Proximity search and navigation for functional information systems

• US 9,990,380 B2
• Filed: 04/14/2017
• Issued: 06/05/2018
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Locus LP
• Original Assignee: Locus LP
• Inventors: Riggs, Rory, Bowling, Elizabeth, Breitmeyer, James, Chandler, Jonathan, Chew, Vin Harng, Fox, Barry, Goldman, Daniel, Hsu, Lauren, Kahn, Richard, McKeage, Diana, Sandys, Sean, Silkworth, Christopher, Whitaker, Amy
• Primary Examiner(s): Burke, Jeffrey A
• Assistant Examiner(s): Vu, Thong
• Application Number:
  US15/488,433
• Publication Number:
  US 20170220603A1
• Time in Patent Office: 417 Days
• Field of Search: 707769, 707602, 707693, 707724, 707737, 707803, 707723, 706 12, 706 46, 709202, 709203, 709224, 709225, 345108, 345157, 345440, 345470, 705 2, 705310, 705319, 705 2635, 705 2644, 715238, 715853, 704 2, 704245, 702 19, 702118
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/2358 Change logging, detection, and notification replication G06F16/27
• CPC Class Codes: G06F 16/2379 Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/2458 Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
• CPC Class Codes: G06F 16/248 Presentation of query results
• CPC Class Codes: G06F 16/287 Visualization; Browsing
• CPC Class Codes: G06F 16/29 Geographical information databases
• CPC Class Codes: G06F 16/9024 Graphs; Linked lists G06F16/9027 takes precedence
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

The invention includes an algorithmic method for dynamically computing complex relationships among objects of an underlying functional system. The invention includes a method to algorithmically determine a set of functional locations in n-dimensional functional space of a set of elements of a functional system by electronically representing a set of data entities in a database system, the database system comprising a logical data model for structuring data sets from which functional information can be derived, using the logical data model to associate a set of characteristics with a reference point in the functional information system, selecting a functional positioning algorithm, and wherein the functional positioning algorithm executes a set of steps that takes as input a set of characteristics and returns as output a set of locations in n-dimensional functional space.

Claims

1. A computer-implemented method for using a computing environment for algorithmically determining functional proximity among elements of underlying functional systems represented in n-dimensional space, the method comprising, executing on a processor:

electronically representing a set of data entities in a functional information system database, the data entities representing elements of the underlying functional system, and wherein the functional system represents processes converting a set of input elements to a set of output elements;

the database system electronically representing a logical data model for structuring the set of data entities;

wherein;

the logical data model comprises a functional system syntax comprising an ordered set of interrelationships among the elements in the underlying functional system, wherein the functional system syntax can be applied by the computer processor to evaluate validity of expressions of elements according to their functional properties, roles or locations in processes converting inputs to outputs; and

electronically assigning tags to the data entities representing reference points in an n-dimensional space associated with functional properties of the elements in the functional information system;

receiving an electronic signal comprising a selection of a data entity from a user at a location remote from or co-located with the functional information system;

executing a functional proximity algorithm that measures the functional distance among a set of data entities with respect to a specific data entity according to a set of steps that takes a tagged data entity and functional location of the tagged data entity, and returns results comprising a set of measures of proximity of specific data entities to other functionally tagged data entities within the structure of the logical data model established by the functional system based on a magnitude and a category of relationships among the tags associated with the data entities in the functional system, wherein the distance among the tags are representative of the relationship among the entities; and

storing the result of the functional proximity algorithm and electronically sending an electronic signal to the user at the remote or a current location.

2. The method of claim 1, further comprising

defining a functional vicinity with respect to a reference point, wherein the functional vicinity comprises a subset or region of n-dimensional space around the reference point;

electronically searching the database system to identify a subset of data entities within the functional vicinity of a defined reference point;

retrieving the subset of data entities within the vicinity of the first reference point;

associating a property with the functional vicinity, wherein the property may be derived from shared properties of the members of the vicinity; and

inferring a property associated with a selected reference point based on its membership in a functional vicinity.

3. The method of claim 2, wherein the reference point corresponds to a biological process and the functional vicinity corresponds to related morphological and physiological systems, further comprising using the data related to the functional vicinity for prognostic purposes.

4. The method of claim 3, further comprising using the functional navigation algorithm to improve a statistical outcome in a biological system, and using a set of parameters as inputs to select a target set of reference points for a biological process from the subset.

