Computing systems and methods featuring synthetic computing operators and collaboration

Abstract

A method for implementing a synthetic participation system for process-based problem resolution is disclosed. One variation includes: a computing system including one or more operably coupled computing resources; a user interface operated by the computing system, the user interface configured to engage a human operator according to a predetermined process configuration towards a predetermined requirement based at least in part on one or more particular facts; wherein the user interface is configured to allow a human operator to select and interactively participate in one or more synthetic operators operated by the computing system to achieve a predetermined process configuration, and to return a result selected to at least partially meet the predetermined requirement to the human operator; and wherein each of the one or more synthetic operators is notified by a convolutional neural network, which is notified at least in part by historical actions of a particular actual human operator.

Description

Technical Field

[0001] The present invention generally relates to systems and methods for configuring, organizing and utilizing computing resources, and more particularly to computing systems, methods and configurations featuring one or more composite computing interface operators configured to assist in applying and controlling associated resources. Background Art

[0002] Computing systems of all types have become ubiquitous in modern life, and have greatly improved all aspects of productivity. However, human endeavors to expand and scale through computing are limited, in part because of traditional paradigms for how humans interact with and utilize computing resources, and because of the complexity of the human challenges in question. For example, the interface for using computers to solve specific technical challenges still involves obscure operator interfaces, such as Figure 1A The "command line" interface in (2) and Figure 1B The operator interface shown in the "Visual Studio" interface (4) in , and specific background knowledge and experience for optimization. Of course, more user-friendly interfaces have been developed to expand computing access, and some challenges can be solved by accessing portals (such as web browser interfaces (e.g. Figure 2 as shown in (6)) or by, for example Figure 3 The problem of using a voice-based computing interface for a device such as (8) shown in FIG) is relatively easy to solve. However, in many cases, the ultimate collaborative resource for complex tasks is still not a computing resource, but another type of human resource or a team thereof, with unique skills, experience, and capabilities, such as those related to operating and utilizing computing resources, as well as many other skills, experience, and capabilities.

[0003] Off-the-shelf general-purpose “artificial intelligence” (or “AI”) computing systems (e.g., those available from providers such as Amazon or Google (e.g., under the trade name Alexa) TM or Google Assistant TMThe advent of AI has helped provide relatively convenient, hands-free, low-latency responses to challenges such as “What is the capital of Oregon?” However, such systems are generally not well suited for complex, multi-factorial challenges such as: a) design the next successful Ford Mustang and return design and manufacturing documents that are ready to manufacture; b) compose the music for the next Beatles album; or c) create the next successful major iteration of a consumer electronics product and return design and manufacturing documents that are ready to manufacture. Again, these challenges are often led by teams of talented, experienced people and inherently have problems associated with the human element, such as: finding the best talent, getting them to work on the challenge, and co-locating them at the right time so that they can provide functional synergy to each other and the overall goal. In other words, recruiting and maintaining the best team for a specific challenge is complex, difficult, expensive, and difficult to scale.

[0004] In fact, referring to the three challenges in more detail, a typical high-level example for the first challenge (designing the next successful Ford Mustang) might involve something like Figure 4 As shown in the following example: a) assemble a core team of designers, mechanical engineers, electrical engineers, suspension engineers, powertrain engineers, materials experts, regulatory experts, product marketing experts, manufacturing experts, cost control experts, external marketing experts, sales experts, project managers, and technical and overall management experts (10); b) collaborate to understand the Ford Mustang’s past, what worked well, what didn’t work well, and where the product or product line needs to go, taking into account not only artistic and performance constraints, but also regulatory and cost control factors (12); c) determine the high-level design in a collaborative manner to benefit from collective expertise (14); d) iterate many details to develop one or more detailed designs that can be physically prototyped and / or tested (16); e) manufacture, market, and sell the required number of new Ford Mustangs to achieve the required operating profit margin and generate positive contribution to the entity (18). Undertaking such a multivariate and complex project, or even obtaining and retaining the required resources to undertake it, is a daunting challenge that is difficult to successfully overcome; many would argue that the likelihood of ultimately overcoming this challenge with a net positive contribution margin is quite low, and the upfront costs are extremely high.

[0005] For the second challenge mentioned above (writing the music for the next Beatles album), a typical high-level example might involve different resources, but arguably is no less complex and risky, e.g. Figure 5As shown: a) select a producer who has a deep understanding of the Beatles’ music, understands why they were great, how their music evolved when they broke up, what the Beatles should and should not sound like, what they might have written at the time, what instruments at the time should sound like, how to recreate that sound using modern and / or period equipment, and anything and everything else about each of Ringo, John, Paul, and George (20); b) select musicians who have a deep understanding of the Beatles’ music, understands why they were great, how their music evolved when they broke up, what the Beatles should and should not sound like, what they might have written at the time, what instruments at the time should sound like, and how to recreate their specific instruments using modern and / or period equipment (22); c) collaborate to write and record songs for a new album in a way that produces a product that is consistent with the mission (24). Again, undertaking such a multivariate and complex project, or even obtaining and retaining the optimal resources to do so, is a huge challenge that is difficult to successfully address; many believe that the odds of overcoming this challenge are quite low and the upfront costs are relatively high.

[0006] If a user tries to use Alexa TM If one attempts to leverage traditional computational resources and utility paradigms (e.g., search queries, audio files, video files, etc.) to accomplish one of the challenges above, the answer will likely be something like, “I can’t do that.” If one attempts to leverage traditional computational resources and utility paradigms (e.g., search queries, audio files, video files, etc.), the challenge will be extremely difficult, inefficient, and difficult to scale, in part due to the complexity of these challenges and in part due to the traditional paradigms of interacting with and leveraging computational resources, which is why, as mentioned above, the best collaborative resource for these types of tasks is often: a team of talented individuals — with the associated challenge of recruiting, retaining, engaging, and executing with these individuals in a way that allows for success. In fact, trying to leverage a single person, let alone a team, to accomplish a complex task is inherently very challenging. For example, refer to Fig. 6A , shows a variation of a model (30) for increasing an individual's (28) chance of success under a particular challenge (32), wherein a number of inputs and factors, including but not limited to knowledge (34), experience (36), resources (38), analytical skills (40), technical skills (42), efficiency (44), an environment that appropriately promotes success (46), an appropriate risk / reward paradigm (48), collaborative / "people" skills (50), hard work (52), an intuition about the marketing and / or value of various alternatives (54), an understanding of business opportunities (56), communication skills (58), time (60), and a desire / ability to overcome adversity (62), may contribute to meeting challenges and successfully achieving goals / objectives (32).

[0007] Although many people believe Fig. 6A Only one of many models that might help describe the multifaceted challenge that one faces in achieving one’s goals is illustrated, but few would dispute that such challenges are multifactorial, complex, and difficult to address—and, again, this refers to using a single resource to attempt to address a complex challenge.

[0008] Figure 6B A variation of a related process is shown in which a challenge is identified, outlined in detail, and considered to be resourced by a single human resource (64). A single human resource may be identified and / or assigned (66). Where appropriate, the resource may clarify the understanding of goals and objectives related to the challenge, as well as available resources and context about the relevant business opportunity (68). At this point, the resource may be in a "ready to execute" state (70). Using assets such as skills, knowledge, experience, and intuition, and driven by factors such as hard work, time, collaboration / interpersonal skills, appropriate risk / reward paradigms, an environment configured to promote success, efficiency, resources (such as information, computing, etc.), desire / ability to overcome problems and adversity, and communication skills (72), the resource initiates and overcomes the challenge. The resource may leverage similar assets and facilitators to iterate and improve interim solutions (74). Finally, the resource may produce a final solution to achieve the goal / objective (76).

[0009] Likewise, the above example of a single HR process addressing a specific challenge is complex and has the potential to fail or achieve suboptimal results at many stages. In fact, as with any HR-related process, there are human factors that can affect the process, such as difficulty in recruiting, lack of the right people, interpersonal issues, throughput limitations due to human capabilities, vacation days, family issues, etc. Adding further complexity are the resources and size required for teams and to optimally address complex challenges (such as the above challenges regarding vehicle design goals, music production goals, and consumer product goals), and these examples can result in relatively high failure rates when attempting to address challenges of equal complexity (many vehicle designs fail, many attempts to make successful music fail, and many iterations of consumer electronics fail).

[0010] refer to Figures 7A-8C and 9A-10, some advances in computing have helped solve some human-scale challenges of varying complexity. For example, ref. Fig. 7A A robot (78) (e.g., one sold under the trade name PR2(RTM) or "Personal Robot 2") typically has a head module (84) having various visual and sensory equipment, a mobile base (86), a left arm (80) with a gripper, and a right arm (82) with a gripper. Figure 7B-7K , such robots (78) have been used to address certain challenges, such as Figure 7B-7K The sequence of steps of approaching a pile of towels (88) on a first table (92), selecting a single towel (90), and folding the single towel (90) on a second table (93) is described below. Fig. 8A , shows an event graph in which such a robot may be configured to execute a series of events (eg, events E1-E10) in sequence to fold a towel. Figure 8B A list of related event sequences (96) is shown to show that events E1-E10 are processed in order. Figure 8C , a process diagram is shown to show that the seemingly at least somewhat complex task of folding towels can be solved by a series of steps, such as powering up the system, preparing it and placing it on a first laundry table (102), identifying and picking up a single towel on the first table (104), identifying a first corner of the single towel (106), identifying a second corner of the selected towel (108), moving to a second table (110), applying tension between two adjacent corners of the towel and dragging the towel onto the table for folding (112), making a first fold of the towel (114), making a second fold of the towel (116), picking up the twice-folded towel and moving it to a stacking position on the second table (118), and finally flattening the folded towel (120). The system utilizes a series of events executed in a single thread to perform the human-scale challenge of folding towels. However, getting such a system to complete such a challenge requires a great deal of programming and experimentation, and typically runs much slower than a human with only minimal attention to simple tasks.

[0011] refer to Figure 9A-10 , illustrates another at least somewhat complex challenge in which a small robotic system, such as a TurtleBot (RTM) (126), can be programmed and prepared using machine learning techniques to utilize a LIDAR scanner device (130) and a mobile base (132) to scan for obstacles (134) and to successfully navigate a real room (136) at runtime based on training using a synthetic environment (122) with synthetic obstacles (124) and simulation of the LIDAR scanning capability (128) for learning purposes. For example, referring to Fig.10, robot and sensor hardware can be selected for the navigation challenge (140); a goal can be set for a reinforcement learning approach (i.e., have the robot autonomously reach a specified goal at a certain X / Y coordinate in a maze defined by walls / objects placed on a substantially flat surface) (142); a synthetic training environment can be created so that a synthetic robot can synthetically / autonomously explore the synthetic maze to repeatedly reach various specified goal locations (144); and at runtime, an actual robot can use the trained convolutional neural network (“CNN”) to navigate an actual maze or room with the goal of reaching an actual pre-selected goal in the room (146). Thus, certain machine learning techniques can also be used to solve computational challenges, such as relatively single-threaded sequences of smaller decisions to navigate a maze or room obstacle, but access to such solutions remains limited and suboptimal, typically requiring extensive computational, sensor, robotic, etc. knowledge.

[0012] There remains a need for computing techniques and configurations that assist users in completing highly complex and sophisticated human tasks in a scalable and efficient manner. Systems, methods, and configurations for enhancing interactivity between human users and computing resources are described herein for various purposes, including but not limited to computing systems, methods, and configurations having one or more synthetic computing interface operators configured to assist applications and control associated resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1A and 1B Various aspects of the computing interface are shown.

[0014] Figure 2 and Figure 3 Various aspects of the computing interface are shown.

[0015] Figure 4 Various aspects of a hypothetical engineering project process are shown.

[0016] Figure 5 Various aspects of a hypothetical music project process are shown.

[0017] Fig. 6A and 6B Various aspects of the paradigm of utilizing human resources to achieve goals or objectives are shown.

[0018] Figures 7A-7K 8A-8C illustrate various aspects of the complexity that may be involved in accomplishing a task or goal by a computer-based robotic system.

[0019] Figures 9A-9C Various aspects of an electromechanical configuration that may be used to navigate and / or map an environment are shown.

[0020] Fig.10Various aspects of a process configuration for utilizing an electromechanical system for navigation to achieve a goal such as maze navigation are shown.

[0021] Fig.11A -B, 12A-D, 13A-13C and 14A-E, 15A-B and 16 illustrate various aspects of configurations in which relatively simple line drawings can be utilized to assist an automated system in producing more detailed art or graphic products.

[0022] Fig.17A -G and 18A-G illustrate various aspects of automated design configurations and process examples in which the subject computerized configurations may be used to improve complex products (e.g., shoes, automobiles, or parts thereof).

[0023] Figures 19A-19D 20A-20C illustrate various aspects of a convolutional neural network configuration that can be used to assist in solving complex problems.

[0024] Figures 21A-21C , 22, 23A-23C, and 24A-24C illustrate various complex configuration variations that may be used to help solve complex problems (eg, problems typically solved by teams of humans).

[0025] Fig.25 , 26 and 27A-27B illustrate various aspects of interfaces that may be used to assist users in providing feedback and controlling issues related to team functionality, cost, and time domains.

