Systems and methods for synthetic computing operators and collaborative computing

Abstract

Disclosed is an approach for implementing a composite engagement system for process-based problem solving. One variation includes a computing system with one or more operably coupled computing resources, and a user interface operated by the computing system and configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more specific facts, where the user interface enables the human operator to select and interactively engage with one or more composite operators operated by the computing system.

Description

[Technical Field]

[0001] The present invention relates generally 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 the application and control of associated resources. [Background technology]

[0002] Computing systems of various types have become ubiquitous to modern life, resulting in significant improvements in various aspects of productivity. However, the expansion and amplification of human endeavors through computing has been limited, in part, due to factors such as the traditional paradigm through which humans interact with and utilize computing resources and the complexity of many aspects of the human problems at stake. For example, interfaces for utilizing computers to address specific technical problems continue to involve arcane operational interfaces, such as those illustrated in the "command line" interface (2) of FIG. 1A and the "Visual Studio" interface (4) of FIG. 1B, and specific background knowledge and experience for optimization. Of course, more user-friendly interfaces for expanding access to computing have also been developed, and some problems can be addressed relatively simply through access portals, such as web browser interfaces, such as those illustrated in FIG. 2 (6), or voice-based computing interfaces through devices, such as those illustrated in FIG. 3 (8). However, in many scenarios, the ultimate collaborating resource for a complex task remains not a computing resource, but another human resource or team of human resources with unique skills, experience, and capabilities, such as those related to operating and utilizing the computing resource, along with many other skills, experience, and capabilities.

[0003] (For example, the trademark AIexa TM or Google Assistant TM The advent of readily available generalized "artificial intelligence" (or "AI") computing systems, such as those available from providers such as Amazon, Inc. or Google, Inc. (below), has helped provide relatively expedient, hands-free, and low-latency responses to problems such as "What is the capital of Oregon?" However, such systems are generally less well-suited for complex, multifactorial problems such as a) designing the next successful Ford Mustang and returning production-ready design and manufacturing documentation, b) creating the music that will become the next Beatles album, or c) creating the next significant improvement to a successful consumer electronics product and returning production-ready design and manufacturing documentation. Again, such problems are typically assigned to teams of talented and experienced humans, and inherently present the associated human factors-related challenges of finding the best people, keeping them motivated, focusing them on the task, bringing them together as needed, and enabling them to provide functional synergy for mutual and overall purposes. In other words, engaging and maintaining the very best team for a given task is very complex, difficult, expensive, and hard to scale.

[0004] Indeed, looking more closely at these three stated challenges, a typical high-level paradigm for the first challenge (designing the next successful Ford Mustang), as illustrated in FIG. 4, might involve the following: a) assemble a core team of designers, mechanical engineers, electrical engineers, suspension engineers, drivetrain engineers, materials experts, regulatory experts, product marketing experts, manufacturing experts, cost control experts, external marketing experts, sales experts, project managers, and technical and general management experts (10); b) work collaboratively to understand what the Ford Mustang has been like in the past, what has worked, what has not worked, and the goals the product or product line needs to move toward, not only in light of artistic and performance constraints but also, among other things, regulatory and cost controls (12); c) determine a high-level design that will benefit from their collective expertise in a collaborative manner (14); d) develop one or more detailed designs that can be iterated through in many details and physically prototyped and / or tested (16); and e) manufacture, market, and sell the new Ford Mustang in the required numbers and at the required operating margins, providing a positive contribution to the company (18). Carrying out such a multivariate and complex project, or even acquiring and reserving the appropriate resources to do so, is a tremendous challenge that is very difficult to undertake successfully, and many would point out that the likelihood of achieving such a challenge with an ultimate positive contribution margin is very low, and the initial costs are extremely high.

[0005] A typical high-level paradigm for the second aforementioned task (creating the music that will become the next Beatles album), for example, as illustrated in Figure 5, may involve different resources, but is likely no less complex or risky. a) Select a producer who is familiar with the Beatles' music and has knowledge of what made them great, their musical evolution at the time of their breakup, what the Beatles should and should not have sounded like, what they sang about during that time, the sounds of instruments from that time and how to use modern and / or period instruments to recreate those sounds, and everything possible about Ringo, John, Paul, and George (20); b) Select musicians who are familiar with the Beatles' music and have knowledge of what made them great, their musical evolution at the time of their breakup, what the Beatles should and should not have sounded like, what they sang about during that time, the sounds of instruments from that time and how to use modern and / or period instruments to recreate those particular instruments (22); and c) Work together to compose and record a new album's worth of songs in a manner that will result in a deliverable commensurate with that mission (24). Again, carrying out such a multivariate and complex project, or even acquiring and reserving the appropriate resources to do so, is a tremendous challenge that is very difficult to successfully address, and many will point out that the likelihood of accomplishing such a task is very low and the initial costs are extremely high.

[0006] Users can register for AIexa TMIf one were to attempt to accomplish one of the aforementioned tasks using a generalized AI system such as a bot, the answer would likely be something similar to, "It can't be done." If a user attempts to utilize traditional computing resources and utility paradigms (such as search queries, audio files, video files, and the like), the tasks are highly complex, inefficient, and difficult to scale, in part due to the complexity of these tasks and in part due to traditional paradigms for interacting with and utilizing computing resources, which, as noted above, is why the best collaborative resources for these types of tasks are often teams of talented individuals; naturally, the associated challenges are in recruiting, retaining, engaging, and getting such individuals to perform in a successful manner. Indeed, the concept of attempting access to accomplish complex tasks can itself be extremely difficult, even for an individual human being, much less a team. Referring to FIG. 6A, for example, one variation of a model (30) for increasing the likelihood of success for an individual (28) given a particular challenge (32) is illustrated, illustrating that many inputs and factors can influence meeting a challenge and successfully meeting goals / objectives (32), including, but not limited to, knowledge (34), experience (36), resources (38), analytical skills (40), technical skills (42), efficiency (44), an environment that appropriately fosters success (46), an appropriate risk / reward paradigm (48), collaboration / "interpersonal" skills (50), hard work (52), intuition regarding marketability and / or the value of various alternatives (54), understanding of the business opportunity (56), communication skills (58), time (60), and willingness / ability to overcome adversity (62).

[0007] While many will point out that Figure 6A illustrates only one of many models that can help characterize the multifaceted challenges of getting a person to reach a goal, few would argue that such challenges are multifactorial, complex, and difficult to address, again with reference to assigning a single resource to attempting to address a complex challenge.

[0008] Figure 6B illustrates one variation of a related process flow, in which a problem is identified, outlined in detail, and considered to be resourced by a single human resource (64). The single human resource may be identified and / or assigned (66). The resource may clarify their understanding of the problem's goals and objectives, along with background on available resources and related business opportunities, as needed (68). At this point, the resource may be in a "ready to execute" condition (70). Using assets such as skills, knowledge, experience, and intuition, the resource initiates and works through the problem (72), driven by factors such as diligence, time, collaboration / interpersonal skills, an appropriate risk / reward paradigm, an environment structured to foster success, efficiency, resources (information, computing, etc.), the willingness / ability to overcome challenges and adversity, and communication skills. The resource may utilize similar assets to drive factors (74) to iterate and improve the tentative solution. Finally, the resource may generate a final solution to address the goal / objective (76).

[0009] Again, the above sample process for a single human resource to address a particular challenge is complex, with opportunities for failure or suboptimal results at many stages. Indeed, as with any human resource-related process, there are additional human factors issues that can affect the process, such as hiring difficulties, lack of suitable personnel, interpersonal issues, limitations on throughput due to human capacity, vacation days, family issues, etc. The teams and resources and scale required to optimally address complex challenges such as those described above with reference to vehicle design goals, music production goals, and consumer product goals add significantly more complexity, and these paradigms likely contribute to relatively high failure rates in attempts to address challenges of comparable complexity (many vehicle designs fail, many attempts at successful music creation fail, many iterative manufacturing of consumer electronics devices fail).

