Abstract: Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Abstract: A method includes receiving, by a server computer, data of a communication device; training, by the server computer, a neural network model based on the data of the communication device and communication device metadata from one or more additional communication devices, to generate a machine learning model configured to determine, based on a metadata associated with an application, a security value related to an indication of a security threat; and transmitting the machine learning model to the communication device. The communication device can use the machine learning model to determine the security value, by inputting the metadata associated with the application, as a vectorized data into the machine learning model. The communication device can determine whether to run or install the application based upon the security value.

Abstract: Embodiments are directed to a method for determining an interview script in a claims submission. The method may comprising receiving data relating to a claim being submitted, which may include claims submission data input by a user, information relating to the user, and one or more features. Data associated with the one or more features may be determined from an artificial intelligence model. A first score based on the data associated with the one or more features and data associated with the information relating to the user may be determined and used to determine an interview script. In one embodiment, questions in the interview script may continue to be provided to the interviewer computer if a continually updated score remains above a predetermined threshold. In another embodiment, the user may be routed to a live interview with a human representative if a continually updated score drops below a predetermined threshold.

Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Abstract: A method is disclosed. The method includes receiving a text phrase from a user and parsing the text phrase using a natural language parser to generate distinct words. A vector of values relating to the distinct words is generated and compared to vectors in a graph database which relate to learned communities of language. Based on the comparing, a most similar community in the learned communities of language is determined and an action set action set associated with the most similar community is queried. A response to the text phrase from the user is then generated based on the action set and provided to the user.

Abstract: A method is disclosed. The method includes receiving a text phrase from a user and parsing the text phrase using a natural language parser to generate distinct words. A vector of values relating to the distinct words is generated and compared to vectors in a graph database which relate to learned communities of language. Based on the comparing, a most similar community in the learned communities of language is determined and an action set action set associated with the most similar community is queried. A response to the text phrase from the user is then generated based on the action set and provided to the user.

Abstract: Embodiments are directed to a method of performing autonomous learning for updating input features used for an artificial intelligence model, the method comprising receiving updated data of an information space that includes a graph of nodes having a defined topology, the updated data including historical data of requests to the artificial intelligence model and output results associated with the requests, wherein different categories of input data corresponds to different input nodes of the graph. The method may further comprise updating edge connections between the nodes of the graph by performing path optimizations that each use a set of agents to explore the information space over cycles to reduce a cost function, each connection including a strength value, wherein during each path optimization, path information is shared between the rest of agents at each cycle for determining a next position value for each of the set of agents in the graph.

Abstract: A first node on the blockchain storage system node may include data from other blocks on the blockchain used for blockchain verification and an additional node which may include an analysis element. The analysis element may include computer executable code for receiving data added to the blockchain, determining a risk score for the data added to the blockchain based on past performance and in response to the risk score being over a threshold, alerting members of blockchain of the risk score.

Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Abstract: A system receives transaction data from payment devices of a user. The system generates a transaction profile of the user based on the received transaction data. Based on the transactional parameters, the system determines a community of the user. The system further receives one or more predefined sub-groups of the community from a database. The one or more sub-groups define subsets of activities, geographic locations, and times. The system identifies one or more rules setting limits on the transactional parameters. Absent receiving additional user interactions, the system generates a suggested community for the user as a function of the one or more rules, the activities, geographic locations, and times of the one or more sub-groups of the community. The system generates a suggested activity to the user based on the suggested community.

Abstract: A first node on the blockchain storage system node may include data from other blocks on the blockchain used for blockchain verification and an additional node which may include an analysis element. The analysis element may include computer executable code for receiving data added to the blockchain, determining a risk score for the data added to the blockchain based on past performance and in response to the risk score being over a threshold, alerting members of blockchain of the risk score.

Abstract: A disclosed method an analysis computer determining a rolling window associated with interaction data for interactions that occur over time. The analysis computer can retrieve interaction data for interactions occurring in the rolling window. The analysis computer can then generate pseudo interaction data based upon historical interaction data. The analysis computer can optionally embed the interaction data for the interactions occurring within the rolling window and the pseudo interaction data to form interaction data matrices. The analysis computer can then form a neural network model using the interaction data matrices, which is derived from the interaction data in the rolling window and the pseudo interaction data.