[0026] Figures 28A-28C , 29A-29C, 30A-30D and 31 illustrate various aspects of system configurations that can be used to precisely control computer processing to solve complex challenges that are typically solved by human teams. Summary of the invention

[0027] One embodiment relates to a synthetic engagement system for process-based problem solving, comprising: a computing system including one or more operably coupled computing resources; and a user interface operated by the computing system, the user interface configured to engage a human operator based at least in part on one or more specific facts, according to a predetermined process configuration toward a given requirement; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to implement the predetermined process configuration, and return the result selected to at least partially meet the given requirement to the human operator; and wherein each of the one or more synthetic operators is informed by a convolutional neural network, which is informed at least in part by historical actions of a particular actual human operator. The one or more specific facts may be selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration. The one or more specific facts may include text information related to specific context information from historical storage. The one or more specific facts may include text information related to an actual operator. The one or more specific facts may include text information related to a synthetic operator. The specific facts may include a predetermined profile of the specific facts developed as a startup module for a specific synthetic operator profile. One or more operably coupled computing resources may include a local computing resource. The local computing resource may be selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource. The local computing resource may include an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array. One or more operably coupled computing resources may include a resource selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location. The system may also include a positioning element, which is operably coupled to the computing system and configured to determine the position of a human operator relative to a global coordinate system. The positioning element may be selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor. One or more operably coupled computing resources may be activated based on the determined position of the human operator. The user interface may include a graphical user interface. The user interface may include an audio user interface. The graphical user interface may be configured to engage a human operator using elements selected from the group consisting of: computer graphics engaging the display, video graphics engaging the display, and audio engaging accompanying the displayed graphics.The graphical user interface may include a video graphic participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which a human operator can converse. The video interface participant character may be selected from the group consisting of: a humanoid character, an animal character, and a cartoon character. The user interface may be configured to allow the human operator to select a visual presentation feature of the video interface participant character, the visual presentation feature selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape. The visual presentation of the video interface participant character may be modeled after a selected actual human. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character, the audio presentation aspects selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range. One or more audio presentation aspects of the video interface participant character may be modeled after a selected actual human. The predetermined process configuration may include a finite set of steps by which the participation will further achieve the established requirements. The predetermined process configuration may include process elements selected from the group consisting of: one or more general operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings. A limited set of steps includes steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design. The predetermined process configuration may include a human operator's selection of elements. The human operator's selection of elements may include selecting synthetic operator resources for one or more aspects of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific part of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a second specific part of the predetermined process configuration, which is different from the specific resources for the first specific part of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific part of the predetermined process configuration based on multiple synthetic operator roles. Each of the multiple synthetic operator roles can be applied sequentially to the first specific part. Each of the multiple synthetic operator roles can be applied to the first specific part simultaneously. The system can be configured to allow a human operator to specify that specific resources for a first specific portion of a predetermined process configuration are based on one or more hybrid composite operator roles. The one or more hybrid composite operator roles can include a combination of other individual composite operator roles that can be applied to the first specific portion simultaneously.The convolutional neural network may be informed using input from a training data set, the training data set including data related to historical actions of a particular actual human operator. The convolutional neural network may be informed using a supervised learning model using input from the training data set. The convolutional neural network may be informed using analysis of established requirements using a reinforcement learning model using input from the training data set. Each of the one or more synthetic operators may be informed by a convolutional neural network informed at least in part by a curated selection of synthetic action records related to synthetic actions of actual human operators. Each of the one or more synthetic operators may be informed by a convolutional neural network informed at least in part by a curated selection of synthetic action records related to synthetic operations of the synthetic operators. The computing system may be configured to separate each of a finite set of steps from an execution step during which the one or more synthetic operators are configured to implement established requirements according to one or more execution behaviors associated with the associated convolutional neural network. At least one of the one or more execution behaviors may be based on project leadership influence on the associated convolutional neural network. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to project manage the completion of the plurality of tasks to achieve one or more milestones of the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at one or more stages of the execution steps. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at the end of each execution step for consideration in each of a finite set of steps in the process configuration. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally present the updates using a user interface operated by the computing system for consideration by a human operator. Based at least in part on at least one execution behavior influenced by the project leadership, the computing system may be further configured to incorporate instructions from a human operator regarding updates to the presentation as limited steps of the process configuration proceed, utilizing a user interface operated by the computing system. The user interface may be configured to allow the human operator to pause the computing system while it otherwise implements the predetermined process configuration so that the human operator can examine one or more intermediate results related to the established requirements. The user interface may be configured to allow the human operator to change one or more aspects of one or more specific facts during the pause of the computing system to facilitate forward execution based on the change.The user interface is configured to provide a human operator with resource costs calculated based at least in part on utilization of operably coupled computing resources in a predetermined process configuration.

[0028] Another embodiment relates to a synthetic engagement system for process-based problem solving, comprising: a computing system including one or more operably coupled computing resources; a user interface operated by the computing system, the user interface configured to engage a human operator based at least in part on one or more specific facts, according to a predetermined process configuration toward a given requirement; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to collaboratively implement the predetermined process configuration and return the results selected to at least partially meet the given requirement to the human operator; and wherein each of the two or more synthetic operators is informed by a convolutional neural network, which is informed at least in part by historical actions of a particular actual human operator. The one or more specific facts may be selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration. The one or more specific facts may include text information related to specific context information from historical storage. The one or more specific facts may include text information related to an actual operator. The one or more specific facts may include text information related to a synthetic operator. The specific facts may include a predetermined profile of the specific facts developed as a startup module for a specific synthetic operator profile. One or more operably coupled computing resources may include a local computing resource. The local computing resource may be selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource. The local computing resource may include an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array. One or more operably coupled computing resources may be a resource selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location. The system may also include a positioning element, which is operably coupled to the computing system and is configured to be the position of a human operator relative to a global coordinate system. The positioning element may be selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor. One or more operably coupled computing resources may be activated based on the determined position of the human operator. The user interface may include a graphical user interface. The user interface may include an audio user interface. The graphical user interface may be configured to engage a human operator using elements selected from the group consisting of: computer graphics engaging the display, video graphics engaging the display, and audio engaging accompanying the displayed graphics.The graphical user interface may include a video graphic participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which a human operator can converse. The video interface participant character may be selected from the group consisting of: a humanoid character, an animal character, and a cartoon character. The user interface may be configured to allow the human operator to select a visual presentation of the video interface participant character. The user interface may be configured to allow the human operator to select a visual presentation feature of the video interface participant character, the visual presentation feature selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape. The visual presentation of the video interface participant character may be modeled after the selected actual human. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character, the audio presentation aspects selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range. One or more audio presentation aspects of the video interface participant character may be modeled after the selected actual human. The predetermined process configuration may include a limited set of steps by which the participation will further achieve the established requirements. The predetermined process configuration may include process elements selected from the group consisting of: one or more general operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings. The limited set of steps may include steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design. The predetermined process configuration may include the selection of elements by a human operator. The selection of elements by a human operator may include selecting synthetic operator resources for one or more aspects of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific part of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a second specific part of the predetermined process configuration, which is different from the specific resources for the first specific part of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific part of the predetermined process configuration based on multiple synthetic operator roles. Each of the multiple synthetic operator roles may be applied sequentially to the first specific part. Each of the multiple synthetic operator roles may be applied to the first specific part simultaneously. The system can be configured to allow a human operator to specify that specific resources for a first specific portion of a predetermined process configuration are based on one or more hybrid composite operator roles. The one or more hybrid composite operator roles can include a combination of other individual composite operator roles that can be applied to the first specific portion simultaneously.The convolutional neural network may be informed using input from a training data set, the training data set including data related to historical actions of a particular actual human operator. The convolutional neural network may be informed using a supervised learning model using input from the training data set. The convolutional neural network may be informed using analysis of established requirements using a reinforcement learning model using input from the training data set. Each of the two or more synthetic operators may be informed by a convolutional neural network informed at least in part by a curated selection of synthetic action records related to synthetic actions of one or more actual human operators. Each of the two or more synthetic operators may be informed by a convolutional neural network informed at least in part by a curated selection of synthetic action records related to synthetic actions of one or more synthetic operators. The computing system may be configured to separate each of a finite set of steps from an execution step during which the two or more synthetic operators are configured to implement the established requirements according to one or more execution behaviors associated with the associated convolutional neural network. At least one of the one or more execution behaviors may be based on project leadership influence on the associated convolutional neural network. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to project manage the completion of the plurality of tasks to achieve one or more milestones of the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at one or more stages of the execution steps. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at the end of each execution step for consideration in each of a finite set of steps in the process configuration. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally present the updates using a user interface operated by the computing system for consideration by a human operator. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to incorporate instructions from a human operator regarding updates to the presentation as the limited steps of the process configuration proceed, utilizing a user interface operated by the computing system. The user interface may be configured to allow the human operator to pause the computing system while it otherwise implements the predetermined process configuration so that the human operator can examine one or more intermediate results related to the established requirements. The user interface may be configured to allow the human operator to change one or more aspects of the one or more specific facts during the pause of the computing system to facilitate forward execution based on the changes.The user interface is configured to provide a human operator with a resource cost calculated based at least in part on the utilization of computational resources that can be operably coupled in a predetermined process configuration. The system can be configured to allow a human operator to specify that two or more composite operators are different. The system can be configured to allow a human operator to specify that two or more composite operators are the same, and can be configured to collaboratively expand their productivity when they execute the predetermined process configuration. Two or more composite operators can be configured to automatically optimize their application as resources when they implement the predetermined process configuration. The system can be configured to utilize two or more composite operators, at least in part based on the characteristics of the two or more composite operators, to generate an initial decision node group related to a given requirement. The system can be further configured to create an intermediary (mediated) decision node group based on the initial decision node group. The system can be further configured to create an operational decision node group based on the intermediary decision node group. The two or more composite operators are operated by the computing system to collaboratively implement the predetermined process configuration by sorting the operational decision nodes to implement the given requirements. The two or more composite operators can include a plurality that are limited only by the computational resources that can be operably coupled.

[0029] Another embodiment relates to a synthetic participation method for process-based problem solving, including: providing a computing system including one or more operably coupled computing resources; and using the computing system to present a user interface, the user interface is configured to enable a human operator to participate based at least in part on one or more specific facts, according to a predetermined process configuration toward a given requirement; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to achieve the predetermined process configuration, and return the results selected to at least partially meet the given requirements to the human operator; and wherein each of the one or more synthetic operators is informed by a convolutional neural network, which is informed at least in part by historical actions of a specific actual human operator. The one or more specific facts can be selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration. The one or more specific facts can include text information related to specific background information from historical storage. The one or more specific facts can include text information related to the actual operator. The one or more specific facts can include text information related to the synthetic operator. The specific facts may include a predetermined profile of the specific facts developed as a launch module for a specific synthetic operator profile. One or more operably coupled computing resources may include a local computing resource. The local computing resource may be selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource. The local computing resource may include an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array. One or more operably coupled computing resources may include a resource selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location. The method may also include operably coupling a positioning element to the computing system, the positioning element being configured to determine the position of a human operator relative to a global coordinate system. The positioning element may be selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor. The method may also include activating one or more operably coupled computing resources based on the determined position of the human operator. Presenting a user interface may include presenting a graphical user interface. Presenting a user interface includes presenting an audio user interface. Presenting the graphical user interface may include engaging a human operator using elements selected from the group consisting of: computer graphics engaging the display, video graphics engaging the display, and audio engaging accompanying the displayed graphics.Presenting a graphical user interface may include presenting a video graphical participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which a human operator can converse. The video interface participant character may be selected from the group consisting of: a humanoid character, an animal character, and a cartoon character. The user interface may be configured to allow the human operator to select a visual presentation feature of the video interface participant character, the visual presentation feature selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape. The visual presentation of the video interface participant character may be modeled after a selected actual human being. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character, the audio presentation aspects selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range. One or more audio presentation aspects of the video interface participant character may be modeled after a selected actual human being. The predetermined process configuration may include a finite set of steps by which the participation will further achieve the established requirements. The predetermined process configuration may include process elements selected from the group consisting of: one or more general operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings. The limited set of steps includes steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design. The predetermined process configuration may include a selection of elements by a human operator. The selection of elements by a human operator may include selecting synthetic operator resources for one or more aspects of the predetermined process configuration. The user interface may be configured to allow a human operator to specify specific resources for a first specific portion of the predetermined process configuration. The user interface may be configured to allow a human operator to specify specific resources for a second specific portion of the predetermined process configuration, the specific resources for the second specific portion of the predetermined process configuration being different from the specific resources for the first specific portion of the predetermined process configuration. The user interface may be configured to allow a human operator to specify that the specific resources for the first specific portion of the predetermined process configuration are based on multiple synthetic operator roles. The method may also include applying each of the multiple synthetic operator roles sequentially to the first specific portion. The method may also include applying each of the multiple synthetic operator roles simultaneously to the first specific portion. The user interface may be configured to allow a human operator to specify that a particular resource for a first particular portion of a predetermined process configuration is based on one or more hybrid composite operator roles.The one or more hybrid synthetic operator roles may include a combination of other individual synthetic operator roles that may be applied to the first specific portion simultaneously. The convolutional neural network may be informed using input from a training data set, the training data set including data related to historical actions of a specific actual human operator. The convolutional neural network may be informed using a supervised learning model using input from the training data set. The convolutional neural network may be informed using input from the training data set using analysis of established requirements using a reinforcement learning model. Each of the one or more synthetic operators may be informed by a convolutional neural network that is informed at least in part by a curated selection of synthetic action records related to synthetic actions of actual human operators. Each of the one or more synthetic operators may be informed by a convolutional neural network that is informed at least in part by a curated selection of synthetic action records related to synthetic operations of the synthetic operators. The computing system may be configured to separate each of a finite set of steps from an execution step during which the one or more synthetic operators are configured to implement established requirements according to one or more execution behaviors associated with the associated convolutional neural network. At least one of the one or more execution behaviors may be based on project leadership influence on the associated convolutional neural network. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to project manage the completion of the plurality of tasks to achieve one or more milestones of the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at one or more stages of the execution steps. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at the end of each execution step for consideration in each of a finite set of steps in the process configuration. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally present updates for consideration by a human operator utilizing a user interface operated by the computing system. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to incorporate instructions from a human operator regarding the presented updates as the limited steps of the process configuration proceed, utilizing a user interface operated by the computing system. The user interface may be configured to allow the human operator to pause the computing system while it is otherwise implementing the predetermined process configuration so that the human operator can inspect one or more intermediate results related to the established requirements.The user interface may be configured to allow a human operator to change one or more aspects of one or more specific facts during a pause of the computing system to facilitate forward execution based on the changes. The user interface is configured to provide the human operator with resource costs calculated at least in part based at least in part on utilization of computing resources operably coupled in a predetermined process configuration.

