A method, computer system, and a computer program product for assessing a likelihood of success associated with developing at least one machine learning (ML) solution is provided. The present invention may include generating a set of questions based on a set of raw training data. The present invention may also include computing a feasibility score based on an answer corresponding with each question from the generated set of questions. The present invention may then include, in response to determining that the computed feasibility score satisfies a threshold, computing a level of effort associated with developing the at least one ML solution to address a problem. The present invention may further include presenting, to a user, a plurality of results associated with assessing the likelihood of success of the at least one ML solution.

Techniques for structuring knowledge bases specific to a user or group of users and techniques for using the knowledge bases to answer user inputs are described. A knowledge base may be populated with information provided by users associated with the knowledge base. Users associated with a knowledge base may be proactive in providing content to the knowledge base and/or a system may solicit an answer to a user input from users associated with a particular knowledge base. When the system receives an answer, the system may populate the knowledge base with the answer and may output the answer to the user that originated the user input. The system may output user inputs to be answered using messages or by establishing two-way communication sessions.

Methods, systems, and machine-readable media are provided to facilitate content receiver control for particularized output of content items based on intra-content metrics. Observation data, corresponding to indications of detected content receiver operations associated with a content receiver and mapped to a first set of content items, may be processed. A first set of intra-content metrics may be detected. An audiovisual pattern of intra-content metrics may be mapped based on correlating the set of observation data with the first set of intra-content metrics. A second set of content items may be processed to detect a second set of intra-content metrics. A subset of the second set of content items may be selected based on a visual category and/or an audio category of the audiovisual pattern of intra-content metrics. The subset may be specified to cause a content receiver to modify operations to record and/or output content corresponding to the subset.

A method for using machine learning techniques to analyze past decisions made by administrators concerning account opening requests and to recommend whether an account opening request should be allowed or denied. Further, the machine learning techniques determine various other products that the customer may be interested in and prioritizes the choices of options that the machine learning algorithm determines appropriate for the customer.

Various embodiments provide an approach to controlled access of websites based on website content, and profile for the person consuming the data. In operation, machine learning techniques are used to classify the websites based on community and social media inputs, crowdsourced data, as well as access rules implemented by parents or system administrators. Feedback from users/admins of the system, including the instances of allowed or denied access to websites, in conjunction with other relevant parameters, is used for iterative machine learning techniques.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include, for a subset of the feature vectors, accessing a first label. The actions further include generating a classifier that is configured to associate a given feature vector with a feature vector of the subset of the feature vectors. The actions further include applying the feature vectors that are not included in the subset of the feature vectors to the classifier. The actions further include generating a dissimilarity matrix. The actions further include, based on the dissimilarity matrix, generating a graph. The actions further include, for each node of the graph, determining a second label. The actions further include, based on the second labels and the first labels, determining a training label for each feature vector.

A computerized-method for dynamic digital-survey-channel selection is provided herein. In a computerized system having a processor, a memory to store a database of survey responses and a database of customers details, and a Voice of the Customer (VOC) platform having an outbound-message Application Programming Interface (API) to send a digital survey to a customer, via a plurality of digital survey channel types, when a customer is nominated for a digital survey, the computerized-method included operating by said processor, a digital-survey-channel-selection module. The digital-survey-channel-selection module includes (i) determining a digital-survey-channel type to elevate customers-response-rate to a digital survey; and (ii) sending the determined digital-survey-channel type to the outbound-message API to trigger the digital survey to a computerized device of the customer, via the determined digital-survey-channel type.

The present disclosure provides a novel integrated entropy-based method that combines genome-wide profiling and network analyses for diagnostic and prognostic applications. The present disclosure further provides the integration of multiomics datasets, network analyses and machine learning that enable predictions on diagnosing infectious diseases and predicting the probability that they will escape treatment/the host immune system and/or become antibiotic resistant. The present disclosure provides a primary gateway towards the development of highly accurate infectious disease prognostics.

A method, apparatus and computer program product are provided for validating electronic distribution transactions and reducing non-compliant electronic distribution transactions. A distribution application enables users to enter details relating to a retirement account distribution. A tax service integrated with the distribution application provides scenario-specific tax withholding information, and enforces relevant tax withholding rules. A user provides withholding information and the system ensures compliance by validating the transaction against the withholding rules. The system displays to the user any errors that need to be corrected, and a breakdown of the proceeds from the transaction. The tax service is implemented remotely from the distribution application so that changing tax regulations may be implemented into the tax service without impacting the distribution application. The tax service may utilize a customer's state of residence, age (and/or date of birth), and citizenship status to calculate and validate tax withholding information and required minimum distribution rules.

A method, apparatus and computer program product are provided for validating electronic distribution transactions and reducing non-compliant electronic distribution transactions. A distribution application enables users to enter details relating to a retirement account distribution. A tax service integrated with the distribution application provides scenario-specific tax withholding information, and enforces relevant tax withholding rules. A user provides withholding information and the system ensures compliance by validating the transaction against the withholding rules. The system displays to the user any errors that need to be corrected, and a breakdown of the proceeds from the transaction. The tax service is implemented remotely from the distribution application so that changing tax regulations may be implemented into the tax service without impacting the distribution application. The tax service may utilize a customer's state of residence, age (and/or date of birth), and citizenship status to calculate and validate tax withholding information and required minimum distribution rules.