Abstract: Disclosed herein is a machine learning platform and system for data analysis including for purposes of providing digital evaluations and therapeutics.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: Disclosed herein is a machine learning platform and system for data analysis including for purposes of providing digital evaluations and therapeutics.

Abstract: Described herein are methods, devices, systems, software, and platforms used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurologic impairments. Specifically, described herein are methods, devices, systems, software, and platforms that are used to analyze video and/or audio recordings of individuals having one or more behavioral disorders, developmental delays, and neurologic impairments.

Abstract: Described herein is software used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurological impairments. Specifically, described herein is software configured for use along with methods, devices, systems, and platforms that are used to analyze to aid in the positive or negative diagnosis of individuals for one or more behavioral disorders, developmental delays, and neurological impairments.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: The methods and apparatus disclosed herein can evaluate a subject for a developmental condition or conditions and provide improved sensitivity and specificity for categorical determinations indicating the presence or absence of the developmental condition by isolating hard-to-screen cases as inconclusive. The methods and apparatus disclosed herein can be configured to be tunable to control the tradeoff between coverage and reliability and to adapt to different application settings and can further be specialized to handle different population groups.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: Disclosed herein are platforms, systems, devices, methods and media for model optimization and data analysis using machine learning. Input data can be processed and analyzed to identify relevant discriminating features, which can be modeled using a plurality of machine learning models. A computing device can be configured with one or more optimized models for categorizing input data.

Abstract: Various embodiments include systems and methods for search result ranking using machine learning. A goal model can be created using machine learning. Responsive to a search query, a plurality of data factors can be inputted into the goal model to create a model output. Search results can be presented to a user based on the model output.

Abstract: The methods and apparatus disclosed herein provide digital diagnostics and digital therapeutics to patients. The digital personalized medicine system uses digital data to assess or diagnose symptoms of a patient, and feedback from the patient response to treatment is considered to update the personalized therapeutic interventions. The methods and apparatus disclosed herein can also diagnose and treat cognitive function of a subject, with fewer questions, decreased amounts of time, and determine a plurality of behavioral, neurological or mental health disorders, and provide clinically acceptable sensitivity and specificity in the diagnosis and treatment.

Abstract: An application classifier classifies applications using latent semantic indexing (LSI) vectors of the applications. The application classifier uses a machine-learned model generated based on pairs of LSI vectors of positive and negative training sets of applications, where the positive training set includes applications within a desired category and the negative training set includes applications outside of the desired category. For a given application, the application classifier determines whether the application belongs to the desired category based on similarity of an LSI vector of the application and LSI vectors of positive and negative exemplar applications, as determined by the machine-learned model. If the LSI vector of the application is similar to an LSI vector of at least one positive exemplar application and not similar to an LSI vector of any of the negative exemplar applications, the application is determined to belong to the desired category.

Abstract: The methods and apparatus disclosed herein can evaluate a subject for a developmental condition or conditions and provide improved sensitivity and specificity for categorical determinations indicating the presence or absence of the developmental condition by isolating hard-to-screen cases as inconclusive. The methods and apparatus disclosed herein can be configured to be tunable to control the tradeoff between coverage and reliability and to adapt to different application settings and can further be specialized to handle different population groups.

Abstract: The methods and apparatus disclosed herein can evaluate a subject for a developmental condition or conditions and provide improved sensitivity and specificity for categorical determinations indicating the presence or absence of the developmental condition by isolating hard-to-screen cases as inconclusive. The methods and apparatus disclosed herein can be configured to be tunable to control the tradeoff between coverage and reliability and to adapt to different application settings and can further be specialized to handle different population groups.

Abstract: The methods and apparatus disclosed herein provide digital diagnostics and digital therapeutics to patients. The digital personalized medicine system uses digital data to assess or diagnose symptoms of a patient, and feedback from the patient response to treatment is considered to update the personalized therapeutic interventions. The methods and apparatus disclosed herein can also diagnose and treat cognitive function of a subject, with fewer questions, decreased amounts of time, and determine a plurality of behavioral, neurological or mental health disorders, and provide clinically acceptable sensitivity and specificity in the diagnosis and treatment.

Abstract: A system and method of click modeling are disclosed. A ranking algorithm is modified using a click model. The click model makes inferences based on user click data. The user click data includes indications of user intent to transact a shopping action for an item for sale. Item listings are ranked for a search results page using the ranking algorithm in response to receiving a search query. Each item listing comprises an item for sale on an e-commerce site. The user click data can also include user clicks of item listings in a search results page. The shopping action can include bidding on an item for sale or purchasing an item for sale.

Abstract: A method includes performing a search of an application data store using a search query. The application data store includes application data for a plurality of software applications available from a software-application marketplace. The application data includes a set of application statistics for each software application. Each set of application statistics includes statistical data points over time. The application data also includes a set of market-wide statistics that is an aggregate of the sets of application statistics over time. The method further includes generating a list of software applications identified during the search. Additionally, the method includes generating a result score for each software application in the list based on a set of market-adjusted statistics associated with the software application.

Abstract: An application classifier classifies applications using latent semantic indexing (LSI) vectors of the applications. The application classifier uses a machine-learned model generated based on pairs of LSI vectors of positive and negative training sets of applications, where the positive training set includes applications within a desired category and the negative training set includes applications outside of the desired category. For a given application, the application classifier determines whether the application belongs to the desired category based on similarity of an LSI vector of the application and LSI vectors of positive and negative exemplar applications, as determined by the machine-learned model. If the LSI vector of the application is similar to an LSI vector of at least one positive exemplar application and not similar to an LSI vector of any of the negative exemplar applications, the application is determined to belong to the desired category.

Abstract: Systems and methods are disclosed for performing man-machine interaction with a user by capturing audible signals and video signals from an environment; detecting a communication context from the audio and video signals; looking up the context in an etiquette database; communicating without disrupting the user and if not possible, determining an appropriate time and fashion to interrupt the user; and communicating with the user at the appropriate time in the appropriate fashion.

Abstract: Various embodiments include systems and methods for search result ranking using machine learning. A goal model can be created using machine learning. Responsive to a search query, a plurality of data factors can be inputted into the goal model to create a model output. Search results can be presented to a user based on the model output.