Abstract: An example method of document cluster labeling comprises: selecting a current document cluster of a plurality of document clusters (e.g., the current document cluster can have documents organized using a DBSCAN or OPTICS algorithm); initializing a label associated with the current document cluster; selecting a term from a list of terms comprised by the document cluster; appending the term to the label associated with the current document cluster; responsive to determining that the label is found in a label dictionary, iteratively selecting a next term from the list of terms comprised by the document cluster and appending the next term to the label associated with the current document cluster; responsive to failing to locate the label in the label dictionary, inserting the label into the label dictionary; and associating the label with the current document cluster.

Abstract: Electrochemical devices including electrochemically-driven carbon dioxide separators are disclosed, the devices including electrodes comprised of an anion exchange polymer and a charge storage compound such as nickel hydroxide and a membrane comprising an anion exchange polymer, the membrane having a channel for inflow of a carbon dioxide-containing gas within the membrane.

Abstract: An intelligent flow agent system comprising: a processor and a memory element, the memory element comprising a non-transitory computer-readable medium storing instructions that, when executed by the processor, cause the system to: receive input from an actor, the input comprising at least one of an event, a task, or a mission; embed contextual information into the input via a contextual unit, the contextual information including at least one of a system state, environmental conditions, user behavior, or historical interactions; construct the mission based on the received input and the embedded contextual information; evaluate the mission using the intelligent flow agent to determine one or more workflows or actions suitable for execution, wherein the evaluation includes a context-aware decision process to select, sequence, or delegate actions based on at least one of the contextual relevance, system policies, or optimization criteria; and initiate an intelligent workflow comprising dynamically adaptive and co

Abstract: The present invention relates to a system and a method implemented by an intelligent module. The system comprises an interface, an artificial intelligence module, and an intelligent flow framework module. The intelligent flow framework module is communicatively coupled to the interface and the artificial intelligence module. The intelligent flow framework module is configured to define at least one task based on an event and contextual data for completing a mission. The system provides the ability to adapt quickly to changing circumstances and make intelligent decisions to ensure the successful completion of missions/objectives.

Abstract: Methods and systems are described for audio pipelines between a user and a machine learning (ML) model. The method involves obtaining digital audio data associated with an audio signal from a user's equipment. This data is then analyzed using a trained ML model, which includes one or more neural networks. The ML model determines an indication related to the digital audio data, which can be a prediction of the ending of one or more portions of the data, an intention behind the data, a key term within the data, an action to take based on the data, or the context associated with the data. Based on the determined indication, a response is generated and subsequently transmitted back to the user's equipment.

Abstract: The present invention relates to a system and a method implemented by an intelligent module. The system comprises an interface, an artificial intelligence module, and an intelligent flow framework module. The intelligent flow framework module is communicatively coupled to the interface and the artificial intelligence module. The intelligent flow framework module is configured to define at least one task based on an event and contextual data for completing a mission. The system provides the ability to adapt quickly to changing circumstances and make intelligent decisions to ensure the successful completion of missions/objectives.

Abstract: Systems, apparatuses and methods provide technology that identifies a plurality of data samples of a data set. In response to a trigger, the technology identifies a first subset of samples of the plurality of data samples that are outside a range associated with a decision boundary of a machine learning model, and removes the first subset of samples from the data set to generate a modified training set. The technology train the machine learning model based on the modified training set.

Abstract: Electrochemical devices including electrochemically-driven carbon dioxide separators are disclosed, the devices including electrodes comprised of an anion exchange polymer and a charge storage compound such as nickel hydroxide and a membrane comprising an anion exchange poiymer, the membrane having a channel for inflow of a carbon dioxide-containing gas within the membrane.

Abstract: An example method of document cluster labeling comprises: selecting a current document cluster of a plurality of document clusters (e.g., the current document cluster can have documents organized using a DBSCAN or OPTICS algorithm); initializing a label associated with the current document cluster; selecting a term from a list of terms comprised by the document cluster; appending the term to the label associated with the current document cluster; responsive to determining that the label is found in a label dictionary, iteratively selecting a next term from the list of terms comprised by the document cluster and appending the next term to the label associated with the current document cluster; responsive to failing to locate the label in the label dictionary, inserting the label into the label dictionary; and associating the label with the current document cluster.

