Matching multidimensional projections of functional space

• US 10,599,623 B2
• Filed: 08/15/2019
• Issued: 03/24/2020
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Locus LP
• Original Assignee: Locus LP
• Inventors: Riggs, Rory, Bowling, Elizabeth, Fox, Barry, Goldman, Daniel, Hall, Zachary, Johnson, Jordan, Prior, Elizabeth, Sandys, Sean, Wengert, Dylan
• Primary Examiner(s): Casanova, Jorge A
• Application Number:
  US16/541,303
• Publication Number:
  US 20190370231A1
• Time in Patent Office: 222 Days
• CPC Class Codes: G06F 16/21 Design, administration or maintenance of databases
• CPC Class Codes: G06F 16/2264 Multidimensional index structures
• 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 executing on a processor coupled to a memory for algorithmically using an n-dimensional representation derived from a systems syntax for projecting location or relationships associated with data entities in n+k-dimensional space, the method comprising:

electronically representing a systems syntax, wherein the systems syntax comprises a logical data model that can be applied by a computer processor to evaluate or generate expressions of elements, wherein the elements represent parts, processes, and interactions of an underlying system;

electronically receiving an input from a computing device, wherein the input is capable of being represented as a functional location in n-dimensional space, and wherein the data entities at a functional location characterize one or more of the elements, and storing the input as a data entity;

wherein at least one of the dimensions in the n-dimensional space represents a functional domain, the functional domain comprising attributes of roles, order, or relationships among the elements;

electronically assigning a set of functional locations in the n-dimensional space to the data entity, the locations based on attributes of the data entity;

algorithmically computing a syntactic proximity among a set of functional locations by executing an algorithm based on the systems syntax on a set of the locations in n-dimensional space,

wherein the syntactic proximity characterizes a quantitative or qualitative measure of similarity amongst attributes of the elements of the underlying system with respect to a projection of the functional location;

algorithmically computing a projection at a point in time of location or relationships associated with one or more of the elements in the underlying system as represented by the data entities in the n-dimensional space, based on the computed syntactic proximity.

2. The method of claim 1, further comprising:

algorithmically computing a functional trajectory of the data entity based on a relationship of the data entity to reference points in the n-dimensional space or movement of the data entity with respect to the reference points, the trajectory representing a direction associated with a set of paths through functional locations in the n-dimensional space;

algorithmically computing a functional velocity of the data entity based on a measurement of a change of the location or relationships relative to positions or the reference points at two or more points in time;

using the functional trajectory and functional velocity as an algorithmic input to improve projective precision or accuracy.

3. The method of claim 2, further comprising:

associating one or more elements and a functional location with a set of users;

algorithmically computing the syntactic proximity amongst a plurality of users or elements;

applying a syntactic vicinity algorithm to define a group of users and elements based on the computed syntactic proximity;

predicting a quantitative or qualitative outcome for the group;

wherein the group exhibits a higher correlation to the predicted outcome than the elements that are not in the group, as determined by a test of statistical significance.

4. The method of claim 3, wherein the group comprises a statistical control group, and wherein the syntactic vicinity algorithm 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 syntactic proximity of one or more elements represented by data entities, further comprising:

applying the syntactic vicinity algorithm to a second group of users and elements;

computing the syntactic proximity between the statistical control group and the second group;

using the syntactic proximity and the predictive quantitative or qualitative outcome or an empirical outcome from the statistical control group to project an outcome for the second group.

5. The method of claim 4, wherein the statistical control group comprises a work or labor force distribution of a region, company, sector, or industry, further comprising:

associating a set of metrics or outcomes with the statistical control group;

computing predictions of a set of paths or locations in the underlying system related to the work or labor force distribution of a second region, company, sector, or industry;

wherein a user is associated with the second region, company, sector, or industry;

providing recommendations to the user regarding paths or locations.

