Mechanized Intelligence Orchestration

Abstract

"The Organization of Mechanized Intelligence" The present invention relates to a mechanized intelligence orchestration process and apparatus for facilitating the process of problem solving and decision making, further the mechanized intelligence orchestration mimics human intelligence as a mechanized intelligence and acts as an intelligence mentor. The mechanized intelligence comprises of subdivisions of Intelligence Acquisition (IQ), Intelligence Application (IP), and Intelligence Verification (IX). The mechanized intelligence is developed by applying factors as functions to achieve a subjective outcome. The mechanized intelligence acquires, applies, and verifies solutions.

Description

FIELD OF THEINVENTION

[0001] The present invention relates to mechanized intelligence orchestration, where a machine mimics human intelligence to operate as a mechanized intelligence and act as an intelligence mentor. In particular, the present invention relates to a machine learning intelligence capabilities and drivers through human intervention, problem solving and decision making processes through a human natural language interface with the skills to intelligently acquire knowledge from diverse computational sources.

[0002] Background of the invention:

[0003] Mimicking human intelligence in machines can leverage the field of cognitive science. Intelligence is more than memory; intelligence works with memory and identity. Intelligent engines are designed to manage and solve complex problems in the workings of intelligence. Intelligent engines provide knowledge and use it for problem solving and decision making. Knowledge-based intelligent engines learn from rules provided to them or learn on their own using machine learning techniques.

[0004] Knowledge representation is part of the intelligent system of artificial intelligence approach. To represent knowledge as part of intelligence, it needs to be considered in a multi-dimensional way to cover important aspects of intelligence. However, traditional methods have not been able to comprehensively orchestrate the configuration, coordination and management of intelligence. This is provided by the orchestration process of mechanized intelligence capabilities and factors.

[0005] Leveraging existing content is the domain of data acquisition, information acquisition, and knowledge acquisition. It requests information from data, information, or knowledge and provides solutions. Data acquisition is from structured data, information acquisition is from semi-structured or unstructured data, and knowledge acquisition is from knowledge sources. There are several approaches to acquire post-integration from diverse sources. Moreover, the traditional process of post-integration acquisition and indexing is time-consuming, cumbersome, and error-prone. There are also traditional question answering methods, which utilize predefined patterns to acquire from various sources, but traditional methods fail to intelligently acquire from diverse sources. Hence, traditional automated approaches fail to acquire, apply, and evaluate intelligence.

[0006] Thus, there is an unmet need for mechanized intelligence methods to efficiently acquire, apply, validate, and evaluate intelligence.

[0007] Prior art and its shortcomings:

[0008] ·US Patent US8935277B2 provides a context-aware question answering (QA) system. A question may be a business intelligence question expressed in natural language. The parsed question is matched to a pattern among the generated patterns. A predefined technical query associated with the matched pattern is processed to retrieve data relevant to the question from multiple data sources. The QA system generates an answer to the question based on the retrieved data.

[0009] However, the prior art generates solutions based on pattern matching techniques. Moreover, the prior art does not consider the multiple factors and divergent approaches of natural query perception.

[0010] Shortcomings of the prior art:

[0011] The prior art suffers from some or all of the following deficiencies: · Prior art fails to provide an orchestration process that mimics Multidimensional Intelligence as a mechanized intelligence and acts as an intelligence mentor. · The prior art fails to provide a mechanism for mechanized intelligence to intelligently retrieve from diverse computational sources without transformation of the content. · The prior art is unable to mimic the mechanized intelligence process to acquire, apply, and evaluate solutions. · The prior art fails to generate intermediate elements of human natural language based user request recognition based on the “examples and principles” approach using the divergent capabilities of mechanized intelligence factors. The prior art is unable to produce a general purpose multi-dimensional eta content product for determining computer resources. · The prior art fails to recognize and retain meta-subjective achievements. The prior art fails to provide any implicit hierarchical examples and principles for determining computer resources. The prior art does not even contemplate one principle for determining resources based on the hierarchy of data, information, knowledge, and wisdom of computer resources. Prior art is unable to generate generic query translations from natural language processed user requests. The prior art fails to generate straightforward solutions using the convergent capabilities of mechanized intelligence for computational resource determination, generic query transformation, and result generation assistance, taking into account the “cases and principles” approach.

