# US 2019/0244094 A1 · Machine learning driven data management > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0244094 A1 - Document ID: US20190244094A1 - Status: Published application - Kind: A1 - Application number: 15/890,184 - Filed: 2018-02-06 - Published: 2019-08-08 - Earliest claimed: 2018-02-06 - Assignees: Sap SE - Applicants: Sap SE - Inventors: Hans-Martin Ramsl - Web page: https://patentgrep.com/patent/US20190244094A1 ## CPC - G06N 3/08: Learning methods - G06F 9/453: {Help systems} - G06F 16/30: of unstructured textual data (document management systems G06F16/93) - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 3/02: Neural networks - G06N 3/0499: Feedforward networks - G06N 20/00: Machine learning - G06Q 10/00: Administration; Management - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR ## Abstract A system for machine learning driven data management is provided. In some implementations, the system performs operations including receiving, by a neural network, first and second textual data associated with a first item and a second item. The operations further include converting, by the neural network, the first and second textual data to a first vector and a second vector. The operations further include determining, by the neural network, whether the first item and the second item satisfy, based on a comparison of the first vector with the second vector, a similarity threshold. The operations further include selecting, by the neural network and in response to satisfaction of the similarity threshold, one of the first item and the second item, the selecting based on a selection criteria. The operations further include providing, by the neural network, a recommendation on a user interface regarding the selected first item or second item. ## Claims ### Claim 1 A system, comprising: at least one data processor; and at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising: receiving, by a neural network, first textual data associated with a first item and second textual data associated with a second item; converting, by the neural network, the first textual data to a first vector and the second textual data to a second vector, the first vector indicating one or more words associated with the first item, the second vector indicating one or more words associated with the second item; determining, by the neural network, whether the first item and the second item satisfy, based on a comparison of the first vector with the second vector, a similarity threshold; selecting, by the neural network and in response to satisfaction of the similarity threshold, one of the first item and the second item, the selecting based on a selection criteria; and providing, by the neural network, a recommendation on a user interface regarding the selected first item or second item. ### Claim 2 (depends on 1) The system of claim 1, wherein the receiving and the converting are performed by an input layer of the neural network, wherein the determining is performed by an embedding layer of the neural network, and wherein the selecting and the providing are performed by a comparison layer of the neural network. ### Claim 3 (depends on 1) The system of claim 1, wherein the operations further comprise preprocessing the first textual data and/or the second textual data to remove at least a portion of the first textual data and/or the second textual data. ### Claim 4 (depends on 1) The system of claim 1, wherein the converting comprises training a word embedding model and converting the first textual data to the first vector and the second textual data to the second vector using the trained word embedding model. ### Claim 5 (depends on 4) The system of claim 4, wherein the word embedding model comprises a skip-gram model. ### Claim 6 (depends on 1) The system of claim 1, wherein the determining comprises: comparing, in response to receiving the first and the second vectors, the one or more words associated with the first item and the one or more words associated with the second item; and determining, based on the comparing the one or more words associated with the first item and the one or more words associated with the second item, a degree of similarity between the first item and the second item, wherein the similarity threshold comprises a threshold degree of similarity value. ### Claim 7 (depends on 1) The system of claim 1, wherein the selection criteria comprises user-selected criteria. ### Claim 8 (depends on 1) The system of claim 1, wherein the selecting comprises: correlating a first weighted score with the first item and a second weighted score with the second item, the selection criteria comprising a weighted score value; selecting the first item, when the first weighted score is higher than the second weighted score. ### Claim 9 (depends on 8) The system of claim 8, wherein the selecting comprises removing the second item from a database. ### Claim 10 (depends on 1) The system of claim 1, wherein the recommendation comprises a first indication to store the first item in a database and/or a second indication to remove the second item from the database. ### Claim 11 A method comprising: receiving, by a neural network, first textual data associated with a first item and second textual data associated with a second item; converting, by the neural network, the first textual data to a first vector and the second textual data to a second vector, the first vector indicating one or more words associated with the first item, the second vector indicating one or more words associated with the second item; determining, by the neural network, whether the first item and the second item satisfy, based on a comparison of the first vector with the second vector, a similarity threshold; selecting, by the neural network and in response to satisfaction of the similarity threshold, one of the first item and the second item, the selecting based on a selection criteria; and providing, by the neural network, a recommendation on a user interface regarding the selected first item or second item. ### Claim 12 (depends on 11) The method of claim 11, wherein the receiving and the converting are performed by an input layer of the neural network, wherein the determining is performed by an embedding layer of the neural network, and wherein the selecting and the providing are performed by a comparison layer of the neural network. ### Claim 13 (depends on 11) The method of claim 11, wherein the operations further comprise preprocessing the first textual data and/or the second textual data to remove at least a portion of the first textual data and/or the second textual data. ### Claim 14 (depends on 11) The method of claim 11, wherein the converting comprises training a word embedding model and converting the first textual data to the first vector and the second textual data to the second vector using the trained word embedding model. ### Claim 15 (depends on 11) The method of claim 11, wherein the determining comprises: comparing, in response to receiving the first and the second vectors, the one or more words associated with the first item and the one or more words associated with the second item; and determining, based on the comparing the one or more words associated with the first item and the one or more words associated with the second item, a degree of similarity between the first item and the second item, wherein the similarity threshold comprises a threshold degree of similarity value. ### Claim 16 (depends on 11) The method of claim 11, wherein the selection criteria comprises user-selected criteria. ### Claim 17 (depends on 11) The method of claim 11, wherein the selecting comprises: correlating a first weighted score with the first item and a second weighted score with the second item, the selection criteria comprising a weighted score value; selecting the first item, when the first weighted score is higher than the second weighted score. ### Claim 18 (depends on 17) The method of claim 17, wherein the selecting removing the second item from a database. ### Claim 19 (depends on 11) The method of claim 11, wherein the recommendation comprises a first indication to store the first item in a database and/or a second indication to remove the second item from the database. ### Claim 20 A non-transitory computer program product storing instructions which, when executed by at least one data processor, causes operations comprising: receiving, by a neural network, first textual data associated with a first item and second textual data associated with a second item; converting, by the neural network, the first textual data to a first vector and the second textual data to a second vector, the first vector indicating one or more words associated with the first item, the second vector indicating one or more words associated with the second item; determining, by the neural network, whether the first item and the second item satisfy, based on a comparison of the first vector with the second vector, a similarity threshold; selecting, by the neural network and in response to satisfaction of the similarity threshold, one of the first item and the second item, the selecting based on a selection criteria; and providing, by the neural network, a recommendation on a user interface regarding the selected first item or second item.