Abstract and Claim for a Pending General Endeavor Management (GEM) US Patent (Priority Date 03/10/2016)

Abstract

The General Endeavor Management (GEM) method provides knowledge-workers with a general-use intelligence, viewpoint alignment, access-control, and operations management capability. All humans, and increasingly some artificial intelligence (AI) software, use recorded structured knowledge to perform activities and to increase, refine, and use intelligence. The GEM method provides a generalized, holistic and consistent: value-driven endeavor-operations lifecycle process, integrated terminology-based intelligence management, and access control method, for human and AI use in their operations and inter-operations.

Claim

What: General Endeavor Management (GEM) provides a consistent method for whole-endeavor terminology management within the broader activity of Technical Communication Management. The GEM method and mechanism consists of integrated intelligence and operations lifecycle management capabilities, usable by any endeavor, at any scale.

The GEM method provides a process, using current internet, database, and modeling technology, for consistently building, extending, and applying a general-use, holistic, outside-in terminology-management service to enable, discipline, and improve intelligence and operations management. 

At the global and species scales, the GEM terminology-management service enables effective and efficient communication of intelligence and operations for the entire human species and Artificial Intelligence (AI) services. At the smaller scale, GEM provides every member and group of the human species and AI machine learning system with the means to be effective and efficient when interacting with local to global intelligence and operations. Integration using GEM provides the means for dynamic and informed perspectives for local to global operations.

At all scales, GEM provides a continuously self-refining analytics environment, producing increasing value to its users through integrated: endeavor descriptive analytics, diagnostic analytics, prescriptive analytics, and predictive analytics. This endeavor analytics environment provides the knowledge-base for cognitive value processing, machine processing, and cognitive decision-making.

How: The foundation of the GEM terminology-management service is the collected vocabulary “terms” of the GEM users and public information content, whether single terms or compound terms, whether nouns, verbs, or their modifiers. This terminology-management service consists of the following integrated and distributed cloud GEM components and their interfaces:

1)     vocabulary discovery tools (e.g., source-tracking, and extraction),

2)     vocabulary term repository,

3)     term ungoverned taxonomy (i.e., broad to narrower meaning term hierarchy, with high ambiguity),

4)     term definition repository,

5)     term+definition governed taxonomy (i.e., reduced ambiguity),

6)     meaning and context repository (i.e., meta structures, definition/semantic and taxonomic context of term relation models),

7)     term thesaurus repository (e.g., term translator, translation memory),

8)     ungoverned knowledge repository (i.e., descriptive term compound-relation models),

9)     governed knowledge comparison repository (e.g., descriptive analytic models of a topic’s viewpoints, views, ideas, ideologies, beliefs, news, interpretations),

10)  governed knowledge repository (e.g., descriptive analytic models of general/variant/disjointed knowledge, concept translator, viewpoint translation memory),

11)  governed-messaging repository (i.e., Technical Communication reference/dictionary)

12)  continuous improvement terminology-refinement repository (e.g., GEM components 1-11 content-change tracking, history).

 Each higher-numbered component is built by integrating and adding additional terminology-management features to the preceding lower-numbered component, thus increasing the intelligence and operations value of each component to GEM users.

Building the GEM terminology-service starts with building a top-level vocabulary-term repository, as an extension of an industry-standard taxonomy structure, using commercially-available and open-source globally-distributed cloud graph-database products. This vocabulary-term repository is populated with the captured raw vocabulary terms from public and private information content stores (e.g., structured, unstructured, semi-structured, big data, and big table content), and the available Dublin-Core metadata (e.g., author, organization, create-date) of this information content.

The top-level root noun vocabulary of the GEM vocabulary-term taxonomy consists of seven basic operation questions of:

a)      where is the operation,

b)     who’s responsible for the operation,

c)      who performs the operation,

d)     what operation is done,

e)     how the operation activity flows,

f)       what goes in and comes out of the operation, and

g)      why and when of the operation? 

 These basic questions are captured in the taxonomy as noun-types, respectively as:

a)      location (where),

b)     endeavor (who’s responsible),

c)      performer (who performs),

d)     function (what is done),

e)     activity (how),

f)       resource (activity in and out), and

h)     required-capability (i.e., when and why, required functions of performer+endeavor+location, function capability provided as activity+resource).

 These noun-types will have noun subtypes and instances within the evolving noun taxonomy in GEM components 2 through 6 above.

The top-level root verb vocabulary has the following relation-types:

a)      equivalence

b)     containment

c)      categorization

d)     sequence

e)     version

f)       variance

g)      description.

 These relation-types will categorize verb-term subtypes and instances in GEM components 2 through 6 above. 

GEM provides a method for consistently modeling term relations as “noun-verb-noun” structures between the noun-repository and the verb repository entries. The data industry names for such a term relation is: triple, directed-labeled-graph (DLG), and semantic (as in a “sentence diagram”). 

Triples (or equivalent DLG or semantics) will be captured and managed in GEM component 6 above. These triples will be further connected and expanded to build the following terminology products: 

a)      thesaurus (component 7),

b)     process models (components 8, 9, 10),

c)      data models (components 8, 9, 10),

d)     knowledge models (e.g., descriptive analytic models, ontologies, metamodels, architectures) (components 8, 9, 10),

e)     value-chain (e.g., supply, dependency) process models (components 8, 9, 10),

f)       dictionary (component 11).

 The GEM users’ raw vocabulary terms, managed using GEM component 2, are either automatically or manually queried for term-definitions, managed by GEM component 4, from Internet, industry association, local and associated organization, internal organization group, and individual definition sources. This provides the GEM component 4 with a “table” of terms having zero or more related definitions for each raw vocabulary term. The GEM component 4 definitions table is then applied as the lookup source in building an increasingly-governed and semantically-refined GEM component 5 taxonomy as a hierarchical broader-to-narrower term+definition controlled-vocabulary. 

When communicating within GEM, the term+definition query from the GEM component 5 governed taxonomy provides a lookup source for the nouns and verbs in GEM component 6 modeling and messaging, thus providing broader semantical context and relevant definitions for model and message meaning, thus reducing the ambiguity of the message across diverse message recipients, beyond those who are also using GEM.

To further expand the GEM component 5 taxonomy’s capability, the term and term+definition will also be automatically and manually queried for term acronyms, abbreviations, alternate spellings, and synonyms from Internet, industry, organization, group, and individual sources, to create the GEM component 7 Thesaurus. This GEM thesaurus will then be applied as a mechanism to aid in the translation of messages across senders and receivers that might have differing jargon and natural languages.

The component 7 Thesaurus can be viewed with alpha-numerical term sorting and additional data to provide the GEM component 11 Reference/Dictionary.

GEM component 6 descriptive modeling provides the base to consistently model GEM component 8, 9, and 10 term-focused views and viewpoints, and to consistently compare, merge, and distinguish differences in term-focus views and viewpoints.

GEM components 7, 8, 9, and 10s’ resultant compared views and viewpoints provide a method to capture, share, and integrate recorded human and artificial intelligence, accessible under fine-grained descriptive-model-based access controls for user authentication “key” and resource authorization “lock” capabilities.

GEM provides a method for consistently applying the above fine-grained key-lock access controls as a dynamic and evolving knowledge base to inform and extend a “situationally-adaptive” and “semantics-driven” general endeavor-operations lifecycle management process with descriptive-model-based “need to know” access control. 

All the above provides a unifying, holistic, top-down, end-to-end, adaptively-secured Generalized Operations Lifecycle management method implementable using public-cloud services, enabling linking together all the diverse activities, viewpoints, and views within and around an endeavor into a single intelligence-governed management system.