5. The method of claim 2, further comprising executing:

(a) a graph representation algorithm that executes steps for representing the set of input elements including the selected data entity as nodes, the set of output elements as nodes, and a set of related processes as edges such that the graph representation can be used for comparison of relative structure and outcomes of elements of the underlying functional system, prognosis or diagnosis within the underlying functional system, or recommendation of changes within the underlying functional system to achieve a normative or desired outcome;

or

(b) a functional trajectory algorithm that executes steps for representing a set of paths through functional locations including the selected data entity in n-dimensional space to infer an outcome in the underlying functional system, as verifiable through a test of statistical significance;

or

(c) a functional navigation algorithm that executes steps for defining a magnitude, distance, or route among a series of reference points including the selected data entity within the n-dimensional space;

or

(d) a functional vicinity algorithm that executes steps for identifying a subset of the data entities including the selected data entity in n-dimensional space which are located within a threshold functional proximity of one or more elements represented by data entities;

or

(e) a functional machine learning algorithm that executes steps for predicting a functional location including the selected data entity in n-dimensional space by assigning functional tags and then probabilistically assigning functional locations to the entities based on the tags;

or

(f) a functional machine learning algorithm that executes steps for predicting an outcome relating to the selected data entity in the functional system by using representations in n-dimensional space of prior outcomes in the underlying functional system as a training set.

6. The method of claim 2, further comprising:

for an untagged data entity, assessing the probability of membership in the vicinity by comparing the properties associated with the vicinity with those associated with the untagged data entity; and

incorporating probability of membership in multiple vicinities into a prediction of precise functional location for the untagged data entity.

7. The method of claim 1, further comprising:

defining m non-functional attributes of the data entities;

electronically representing the data entities in an m-dimensional space, wherein the m dimensions correspond to the m non-functional attributes;

forming an n+m dimensional space as the cartesian product of the n-dimensional functional space and the m-dimensional non-functional space;

extending the functional proximity algorithm defined on the n-dimensional space to a proximity algorithm defined on the n+m-dimensional space.

8. The method of claim 1, further comprising iteratively scoring and ranking data entities associated with elements in the functional system based on the quantity and quality of direct and indirect relationships codified in functional space.

9. The method of claim 1, further comprising:

constructing at least one normative reference graph and at least one test graph by assigning nodes to data entities and edges to the interrelationships;

visually representing the graphs in the n-dimensional space projected onto a set of coordinate planes;

using statistical analysis of a population associated with the functional system to determine normative outcomes;

performing comparative analytics for diagnostic purposes between the reference graph and the test graph to consider potential modifications to the underlying system corresponding to the test graph.

10. The method of claim 1, further comprising delineating a functional trajectory over time by electronically representing a set of paths through functional locations in the n-dimensional space to infer an outcome in the underlying functional system, as verifiable through a test of statistical significance.

11. The method of claim 1, further comprising:

assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein the assignment of n-dimensional tensors organizes k tensors in n-dimensional space based on the functional properties of the elements with respect to a set of variables within the functional system;

using the result of the functional proximity algorithm as an input to a matrix of the output of the feature values of dimension ≤

m²×

k²;

wherein the matrix comprises a set of results of the functional proximity algorithm among a plurality of tensors.

12. The method of claim 11, further comprising:

electronically using a machine learning process to construct a probability space based on the matrix for the assignment of positions in n-dimensional space to an l-dimensional array of j tensors;

outputting results comprising a results matrix of dimension ≤

l×

n;

wherein the set of entries of the results matrix probabilistically organize the l tensors in n-dimensional space based on the predictions of the machine learning technique of the functional properties of the elements with respect to a variable within the functional system.

13. A system for using a computing environment for algorithmically determining proximity among elements of underlying functional systems represented in n-dimensional space, the method comprising, the system comprising:

a computer processor configured for;

electronically representing a set of data entities in a functional information system database, the data entities representing elements of the underlying functional system, and wherein the functional system represents processes converting a set of input elements to a set of output elements;

the database system electronically representing a logical data model for structuring the set of data entities;

wherein;

the logical data model comprises a functional system syntax comprising an ordered set of interrelationships among the elements in the underlying functional system, wherein the functional system syntax can be applied by the computer processor to evaluate validity of expressions of elements according to their functional properties, roles or locations in processes converting inputs to outputs; and

electronically assigning tags to the data entities representing reference points in an n-dimensional space associated with functional properties of the elements in the functional information system;

receiving an electronic signal comprising a selection of a data entity from a user at a location remote from or co-located with the functional information system;

executing a functional proximity algorithm that measures the functional distance among a set of data entities with respect to a specific data entity according to a set of steps that takes a tagged data entity and functional location of the tagged data entity, and returns results comprising a set of measures of proximity of specific data entities to other functionally tagged data entities within the structure of the logical data model established by the functional system based on a magnitude and a category of relationships among the tags associated with the data entities in the functional system, wherein the distance among the tags are representative of the relationship among the entities; and

storing the result of the functional proximity algorithm and electronically sending an electronic signal to the user at the remote or a current location.