[0030] Another embodiment relates to a synthetic participation method for process-based problem solving, comprising: providing a computing system including one or more operably coupled computing resources; presenting a user interface using the computing system, the user interface being configured to engage a human operator based at least in part on one or more specific facts, according to a predetermined process configuration toward a given requirement; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to collaboratively implement the predetermined process configuration and return the results selected to at least partially meet the given requirement to the human operator; and wherein each of the two or more synthetic operators is informed by a convolutional neural network, which is informed at least in part by historical actions of a particular actual human operator. The one or more specific facts may be selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration. The one or more specific facts may include text information related to specific context information from historical storage. The one or more specific facts may include text information related to an actual operator. The one or more specific facts may include text information related to a synthetic operator. The specific facts may include a predetermined profile of the specific facts developed as a launch module for a specific synthetic operator profile. One or more operably coupled computing resources may include a local computing resource. The local computing resource may be selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource. The local computing resource may include an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array. One or more operably coupled computing resources may include a resource selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location. The method may also include operably coupling a positioning element to the computing system, the positioning element being configured to determine the position of a human operator relative to a global coordinate system. The positioning element may be selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor. The method may also include activating one or more operably coupled computing resources based on the determined position of the human operator. Presenting a user interface may include presenting a graphical user interface. Presenting a user interface may include presenting an audio user interface. Presenting the graphical user interface may include engaging a human operator using elements selected from the group consisting of: computer graphics engaging the display, video graphics engaging the display, and audio engaging accompanying the displayed graphics.Presenting a graphical user interface may include presenting a video graphic participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which a human operator can converse. The video interface participant character may be selected from the group consisting of: a humanoid character, an animal character, and a cartoon character. The user interface may be configured to allow the human operator to select a visual presentation feature of the video interface participant character, the visual presentation feature selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape. The visual presentation of the video interface participant character may be modeled after the selected actual human being. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character, the audio presentation aspects selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range. One or more audio presentation aspects of the video interface participant character may be modeled after the selected actual human being. The predetermined process configuration may include a finite set of steps by which the participation will further achieve the established requirements. The predetermined process configuration may include process elements selected from the group consisting of: one or more general operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings. The limited set of steps may include steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design. The predetermined process configuration may include a human operator's selection of elements. The human operator's selection of elements may include selecting synthetic operator resources for one or more aspects of the predetermined process configuration. The user interface may be configured to allow a human operator to specify specific resources for a first specific portion of the predetermined process configuration. The user interface may be configured to allow a human operator to specify specific resources for a second specific portion of the predetermined process configuration, which is different from the specific resources for the first specific portion of the predetermined process configuration. The user interface may be configured to allow a human operator to specify that the specific resources for the first specific portion of the predetermined process configuration are based on multiple synthetic operator roles. The method may also include applying each of the multiple synthetic operator roles sequentially to the first specific portion. The method may include applying each of the multiple synthetic operator roles simultaneously to the first specific portion. The user interface can be configured to allow a human operator to specify that a particular resource for a first particular portion of a predetermined process configuration is based on two or more hybrid composite operator roles. The two or more hybrid composite operator roles include a combination of other individual composite operator roles that can be applied to the first particular portion simultaneously.The convolutional neural network may be informed using input from a training data set, the training data set including data related to historical actions of a particular actual human operator. The convolutional neural network may be informed using a supervised learning model using input from the training data set. The convolutional neural network may be informed using analysis of established requirements using a reinforcement learning model using input from the training data set. Each of the two or more synthetic operators may be informed by a convolutional neural network informed at least in part by a curated selection of synthetic action records related to synthetic actions of actual human operators. Each of the two or more synthetic operators may be informed by a convolutional neural network informed at least in part by a curated selection of synthetic action records related to synthetic operations of the synthetic operators. The computing system may be configured to separate each of a finite set of steps from an execution step during which one or more synthetic operators are configured to implement established requirements according to one or more execution behaviors associated with the associated convolutional neural network. At least one of the one or more execution behaviors may be based on project leadership influence on the associated convolutional neural network. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to project manage the completion of the plurality of tasks to achieve one or more milestones of the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at one or more stages of the execution steps. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at the end of each execution step for consideration in each of a finite set of steps in the process configuration. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally present the updates using a user interface operated by the computing system for consideration by a human operator. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to incorporate instructions from a human operator regarding the presented updates using a user interface operated by the computing system as the limited steps of the process configuration proceed. The user interface may be configured to allow the human operator to pause the computing system while it is otherwise implementing the predetermined process configuration so that the human operator can examine one or more intermediate results related to the established requirements. The user interface may be configured to allow the human operator to change one or more aspects of the one or more specific facts during the pause of the computing system to facilitate forward execution based on the change.The user interface is configured to provide a human operator with a resource cost calculated based at least in part on the utilization of the computational resources operably coupled in a predetermined process configuration. Two or more composite operators may be configured to automatically optimize their application as resources when they implement the predetermined process configuration. The system may be configured to generate an initial decision node group associated with a given requirement using two or more composite operators, based at least in part on the characteristics of the two or more composite operators. The system may be further configured to create an intermediate decision node group based on the initial decision node group. The system may be further configured to create an operational decision node group based on the intermediate decision node group. Two or more composite operators are operated by the computing system to collaboratively implement the predetermined process configuration by sorting the operational decision nodes to implement the given requirement. The two or more composite operators may include a plurality that is limited only by the computational resources operably coupled. DETAILED DESCRIPTION

[0031] refer to Figure 11A-16 , the relatively simple challenge of creating a color cartoon is used to illustrate a synthesis operator configuration by which the user can leverage large computational resources to tackle the challenge.

[0032] See also Fig.11A , the figure shows an "Andy" cartoon character (150) which consists of a relatively simple wireframe. Fig. 11B , the basic structure of the character can be represented using a stick figure or a set of segments (152), where the segments represent the positions of the character's head (154), neck (156), shoulders (158), left arm (160), right arm (162), torso (164), hips (166), left leg (168), and right leg (170). See Figures 12A-12D For example, a very simple cartoon sequence may include a series of views: the character (150) stands up, raises his right hand (160), lowers his right hand, and then raises his left hand (162). Fig.13A , suppose a user wishes to have a computing system automatically generate a series of cartoon images and colorize those images so that they can be viewed as simple color cartoons in sequence (172). The user may provide requirements such that the user wishes the cartoon character "Andy" to do some simple arm movements against a generic outdoor background in "old-style cartoon form" with "basic colors" to keep Andy in black and white; "VGA frames (640x480) are fine"; "total length is 30 seconds" (174). The computing system may be configured to obtain certain specific facts from the input and searches performed, such as:

[0033] "Andy" is a generic boy character and samples can be obtained through search; "old cartoon" form can be interpreted from other searched references at approximately 25 frames per second; "generic outdoor background" can be interpreted based on available benchmarks as a line of cartoon ground with a simple cloud in the sky; "basic coloring" of these can be interpreted based on similar reference benchmarks as green ground, blue sky, white clouds (176). The system can configure certain process configurations to meet the challenges, such as: utilizing a stick figure type configuration and waypoints or benchmarks developed according to user instructions; importing the Andy generic configuration; inserting the Andy character sketch for waypoints to obtain enough frames to move smoothly at 25 frames per second for 30 seconds (750 frames total); exporting the black and white 30 second viewer to the user for approval; after approval, colorizing the 750 frames and returning the final product to the user (178). The system can provide resources such as a standard desktop computer connected to Internet resources, a general AI for user communication and basic information acquisition, and a synthesis operator configuration designed to execute and return results to the user (180). By utilizing these instructions, requirements, facts, process configurations, and resources, a composition operator may be configured to work through a sequence (such as a single-threaded type of sequence as shown herein) to execute and return results at runtime (182).

[0034] refer to Fig. 13B and 13C , the operations of the illustrative synthesis operators may be decomposed at a finer granularity. For example, the challenge may be addressed by selecting a first relatively "narrowband" synthesis operator operably coupled to a computing resource that may be configured through training (e.g., through neural network training) to do no more than (i.e., narrow training / narrowband; i.e., such a configuration may only be able to perform functional skills based on training to complete this type of narrow task) generate a sequence of wireframe sketches (184) of a simple character (e.g., Andy) by interpolating between endpoints or waypoints. Four endpoints may be received (Andy stands up straight; Andy raises his left hand; Andy resumes standing straight; Andy raises his right hand) and instructions to smoothly pass through the waypoints at a rate of 25 frames per second for 30 seconds (i.e., 750 frames; 4 benchmarks) (186). The narrowband synthesis operator may be configured to simply digitally interpolate (i.e., average between the two) to create 750 frames of black and white images (188). The synthesis operator may be configured to return a stack of 750 black and white digital images to the user for review and approval (190).

[0035] refer to Fig. 13C , upon approval Fig.13AAfter the image in (190), a different narrow-band synthesis operator (e.g., trained only to simply provide the most basic colorization of a wireframe sketch based on a simple input) can be used to perform (198) colorization on the image (192) using the provided basic input (194) and the black and white wireframe (196), and the result is returned to the user (200).

[0036] Therefore, reference Fig.14A , the synthetic operator (212) can be considered as a synthetic character with certain human-like capabilities and presented to a human user through a user interface. Depending on the configuration and challenge, the synthetic operator can be configured to communicate with the user (208), such as through natural language general AI for verbal instructions, typed instructions, direct computing interface-based commands, etc. The relevant system can be configured to assist the user in providing requirements related to the challenge (202), providing specific facts related to the challenge (204), coupling with computing resources (206), and receiving certain process configurations related to the challenge (210).

[0037] For example, with Fig.14AAn embodiment related to the embodiment shown in may include a synthetic participation system for process-based problem solving, including: a computing system including one or more operably coupled computing resources; and a user interface operated by the computing system, the user interface being configured to enable human operators to participate based at least in part on one or more specific facts, according to a predetermined process configuration toward a given requirement; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to achieve the predetermined process configuration, and return the results selected to at least partially meet the given requirements to the human operator; and wherein each of the one or more synthetic operators is informed by a convolutional neural network, which is informed at least in part by historical actions of a specific actual human operator. The one or more specific facts may be selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration. The one or more specific facts may include text information related to specific background information from historical storage. The one or more specific facts may include text information related to the actual operator. The one or more specific facts may include text information related to the synthetic operator. The specific facts may include a predetermined profile of the specific facts developed as a launch module for a specific synthetic operator profile. One or more operably coupled computing resources may include a local computing resource. The local computing resource may be selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource. The local computing resource may include an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array. One or more operably coupled computing resources may include a resource selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and a computing system assembly at a remote location. The system may also include a positioning element, which is operably coupled to the computing system and configured to determine the position of a human operator relative to a global coordinate system. The positioning element may be selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor. One or more operably coupled computing resources may be activated based on the determined position of the human operator. The user interface may include a graphical user interface. The user interface may include an audio user interface. The graphical user interface may be configured to enable human operator participation using elements selected from the group consisting of: computer graphics participation display; video graphics participation display; and audio participation accompanying the displayed graphics.The graphical user interface may include a video graphical participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which a human operator can converse. The video interface participant character may be selected from the group consisting of: a humanoid character, an animal character, and a cartoon character. The user interface may be configured to allow the human operator to select a visual presentation of the video interface participant character. The user interface may be configured to allow the human operator to select a visual presentation feature of the video interface participant character selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape. The visual presentation of the video interface participant character may be modeled after the selected actual human being. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character. The user interface may be configured to allow the human operator to select one or more audio presentation aspects of the video interface participant character from the group consisting of: character voice intonation; character voice loudness; character speaking language; character speaking dialect; and character voice dynamic range. One or more audio presentation aspects of the video interface participant character may be modeled after the selected actual human being. The predetermined process configuration may include a limited set of steps through which the engagement will further achieve the established requirements. The predetermined process configuration may include process elements selected from the group consisting of: one or more common operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings. The limited set of steps may include steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design. The predetermined process configuration may include a human operator's selection of elements. The human operator's selection of elements may include: selecting synthetic operator resources for one or more aspects of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific portion of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a second specific portion of the predetermined process configuration, and the specific resources for the second specific portion of the predetermined process configuration are different from the specific resources for the first specific portion of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific portion of the predetermined process configuration based on multiple synthetic operator roles. Each of the multiple synthetic operator roles may be applied sequentially to the first specific portion. Each of the multiple synthetic operator roles may be applied simultaneously to the first specific portion. The system can be configured to allow a human operator to specify that specific resources for a first specific portion of a predetermined process configuration are based on one or more hybrid composite operator roles. The one or more hybrid composite operator roles can include a combination of other individual composite operator roles that can be applied to the first specific portion simultaneously.The convolutional neural network may be informed using input from a training data set, the training data set including data related to historical actions of a particular actual human operator. The convolutional neural network may be informed using a supervised learning model using input from the training data set. The convolutional neural network may be informed using analysis of the established requirements using a reinforcement learning model. Each of the one or more synthetic operators may be informed by a convolutional neural network, the convolutional neural network being informed at least in part by a curated selection of synthetic action records related to synthetic actions of actual human operators. Each of the one or more synthetic operators may be informed by a convolutional neural network, the convolutional neural network being informed at least in part by a curated selection of synthetic action records related to synthetic actions of the synthetic operators. The computing system may be configured to separate each of a finite set of steps from an execution step during which the one or more synthetic operators are configured to achieve the established requirements according to one or more execution behaviors associated with the associated convolutional neural network. At least one of the one or more execution behaviors may be based on a project leadership influence on the associated convolutional neural network. Based at least in part on at least one execution behavior based on the project leadership influence, the computing system may be configured to divide the execution step into a plurality of tasks that may be processed by available resources to achieve the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to project manage the completion of a plurality of tasks to achieve one or more milestones in the established requirements. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of a plurality of tasks at one or more stages of the execution steps. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to complete updates related to the completion of a plurality of tasks at the end of each execution step for consideration in each of a limited set of steps in the process configuration. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to functionally present updates for consideration by a human operator using a user interface operated by the computing system. Based at least in part on at least one execution behavior based on project leadership influence, the computing system may be further configured to incorporate instructions from a human operator related to the presented updates using a user interface operated by the computing system as the limited steps of the process configuration continue. The user interface may be configured to allow a human operator to pause the computing system while it is otherwise implementing a predetermined process configuration so that the human operator can examine one or more intermediate results related to a given requirement. The user interface may be configured to allow a human operator to change one or more aspects of one or more specific facts during the pause of the computing system to facilitate forward execution based on the change.The user interface may be configured to provide a human operator with resource costs calculated based at least in part on utilization of computing resources operably coupled in a predetermined process configuration.

[0038] Figures 14B-14E Further details of these components are shown in relation to various hypothetical problems or challenge scenarios. Fig. 14B , the user's requirements for the synthesis operator (202) may include: general project constraints (time window, specifications for the synthesis operator, resources available to the synthesis operator, I / O, interaction, or communication model with the synthesis operator in the time and schedule domains); specific project constraints (goal / objective details, what is important in the solution, which features of the synthesis operator may be most important, specific facts or inputs to be prepared and loaded and / or immediately provided to the synthesis operator); and specific operational constraints (nuances / shadow inputs related to specific solution problems, AI presence and adjustments, launch and perturbation presence and adjustments, target market / domain / cultural adjustments).

[0039] refer to Fig. 14C The interconnected resources (206) may include one or more desktop or laptop type computing systems (230), one or more interconnected datacenter type computing assemblies (232), and smaller computing systems, such as those used in mobile systems or "edge" or "internet of things" (IoT) (234) computing configurations.

[0040] refer to Fig.14D , the specific facts (204) provided may include, for example, specific inputs indicated by the user to assist in the process and solution and to be loaded into the synthesis operator and / or immediately provided to the synthesis operator (i.e., in the presence of a computing RAM type); specific background information from historical storage (e.g., the complete work of the Beatles; Bureau of Labor Statistics data for the past 25 years; academic publications of a particular group; detailed drawings of each generation of the Ford Mustang; a significant publishing analysis of Max Martin and the most successful singles in pop music; a detailed electronic configuration and sales cost analysis of the top 100 consumer electronic products in the past decade); and specific facts or inputs related to past actual operators or other synthesis operators (a case study of Andy Grove's persona and technical leadership approach; Elon Musk's risk-taking profile; Paul McCartney's personality aspects as seen through his growth and evolution as a musician; Matt Chamberlain's drumming style on Fiona Apple's Tidal album; the sound characteristics of a typical 1959 Les Paul guitar through vintage electronics and speakers).

