# US 10,372,724 B2 · Relativistic concept measuring system for data clustering

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- Number: US 10,372,724 B2
- Document ID: US10372724B2
- Status: Granted
- Kind: B2
- Application number: 14/040,197
- Filed: 2013-09-27
- Published: 2014-01-30
- Granted: 2019-08-06
- Earliest claimed: 2011-03-11
- Assignees: Kyndi, Inc.
- Applicants: Kyndi Inc.
- Inventors: Arun Majumdar
- Web page: https://patentgrep.com/patent/US10372724B2

## CPC

- G06F 16/258: {Data format conversion from or to a database}
- G06F 16/355: Creation or modification of classes or clusters
- G06F 16/367: {Ontology}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

A method and apparatus for mapping concepts and attributes to distance fields via rvachev-functions. The steps including generating, for a plurality of objects, equations representing boundaries of attributes for each respective object, converting, for a plurality of objects, the equations into greater than or equal to zero type inequalities, generating, for a plurality of objects, a logical expression combining regions of space defined by the inequalities into a semantic entity, and substituting, for a plurality of objects, the logical expression with a corresponding rvachev-function such that the resulting rvachev-function is equal to 0 on a boundary of the semantic entity, greater then 0 inside a region of the semantic entity, and less then 0 outside the region of the semantic entity. Also included is the step of generating a composite rvachev-function representing logical statements corresponding to the plurality of objects using the respective rvachev-functions of the objects.

## Claims

### Claim 1

A method of generating, within a framework that represents data as particles in a vector field, an implicit structure to derive conclusions about data having an implicit ontology, the method comprising:
obtaining the data having the implicit ontology and being represented at a plurality of data points;
representing a superposition of the obtained data by a superposition of multiple fields including vector fields based on solutions of respective distance field functions, the representation of the superposition corresponding to a representation of a superposed state, wherein each data point is represented as a particle in the vector fields and the vector fields include one or more concepts also represented as respective particles;
determining, based on the superposition of the multiple fields, forces at respective data points of the obtained data within the vector fields;
separating the superposed state by shifting each concept of the obtained data along respective directions of the forces and then updating the superposition of the multiple fields based on the evolved data by re-solving the respective distance field functions with the positions of the shifted concepts using a particularly programmed computing device;
for a data point of the data points,
repeating the separating step until the data point is shifted along the forces and converges to a stable point based on a pre-defined threshold, wherein the repeated shifting generates a path of the data point, and the one of the one or more concepts forms a leaf node in the implicit data structure;
measuring distances between the data point and concepts positioned along the path of the data point within the separated superposed state, and, for concepts within a clustering threshold distance of the path of the data point, linking the leaf node to nodes of the concepts within the clustering threshold distance as to generate the implicit structure as the implicit ontology;
storing said implicit ontology and similarity in a memory; and
performing a search of said stored implicit ontology including deriving a similarity of a first piece of the data to a second piece of the data based on the measured relative distances between the first piece and the second piece in the implicit structure.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the distance field functions are Rvachev-functions.

### Claim 3 (depends on 1)

The method according to claim 1, wherein the data is language data.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the particularly programmed computing device includes memory and a microprocessor.

### Claim 5 (depends on 1)

The method according to claim 1, wherein the data is medical data.

### Claim 6 (depends on 1)

The method according to claim 1, wherein
the relative distances is induced based on setting a fixed distance between a top layer of the implicit ontology and any other layer of the implicit data ontology to a constant value,
the distance between the top layer and a bottom layer of the implicit ontology is set to a first predetermined constant distance, and
a distance between an intermediate layer and the top layer of the implicit ontology is set to a second predetermined constant distance, the first predetermined constant distance being different than the second predetermined constant distance.

### Claim 7 (depends on 6)

The method according to claim 6, wherein the first predetermined constant distance is one, and the second predetermined constant distance is zero.

