# US 12,602,595 B2 · System and method of using a knowledge representation for features in a machine learning classifier > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,595 B2 - Document ID: US12602595B2 - Status: Granted - Kind: B2 - Application number: 15/360,454 - Filed: 2016-11-23 - Published: 2018-05-24 - Granted: 2026-04-14 - Earliest claimed: 2016-11-23 - Assignees: Primal Fusion Inc. - Applicants: Primal Fusion Inc. - Inventors: Mathew Whitney Wilson; Ihab Ilyas; Peter J. Sweeney - Web page: https://patentgrep.com/patent/US12602595B2 ## CPC - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 20/00: Machine learning - G06Q 30/0631: {Recommending goods or services} - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 Systems and methods are provided for classifying at least one unlabeled content item with a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used as a source of features for which training data is evaluated. The machine learning classifier is trained based on features based on the attributes and the training data. ## Claims ### Claim 1 A computer-implemented method of transforming a set of raw data into a set of featurized data using an object of interest comprising a set of unstructured data for training machine learning models, the method comprising executing via a computer system comprising at least one processor and at least one non-transitory memory: receiving, by the at least one processor, the set of raw data for use with the machine learning models, the set of raw data comprising content items; receiving, by the at least one processor, the object of interest containing terms and/or properties; synthesizing, by the at least one processor executing executable instructions stored in the at least one non-transitory memory, a knowledge representation based on terms and/or properties of the object of interest, the knowledge representation being encoded as a non-transitory computer-readable data structure, the synthesizing comprising: generating an initial knowledge representation using the object of interest as input, the initial knowledge representation containing a plurality of concepts and at least one relationship between the plurality of concepts; executing a semantic synthesis process on the initial knowledge representation to obtain an augmented initial knowledge representation, the semantic synthesis process including (i) augmenting the initial knowledge representation using at least one information source external to the object of interest, (ii) deriving at least a first concept or a first relationship between two or more concepts that is not present in the initial knowledge representation based on a semantic relationship between the plurality of concepts in the initial knowledge representation and the at least a first concept or a first relationship, and (iii) adding the at least a first concept or a first relationship to the initial knowledge representation; deriving one or more attributes from the augmented initial knowledge representation, wherein the one or more attributes are properties of the augmented initial knowledge representation; deriving a first set of one or more content item features from the derived one or more attributes for use in obtaining featurized content items with derived features; determining evaluation values of the derived first set of one or more content item features for each of the content items of the set of raw data by evaluating the content items of the set of raw data with respect to the derived first set of one or more content item features; associating the evaluation values of the derived first set of one or more content item features with each of the content items of the set of raw data to obtain the set of featurized data; and training the machine learning models using the set of featurized data as training data. ### Claim 2 (depends on 1) The method of claim 1, further comprising executing the machine learning models using the set of featurized data as input data. ### Claim 3 (depends on 2) The method of claim 2, wherein the input data is training labeled data. ### Claim 4 (depends on 2) The method of claim 2, wherein the input data is evaluation data. ### Claim 5 (depends on 1) The method of claim 1, wherein the machine learning models use supervised learning. ### Claim 6 (depends on 1) The method of claim 1, wherein the object of interest is one of a topic, a tweet, a webpage, a website, a document, a document title, a message, an advertisement, and a search query. ### Claim 7 (depends on 1) The method of claim 1, wherein the semantic synthesis process comprises generative rules applied to an elemental data structure to generate a new complex knowledge representation. ### Claim 8 (depends on 1) The method of claim 1, wherein the one or more content item features include one of: a total number of concepts intersecting between the augmented initial knowledge representation and at least one content item; a number of broader concepts intersecting between the augmented initial knowledge representation and at least one content item; a number of narrower concepts intersecting between the augmented initial knowledge representation and at least one content item; and a sum of weights of concepts intersecting between the augmented initial knowledge representation and at least one content item. ### Claim 9 A system for transforming a set of raw data into a set of featurized data using an object of interest comprising a set of unstructured data for training machine learning models, the system comprising: at least one non-transitory memory, and at least one processor configured to perform a method comprising: receiving the set of raw data for use with the machine learning models, the set of raw data comprising content items; receiving the object of interest containing terms and/or properties; synthesizing, by the at least one processor executing executable instructions stored in the at least one non-transitory memory, a knowledge representation based on terms and/or properties of the object of interest, the knowledge representation being encoded as a non-transitory computer-readable data structure, the