[0010] With reference to Figures 7A-8C and 9A-10, several advances in computing support human-scale tasks of a certain level of complexity. For example, with reference to Figure 7A, a robot (78) such as that available under the trade name PR2™, or "Personal Robot 2," generally features a head module (84) featuring various vision and sensing devices, a mobile base (86), a left arm (80) with a gripper, and a right arm (82) with a gripper. With reference to Figures 7B-7K, such a robot (78) is utilized to address a task, such as approaching a stack of towels (88) on a first platform (92), selecting a towel (90), and folding the towel (90) on a second platform (93), as shown in the sequence of Figures 7B-7K. Referring to Figure 8A, an event chart is illustrated in which such a robot may be configured to progress through a series of events (such as events E1-E10) in sequence to fold a towel. Figure 8B illustrates an associated event sequence (96) list, showing that events E1-E10 are addressed in sequence. 8C, an associated flowchart illustrates how the seemingly at least somewhat complex task of folding towels can be addressed using a sequence of steps, such as powering on the system, becoming ready at a first laundry work station (102), identifying and lifting one towel at the first station (104), identifying a first corner of one towel (106), identifying a second corner of the selected towel (108), moving to a second station (110), applying tension between two adjacent corners of the towel, spreading the towel on the station for folding (112), performing a first fold of the towel (114), performing a second fold of the towel (116), lifting the twice-folded towel and moving it to a stack destination on the second station (118), and performing a final flattening of the folded towel (120). This sequence of events is utilized by the system in a single-threaded execution to perform the human-scale task of folding towels.However, getting such a system to perform such a task requires a significant amount of programming and experimentation, and is generally much slower at runtime than human performance with only the most basic level of attention for simple tasks.

[0011] 9A-10, another at least somewhat complex challenge is illustrated in which a small robotic system, such as that available under the trademark name TurtleBot™ (126), utilizing a LIDAR scanner device (130) and a mobile base (132), can be programmed and prepared using machine learning techniques to scan obstacles (134) and successfully navigate within a real room (136) at runtime based on training using a synthetic environment (122) with synthetic obstacles (124) and a simulation of LIDAR scanning capabilities (128) for learning purposes. 10 , robot and sensor hardware may be selected for a navigation challenge (140), a goal may be established for a reinforcement learning approach (i.e., for the robot to autonomously reach a specified target in X / Y coordinates at a location within a maze defined by walls / objects placed on a generally planar surface) (142), a synthetic training environment may be created so that the synthetic robot may synthetically / autonomously explore the synthetic maze and iteratively reach various specified goal locations (144), and at runtime, a real robot may navigate the real maze or room using a trained convolutional neural network (“CNN”) with the goal of reaching a real pre-selected target within the room (146). Thus, while certain machine learning techniques, such as a nearly single-threaded sequence of smaller decisions to navigate obstacles in a maze or room, may similarly be utilized to address the computing challenge, access to such solutions remains limited, suboptimal, and generally requires significant knowledge of computing, sensors, robots, and the like.

[0012] There is a continuing need for computing techniques and configurations to assist users in scalably and efficiently performing tasks of significant human complexity and sophistication. Described herein are systems, methods, and configurations for improving the interaction between human users and computing resources for a variety of purposes, including, but not limited to, computing systems, methods, and configurations featuring one or more synthetic computing interface operators configured to assist in the application and control of associated resources. Summary of the Invention [Means for solving the problem]

[0013] One embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operably coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more specific facts, the user interface being configured to enable the human operator to select, interactively engage with, and progress through the predetermined process configuration of one or more synthetic operators operated by the computing system, and return results to the human operator that are selected, at least in part, to satisfy the established requirements, the one or more synthetic operators each being informed, at least in part, by a convolutional neural network informed by the historical actions of a particular actual human operator. The one or more specific facts may be selected from the group consisting of text information, numerical data, audio information, video information, emotional state information, analog chaotic input selection, activity disturbance selection, curiosity selection, memory configuration, learning model, filtration configuration, and encryption configuration. The one or more concrete facts may include text information regarding concrete background information from a history storage device. The one or more concrete facts may include text information regarding an actual operator. The one or more concrete facts may include text information regarding a composite operator. The concrete facts may include a predetermined profile of concrete facts developed as a starting module for a concrete composite operator profile. The one or more operably coupled computing resources may comprise 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 comprise an embedded computing resource selected from the group consisting of an embedded microcontroller, an embedded microprocessor, and an embedded gate array. The one or more operably coupled computing resources may comprise 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 further comprise a location element operably coupled to the computing system and configured to determine a location of the human operator relative to a global coordinate system. The location element may be selected from the group consisting of a GPS sensor, an IP address detector, a connectivity triangulation detector, an electromagnetic location sensor, and an optical location sensor. The one or more operably coupled computing resources may be activated based on the determined location of the human operator. The user interface may comprise a graphical user interface. The user interface may comprise an audio user interface. The graphical user interface may be configured to engage the human operator using an element selected from the group consisting of a computer graphic engagement display, a video graphic engagement display, and audio engagement with a graphic display. The graphical user interface may comprise a video graphic engagement display configured to present a real-time or near real-time graphical representation of a video interface engagement character with which a human operator may interact. The video interface engagement 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 engagement character.The user interface may be configured to allow a human operator to select visual presentation characteristics of the video interface participating characters 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 participating characters may be modeled from a 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 participating characters selected from the group consisting of character voice intonation, character voice volume, character speaking language, character speaking dialect, and character voice dynamic range. The one or more audio presentation aspects of the video interface participating characters may be modeled from a selected actual human. The predetermined process configuration may include a finite set of steps through which participation will proceed in furtherance of the established requirements. The predetermined process configuration may include process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, a process sequential paradigm, a process cyclical / iterative paradigm, and AI utilization and configuration settings. The finite set of steps may include steps selected from the group consisting of a problem definition, a potential solution outline, a preliminary design, and a detailed design. The predetermined process configuration may include selection of elements by a human operator. Selection of elements by a human operator may include selecting a composite operator resource allocation for one or more aspects of the predetermined process configuration. The system may be configured to enable a human operator to specify a specific resource allocation for a first specific portion of the predetermined process configuration.The system may be configured to enable a human operator to specify a specific resource allocation for a second concrete portion of a predetermined process configuration that differs from the specific resource allocation for the first concrete portion of the predetermined process configuration. The system may be configured to enable a human operator to specify a specific resource allocation for the first concrete portion of a predetermined process configuration based on multiple composite operator characters. Each of the multiple composite operator characters may be applied to the first concrete portion sequentially. Each of the multiple composite operator characters may be applied to the first concrete portion simultaneously. The system may be configured to enable a human operator to specify a specific resource allocation for the first concrete portion of a predetermined process configuration based on one or more hybrid composite operator characters. The one or more hybrid composite operator characters may comprise a combination of otherwise distinct composite operator characters that may be applied to the first concrete portion simultaneously. The convolutional neural network may be informed using input from a training dataset that includes data regarding the historical actions of a particular actual human operator. The convolutional neural network may be informed using input from a training dataset using a supervised learning model. The convolutional neural network may be informed using inputs from a training dataset along with an analysis of the established requirements using a reinforcement learning model. Each of the one or more composite operators may be informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthetic action records for the composite actions of actual human operators. Each of the one or more composite operators may be informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthetic action records for the composite operators' synthetic actions.The computing system may be configured to separate each of the finite steps with an execution step, during which one or more composition operators are configured to progress toward 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. The computing system may be configured to divide the execution steps into multiple tasks that can be addressed by available resources in furtherance of the established requirements based at least in part on the at least one execution behavior based on project leadership influence. The computing system may be further configured to project manage the performance of the multiple tasks toward one or more milestones in pursuit of the established requirements based at least in part on the at least one execution behavior based on project leadership influence. The computing system may be further configured to functionally provide updates regarding the performance of the multiple tasks during one or more phases of the execution steps based at least in part on the at least one execution behavior based on project leadership influence. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for review at each of the finite steps within the process configuration based at least in part on the at least one execution behavior based on the influence of project leadership. The computing system may be further configured to functionally present the updates for review by a human operator utilizing a user interface operated by the computing system based at least in part on the at least one execution behavior based on the influence of project leadership.The computing system may be further configured to incorporate instructions regarding presented updates from a human operator utilizing a user interface operated by the computing system as finite steps of the process configuration continue based at least in part on at least one execution behavior based on the influence of project leadership. The user interface may be configured to allow the human operator to pause the computing system while proceeding through an otherwise predetermined process configuration so that one or more intermediate results may be inspected by the human operator with respect to established requirements. The user interface may allow the human operator to review one or more of the one or more specific facts during the suspension of the computing system. The user interface may be configured to allow a human operator to modify aspects and facilitate future execution based on the modifications. The user interface may be configured to provide a human operator with calculated resource allocation costs based, at least in part, on utilization of operably coupled computing resources in a predetermined process configuration.

