Abstract: Methods and apparatuses are described for automatically identifying text sections of a document to generate a searchable hierarchical data structure. A computing device receives a document comprising text entities and converts the document from a first format to a second format, including generating metadata associated with text alignment, text position, text spacing, or fonts. The computing device extracts the text blocks, including determining coordinates associated with each text block using the metadata. The computing device determines document sections using the document metadata by identifying strings in the extracted text blocks that indicate a presence of a bullet point in the document, assigns a hierarchical category to each identified document section, and inserts text of each document section into a hierarchical data structure based upon the assigned hierarchical category.

Abstract: Methods and apparatuses are described for automatically identifying text sections of a document to generate a searchable hierarchical data structure. A computing device receives a document comprising text entities and converts the document from a first format to a second format, including generating metadata associated with text alignment, text position, text spacing, or fonts. The computing device extracts the text blocks, including determining coordinates associated with each text block using the metadata. The computing device determines document sections using the document metadata by identifying strings in the extracted text blocks that indicate a presence of a bullet point in the document, assigns a hierarchical category to each identified document section, and inserts text of each document section into a hierarchical data structure based upon the assigned hierarchical category.

Abstract: Systems and methods for extracting data from electronic documents based on data patterns. The method includes receiving electronic template documents. Each template document corresponds to a type of electronic document. The method further includes, for each template document, processing the template document using a text extraction and data processing application. The method also includes, for each template document, determining a data extraction formula corresponding to the type of electronic document. The method further includes, storing the data extraction formula in a first database. The method also includes, receiving an electronic document including user data and a Unicode corresponding to the type of document. The method also includes, processing and classifying the electronic document into the type of document corresponding to the Unicode.

Abstract: Systems and methods for extracting data from electronic documents based on data patterns. The method includes receiving electronic template documents. Each template document corresponds to a type of electronic document. The method further includes, for each template document, processing the template document using a text extraction and data processing application. The method also includes, for each template document, determining a data extraction formula corresponding to the type of electronic document. The method further includes, storing the data extraction formula in a first database. The method also includes, receiving an electronic document including user data and a Unicode corresponding to the type of document. The method also includes, processing and classifying the electronic document into the type of document corresponding to the Unicode.

Abstract: Systems and methods for deriving classification rules from documents and a database using rule-based machine learning. The method includes extracting first variables from documents corresponding to an organization. The method further includes extracting second variables from a database corresponding to the organization. The method also includes filtering the extracted second variables based on at least one of null values, repeat variables, location variables, ID variables, or data variables. The method further includes deriving first classification rules based on the first variables using a rule-based machine learning algorithm. The method also includes calculating an accuracy of the derived first classification rules. The method also includes deriving second classification rules based on the first variables and the filtered second variables. The method further includes determining a suggested additional variable based on the derived second classification rules and the calculated accuracy.

Abstract: Methods and apparatuses, including computer program products, are described for customer relationship capacity planning. A computing device receives (i) financial plan attributes associated with a financial plan of a customer and (ii) personality characteristics associated with the customer. The computing device determines a relationship complexity score associated with the customer based upon the financial plan attributes and the personality characteristics. The computing device determines a target workload value for customer service managers of the financial services entity based upon the relationship complexity score for a plurality of customers and a past number of relationship service hours associated with the plurality of customers. The computing device allocates the customer to a customer service manager based upon the target workload value and a current workload of the customer service manager.

Abstract: Disclosed is a method for enhancing the effect of a protein modulator on a protein by modifying the protein modulator so that the protein modulator binds with the surface of the protein, along with a method for modulating a protein's biological function by contacting the protein with such a modified protein modulator. Also described are modified protein modulators having the formula PM-SP-(LK)p-MCG-(M)q, where PM is a protein modulator which interacts with an active site or allosteric site of a protein; SP is a spacer; LK is a linker; p is 0 or 1; q is an integer greater than or equal to one; MCG is a metal chelating group; and M is a metal ion.