[0012] Objective of the invention:

[0013] The main objective of this invention is to provide an orchestration process that mimics human intelligence and provides problem solving for decision making.

[0014] Another object of the present invention is to provide an orchestration process of mechanized intelligence by configuring, coordinating and managing it.

[0015] Another object of the present invention is to acquire, apply, and validate mechanized intelligence.

[0016] Yet another object of the present invention is to generate a generic metamodel of subjective factors of mechanized intelligence.

[0017] Yet another object of the present invention is to provide intellectual acquisition skills that provide problem solving and aid in decision making.

[0018] It is yet another object of the present invention to provide a mechanism for intelligent acquisition from diverse sources while utilizing natural human language.

[0019] Yet another object of the present invention is to utilize the divergent capabilities of Mechanized Intelligence (MI) to analyze human natural language based user requests in an MI manner.

[0020] Yet another objective of the present invention is to provide hierarchical tacit knowledge for determining computational resources and to provide generic query transformation and result generation by utilizing MI convergence capabilities and the “cases and principles” approach. [Brief description of the drawings]

[0021] [Figure 1] It shows the fragmented capabilities of mechanized intelligence. [Diagram 2] We present a metamodel of the causes of mechanized intelligence. [Diagram 3] Here is a block diagram of the acquisition of intelligence for intelligent acquisition. [Figure 4(a)] Demonstrates the application and validation of mechanized intelligence. [Figure 4(b)] It illustrates the behavioral process of a human natural language request for recognition. [Figure 4(c)] Presents a compromise approach to determining computer resources. [Figure 4(d)] It shows the building blocks that transform a generic query into a standard technical query. [Figure 4(e)]We present a comprehensive approach to generating results using result generation assistance. [Diagram 5] 1 shows a representation of the orchestration environment of the present invention. [Figure 6] A conceptual diagram showing an orchestration device connected to a network and a repository is provided.

[0022] What do the reference numbers for each component in machine learning for mechanized intelligence mean? IQ: The subdivision of intelligence acquisition IX: Subdivision of Intelligence Tests IP: Subdivision of Intellectual Application S: Subject SL: Subject language SD: Subject domain SC: Subject Computer Resources SS: Contextual situation of the subject F: Function FC: Functional cognition FT: Function retention FR: Feature Recall FD: Functional Divergence FC: Functional convergence FE: Functional evaluation O: Result OU: Result Unit OC: Result Class OR: Output relation OS: Output system 300: Intelligence acquisition 301: Expert team 302: Mechanized Objects 303: Subjective Perception of Outcomes 303.1: Recognition of Linguistic Subjects 303.2: Recognition of Domain Subjects 303.3: Computer Resource Subject Awareness 303.4: Contextual Situational Theme Awareness 303.5: The "cases and principles" approach to intelligence acquisition 304: Subjective Retention 305: Subjective Outcomes 400: Application and validation of intelligence for intellectual acquisition and intellectual search skills 401: User request for acknowledgement 401.1: User request in natural human language 401.2: Subjective Recall of Results 401.2.1: Recall of Language Results 401.2.2: Recall of Domain Results 401.2.3: Recall of Computer Resource Results 401.2.4: Recall of Contextual Situational Outcomes 401.3: User request diverged 401.4: Check for existence of evaluated ICUR 401.5: Recognition of UR Units 401.6: UR Class Recognition 401.7: Recognition of UR Relationships 401.8: Retention of ICUR 401.9: ICUR Recall 401.n: Interim acknowledged user request 402: Computer resource determiner 402.1: Recall of intermediate acknowledged user request 402.2: Check for Evaluated DCR Results 402.3: Recall of Determined Computer Resources 402.4: Convergence of Computer Resources 402.4.1: "Examples and Principles" for Computer Resource Determinators 402.5: Perceived Determined Computer Resources 402.6: Determined Computer Resources Retained 402.n: Determined Computer Resources 403: Generic Query Translation 403.1: Check for the existence of a standard technical query that has been evaluated 403.2: Recall of standard technical queries 403.3: Recall of Determined Computer Resources 403.4: Convergence of Standard Technical Queries 403.4.1: General Query Translation "Examples and Principles" 403.5: Recognition of Standard Technical Queries 403.6: Preservation of Standard Technical Queries 404: Result generation support 404.1: Determine if a standard visualization component exists for a given user request 404.2: Recall of standard visualization components 404.3: Recall of standard technical queries 404.4: Convergence of Standard Results Visualization 404.4.1: "Examples and Principles" for Result Generation Support 404.5: Recognition of Standard Visualization Components 404.6: Preservation of standard visualization components 404.n: Proposed visualization components 405: User rating rating 500: Mechanized Intelligence Orchestration Device 503: Processor 504: Network Interface 505: Storage devices 506: Memory 507: User Interface 508: Power supply 509: Operating System 510: Application 511: Mechanized Intelligence Solutions 600: MI Conceptual Diagram 601: Objects / equipment / devices 602: Computer Resources 603: Network