6. The method of claim 3, wherein the functional domain is biological, and a plurality of elements are selected from among genes, nucleotides, proteins, molecules, organelles, organs, organ systems, organisms, species, populations, and ecosystems, further comprising using the syntactic proximity as a basis for predicting, recommending, or engineering biological composition, structure, location, relationships, or outcome.

7. The method of claim 3, wherein the functional domain is economic, and a plurality of the elements are selected from among individuals, jobs, skills, products, companies, resources, and activities;

further comprising using the syntactic proximity as a basis for predicting, recommending, or engineering economic composition, structure, location, relationships, or outcome.

8. The method of claim 3, wherein the group represents a geographic region, biological system, or grouping of functional assignments,

wherein the functional trajectory represents a developmental path for the geographic region, biological system, or grouping of functional assignments, and

wherein the functional velocity correlates to a growth rate, further comprising computing the predictive functional location as an input to a recommendation engine.

9. The method of claim 2, wherein the functional domain is economic and the syntactic proximity defines a functional distance comprising a mapping from at least two points in at least one dimension in the n-dimensional space to a real number, further comprising:

algorithmically comparing the functional trajectory of a plurality of economic elements based on a set of qualitative, quantitative, geographic, or temporal attributes of the economic elements;

using the functional trajectory as an input to a search, recommendation, matching, or analytical result provided to a user.

10. The method of claim 9, further comprising:

identifying a functional region proximate to the data entity in n+k-dimensional space;

modifying the functional region based on a preference, or from interactions between the data entity and other elements of the functional system;

wherein the functional trajectory or functional velocity of the data entity corresponds to a path in the underlying system or in a digital representation of the underlying system; and

providing a set of projections of code sequences, paths, pathways, tags, or metatags for modifying outcomes in the functional system.

11. The method of claim 9, wherein the search or recommendation result relates to economic, financial, or policy objectives, further comprising providing a set of potential or actual economic incentives, tax policies, labor policies, workforce policies, industrial policies, monetary policies, fiscal policies, acquisitions, mergers, divestitures, or investments to a user.

12. The method of claim 9, wherein the search or recommendation result relates to workforce development or labor, further comprising providing a set of career paths, job opportunities, labor distributions, educational distributions, recruitment pools, skill levels, job responsibilities, economic metrics, staffing recommendations, or advertisements to a user.

13. The method of claim 2, further comprising:

assigning k-dimensions in the n+k-dimensional space to temporal, geographic, demographic, biological, genetic, financial, policy or economic data;

wherein the data comprises quantitative, qualitative, textual, visual, aural, audiovisual, tactile information;

computing a predictive functional location in the n-dimensional space based on a set of the k-dimensions.

14. The method of claim 13, further comprising using a word embedding algorithm that renders textual data based on semantic proximity to construct a subset of the n+k dimensional space based on a corpus;

and

integrating the semantic proximity and the syntactic proximity to enhance the computation of predictive functional location.

15. The method of claim 13, further comprising:

constructing or augmenting a corpus based on syntactic proximity in n+k dimensional space;

using the corpus to provide or augment the predictive functional location or a search or recommendation result.

16. The method of claim 1, further comprising:

electronically constructing a graph representation of a set of the locations in n-dimensional space by assigning nodes to a set of the parts and processes and edges to a set of the interactions or relationships amongst the parts and processes;

algorithmically computing a graph proximity by executing an algorithm that computes similarity amongst the elements based on strength, quantity, or degree of connection amongst the nodes and the attributes of the nodes in the graph representation; and

algorithmically computing a composite proximity by executing an algorithm that integrates the computed syntactic proximity and output of the graph proximity algorithm;

using the composite proximity to increase the precision or accuracy of the projected location or relationships.

17. The method of claim 16, further comprising:

integrating the graph representation with a second graph representation such that the first graph representation comprises a subgraph;

associating one or more elements with a user;

associating a set of metrics related to syntactic proximity to route the user to alternate paths; and

relating the set of metrics to predicted outcomes in the underlying system.

