Modality.ai

Hardik Kothare

Vikram Ramanarayanan

Michael Neumann

David Suendermann-Oeft

Andrew Cornish

Jackson Liscombe

Oliver Roesler

David Pautler

Doug Habberstad

William Burke

A system for remotely determining the potential presence of depression in a user. The system includes a virtual agent that administers one or more tasks to the user. The user performs the tasks and the performance is captured by a camera. The captured audiovisual data is sent to a server that derives objective metrics which are then applied to a classifying algorithm. If certain metrics meet certain thresholds, the system determines that the user is exhibiting depression symptoms. In embodiments, the system can further determine whether a user has been taking depression medication.

Multimodal investigation of speech, text, cognitive and facial video features for characterizing depression with and without medication

A system for identifying efficacious markers comprises memory storing collected multimodal digital markers from a first cohort experiencing a sign onset and a second cohort experiencing a non-sign onset. The system further comprises selection logic that identifies a subset of markers that best capture differences between the sign and non-sign onsets. The system further comprises responsiveness logic that determines a responsiveness parameter that specifies a rate of change in the subset. The system further comprises time to detect change logic that determines a time parameter that specifies a time required to detect change in the subset. The system further comprises sample size logic that determines how the responsiveness parameter and the time parameter change depending on sample size. The system further comprises sensitivity logic that determines a sensitivity parameter that specifies whether the subset detects condition deterioration during intervals when no changes are reported in an external standard.

Multimodal metrics for remote monitoring of condition progression

A computer-implemented method of generating interpretable, composite marker indexes that are discriminative and noise-robust is provided. The method comprises storing remotely collected multimodal digital markers from a first cohort and a second cohort. The method further comprises grouping multicollinear features in the multimodal digital markers into clusters, and then selecting representative features for the clusters for multiple classification tasks that require discrimination between the first cohort and the second cohort. The method further comprises linearly combining the representative features into an interpretable, composite marker index such that relative contributions of each of the representative features to the interpretable, composite marker index are known.

Interpretable index score for combining multimodal metrics for remote monitoring of condition progression

A computer-generated dialog session is customized for a user having a pathology characterized at least in part by a speech pathology. The user's speech is analyzed for spans of speech in which the starts and ends of the spans satisfy predetermined thresholds of time. Customization occurs by altering at least one of the following configurable parameters: (a) a threshold minimum signal strength of speech (dB) to consider as the start of the span of speech; (b) an adjustment factor by which signal strengths of background noise increases between consecutive spans of speech; (c) a threshold between signal strength during the span of speech and signal strength during the span of non-speech; (d) a start speech time threshold; and (e) an end speech time threshold.

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Burlingame, CA, United States of America

Hardik Kothare