Methods, apparatuses, systems, and computer-readable media for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

A method for concept based segmentation, the method may include (a) detecting an object within a region of an image; wherein the object is associated with characteristic pixels metadata that indicative of multiple examples of pixels properties of pixels that are included in at least one appearance of the object within at least one image; and (b) finding, within the region, one or more object boundaries, based on the characteristic pixels metadata.

An Artificial Intelligence (AI) based document processing system receives a request including one or more of a message and documents related to a process to be automatically executed. A process identifier is extracted and used for retrieving guidelines for the automatic execution of the document processing task. Machine Learning (ML) models, each corresponding to a guideline, are used to extract data responsive to the guidelines. Based on the responsive data meeting the approval threshold and the automatic document processing task executed, one or more of a recommendation to accept or reject the request, and a corresponding letter can be automatically generated.

Systems, apparatuses, and methods for implementing a safety monitor framework for a safety-critical inference application are disclosed. A system includes a safety-critical inference application, a safety monitor, and an inference accelerator engine. The safety monitor receives an input image, test data, and a neural network specification from the safety-critical inference application. The safety monitor generates a modified image by adding additional objects outside of the input image. The safety monitor provides the modified image and neural network specification to the inference accelerator engine which processes the modified image and provides outputs to the safety monitor. The safety monitor determines the likelihood of erroneous processing of the original input image by comparing the outputs for the additional objects with a known good result. The safety monitor complements the overall fault coverage of the inference accelerator engine and covers faults only observable at the network level.

Methods, systems, and apparatus for efficient image analysis. In some aspects, a system includes a camera configured to capture images, one or more environment sensors configured to detect movement of the camera, a data processing apparatus, and a memory storage apparatus in data communication with the data processing apparatus. The data processing apparatus can access, for each of a multitude of images captured by a mobile device camera, data indicative of movement of the camera at a time at which the camera captured the image. The data processing apparatus can also select, from the images, a particular image for analysis based on the data indicative of the movement of the camera for each image, analyze the particular image to recognize one or more objects depicted in the particular image, and present content related to the one or more recognized objects.

A method and a system for producing a story video are provided. A method for producing a story video, according to one embodiment, can produce a specific story video by determining a theme of a story that is suitable for collected videos and selecting and arranging an appropriate video for each frame of a template associated with the theme.

A method for generating training examples for a product recognition model is disclosed. The method includes capturing images of a product using an array of cameras. A product identifier for the product is associated with each of the images. A bounding box for the product is identified in each of the images. The bounding boxes are smoothed temporally. A segmentation mask for the product is identified in each bounding box. The segmentation masks are optimized to generate an optimized set of segmentation masks. A machine learning model is trained using the optimized set of segmentation masks to recognize an outline of the product. The machine learning model is run to generate a set of further-optimized segmentation masks. The bounding box and further-optimized segmentation masks from each image are stored in a master training set with its product identifier as a training example to be used to train a product recognition model.

Techniques are described including displaying an interface associated with a computing device, the interface being configured to present a form including a text entry box, receiving data from an activity information pipeline associated with a self-reporting service, the activity information pipeline having a source of activity information associated with a tool configured to geotag the activity information and another source of associated with a mobile sensor configured to capture other activity information geotagged by the tool, the other activity information being processed by the self-reporting service to deprioritize a suggestion over another suggestion, displaying, in response to selection of the text entry box, a suggestion indicative of a journey and identified as prioritized and presented proximate to the center of the interface of the display device, and dynamically adjusting the suggestion while prioritizing display of the suggestion over a generic suggestion.

Methods, devices, and systems for segmenting and annotating videos for analysis are disclosed. A user identifies specific moments of the video that provide a teachable moment. A pre-context and a post-context portion of the video surrounding the identified moment are used to create a tile video. One or more tile videos are compiled in a user-defined order to generate a weave video with a specific focus or theme. The generated weave video is shared with one or more users and can be annotated to facilitate teaching and/or discussion.

A method for processing sensor data includes assessing the sensor data of a sensor using metadata of the sensor as well as sensor data of at least one additional sensor using the metadata of the additional sensor, in order to receive assessed sensor data of the sensors. The method further includes merging the assessed sensor data in order to receive merged sensor data.