Edward W Hunter

US Patent:

20230075341, Mar 9, 2023

Filed:

Aug 19, 2022

Appl. No.:

17/892033

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 40/30
G06F 40/284
G06F 40/211
G06F 16/34

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.

US Patent:

20230056987, Feb 23, 2023

Filed:

Aug 19, 2022

Appl. No.:

17/892030

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 40/30
G06F 40/137
G06F 40/284
G06F 40/117
G06F 40/169
G06F 16/9038
G06F 16/93

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 condition of the first category; and storing, with the computer system, the data model object in memory.

US Patent:

20230059494, Feb 23, 2023

Filed:

Aug 19, 2022

Appl. No.:

17/892026

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 40/30
G06F 40/284
G06F 40/169
G06F 40/137
G06F 40/117
G06N 5/02

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.

US Patent:

20210383070, Dec 9, 2021

Filed:

Jun 2, 2021

Appl. No.:

17/337239

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 40/30
G06F 40/103
G06N 20/00
G06F 40/284

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.

US Patent:

20210149958, May 20, 2021

Filed:

Dec 15, 2020

Appl. No.:

17/121915

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 16/901
G06F 17/18
G06F 9/54
G06N 3/08

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.

US Patent:

20210073282, Mar 11, 2021

Filed:

Jun 4, 2020

Appl. No.:

16/893290

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 16/901
H04L 29/06
H04L 9/32

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.

US Patent:

20210073284, Mar 11, 2021

Filed:

Sep 8, 2020

Appl. No.:

17/015028

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 16/901
H04L 29/06
H04L 9/32

Abstract:

A process includes receiving a request via an API, determining a query based on a set of query parameters, and determining a target graph portion template based on the query, where the request includes a callback address. The process may include searching a set of directed graphs to determine a set of graph portions based on the query. Each respective directed graph of the set of directed graphs may include a set of vertices and a set of directed edges connecting respective pairs of vertices among the set of vertices, where each respective vertex of the set of vertices is associated with a respective category label of a set of mutually exclusive categories. The process may include selecting a set of event records and sending a value of the set of event records to the callback address.

US Patent:

20210073285, Mar 11, 2021

Filed:

Sep 8, 2020

Appl. No.:

17/015038

Inventors:

- Gaithersburg MD, US
Edward Hunter - Gaithersburg MD, US

International Classification:

G06F 16/901
H04L 29/06
H04L 9/32

Abstract:

A method includes executing an instance of an application, where a program state includes a set of vertices and a set of directed graph edges, serializing the set of vertices in a serialized data format, and storing a record in a first persistent storage of a first computing device. The method includes distributing the serialized set of vertices to a second computing device and deserializing the serialized set of vertices to generate a second instance of a directed graph in non-serialized data format. The method includes storing the second instance in a second persistent storage, receiving a message encoding a graph portion template, and querying the second persistent storage based on the graph portion template to retrieve a subset of vertices encoded in the second instance. The method includes computing a response value based on the subset of vertices and transmitting the response value.