[0023] Summary of the Invention In an embodiment of the present invention, a mechanical intelligence (MI) orchestration process is provided as an orchestration device or, in other examples, as a means of an MI orchestration device or MI orchestration system. This orchestration process integrates intelligence to support human problem solving and decision making. Intelligence is developed through the abilities of acquisition, application, and validation, and therefore this mechanical intelligence consists of the divisions of intelligence acquisition, application, and validation. Mechanical intelligence is developed to achieve subjective outcomes by applying its elements as functions. Intelligence is mechanically acquired on identified targets. Identified targets include domains, languages, computational resources, and contextual situations. The outcomes after applying functions on targets are actual or transformed units, classes, relations, and systems. Intelligence is acquired through subjective perception and retention functions.

[0024] When a problem occurs or a decision is required, the user makes a request to the MI to request intelligent retrieval skills through the application of intelligence. To obtain a solution, the user makes a request through a natural human language (HKL) interface, and the retrieved intelligence processes the user request using subjective recall and branching functions. It then determines the computer resources and identifies where the answer exists, primarily with the convergence function. It further translates from HKL to a general query using the convergence function for technical query translation, and performs result generation assistance to generate results through subjective recall and convergence functions. This application of intelligence is achieved with the help of a specific and prescriptive approach, where the problem is solved by the use of specifics as well as prescriptive, and a decision is made. Finally, an evaluation of the intelligent retrieval skills by the user is performed through the subjective recall and evaluation functions.

[0025] Detailed Description of the Invention The disclosed embodiments of the present invention provide a solution to one or more technical problems identified by the inventors in the prior art or current state of the art. The accompanying drawings, which are part of this complete specification, illustrate one or more embodiments of the present invention. Preferred embodiments of the present invention are described below with reference to the drawings, which are intended to illustrate presently preferred embodiments of the present invention and are not intended to be limiting.

[0026] The following detailed description illustrates embodiments of the present disclosure and shows how the disclosed embodiments can be implemented. Although several modes of implementation of the embodiments of the present disclosure are disclosed, those skilled in the art will recognize that other embodiments and methods of implementing the present disclosure are possible and will therefore become apparent to those skilled in the art.

[0027] See Figure 1. An embodiment of the present invention provides an orchestration process that configures, coordinates, and manages Mechanical Intelligence (MI). It first acquires intelligence through the elements of MI and then leverages the acquired intelligence for retrieval. This orchestration works through three subdivisions: intelligence acquisition, validation, and application. MI also provides intelligent retrieval skills from diverse computational sources.

[0028] The intelligence acquisition section (IQ) acquires the required elements, their general attributes, specific features, their functions and relationships with contrasting elements with the help of a team of human experts or mechanized ones. The intelligence verification section (IX) thoroughly verifies the acquired knowledge with the help of the intelligence application section (IP). Therefore, the intelligence verification section (IX) and the intelligence application section (IP) will continue to be used. Furthermore, the intelligence verification section (IX) simultaneously recalls the acquired objects and verifies the obtained results. The intelligence verification section (IX) and the intelligence application section (IP) depend on the intelligence acquisition section (IQ). Therefore, the intelligence acquisition section (IQ) is the precursor of (IX) and (IP). This orchestration process improves intelligence cognition and learning by acquiring intelligence capabilities through the virtues of intelligence elements and improving problem-solving and decision-making capabilities through the particulars and norms (PwP) approach.