[0041] refer to Fig.14E, the process configuration (210) indicated by the user and / or supervisory role may include, for example: general operating parameters (i.e., how the supervisor wishes to work with the synthetic operator ("SO") to complete this engagement / challenge; the SO may generally be configured to operate at high frequency, 24x7 relative to human scale and human factors; supervisor adjustable preferences may be no more than 1 output / engagement per workday; supervisor adjustable I / O for engagement may be configured to include outline reports, emails, natural language audio summary updates, visuals; explicit limits on SO's authority); resource / input awareness and utilization (i.e., the SO needs to be appropriately loaded, connected to, and / or ready to utilize information, management, and computing resources, including project inputs and I / O from the supervisor); domain expertise modules (business, music, finance, etc.; level and depth of expertise; SOs may be specifically configured or pre-configured for various types of expertise and role expectations; thus, the CFO SOs may be pre-configured to have a general understanding of GAAP accounting practices, U.S. securities offerings, and financial statements; a drummer musician SO may be pre-configured to have a general understanding of American music, how drums are typically used to set the tone for a band, and how bass drums are typically used with snare drums; these may be adjusted by the supervisor, for example, through project inputs provided to the SO); sequencing paradigm (specific to the domain and level of expertise; i.e., there may be a potential expected sequence when the SO is typically building towards a solution in a given domain, and this may be adjusted by the supervisor; for example, rearview mirror shape may not be the first expected outcome of a project to design the next successful Ford Mustang, and a drum solo may not be the first expected outcome of a project to write the next hit single); loop / iteration paradigm Examples include priming and perturbation configuration (domain and expertise level specific; i.e., there may often be an underlying expected loop / iteration paradigm when an SO builds a solution in a given domain, and this may be adjusted by the supervisor; e.g., it may not be helpful for an SO to return to 1,000 song melody iterations every day on a project to write the next hit single; priming and perturbation configurations may be adjustable and may be important to bridge gaps or pauses, kick off tasks or subtasks, or introduce enough perturbations to prevent reaching steady states too early in the process); and / or AI utilization and configuration (AI, neural networks, deep networks, and / or training datasets can be used in nearly every process and exchange, but a balance may need to be maintained to avoid too much AI insertion).

[0042] refer to Fig.15A , shows the event flow (236) of the related cartoon challenge, where a series of events (E1-E10) can be used to sequentially complete the process of returning a color image stack to the user for presentation as a short cartoon. Fig. 15BThe relevant simplified sequence of events (238) is shown to again demonstrate that the cartoon challenge can be accomplished through a series of smaller challenges and requires the participation of a properly resourced and guided compositing operator in an effective manner. For example, referring to Fig.16 , showing the specific involvement steps of the synthesis operator. The synthesis operator integration system can be powered on and ready to receive instructions from the user (252). The user can make requests through user input devices, such as general natural language AI and / or other synthesis operator communication interactions.

[0043] The Andy cartoon is in old-fashioned cartoon form, with basic colors, VGA, and a duration of approximately 30 seconds (254). The compositing operator may be configured to interpret the requirements (old-fashioned cartoon form; basic colors; generic outdoor background, VGA, simple arm movements) to identify specific facts, process configurations, and resources (256). The compositing operator may be configured to create an execution plan (interpolate wireframes; present to user for approval; upon approval, colorize; return product to user) (258). The compositing operator may use the computing resources to create 750 wireframes by interpolating using the provided endpoints (260). The compositing operator may use the mutually coupled computing resources to present the black and white wireframes to the user for approval (262). If the user approves, such approval may be communicated to the compositing operator, such as via the mutually coupled computing resources (264). The compositing operator (perhaps a different compositing operator that may be better suited for a particular task) may colorize the 750 frames (266) using the mutually coupled computing resources and package them for delivery to the user as a returned final product (270) (268).

[0044] Therefore, a synthetic operator configuration can be utilized to execute certain somewhat complex instructions in order to return results to the user by using a properly trained, informed, and resourced computing system.

[0045] refer to Figures 17A-17G , shows another illustrative example that utilizes synthetic operator configuration to perform challenges that are typically the domain of mechanical or systems engineers. Fig.17AAs shown, in this case, Volkswagen has decided to build a compact electric pickup truck for the U.S. market, and a basic design is needed before the body and exterior can be customized (272). The following requirements can be provided: the vehicle needs to have two rows of seats and four doors; the hood should be 6' and should be able to support a full 8' x 4' sheet of plywood with the tailgate folded down; it should be fully electric; the minimum range should be 200 miles; and the chassis should look like a member of the current Volkswagen product line (274). Resources can be specified and provided, such as: full access to data or computing centers (such as AWS); access to the Internet; and electronic access to relevant specific facts (276). Specific facts can be provided, such as: full access to Volkswagen's historical design documents and all available design documents related to electric drivetrains and related to reliability, maintenance, life, cost, and efficiency; regulatory information related to safety, emissions, weight, and size (278). Process configurations may be provided, such as: Assume that the aerodynamic efficiency of a standard Toyota Tacoma can be improved by up to 15% gain through wind tunnel conditioning; a 4-door upright seat cab is desired; an opening canopy is desired for side / top / rear access; acceleration of a standard Toyota Tacoma is desired; and the user is presented with viable drivetrain and battery chemistry alternatives and basic chassis configurations (280). Finally, the system may be configured to utilize these inputs and resources to execute and present results at runtime (282). Reference Fig. 17B , requirements (202) from the user may include, for example: chassis, drivetrain, and battery chemistry design alternatives are required as primary outputs; the vehicle is a pickup truck configuration and requires a 4-door cab; the pickup truck hood should be at least 6' long and should be able to support a full 8' x 4' sheet of plywood with the tailgate folded down; the drivetrain needs to be fully electric; the vehicle's range should be at least 200 miles; the chassis needs to look like a member of the current Volkswagen product line.

[0046] refer to Fig. 17C , computing resources (206) may include interconnected data centers (232), desktops (230), and edge / IoT type systems, as well as interconnected access to the Internet / network access (240) and electronic access to specific factual data (242).

[0047] refer to Fig.17D , specific facts (204) for a specific challenge might include: full access to VW's historical design documentation and all available design documentation on chassis and suspension design, as well as electric drivetrain and related to reliability, maintenance, life, cost and efficiency; and regulatory information related to safety, emissions, weight, size.

[0048] refer to Fig.17E, a process configuration (210) for a particular challenge might include: as initial process inputs, assuming standard Toyota Tacoma aerodynamic efficiency, but gains of up to 15% can be obtained through wind tunnel-based aerodynamic tuning and optimization; as further key initial process inputs for chassis design: requiring a 4-door cab with upright seats, and an opening roof for side / top / rear entry; from a road performance perspective, requiring acceleration to at least match that of a standard Toyota Tacoma; using these initial inputs, along with searchable resources and specific facts, to develop a list of candidate powertrain, battery chemistry, and chassis alternative combinations; presenting the permutations to the user.

[0049] Therefore, reference Fig.17F During the process, the synthetic operator enabled system can be powered on and ready to receive instructions from the user (284). Through one or more user input devices, such as general purpose natural language AI and / or other synthetic operator interactions, the user can request the powertrain, battery chemistry, and chassis options for a new Volkswagen all-electric truck design, requiring a 4-door upright cab, a lid of at least 6' (capable of accommodating 8' x 4' with the tailgate folded down), a minimum range of 200 miles, and a chassis that looks like a member of the current Volkswagen product line (286). The synthesis operator can be configured to connect available resources (full AWS and internal computing access; full network access; electronic access to specific facts), load specific facts (full access to VW historical design documents and all available design documents related to electric drivetrains and related to reliability, maintenance, life, cost and efficiency; regulatory information related to safety, emissions, weight, size) and process configuration (assumes standard Toyota Tacoma aerodynamic efficiency improvement of up to 15% gain from wind tunnel tuning; requires 4 doors; upright seat cab; requires open canopy for side / top / rear entry; requires acceleration of standard Toyota Tacoma; presents user with viable drivetrain and battery chemistry alternatives and basic chassis configuration) (288). The synthesis operator can be configured to implement an execution plan based on all inputs including the process configuration; utilizes available resources to assemble a list of candidate combinations and permutations of drivetrain, battery chemistry and chassis configurations taking into account all requirements, specific inputs and process configuration (290). Finally, the system can be configured to return the results to the user (292).

[0050] refer to Figure 17G, showing the Synthetic Operator ("SO") centric process for the challenge. With all the inputs for a particular challenge, the SO can be configured to have certain system-level problem solving capabilities (302). The SO can be configured to first record the requirements / goals at a very basic level (e.g., the goal is a candidate for battery chemistry / powertrain / chassis) and develop a basic forward paradigm to achieve the goal based on the prohibited process of using the inputs and resources to achieve the goal (e.g., understand the requirements; use available information to find candidate solutions; analyze candidate solutions; present results) (304). The SO can be configured to search for the aerodynamic efficiency and acceleration of the Toyota Tacoma to better refine the requirements (the CD of the Tacoma is about 0.39; 15% better is about 0.33, which is exactly the CD of the Subaru Forester; the 0-60 acceleration time of the Tacoma is 8.2 seconds) (306). The SO can be configured to search and determine that a pickup truck is a four-wheel vehicle with a tailgate cover in the rear and a four-door cab in the front, and the basic chassis design candidate is obvious, and its CD should be close to the Subaru Forester (308). The SO may be configured to search and determine that the most efficient drivetrain appears to be an electric motor coupled to a single or two speed transmission, and that there are many drivetrains available that should be able to meet the 8.2 second 0-60 requirement (based on a known benchmark estimating the mass of a new car) and 0.33 CD (310). The SO may be configured to search and determine that lithium battery chemistry has superior energy density relative to mass and is used in many electric drivetrains (312). The SO may be configured to roughly calculate estimated range and acceleration based on the overall mass and CD benchmark to present various candidate results (e.g., a heavier battery may provide greater instantaneous current / acceleration, but will reduce range; similarly a larger electric motor may be able to handle more current and produce greater output torque to achieve instantaneous acceleration, but may reduce overall range) (314). Finally, the SO may be configured to present the results to the user (316).

[0051] refer to Figures 18A-18G , shows another illustrative example that utilizes synthetic operator configuration to perform challenges that are typically within the domain of materials engineers. Fig.18A, Nike has decided to design a new forefoot strike / enlarged toe box running shoe for the U.S. market and requires a basic sole design for further industrial design, coloring, and finishing materials, but the final configuration should conform to Nike's design vocabulary (318). User requirements for the Synthetic Operator's augmented system configuration may include: the toe box needs to accommodate the non-laterally compressive foot geometry of 80% of the anthropometric market; the sole ground contact profile should mimic the Nike React Infinity Run v2 (RTM). The Synthetic Operator's resources may include full Amazon Web Services ("AWS") and internal computing access, including solid modeling capabilities based on selected materials and geometries; full network access; and electronic access to specific facts (322). Specific facts for a specific challenge may include: full access to Nike historical design documents and all available design documents related to sole and composite configurations, modulus data, and testing information; a library of mechanical properties and wear information related to injection molded polymers; regulatory information related to safety and hazardous materials; and anthropometric data (i.e., based on actual human anatomy statistics) (324). The configuration of the process for the synthesis operator to address a particular challenge may include: assuming the assembly of an injection molded cushioning material and a structural / traction sole element coupled thereto; presenting to the user possible sole designs and associated geometries along with estimated performance data related to wear and local / bulk modulus (326). Finally, the system may be configured so that the synthesis operator can execute and present the results to the user (328).

[0052] refer to Fig.18B , requirements for a specific challenge (202) may include: requiring a basic sole design as the primary output (before industrial design, color, and decorative materials; ultimately needing to conform to Nike's design vocabulary); the toe box of the sole design needs to accommodate the non-laterally compressive foot geometry of 80% of the anthropometric market; the sole ground contact profile should mimic the Nike React Infinity Run v2 (RTM).

[0053] refer to Fig.18C , computing resources (206) may include interconnected data centers (232), desktops (230), and edge / IoT type systems, as well as interconnected access to the Internet / network (240), electronic access to certain specific fact data (242), and electronic access to computerized solid modeling capabilities for material and geometry dynamics (330).

[0054] refer to Fig.18D, specific facts (204) related to a particular challenge might include: full access to Nike historical design files and all available design files related to sole and composite material configurations, modulus data, and testing information; a library of mechanical properties and wear information related to injection molded polymers; regulatory information related to safety and hazardous materials; and anthropometric data related to the target market population.

[0055] refer to Fig.18E , a process configuration (210) for a particular synthetic operator augmentation scenario may include: as initial process inputs: assuming the assembly of an injection molded cushioning material and a structural / traction sole element coupled thereto; utilizing these initial inputs along with searchable resources and specific facts to develop a list of candidate sole configurations; and presenting the candidate configurations to a user.

[0056] Therefore, reference Fig.18F The process, the synthesis operator enabled system can be powered on and ready to receive instructions from the user (332). Through a user input device (such as a general natural language AI and / or other synthesis operator interaction), the user can request a basic sole design for a forefoot landing / enlarged toe running shoe for the US market (only the basic sole design is requested, and further industrial design, coloring and decorative materials will be performed, but the final sole design should be able to meet the Nike design vocabulary) (334). The synthesis operator can be configured to connect available resources (full AWS and internal computing access; full network access; solid modeling capabilities; electronic access to specific facts), load specific facts (full access to Nike historical design documents and all available design documents related to soles and composite material configurations, modulus data and testing information; mechanical properties and wear information library related to injection molded polymers; regulatory information related to safety and hazardous materials; anthropometric data) and process configuration (assuming the assembly of an injection molded cushioning material and a structural / traction sole element coupled thereto; presenting the user with feasible sole designs and related geometries and estimated performance data related to wear and local / bulk modulus) (336). The synthesis operator can be configured to implement the execution plan based on all inputs including the process configuration; for example, taking into account all requirements, specific inputs and process configurations, assembling a list of candidate sole configurations using available resources (338). Finally, the synthesis operator can be configured to return the results to the user (340).

