Abstract: Systems, methods, and computer-readable storage media for building knowledge graphs which map the aspects of entities, then transforming the graphs into vectors for a similarity comparison. The resulting vectors can be used to identify skills and competencies of individuals, which the system uses to compare those individuals to others. The system can then quantify who would make for a good replacement for a given individual and make an associated recommendation.

Abstract: A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

Abstract: A method for fact retrieval includes receiving an input from a user, said input including a string of text that represents a user intent. The method includes performing natural language processing on the input to generate an embedding that corresponds to a semantic representation of the string of text, and based on the generated embedding, identifying an action that is associated with the user intent. The method includes executing the identified action, such that the executed action returns a result associated with the user intent, and providing the result to the user.

Abstract: A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

Abstract: A method for fact retrieval includes receiving an input from a user, said input including a string of text that represents a user intent. The method includes performing natural language processing on the input to generate an embedding that corresponds to a semantic representation of the string of text, and based on the generated embedding, identifying an action that is associated with the user intent. The method includes executing the identified action, such that the executed action returns a result associated with the user intent, and providing the result to the user.

Abstract: Systems, methods, and computer-readable storage media for recommending computational commands based on a weighted, ranked hierarchical graph. The system converts a map of intended user actions to canonical actions (commands) into a graph representation. Then, via a chatbot algorithm, the system receives an initial intent as a request by a user of the chatbot algorithm. Based on the popularity of alternative canonical actions, the system makes recommendations of alternative actions which are similar to the initial intent provided by the user.

Abstract: Technical solutions include an ML based multi-model architecture to generate responses to enterprise employee queries. A processor can receive a query on a topic and identify ML models for a plurality of domains, trained using texts on a respective domain of the plurality of domains for each respective ML model and covering a plurality of topics corresponding geographic areas. The processor can select, using a first portion of the query and a classification model trained to classify the ML models according to the topics, a first ML model trained on a domain associated with the topic of the query. The processor can generate, using a second portion of the query corresponding to a geographic area of the geographic areas and the first ML model, a response to the query and provide the response.

Abstract: Systems, methods, and computer-readable storage media for recommending computational commands based on a weighted, ranked hierarchical graph. The system converts a map of intended user actions to canonical actions (commands) into a graph representation. Then, via a chatbot algorithm, the system receives an initial intent as a request by a user of the chatbot algorithm. Based on the popularity of alternative canonical actions, the system makes recommendations of alternative actions which are similar to the initial intent provided by the user.

Abstract: A system can receive textual input from a client device via a chatbot interface. The system can classify, by machine learning models, the textual input into categories, where the categories correspond to content-based categories and attribute categories. The system can determine, by the machine learning models, a polarity score for the textual input based on the categories. The system can detect, via a payroll processing system, a payroll-related event based on the polarity score and the profile. The system can identify, using the machine learning models, a churning event based on the payroll-related event. The system can generate, by the machine learning models, a command to adjust access control policies associated with the profile, where the command modifies access to a subset of payroll records maintained in a database.

Abstract: A system can receive textual input from a client device via a chatbot interface. The system can identify a profile data structure based on an identifier of the client device. The system can classify, by machine learning models, the textual input into categories, where the categories correspond to content-based categories and attribute categories. The system can determine, by the machine learning models, a polarity score for the textual input based on the categories. The system can input the categories, the polarity score, and the identifier into a payroll processing system to identify a payroll-related event and extract data corresponding to the payroll-related event. The system can provide the extracted data to the machine learning models to generate an output response data structure. The system can transmit the output response data structure to the client device to parse the output response data structure and present a corresponding message dialog.

Abstract: A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

Abstract: Systems, methods, and computer-readable storage media for using graph theory to identify conflicts of interests between entities. A system can receive first data from a first entity, the first data identifying relationships associated with the first entity, as well as second data from a second entity, the second data identifying relationships associated with the second entity. The system can also receive information about the first and second entity being competitors, and using the first data, second data, and competitor data, construct a knowledge graph regarding the relationships between the entities. The system can then analyze the graph model, resulting in a subset of the graph model which identifies any competitive relationships. This subset can then be output to the first and/or second entity.

Abstract: Systems, methods, and computer-readable storage media for building knowledge graphs which map the aspects of entities, then transforming the graphs into vectors for a similarity comparison. The resulting vectors can be used to identify skills and competencies of individuals, which the system uses to compare those individuals to others. The system can then quantify who would make for a good replacement for a given individual and make an associated recommendation.

Abstract: Systems, methods, and computer-readable storage media for using graph theory to identify conflicts of interests between entities. A system can receive first data from a first entity, the first data identifying relationships associated with the first entity, as well as second data from a second entity, the second data identifying relationships associated with the second entity. The system can also receive information about the first and second entity being competitors, and using the first data, second data, and competitor data, construct a knowledge graph regarding the relationships between the entities. The system can then analyze the graph model, resulting in a subset of the graph model which identifies any competitive relationships. This subset can then be output to the first and/or second entity.

Abstract: A method for fact retrieval includes receiving an input from a user, said input including a string of text that represents a user intent. The method includes performing natural language processing on the input to generate an embedding that corresponds to a semantic representation of the string of text, and based on the generated embedding, identifying an action that is associated with the user intent. The method includes executing the identified action, such that the executed action returns a result associated with the user intent, and providing the result to the user.

Abstract: A method receives an electronic image and uses the image as an input to a neural network. Based on a determination that the image represents a document, the method uses the image as an input to another neural network to identify a portion of the document containing an identifier. The method extracts the identifier by performing character recognition on the identified portion and determines whether the identifier is valid by using a validation API to determine whether the identifier is associated with a valid account at an institution. Based on a determination that the identifier is associated with a valid account, the method authorizes a transaction associated with the identifier. Based on a determination that the identifier is not associated with a valid account, the method denies the transaction. The first neural network classifies the electronic image into one of multiple valid document types and an invalid document type.

Abstract: Systems, methods, and computer-readable storage media for building knowledge graphs which map the aspects of entities, then transforming the graphs into vectors for a similarity comparison. The resulting vectors can be used to identify skills and competencies of individuals, which the system uses to compare those individuals to others. The system can then quantify who would make for a good replacement for a given individual and make an associated recommendation.

Abstract: Systems, methods, and computer-readable storage media for recommending computational commands based on a weighted, ranked hierarchical graph. The system converts a map of intended user actions to canonical actions (commands) into a graph representation. Then, via a chatbot algorithm, the system receives an initial intent as a request by a user of the chatbot algorithm. Based on the popularity of alternative canonical actions, the system makes recommendations of alternative actions which are similar to the initial intent provided by the user.

Abstract: A method for predicting customer churn includes receiving a graph data structure storing data associated with activity of a user, the graph data structure having multiple nodes, including a user input node associated with the user. The method includes updating at least the user input node with a vector representation of a received user input, and using historical user input, training a sentiment model to classify user input according to one of multiple sentiments. The method includes using the trained sentiment model to classify the received user input as a particular sentiment, and adding to the graph data structure a sentiment node that is associated with the particular sentiment and that is connected to the user input node. The method includes, using the graph data structure, training a churn model to estimate customer churn probability, and using the trained churned model to estimate a particular churn probability for the user.

Abstract: Systems, methods, and computer-readable storage media for identifying and generating insights using machine learning and serverless computing systems. When events are detected, data from those events is collected and formatted into predefined formats. That formatted data is then used within an instance, where a machine learning algorithm is executed by a serverless computing system using the data within the instance as input. The result from the machine learning algorithm is an insight into the event and event data, which can be presented to users for interpretation.