[0029] See Figure 2. In another embodiment of the present invention, the elements of mechanical intelligence are based on functions, objects, and outcomes. The intelligence acquisition category (IQ) ability mimics the functions of the human mind: identity, cognition by specifics and norms (scope rules), and storing in memory as mind maps. IQ must acquire intelligence by applying functions (F) to various objects (S) based on their nature and categories of outcomes (O). This ability is acquired by competing functions on objects to derive subjective outcomes.

[0030] In other embodiments, a single MI device performs the IQ, IP, and / or IX MI capabilities on the MI device as an MI system. In some cases, multiple devices may be decentralized or blockchained to perform the IQ, IP, and / or IX as separate MI modules. In some examples, one MI device acts as a server and performs the IQ and IX capabilities of MI, while other devices perform the intelligent retrieval skills through the IP capabilities.

[0031] The intelligence element, Object (S), is related to the field of information that promotes intelligence. All intellectual functions and activities are performed in the object element to obtain subjective results. The object element distinguishes the information field for developing mechanical intelligence. This object dimension includes four subfactors: Language (SL), Domain (SD), Computational Resources (SC), and Contextual Situation (SS).

[0032] Subsequent text further describes this process of intelligence acquisition, application, and validation in detail, detailing the components of intelligence and the role of each function.

[0033] [Table 1]

[0034] Table 1 shows various factors for recognizing and retaining language outcomes, where the recognition and retention of linguistic units is based on character, token and sentence units. The recognition and retention of language classes is determined by character classification, token classification and parts of speech. The recognition and retention of linguistic relationships is determined by identifying the relationships between characters for spelling, morphology and lexical analysis. Furthermore, by identifying the relationships of recognition and retention of unit classes and systems, network-like properties with language components themselves and other informational subjects are identified. After recognizing and retaining linguistic knowledge at various stages, language outcomes (305.1) are generated, which represent the linguistic analysis of character, token and sentence differences.

[0035] After the recognition and retention of linguistic subjects, domains (303.2) are recognized and retained as outcomes of the subjective domain (305.2). Knowledge of hierarchical domains (303.2) in the fields of classical and modern knowledge in commerce, science, and art is generated and aids in the acquisition of intelligence. Domains (303.2) identify the units of acquired linguistic knowledge, compare them with opposing domains, and establish relationships between variable domains.

[0036] [Table 2] Table 2 shows various factors that influence the recognition and retention of domain outcomes. The recognition and retention of domain units is based on the description of the domain using linguistic components. After identifying the domain units, the classes and subclasses of the domain are determined. Further, relationships between different domains are established and knowledge about them is recognized and retained. After the recognition and retention of linguistic knowledge at different stages, the domain outcomes (305.2) are generated and the domain of the acquired knowledge is determined.

[0037] After the subjective domain outcomes (305.2), computer resources are recognized and retained, resulting in subjective computer resource outcomes (305.3). Computer resources are software structures that facilitate content storage and retrieval. Multiple computer resources include multimedia audio, video, images, structured transactional, aggregated, relational, hierarchical or networked data or information, unstructured text, semi-structured markup, delimited values, etc. Computer resources vary in storage based on thematic content differentiation. The recognition of computer resource subjects (303.3) is essential to gain intelligence. Each computer resource has different metadata formations to identify the computer resource and retrieve the content. Metacontent plays an important role. Therefore, accurate recall of the content of computer resources with metacontent requires classified storage in memory. Various computer resources are stored for further access to information, and the meta properties of these resources need to be retained as subjective retention (304) for further intellectual actions.

[0038] A subdivision of intelligence acquisition (IQ) is the memorization of recognized or learned subjects through the human mind or machine learning. Memory is an ability that has two subfunctions: memory recording and recall. Once recognition occurs, its meta properties are stored as a multi-layered hierarchical system structure, and this memorized meta helps determine the outcome of computer resources.