[0057] refer to Figure 18G, showing the Synthetic Operator ("SO") centric process for this challenge. With all the inputs for a particular challenge, the SO may be configured to have certain system-level problem solving capabilities (352). The SO may be configured to initially record the requirements / goals at a very basic level (e.g.: the goal is a sole shape with two materials) and implement a basic forward paradigm based on the process of enforcing the inputs and resources to achieve the goal (e.g.: understand the requirements; find candidate solutions using available information; analyze candidate solutions; present results) (354). The SO may be configured to search to determine what the toe box inside the shoe is and which geometry is appropriate for 80% of the anthropometric market (356). The SO may be configured to search to determine the sole ground contact profile of the Nike React Infinity Run v2 (RTM) (358). The SO may be configured to search to determine that the controlling factor in the sole design is cushioning performance, and the controlling factor in cushioning performance is related to material modulus, shape, and structural constraints (360). The SO may be configured to determine that the sole ground contact profile is similar to the Nike React Infinity Run v2 (RTM), and the Nike design language provides some surface configurations but generally uses open foam on the side of the shoe, and the main variables in this challenge are the cushioning foam material, its thickness, and the area / shape of the toe box (determined by anthropometric data) (362). The SO may be configured to analyze variations / combinations / permutations of sole assemblies using various cushioning materials and thicknesses (again, working within the range of the sole ground contact profile of the Nike React Infinity Run v2 and anthropometric data) (364). Finally, the synthesis operator may be configured to present the results to the user (366).

[0058] In various embodiments, it may be useful to have a composition operator capability configured to solve multi-threaded challenges, such as simulating the participation of multiple players, multiple subprocesses, etc. in many human-scale challenges. For example, referring to Fig.19A , shows a composite operator (212) configuration, where a composite artificial intelligence configuration may be employed, such as a configuration utilizing a convolutional neural network ("CNN") (376). For example, referring to Fig.19A, the CNN that drives the functionality of the synthetic operator (212) can be informed by a supervised learning configuration, where interviews with appropriate experts in the subject area can be utilized, as well as repeated and varied scenario demonstrations and case studies from past processes (368). For example, to build a synthetic operator capability similar to that of David Packard, a famous engineering manager and founder of Hewlett-Packard, interviews, scenarios, and case studies of what David Packard actually did in various situations can be studied. Decision nodes and related decisions (370) can be labeled based on such research and input to a supervised learning model related to these decision nodes and decisions, so that a CNN (376) can be created and operated. Further feedback improvement and evolution of the synthetic operator over time and the experience of the synthetic operator using actual result data is promoted by using an audit trail of labeled data recorded by the relevant CNN-based synthetic operator from actual results. In addition, synthetic scenarios with decision nodes, decisions, and results can be created. For example, a simulation scenario related to the situation can be created, as well as speculation about what David Packard might do in a specific engineering management situation, as well as detailed information about the synthetic scenario, such as decision nodes, decisions, and results. To increase the amount of synthetic data from such configurations, techniques for simulating variability in various variables in such processes or subprocesses can be used to generate more synthetic data, which can be automatically labeled and utilized (374) to further train the CNN in a supervised learning configuration.

[0059] refer to Fig.19B , in various complex synthesis operator enhancement processes, it may be necessary to have hybrid capabilities where two different synthesis operator configurations (380, 382) can be used together to solve specific challenges. Fig.19B The configuration shows two different composition operators using the same input (384) in a parallel configuration, whereby the system can be configured to receive each independent result (386, 388), weight and / or combine them according to user preferences, and present a combined or blended result (392).

[0060] refer to Fig.19C , showing a configuration in which, after a process is deconstructed to determine which nodes of the process will be applied in sequence by which of two or more composition operators, sequential operations are performed such that a first (394) composition operator processes a first portion of the challenge and then hands over to a second (396) composition operator to process the remainder of the challenge and present a blended result (393).

[0061] See also Fig.19D, shows a hybrid configuration with serial and parallel synthesis operator activity, where a first row of synthesis operator configurations (590, 382, ​​592 for synthesis operators 7 (414), 2 (396), and 5 (412)) operates in parallel with a second row and a third row, the second row having a single synthesis operator configuration (594) for synthesis operator 3 (408) and the third row having two serial synthesis operator configurations (596, 598) for synthesis operator 9 (416) and synthesis operator 4 (410). The results (402, 404, 406) can be weighted and / or combined (390) as specified by the user, and the results (392) are presented.

[0062] therefore, Figures 19A-19DVarious configurations are shown in which various types of synthetic operator configurations can be utilized to solve complex challenges, and a human user or operator can be allowed to select a single synthetic operator, multiple synthetic operators, and hybrid operator configurations (e.g., a hybrid operator configuration in which a single synthetic operator is configured to have various characteristics of two other separate synthetic operators, or to have multiple synthetic operators with process mitigation, as described herein) through a user interface. Thus, various embodiments can be directed to a synthetic engagement system for process-based problem solving, including: a computing system including one or more operably coupled computing resources; a user interface operated by the computing system, the user interface configured to engage a human operator based at least in part on one or more specific facts, according to a predetermined process configuration toward a given requirement; wherein the user interface is configured to allow a human operator to select and interactively engage one or more synthetic operators operated by the computing system to collaboratively complete the predetermined process configuration and return a result selected to at least partially meet the given requirement to the human operator; and wherein each of the two or more synthetic operators is informed by a convolutional neural network, the convolutional neural network being informed at least in part by historical actions of a particular actual human operator. One or more specific facts may be selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity disturbance selection, curiosity selection, storage configuration, learning model, filtering configuration and encryption configuration. One or more specific facts may include text information related to specific background information from historical storage. One or more specific facts may include text information related to actual operators. One or more specific facts may include text information related to synthetic operators. Specific facts may include a predetermined profile of specific facts developed as a startup module for a specific synthetic operator profile. One or more operably coupled computing resources may include local computing resources. Local computing resources may be selected from the group consisting of: mobile computing resources, desktop computing resources, laptop computing resources, and embedded computing resources. Local computing resources may be embedded computing resources selected from the group consisting of: embedded microcontrollers, embedded microprocessors, and embedded gate arrays. One or more operably coupled computing resources include resources selected from the group consisting of: remote data centers, remote servers, remote computing clusters, and assemblies of computing systems at remote locations. The system may also include a positioning element operably coupled to the computing system and configured to determine the position of the human operator relative to the global coordinate system. The positioning element may be selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor. One or more operably coupled computing resources may be activated based on the determined position of the human operator.The user interface may include a graphical user interface. The user interface may include an audio user interface. The graphical user interface may be configured to enable human operators to participate using elements selected from the group consisting of the following items: a computer graphics participation display, a video graphics participation display, and audio participation accompanying the displayed graphics. The graphical user interface may include a video graphics participation display configured to present a real-time or near-real-time graphical representation of a video interface participant role with which a human operator can talk. The video interface participant role may be selected from the group consisting of the following items: a humanoid role, an animal role, and a cartoon role. The user interface may be configured to allow a human operator to select a visual presentation of a video interface participant role. The user interface may be configured to allow a human operator to select a visual presentation feature of a video interface participant role, which is selected from the group consisting of the following items: role gender, role hair color, role hairstyle, role skin color, role eye color, and role shape. The visual presentation of the video interface participant role may be modeled after the selected actual human. The user interface may be configured to allow a human operator to select one or more audio presentation aspects of the video interface participant role. The user interface may be configured to allow a human operator to select one or more audio rendering aspects of a video interface participant role, the audio rendering aspects being selected from the group consisting of: role voice intonation, role voice loudness, role speaking language, role speaking dialect, and role voice dynamic range. One or more audio rendering aspects of a video interface participant role may be modeled after the selected actual human. The predetermined process configuration may include a limited set of steps through which the participation will further achieve the established requirements. The predetermined process configuration may include process elements selected from the group consisting of: one or more general operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings. The limited set of steps may include steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design. The predetermined process configuration may include a human operator's selection of elements. The human operator's selection of elements may include selecting synthetic operator resources for one or more aspects of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific portion of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a second specific portion of a predetermined process configuration, the specific resources for the second specific portion of the predetermined process configuration being different from the specific resources for a first specific portion of the predetermined process configuration. The system may be configured to allow a human operator to specify specific resources for a first specific portion of the predetermined process configuration based on a plurality of synthetic operator roles. Each of the plurality of synthetic operator roles may be sequentially applied to the first specific portion.Each of the plurality of synthetic operator roles may be simultaneously applicable to the first specific portion. The system may be configured to allow a human operator to specify that a specific resource for the first specific portion of the predetermined process configuration is based on one or more hybrid synthetic operator roles. One or more hybrid synthetic operator roles may include a combination of other individual synthetic operator roles that may be simultaneously applied to the first specific portion. A convolutional neural network may be informed using input from a training data set, the training data set including data related to historical actions of a particular actual human operator. A supervised learning model may be used to inform the convolutional neural network using input from the training data set. An analysis of established requirements of a reinforcement learning model may be used to inform the convolutional neural network using input from the training data set. Each of the two or more synthetic operators may be informed by a convolutional neural network that is at least partially informed by a curated selection of synthetic action records related to the synthetic actions of an actual human operator. Each of the two or more synthetic operators may be informed by a convolutional neural network that is at least partially informed by a curated selection of synthetic action records related to the synthetic actions of one or more synthetic operators. The computing system may be configured to separate each of a finite set of steps from an execution step during which two or more synthesis operators are configured to achieve a given requirement based on one or more execution behaviors associated with a related convolutional neural network. At least one of the one or more execution behaviors may be based on a project leadership influence on the related convolutional neural network. Based at least in part on at least one execution behavior based on the project leadership influence, the computing system may be configured to divide the execution step into a plurality of tasks that can be processed by available resources to achieve the given requirement. Based at least in part on at least one execution behavior based on the project leadership influence, the computing system may be further configured to project manage the completion of the plurality of tasks to achieve one or more milestones of the given requirement. Based at least in part on at least one execution behavior based on the project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at one or more stages of the execution step. Based at least in part on at least one execution behavior based on the project leadership influence, the computing system may be further configured to functionally provide updates related to the completion of the plurality of tasks at the end of each execution step for consideration in each of a finite set of steps in the process configuration. Based at least in part on the at least one execution action based on the project leadership influence, the computing system may be further configured to functionally present the update for consideration by the human operator using a user interface operated by the computing system. Based at least in part on the at least one execution action based on the project leadership influence, the computing system may be further configured to incorporate instructions from the human operator regarding the presented update using a user interface operated by the computing system as the limited steps of the process configuration proceed.The user interface may be configured to allow a human operator to pause the computing system while it otherwise implements the predetermined process configuration so that the human operator can examine one or more intermediate results related to the established requirements. The user interface may be configured to allow the human operator to change one or more aspects of one or more specific facts during the computing system pause to facilitate forward execution based on the change. The user interface is configured to provide the human operator with a resource cost calculated based at least in part on the utilization of the computing resources operably coupled in the predetermined process configuration. The system may be configured to allow the human operator to specify that two or more synthetic operators are different. The system may be configured to allow the human operator to specify that two or more synthetic operators are the same and may be configured to collaboratively expand their productivity when they implement the predetermined process configuration. The two or more synthetic operators may be configured to automatically optimize their application as resources when they implement the predetermined process configuration. The system may be configured to generate an initial decision node group related to the established requirements using two or more synthetic operators, at least in part based on the characteristics of the two or more synthetic operators. The system may be further configured to create an intermediate decision node group based on the initial decision node group. The system may be further configured to create an operational decision node group based on the intermediate decision node group. The two or more composite operators are operated by a computing system to collaboratively implement a predetermined process configuration by sequencing operational decision nodes to achieve a given requirement. The two or more composite operators may include a number limited only by the computing resources that are operably coupled.

[0063] refer to Fig. 20A For example, a configuration for creating and updating a mechanical engineer synthetic operator "2" (396) is shown, wherein a continuously updated CNN can be utilized to generate a set of optimized decision nodes (422) for that particular synthetic operator, Mechanical Engineer 2 (i.e., somewhat similar to the process of how that engineer approaches and resolves challenges).

[0064] refer to Fig. 20B For example, a configuration for creating and updating an accounting composite operator "11" (418) is shown, wherein a continuously updated CNN can be utilized to generate a set of optimized decision nodes (420) for that particular composite operator, Accountant 11 (i.e., somewhat similar to the process of how that accountant responds to and resolves challenges).

[0065] refer to Fig. 20C , in order for two different synthesis operators to jointly obtain a result through specific process steps (i.e., as opposed to independent parallel or sequential operations and then combining the results at the end), which is very similar to how complex human teams operate, it may be useful to develop a CNN (428) consisting of optimized decision nodes ( Fig. 20A and 20B422, 420 in the illustrative example of a mechanical engineer 2 and an accountant 11) and provides information about how these decision nodes should be combined and mediated by actual (424) and synthetic (426) data. This CNN can be used to create operational decision nodes for the synthetic operator mechanical engineer 2 through a given process, and the mechanical engineer 2 works with the synthetic operator accountant 11. In other words, a set of decision nodes can now be used to collaborate based on the previously dispersed set of decision nodes, and the synthetic operator configuration (436) can now be executed at runtime (i.e., in this particular illustrative scenario, related to mechanical engineer 2 and accountant 11, as shown in FIG. Fig. 20A and 20B ) and used to generate a result (434).

[0066] refer to Fig.21A , there are two composite operators (e.g., a mechanical engineer composite operator configuration 438 and an accounting composite operator configuration 440), there is essentially a relationship (442) between the two, and a process intermediary needs to be processed to bring the two into a coherent process. Fig. 21B , the process mediation is further complicated by introducing additional composition operators, such as the product marketing composition operator configuration (444), each of which theoretically has two different relationships (442, 446, 448). Fig. 21C , a configuration with five composite operator configurations (438, 440, 452, 454, 444) is shown to show the multiplication of the complexity of the relationships (442, 456, 462, 468, 446, 448, 458, 464, 460, 466) mediated in the process.

[0067] refer to Fig. 22 , this complexity can be addressed through a variety of configurations. After defining the challenge (470) and deciding on the specialized functional groups (472) to include in a particular process, the user or supervisor can decide on the model of process interoperability (474); for example, one can decide to model each relationship 1:1 for each composite operator; one can decide to model each composite operator only with respect to the rest of the entire group ("1:(G-1)"); one can decide that the user or supervisor will specify the process intermediary for the group as a unified whole ("G-unified") (i.e., "this is the process we will all run"). After determining the operational decision nodes (476) for the functional group's collaborative working process, the composite operator configuration (436) can be used to execute (432) at runtime and produce results (434).

[0068] refer to Fig.23A, as an example, a Nike (RTM) sole design challenge is defined (478). A simplified grouping of mechanical engineer synthesis operators will be combined with an accounting synthesis operator (480). With only two synthesis operators, a relationship and process mediation (474) is required; this can be determined by, for example, a user or supervisor, such as Fig. 23B As shown, where accounting synthesis operators enter the process which is primarily an engineering process in only two locations.