Abstract: An example method comprises: receiving a natural language text; transforming, by a neural network, the natural language text into a numeric representation comprising a plurality of numeric values; discarding the natural language text; and performing, using the numeric representation of the natural language text, an information extraction task.

Abstract: An example method of employee experience and efficiency evaluation based on the employee's collaboration circles comprises: identifying, by a computer system, based on processing a plurality of documents reflecting communications of a specified person, a collaboration circle of the specified person; generating, based on a set of previously collected responses reflecting experience and efficiency of the employee, a set of questions with respect to experience and efficiency of the employee; presenting the set of questions to a plurality of persons comprised by the collaboration circle; collecting responses to the set of questions from the plurality of persons comprised by the collaboration circle; and generating a dashboard reflecting the collected responses.

Abstract: A method to predict problematic actions in an organization, executed by a processing device, includes accessing stored employee-related data, such as at least one of emails, surveys, minutes, or records of conversations, identifying a subset of the employee-related data that is associated with an employee, and predicting, based on the subset of the employee-related data associated with the employee, at least one of a likelihood that the employee is engaged in an adverse relation with other employees or a likelihood that the employee is to resign from the organization within a period of time.

Abstract: An example method of method of document cluster labeling comprises: selecting a current document cluster of a plurality of document clusters; initializing a label associated with the current document cluster; selecting a term from a list of terms comprised by the document cluster; appending the term to the label associated with the current document cluster; responsive to determining that the label is found in a label dictionary, iteratively selecting a next term from the list of terms comprised by the document cluster and appending the next term to the label associated with the current document cluster; responsive to failing to locate the label in the label dictionary, inserting the label into the label dictionary; and associating the label with the current document cluster.

Abstract: An example method of document clustering comprises: representing each document of a plurality of documents by a vector comprising a first plurality of real values, wherein each real value of the first plurality of real values reflects a first frequency-based metric of a term comprised by the document; partitioning the plurality of documents into a first set of document clusters based on distances between vectors representing the documents; representing each document cluster of the first set of document clusters by a vector comprising a second plurality of real values, wherein each real value of the second plurality of real values reflects a second frequency-based metric of a term comprised by the document cluster; and partitioning the first set of document clusters into a second set of document clusters based on distances between vectors representing the document clusters of the first set of document clusters.

Abstract: A task assistant identifies a correspondence received by a source associated with a user and determines that the correspondence includes a request. The task assistant further determines a ranking associated with the request based on one or more characteristics of the request and of the correspondence. In response to the ranking of the request exceeding a threshold, the task assistant generates a notification associated with the request and provides the notification to a client device associated with the user.

Abstract: An example method comprises: receiving a natural language text; transforming, by a neural network, the natural language text into a numeric representation comprising a plurality of numeric values; discarding the natural language text; and performing, using the numeric representation of the natural language text, an information extraction task.

Abstract: A task assistant identifies a correspondence received by a source associated with a user and determines that the correspondence includes a request. The task assistant further determines a ranking associated with the request based on one or more characteristics of the request and of the correspondence. In response to the ranking of the request exceeding a threshold, the task assistant generates a notification associated with the request and provides the notification to a client device associated with the user.

Abstract: A task assistant identifies a correspondence received by a source associated with a user and determines that the correspondence includes a request. The task assistant further determines a ranking associated with the request based on one or more characteristics of the request and of the correspondence. In response to the ranking of the request exceeding a threshold, the task assistant generates a notification associated with the request and provides the notification to a client device associated with the user.

Abstract: A method to predict problematic actions in an organization, executed by a processing device, includes accessing stored employee-related data, such as at least one of emails, surveys, minutes, or records of conversations, identifying a subset of the employee-related data that is associated with an employee, and predicting, based on the subset of the employee-related data associated with the employee, at least one of a likelihood that the employee is engaged in an adverse relation with other employees or a likelihood that the employee is to resign from the organization within a period of time.

Abstract: An illustrative method according to a set of instructions stored on a memory of a computing device includes receiving, by a processor of the computing device, a search input. The method further includes searching, by the processor, a plurality of electronic storage locations for electronic files related to the search input. The method further includes determining, by the processor, the presence of an electronic file on a first electronic storage location of the plurality of electronic storage locations that is related to the search input. The method further includes displaying, by the processor, on a graphical user interface (GUI), representative information of the electronic file. The representative information includes descriptive information relating to the electronic file.