[0014] Another embodiment is directed to a synthetic engagement system for process-based problem solving, comprising: a computing system having one or more operably coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more specific facts, the user interface being configured to enable the human operator to select, interactively engage with, and collaboratively progress through the predetermined process configuration with two or more synthetic operators operated by the computing system, and return results to the human operator that are selected, at least in part, to satisfy the established requirements, the two or more synthetic operators each being informed, at least in part, by a convolutional neural network informed by the historical actions of a particular actual human operator. The one or more specific facts may be selected from the group consisting of text information, numerical data, audio information, video information, emotional state information, analog chaotic input selection, activity disturbance selection, curiosity selection, memory configuration, learning model, filtration configuration, and encryption configuration. The one or more concrete facts may include text information regarding concrete background information from a history store. The one or more concrete facts may include text information regarding an actual operator. The one or more concrete facts may include text information regarding a composite operator. The concrete facts may include a predetermined profile of a concrete fact developed as a starting module for a concrete composite operator profile. The one or more operably coupled computing resources may comprise 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 comprise an embedded computing resource selected from the group consisting of an embedded microcontroller, an embedded microprocessor, and an embedded gate array. The one or more operably coupled computing resources may comprise 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 further comprise a location element operably coupled to the computing system and configured to determine a location of the human operator relative to a global coordinate system. The location element may be selected from the group consisting of a GPS sensor, an IP address detector, a connectivity triangulation detector, an electromagnetic location sensor, and an optical location sensor. The one or more operably coupled computing resources may be activated based on the determined location of the human operator. The user interface may comprise a graphical user interface. The user interface may comprise an audio user interface. The graphical user interface may be configured to engage with a human operator using elements selected from the group consisting of a computer graphic engagement display, a video graphic engagement display, and audio engagement with a graphic display. The graphical user interface may comprise a video graphic engagement display configured to present a real-time or near real-time graphical representation of a video interface engagement character with which the human operator may interact. The video interface engagement 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 a human operator to select a visual presentation of a video interface participating character. The user interface may be configured to allow a human operator to select a visual presentation characteristic of a video interface participating 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 participating character may be modeled from a selected actual human. The user interface may be configured to allow a human operator to select one or more audio presentation aspects of a video interface participating character selected from the group consisting of character voice intonation, character voice volume, character speaking language, character speaking dialect, and character voice dynamic range. The one or more audio presentation aspects of a video interface participating character may be modeled from a selected actual human. The predetermined process configuration may include a finite set of steps through which participation will proceed in furtherance of the established requirements. The predetermined process configuration may include process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, a process sequential paradigm, a process cyclical / iterative paradigm, and AI utilization and configuration settings. The finite set of steps may include steps selected from the group consisting of a problem definition, a potential solution outline, a preliminary design, and a detailed design. The predetermined process configuration may include selection of elements by a human operator. Selection of elements by a human operator may include selecting a composite operator resource allocation for one or more aspects of the predetermined process configuration.The system may be configured to enable a human operator to specify a specific resource allocation for a first concrete portion of a predetermined process configuration. The system may be configured to enable a human operator to specify a specific resource allocation for a second concrete portion of a predetermined process configuration that differs from the specific resource allocation for the first concrete portion of the predetermined process configuration. The system may be configured to enable a human operator to specify a specific resource allocation for a first concrete portion of a predetermined process configuration based on multiple composite operator characters. Each of the multiple composite operator characters may be applied to the first concrete portion sequentially. Each of the multiple composite operator characters may be applied to the first concrete portion simultaneously. The system may be configured to enable a human operator to specify a specific resource allocation for a first concrete portion of a predetermined process configuration based on one or more hybrid composite operator characters. The one or more hybrid composite operator characters may comprise a combination of otherwise distinct composite operator characters that may be applied to the first concrete portion simultaneously. The convolutional neural network may be informed using input from a training dataset that includes data regarding the historical actions of a particular actual human operator. The convolutional neural network may be informed using inputs from a training data set using a supervised learning model. The convolutional neural network may be informed using inputs from a training data set along with an analysis of established requirements using a reinforcement learning model. Two or more synthetic operators may each be informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthetic action recordings of synthetic actions of actual human operators.The two or more composition operators may each be informed by a convolutional neural network, which is informed, at least in part, by a curated selection of composition action records related to the composition operators' composition actions. The computing system may be configured to separate each of the finite sets of steps with an execution step, during which the two or more composition operators are configured to progress toward 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. The computing system may be configured to divide the execution steps into multiple tasks that can be addressed by available resources in furtherance of the established requirements based, at least in part, on the at least one execution behavior based on project leadership influence. The computing system may be further configured to project manage the performance of the multiple tasks toward one or more milestones in pursuit of the established requirements based, at least in part, on the at least one execution behavior based on project leadership influence. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at one or more stages of the execution steps based at least in part on the at least one execution behavior based on the influence of the project leadership. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for consideration at each of the finite steps in the process configuration based at least in part on the at least one execution behavior based on the influence of the project leadership.The computing system may be further configured to functionally present updates for review 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 the influence of project leadership. The computing system may be further configured to incorporate instructions regarding the presented updates from a human operator utilizing a user interface operated by the computing system as finite steps of the process configuration continue based at least in part on at least one execution behavior based on the influence of project leadership. The user interface may be configured to allow the human operator to pause the computing system while the human operator progresses through an otherwise predetermined process configuration so that one or more intermediate results may be inspected by the human operator with respect to established requirements. The user interface may allow the human operator to review one or more specific facts during the pause of the computing system. The system may be configured to allow a human operator to modify aspects thereof and facilitate future execution based on the modifications. The user interface may be configured to provide the human operator with calculated resource allocation costs based, at least in part, on utilization of operably coupled computing resources in the predetermined process configuration. The system may be configured to allow a human operator to specify that two or more composite operators are different. The system may be configured to allow a human operator to specify that two or more composite operators are identical and may be configured to collaboratively expand their productivity as they progress through the predetermined process configuration. The two or more composite operators may be configured to automatically optimize their application as resources progress through the predetermined process configuration. The system may be configured to generate initial decision nodes associated with the established requirements utilizing two or more composite operators based, at least in part, on characteristics of the two or more composite operators. The system may be further configured to create intermediary decision nodes based on the initial decision nodes. The system may be further configured to create operational decision nodes based on the intermediary decision nodes. Two or more composite operators may be operated by a computing system to cooperatively advance through a predetermined process configuration by sequentially progressing through operational decision nodes in furtherance of established requirements. The two or more composite operators may have a plurality, limited only by the computing resources to which they are operatively coupled.

