The Boeing Company

Brian Warn

Leonard Jon Quadracci

Jaime Antonio Flores, Jr

William G Arnold

Danielle C Young

Patrick Harris

Kyle Masao Nakamoto

John Desmond Whelan

Fredwilliam Esguerra

Leonard Jon Ouadracci

Kyle M Nakamoto

John Whelan

Jr Flores Jaime Antonio

A system for assisting a user in determining a cause of a manufacturing non-conformance situation in a manufacturing application. The system may include an associative memory subsystem that is populated with a plurality of entity types, with each entity type including at least one entity, to form an associative memory. A user input device enables a user to input manufacturing non-conformance information into the associative memory subsystem that causes the associative memory subsystem to perform an initial search. The initial search generates a plurality of the entities that has a primary relevance useful for investigating the manufacturing non-conformance situation. An output device is responsive to the associative memory subsystem presents the plurality of entities found during the initial search to the user.

Associative memory learning agent for analysis of manufacturing non-conformance applications

There is provided a computer implemented data driven classification and troubleshooting system and method. The system has an interface application enabled to receive data. The system has an associative memory software in communication with the interface application via an API. The associative memory software has an associative memory and a machine learning algorithm. The system has one or more individual areas, within the associative memory, requiring one or more troubleshooting actions to improve accuracy of the individual areas. The system has at least one troubleshooting tool enabled by the interface application. The at least one troubleshooting tool enables or performs the troubleshooting actions. The system has a quality rating metric (QRM) that measures a strength and an assurance that one or more predictions of the associative memory are correct. The one or more troubleshooting actions results in improving the accuracy and the performance of the associative memory.

Data driven classification and troubleshooting system and method using associative memory and a machine learning algorithm to improve the accuracy and performance of the associative memory

A non-conformance analysis system may have an associative memory subsystem populated with information involving a plurality of entities defining different attributes of a component, with each entity being categorized under a user defined entity type, the entities and entity types forming an associative memory. A user input device may be used for enabling a user to input a query concerning the component, and to obtain information useful for managing a lifecycle of said component. An associative memory entity analytics engine in communication with the associative memory subsystem, and responsive to said user input device, searches the associative memory using the information provided in the query to retrieve entities helpful to the user in assessing the lifecycle of the component.

Employing associative memory for enhanced lifecycle management

A computer implemented data driven classification and data quality checking system is provided. The system has an interface application enabled to receive data and has an associative memory software. The system has a data driven associative memory model configured to categorize one or more fields of received data and to analyze the received data. The system has a data quality rating metric associated with the received data. The system has a machine learning data quality checker for the received data, and is configured to add the received data to a pool of neighboring data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold. The machine learning data quality checker is configured to generate and communicate an alert of a potential error in the received data, if the data quality rating metric is less than the data quality rating metric threshold.

Data driven classification and data quality checking system

A method for improving accuracy and quality of received data is provided. The method provides a computer implemented data driven classification and data quality checking system. The method uses the associative memory software to build a data driven associative memory model that enables a machine learning data quality checker for receiving data. The method categorizes one or more fields of received data, analyzes the received data, and calculates a data quality rating metric, by comparing the received data with a pool of neighboring data in the category of field of the received data. The method accepts and adds the received data, if the data quality rating metric is greater than or equal to a data quality rating metric threshold, and generates and communicates an alert of a potential error in the received data, if the data quality rating metric is less than the data quality rating metric threshold.

Data driven classification and data quality checking method

A system for analyzing a request for proposal. The system includes an associative memory including a plurality of data having a plurality of associations. The system also includes an input device in communication with the associative memory. The input device is configured to provide the request for proposal to the associative memory in a format understandable to the associative memory. The associative memory is configured to digest the request for proposal to be included within the plurality of data, wherein a digested request for proposal is formed. The system also includes an analyzer in communication with the associative memory. The associative memory is configured to analyze the digested request for proposal by receiving a query comprising one or more terms, creating relationships among the plurality of data in the associative memory based on the query, and returning an output that includes requirements or other attributes presented in the digested request for proposal based on the query.

Associative memory technology for analysis of requests for proposal

A method for processing at least partially unstructured data is provided. The method includes receiving, at a data processing tool, at least partially unstructured data from at least one data source, and processing the at least partially unstructured data to generate at least partially structured data that includes tagged data, wherein processing the at least partially unstructured data includes at least one of processing the at least partially unstructured data using an associative memory application, and processing the at least partially unstructured data using a regular expression processing program. The method further includes transmitting the at least partially structured data to a main application, and incorporating the at least partially structured data into the main application based at least in part on the tagged data, wherein incorporating the at least partially structured data includes at least one of including and excluding data based on the existence, content and/or type of a tag.

Systems and methods for processing data

According to an embodiment, a non-conformance analysis system may include at least one information storage tool that stores previously generated non-conformance information; a data mining tool that retrieves specific attributes of the previously generated non-conformance information stored in the at least one information storage tool; an associative memory subsystem that is populated with information involving a plurality of entity types, with each entity type including at least one entity, to form an associative memory; and a user input device that enables a user to input a non-conformance query into the associative memory subsystem, that causes the associative memory subsystem to generate all of the entity types and entities that include information useful for investigating the non-conformance query.

Non-conformance analysis using an associative memory learning agent

A method, apparatus, and non-transitory computer readable storage medium for validating content is provided. Data is parsed into at least a first group of data and a second group of data according to a plurality of types of content present in the data. The data is ingested into an associative memory. The associative memory forms a plurality of associations among the data. The associative memory is configured to be queried based on at least one relationship selected from a group consisting of direct relationships and indirect relationships among the data. The associative memory comprises a content-addressable structure, the content-addressable structure comprising a memory organization in which the data is configured to be accessed by the content as opposed to being configured to be accessed by addresses for the data. The first group of data and the second group of data are communicated in a graphical representation.

Associative memory visual evaluation tool

The present invention provides a computer implemented method, apparatus, and computer usable program code for identifying interchangeable parts in parts catalogs. In one illustrative embodiment, a set of catalogs is searched for first attributes for a set of parts using an intelligent agent. The intelligent agent compares the first attributes for the set of parts to a set of second attributes for a selected part. A weight is assigned to each of the first attributes based on whether each of the first attributes is correlated to an attribute in the set of second attributes. Each part in the set of parts is ranked using the weight assigned to each attribute in the first attributes. A set of interchangeable parts is identified using the ranking for each part in the set of parts.

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Seattle, WA, United States of America

Brian Warn