[0039] [Table 3] JPEG2025510831000005.jpg48167

[0040] Table 3 shows various ways to encode and preserve meta. Meta content is proposed and divided into a hierarchy of levels, divided into units, classes, relationships and system properties of meta content of any computer resource. Recognition of computer resources has both general (g) and specific (s) properties that help to identify the resource. These g and s factors are very useful for two major capabilities: computer resource decider and general query transformation. Standardized g and s (gs) factors metadata for electronic sources are necessary to recognize for intelligence application and validation.

[0041] Awareness of domains (303.2) and computer resources (303.3) is always related to language (303.1) knowledge and contextual situation. Awareness of contextual subject matter (303.4) aids knowledge and ultimately intelligence acquisition. Components of the contextual situation include, but are not limited to, places, news, weather, government, demographics, etc. The Intelligence Acquisition subdivision (IQ) recognizes the meta-content of the contextual situation and retains it for further intelligence application and validation.

[0042] [Table 4] Table 4 shows that knowledge is acquired and applied in different context situations. Recognition of context situation units, recognition of context situation classes, recognition of context situation relations, and recognition of context situation systems generate context situation outcomes (305.4). In other examples, there may be fewer or more elements or factors among those in Tables 1 to 4, which may be utilized or extended internally and externally.

[0043] Based on the determined intelligence acquisition subdivision (IQ), it indicates that there are operational factors necessary to arrive at a solution when a problem arises. The problem must be understood and constructed as a system outcome before progress towards a solution can be made. After the problem is recognized, alternative solutions are generated as a divergent generation function of the outcome, or convergent generation occurs when a unique solution exists based on sufficient information. The problem solving process has an evaluation mechanism that accepts or rejects the recognition of the problem and the generated solution.

[0044] Referring to Figure 4(a) to Figure 4(e), another embodiment of the present invention shows a process of applying or testing intelligence using intelligence application and intelligence testing (400). Intelligence acquisition follows acquiring intelligence through a process of developing sub-capabilities as user request for cognition (401), computer resource determiner (402), generic query translation (403), result generation assistance (404), and user evaluation evaluation (405). Then, (400.n) is generated as meta-response (MR) to be utilized in the intelligence testing process.

[0045] A user presents a problem and requests a solution for problem solving and decision making. This is done through a User Request for Cognition (401) segment. A Request for Cognition (401) is a request in Human Natural Language [HKL] for recognizing a user's request for generating knowledge. Humans may answer by understanding the whole question, or they may understand nouns and verbs as part of a query, identify keywords from the request, and sometimes link knowledge to generate an answer. Based on the definition of the terms given, we classify these terms here in a higher hierarchy as Request for Cognition (RFC). A Request for Cognition (RFC) is a standard request that aims to recognize a user's request for generating knowledge. It is important to understand the whole picture of an RFC from all possible perspectives before fulfilling it in the form of a response to a user's request. One User Request (UR) contains multiple queries, and one query contains multiple sentences formed from words. A group of correctly arranged words with meaning is called a sentence, words arranged to have a particular meaning are called sentences, and one RFC may consist of multiple sentences.

[0046] The proposed solution recognizes user requests by subject. Identifying the type of user request includes searching, exploring, questioning, researching, inspecting, probing, investigating, inquiring, etc. The user requests in natural language as a user request (401.1), which is entered into the request for recognition (401), and finally intermediate cognitive user requests (401.n) are generated. (401.4) checks whether there exists an intermediate cognitive user request (ICUR) evaluated for the given user request (UR). If there is, it invokes ICUR (401.9) and terminates. But if there is not, it proceeds to the recognition of the user request outcome. The recognition of the user request outcome is obtained by the recognition of user request units (401.5), user request classes (401.6), and user request relations (401.7). The recognition of the user request outcome is achieved with the help of the invocation of subjective outcomes (401.2) and the emanations of user request outcomes (401.3). The Cognitive Requirement (401) recognizes the semantic elements of the user request and checks if the syntax of the sentence is correct. It identifies the different features of the sentence lexically and syntactically, preprocesses the tokens such as spelling of words and abbreviations, parses the tokens of the sentence with different classifications as parts of speech, and finally generates intermediate analyzed elements for the user request. The analyzed required elements of this Cognitive Requirement (401) are stored in a semi-structured markup language format as Intermediate Cognitive User Request [ICUR] (401.n).

