Abstract: Methods comprising determining a dataset of entries, each comprising an entity identifier and associated application identifier(s) are provided. One method comprises determining a set of application pairs from the dataset; determining a correlation score for each application pair based; responsive to the correlation score exceeding a threshold correlation value: determining application identifiers of the application pair as being correlated; providing a correlation write request comprising the applications identifiers of the pair to an app recommender database.

Abstract: A method comprises determining token(s) from financial documents and determining a set of preliminary attribute labels for the token(s), wherein the set is associated with attribute type(s). The method further comprises providing the set for each token to an attribute prediction model to determine, for the token, a confidence value for each attribute type(s), determining subsets of token, each subset being associated with a respective document of the plurality of documents and determining a set of refined labels for each document based on the confidence values, wherein the set of refined labels comprises a value for attribute type(s).

Abstract: A method comprises determining a plurality of documents and for each document of the plurality of documents: (i) providing the document to a numerical representation generation model; (ii) generating, by the numerical representation generation model, a numerical representation of the document; and (iii) determining a document score for the document based on the numerical representation. The method further comprises c) providing the document scores to a clustering module; d) determining, by the clustering module, one or more clusters, each cluster being associated with a class of the documents; e) outputting. by the clustering module, a cluster identifier indicative of the class of each document; and f) associating each document with its respective cluster identifier.

Abstract: A computer-implemented method comprises determining a dataset of transactions occurring during a first time period; determining a subset of related transactions from the dataset of transactions, where each transaction in the subset of related transactions shares at least one common attribute; selecting a first transaction interval pattern; selecting a first clustering criteria; based on the first transaction interval pattern and the first clustering criteria, identifying a cluster of transactions from the subset of related transactions; performing a viability check on the cluster; and in response to the cluster passing the viability check, generating a model of periodic transactions, the model including an interval related to the first transaction interval pattern; and a common attribute based on the at least one common attribute.

Abstract: Described embodiments relate to determining a candidate financial record associated with a transaction between a first accounting entity and a second entity, and determining, using a numerical representation generation model, a numerical representation of the candidate financial record, the numerical representation generation model having been trained on a corpus generated from historical transaction records. The method further comprises providing, to a transaction attribute prediction model, the numerical representation of the candidate financial record, the transaction attribute prediction model having been trained using a dataset of previously reconciled financial records, each associated with a respective first transaction attribute; and determining, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record.

Abstract: Methods comprising determining a dataset of entries, each comprising an entity identifier and associated application identifier(s) are provided. One method comprises determining a set of application pairs from the dataset; determining a correlation score for each application pair based; responsive to the correlation score exceeding a threshold correlation value: determining application identifiers of the application pair as being correlated; providing a correlation write request comprising the applications identifiers of the pair to an app recommender database.

Abstract: Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data.

Abstract: Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data.

Abstract: Described embodiments relate to determining a candidate financial record associated with a transaction between a first accounting entity and a second entity, and determining, using a numerical representation generation model, a numerical representation of the candidate financial record, the numerical representation generation model having been trained on a corpus generated from historical transaction records. The method further comprises providing, to a transaction attribute prediction model, the numerical representation of the candidate financial record, the transaction attribute prediction model having been trained using a dataset of previously reconciled financial records, each associated with a respective first transaction attribute; and determining, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record.

Abstract: Described embodiments relate to determining a candidate financial record associated with a transaction between a first accounting entity and a second entity, and determining, using a numerical representation generation model, a numerical representation of the candidate financial record, the numerical representation generation model having been trained on a corpus generated from historical transaction records. The method further comprises providing, to a transaction attribute prediction model, the numerical representation of the candidate financial record, the transaction attribute prediction model having been trained using a dataset of previously reconciled financial records, each associated with a respective first transaction attribute; and determining, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record.

Abstract: Described embodiments relate to a method comprising: determining a candidate document comprising image data and character data and extracting the image data and the character data from the candidate document. The method comprises providing, to an image-based numerical representation generation model, the image data, and generating, by the image-based numerical representation generation model, an image-based numerical representation of the image data. The method comprises providing, to a character-based numerical representation generation model, the character data; and generating, by the character-based numerical representation generation model, a character-based numerical representation of the character data.

