Mental Model Elicitation Device (MMED) Methods and Apparatus

• US 20120330869A1
• Filed: 06/03/2012
• Published: 12/27/2012
• Est. Priority Date: 06/25/2011
• Status: Abandoned Application

• Current Assignee: Jayson Theordore Durham
• Original Assignee: Jayson Theordore Durham
• Inventors: Durham, Jayson Theordore
• Application Number:
  US13/487,249
• Publication Number:
  US 20120330869A1
• Time in Patent Office: Days
• US Class Current: 706/16
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition

Abstract

A mental-model elicitation process and apparatus, called the Mental-Model Elicitation Device (MMED) is described. The MMED is used to give rise to more effective end-user mental-modeling activities that require executive function and working memory functionality. The method and apparatus is visual analysis based, allowing visual and other sensory representations to be given to thoughts, attitudes, and interpretations of a user about a given visualization of a mental-model, or aggregations of such visualizations and their respective blending. Other configurations of the apparatus and steps of the process may be created without departing from the spirit of the invention as disclosed.

Claims

1. A method for eliciting mental models, comprising:

(a) communicating predetermined representations of predetermined models and predetermined associations of elements of said models

(b) storing and retrieving information elements that incorporate information, or transformations of such information as provided by said communications of said representations

(c) interactively exchanging and manipulating said information elements

(d) synthesizing new elements

(e) wherein said new elements incorporate information derived from said interactive exchanges of elements, representations, or associations.

(e) whereby said communication, storage, retrieval, and synthesis operations enhance the working memory and executive functionality of said operator

(f) whereby said enhanced operator functionality and associated capabilities facilitate self-defining and self-improving of task performance of said operator.

2. The method for eliciting mental models of claim 1, comprising:

(a) pedagogical orientation with initial mental-model elicitation

(b) mental model elicitation for domains of interest

(c) clarification and synthesis of said elicited mental models

(d) production of knowledge artifacts

(e) whereby said knowledge artifacts represent transformations of said mental model elicitation and synthesis, thus incorporating information originally implicit in said mental models.

3. The method for eliciting mental models of claim 1,

(a) wherein said predetermined models and associated representations are selected from a group consisting of predetermined subgroups of predetermined representations and models

(b) wherein said predetermined subgroups are selected from the group comprising a plurality of the following domains:

(a) Phenomenology and semiotics;

(b) Emotions;

(c) Genomics and genetics;

(d) Physiology and endophenotypes;

(e) Brain science;

(f) Behavioral neuroscience;

(g) Intelligent systems;

(h) Systems Engineering;

(i) Organizational theory;

(j) Human development psychology;

(k) Sports psychology;

(l) Personal genomics, family genetics, personal history, family history, genealogy;

(m) Values, interests, goals, objectives, plans, milestones, schedules, daily activities, education, vocations, occupations, industries, patents, knowledge, skills, abilities, user assessments.

4. The method for eliciting mental models of claim 1,

(a) wherein said elicitation operations comprise a method of assigning relationships

(b) wherein the operator dynamically designates a degree of said relationship, or concurrence with a predetermined designation and scoring of degree of relationship.

5. The method for eliciting mental models of claim 1,

(a) wherein said elicitation operations comprises presentation processing of said predetermined representations

(b) wherein said presentation processing comprises assigning relatedness scores between elements of said representations and a plurality of interactively selected predetermined topics stored within a predetermined repository

(c) whereby said relatedness scores are utilized for follow-on interactive discovery and elicitation of additional topics and associated types of representations.

6. The method for eliciting mental models of claim 1,

(a) wherein said elicitation operations include assigning said related topics and representations to clusters of said topics and representations

(b) wherein the clusters are hierarchically related.

7. The method for eliciting mental models of claim 1,

(a) wherein said elicitation operations include artificial intelligence (AI) methods or use of AI devices

(b) wherein said artificial intelligence (AI) methods are selected from a group comprising methods of emulation of human intelligence, methods of machine learning, and methods of knowledge processing.

8. A method for eliciting mental models of claim 1,

(a) wherein said elicitation operations facilitate interactive discovery and elicitation of (i) operator genomic predispositions, (ii) intrinsic values and interests, (iii) assessing of said operator capabilities, (iv) establishing goals and milestones

(b) whereby said discoveries and elicitations aid the transformation of said discoveries and elicitations into plans and support elements for executing and monitoring said plans.

9. The method for eliciting mental models of claim 1, wherein said elicitation comprises:

(a) discovering representations relating to neuroscience and genomics

(b) associating said discoveries to neuromuscular activity

(c) associating said associated discoveries with predetermined representations of other models and activities

(d) relating said combinations of discoveries, elicitations, and associations to operator specified interests, goals, and objectives

(e) whereby enabling an operator to transform individual instances of said representations into a blended representation

(f) whereby said relationships enable kinesthetic learning that pertains to the executive function and working memory aspects of neuromuscular activities

(g) whereby said neuromuscular activities comprise athletic sports activities.