[0047] The divergence of user request outcomes (401.3) provides a combination of different possible classes for the thorough recognition of user requests. The divergence of user requests (401.3) uses the invocation function of subjective outcomes (401.2) to generate multiple solutions for the recognition of user requests. The invocation function of subjective outcomes (401.2) invokes language outcomes (401.2.1), domain outcomes (401.2.2), computer resource outcomes (401.2.3), and context situation outcomes (401.2.4) to understand the divergence of user requests. The recognition of user request units (401.4) is considered as the knowledge of language units based on domain and context situation. The language scanner parser and sentence boundary identifier are used to identify user request units (characters, tokens, sentences, etc.). The characteristics of these three language units are invoked from the retained subjective outcomes. The recognition of user request classes (401.5) classifies user requests based on language, domain, and context. Recognizing user request relations (401.6) associates elements of a user request internally and externally. The linguistic relations of characters constitute words, and the relations of word tokens provide the semantics of the user request. The relations between tokens identify the action or state of a specified requested situation. The relations of two sentences in one request are effective in identifying the semantics of that request. The relations between the tokens of a sentence lead to the grammatical syntax of the request, which further helps in recognizing the user request. Retention of recognized user request (401.7) is taken into account for further (IP) and (IX) processes. Retention of recognized user request (401.7) encodes and records the information in an appropriate structure as an intermediate cognitive user request.

[0048] Intermediate-cognitive user requests (401.n) contain encoded information about the characteristics of user request units, classes, and relationships, and are structured as user request unit recognition (401.4), user request class recognition (401.5), and user request relationship recognition (401.6). The format of intermediate-cognitive user requests is a structure based on hierarchical markup and key-value pairs.

[0049] Furthermore, the computer resource determiner (402) selects a resource based on the acquired knowledge and the user's RFC as a human natural language [HKL] query. Based on the given UR, it determines a computer resource based on existing evaluated determined computer resources (402.n) or by knowledge of intermediate recognized user requests (401.n). In the computer resource determiner (402), the recognized user's request is called and evaluated whether it already exists. If it does, it calls the determined computer resource (402.3). The determined computer resource contains an entry of a computer resource with a solution for the user's request. When the determined computer resource is recognized, the function (402) ends. However, if there is no existing evaluation result, the process moves to (402.5) and performs convergence of computer resources by utilizing the characteristics of the computer resource determiner (402) (premise (402.5.1)). Even in (402.5), knowledge of subjective outcomes (305) is required. It utilizes the invocation of subjective outcomes (401.2) and invokes intermediate recognized user requests (401.n).The characteristics of the computer resource determiner include, but are not limited to, the type of user request (e.g., command question), the class of linguistic units in the user request, and any of the content classes that identify data, information, knowledge, or wisdom.

[0050] Generic Query Translation (403) applies a process that translates a natural language query into a technical query. A user's natural language request may be fulfilled from any connected computer resource. Each computer resource has different means of connection and retrieval.

[0051] In the general query transformation (403), the invocation of the intermediate recognized user request (401.n) is performed. After the recognized user request is invoked, it is checked (function 403.1) whether an evaluation result exists for the given intermediate recognized user request (401.n) and the 15 determined computer resources (402.n). If it exists, it invokes the standard technical query (403.2). The standard technical query has a technical query for the selected computer resource and the given intermediate recognized user request, and contains an entry on how to access the computer resource and generate a solution. If the standard technical query is recognized, the function (403) ends. However, if there is no existing evaluation result, the process moves to (403.4) to perform the convergence of the standard technical query using the characteristics of the general query transformation (403) (prerequisite (403.4.1)). (403.4) also requires knowledge of the subjective outcome (305). Invoke intermediate recognized user requests (401.n), perform subjective outcomes (402.4), perform language outcomes (402.4.1), domain outcomes (402.4.2), computer resource outcomes (402.4.3), context situation outcomes (402.4.4), determine computer resources (403.3), and conclusively recognize and maintain standard technical queries.