[0015] Another embodiment is directed to a synthetic engagement method for process-based problem solving, comprising: providing a computing system with one or more operably coupled computing resources; and presenting a user interface configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more concrete facts, the user interface configured to enable the human operator to select and interactively engage with one or more synthetic operators operated by the computing system to progress through the predetermined process configuration and return results to the human operator that are selected, at least in part, to satisfy the established requirements, the one or more synthetic operators each being informed, at least in part, by a convolutional neural network informed by the historical actions of a particular actual human operator. The one or more concrete facts may be selected from the group consisting of text information, numerical data, audio information, video information, emotional state information, analog chaotic input selection, activity disturbance selection, curiosity selection, memory configuration, learning model, filtration configuration, and encryption configuration. The one or more concrete facts may include text information regarding concrete background information from a history storage device. The one or more concrete facts may include text information regarding an actual operator. The one or more concrete facts may include text information regarding a composite operator. The concrete facts may include a predetermined profile of concrete facts developed as a starting module for a concrete composite operator profile. The one or more operably coupled computing resources may comprise 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 comprise an embedded computing resource selected from the group consisting of an embedded microcontroller, an embedded microprocessor, and an embedded gate array. The one or more operably coupled computing resources may comprise 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. The method may further include operably coupling a location element to a computing system configured to determine a location of the human operator relative to a global coordinate system. The location element may be selected from the group consisting of a GPS sensor, an IP address detector, a connectivity triangulation detector, an electromagnetic location sensor, and an optical location sensor. The method may further include activating the one or more operably coupled computing resources based on the determined location 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 a graphical user interface may include engaging the human operator using an element selected from the group consisting of a computer graphic engagement display, a video graphic engagement display, and audio engagement with a graphical display. Presenting the graphical user interface may include presenting a video graphic engagement display configured to present a real-time or near real-time graphical representation of a video interface engagement character with which a human operator may interact. The video interface engagement 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 engagement character.The user interface may be configured to allow a human operator to select visual presentation characteristics of the video interface participating characters 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 participating characters may be modeled from a 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 participating characters selected from the group consisting of character voice intonation, character voice volume, character speaking language, character speaking dialect, and character voice dynamic range. The one or more audio presentation aspects of the video interface participating characters may be modeled from a selected actual human. The predetermined process configuration may include a finite set of steps through which participation will proceed in furtherance of the established requirements. The predetermined process configuration may include process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, a process sequential paradigm, a process cyclical / iterative paradigm, and AI utilization and configuration settings. The finite set of steps may include steps selected from the group consisting of a problem definition, a potential solution outline, a preliminary design, and a detailed design. The predetermined process configuration may include selection of elements by a human operator. Selection of elements by the human operator may include selecting a composite operator resource allocation for one or more aspects of the predetermined process configuration. The user interface may be configured to enable the human operator to specify a specific resource allocation for a first specific portion of the predetermined process configuration.The user interface may be configured to allow a human operator to specify a specific resource allocation for a second concrete portion of the predetermined process configuration that differs from the specific resource allocation for the first concrete portion of the predetermined process configuration. The user interface may be configured to allow a human operator to specify a specific resource allocation for the first concrete portion of the predetermined process configuration based on a plurality of composite operator characters. The method may further include sequentially applying each of the plurality of composite operator characters to the first concrete portion. The method may further include simultaneously applying each of the plurality of composite operator characters to the first concrete portion. The user interface may be configured to allow a human operator to specify a specific resource allocation for the first concrete portion of the predetermined process configuration based on one or more hybrid composite operator characters. The one or more hybrid composite operator characters may comprise a combination of otherwise distinct composite operator characters that may be applied to the first concrete portion simultaneously. The convolutional neural network may be informed using input from a training dataset that includes data regarding the historical actions of certain actual human operators. The convolutional neural network may be informed using inputs from a training data set using a supervised learning model. The convolutional neural network may be informed using inputs from a training data set along with an analysis of established requirements using a reinforcement learning model. One or more synthetic operators may each be informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthetic action recordings of synthetic actions of actual human operators.The one or more composition operators may each be informed by a convolutional neural network, which is informed, at least in part, by a curated selection of composition action records related to the composition operators' composition actions. The computing system may be configured to separate each of the finite set of steps with an execution step, during which the one or more composition operators are configured to progress toward 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. The computing system may be configured to divide the execution steps into multiple tasks that can be addressed by available resources in furtherance of the established requirements based, at least in part, on the at least one execution behavior based on project leadership influence. The computing system may be further configured to project manage the performance of the multiple tasks toward one or more milestones in pursuit of the established requirements based, at least in part, on the at least one execution behavior based on project leadership influence. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at one or more stages of the execution steps based at least in part on the at least one execution behavior based on the influence of the project leadership. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for consideration at each of the finite steps in the process configuration based at least in part on the at least one execution behavior based on the influence of the project leadership.The computing system may be further configured to functionally present updates for review 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 the influence of project leadership. The computing system may be further configured to incorporate instructions regarding the presented updates from the human operator utilizing a user interface operated by the computing system as finite steps of the process configuration continue based at least in part on at least one execution behavior based on the influence of project leadership. The user interface may be configured to provide instructions regarding the presented updates from the human operator utilizing a user interface operated by the computing system as the human operator progresses through an otherwise predetermined process configuration. The user interface may be configured to allow a human operator to pause the computing system so that the system can be inspected for established requirements. The user interface may be configured to allow a human operator to modify one or more aspects of one or more specific facts during the suspension of the computing system and facilitate future execution based on the modifications. The user interface may be configured to provide the human operator with calculated resource allocation costs based, at least in part, on utilization of computing resources operably coupled in a predetermined process configuration.

[0016] Another embodiment is directed to a synthetic engagement method for process-based problem solving, comprising: providing a computing system with one or more operably coupled computing resources; and presenting, in a user interface, the computing system configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more concrete facts, the user interface configured to enable the human operator to select, interactively engage with, and collaboratively progress through the predetermined process configuration with two or more synthetic operators operated by the computing system, and return results to the human operator that are selected, at least in part, to satisfy the established requirements, the two or more synthetic operators each being informed, at least in part, by a convolutional neural network informed by the historical actions of a particular actual human operator. The one or more concrete facts may be selected from the group consisting of text information, numerical data, audio information, video information, emotional state information, analog chaotic input selection, activity disturbance selection, curiosity selection, memory configuration, learning model, filtration configuration, and encryption configuration. The one or more concrete facts may include text information regarding concrete background information from a history store. The one or more concrete facts may include text information regarding an actual operator. The one or more concrete facts may include text information regarding a composite operator. The concrete facts may include a predetermined profile of a concrete fact developed as a starting module for a concrete composite operator profile. The one or more operably coupled computing resources may comprise 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 comprise an embedded computing resource selected from the group consisting of an embedded microcontroller, an embedded microprocessor, and an embedded gate array. The one or more operably coupled computing resources may comprise 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 further include operably coupling a localization element to a computing system configured to determine a location of the human operator relative to a global coordinate system. The localization element may be selected from the group consisting of a GPS sensor, an IP address detector, a connectivity triangulation detector, an electromagnetic location sensor, and an optical location sensor. The method may further include activating the one or more operably coupled computing resources based on the determined location 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 the human operator using an element selected from the group consisting of a computer graphic engagement display, a video graphic engagement display, and audio engagement with a graphic display. Presenting the graphical user interface may include presenting a video graphic engagement display configured to present a real-time or near real-time graphical representation of a video interface engagement character with which the human operator may converse.The video interface participating characters may be selected from the group consisting of humanoid characters, animal characters, and cartoon characters. The user interface may be configured to enable a human operator to select a visual presentation of the video interface participating characters. The user interface may be configured to enable a human operator to select visual presentation characteristics of the video interface participating characters 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 participating characters may be modeled after a selected actual human. The user interface may be configured to enable a human operator to select one or more audio presentation aspects of the video interface participating characters selected from the group consisting of character voice intonation, character voice volume, character speaking language, character speaking dialect, and character voice dynamic range. The one or more audio presentation aspects of the video interface participating characters may be modeled after a selected actual human. The predetermined process configuration may include a finite set of steps through which participation will proceed in furtherance of the established requirements. The predetermined process configuration may include process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, a process sequential paradigm, a process cyclical / iterative paradigm, and AI utilization and configuration settings. The finite set of steps may include steps selected from the group consisting of a problem definition, a potential solution outline, a preliminary design, and a detailed design. The predetermined process configuration may include element selection by a human operator.The selection of elements by the human operator may include selecting a composite operator resource allocation for one or more aspects of the predetermined process configuration. The user interface may be configured to enable the human operator to specify a specific resource allocation for a first concrete portion of the predetermined process configuration. The user interface may be configured to enable the human operator to specify a specific resource allocation for a second concrete portion of the predetermined process configuration that differs from the specific resource allocation for the first concrete portion of the predetermined process configuration. The user interface may be configured to enable the human operator to specify a specific resource allocation for the first concrete portion of the predetermined process configuration based on multiple composite operator characters. The method may further include sequentially applying each of the multiple composite operator characters to the first concrete portion. The method may also include simultaneously applying each of the multiple composite operator characters to the first concrete portion. The user interface may be configured to enable the human operator to specify a specific resource allocation for the first concrete portion of the predetermined process configuration based on two or more hybrid composite operator characters. The convolutional neural network may be trained using inputs from a training dataset that includes data regarding the historical actions of a particular actual human operator. The convolutional neural network may be trained using inputs from a training dataset using a supervised learning model. The convolutional neural network may be trained using inputs from a training dataset along with an analysis of established requirements using a reinforcement learning model.Each of the two or more composite operators may be informed by a convolutional neural network that is informed, at least in part, by a curated selection of composite action records of composite actions of actual human operators. Each of the two or more composite operators may be informed by a convolutional neural network that is informed, at least in part, by a curated selection of composite action records of the composite operators' composite actions. The computing system may be configured to separate each of the finite sets of steps with an execution step, during which the two or more composite operators are configured to progress toward 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. The computing system may be configured to divide the execution steps into multiple tasks that can be addressed by available resources in furtherance of the established requirements based, at least in part, on the at least one execution behavior that is based on project leadership influence. The computing system may be further configured to project manage the performance of the plurality of tasks toward one or more milestones in pursuit of the established requirements based at least in part on the at least one execution behavior based on the influence of project leadership. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks in one or more stages of execution based at least in part on the at least one execution behavior based on the influence of project leadership.The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for review at each of the finite steps within the process configuration based at least in part on at least one execution behavior based on the influence of project leadership. The computing system may be further configured to functionally present the updates for review 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 the influence of project leadership. The computing system may be further configured to incorporate instructions regarding the presented updates from the human operator utilizing a user interface operated by the computing system as the finite steps of the process configuration continue based at least in part on at least one execution behavior based on the influence of project leadership. The user interface may present one or more intermediate results to the human operator as the human operator progresses through the otherwise predetermined process configuration. The system may be configured to allow a computing system to be paused so that the established requirements can be examined by the user interface. The user interface may be configured to allow a human operator to modify one or more aspects of one or more specific facts during the suspension of the computing system and to facilitate future execution based on the modifications. The user interface may be configured to provide the human operator with a calculated resource allocation cost based, at least in part, on utilization of the computing resources operably coupled in the predetermined process configuration. The two or more composition operators may be configured to automatically optimize their application as resources progress through the predetermined process configuration. The system may be configured to generate initial decision nodes associated with the established requirements utilizing two or more composition operators based, at least in part, on characteristics of the two or more composition operators. The system may be further configured to create intermediary decision nodes based on the initial decision nodes. The system may be further configured to create action decision nodes based on the intermediary decision nodes. The two or more composition operators may be operated by the computing system to cooperatively progress through the predetermined process configuration by sequentially progressing through the action decision nodes in furtherance of the established requirements. The two or more compositing operators may have a plurality, limited only by the computing resources to which they are operatively coupled. [Brief explanation of the drawings]

