# US 12,387,479 B2 · Creating data shapes for pattern recognition systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,387,479 B2 - Document ID: US12387479B2 - Status: Granted - Kind: B2 - Application number: 18/615,335 - Filed: 2024-03-25 - Published: 2024-08-08 - Granted: 2025-08-12 - Earliest claimed: 2006-08-14 - Assignees: DataShapes, Inc. - Applicants: DataShapes, Inc. - Inventors: Jeffrey Brian Adams - Web page: https://patentgrep.com/patent/US12387479B2 ## CPC - G06V 10/955: {using specific electronic processors} - G06F 18/40: Software arrangements specially adapted for pattern recognition, e.g. user interfaces or toolboxes therefor - G06V 10/945: {User interactive design; Environments; Toolboxes} - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, apparatuses and systems directed to pattern identification and pattern recognition. In some particular implementations, the invention provides a flexible pattern recognition platform including pattern recognition engines that can be dynamically adjusted to implement specific pattern recognition configurations for individual pattern recognition applications. In some implementations, the present invention also provides for a partition configuration where knowledge elements can be grouped and pattern recognition operations can be individually configured and arranged to allow for multi-level pattern recognition schemes. ## Claims ### Claim 1 A computer-implemented method, comprising: receiving input data; generating a plurality of feature sets from the input data, each feature set representing a corresponding one of a plurality of portions of the input data, each feature set also representing a point in a corresponding multi-dimensional space; generating a plurality of data objects from the feature sets, each data object corresponding to one or more of the feature sets, each data object representing a shape that includes the one or more points that the corresponding one or more feature sets represent; and associating identification data with each data object, the identification data for each data object including a reference to each portion of the input data to which the data object corresponds. ### Claim 2 (depends on 1) The method of claim 1, wherein the identification data for each data object also identifies a source of each portion of the input data to which the data object corresponds. ### Claim 3 (depends on 1) The method of claim 1, wherein the correspondence between each data object and each of the corresponding one or more feature sets is established in a supervised learning operation, an unsupervised learning operation, or a recognition operation. ### Claim 4 (depends on 1) The method of claim 1, wherein each data object is a knowledge element of a knowledge base, the knowledge elements and the knowledge base being configured to support pattern recognition operations, the method further comprising performing a first pattern recognition operation using additional input data by matching a feature set representing the additional input data to one or more of the knowledge elements. ### Claim 5 (depends on 4) The method of claim 4, wherein the knowledge elements of the knowledge base are organized in a plurality of partitions, each partition being configured to support a corresponding subset of the pattern recognition operations. ### Claim 6 (depends on 5) The method of claim 5, further comprising combining pattern recognition results from more than one of the partitions to generate a higher-level result. ### Claim 7 (depends on 1) The method of claim 1, further comprising maintaining a count for each data object, each count representing a number of the feature sets corresponding to that data object, the counts collectively representing statistical regularities of patterns represented by the input data. ### Claim 8 (depends on 1) The method of claim 1, wherein generating a first data object is done using one or more learning operations, and wherein the identification data associated with the first data object identifies training data used in the one or more learning operations. ### Claim 9 (depends on 1) The method of claim 1, wherein different subsets of the feature sets include different numbers of features. ### Claim 10 (depends on 1) The method of claim 1, wherein the input data represent signals generated by one or more sensors, and wherein the identification data associated with each data object identifies either or both of a corresponding sensor or corresponding sensor type. ### Claim 11 (depends on 1) The method of claim 1, wherein the input data represent preexisting digital data. ### Claim 12 (depends on 1) The method of claim 1, wherein the receiving, generating, generating, and associating are all performed by hardware or software that is included in a system that also generates the input data. ### Claim 13 (depends on 1) The method of claim 1, wherein a first portion of the input data represents training data, and wherein generating the data objects includes: in a learning operation, associating a first feature set corresponding to the first portion of the input data with a first data object; and storing an association between the training data and the first data object; the method further comprising: in a recognition operation, matching a second feature set corresponding to a second portion of the input data to the first data object; and in response to the second feature set matching the first data object, retrieving the training data based on the association between the training data and the first data object. ### Claim 14 (depends on 1) The method of claim 1, wherein generating the data objects includes: in a learning operation, associating a first feature set corresponding to a first portion of the input data with