[0052] The result generation assistance (404) assists in identifying the proposed visualization components (404.n). In the result generation assistance (404), the invocation function for the intermediate recognized user request (401.n) is executed. After invoking the necessary components based on the user request, it checks (function 404.1) if an evaluation result exists for the given intermediate recognized user request (401.n), the determined computer resources (402.n) and the standard technical query (403.n). If it exists, it invokes the standard visualization component (404.2). The standard visualization component (404.2) has a visualization component identification for the given component and contains a visualization component entry. Visualization components include, but are not limited to, single or multi-valued, text, n-dimensional tables, charts, shapes, pictorial marks, text, graphs, images, audio, video, etc. The function (404) ends when a standard visualization component is recognized. However, if no existing evaluation results exist, the process moves to (404.4) where the result generation assistance characteristics are used to converge the standard visualization components (prerequisite (404.4.1)). (404.4) also requires knowledge of the computer resource outcomes (402.4.3) as well as knowledge of the subjective outcomes (305).

[0053] The result generation assistance (404) makes a decision on the visualization component based on the characteristics of the result generation assistance (premises (404.4.1)). As a result, it recognizes the standard visualization component (404.5) and stores it in the standard visualization component (404.n) as (404.6). Based on the request and the intermediate responses, the user provides satisfactory feedback. The user's evaluation evaluation (405) evaluates the solution of the machine intelligence. The generated solution may be correct or incorrect, complete or incomplete, finite or infinite. The expert user provides feedback on the requested characteristics and stores it in the feedback (405.n). The user's evaluation evaluation consists of the evaluation of the user's request for recognition (401 factor), the evaluation of the computer resource determiner (402 factor), the evaluation of the general query transformation (403 factor) and the evaluation of the result generation assistance (404 factor). Other examples may have fewer, more, enhanced or expanded steps and may occur within or outside the examples of Figures 4(a) through 4(e). The present disclosure may be used in a variety of contextual situations, through the use of diverse natural languages, and in domains that rely on or are independent of a variety of computational resources.

[0054] Referring to FIG. 5, the present intelligence implementation (511) is a solution of the orchestration process of mechanical intelligence. The process is implemented as an application (510) residing in an operating system (509). The implementation (511) requires a processor (503) and a network interface (504) to communicate. In addition, the implementation (511) has a storage device (505) and a memory (506). To interact with the implementation, a user interface (507) provides input and output solutions. One example of a storage device (505) might include a computer resource (602) that specifies that the resource is on the same machine. Interaction with the system is through the network interface (504) and the user interface (507). A power supply (508) provides power to start the implementation (511). Each component (503, 504, 505, 506, 507, 508, 509, 510, 511) may be operationally, physically, or communicatively connected for inter-component communication. The orchestration device (500) may include additional components and run different applications. The MI device (500) may be mobile, fixed, or portable. In one example, a single user may own the MI device for the orchestration process. In another example, two or more users may share the MI device for orchestration. Conceptually, the mechanical intelligence orchestration device (500x) is connected for communication with other objects / devices / apparatus (601) on the computer resource (602) via the network (603). In some examples, the MI device (500) utilizes a network interface (504) to wirelessly communicate with (601), such as a mobile phone, a server, or other network-connected computer device. The computing environment of (601) utilizes one or more of the same components as the MI device (500).In one example, (500x) indicates one or more (500) devices used for a single orchestration process, in another example, (500x) specifies the distribution of a complete (500) MI process over multiple devices, and in yet another example, one (500x) device connects to another (500x) device over a wired or wireless network (603).

[0055] The network (603) may be based on any communication protocol that allows for data transfer between two or more devices. The network (603) may be embodied as one or more of the Internet, a fiber optic network, or a wired network, including but not limited to WiFi, a local area network (LAN), 3G, 4G, WiMax, a cellular network, a satellite network, etc. The network interface (504) may include but is not limited to an optical transceiver, an Ethernet card, a radio frequency transceiver, 3G, 4G, WiFi in a mobile computing device, USB, or any type of device capable of sending and receiving information.