[0017] [Figure 1A] 1A and 1B illustrate aspects of a computing interface. [Figure 1B] 1A and 1B illustrate aspects of a computing interface.

[0018] [Figure 2] 2 and 3 illustrate aspects of the computing interface. [Figure 3] 2 and 3 illustrate aspects of the computing interface.

[0019] [Figure 4] Figure 4 illustrates aspects of the process for a hypothetical engineering project.

[0020] [Figure 5] Figure 5 illustrates aspects of the process for a hypothetical music project.

[0021] [Figure 6A] 6A and 6B illustrate aspects of a paradigm for engaging human resources and moving toward a goal or objective. [Figure 6B] 6A and 6B illustrate aspects of a paradigm for engaging human resources and moving toward a goal or objective.

[0022] [Figure 7A] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7B] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7C] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7D] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7E]7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7F] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7G] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7H] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7I] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7J] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 7K] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 8A] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 8B] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal. [Figure 8C] 7A-7K and 8A-8C illustrate aspects of the complexity that may be involved in having a computer-based robotic system accomplish a task or goal.

[0023] [Figure 9A]9A-9C illustrate aspects of electromechanical configurations that may be utilized to navigate and / or map an environment. [Figure 9B] 9A-9C illustrate aspects of electromechanical configurations that may be utilized to navigate and / or map an environment. [Figure 9C] 9A-9C illustrate aspects of electromechanical configurations that may be utilized to navigate and / or map an environment.

[0024] [Figure 10] FIG. 10 illustrates aspects of a process configuration for navigating utilizing an electromechanical system to address objectives such as maze navigation.

[0025] [Figure 11] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 12] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 13A] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 13B] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 13C]11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 14A] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 14B] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 14C] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 14D] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 14E] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 15] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts. [Figure 16] 11A-B, 12A-D, 13A-13C, and 14A-E, 15A-B, and 16 illustrate aspects of compositions in which relatively simple line drawings can be utilized to assist an automated system in generating more detailed artistic or graphical artifacts.

[0026] [Figure 17A] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 17B] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 17C] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 17D] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 17E] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 17F] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 17G]17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18A] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18B] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18C] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18D] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18E] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18F] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter. [Figure 18G] 17A-G and 18A-G illustrate aspects of automated design configuration and process embodiments in which complex artifacts such as shoes, automobiles, or component parts thereof, can be fabricated using the computerized configuration of the present subject matter.

[0027] [Figure 19A] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems. [Figure 19B] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems. [Figure 19C] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems. [Figure 19D] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems. [Figure 20A] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems. [Figure 20B] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems. [Figure 20C] 19A-19D and 20A-20C illustrate various aspects of convolutional neural network configurations that can be utilized to help solve complex problems.

[0028] [Figure 21A] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 21B]21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 21C] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 22] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 23A] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 23B] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 23C] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 24A] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 24B] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans. [Figure 24C] 21A-21C, 22, 23A-23C, and 24A-24C illustrate various complexities of configuration variations that can be utilized to help solve complex problems such as those more commonly addressed by teams of humans.

[0029] [Figure 25] 25, 26, and 27A-27B illustrate various aspects of interfaces that may be utilized to assist user feedback and control regarding team function, cost, and time domain related issues. [Figure 26] 25, 26, and 27A-27B illustrate various aspects of interfaces that may be utilized to assist user feedback and control regarding team function, cost, and time domain related issues. [Figure 27A] 25, 26, and 27A-27B illustrate various aspects of interfaces that may be utilized to assist user feedback and control regarding team function, cost, and time domain related issues. [Figure 27B] 25, 26, and 27A-27B illustrate various aspects of interfaces that may be utilized to assist user feedback and control regarding team function, cost, and time domain related issues.

[0030] [Figure 28A] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 28B] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 28C]28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 29A] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 29B] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 29C] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 30A] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 30B] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 30C] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 30D]28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. [Figure 31] 28A-28C, 29A-29C, 30A-30D and 31 illustrate aspects of system configurations that can be utilized to provide precise control of computerized processing to address complex challenges more commonly addressed by human teams. DETAILED DESCRIPTION OF THE INVENTION

[0031] Detailed Description With reference to FIGS. 11A-16, a relatively simple task of creating a colored cartoon is utilized to illustrate compositing operator configuration, whereby a user may utilize significant computing resources to address the task.

