent storage device.