Bank of America Corporation

Other

Matthew I Cobb

Arjun Thimmareddy

Melissa A Fraser

Kimberly S Smith

Timothy Bryan Vannatter

A device that includes an enterprise data indexing engine (EDIE) configured to obtain a set of sentences from a document and to compare the words from each of the sentences to a set of keywords. The EDIE is further configured to identify one or more sentences that do not contain any of the keywords and to associate the identified sentences with a first classification type. The EDIE is further configured to identify a sentence that contains one or more keywords and to associate the sentence with a second classification type. The EDIE is further configured to link together the sentence that is associated with the second classification type and the sentences that are associated with the first classification type. The EDIE is further configured to obtain a classification description and a token and to link the classification description and its token with the classified sentences.

Classifying and mapping sentences using machine learning

A device that includes an enterprise data indexing engine (EDIE) configured to receive a set of sentences and to compare the words in the sentences to a set of predefined keywords. The EDIE is further configured to identify one or more sentences that do not contain any of the keywords and to associate the identified sentences with a first classification type. The EDIE is further configured to identify a sentence that contains one or more keywords and to associate the sentence with a second classification type. The EDIE is further configured to link together the sentence that is associated with the second classification type and the sentences that are associated with the first classification type.

Classifying and grouping sentences using machine learning

A device that includes an enterprise data indexing engine (EDIE) configured to receive a set of input sentences and to determine a set of similarity scores between the set of input sentences and a plurality of classification descriptions. The EDIE is further configured to identify a classification description that is associated with the highest similarity score and to identify a token that is associated with the identified classification description. The EDIE is further configured to link the identified token with the set of input sentences.

Identifying relationships between sentences using machine learning

A device that includes an enterprise data indexing engine (EDIE) configured to obtain a set of sentences from a document, to associate one or more of the sentences with a first classification type, to associate one of the sentences with a second classification type, and to link the classified sentences together. The EDIE is further configured to determine a set of similarity scores between the classified sentences and a plurality of classification descriptions. The EDIE is further configured to identify a classification description that is associated with the highest similarity, to identify a token associated with the identified classification description, and to output the token.

Mapping documents using machine learning

A device that includes an enterprise data indexing engine (EDIE) configured to receive a document that contains text and to convert the document into a markup language code. The EDIE is further configured to identify tags within the markup language code and to parse the markup language code into logical sections based on the tags. The EDIE is further configured to identify sentences within each of the logical sections and to link the identified sentences with a location identifier that identifies a location within the document where a sentence is located.

Parsing documents using markup language tags

A device that includes an enterprise data indexing engine (EDIE) configured to determine a first set of similarity scores between a first set of sentences from a first document and a plurality of classification descriptions. The EDIE is further configured to identify one or more classification descriptions that have a similarity score that exceeds a predetermined threshold value. The EDIE is further configured to determine a second set of similarity scores between a second set of sentences from a second document and the plurality of classification descriptions. The EDIE is further configured to identify one or more classification descriptions that have a similarity score that exceeds the predetermined threshold value. The EDIE is further configured to populate a data structure that identifies the tokens within the first set of tokens and the second set of tokens and the number of times each token appears.

Validating mappings between documents using machine learning

A device that includes an enterprise data indexing engine (EDIE) configured to receive a document comprising text and to convert the document into a markup language code. The EDIE is further configured to identify tags within the markup language code and to parse the markup language code into logical sections based on the tags. The EDIE is further configured to identify sentences within each of the logical sections and to link the identified sentences with a classification type. The EDIE is further configured to use the classified sentences to train a custom machine learning model.

Generating machine learning models for understanding sentence context

Counterrotating cam and follower apparatuses (C-CAFA) capable of converting reciprocating to rotational motion, and visa versa, utilizing counterrotating cams cooperating with at least one reciprocating cam follower. Apparatus cam counterrotation is ensured to be synchronously timed without necessity of prior art stationary geartrains, by and through apparatus cam follower(s) combining with counterrotating cam surfaces acting as moving follower constraints, provided that disclosed structural limitations, including those regarding cam and follower surface interface engineering slop, multiple degree of freedom cams, rotating and reciprocating follower(s), and electromechanical limitations are met when specified. Optional apparatus usages include reciprocating mass balancer, differential to reverse and or multiply shaft rotations, and apparatus combinations with connecting rods, pistons, cylinders, and or engines. 'Adequacy of Constraint and Newtonian Force Analysis, Cam and Follower Surface Kinematical Analysis, and Electromagnetic Cam Analysis' are applied to various disclosed embodiments to teach making, using, and correlation of disclosed apparatus structure to function.

Synchronously timed counterrotating cam and follower apparatus

A method for operating an electronic self-service device is provided. The method may include using an electronic receiver module to receive electronic data from the self-service device. The electronic data may include information regarding a limited access personal identification number ('PIN'). The method may further include using an electronic computational module to test the PIN using a plurality of algorithms. The testing preferably determines whether the PIN corresponds to one of a plurality of stored PIN offset values. The method may also include identifying an algorithm that obtains a correspondence between the PIN and one of the plurality of stored PIN offset values. The method may further include using an electronic transmitter to transmit a signal corresponding to the algorithm used to obtain the correspondence and receiving an electronic authorization message that comprises instructions. The instructions may provide limited access authorization information.

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Huntersville, NC, United States of America

Matthew I Cobb