[0032] With reference to Figure 11A, a cartoon character named "Andy" (150) is illustrated comprising a relatively simple wireframe drawing. With reference to Figure 11B, the character's basic structure may be represented using a stick figure or a collection of line segments (152), with the segments representing the positioning of the character's head (154), neck (156), shoulders (158), left arm (160), right arm (162), torso (164), waist (166), left leg (168), and right leg (170). With reference to Figures 12A-12D, for example, a very simple cartoon sequence may comprise a series of views of the character (150) standing upright, raising his right hand (160), lowering his right hand, and then raising his left hand (162). Indeed, referring to Figure 13A, suppose a user desires a computing system to automatically generate a series of cartoon images and colorize them sequentially so that they can be viewed as perceived as a simple color cartoon (172). The user may provide requirements such as the user prefers that the cartoon character "Andy" perform some simple arm movements against a generic outdoor background in "old cartoon style," that in "basic coloring," Andy remain in black and white, that "VGA frames (640x480) are sufficient," and that the total length be "30 seconds" (174). The computing system may be configured to have certain specific facts from the input and the search performed, such as that "Andy" is a generic boy character, samples are available from the search, the "old cartoon" format may be interpreted from other searched references to be approximately 25 frames per second, "generic outdoor background" may be interpreted as lines on the cartoon ground with simple clouds in the sky based on available benchmarks, and the "base coloring" for these may be interpreted as green ground, blue sky, white clouds based on similarity benchmarking (176).To address the challenge, the system may be configured with a process configuration, such as utilizing stick figure-type configurations and waypoints or benchmarks developed from user instructions, importing Andy's generic configuration, interpolating Andy's character sketches for waypoints to have enough frames for smooth motion at 25 frames per second for 30 seconds (750 frames total), exporting a black and white 30-second viewer to the user for approval, coloring the 750 frames upon approval, and returning the final product to the user (178). The system may be provided with resources, such as a standard desktop computer connected to Internet resources, a generalized AI for user communication and basic information acquisition, and a composition operator configuration designed to execute and return results to the user (180). By utilizing such instructions, requirements, facts, process configurations, and resources, the composition operator may be configured to function through a sequence, such as the single-threaded sequence illustrated herein, execute at runtime, and return results (182).

[0033] 13B and 13C, the operation of the illustrative synthesis operators may be more finely differentiated. For example, the challenge may be addressed by selecting a first, relatively “narrow-band” synthesis operator operably coupled to the computing resource, which may be configured through training (e.g., via neural network training) to do little more than generate a sequence of wireframe sketches of a simple character, such as Andy, by interpolating between endpoints or waypoints (i.e., narrow-range training / narrow-band; such configuration may, based on training, only be capable of the functional skills to perform a narrow range of tasks of this type). Four endpoints (Andy standing upright, Andy raising his left hand, Andy returning to standing upright, Andy raising his right hand) may be received, along with instructions to smoothly sequence through the waypoints at 25 frames per second for 30 seconds (i.e., 750 frames, four benchmarks) (186). The narrowband compositing operator may be configured to simply digitally interpolate (i.e., average between) and create 750 frames in black and white (188). The compositing operator may be configured to return a stack of 750 black and white digital images to the user for viewing and approval (190).

[0034] Referring to FIG. 13C, after approval (190) of the image from FIG. 13A, a different narrowband synthesis operator, for example, trained to provide only the most basic coloring of a wireframe sketch based on simple input, may be utilized to perform (198) coloring of the image (192) using the provided basic input (194) and black and white wireframe (196) and return (200) the results to the user.

[0035] 14A, a synthetic operator (212) may be considered a synthetic character with certain human-like capabilities depending on the configuration and the task, and as such may be presented to a human user via a user interface, which may be configured to communicate (208) with the user via spoken instructions, typed instructions, direct computing interface-based commands, and the like, natural language generalized to AI, etc. An associated system may be configured to assist the user in providing requirements (202) for the task, providing specific facts (204) about the task, interconnecting with computing resources (206), and receiving certain process configurations (210) related to the task.

[0036] 14A , for example, an embodiment may comprise a synthetic engagement system for process-based problem solving, comprising: a computing system comprising one or more operably coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more specific facts, the user interface configured to enable the human operator to select and interactively engage with one or more synthetic operators operated by the computing system to progress through the predetermined process configuration, and return results to the human operator that are selected, at least in part, to satisfy the established requirements, the one or more synthetic operators each being informed, at least in part, by a convolutional neural network informed by the historical actions of a particular actual human operator. The one or more specific facts may be selected from the group consisting of text information, numerical data, audio information, video information, emotional state information, analog chaotic input selection, activity disturbance selection, curiosity selection, memory configuration, learning model, filtration configuration, and encryption configuration. The one or more concrete facts may include text information regarding concrete background information from a history store. The one or more concrete facts may include text information regarding an actual operator. The one or more concrete facts may include text information regarding a composite operator. The concrete facts may include a predetermined profile of a concrete fact developed as a starting module for a concrete composite operator profile. The one or more operably coupled computing resources may comprise 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 comprise an embedded computing resource selected from the group consisting of an embedded microcontroller, an embedded microprocessor, and an embedded gate array. The one or more operably coupled computing resources may comprise 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 further comprise a location element operably coupled to the computing system and configured to determine a location of the human operator relative to a global coordinate system. The location element may be selected from the group consisting of a GPS sensor, an IP address detector, a connectivity triangulation detector, an electromagnetic location sensor, and an optical location sensor. The one or more operably coupled computing resources may be activated based on the determined location of the human operator. The user interface may comprise a graphical user interface. The user interface may comprise an audio user interface. The graphical user interface may be configured to engage with a human operator using elements selected from the group consisting of a computer graphic engagement display, a video graphic engagement display, and audio engagement with a graphic display. The graphical user interface may comprise a video graphic engagement display configured to present a real-time or near real-time graphical representation of a video interface engagement character with which the human operator may interact. The video interface engagement 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 a human operator to select a visual presentation of a video interface participating character. The user interface may be configured to allow a human operator to select a visual presentation characteristic of a video interface participating 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 participating character may be modeled from a selected actual human. The user interface may be configured to allow a human operator to select one or more audio presentation aspects of a video interface participating character selected from the group consisting of character voice intonation, character voice volume, character speaking language, character speaking dialect, and character voice dynamic range. The one or more audio presentation aspects of a video interface participating character may be modeled from a selected actual human. The predetermined process configuration may include a finite set of steps through which participation will proceed in furtherance of the established requirements. The predetermined process configuration may include process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, a process sequential paradigm, a process cyclical / iterative paradigm, and AI utilization and configuration settings. The finite set of steps may include steps selected from the group consisting of a problem definition, a potential solution outline, a preliminary design, and a detailed design. The predetermined process configuration may include selection of elements by a human operator. Selection of elements by a human operator may include selecting a composite operator resource allocation for one or more aspects of the predetermined process configuration.The system may be configured to enable a human operator to specify a specific resource allocation for a first concrete portion of a predetermined process configuration. The system may be configured to enable a human operator to specify a specific resource allocation for a second concrete portion of a predetermined process configuration that differs from the specific resource allocation for the first concrete portion of the predetermined process configuration. The system may be configured to enable a human operator to specify a specific resource allocation for a first concrete portion of a predetermined process configuration based on multiple composite operator characters. Each of the multiple composite operator characters may be applied to the first concrete portion sequentially. Each of the multiple composite operator characters may be applied to the first concrete portion simultaneously. The system may be configured to enable a human operator to specify a specific resource allocation for a first concrete portion of a predetermined process configuration based on one or more hybrid composite operator characters. The one or more hybrid composite operator characters may comprise a combination of otherwise distinct composite operator characters that may be applied to the first concrete portion simultaneously. The convolutional neural network may be informed using input from a training dataset that includes data regarding the historical actions of a particular actual human operator. The convolutional neural network may be informed using inputs from a training data set using a supervised learning model. The convolutional neural network may be informed using inputs from a training data set along with an analysis of established requirements using a reinforcement learning model. One or more synthetic operators may each be informed by a convolutional neural network that is informed, at least in part, by a curated selection of synthetic action recordings of synthetic actions of actual human operators.The one or more composition operators may each be informed by a convolutional neural network, which is informed, at least in part, by a curated selection of composition action records related to the composition operators' composition actions. The computing system may be configured to separate each of the finite set of steps with an execution step, during which the one or more composition operators are configured to progress toward 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. The computing system may be configured to divide the execution steps into multiple tasks that can be addressed by available resources in furtherance of the established requirements based, at least in part, on the at least one execution behavior based on project leadership influence. The computing system may be further configured to project manage the performance of the multiple tasks toward one or more milestones in pursuit of the established requirements based, at least in part, on the at least one execution behavior based on project leadership influence. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at one or more stages of the execution steps based at least in part on the at least one execution behavior based on the influence of the project leadership. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for consideration at each of the finite steps in the process configuration based at least in part on the at least one execution behavior based on the influence of the project leadership.The computing system may be further configured to functionally present updates for review 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 the influence of project leadership. The computing system may be further configured to incorporate instructions regarding the presented updates from a human operator utilizing a user interface operated by the computing system as finite steps of the process configuration continue based at least in part on at least one execution behavior based on the influence of project leadership. The user interface may be configured to allow the human operator to pause the computing system while the human operator progresses through an otherwise predetermined process configuration so that one or more intermediate results may be inspected by the human operator with respect to established requirements. The user interface may allow the human operator to pause one or more of the computing system during the pause of the computing system. The user interface may be configured to allow a human operator to modify one or more aspects of the specific facts above and to facilitate future execution based on the modifications. The user interface may be configured to provide a human operator with calculated resource allocation costs based, at least in part, on utilization of the computing resources operably coupled in the predetermined process configuration.