[0069] Therefore, reference Fig. 23B , the Mechanical Engineer (“ME”) SO and the Accounting SO have all the inputs to the challenge; the Synthesis Operator may be configured to have some system-level problem solving capabilities (482), and the Accounting SO may be configured to provide a cost of goods sold (“COGS”) envelope and discuss possible supply chain issues with certain materials (484). The ME SO may be configured to initially document the requirements / goals at a very basic level (e.g., the goal is a sole shape with two materials) and develop a forward basic paradigm based on a prohibited process using the inputs and resources to achieve the goal (e.g., understand the requirements; use available information to find candidate solutions; analyze candidate solutions; present results) (486). The ME SO may be configured to search to determine what the toe box inside the shoe is and what geometry is appropriate for 80% of the anthropometric market (488). The ME SO may be configured to search to determine the sole ground contact profile of the Nike React Infinity Run v2 (RTM) (490). The MESO may be configured to search to determine that the controlling factor in the sole design is cushioning performance, and the controlling factors in cushioning performance involve material modulus, shape, and structural containment (492). The ME SO may be configured to determine that the sole ground contact profile is similar to the Nike React Infinity Run v2, and the Nike design language provides some surface configurations, but generally uses open foam on the side of the shoe, so the main variables in this challenge are the cushioning foam material, its thickness, and the area / shape of the toe box (determined by anthropometric data) (494). The accounting SO may be configured to alert to possible COGS envelope and supply chain issues for certain materials (496). The ME SO may be configured to analyze variations / combinations / permutations of sole assemblies using various cushioning materials and thicknesses (again, working within the range of the sole ground contact profile of the Nike React Infinity Run v2 and anthropometric data) (498). The results of the complex process configuration may be presented to the user (500).

[0070] As mentioned above, for example, reference Fig. 20C and Fig. 22, the decision node process mediator plays a key role in the runtime (432) in synthesizing operator configuration (436) and determining the operation nodes for the group of functions (430, 476) working together. Fig.23C ,about Fig.23A and 23B As an illustrative example, a ME synthesis operator configuration may be initiated (502); user, management and / or supervisor discussion or input may be similar to: "This is a critical product; it needs to work the first time; engineer Bob Smith always gets things like this done successfully; bring in Bob Smith" (504). An accounting synthesis operator configuration may be initiated (506); user, management and / or supervisor discussion or input may be similar to: "Let's not get in the way of upfront engineering; bring in the ever-friendly / efficient accountant Sally Jones to get the work started, but bring in accountant Eeyore Johnson at the end to make sure we hit the COGS numbers." (508). The system can be configured to initiate analysis and selection (510) of operational decision nodes for groups of functions working together (ME, accounting) with user, management and / or supervisor discussions or input similar to: "This is mostly about engineering; let them control the process, but they will have COGS and supply chain input up front, and in the end, COGS needs to be a control filter." With such input, operational decision nodes can be developed, as discussed from the process mediation (430), with associated composite operator configurations (436), runtimes (432), and results (434).

[0071] refer to Figures 24A-24C , shows a complex configuration in which a supplement to a previous album can be created using synthesis operators associated with the four Beatles (RTM), their producers and their managers. Fig.24A As mentioned above, due to the large number of composition operators (514, 516, 524, 520, 518, 522), the number of relations (526) is also very large. Fig. 24B, a challenge can be defined: develop aligned verses, chorus, bridge, and solos for a Beatles mid-tempo rock song that can complement a Sgt Peppers album (530). A decision can be made about the functional expertise groups to be brought into the process: six (Ringo, McCartney, Lennon, Harrison, George Martin, Brian Epstein); synthesis operator models developed for each based on historical / anecdotal information (532). To model the interoperation of the functional groups, a decision can be made about the technique for deriving an intermediate decision node using this large set of synthesis operators (e.g., 1:1 analysis; 1:(G-1) analysis; G-unification); in this case it might be verbal (say G-unification based on historical / anecdotal information about how they worked together on the Sgt Peppers album) (534). With such decisions and configurations, operational decision nodes (476) can be used along with synthesis operator configurations (436) created for these specific roles, and these can be used at runtime (432) and the results (434) can be obtained, such as Fig.24C As further shown in .

[0072] refer to Fig.24C, the user specifies the process intermediates in the boxes shown on the right (536, 536, 540, 542, 544, 546, 548, 550). SO Harrison and SO McCartney experimentally develop a "rif" combination of bass and guitar that can be used as a chorus (552). SO Lennon and SO Ringo provide input, but control initially remains in the hands of So Harrison and SO McCartney (554). SO Lennon and SO Ringo develop multiple verses associated with the chorus (556). SO Lennon and SO Ringo provide further input, but control initially remains in the hands of So Harrison and SO McCartney (558). SO Lennon and SO Ringo develop a bridge to handle the verse and chorus material (560). The foundation of a song is coming together; it is now possible to play verse-chorus-verse-chorus-bridge, with SO Harrison on lead guitar for verses, chorus, and bridge; SO McCartney on bass for verses, chorus, and bridge; SO Ringo on drums throughout; SO Lennon on rhythm guitar throughout; all continue to provide input to the overall configuration and contribute to each other (562). As the song develops, Epstein begins recording and operates the mixing board; George Martin provides very minimal input (564). SO Harrison develops a basic guitar solo, which is arranged in sequence after the bridge, with minimal input from SO McCartney and SO Lennon (566). The result is complete and can be presented (568).

[0073] refer to Fig.25 , presents an example user interface in which a representation of a sequence of events may be presented to a user ( 570 ), and the user is able to click or right-click on a particular event to further present a sub-representation (e.g., a box or bubble) ( 572 ) containing further information about the synthesis operator enhanced computation operation and status.

[0074] refer to Fig.26 , a calculation table portion (574) is shown to illustrate that various business models can be utilized to provide significant value to users / customers while also creating positive operating profit opportunities, depending on the costs associated with computing resources.

[0075] refer to Fig.27AAs described above, many human processes are complex and varied, and it may be useful to combine many different types of synthetic operators (576, 578, 580, 582, 584, 586) to solve various complexity challenges. In fact, in various embodiments, it is best for various system instances to utilize synthetic operator resources in a cohesive and connected manner (588), somewhat similar to actual human processes, where the best talents are combined to solve complex challenges.

[0076] refer to Fig.28A , a synthetic operator (212) configuration (380) is shown, with additional details interrelated about how continuous learning and evolution are achieved using various factors. For example, as described above, the neural network configured to operate various aspects of the synthetic operator can be informed by actual historical data, synthetic data, and audit data related to utilization. The learning model (614) can be configured to help filter, protect, and encrypt inputs to continuously adjust the process of the neural network. For example, in various embodiments, a control or control panel can be presented to the user to allow configuration of mood / emotional states (e.g., by selecting areas on a mood state map) (602), access to various others' experiences and teachings (604), simulated chaotic input selection (606), activity perturbation selection (608), curiosity selection (610), and storage configuration (612). For example, after selecting a positive emotional state, the synthetic operator can be configured to engage with more positive information and methods. More access to teaching and experience may broaden the potential of the synthetic operator configuration. Additional chaos in the synthetic operator process can be a good thing or a bad thing; for example, it can make the activity very active or it can cause cycles to be wasted. High levels of activity perturbations may help keep processes, learning, and other activities at high levels. High levels of curiosity may enhance learning and assimilation as input to neural networks. Memory configurations with large amounts of long-term and short-term storage may help the development of neural networks.

[0077] refer to Fig.28B , various aspects of the learning model configuration can be informed by: actual human teaching and experience (616), actual experience input from real human scenarios (618), teaching from synthetic facts and scenarios (620) (e.g.: a synthetic scenario about how Cyberdine Systems takes over the world like in the movie "Terminator" RTM), and other synthetic experience input (622) (e.g.: how a war between Cyberdine Systems and humans would happen).

[0078] refer to Fig.28C, various aspects of the learning model configuration may be further informed by interactions with composition relationships (624), which may exist between composition operators, and composition environments (626), which may be configured to help composition operators participate in various composition experiences, teachings, and encounters, for example, as influenced by the user settings of the learning model configuration at the time. For example, Figures 29A-29C , a synthetic world (624, 626, 628) is shown in FIG. The system can be configured to utilize the synthetic operator configuration as well as the learning model settings to help a given synthetic operator synthetically navigate in such a world and gain associated experiences and learning. For example, if SO#27 is a heavy metal guitarist and the emotional state is set to black for a period of time in the associated learning model, then SO#27 may gravitate toward the darker, heavier aspects of the associated synthetic world, which may be associated with darker, heavier information and experiences, such as a dark cave full of scorpions. Conversely, a yoga instructor SO with a very positive emotional state selection may gravitate toward the brighter, sunnier, more positive aspects of the synthetic world and gain more positive information and experiences at that stage of evolution.

[0079] refer to Figures 30A-30D , the system can be configured to help users configure various sequences and customize the results based on sequence and time domain issues. For example, Fig. 30A A process description is shown, which involves ten stages of a process involving four musicians, a producer, and a manager. The depicted configuration has the Beatles members through the entire 10-stage process. Fig. 30B In the configuration shown in (632), Eddie Van Halen was replaced as the lead guitarist in stages 6 and 7, Alex Van Halen was replaced as the drummer in stages 8, 9 and 10, and Jimi Hendrix was on the mixing desk as the producer in stages 8, 9 and 10. Fig. 30B The final result of the configuration is not satisfactory, so the time domain selector (636) can be used to rewind the process to the beginning of stage 8, such as Fig. 30C As shown, then Fig.30D As shown, the process can be run forward again from there, with Ringo returning to the drummer position for stages 8, 9 and 10, but Jimi Hendrix still on the mixing desk in the producer role for stages 8, 9 and 10 to see how this affects the results.

[0080] refer to Fig.31, a process configuration is shown in which a computing system (the computing system includes operably coupled resources, such as local and / or remote computing systems and subsystems) is provided to a user (702). The computing system can be configured to present a user interface (e.g., graphics, audio, video) so that a human operator can participate to complete a predetermined process configuration toward a given requirement (i.e., such as a goal or purpose); specific facts can be used to inform the process and computing configuration (704). The user interface can be configured to allow the human operator to select and interactively participate in one or more composite operators operated by the computing system to continue the predetermined process configuration and return to the human operator (e.g., through the user interface) a partial or complete result that at least partially satisfies the given requirement (706). In embodiments using two or more composite operators, they can be configured to work together to complete the predetermined process configuration toward the given requirement, subject to configuration such as a decision node intermediary (708).

[0081] Various exemplary embodiments of the present invention are described herein. Reference is made to these examples in a non-limiting sense. These examples are provided to illustrate the more widely applicable aspects of the present invention. Various changes may be made to the described invention, and equivalents may be substituted without departing from the true spirit and scope of the present invention. In addition, many modifications may be made to adapt specific circumstances, materials, compositions of matter, processes, process actions or steps to the purpose, spirit or scope of the present invention. In addition, it will be appreciated by those skilled in the art that each individual variant described and illustrated herein has discrete components and features that can be easily separated or combined with the features of any one of the other several embodiments without departing from the scope or spirit of the present invention. All such modifications are intended to fall within the scope of the claims associated with this disclosure.

[0082] Any device used to perform a diagnostic or interventional procedure on a subject may be provided in a packaged combination for use in performing such an intervention. These supply "kits" may also include instructions for use and be packaged in a sterile tray or container commonly used for such purposes.

[0083] The present invention includes methods that can be performed using subject equipment. These methods may include the act of providing such suitable equipment. This provision may be performed by the end user. In other words, the "providing" behavior only requires the end user to obtain, access, approach, locate, set up, activate, start or otherwise provide the necessary equipment in the subject method. The methods described herein may be performed in any logically feasible order of events and the order of events.

[0084] Exemplary aspects of the present invention and details of material selection and manufacturing have been described above. As for other details of the present invention, these details can be understood in conjunction with the patents and publications cited above, as well as those generally known or understood by those skilled in the art. The same is true for the method-based aspects of the present invention, in terms of additional actions that are generally or logically employed.

[0085] In addition, although the present invention has been described with reference to several examples optionally including various features, the present invention is not limited to the content described or indicated for each variant of the present invention. Various changes can be made to the described invention and can be replaced with equivalents (whether listed herein or not included for brevity) without departing from the true spirit and scope of the present invention. In addition, when a range of values ​​is provided, it should be understood that each intermediate value between the upper and lower limits of the range and any other described or intermediate values ​​within the range are included in the present invention.

[0086] In addition, it is contemplated that any optional features of the invention variants may be set forth and claimed independently or in combination with any one or more features described herein. References to singular items include the possibility of the presence of plural identical items. More specifically, as used herein and in the claims associated therewith, the singular forms "one", "an", "said", and "the" include plural references unless expressly stated otherwise. In other words, the use of these articles allows reference to "at least one" subject item in the above description and in the claims associated with the present disclosure. It is further noted that such claims may be drafted to exclude any optional elements. Therefore, this statement is intended to serve as a prior basis for the use of exclusive terms such as "only", "only" or the use of "negative" limitations when reciting claim elements.

[0087] Without using these exclusive terms, the term "comprising" in the claims related to the present disclosure shall allow for the inclusion of any additional elements - regardless of whether a given number of elements are recited in these claims or whether the addition of features may be considered to alter the nature of the elements recited in these claims. Unless otherwise expressly defined herein, all technical and scientific terms used herein shall be given the broadest possible commonly understood meaning while maintaining claim validity.

[0088] The scope of the present invention is not limited to the examples and / or subject description provided, but is limited only by the scope of the language of the claims associated with this disclosure.

Claims

1. A synthetic engagement system for process-based problem solving, comprising: a. A computing system comprising one or more operably coupled computing resources; as well as b. a user interface operated by the computing system, the user interface being configured to engage a human operator according to a predetermined process configuration toward a given requirement based at least in part on one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to implement the predetermined process configuration and return results selected to at least partially satisfy the established requirements to the human operator; and Wherein each of the one or more synthetic operators is informed by a convolutional neural network that is at least partially informed by historical actions of a particular actual human operator.

2. The system according to claim 1, wherein: The one or more specific facts are selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration.

3. The system according to claim 2, wherein: The one or more specific facts include text information related to specific context information from historical storage.

4. The system according to claim 2, wherein: The one or more specific facts include: text information related to the actual operator.

5. The system according to claim 2, wherein: The one or more specific facts include: text information related to the synthesis operator.

6. The system according to claim 1, wherein: The specific facts include a predetermined profile of specific facts developed as a starting module for a specific synthetic operator profile.

7. The system according to claim 1, wherein: The one or more operably coupled computing resources include a local computing resource.

8. The system according to claim 7, wherein: The local computing resource is selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource.

9. The system according to claim 8, wherein: The local computing resource comprises an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array.

10. The system according to claim 1, wherein: The one or more operably coupled computing resources include resources selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location.