[0056] The storage device (505) may be configured to store a larger amount of information than the memory (506). The embodiments disclosed herein may be recorded in the storage device (505). Optionally, the storage device (505) may include one or more computer readable storage media. The storage device (505) may be configured for long-term storage of information. In another embodiment, the storage device (505) may include, but is not limited to, non-volatile storage devices such as optical disks, CDs, DVDs, floppy disks, magnetic hard disks, flash memory, or electrically erasable programmable memory (EEPROM), electrically programmable memory (EPROM), and the like.

[0057] In another embodiment, the MI device (500) may include one or more power sources (508) for providing power to the MI device (500). In some examples, the power source (508) may be capable of providing power storage or voltage from another stored energy source, such as a capacitor or a fuel cell. The power source (508) may include a battery or circuitry that generates power from an AC or DC source.

[0058] The operating system (509) controls the operation of the components of the MI device (500) as needed, for example, the operating system (509) along with the processor (503), the network interface (504), the storage device (505), the memory (506), and the user interface (507) to facilitate the operation of the applications (510).

[0059] The applications (510) are software and / or hardware modules. In some examples, the applications (510) function as subroutines of the operating system (509) or as independent software or submodules that perform various aspects of a communication process. In some examples, a user may start an application from a list of applications (510). Any application (510) or module implemented, processed, operated, executed, or communicated by components of the MI device (500) is also included.

[0060] Advantages of the invention: The present invention provides an orchestration process through configuration, coordination, and management to mimic human intelligence as a mechanized intelligence of cognitive science.

[0061] The invention provides mechanized intelligence to obtain, apply and validate solutions.

[0062] The invention provides intelligent search skills that contribute to problem solving and facilitate decision making.

[0063] The present invention provides a computational process that uses human instinctive language to perform intelligent searches from diverse computer resources.

[0064] The invention generates general to specific meta content for subjective results, aiding in subject recognition and contributing to improved IQ and IP.

[0065] This invention uses MI to analyze user requirements based on human instinctive language.

[0066] The present invention uses a learned approach that leverages MI convergence to identify computational resources, translate them into standard technical queries, and help generate results.

Claims

1. Orchestrating Mechanized Intelligence Processes and Equipment The process consists of configuration, coordination, and management, including: The power of mechanized intelligence, Elements of the mechanized intelligence metamodel, Intelligent search skills. Where: The capabilities of said mechanized intelligence consist of: Acquisition of intelligence (IQ), Application of Intelligence (IP), Verification of Intelligence (IX). The elements of the mechanized intelligence metamodel consist of Subject (S), Function (F), and Outcome (O). The intelligent search uses mechanized intelligence capabilities and elements of the mechanized intelligence metamodel to intelligently search diverse computing sources using Human Knockout Language (HKL).

2. The computational process of intelligent search according to claim 1. Said intelligent search is realized through IQ, IP, and IX; steps of knowledge acquisition, subject recognition and retention are necessary to obtain a solution. Knowledge recognition and retention are determined by subject domain (SD), computational resources (SC), language (SL), and situational knowledge (SS). General meta-content is recognized and retained for subjective results. Information recognition and retention is general and specific, and acquired intelligence solutions are retained for presented problems and decisions.

3. The intelligent search process according to claim 1. Said application of intelligence (IP) receives a user request (UR) to generate a solution to the problem through the acquired information. Said User Requirement (UR) is processed by applying branching functions to recognize Unit Results (OU), Class Results (OC), Relationship Results (OR) and System Results (OS) based on the Subject Language (SL), Subject Domain (SD) and Situational Situation (SS) and generate intermediate recognized User Requirement (401.n) based on the subjective results. The generated intermediate recognition user request (401.n) is used to determine computational resources (SC) using the CRD "Precepts Approach" (402.5.1); thus, intelligence is obtained using the subject language, domain, computational resources, and situational results. The obtained intelligence is used to convert the general query into a standard technical query, and the GQC "Precepts Approach" (403.4.1) is applied to obtain the standard technical query. Convergence is applied to determine standard visualization components using the specific "Precepts Approach" (404.4.1).

4. An intelligent search process according to claim 1. The evaluation of the generated responses is determined through the Intelligence Verification Subsystem (IXs).

5. Mechanized intelligence capabilities, elements, and orchestration processes for intelligent search The method captures, applies, and validates intelligence based on meta-elements to aid in solution capture and decision making.

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