Abstract: Described embodiments relate to determining a candidate financial record associated with a transaction between a first accounting entity and a second entity, and determining, using a numerical representation generation model, a numerical representation of the candidate financial record, the numerical representation generation model having been trained on a corpus generated from historical transaction records. The method further comprises providing, to a transaction attribute prediction model, the numerical representation of the candidate financial record, the transaction attribute prediction model having been trained using a dataset of previously reconciled financial records, each associated with a respective first transaction attribute; and determining, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record.

Abstract: The system obtains a set of tickets representing customer requests for a customer-support ticketing system. Next, the system produces a set of request vectors by feeding words from each ticket through a model to generate a request vector for the ticket, wherein the set of request vectors is represented as a set of points in a vector space. The system then performs a clustering operation on the set of points to form clusters representing support topics, wherein the clustering operation creates a new point for a new ticket in the vector space when the new ticket is received, and incrementally updates existing clusters to accommodate the new point. Finally, the system presents a user interface to a customer-support agent, wherein the user interface uses the support topics to organize the customer requests, and enables the customer-support agent to perform a customer-support operation in response to a customer request.

Abstract: Described embodiments relate to determining a candidate financial record associated with a transaction between a first accounting entity and a second entity, and determining, using a numerical representation generation model, a numerical representation of the candidate financial record, the numerical representation generation model having been trained on a corpus generated from historical transaction records. The method further comprises providing, to a transaction attribute prediction model, the numerical representation of the candidate financial record, the transaction attribute prediction model having been trained using a dataset of previously reconciled financial records, each associated with a respective first transaction attribute; and determining, by the transaction attribute prediction model, at least one first transaction attribute associated with the candidate financial record.

Abstract: The disclosed embodiments relate to a system that automatically interacts with a customer during an automated customer-support conversation. The system first receives a textual input from the customer during the automated customer-support conversation, wherein the conversation relates to an issue the customer has with a product or a service used by the customer. Next, the system calculates a semantic embedding in a vector space for the textual input. The system then determines a new position in a predefined dialogue tree based on the calculated semantic embedding and a current position of the conversation in the dialogue tree, wherein the dialogue tree defines a structure for the conversation, including dialogue text, and predefined responsive customer-support actions for various customer inputs. Finally, the system navigates to the new position in the dialogue tree and performs a responsive customer-support action associated with the new position.

Abstract: The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.

Abstract: The disclosed embodiments relate to a system that suggests helpful articles to resolve a customer request. During operation, the system receives the customer request, wherein the customer request is associated with a product or a service used by the customer. Next, the system feeds a set of words from the customer request through a model to generate a request vector comprising numerical values representing words in the customer request. The system then compares the request vector against article vectors representing articles in a set of help center articles to determine whether the customer request matches one or more help center articles. If the customer request matches one or more help center articles, the system presents the one or more help center articles to the customer to facilitate automatically resolving the customer request.

Abstract: The system obtains a set of tickets representing customer requests for a customer-support ticketing system. Next, the system produces a set of request vectors by feeding words from each ticket through a model to generate a request vector for the ticket, wherein the set of request vectors is represented as a set of points in a vector space. The system then performs a clustering operation on the set of points to form clusters representing support topics, wherein the clustering operation creates a new point for a new ticket in the vector space when the new ticket is received, and incrementally updates existing clusters to accommodate the new point. Finally, the system presents a user interface to a customer-support agent, wherein the user interface uses the support topics to organize the customer requests, and enables the customer-support agent to perform a customer-support operation in response to a customer request.

Abstract: The system obtains a set of tickets representing customer requests generated by a customer-support ticketing system. Next, the system feeds words from each ticket through a model to generate a request vector for the ticket, wherein the request vector comprises numerical values representing words in the ticket. The system then embeds the request vectors in a vector space. If help center articles already exist, the system embeds article vectors for the existing help center articles in the vector space. Next, the system identifies clusters of request vectors, which are within a pre-specified distance of each other in the vector space. If an identified cluster is more than a pre-specified distance away from a closest article vector in the vector space, the system notifies a content creator that a new article needs to be written, or an existing article needs to be updated, to cover the identified cluster.

Abstract: The disclosed embodiments relate to a system that suggests helpful articles to resolve a customer request. During operation, the system receives the customer request, wherein the customer request is associated with a product or a service used by the customer. Next, the system feeds a set of words from the customer request through a model to generate a request vector comprising numerical values representing words in the customer request. The system then compares the request vector against article vectors representing articles in a set of help center articles to determine whether the customer request matches one or more help center articles. If the customer request matches one or more help center articles, the system presents the one or more help center articles to the customer to facilitate automatically resolving the customer request.