[0037] 14B-14E illustrate further details regarding various of these components in relation to various hypothetical problem or challenge scenarios. For example, referring to FIG. 14B, user-to-composite operator requirements (202) may include general project constraints (time window, specifications for the composite operator, resources that should be available to the composite operator, I / O, interactions, or communication models with the composite operator in the time and progress domains), specific project constraints (goal / objective details, what is important in the solution, perhaps most important, characteristics of the composite operator, specific facts or inputs that should be prepared, loaded, and / or made immediately available to the composite operator), and specific operational constraints (input nuances / subtleties related to the specific solution problem, AI presence and coordination, initiation and disturbance presence and coordination, target market / area / cultural coordination).

[0038] Referring to FIG. 14C, the interconnected resources (206) may comprise one or more desktop or laptop type computing systems (230), one or more interconnected data center type computing assemblies (232), and smaller computing systems such as mobile systems or those utilized in "edge" or "Internet of Things" (IOT) (234) computing configurations.

[0039] Referring to FIG. 14D, the provided specific facts (204) may, for example, aid in the process and solution and may be loaded into and / or made immediately available to the synthesis operator (i.e., in the presence of computing RAM type), specific inputs directed by the user, specific background information from historical storage (such as the complete Beatles album, Bureau of Labor Statistics data for the past 25 years, specific groups of academic research publications, detailed drawings of all generations of the Ford Mustang, critical published analysis of the most successful singles in Max Martin and music in general, detailed electronic configuration and sales analysis of the top 100 consumer electronic products of the past 10 years, etc.), and specific facts or inputs about past actual operators or other synthesis operators (such as case studies of Andy Grove's personality profile and technical leadership approach, Elon Musk's risk-taking profile, Paul McCartney's personality profile in light of his growth and evolution as a musician up to a certain point, Matt McCartney's performance on Fiona Apple's album "Tidal"). These may include the sound profile of a classic 1959 Les Paul guitar played through Chamberlain drumming styles, vintage electronics and speakers.

[0040] Referring to FIG. 14E, process configuration (210), as dictated by the user and / or supervisor role, may include, for example, generalized operating parameters (i.e., how the supervisor desires to interact with the synthetic operator ("SO") regarding this engagement / issue; the SO may be configured to operate generally at a high frequency (24×7) for human scale and human factors, and may be limited to no more than one output / engagement per business day as a supervisor-adjustable preference; supervisor-adjustable I / O for engagement may be configured to include summary reports, emails, natural language audio summary updates, video, with explicit constraints depending on the authority of the SO); resource / input awareness and utilization (i.e., the SO must be properly loaded, connected to, and / or ready to utilize information, management, and computing resources, including project input and I / O, from the supervisor); domain expertise modules (a level and depth of expertise, such as business, music, financing, etc., where the SO may be specifically configured or utilized with respect to various types of expertise and role expectations); Examples of project management paradigms include: a sequential paradigm (which is domain and level of expertise specific, i.e., generally, as an SO builds towards a solution in a given domain, there may be an underlying expected sequence that is adjustable by a supervisor; for example, a rearview mirror shape is probably not the first expected outcome from a project to design the next successful Ford Mustang, nor is a drum solo likely the first expected outcome from a project to compose the next top pop single); a cyclical / iterative paradigm (which is domain and level of expertise specific, i.e., generally, as an SO builds towards a solution in a given domain, there may be an underlying expected sequence that is adjustable by a supervisor; for example, a rearview mirror shape is probably not the first expected outcome from a project to design the next successful Ford Mustang, nor is a drum solo likely the first expected outcome from a project to compose the next top pop single); a sequential paradigm (which is domain and level of expertise specific, i.e., generally, as an SO builds towards a solution in a given domain, there may be an underlying expected sequence that is adjustable by a supervisor; for example, a rearview mirror shape is probably not the first expected outcome from a project to design the next successful Ford Mustang, nor is a drum solo likely the first expected outcome from a project to compose the next top pop single);That is, generally, as the SO builds toward a solution in a given domain, there may be an underlying expected cyclical / iterative paradigm that can be adjusted by a supervisor (e.g., in a project to compose the next top pop single, it may not be useful for the SO to return 1,000 iterations of a melody per day; initiation and disturbance configurations may be adjustable; filling gaps or pauses, initiating tasks or subtasks, or introducing enough disturbances to prevent steady state too early in the process may be important), and / or AI utilization and configuration (AI, neural networks, deep networks, and / or training datasets may be utilized in nearly all processes and exchanges, although balance may be desired to avoid excessive AI intervention).

[0041] Referring to FIG. 15A, an event flow (236) is illustrated for an associated cartoon task, where a sequence of events (E1-E10) may be utilized to progress through the process of returning a color image stack to the user for presentation as a short cartoon. FIG. 15B illustrates an associated simplified event sequence (238), again demonstrating that a cartoon task may be accomplished in an efficient manner with appropriately resourced and directed composition operator involvement through a series of smaller tasks. For example, referring to FIG. 16, specific composition operator involvement steps are shown. The system with integrated composition operator may be powered on and ready to receive instructions from the user (252). Through user input devices such as a generalized natural language AI and / or other composition operator communication interactions, the user may:

[0042] A user may request an approximately 30-second cartoon of Andy in VGA format with basic coloring on a generic outdoor background in a classic cartoon style (254). A compositing operator may be configured to interpret the requirements (classic cartoon style, basic coloring, generic outdoor background, VGA, simple arm movement) and 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, receive approval, color, and return deliverable to user) (258). Computing resources may be used by the compositing operator to create 750 wireframes by interpolating using provided endpoints (260). The compositing operator may use interconnected 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 through the interconnected computing resources, etc. (264). A compositing operator (which may be a different compositing operator better suited to a particular task) may utilize interconnected computing resources to colorize (266) the 750 frames and package (268) them for delivery to the user as a returned final product (270).

[0043] Thus, a composite operator configuration can be utilized to execute in response to certain somewhat complex instructions and return results to a user through the use of an appropriately trained, informed, and resourced computing system.

[0044] Referring to Figures 17A-17G, another illustrative example is shown utilizing a synthetic operator configuration to respond to challenges that may traditionally be the domain of mechanical or systems engineers. In one such scenario, as shown in Figure 17A, Volkswagen has decided to build a compact electric pickup truck for the U.S. market and requires a basic design prior to bodywork and exterior customization (272). Requirements may be provided, such as the vehicle must have two rows of seats and four doors, the bed should be six feet long, be capable of supporting an eight-foot-by-four-foot sheet of plywood with the rear doors folded, be fully electric, have a minimum range of 200 miles, and the chassis must be considered an assembly line for the current Volkswagen product family (274). Resources may be determined and provided, such as full access to a data or computing center such as AWS, access to the Internet, and electronic access to relevant specific facts (276). Specific facts may be provided, such as full access to Volkswagen historical design documents and all available design documents, safety, emissions, weight, and dimensional regulatory information, regarding electric drivetrains and associated reliability, maintenance, lifespan, cost, and efficiency (278). Process configurations may be provided, such as assuming the aerodynamic efficiency of a standard Toyota Tacoma with up to 15% gain from wind tunnel tuning, requiring a four-door stand-up cab, requiring an open-top cargo area for side / top / rear access, and requiring the acceleration performance of a standard Toyota Tacoma, presenting the user with practical drivetrain and battery chemistry alternatives along with base chassis configurations (280). Finally, the system may be configured to utilize these inputs and resources to execute and present results at runtime (282).Referring to FIG. 17B, requirements (202) from the user may include, for example, requiring chassis, drivetrain, and battery chemistry design alternatives as primary outputs; that the vehicle be in a pickup truck style configuration with a four-door cab; that the pickup truck bed should be at least six feet long and be capable of supporting an eight-foot by four-foot plywood sheet with the rear doors folded; that the drivetrain be fully electric; that the fully equipped vehicle would need to have a minimum range of 200 miles; and that the chassis be considered an assembly member of the current Volkswagen product family.

