Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: The present invention relates to cyclopropylamide compounds that are useful in the treatment of parasitic infestations of fish. The compounds have the formula (I).

Abstract: In some examples, apparatus comprises a multiplexer (MUX) adapted to be coupled to a set of battery cells and configured to provide a voltage of a different battery cell in the set of battery cells based on a MUX control signal. Apparatus comprises a comparator coupled to the MUX and configured to compare a MUX output signal to a threshold voltage to provide a comparator output signal. Apparatus comprises a digital control circuit configured to provide the MUX control signal to the MUX, to store the comparator output signal, and to use a logic AND gate to provide an AND gate output signal based on the stored comparator output signal.

Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: In accordance with a version, one or more identification clips, identification clip systems, and methods are provided which safely attach to and identify a plurality of damaged shingles to visually demonstrate and ascertain the extent of roof damage from a distance, particularly from the perspective of someone standing on the ground.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: In accordance with a version, one or more identification clips, identification clip systems, and methods are provided which safely attach to and identify a plurality of damaged shingles to visually demonstrate and ascertain the extent of roof damage from a distance, particularly from the perspective of someone standing on the ground.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for creating an effective baseline for controlling software and programs running on a user device or virtual machine environment of an enterprise network. Historical data may be used to train one or more iterative machine learning processes for intelligently identifying relevant program characteristics and aiding or automating a determination as to a software or program's need, usage, redundancy, or security. Determinations regarding software or program classifications may be used for further review or automated control of software or program execution within a network environment.

Abstract: Techniques include obtaining a location of a trigger word located in unstructured text of a natural-language-text document; determining, based on the location, a set of words following the trigger word in the unstructured text; conducting a lattice decoding operation for the set of words to determine a clause associated with the trigger word, the operation comprising: determining a clause decoding lattice for the set of words defining one or more paths, between the trigger word and the end of clause token, through the set of words; selecting a path of the clause decoding lattice; and determining, based on the path selected, the clause including one or more words of the set of words that correspond to the path selected; and generating and storing a data model object including the clause associated with the trigger word.

Abstract: Techniques include obtaining, with a computer system, a natural-language-text document comprising unstructured text; determining, with the computer system, a document structure corresponding to the unstructured text of the natural language text document; identifying, with the computer system, a plurality of clauses within the document structure; generating, with the computer system, using a trained machine learning model, a hierarchical structure; determining, with the computer system, based on a trigger word associated with a first category within the hierarchical structure and a first set of machine learning model parameters, a region within the unstructured text comprising descriptors associated with the first category; determining, with the computer system, from the region based on a second set of machine learning model parameters, a descriptor describing an action or condition of the first category; generating, with the computer system, a data model object comprising the descriptor defining an action or

Abstract: Techniques include obtaining, with a computer system, a natural-language-text document comprising unstructured text; generating, with the computer system, based on a first set of machine learning model parameters, a neural representation of the unstructured text; identifying, with the computer system, based on the neural representation, a trigger word located within the unstructured text and associated with a first category; determining, with the computer system, based on the trigger word, a region within the unstructured text comprising descriptors associated with the first category; determining, with the computer system, from the region based on a second set of machine learning model parameters, a descriptor describing an action or condition of the first category; generating, with the computer system, a data model object comprising the descriptor defining an action or condition of the first category; and storing, with the computer system, the data model object in memory.

Abstract: A method includes obtaining identifiers of entities and symbolic artificial intelligence (AI) models configured to produce outputs responsive to inputs based on events caused by at least one of the entities. At least some of the entities are associated with outputs of respective symbolic AI models and have respective scores corresponding to the respective outputs of the symbolic AI models. The method may include obtaining scenarios, where each scenario includes simulated inputs corresponding to one or more simulated events, and at least some scenarios include a plurality of simulated inputs. The method may also include determining a population of scores of a given entity among the entities, where respective members of the population of scores correspond to respective outputs of the plurality of symbolic AI models, and where the respective outputs correspond to respective scenarios among the scenarios and storing the population of scores in memory.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for creating an effective baseline for controlling software and programs running on a user device or virtual machine environment of an enterprise network. Historical data may be used to train one or more iterative machine learning processes for intelligently identifying relevant program characteristics and aiding or automating a determination as to a software or program's need, usage, redundancy, or security. Determinations regarding software or program classifications may be used for further review or automated control of software or program execution within a network environment.

Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: A computer-implemented process includes obtaining a natural-language-text document comprising a first and second clause and determining first and second embedding sequences based on n-grams of the first and second clauses. The process includes generating data model objects based on the embedding sequences and determining an association between the first data model object and the second data model object based on a shared parameter of the first and second clauses. The process includes receiving a query including the first category and the first n-gram and causing a presentation of a visualization of data model objects that includes shapes based on the data model objects and a third shape based on the association between the first data model object and the second data model object.

Abstract: In accordance with a version, one or more identification clips, identification clip systems, and methods are provided which safely attach to and identify a plurality of damaged shingles to visually demonstrate and ascertain the extent of roof damage from a distance, particularly from the perspective of someone standing on the ground.

Abstract: A method includes finding a smart contract that includes an associative array of entities, an associative array of conditions, and a serialized array of vertices. The method also includes deserializing the serialized array of vertices to generate a directed graph and determining a set of triggered vertices based on the directed graph and the event. Each of the set of triggered vertices is indicated as triggerable and is associated with a norm condition that is triggered by the event. The method includes updating the directed graph by updating a norm status associated with the triggered vertices and updating child vertices of the triggered vertices. The method includes updating the serialized array of vertices by serializing the updated directed graph and persisting the serialized array of vertices to a storage of the computer system.