11. The system of claim 1 , further comprising a positioning element operably coupled to the computing system and configured to determine a position of the human operator relative to a global coordinate system.

12. The system according to claim 11, wherein: The positioning element is selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor.

13. The system according to claim 11, wherein: Based on the determined location of the human operator, the one or more operably coupled computing resources are activated.

14. The system of claim 1, wherein: The user interface includes a graphical user interface.

15. The system of claim 1, wherein: The user interface includes an audio user interface.

16. The system of claim 14, wherein: The graphical user interface is configured to engage the human operator using elements selected from the group consisting of: computer graphics engagement display, video graphics engagement display, and audio engagement accompanying the displayed graphics.

17. The system of claim 14, wherein: The graphical user interface includes a video graphical participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which the human operator can converse.

18. The system of claim 17, wherein: The video interface participating characters are selected from the group consisting of: humanoid characters, animal characters, and cartoon characters.

19. The system of claim 18, wherein: The user interface is configured to allow the human operator to select a visual representation of the video interface participating character.

20. The system of claim 19, wherein: The user interface is configured to allow the human operator to select visual presentation characteristics of the video interface participating character, the visual presentation characteristics being selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape.

21. The system of claim 19, wherein: The visual presentation of the video interface participating character is modeled after the selected actual human being.

22. The system of claim 18, wherein: The user interface is configured to allow the human operator to select one or more audio presentation aspects of the video interface participating character.

23. The system of claim 22, wherein: The user interface is configured to allow the human operator to select one or more audio rendering aspects of the video interface participating character, the audio rendering aspects being selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range.

24. The system of claim 23, wherein: One or more audio rendering aspects of the video interface participating character are modeled after the selected actual human being.

25. The system of claim 1, wherein: The predetermined process configuration includes a finite set of steps through which the participation will further achieve the established requirements.

26. The system of claim 1, wherein: The predetermined process configuration includes process elements selected from the group consisting of: one or more common operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings.

27. The system of claim 25, wherein: The limited set of steps includes steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design.

28. The system of claim 25, wherein: The predetermined process configuration includes selection of elements by the human operator.

29. The system of claim 28, wherein: The human operator's selection of elements includes selecting a synthetic operator resource for one or more aspects of the predetermined process configuration.

30. The system of claim 29, wherein: The system is configured to allow the human operator to specify specific resources for a first specific portion of the predetermined process configuration.

31. The system of claim 30, wherein: The system is configured to allow the human operator to specify specific resources for a second specific portion of the predetermined process configuration, the specific resources for the second specific portion of the predetermined process configuration being different from the specific resources for the first specific portion of the predetermined process configuration.

32. The system of claim 30, wherein: The system is configured to allow the human operator to specify that a particular resource for a first particular portion of the predetermined process configuration is based on a plurality of composite operator roles.

33. The system of claim 32, wherein: Each of the plurality of composite operator roles is sequentially applied to the first specific portion.

34. The system of claim 32, wherein: Each of the plurality of composite operator roles is simultaneously applied to the first specific portion.

35. The system of claim 30, wherein: The system is configured to allow the human operator to specify specific resources for a first specific portion of the predetermined process configuration based on one or more hybrid composite operator roles.

36. The system of claim 30, wherein: The one or more hybrid composite operator roles include a combination of other individual composite operator roles that are simultaneously applicable to the first specific portion.

37. The system of claim 1, wherein: The convolutional neural network is informed using input from a training dataset comprising data relating to the historical actions of the particular actual human operator.

38. The system of claim 37, wherein: A supervised learning model is used to inform the convolutional neural network using input from a training dataset.

39. The system of claim 37, wherein: The convolutional neural network is informed by analysis of the stated requirements using a reinforcement learning model using input from a training dataset.

40. The system of claim 1, wherein: Each of the one or more synthesis operators is informed by a convolutional neural network that is informed at least in part by a curated selection of synthesis action records related to synthesis actions of an actual human operator.

41. The system of claim 1, wherein: Each of the one or more compositing operators is informed by a convolutional neural network that is informed, at least in part, by a curated selection of compositing action records related to the compositing actions of the compositing operator.

42. The system of claim 25, wherein: The computing system is configured to separate each of the finite set of steps from an execution step during which the one or more synthesis operators are configured to implement the established requirements according to one or more execution behaviors associated with the relevant convolutional neural network.

43. The system of claim 42, wherein: At least one of the one or more execution behaviors is based on a project leadership influence on the associated convolutional neural network.

44. The system of claim 43, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements.

45. The system of claim 44, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to project manage completion of the plurality of tasks to achieve one or more milestones in the established requirements.

46. ​​The system of claim 44, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at one or more stages of the execution step.

47. The system of claim 46, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at the conclusion of each execution step for consideration in each of the finite set of steps in the process configuration.

48. The system of claim 47, wherein: Based at least in part on the at least one execution action based on project leadership influence, the computing system is further configured to functionally present the update for consideration by the human operator utilizing the user interface operated by the computing system.

49. The system of claim 48, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to incorporate instructions from the human operator regarding the presented updates using the user interface operated by the computing system as the limited steps of the process configuration proceed.

50. The system of claim 1, wherein: The user interface is configured to allow the human operator to pause the computing system while it is otherwise implementing the predetermined process configuration so that the human operator can inspect one or more intermediate results related to the established requirements.

51. The system of claim 50, wherein: The user interface is configured to allow the human operator to change one or more aspects of the one or more specific facts during a pause of the computing system to facilitate forward execution based on the changes.

52. The system of claim 1, wherein: The user interface is configured to provide the human operator with a resource cost calculated based at least in part on utilization of the operably coupled computing resources in the predetermined process configuration.

53. A synthetic engagement system for process-based problem solving, comprising: a. A computing system comprising one or more operably coupled computing resources; as well as b. a user interface operated by the computing system, the user interface being configured to engage a human operator according to a predetermined process configuration toward a given requirement based at least in part on one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to collaboratively implement the predetermined process configuration and return results selected to at least partially satisfy the established requirements to the human operator; and Wherein each of the two or more synthetic operators is informed by a convolutional neural network that is at least partially informed by historical actions of a particular actual human operator.

54. The system of claim 53, wherein: The one or more specific facts are selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration.

55. The system of claim 54, wherein: The one or more specific facts include text information related to specific context information from historical storage.

56. The system of claim 54, wherein: The one or more specific facts include: text information related to the actual operator.

57. The system of claim 54, wherein: The one or more specific facts include: text information related to the synthesis operator.

58. The system of claim 53, wherein: The specific facts include a predetermined profile of specific facts developed as a starting module for a specific synthetic operator profile.

59. The system of claim 53, wherein: The one or more operably coupled computing resources include a local computing resource.

60. The system of claim 59, wherein: The local computing resource is selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource.

61. The system of claim 60, wherein: The local computing resource comprises an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array.

62. The system of claim 53, wherein: The one or more operably coupled computing resources include resources selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location.

63. The system of claim 53, further comprising a positioning element operably coupled to the computing system and configured to determine a position of the human operator relative to a global coordinate system.

64. The system of claim 63, wherein: The positioning element is selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor.

65. The system of claim 63, wherein: Based on the determined location of the human operator, the one or more operably coupled computing resources are activated.

66. The system of claim 53, wherein: The user interface includes a graphical user interface.

67. The system of claim 53, wherein: The user interface includes an audio user interface.

68. The system of claim 66, wherein: The graphical user interface is configured to engage the human operator using elements selected from the group consisting of: computer graphics engagement display, video graphics engagement display, and audio engagement accompanying the displayed graphics.

69. The system of claim 66, wherein: The graphical user interface includes a video graphical participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which the human operator can converse.

70. The system of claim 69, wherein: The video interface participating characters are selected from the group consisting of: humanoid characters, animal characters, and cartoon characters.

71. The system of claim 70, wherein: The user interface is configured to allow the human operator to select a visual representation of the video interface participating character.

72. The system of claim 71, wherein: The user interface is configured to allow the human operator to select visual presentation characteristics of the video interface participating character, the visual presentation characteristics being selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape.

73. The system of claim 71, wherein: The visual presentation of the video interface participating character is modeled after the selected actual human being.

74. The system of claim 70, wherein: The user interface is configured to allow the human operator to select one or more audio presentation aspects of the video interface participating character.

75. The system of claim 74, wherein: The user interface is configured to allow the human operator to select one or more audio rendering aspects of the video interface participating character, the audio rendering aspects being selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range.

76. The system of claim 75, wherein: One or more audio rendering aspects of the video interface participating character are modeled after the selected actual human being.

77. The system of claim 53, wherein: The predetermined process configuration includes a finite set of steps through which the participation will further achieve the established requirements.

78. The system of claim 53, wherein: The predetermined process configuration includes process elements selected from the group consisting of: one or more common operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings.

79. The system of claim 77, wherein: The limited set of steps includes steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design.

80. The system of claim 77, wherein: The predetermined process configuration includes selection of elements by the human operator.

81. The system of claim 80, wherein: The human operator's selection of elements includes selecting a synthetic operator resource for one or more aspects of the predetermined process configuration.

82. The system of claim 81, wherein: The system is configured to allow the human operator to specify specific resources for a first specific portion of the predetermined process configuration.

83. The system of claim 82, wherein: The system is configured to allow the human operator to specify specific resources for a second specific portion of the predetermined process configuration, the specific resources for the second specific portion of the predetermined process configuration being different from the specific resources for the first specific portion of the predetermined process configuration.

84. The system of claim 82, wherein: The system is configured to allow the human operator to specify that a particular resource for a first particular portion of the predetermined process configuration is based on a plurality of composite operator roles.

85. The system of claim 84, wherein: Each of the plurality of composite operator roles is sequentially applied to the first specific portion.

86. The system of claim 84, wherein: Each of the plurality of composite operator roles is simultaneously applied to the first specific portion.

87. The system of claim 82, wherein: The system is configured to allow the human operator to specify specific resources for a first specific portion of the predetermined process configuration based on one or more hybrid composite operator roles.

88. The system of claim 82, wherein: The one or more hybrid composite operator roles include a combination of other individual composite operator roles that are simultaneously applicable to the first specific portion.

89. The system of claim 53, wherein: The convolutional neural network is informed using input from a training dataset comprising data related to the historical actions of the particular actual human operator.

90. The system of claim 89, wherein: A supervised learning model is used to inform the convolutional neural network using input from a training dataset.

91. The system of claim 89, wherein: The convolutional neural network is informed by analysis of the stated requirements using a reinforcement learning model using input from a training dataset.

92. The system of claim 53, wherein: Each of the two or more synthesis operators is informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthesis action records related to synthesis actions of one or more actual human operators.

93. The system of claim 53, wherein: Each of the two or more compositing operators is informed by a convolutional neural network that is informed, at least in part, by a curated selection of compositing action records related to compositing actions of the one or more compositing operators.

94. The system of claim 77, wherein: The computing system is configured to separate each of the finite set of steps from an execution step during which the two or more synthesis operators are configured to implement the established requirements according to one or more execution behaviors associated with the relevant convolutional neural network.

95. The system of claim 94, wherein: At least one of the one or more execution behaviors is based on a project leadership influence on the associated convolutional neural network.

96. The system of claim 95, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements.

97. The system of claim 96, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to project manage completion of the plurality of tasks to achieve one or more milestones in the established requirements.

98. The system of claim 96, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at one or more stages of the execution step.

99. The system of claim 98, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at the conclusion of each execution step for consideration in each of the finite set of steps in the process configuration.

100. The system of claim 99, wherein: Based at least in part on the at least one execution action based on project leadership influence, the computing system is further configured to functionally present the update for consideration by the human operator utilizing the user interface operated by the computing system.

101. The system of claim 100, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to, as the limited steps of the process configuration continue, incorporate instructions from the human operator regarding the presented updates using the user interface operated by the computing system.

102. The system of claim 53, wherein: The user interface is configured to allow the human operator to pause the computing system while it is otherwise implementing the predetermined process configuration so that the human operator can inspect one or more intermediate results related to the established requirements.

103. The system of claim 102, wherein: The user interface is configured to allow the human operator to change one or more aspects of the one or more specific facts during a pause of the computing system to facilitate forward execution based on the changes.

104. The system of claim 53, wherein: The user interface is configured to provide the human operator with a resource cost calculated based at least in part on utilization of the operably coupled computing resources in the predetermined process configuration.

105. The system of claim 81, wherein: The system is configured to allow the human operator to specify that the two or more composite operators are different.

106. The system of claim 81, wherein: The system is configured to allow the human operator to designate that the two or more composite operators are the same and to collaboratively extend their productivity as they implement the predetermined process configuration.

107. The system of claim 53, wherein: The two or more composite operators are configured to automatically optimize their use as resources as they implement the predetermined process configuration.

108. The system of claim 53, wherein: The system is configured to generate an initial set of decision nodes associated with the given requirement using the two or more composite operators based at least in part on characteristics of the two or more composite operators.

109. The system of claim 108, wherein: The system is further configured to create an intermediate decision node group based on the initial decision node group.

110. The system of claim 109, wherein: The system is further configured to create an operational decision node group based on the intermediate decision node group.

111. The system of claim 109, wherein: The two or more composite operators are operated by the computing system to collaboratively implement the predetermined process configuration by sequencing the operational decision nodes to achieve the established requirements.

112. The system of claim 53, wherein: The two or more composition operators comprise a plurality limited only by the operably coupled computing resources.

113. A synthetic participatory method for process-based problem solving, comprising: a. Providing a computing system comprising one or more operably coupled computing resources; as well as b. presenting a user interface using the computing system, the user interface being configured to engage a human operator according to a predetermined process configuration toward a given requirement based at least in part on one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to implement the predetermined process configuration and return results selected to at least partially satisfy the established requirements to the human operator; and Wherein each of the one or more synthetic operators is informed by a convolutional neural network that is at least partially informed by historical actions of a particular actual human operator.

114. The method of claim 113, wherein: The one or more specific facts are selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration.

115. The method of claim 114, wherein: The one or more specific facts include text information related to specific context information from historical storage.

116. The method of claim 114, wherein: The one or more specific facts include: text information related to the actual operator.

117. The method of claim 114, wherein: The one or more specific facts include: text information related to the synthesis operator.

118. The method of claim 113, wherein: The specific facts include a predetermined profile of specific facts developed as a starting module for a specific synthetic operator profile.

119. The method of claim 113, wherein: The one or more operably coupled computing resources include a local computing resource.

120. The method of claim 119, wherein: The local computing resource is selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource.

121. The method of claim 120, wherein: The local computing resource comprises an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array.

122. The method of claim 113, wherein: The one or more operably coupled computing resources include resources selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location.

