# US 2026/0100259 A1 · Systems and methods for predicting mental health conditions based on passive processing of conversational speech and language > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100259 A1 - Document ID: US20260100259A1 - Status: Published application - Kind: A1 - Application number: 19/417,870 - Filed: 2025-12-12 - Published: 2026-04-09 - Earliest claimed: 2023-06-13 - Assignees: Ellipsis Health, Inc. - Applicants: Ellipsis Health, Inc. - Inventors: Elizabeth Shriberg; Piotr Chlebek; Tomasz Rutowski; Amirhossein Haratinejadtorbati; Michael Aratow; Mainul Mondal; Melissa McCool; Vaishnavi Narayanan; Ishani Tagore; Yang Lu; Tulio Goulart; Robert Rozanski - Web page: https://patentgrep.com/patent/US20260100259A1 ## CPC - G06F 40/30: Semantic analysis - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - G10L 15/16: using artificial neural networks - G16H 10/20: for electronic clinical trials or questionnaires - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 20/00: ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance - G16H 20/70: relating to mental therapies, e.g. psychological therapy or autogenous training - G16H 40/67: for remote operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract Described herein are systems and methods for identifying the severity of a mental health condition or symptoms of same by listening to a human-to-human conversation by receiving conversation data, processing the conversation data to generate a language model output and/or an acoustic model output using one or more language models and/or acoustic models. Further described herein are systems and methods for automatically tracking and providing analytics on self-report questionnaires administered during the conversation. ## Claims ### Claim 1 A system for a behavioral or mental health condition of a subject, the system comprising: at least one input device for receiving conversation data from at least one user; at least one output device for outputting an electronic report; at least one computing device in communication with the at least one input device and the at least one output device, the at least one computing device configured to: receive the conversation data from the at least one input device; process the conversation data to generate a language model output and/or an acoustic model output; apply weights to the language model output and/or the acoustic model output, wherein the language model output and the acoustic model output each comprise a plurality of outputs corresponding to a plurality of time segments of the conversation data, and wherein the weights are optionally temporally-based; generate an electronic report; and transmit the electronic report to the output device. ### Claim 2 (depends on 1) The system of claim 1, wherein the at least one computing device is further configured to: fuse the weighted language model output and the acoustic model output generating a composite output. ### Claim 3 (depends on 1) The system of claim 1, wherein the electronic report identifies a severity of at least one symptom of the behavioral or mental health condition based on the composite output. ### Claim 4 (depends on 1) The system of claim 1, wherein the electronic report comprises an annotation of the language model output indicating salience of at least one of the one or more time segments. ### Claim 5 (depends on 1) The system of claim 1, wherein processing the conversation data to generate the language model output and the acoustic model output comprises using a language model neural network and an acoustic neural network trained on labelled conversation data collected from one or more other subjects, wherein the labelled conversation data comprises is labelled as (i) having, to some level, the behavioral or mental health condition and (ii) not having the behavioral or mental health condition. ### Claim 6 (depends on 1) The system of claim 1, wherein the conversation data is processed based in part on at least one of a patient profile and an agent profile, wherein the profile comprises at least one of historical, biographical, demographic, and longitudinal data. ### Claim 7 (depends on 1) The system of claim 1, wherein the conversation data comprises at least one of speech data and text-based data. ### Claim 8 (depends on 1) The system of claim 1, wherein the at least one computing device is configured to run a model based on human-interpretable features. ### Claim 9 (depends on 1) The system of claim 1, wherein the at least one computing device configured to: determine at least one role of at least one speaker; and wherein the weights are based in part on the at least one role of the at least one speaker during each time segment. ### Claim 10 (depends on 9) The system of claim 9, wherein the at least one speaker comprises an agent and applying the weights to the language model output and the acoustic model output comprises applying a zero weight to acoustic model output corresponding to the agent. ### Claim 11 (depends on 9) The system of claim 9, wherein the at least one role of the at least one speaker comprises at least one of a patient, an agent, an interactive voice response, and bot or AI speaker. ### Claim 12 (depends on 9) The system of claim 9, wherein the weights applied to the language model output and the acoustic model output are based in part on determining that a number of the at least one speaker matches an expected number of speakers. ### Claim 13 (depends on 1) The system of claim 1, wherein: the language model output comprises one or more topics corresponding to one or more time ranges, and the weights are based in part on the one or more topics during each time segment. ### Claim 14 (depends on 13) The system of claim 13, wherein time ranges of the conversation data corresponding to a predefined topic are processed to generate the language model output using more computationally robust models than those used for time ranges of the conversation data not corresponding to the predefined topic. ### Claim 15 (depends on 1) The system of claim 1, wherein the language model output comprises an identification of at least one query based on conversation data from an agent and at least one response to the at least one query based on the conversation data from a patient, wherein the at least one query is mapped onto a predefined query and the at least one response to the at least one query is mapped onto a predefined response to the predefined query. ### Claim 16 (depends on 15) The system of claim 15, wherein the weights are based in part on the at least one query. ### Claim 17 (depends on 1) The system of claim 1, wherein the at least one computing device is further configured to: receive in-context learning comprising a description of the behavioral or mental health condition; prior to processing the conversation data to generate the language model output and/or the acoustic model output, pre-processing the conversation data by performing at least one of: weighing at least one segment of the conversation data based on a relation between the at least one segment of the conversation data and the behavioral or mental health condition; summarizing the at least one segment of the conversation data; providing analytics on the at least one segment of the conversation data; summarizing at least one aspect of the behavioral or mental health condition; and providing analytics on the at least one aspect of the behavioral or mental health condition; and transmit the pre-processed conversation data to one or more models to predict the behavioral or mental health condition of the subject. ### Claim 18 A system for scoring surveys based on a conversation, the system comprising: at least one input device for receiving conversation data from at least one user; at least one output device for outputting an electronic report; at least one computing device in communication with the at least one input device and the at least one output device, the at least one computing device configured to: receive the conversation data from the at least one input device; process the conversation data to generate a language model output, wherein the language model output comprises an identification of at least one query based on conversation data from an agent and at least one response to the at least one query based on the conversation data from a patient, wherein the at least one query is mapped onto a predefined query and the at least one response to the at least one query is mapped onto a predefined response to the predefined query; and generate an electronic report; and transmit the electronic report to the output device. ### Claim 19 A system for predicting a severity of at least one symptom of a behavioral or mental health condition of a subject, the system comprising: at least one input device for receiving conversation data from the subject; at least one computing device in communication with the at least one input device, the at least one computing device configured to: receive in-context learning comprising an explanation related to one or more questions of a questionnaire; receive the conversation data from the at least one input device; predict the severity of the at least one symptom of the behavioral or mental health condition of the subject based on the in-context learning and the conversation data.