10. An apparatus for eliciting mental models, comprising of:

(a) an electrical communications element (1002), a memory management element (1004), a logic and data processing element (1005), an operator interface data processing element (1012), a presentation processing of document data processing element (1014), and an education and demonstration element (1016)

(b) wherein said communications element (1002) communicates predetermined representations of predetermined models and predetermined associations of elements of said models through interconnectivity to said memory management element (1004), said logic and data processing element (1005), said operator interface element (1012), said presentation processing of document data processing element (1014), and said education and demonstration element (1016)

(c) wherein said communication events comprise of exchanges of information elements relating to said predetermined representations of said predetermined models and said predetermined associations of elements of said representations and models

(d) wherein said communication events comprises storing and retrieving information elements that incorporate information, or transformations of such information

(e) wherein said communication events comprises interactive exchanging and manipulating of said information elements

(f) wherein said communication events comprises synthesizing elements and representations

(g) wherein said new elements incorporate information derived from said interactive exchanges of elements, representations, or associations.

(h) whereby said communication, storage, retrieval, and synthesis operations enhance the working memory and executive functionality of said operator

(i) whereby said enhanced operator functionality and associated capabilities facilitate self-defining and self-improving of task performance of said operator.

11. The apparatus for eliciting mental models of claim 10, wherein the method of use comprises

(a) pedagogical orientation with initial mental-model elicitation

(b) mental model elicitation for domains of interest

(c) clarification and synthesis of said elicited mental models

(d) production of knowledge artifacts

(e) whereby said knowledge artifacts represent transformations of said mental model elicitation and synthesis, thus incorporating information originally implicit in said mental models.

12. The apparatus for eliciting mental models of claim 11,

(a) wherein said predetermined representations are selected from a group consisting of predetermined subgroups of representations

(b) wherein said predetermined subgroups are selected from the group comprising a plurality of the following domains:

(i) phenomenology and semiotics;

(ii) emotions;

(iii) genomics and genetics;

(iv) physiology and endophenotypes;

(v) brain science;

(vi) behavioral neuroscience;

(vii) intelligent systems;

(viii) systems engineering;

(ix) organizational theory;

(x) human development psychology;

(xi) sports psychology;

(xii) personal genomics, family genetics, personal history, family history, genealogy;

(xiii) values, interests, goals, objectives, plans, milestones, schedules, daily activities, education, vocations, occupations, industries, patents, knowledge, skills, abilities, user assessments.

13. The apparatus for eliciting mental models of claim 10, wherein, wherein said elicitation functions include a method of selecting related prior art from a plurality of candidate references wherein the operator dynamically designates a degree of relationship, or concurrence with predetermined designation and scoring of degree of relationship, of information elements of said prior art to information elements of said candidate reference.

14. The apparatus for eliciting mental models of claim 10,

(a) wherein said elicitation operations include artificial intelligence (AI) methods or use of AI devices

15. The apparatus for eliciting mental models of claim 14, wherein said artificial intelligence (AI) methods are selected from a group comprising methods of emulation of human intelligence, methods of machine learning, and methods of knowledge processing.

16. The apparatus for eliciting mental models of claim 10, wherein said elicitation functions include updating and evolving associated elements and attributes of said representations by collectively integrating and storing said updated and evolved associations and their transitive relations.

17. The apparatus for eliciting mental models of claim 10, wherein said elicitation functions facilitate interactive discovery and elicitation of a plurality of the following:

(a) operator genomic predispositions, (b) intrinsic values and interests, (c) assessing of said operator capabilities, (d) establishing goals and milestones, whereby said discoveries and elicitations aid the transformation of such knowledge into plans and supporting infrastructure for executing and monitoring said plans.

18. A method of building an apparatus for eliciting mental models, comprising:

(a) providing an electrical communications subelement

(b) providing a dynamically extensible information storage and retrieval subelement

(c) gathering representations of data models comprising of schema, schema elements, and related knowledge representation artifacts

(d) constructing association matrices that explicitly relate elements of said representations, data models and associations of said elements with a plurality of other model domains

(e) constructing memory elements comprising of said representations, data models, schema, schema elements, topics, and respective associations thereof

(f) providing operator interface subelements.

19. The method of building an apparatus for eliciting mental models of claim 18, comprising:

(a) gathering representations and data models comprising of schema and other knowledge representation artifacts relating to predetermined models selected from the group comprising a plurality of (i) genomics and related biochemical models;

(ii) biomedical models;

(iii) brain models including physiological, psychological, or behavioral subelements;

(iv) cognitive function models comprising of executive function and working-memory subelements;

(v) artificial intelligence models comprising machine learning and knowledge processing subelements;

(vi) process models including business process and enterprise architecture models;

(b) constructing association matrices that explicitly relate elements of said data models and associations of said elements with topics selected from the group comprising genomic predispositions, cognitive function, personal history, family history, values, interests, goals, plans, milestones, schedules, activities, education, vocations, occupations, industries, knowledge, skills, and abilities.

20. The method of building an apparatus for eliciting mental models of claim 18, comprising methods of manufacturing domain specific knowledge repositories and associated operator resources from predetermined classification systems (CS), whereby said repositories concentrate on one or more of the preexisting domain nodes that are elements of the said CS.