123. The method of claim 113, further comprising: A positioning element is operably coupled to the computing system, the positioning element being configured to determine a position of the human operator relative to a global coordinate system.

124. The method of claim 123, wherein: The positioning element is selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor.

125. The method of claim 123, wherein: Further comprising: activating the one or more operably coupled computing resources based on the determined location of the human operator.

126. The method of claim 113, wherein: Presenting the user interface includes presenting a graphical user interface.

127. The method of claim 113, wherein: Presenting the user interface includes presenting an audio user interface.

128. The method of claim 126, wherein: The graphical user interface is presented to engage the human operator using elements selected from the group consisting of: computer graphics engagement display, video graphics engagement display, and audio engagement accompanying the displayed graphics.

129. The method of claim 126, wherein: Presenting the graphical user interface includes presenting a video graphical participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which the human operator can converse.

130. The method of claim 129, wherein: The video interface participating characters are selected from the group consisting of: humanoid characters, animal characters, and cartoon characters.

131. The method of claim 130, wherein: The user interface is configured to allow the human operator to select a visual representation of the video interface participating character.

132. The method of claim 131, wherein: The user interface is configured to allow the human operator to select visual presentation characteristics of the video interface participating character, the visual presentation characteristics being selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape.

133. The method of claim 131, wherein: The visual presentation of the video interface participating character is modeled after the selected actual human being.

134. The method of claim 130, wherein: The user interface is configured to allow the human operator to select one or more audio presentation aspects of the video interface participating character.

135. The method of claim 134, wherein: The user interface is configured to allow the human operator to select one or more audio rendering aspects of the video interface participating character, the audio rendering aspects being selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range.

136. The method of claim 135, wherein: One or more audio rendering aspects of the video interface participating character are modeled after the selected actual human being.

137. The method of claim 113, wherein: The predetermined process configuration includes a finite set of steps through which the participation will further achieve the established requirements.

138. The method of claim 113, wherein: The predetermined process configuration includes process elements selected from the group consisting of: one or more common operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings.

139. The method of claim 137, wherein: The limited set of steps includes steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design.

140. The method of claim 137, wherein: The predetermined process configuration includes selection of elements by the human operator.

141. The method of claim 140, wherein: The human operator's selection of elements includes selecting a synthetic operator resource for one or more aspects of the predetermined process configuration.

142. The method of claim 141, wherein: The user interface is configured to allow the human operator to specify specific resources for a first specific portion of the predetermined process configuration.

143. The method of claim 142, wherein: The user interface is configured to allow the human operator to specify specific resources for a second specific portion of the predetermined process configuration, the specific resources for the second specific portion of the predetermined process configuration being different from the specific resources for the first specific portion of the predetermined process configuration.

144. The method of claim 142, wherein: The user interface is configured to allow the human operator to specify that a particular resource of a first particular portion of the predetermined process configuration is based on a plurality of composite operator roles.

145. The method of claim 144, further comprising: Each of the plurality of composite operator roles is sequentially applied to the first specific portion.

146. The method of claim 144, further comprising: Each of the plurality of composite operator roles is simultaneously applied to the first specific portion.

147. The method of claim 142, wherein: The user interface is configured to allow the human operator to specify that specific resources for a first specific portion of the predetermined process configuration are based on one or more hybrid composite operator roles.

148. The method of claim 142, wherein: The one or more hybrid composite operator roles include a combination of other individual composite operator roles that are simultaneously applicable to the first specific portion.

149. The method of claim 113, wherein: The convolutional neural network is informed using input from a training dataset comprising data related to the historical actions of the particular actual human operator.

150. The method of claim 149, wherein: A supervised learning model is used to inform the convolutional neural network using input from a training dataset.

151. The method of claim 149, wherein: The convolutional neural network is informed by analysis of the stated requirements using a reinforcement learning model using input from a training dataset.

152. The method of claim 113, wherein: Each of the one or more synthesis operators is informed by a convolutional neural network that is informed at least in part by a curated selection of synthesis action records related to synthesis actions of an actual human operator.

153. The method of claim 113, wherein: Each of the one or more compositing operators is informed by a convolutional neural network that is informed, at least in part, by a curated selection of compositing action records related to the compositing actions of the compositing operator.

154. The method of claim 137, wherein: The computing system is configured to separate each of the finite set of steps from an execution step during which the one or more synthesis operators are configured to implement the established requirements according to one or more execution behaviors associated with the relevant convolutional neural network.

155. The method of claim 154, wherein: At least one of the one or more execution behaviors is based on a project leadership influence on the associated convolutional neural network.

156. The method of claim 155, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is configured to divide the execution steps into a plurality of tasks that can be processed by available resources to achieve the established requirements.

157. The method of claim 156, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to project manage completion of the plurality of tasks to achieve one or more milestones in the established requirements.

158. The method of claim 156, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at one or more stages of the execution step.

159. The method of claim 158, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at the conclusion of each execution step for consideration in each of the finite set of steps in the process configuration.

160. The method of claim 159, wherein: Based at least in part on the at least one executive action based on project leadership influence, the computing system is further configured to functionally present the update for consideration by the human operator utilizing the user interface operated by the computing system.

161. The method of claim 160, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to, as the limited steps of the process configuration continue, incorporate instructions from the human operator regarding the presented updates using the user interface operated by the computing system.

162. The method of claim 113, wherein: The user interface is configured to allow the human operator to pause the computing system while it is otherwise implementing the predetermined process configuration so that the human operator can inspect one or more intermediate results related to the established requirements.

163. The method of claim 162, wherein: The user interface is configured to allow the human operator to change one or more aspects of the one or more specific facts during a pause of the computing system to facilitate forward execution based on the changes.

164. The method of claim 113, wherein: The user interface is configured to provide the human operator with a resource cost calculated based at least in part on utilization of the operably coupled computing resources in the predetermined process configuration.

165. A synthetic participatory method for process-based problem solving, comprising: a. Providing a computing system comprising one or more operably coupled computing resources; as well as b. presenting a user interface using the computing system, the user interface being configured to engage a human operator according to a predetermined process configuration toward a given requirement based at least in part on one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to collaboratively implement the predetermined process configuration and return results selected to at least partially satisfy the established requirements to the human operator; and Wherein each of the two or more synthetic operators is informed by a convolutional neural network that is at least partially informed by historical actions of a particular actual human operator.

166. The method of claim 165, wherein: The one or more specific facts are selected from the group consisting of: text information, digital data, audio information, video information, emotional state information, simulated chaotic input selection, activity perturbation selection, curiosity selection, storage configuration, learning model, filtering configuration, and encryption configuration.

167. The method of claim 166, wherein: The one or more specific facts include text information related to specific context information from historical storage.

168. The method of claim 166, wherein: The one or more specific facts include: text information related to the actual operator.

169. The method of claim 166, wherein: The one or more specific facts include: text information related to the synthesis operator.

170. The method of claim 165, wherein: The specific facts include a predetermined profile of specific facts developed as a starting module for a specific synthetic operator profile.

171. The method of claim 165, wherein: The one or more operably coupled computing resources include a local computing resource.

172. The method of claim 171, wherein: The local computing resource is selected from the group consisting of: a mobile computing resource, a desktop computing resource, a laptop computing resource, and an embedded computing resource.

173. The method of claim 172, wherein: The local computing resource comprises an embedded computing resource selected from the group consisting of: an embedded microcontroller, an embedded microprocessor, and an embedded gate array.

174. The method of claim 165, wherein: The one or more operably coupled computing resources include resources selected from the group consisting of: a remote data center, a remote server, a remote computing cluster, and an assembly of computing systems at a remote location.

175. The method of claim 165, further comprising: A positioning element is operably coupled to the computing system, the positioning element being configured to determine a position of the human operator relative to a global coordinate system.

176. The method of claim 175, wherein: The positioning element is selected from the group consisting of: a GPS sensor, an IP address detector, a connection triangulation detector, an electromagnetic positioning sensor, an optical position sensor.

177. The method of claim 175, wherein: Also includes: Based on the determined location of the human operator, the one or more operably coupled computing resources are activated.

178. The method of claim 165, wherein: Presenting the user interface includes presenting a graphical user interface.

179. The method of claim 165, wherein: Presenting the user interface includes presenting an audio user interface.

180. The method of claim 178, wherein: Presenting the graphical user interface includes engaging the human operator using elements selected from the group consisting of: computer graphics engaging display, video graphics engaging display, and audio engaging accompanying displayed graphics.

181. The method of claim 178, wherein: Presenting the graphical user interface includes presenting a video graphical participant display configured to present a real-time or near real-time graphical representation of a video interface participant character with which the human operator can converse.

182. The method of claim 181, wherein: The video interface participating characters are selected from the group consisting of: humanoid characters, animal characters, and cartoon characters.

183. The method of claim 182, wherein: The user interface is configured to allow the human operator to select a visual representation of the video interface participating character.

184. The method of claim 183, wherein: The user interface is configured to allow the human operator to select visual presentation characteristics of the video interface participating character, the visual presentation characteristics being selected from the group consisting of: character gender, character hair color, character hairstyle, character skin color, character eye color, and character shape.

185. The method of claim 183, wherein: The visual presentation of the video interface participating character is modeled after the selected actual human being.

186. The method of claim 182, wherein: The user interface is configured to allow the human operator to select one or more audio presentation aspects of the video interface participating character.

187. The method of claim 186, wherein: The user interface is configured to allow the human operator to select one or more audio rendering aspects of the video interface participating character, the audio rendering aspects being selected from the group consisting of: character voice intonation, character voice loudness, character speaking language, character speaking dialect, and character voice dynamic range.

188. The method of claim 187, wherein: One or more audio rendering aspects of the video interface participating character are modeled after the selected actual human being.

189. The method of claim 165, wherein: The predetermined process configuration includes a finite set of steps through which the participation will further achieve the established requirements.

190. The method of claim 165, wherein: The predetermined process configuration includes process elements selected from the group consisting of: one or more common operating parameters, one or more resource / input perception and utilization settings, domain expertise modules, process sequencing paradigms, process loop / iteration paradigms, and AI utilization and configuration settings.

191. The method of claim 189, wherein: The limited set of steps includes steps selected from the group consisting of: problem definition, potential solution outline, preliminary design, and detailed design.

192. The method of claim 189, wherein: The predetermined process configuration includes selection of elements by the human operator.

193. The method according to claim 192, wherein: The human operator's selection of elements includes selecting a synthetic operator resource for one or more aspects of the predetermined process configuration.

194. The method of claim 193, wherein: The user interface is configured to allow the human operator to specify specific resources for a first specific portion of the predetermined process configuration.

195. The method of claim 194, wherein: The user interface is configured to allow the human operator to specify specific resources for a second specific portion of the predetermined process configuration, the specific resources for the second specific portion of the predetermined process configuration being different from the specific resources for the first specific portion of the predetermined process configuration.

196. The method of claim 194, wherein: The user interface is configured to allow the human operator to specify that a particular resource for a first particular portion of the predetermined process configuration is based on a plurality of composite operator roles.

197. The method according to claim 196, further comprising: Each of the plurality of composite operator roles is sequentially applied to the first specific portion.

198. The method of claim 196, further comprising: Each of the plurality of composite operator roles is simultaneously applied to the first specific portion.

199. The method of claim 194, wherein: The user interface is configured to allow the human operator to specify that a particular resource for a first particular portion of the predetermined process configuration is based on two or more hybrid composite operator roles.

200. The method of claim 194, wherein: The two or more hybrid composite operator roles include a combination of other individual composite operator roles that are simultaneously applicable to the first specific portion.

201. The method of claim 165, wherein: The convolutional neural network is informed using input from a training dataset comprising data related to the historical actions of the particular actual human operator.

202. The method according to claim 201, wherein: A supervised learning model is used to inform the convolutional neural network using input from a training dataset.

203. The method according to claim 201, wherein: The convolutional neural network is informed by analysis of the stated requirements using a reinforcement learning model using input from a training dataset.

204. The method of claim 165, wherein: Each of the two or more synthesis operators is informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthesis action records related to synthesis actions of an actual human operator.

205. The method of claim 165, wherein: Each of the two or more compositing operators is informed by a convolutional neural network that is informed, at least in part, by a curated selection of compositing action records related to the compositing actions of the compositing operator.

206. The method of claim 189, wherein: The computing system is configured to separate each of the finite set of steps from an execution step during which the one or more synthesis operators are configured to implement the established requirements according to one or more execution behaviors associated with the relevant convolutional neural network.

207. The method according to claim 206, wherein: At least one of the one or more execution behaviors is based on a project leadership influence on the associated convolutional neural network.

208. The method according to claim 207, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is configured to divide the execution steps into a plurality of tasks that can be processed by the available resources to achieve the established requirements.

209. The method according to claim 208, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to project manage completion of the plurality of tasks to achieve one or more milestones in the established requirements.

210. The method of claim 208, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at one or more stages of the execution step.

211. The method according to claim 210, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to functionally provide updates related to completion of the plurality of tasks at the conclusion of each execution step for consideration in each of the finite set of steps in the process configuration.

212. The method according to claim 211, wherein: Based at least in part on the at least one execution action based on project leadership influence, the computing system is further configured to functionally present the update for consideration by the human operator utilizing the user interface operated by the computing system.

213. The method according to claim 212, wherein: Based at least in part on the at least one execution behavior based on project leadership influence, the computing system is further configured to, as the limited steps of the process configuration continue, incorporate instructions from the human operator regarding the presented updates using the user interface operated by the computing system.

214. The method of claim 165, wherein: The user interface is configured to allow the human operator to pause the computing system while it is otherwise implementing the predetermined process configuration so that the human operator can inspect one or more intermediate results related to the established requirements.

215. The method of claim 214, wherein: The user interface is configured to allow the human operator to change one or more aspects of the one or more specific facts during a pause of the computing system to facilitate forward execution based on the changes.

216. The method of claim 165, wherein: The user interface is configured to provide the human operator with a resource cost calculated based at least in part on utilization of the operably coupled computing resources in the predetermined process configuration.

217. The method of claim 165, wherein: The two or more composite operators are configured to automatically optimize their use as resources as they implement the predetermined process configuration.

218. The method of claim 165, wherein: The system is configured to utilize the two or more composite operators to generate an initial set of decision nodes associated with the given requirement based at least in part on characteristics of the two or more composite operators.

219. The method according to claim 218, wherein The system is further configured to create an intermediate decision node group based on the initial decision node group.

220. The method of claim 219, wherein: The system is further configured to create an operational decision node group based on the intermediate decision node group.

221. The method of claim 219, wherein: The two or more composite operators are operated by the computing system to collaboratively implement the predetermined process configuration by sequencing the operational decision nodes to achieve the established requirements.

222. The method of claim 165, wherein: The two or more composition operators comprise a plurality limited only by the operably coupled computing resources.

Images