Abstract: Computer executable instructions including code sections are received and compared to previously analyzed computer executable instructions. The code sections are then analyzed and assigned a risk score. If the risk score is over a threshold, an alarm may be communicated or the system may substitute computer executable instructions that may have been created according to a standard or have been previously approved.

Abstract: Systems and methods are provided for transcompiling non-distributed source code for a non-distributed software program into a distributed software package for implementation on a distributed computing system. A transcompiler can identify loops within non-distributed source code written in a data-driven language. The transcompiler can generate MapReduce jobs using mapper keys based on grouping indicators associated with each of the loops. The MapReduce jobs can be linked together based on input-output connections of the loops in the non-distributed source code. Then, the transcompiler can generate a distributed software package including the generated MapReduce jobs to implement the same functionality as the non-distributed source code on the distributed computing system, thereby improving the speed of execution over very large datasets. The distributed software package can be optimized using machine learning searching algorithms.

Abstract: Computer executable instructions including code sections are received and compared to previously analyzed computer executable instructions. The code sections are then analyzed and assigned a risk score. If the risk score is over a threshold, an alarm may be communicated or the system may substitute computer executable instructions that may have been created according to a standard or have been previously approved.

Abstract: Methods for controlling genetic learning for predictive models using predefined strategies may include, for each of a plurality of agents, selecting a type of predictive model. A strategy may be selected from predefined strategies. Candidate genomes may be generated and may include a plurality of genes. Each gene may be associated with a feature of the agent predictive model. A fit of each candidate genome to the agent strategy may be determined. A candidate genome may be selected based on the fit. For each of a plurality of epochs, a plurality of training iterations may be performed for each agent. A fitness of each agent predictive model may be determined. A subset of agents with a highest fitness may be determined. For each agent of the subset, at least one new agent may be generated. The genomes of the new agents may be merged with some genomes of the subset.

Abstract: Embodiments are directed to a method for accelerating machine learning using a plurality of graphics processing units (GPUs), involving receiving data for a graph to generate a plurality of random samples, and distributing the random samples across a plurality of GPUs. The method may comprise determining a plurality of communities from the random samples using unsupervised learning performed by each GPU. A plurality of sample groups may be generated from the communities and may be distributed across the GPUs, wherein each GPU merges communities in each sample group by converging to an optimal degree of similarity. In addition, the method may also comprise generating from the merged communities a plurality of subgraphs, dividing each sub-graph into a plurality of overlapping clusters, distributing the plurality of overlapping clusters across the plurality of GPUs, and scoring each cluster in the plurality of overlapping clusters to train an AI model.

Abstract: Described herein are a system and techniques for enabling access control utilizing one or more blockchains associated with a user. A blockchain provider can manage one or more blockchains specifically associated with a an entity, where each blockchain may be associated with a differing sensitivity level. The entity may be a person or a machine such as an IOT (Internet of Things) device. The blockchain provider can manage access control policies associated with each blockchain such that access to the data of the blockchain may be allowed or restricted to requesting entities according to those access control policies.

Abstract: Embodiments are directed to a method for accelerating machine learning using a plurality of graphics processing units (GPUs), involving receiving data for a graph to generate a plurality of random samples, and distributing the random samples across a plurality of GPUs. The method may comprise determining a plurality of communities from the random samples using unsupervised learning performed by each GPU. A plurality of sample groups may be generated from the communities and may be distributed across the GPUs, wherein each GPU merges communities in each sample group by converging to an optimal degree of similarity. In addition, the method may also comprise generating from the merged communities a plurality of subgraphs, dividing each sub-graph into a plurality of overlapping clusters, distributing the plurality of overlapping clusters across the plurality of GPUs, and scoring each cluster in the plurality of overlapping clusters to train an AI model.

Abstract: A disclosed method includes a) receiving by a server computer network data comprising a plurality of transaction data for a plurality of transactions. Each transaction data comprises a plurality of data elements with data values. At least one of the plurality of data elements comprises a user identifier for a user. The server computer can then b) generate one or more graphs comprising a plurality of communities based on the network data. The server computer can c) determine fuzzy values for at least some of the data values for each transaction of the plurality of transactions. For each user, the server computer can d) determine fuzzy values for communities within the plurality of communities. The server computer can then e) generate a model using the fuzzy values obtained in steps c) and d), and at least some of the data values.