a first data object of a knowledge base; storing an association between the first data object and remote data stored in a remote database separate from the knowledge base; the method further comprising: in a recognition operation, matching a second feature set corresponding to a second portion of the input data to the first data object; and in response to the second feature set matching the first data object, retrieving the remote data from the remote database based on the association between the remote data and the first data object. ### Claim 15 A system, comprising memory and one or more processors configured to: receive input data; generate a plurality of feature sets from the input data, each feature set representing a corresponding one of a plurality of portions of the input data, each feature set also representing a point in a corresponding multi-dimensional space; generate a plurality of data objects from the feature sets, each data object corresponding to one or more of the feature sets, each data object representing a shape that includes the one or more points that the corresponding one or more feature sets represent; and associate identification data with each data object, the identification data for each data object including a reference to each portion of the input data to which the data object corresponds. ### Claim 16 (depends on 15) The system of claim 15, wherein the metadata identification data for each data object also identifies a source of each portion of the input data to which the data object corresponds. ### Claim 17 (depends on 15) The system of claim 15, wherein the correspondence between each data object and each of the corresponding one or more feature sets is established in a supervised learning operation, an unsupervised learning operation, or a recognition operation. ### Claim 18 (depends on 15) The system of claim 15, wherein each data object is a knowledge element of a knowledge base, the knowledge elements and the knowledge base being configured to support pattern recognition operations, and wherein the memory and one or more processors are further configured to perform a first pattern recognition operation using additional input data by matching a feature set representing the additional input data to one or more of the knowledge elements. ### Claim 19 (depends on 18) The system of claim 18, wherein the knowledge elements of the knowledge base are organized in a plurality of partitions, each partition being configured to support a corresponding subset of the pattern recognition operations. ### Claim 20 (depends on 19) The system of claim 19, wherein the memory and one or more processors are further configured to combine pattern recognition results from more than one of the partitions to generate a higher-level result. ### Claim 21 (depends on 15) The system of claim 15, wherein the memory and one or more processors are further configured to maintain a count for each data object, each count representing a number of the feature sets corresponding to that data object, the counts collectively representing statistical regularities of patterns represented by the input data. ### Claim 22 (depends on 15) The system of claim 15, wherein the memory and one or more processors are configured to generate a first data object using one or more learning operations, and wherein the identification data associated with the first data object identifies training data used in the one or more learning operations. ### Claim 23 (depends on 15) The system of claim 15, wherein different subsets of the feature sets include different numbers of features. ### Claim 24 (depends on 15) The system of claim 15, wherein the input data represent signals generated by one or more sensors, and wherein the identification data associated with each data object identifies either or both of a corresponding sensor or corresponding sensor type. ### Claim 25 (depends on 15) The system of claim 15, wherein the input data represent preexisting digital data. ### Claim 26 (depends on 15) The system of claim 15, wherein the system is also configured to generate the input data. ### Claim 27 (depends on 15) The system of claim 15, wherein a first portion of the input data represents training data, and wherein the memory and one or more processors are configured to generate the data objects by: in a learning operation, associating a first feature set corresponding to the first portion of the input data with a first data object; and storing an association between the training data and the first data object; wherein the memory and one or more processors are further configured to in a recognition operation, matching a second feature set corresponding to a second portion of the input data to the first data object; and in response to the second feature set matching the first data object, retrieving the training data based on the association between the training data and the first data object. ### Claim 28 (depends on 15) The system of claim 15, wherein the memory and one or more processors are configured to generate the data objects by: in a learning operation, associating a first feature set corresponding to a first portion of the input data with a first data object of a knowledge base; storing an association between the first data object and remote data stored in a remote database separate from the knowledge base; wherein the memory and one or more processors are further configured to: in a recognition operation, matching a second feature set corresponding to a second portion of the input data to the first data object; and in response to the second feature set matching the first data object, retrieving the remote data from the remote database based on the association between the remote data and the first data object.