Abstract: A company may implement automated workflows for convenience of users or to reduce support costs. For example, allowing a user to change an address using an automated workflow may be faster or less expensive than with a human agent. In some instances, a first communications session may be started between a first user and a second user. During the first communications session, one or more communications may be processed to select an intent of the first user and a value of an information item communicated by the first user. An automated workflow may be selected to continue assisting the first user, and the first user may be transferred to a second communications session with the automated workflow. The automated workflow may be initialized with the value of the information item that was provided during the first communications session so that the first user does not need to repeat information.

Abstract: The present disclosure relates to a system, method, and computer program for extracting utterances corresponding to a user problem statement in a conversation between a human agent and a user. The system obtains a set of utterances from a natural language conversation between the human agent and the user. The system uses a problem-statement classifier to obtain machine-generated predictions as to whether each natural language utterance in the set relates to a problem statement. The system selects one or more utterances from the set as corresponding to a problem statement based on the predictions. The system provides the selected utterances to a downstream system for further processing. In certain embodiments, the problem statement classifier includes an encoder that creates an utterance embedding for each utterance and a prediction module that uses the utterance embeddings to predict whether each utterance corresponds to a user problem statement.

Abstract: Application programming interfaces (APIs) may have different API schemas that may have different organizations of schema properties and entities or use different names for schema properties and entities. Where two or more APIs with different schemas are being used, it may be desired to map schema properties and/or entities between them. Semantic representations of API schema properties and entities may be used to assist with the mapping of schema properties and entities. The semantic representations may be used to suggest possible mappings to a user or to automatically determine a mapping without the involvement of a user.

Abstract: A company may implement automated workflows for convenience of users or to reduce support costs. For example, allowing a user to change an address using an automated workflow may be faster or less expensive than with a human agent. In some instances, a first communications session may be started between a first user and a second user. During the first communications session, one or more communications may be processed to select an intent of the first user and a value of an information item communicated by the first user. An automated workflow may be selected to continue assisting the first user, and the first user may be transferred to a second communications session with the automated workflow. The automated workflow may be initialized with the value of the information item that was provided during the first communications session so that the first user does not need to repeat information.

Abstract: A company may implement automated workflows for convenience of users or to reduce support costs. For example, allowing a user to change an address using an automated workflow may be faster or less expensive than with a human agent. In some instances, a first communications session may be started between a first user and a second user. During the first communications session, one or more communications may be processed to select an intent of the first user and a value of an information item communicated by the first user. An automated workflow may be selected to continue assisting the first user, and the first user may be transferred to a second communications session with the automated workflow. The automated workflow may be initialized with the value of the information item that was provided during the first communications session so that the first user does not need to repeat information.

Abstract: A first application programming interface (API) with a first schema may be adapted to work with a second API with a second schema using mappings of schema properties and a directed graph. An API call specification of the first API may receive first API schema properties as input and provide first API schema properties as outputs. The first API schema properties may be mapped to corresponding second API schema properties, such as using semantic representations of the schema properties. An implementation of an API call for the first API may be created by using the schema mappings and selecting a path from a directed graph corresponding to the second API, where the path includes a node corresponding to an API call of the second API. Computer code may be generated using nodes of the path, and the computer code may be stored for later use.

Abstract: Software for a computer system may be automatically generated to reduce costs. Software may be automatically generated using a set of software components where each component may have one or more input properties and one or more output properties. A property may correspond to a type of data (e.g., a customer ID) used by the computer system. A graph may be created from the components where the graph includes component nodes for the components and property nodes for the input and output properties. To automatically generate software for a task, a task specification may be received that includes a task input property and a task output property. A path on the graph may be determined from the task input property and the task output property, and software for accomplishing the task may be generated using components on the path. The software may then be executed or stored for later execution.

Abstract: Software for a computer system may be automatically generated to reduce costs. Software may be automatically generated using a set of software components where each component may have one or more input properties and one or more output properties. A property may correspond to a type of data (e.g., a customer ID) used by the computer system. A graph may be created from the components where the graph includes component nodes for the components and property nodes for the input and output properties. To automatically generate software for a task, a task specification may be received that includes a task input property and a task output property. A path on the graph may be determined from the task input property and the task output property, and software for accomplishing the task may be generated using components on the path. The software may then be executed or stored for later execution.

Abstract: A company may implement automated workflows for convenience of users or to reduce support costs. For example, allowing a user to change an address using an automated workflow may be faster or less expensive than with a human agent. In some instances, a first communications session may be started between a first user and a second user. During the first communications session, one or more communications may be processed to select an intent of the first user and a value of an information item communicated by the first user. An automated workflow may be selected to continue assisting the first user, and the first user may be transferred to a second communications session with the automated workflow. The automated workflow may be initialized with the value of the information item that was provided during the first communications session so that the first user does not need to repeat information.

Abstract: A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.

Abstract: A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.

Abstract: A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.

Abstract: A third-party company may assist companies in providing natural language interfaces for their customers. To implement a natural language interface for a company, a configuration may be received that includes information, such as a list intents, seed messages for the intents, and hierarchical information of the intents. An intent classifier may be trained using the configuration, and the natural language interface may be deployed for use with customers. Usage data of the natural language classifier may be collected and used to improve the natural language interface. Messages corresponding to an intent may be clustered into clusters of similar messages, and a prototype message may be obtained for each cluster to provide a human understandable description of the cluster. The information about the clusters may be used to improve the natural language interface, such as by creating a new intent with a cluster or moving a cluster to a different intent.

Abstract: Some embodiments are directed to identifying semantic properties of documents using free-text annotations associated with the documents. Semantic properties of documents may be identified by using a model that is trained on a corpus of training documents where one or more of the training documents may include free-text annotations. In some embodiments, the model may identify semantic topics expressed only in free-text annotations or only in the body of a document. The model may applied to identify semantic topics associated with a work document or to summarize the semantic topics present in a plurality of work documents.