18. The method of claim 1, further comprising executing a fuzzy query where elements can be identified or matched based on the intersection of two or more functional areas defined by the syntactic proximity of their functional location.

19. A functional connectivity system comprising a computing environment configured to perform a database operation utilizing a computerized representation of a functional system, the system comprising:

an electronic data store comprising a set of data entities in a database system, the data entities representing elements of the functional system, wherein the functional system comprises a group of related elements ordered by their functional roles in converting inputs to outputs, or as the inputs, or as the outputs;

an electronic representation of a systems syntax, wherein the systems syntax comprises a logical data model that can be applied by a computer processor to evaluate or generate expressions of elements, wherein the elements represent parts, processes, and interactions of an underlying system;

at a computer processor coupled to the electronic data store, receiving a set of functional locations from a database, wherein a function represents a conversion from inputs to outputs or a role or property in the conversion from inputs to outputs in the underlying system and a functional location comprises a position of an entity as an input, output, intermediate, relationship, or process associated with inputs, intermediates or outputs;

electronically assigning a set of functional locations to the data entities, the locations based on attributes of the data entities;

algorithmically computing the syntactic proximity among a plurality of the entities or attributes representing the entities based on their functional locations;

wherein the syntactic proximity characterizes a quantitative or qualitative measure of similarity amongst attributes of the elements of the underlying system with respect to the functional locations;

algorithmically computing a projection at a point in time of a location or a relationship associated with one or more of the elements in the underlying system as represented by the data entities, based on the computed syntactic proximity of their functional locations; and

matching and identifying the entities based on the computed syntactic proximity of their functional locations.

20. The system of claim 19, wherein non-functional attributes can be used as an input in the process of identifying or matching of entities, and wherein the functional locations can be identified semantically, syntactically, graphically, symbolically, visually, or aurally;

and wherein non-functional attributes comprise an economic metric, a financial metric, demographic data, geographic data, temporal data, or experiential data.

21. The system of claim 20, wherein the functional system is an economic system, and wherein the data entities comprise enterprises, individuals, products, franchises, facilities, resources, government entities, industries, sectors, independent contractors, or nonprofits, further comprising identifying or matching entities for communication, transaction, advertising, investment, taxation, incentive programs, policy measures, or donation.

22. The system of claim 20, wherein the functional system is an economic system, and wherein the data entities comprise jobs, skills, tasks, workers, workforces, employers, educational institutions, training institutions, and research institutes, further comprising identifying or matching entities for recruiting, job search, hiring, labor policy, workforce development, skill development, training, apprenticeship, coaching, mentorship, management, leadership development, entrepreneurship, incubation, or acceleration.

23. The system of claim 19, wherein the functional system is a biological system, and wherein the data entities comprise genes, nucleotides, genetic sequences, molecules, expressed proteins, biological organisms, organelles, organs, organ systems, species, or populations, further comprising identifying or matching entities for bioinformatic analysis, synthetic genomics, gene editing, or drug discovery.

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

algorithmically computing a functional trajectory of the data entities based on a relationship of the data entities to reference points in an n-dimensional space or movement of the data entities with respect to the reference points, the trajectory representing a direction associated with a set of paths through functional locations in the n-dimensional space;

algorithmically computing a functional velocity of the data entities based on a measurement of a change of the location or relationships relative to positions or the reference points at two or more points in time;

using the functional trajectory and functional velocity as an algorithmic input to improve projective precision or accuracy.

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

electronically constructing a graph representation of a set of the locations in n-dimensional space by assigning nodes to a set of the parts and processes and edges to a set of the interactions or relationships amongst the parts and processes;

algorithmically computing a graph proximity by executing an algorithm that computes similarity amongst the elements based on strength, quantity, or degree of connection amongst the nodes and the attributes of the nodes in the graph representation; and

algorithmically computing a composite proximity by executing an algorithm that integrates the computed syntactic proximity and output of the graph proximity algorithm;

using the composite proximity to increase the precision or accuracy of the projected location or relationships.