Insight Direct USA, Inc.

A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

A method of preprocessing incoming video data of at least one region of interest from a camera collecting video data having a first field of view is disclosed herein that includes receiving the incoming video data from the camera; preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file, with the preprocessing including formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view; and publishing the first video data of the first region of interest to an endpoint to allow access by a first subscriber.

Apparatus and associated methods relate to enhancing care of a patient using video and audio analytics. Video data, audio data, and semantic text data are extracted from a video stream of the patient. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a health outcome of the patient is predicted based on the first, second, and/or third features sets. The health outcome that is predicted is compared with the set of health outcomes of the training patients classified with the patient classification of the patient. Differences are identified between the feature sets corresponding to the patient and feature sets of the training patients who have better health outcomes the patient's predicted health outcome. The differences identified are then reported.

Apparatus and associated methods relate to assessing a confidence level of a verbal communication of a person as determined by a machine learning model operating on a video stream of the person. Video data, audio data, and semantic text data are extracted from a video stream of the person. The video data are analyzed to identify a first feature set. The audio data are analyzed to identify a second feature set. The semantic text data are analyzed to identify a third feature set. Using a computer-implemented machine-learning model, a confidence level of the verbal communication of the person is assessed. The confidence level is then associated with a time of the video stream to which the confidence level pertains. The confidence level and the associated time of the video stream are then reported.

A method of creating custom video sequences includes presenting an ecommerce website to a customer and automatically collecting electronic customer data from the ecommerce website by a server system. The electronic customer data describes at least one customer interaction with the ecommerce website is automatically collected concurrently with customer interaction(s). The method further includes, concurrently with automatically collecting the electronic customer data, creating a customer profile of a customer based on the electronic customer data, determining a set of offering descriptors based on at least one customer descriptor, automatically selecting a subset of video segments from a plurality of video segments based on the set of offering descriptors, and automatically sequencing the subset of video segments into a custom video. The method further includes automatically modifying a web page of the ecommerce website by inserting the custom video into the web page.

A method of creating custom video sequences includes presenting an ecommerce website to a customer and automatically collecting electronic customer data from the ecommerce website by a server system. The electronic customer data describes at least one customer interaction with the ecommerce website is automatically collected concurrently with customer interaction(s). The method further includes, concurrently with automatically collecting the electronic customer data, creating a customer profile of a customer based on the electronic customer data, determining a set of offering descriptors based on at least one customer descriptor, automatically selecting a subset of video segments from a plurality of video segments based on the set of offering descriptors, and automatically sequencing the subset of video segments into a custom video. The method further includes automatically modifying a web page of the ecommerce website by inserting the custom video into the web page.

A method of scheduled modifications of preprocessing of incoming video data of at least one region of interest from a camera collecting video data having a first field of view includes receiving incoming video data from the camera and preprocessing the incoming video data, by a computer processor, according to preprocessing parameters defined within a runtime configuration file. The preprocessing includes formatting the incoming video data to create first video data of a first region of interest with a second field of view that is less than the first field of view. The method also include publishing the first video data of the first region of interest to an endpoint to allow access and processing by a subscriber; in response to a time schedule, altering the preprocessing parameters defined within the runtime configuration file dependent upon the time schedule to create second video data that is different from the first video data; and publishing the second video data to the endpoint to allow access and processing by the first subscriber.

A method of improving a main output of a main processing application processing first video data includes analyzing incoming video data via a first processing pipeline and analyzing incoming video data via a second processing pipeline. The second processing pipeline includes identifying, by a parameter optimization module, first test preprocessing parameters; preprocessing the incoming video data according to the first test preprocessing parameters, wherein the first test preprocessing includes formatting the incoming video data to create first test video data; processing the first test video data by a test processing application to determine a first test output that is indicative of a first test inference dependent upon the first test video data; and comparing the first test output and the main output to a baseline criterion. In response to the first test output satisfying the baseline criterion, the parameter optimization module can alter the first preprocessing parameters to be similar to the first test preprocessing parameters.

A method of improving a main output of a main AI model [language will be added when claims are finalized]