14. The system of claim 13, wherein the computer processor is further configured for:

defining a functional vicinity with respect to a reference point, wherein the functional vicinity comprises a subset or region of n-dimensional space around the reference point;

electronically searching the database system to identify a subset of data entities within the functional vicinity of a defined reference point;

retrieving the subset of data entities within the vicinity of the first reference point;

associating a property with the functional vicinity, wherein the property may be derived from shared properties of the members of the vicinity; and

inferring a property associated with a selected reference point based on its membership in a functional vicinity.

15. The system of claim 14, wherein the reference point corresponds to a biological process and the functional vicinity corresponds to related morphological and physiological systems, further comprising using the data related to the functional vicinity for prognostic purposes.

16. The system of claim 15, wherein the computer processor is further configured for using the functional navigation algorithm to improve a statistical outcome in a biological system, and using a set of parameters as inputs to select a target set of reference points for a biological process from the subset.

17. The system of claim 14, wherein the computer processor is further configured for:

for an untagged data entity, assessing the probability of membership in the vicinity by comparing the properties associated with the vicinity with those associated with the untagged data entity; and

incorporating probability of membership in multiple vicinities into a prediction of precise functional location for the untagged data entity.

18. The system of claim 13, wherein the computer processor is further configured for executing:

(a) a graph representation algorithm that executes steps for representing the set of input elements including the selected data entity as nodes, the set of output elements as nodes, and a set of related processes as edges such that the graph representation can be used for comparison of relative structure and outcomes of elements of the underlying functional system, prognosis or diagnosis within the underlying functional system, or recommendation of changes within the underlying functional system to achieve a normative or desired outcome;

or

(b) a functional trajectory algorithm that executes steps for representing a set of paths through functional locations including the selected data entity in n-dimensional space to infer an outcome in the underlying functional system, as verifiable through a test of statistical significance;

or

(c) a functional navigation algorithm that executes steps for defining a magnitude, distance, or route among a series of reference points including the selected data entity within the n-dimensional space;

or

(d) a functional vicinity algorithm that executes steps for identifying a subset of the data entities including the selected data entity in n-dimensional space which are located within a threshold functional proximity of one or more elements represented by data entities;

or

(e) a functional machine learning algorithm that executes steps for predicting a functional location including the selected data entity in n-dimensional space by assigning functional tags and then probabilistically assigning functional locations to the entities based on the tags;

or

(f) a functional machine learning algorithm that executes steps for predicting an outcome relating to the selected data entity in the functional system by using representations in n-dimensional space of prior outcomes in the underlying functional system as a training set.

19. The system of claim 13, wherein the computer processor is further configured for:

defining m non-functional attributes of the data entities;

electronically representing the data entities in an m-dimensional space, wherein the m dimensions correspond to the m non-functional attributes;

forming an n+m dimensional space as the cartesian product of the n-dimensional functional space and the m-dimensional non-functional space;

extending the functional proximity algorithm defined on the n-dimensional space to a proximity algorithm defined on the n+m-dimensional space.

20. The system of claim 13, wherein the computer processor is further configured for iteratively scoring and ranking data entities associated with elements in the functional system based on the quantity and quality of direct and indirect relationships codified in functional space.

21. The system of claim 13, wherein the computer processor is further configured for:

constructing at least one normative reference graph and at least one test graph by assigning nodes to data entities and edges to the interrelationships;

visually representing the graphs in the n-dimensional space projected onto a set of coordinate planes;

using statistical analysis of a population associated with the functional system to determine normative outcomes;

performing comparative analytics for diagnostic purposes between the reference graph and the test graph to consider potential modifications to the underlying system corresponding to the test graph.

22. The system of claim 13, wherein the computer processor is further configured for:

delineating a functional trajectory over time by electronically representing a set of paths through functional locations in the n-dimensional space to infer an outcome in the underlying functional system, as verifiable through a test of statistical significance.

23. The system of claim 13, wherein the computer processor is further configured for:

assigning an m-dimensional array of n-dimensional tensors to the data entities, wherein the assignment of n-dimensional tensors organizes k tensors in n-dimensional space based on the functional properties of the elements with respect to a set of variables within the functional system;

using the result of the functional proximity algorithm as an input to a matrix of the output of the feature values of dimension ≤

m²×

k²;

wherein the matrix comprises a set of results of the functional proximity algorithm among a plurality of tensors;

electronically using a machine learning process to construct a probability space based on the matrix for the assignment of positions in n-dimensional space to an l-dimensional array of j tensors;

outputting results comprising a results matrix of dimension ≤

l×

n;

wherein the set of entries of the results matrix probabilistically organize the l tensors in n-dimensional space based on the predictions of the machine learning technique of the functional properties of the elements with respect to a variable within the functional system.