synthesizing comprising: generating an initial knowledge representation using the object of interest as input, the initial knowledge representation containing a plurality of concepts and at least one relationship between the plurality of concepts; executing a semantic synthesis process on the initial knowledge representation to obtain an augmented initial knowledge representation, the semantic synthesis process including (i) augmenting the initial knowledge representation using at least one information source external to the object of interest, (ii) deriving at least a first concept or a first relationship between two or more concepts that is not present in the initial knowledge representation based on a semantic relationship between the plurality of concepts in the initial knowledge representation and the at least a first concept or a first relationship, and (iii) adding the at least a first concept or a first relationship to the initial knowledge representation; deriving one or more attributes from the augmented initial knowledge representation, wherein the one or more attributes are properties of the augmented initial knowledge representation; deriving a first set of one or more content item features from the derived one or more attributes for use in obtaining featurized content items with derived features; determining evaluation values of the derived first set of one or more content item features for each of the content items of the set of raw data by evaluating the content items of the set of raw data with respect to the derived first set of one or more content item features; associating the evaluation values of the derived first set of one or more content item features with each of the content items of the set of raw data to obtain the set of featurized data; and training the machine learning models using the set of featurized data as training data. ### Claim 10 (depends on 9) The system of claim 9, further comprising executing the machine learning models using the set of featurized data as input data. ### Claim 11 (depends on 10) The system of claim 10, wherein the input data is training labeled data. ### Claim 12 (depends on 10) The system of claim 10, wherein the input data is evaluation data. ### Claim 13 (depends on 9) The system of claim 9, wherein the machine learning models use supervised learning. ### Claim 14 (depends on 9) The system of claim 9, wherein the object of interest is one of a topic, a tweet, a webpage, a website, a document, a document title, a message, an advertisement, and a search query. ### Claim 15 (depends on 9) The system of claim 9, wherein the semantic synthesis process comprises generative rules applied to an elemental data structure to generate a new complex knowledge representation. ### Claim 16 (depends on 9) The system of claim 9, wherein the one or more content item features include one of: a total number of concepts intersecting between the augmented initial knowledge representation and at least one content item; a number of broader concepts intersecting between the augmented initial knowledge representation and at least one content item; a number of narrower concepts intersecting between the augmented initial knowledge representation and at least one content item; and a sum of weights of concepts intersecting between the augmented initial knowledge representation and at least one content item. ### Claim 17 At least one non-transitory computer readable storage medium storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to perform a method of transforming a set of raw data into a set of featurized data using an object of interest comprising a set of unstructured data for training machine learning models, the method comprising: receiving the set of raw data for use with the machine learning models, the set of raw data comprising content items; receiving the object of interest containing terms and/or properties; synthesizing a knowledge representation based on terms and/or properties of the object of interest, the knowledge representation being encoded as a non-transitory computer-readable data structure, the synthesizing comprising: generating an initial knowledge representation using the object of interest as input, the initial knowledge representation containing a plurality of concepts and at least one relationship between the plurality of concepts; executing a semantic synthesis process on the initial knowledge representation to obtain an augmented initial knowledge representation, the semantic synthesis process including (i) augmenting the initial knowledge representation using at least one information source external to the object of interest, (ii) deriving at least a first concept or a first relationship between two or more concepts that is not present in the initial knowledge representation based on a semantic relationship between the plurality of concepts in the initial knowledge representation and the at least a first concept or a first relationship, and (iii) adding the at least a first concept or a first relationship to the initial knowledge representation; deriving one or more attributes from the augmented initial knowledge representation, wherein the one or more attributes are properties of the augmented initial knowledge representation; deriving a first set of one or more content item features from the derived one or more attributes for use in obtaining featurized content items with derived features; determining evaluation values of the derived first set of one or more content item features for each of the content items of the set of raw data by evaluating the content items of the set of raw data with respect to the derived first set of one or more content item features; associating the evaluation values of the derived first set of one or more content item features with each of the content items of the set of raw data to obtain the set of featurized data; and training the machine learning models using the set of featurized data as training data. ### Claim 18 (depends on 17) The computer readable storage medium of claim 17, further comprising executing the machine learning models using the set of featurized data as input data.