A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.

A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

Techniques and systems are described that leverage computer vision as part of search to expand functionality of a computing device available to a user and increase operational computational efficiency as well as efficiency in user interaction. In a first example, user interaction with items of digital content is monitored. Computer vision techniques are used to identify digital images in the digital content, objects within the digital images, and characteristics of those objects. This information is used to assign a user to a user segment of a user population which is then used to control output of subsequent digital content to the user, e.g., recommendations, digital marketing content, and so forth.

Various embodiments, methods and systems for implementing a distributed computing system scene assembly engine are provided. Initially, a selection of a first synthetic data asset and a selection of a second synthetic data asset are received from a distributed synthetic data as a service (SDaaS) integrated development environment (IDE). A synthetic data asset is associated with asset-variation parameters and scene-variation parameters, the asset-variation parameters and scene-variation parameters are programmable for machine-learning. Values for generating a synthetic data scene are received. The values correspond to asset-variation parameters or scene-variation parameters. Based on the values, the synthetic data scene is generated using the first synthetic data asset and the second synthetic data asset.

Various embodiments, methods and systems for implementing a distributed computing system scene assembly engine are provided. Initially, a selection of a first synthetic data asset and a selection of a second synthetic data asset are received from a distributed synthetic data as a service (SDaaS) integrated development environment (IDE). A synthetic data asset is associated with asset-variation parameters and scene-variation parameters, the asset-variation parameters and scene-variation parameters are programmable for machine-learning. Values for generating a synthetic data scene are received. The values correspond to asset-variation parameters or scene-variation parameters. Based on the values, the synthetic data scene is generated using the first synthetic data asset and the second synthetic data asset.

A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.

A system for query processing of a frequency of utility indicators comprises a processor operable to receive a transmission from a first wearable device comprising entity file information associated with a first entity. The processor is operable to generate a file vector comprising one or more files of a digital folder based on an association with one or more utility indicators and determine that one of the files corresponds to a greater number of the one or more utility indicators than the remaining files based, at least in part, on the entity file information. The processor is operable to assign the determined one of the one or more files as a first file within the file vector and send a transmission to the first wearable device comprising the file vector and an indication to utilize the first file in an interaction between the first user and the first entity.

A method performed by a computing device includes obtaining a set of image segment identigens for image segments of an image to produce sets of image segment identigens. A set of image segment identigens is a set of possible interpretations of a first image segment of the image segments. The method further includes identifying a subset of valid image segment identigens of each set of image segment identigens by applying identigen rules to the sets of image segment identigens to produce subsets of valid image segment identigens. Each valid image segment identigen of a subset of valid image segment identigens represents a most likely interpretation of a corresponding image segment. The method further includes generating an image entigen group utilizing the subsets of valid image segment identigens, where the image entigen group represents a most likely interpretation of the image.

A method performed by a computing device includes obtaining a set of image segment identigens for image segments of an image to produce sets of image segment identigens. A set of image segment identigens is a set of possible interpretations of a first image segment of the image segments. The method further includes identifying a subset of valid image segment identigens of each set of image segment identigens by applying identigen rules to the sets of image segment identigens to produce subsets of valid image segment identigens. Each valid image segment identigen of a subset of valid image segment identigens represents a most likely interpretation of a corresponding image segment. The method further includes generating an image entigen group utilizing the subsets of valid image segment identigens, where the image entigen group represents a most likely interpretation of the image.