Bank of America Corporation

Aftab Khan

Beena Khushalani

Sean Michael Byrne

Van Nguyen

Syed Talha

Sharad K Kalyani

Ashwin Roongta

Sayan Banerjee

Peter Michael Farrell

An apparatus includes a memory and a processor. The memory stores a dictionary and a machine learning algorithm trained to classify text. The processor receives an image of a page, converts the image into a set of text, and identifies a plurality of tokens within the text. Each token includes one or more contiguous characters that are both preceded and followed by whitespace within the text. The processor identifies invalid tokens by removing tokens of the plurality of tokens that correspond to words of the dictionary. The processor calculates, based on a ratio of a total number of valid tokens to a total number of tokens, a score. In response to determining that the score is greater than a threshold, the processor applies the machine learning algorithm to classify the text into a category and stores the image and/or text in a database according to the category.

Automated categorization and processing of document images of varying degrees of quality

An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

Automated categorization and assembly of low-quality images into electronic documents

Embodiments of the present invention provide a system for electronic identification of attributes for performing maintenance, monitoring, and distribution of designated resource assets. In particular, the system may be configured to extract one or more legacy resources from a data repository of an entity system associated with an entity, wherein the legacy resources are in a first format, convert the one or more legacy resources from the first format to a second format, process the one or more legacy resources, via one or more machine learning models, identify one or more attributes based on processing the one or more legacy resources via the one or machine learning models, and implement one or more actions based on the one or more attributes.

System for electronic identification of attributes for performing maintenance, monitoring, and distribution of designated resources

An apparatus includes a memory and processor. The memory stores document categories, text generated from an image a physical document page, and a machine learning algorithm. The text includes errors associated with noise in the image. The machine learning algorithm is configured to extract features associated with natural language processing and features associated with the errors from the text. The machine learning algorithm is also configured to generate a feature vector that includes the first and second pluralities of features, and to generate, based on the feature vector, a set of probabilities, each of which is associated with a document category and indicates a probability that the physical document from which the text was generated belongs to that document category. The processor applies the machine learning algorithm to the text, to generate the set of probabilities, identifies a largest probability, and assigns the image to the associated document category.

An apparatus includes a memory and processor. The memory stores OCR and NLP algorithms. The processor receives an image of a physical document page and executes the OCR algorithm to convert the image into text. The processor identifies errors in the text, which are associated with noise in the image. The processor generates a feature vector that includes features obtained by executing the NLP algorithm on the text, and features associated with the identified errors in the text. The processor uses the feature vector to assign the image to a document category. Documents assigned to the document category share one or more characteristics, and the feature vector is associated with a probability greater than a threshold that the physical document associated with the image includes those characteristics. The processor then stores the image in a database as a page of an electronic document belonging to the assigned document category.

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Richardson, TX, United States of America

Aftab Khan