Apparatuses, methods, systems, and program products are disclosed for optimizing aggregation routing over a network. An apparatus includes a processor and a memory that stores code executable by the processor. The code is executable by the processor to select a data aggregator server from a plurality of data aggregator servers to service a request for aggregated account data based on a plurality of factors associated with each of the plurality of data aggregator servers, format a request for the aggregated account data to be compatible with the selected data aggregator server, route the formatted request over the network to the selected data aggregator server, receive the requested aggregated account data over the network from the selected data aggregator server, populate a form for a personal financial manager (“PFM”) with the aggregated account data, output the populated form to the PFM, and present the aggregated account data to a user.

An apparatus and method displays an error between a first sequence of numbers and a second sequence of numbers. A plurality of respectively different algorithms is provided for comparing the first sequence of numbers and the second sequence of numbers. At least one of the algorithms is selected to compare the first sequence of numbers and the second sequence of numbers. The selected algorithm(s) are applied to the first sequence of numbers and the second sequence of numbers in order to identify the error, wherein the error is a discrepancy between one of the numbers in the first sequence and another of the numbers in the second sequence. The error is displayed by simultaneously displaying and indicating the numbers from each sequence that have the discrepancy. For at least one of the algorithms the numbers from each of the sequences have a matching associated label. At least one of the algorithms applies fuzzy matching to the numbers from each of the sequences. At least one of the algorithms adds the numbers from one of the sequences to obtain a partial addition sum and displays the partial addition sum with one of the numbers from the other sequence. For at least one of the algorithms the discrepancy is between non-identical positions within the two sequences.

A system, method, and computer program product for implementing cognitive natural language processing software framework optimization is provided. The method includes receiving instructions associated with an audible user input of a user. An AI input intention of the user is determined and key information is extracted from the audible user input. The key information is inputted into a generated database table and additional key information is retrieved from a dialog table. A supplementary database table comprising the additional key information is generated and the key information is spliced with the additional key information. A resulting spliced data structure is merged into a final database table and natural language is converted into a request code structure within an SQL structure and an interactive AI interface presenting results of the converting is generated. Operational functionality of an AI device is enabled for audibly presenting results of the conversion.

Systems and methods for searching data are disclosed. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving a sample dataset and identifying a data schema of the sample dataset. The operations may include generating a sample data vector that includes statistical metrics of the sample dataset and information based on the data schema of the sample dataset. The operations may include searching a data index comprising a plurality of stored data vectors corresponding to a plurality of reference datasets. The stored data vectors may include statistical metrics of the reference datasets and information based on corresponding data schema. The operations may include generating, based on the search and the sample data vector, one or more similarity metrics of the sample dataset to individual ones of the reference datasets.

A computerized method is provided for automatically determining answers to a plurality of questions. The method includes automatically discovering a plurality of questions by processing historical data related to prior customer interactions. The automatically discovering includes applying a linguistic analytical model on the data related to historical customer interactions to detect the plurality of questions, vectoring the plurality of questions to generate mathematical representations of the questions, and grouping the plurality of questions into one or more clusters in accordance with similarities of the questions as measured based on their mathematical representations. The method also includes identifying the questions that do not have an existing answer. The method further includes determining at least one probable answer to each of the representative questions using a content mining technique that mines pertinent data from one or more identified content sources.

A method and system of managing a new client record for a client record management system, including creating a new client record, comparing the new client record against at least an existing client record in a database, generating a table with a picklist, with the picklist presenting a suggested record, which is selected from an existing client record in the database, displaying the table and presenting the picklist for a confirmation by a user, and merging the new client record with the existing client record based on the confirmation by the user, where the picklist presents the suggested record based on a criterion, the criterion being a match in a field between the new client record and the suggested record.

Disclosed is an electronic device including a memory and at least one processor, wherein the at least one processor may calculate a similarity estimate between a first query of a plurality of queries and each of a plurality of keys with respect to a plurality of input entities and select some keys of the plurality of keys as a candidate by comparing the similarity estimate with a threshold, calculate the similarity for the keys included in the candidate in a self-attention operation for the first query, and perform the self-attention operation on the plurality of input entities by repeating a candidate selection process for each of the plurality of queries.

Systems and methods for automatic URL identification from data are provided. The system receives and processes one or more sources of data, such as merchant data, and processes the input data to identify one or more URLs present in the data. The identified URLs are automatically validated by the system using one or more fuzzy and/or exact matching algorithms. The validation could be performed by matching one or more non-URL data items, such as a business name, address, e-mail, country, zip code, or any other suitable non-URL data item, to ensure that only valid URLs are identified. Once the URLs are validated, a report is generated by the system.

The method of linking records between databases uses pre-processing of a first database to enhance matching speed of a fuzzy matching algorithm for linking records with a second database. In the first database, at least one text field is selected for linking. The set of first entries associated with the selected field is divided into sub-sets. Each sub-set contains at least one of the first entries within a predetermined first edit distance of a first string metric applied to a common text block associated with the sub-set. Second entries of the second database, corresponding to the selected text field of the first database, are matched against a set formed from the common text blocks. Each of the matching ones of the second entries is compared with each of the first entries within the sub-set of the corresponding one of the common text blocks and linked using approximate string matching.

Methods and systems for performing a search over hierarchical data are provided. The method may be performed by a server comprising a processor and memory. The method includes receiving a query string from a user device. The query string is searched for via a tensor search of a graph structure to determine node tensor distance score for each node in the graph structure. The query string is searched for via a fuzzy search of the graph structure to determine node fuzzy distance score for each node in the graph structure. Nodes with relevant scores are determined by updating a Bayesian network representation with evidence based on the node tensor distance scores and the node fuzzy distance scores of each node. Relevant data from the nodes is sent with relevant scores to the user device.