### Claim 8

A non-transitory computer readable medium having stored thereon a program that when executed by a particularly programmed computing device causes the particularly programmed computing device to execute a method of generating, within a framework that represents data as particles in a vector field, an implicit structure to derive conclusions about data having an implicit ontology, the method comprising:
obtaining the data having the implicit ontology and being represented at a plurality of data points;
representing a superposition of the obtained data by a superposition of multiple fields including vector fields based on solutions of respective distance field functions, the representation of the superposition corresponding to a representation of a superposed state, wherein each data point is represented as a particle in the vector fields and the vector fields include one or more concepts also represented as respective particles;
determining, based on the superposition of the multiple fields, forces at respective data points of the obtained data within the vector fields;
separating the superposed state by shifting each concept of the obtained data along respective directions of the forces and then updating the superposition of the multiple fields based on the evolved data by re-solving the respective distance field functions with the positions of the shifted concepts;
for a data point of the data points,
repeating the separating step until the data point is shifted along the forces and converges to a stable point based on a pre-defined threshold, wherein the repeated shifting generates a path of the data point, and the one of the one or more concepts forms a leaf node in the implicit data structure,
measuring distances between the data point and concepts positioned along the path of the data point within the separated superposed state, and, for concepts within a clustering threshold distance of the path of the data point, linking the leaf node to nodes of the concepts within the clustering threshold distance as to generate the implicit structure as the implicit ontology,
storing said implicit ontology and similarity in a memory; and
performing a search of said stored implicit ontology including deriving a similarity of a first piece of the data to a second piece of the data based on the measured distances between the first piece and the second piece in the implicit structure.

### Claim 9 (depends on 8)

The non-transitory computer readable medium according to claim 8, wherein the distance field functions are Rvachev-functions.

### Claim 10 (depends on 8)

The non-transitory computer readable medium according to claim 8, wherein the data is language data.

### Claim 11 (depends on 8)

The non-transitory computer readable medium according to claim 8, wherein the particularly programmed computing device includes memory and a microprocessor.

### Claim 12 (depends on 8)

The non-transitory computer readable medium according to claim 8, wherein the data is medical data.

### Claim 13

A device for generating, within a framework that represents data as particles in a vector field, an implicit structure to derive conclusions about data having an implicit ontology, the device comprising:
processing circuitry configured to
obtain the data having the implicit ontology and being represented at a plurality of data points;
represent a superposition of the obtained data by a superposition of multiple fields including vector fields based on solutions of respective distance field functions, the representation of the superposition corresponding to a representation of a superposed state, wherein each data point is represented as a particle in the vector fields and the vector fields include one or more concepts also represented as respective particles;
determine, based on the superposition of the multiple fields, forces at respective data points of the obtained data within the vector fields;
separate the superposed state by shifting each concept of the obtained data along respective directions of the forces and then updating the superposition of the multiple fields based on the evolved data by re-solving the respective distance field functions with the positions of the shifted concepts;
for a data point of the data points,
repeat the separating step until the data point is shifted along the forces and converges to a stable point based on a pre-defined threshold, wherein the repeated shifting generates a path of the data point, and the one of the one or more concepts forms a leaf node in the implicit data structure,
measure distances between the data point and concepts positioned along the path of the data point within the separated superposed state, and, for concepts within a clustering threshold distance of the path of the data point, linking the leaf node to nodes of the concepts within the clustering threshold distance as to generate the implicit structure as the implicit ontology; and
store said implicit ontology and similarity in a memory; and
perform a search of said stored implicit ontology including deriving a similarity of a first piece of the data to a second piece of the data based on the measured distances between the first piece and the second piece in the implicit structure.

### Claim 14 (depends on 13)

The device according to claim 13, wherein the distance field functions are Rvachev-functions.

### Claim 15 (depends on 13)

The device according to claim 13, wherein the data is language data.

### Claim 16 (depends on 13)

The device according to claim 13, wherein the device further includes memory and a microprocessor.