[0045] Referring 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 / Web (240) and electronic access to certain specific factual data (242).

[0046] Referring to FIG. 17D, the specific facts (204) for a particular problem may include full access to Volkswagen historical design documentation and all available design documentation for chassis and suspension design, and electric drivetrains and associated reliability, maintenance, lifespan, cost, and efficiency, as well as regulatory information for safety, emissions, weight, and dimensions.

[0047] Referring to FIG. 17E, a process configuration (210) for a particular problem may include assuming as an initial process input the aerodynamic efficiency of a standard Toyota Tacoma, but with up to a 15% gain from wind tunnel-based aerodynamic tuning and optimization; requiring as additional important initial process inputs for the chassis design a four-door stand-up cab with an open-top cargo area for side / top / rear access; and requiring acceleration performance that matches at least that of a standard Toyota Tacoma in terms of on-road performance; utilizing these initial inputs, along with searchable resources and specific facts, to develop a list of combinations of candidate drivetrains, battery chemistries, and chassis alternatives, and presenting the list and combinations to a user.

[0048] Thus, referring to the process flow of FIG. 17F, a synthetic operator-enabled system may be powered on and ready to receive commands from a user (284). Through one or more user input devices, such as a generalized natural language AI and / or other synthetic operator communication interaction, the user may request drivetrain, battery chemistry, and chassis options for a new Volkswagen all-electric truck design, along with requirements for a four-door upright cab, at least a six-foot bed (capable of fitting 8 feet by 4 feet with the rear doors folded), a minimum range of 200 miles, and the chassis to be considered an assembly member of the current Volkswagen product family (286). The synthetic operator interfaces with available resources (full AWS and in-house computing access, full web access, electronic access to specific facts) and collects specific facts (Volkswagen historical design documentation and all available design documentation regarding the electric drivetrain and associated reliability, maintenance, lifespan, cost, and efficiency). The system may be configured to load (288) a set of requirements (full access to regulatory information for safety, emissions, weight, and dimensions) and a process configuration (assuming aerodynamic efficiency of a standard Toyota Tacoma with up to 15% gain from wind tunnel tuning, requesting a four-door stand-up cab, requesting an open-top loading platform for side / top / rear access, requesting acceleration performance of a standard Toyota Tacoma, and presenting the user with viable drivetrain and battery chemistry alternatives along with a base chassis configuration). The synthesis operator may be configured to progress through an execution plan based on all inputs, including the process configuration, and compile a list of candidate combinations and lists of drivetrain, battery chemistry, and chassis configurations utilizing available resources in light of all requirements, specific inputs, and process configuration. Finally, the system may be configured to return the results to the user (292).

[0049] Referring to FIG. 17G, a composition operator ("SO") central flow is illustrated for a problem. Once all inputs for a particular problem are gathered, the SO may be configured to have some system-level problem-solving capabilities (302). The SO may be configured to first record the requirements / objectives at a very basic level (e.g., the objective is to find candidates for battery chemistry / drivetrain / chassis) and then use the inputs and resources to develop a basic paradigm for moving forward and reaching the objective (e.g., understand the requirements, use available information to find candidate solutions, analyze the candidate solutions, and present the results) based on a specified process (304). The SO may be configured to search for the aerodynamic efficiency and acceleration performance of a Toyota Tacoma to better refine the requirements (the Tacoma's CD is approximately 0.39; a 15% improvement would result in approximately 0.33, which is comparable to the CD of a Subaru Forester; the Tacoma's 0-60 mph time is 8.2 seconds) (306). The SO may be configured to search and determine that a candidate base chassis design is evident, where a pickup is a four-wheeled vehicle with a rear cargo bed with rear doors and a four-door cab up front, which should be capable of having a CD close to that of a Subaru Forester (308). The SO may be configured to search and determine that many drivetrains are available, where the most efficient drivetrain is believed to be an electric motor coupled to a one- or two-speed transmission, with a CD of 0.33, which should meet the 8.2-second 0-60 requirement given the estimated mass of the new vehicle based on known benchmarks (310). The SO may be configured to search and determine that lithium-based battery chemistries have better energy density to mass and are utilized in many electric drivetrains (312).The SO may be configured to roughly calculate estimated range and acceleration performance based on the total mass and CD benchmarks and present various candidate results (e.g., a larger battery may deliver more instantaneous current / acceleration performance but have a reduced range; similarly, a larger electric motor may be able to handle more current and generate more output torque for instantaneous acceleration performance but may reduce overall range) (314). Finally, the SO may be configured to present the results to the user (316).

[0050] Referring to Figures 18A-18G, another illustrative example is shown utilizing a synthesis operator configuration to address challenges that may traditionally be the domain of materials engineers. Referring to Figure 18A, Nike has decided to design a new forefoot sprint / extended toe space running shoe for the US market, and requires a basic sole design before further industrial design, color, and decorative material considerations are made, but ultimately, the configuration should fit the Nike design vocabulary (318). Requirements from the user to the synthesis operator enhancement system configuration may include that the toe space must accommodate the non-lateral compression foot geometry for 80% of the anthropometric market, and that the sole ground contact profile should mimic that of the Nike React Infinity Run v2 (trademarked). Resources for the synthesis operator may include full Amazon Web Services ("AWS") and in-house computing access, full web access, and electronic access to specific facts, including solid modeling capabilities, based on the selected materials and geometry (322). Specific facts about a particular problem may include full access to Nike historical design documents and all available design documents for sole and composite material configurations, modulus data, and testing information; a library of mechanical performance and wear information for injection-moldable polymers; regulatory information for safety and hazardous materials; and anthropometric data (i.e., based on actual human anatomy statistics) (324). Process configurations for a synthesis operator to navigate a particular problem may include assuming an assembly of an injection-molded cushioning material and an associated structural / tracing sole element; and presenting the practical sole design and associated geometry to the user, along with estimated performance data for wear and local / global modulus (326). Finally, the system may be configured so that the synthesis operator can execute and present the results to the user (328).

[0051] Referring to FIG. 18B, requirements (202) for a specific problem may include requirements for a basic sole design as the primary output (which will ultimately need to fit the Nike design vocabulary before considering industrial design, color, and decorative materials), that the toe space of the sole design must accommodate non-lateral compression foot geometry for 80% of the anthropometric market, and that the sole ground contact profile of the shoe should mimic that of the Nike React Infinity Run v2 (trademarked).

[0052] Referring to FIG. 18C, computing resources (206) may include interconnected data centers (232), desktops (230), and edge / IOT type systems, and interconnected access to the Internet / Web (240), electronic access to specific concrete factual data (242), and electronic access to computerized solid modeling capabilities that are dynamic to materials and geometry (330).

[0053] Referring to FIG. 18D, specific facts (204) relating to a particular problem may include Nike historical design documentation and all available design documentation regarding sole and synthetic material construction, modulus data, and testing information, a library of mechanical performance and wear information for injection moldable polymers, regulatory information regarding safety, hazardous materials, and full access to anthropometric data for the target market population.

[0054] Referring to FIG. 18E, process configuration (210) for a particular composite operator improvement scenario may include assuming an assembly of one injection-molded cushioning material and one structural / traction sole element coupled thereto as initial process input, utilizing these initial inputs along with searchable resources and concrete facts to develop a list of candidate sole configurations, and presenting the candidate configurations to a user.

[0055] Thus, referring to the process flow of Figure 18F, a synthetic operator-enabled system may be powered on and ready to receive instructions from a user 332. Through a user input device, such as a generalized natural language AI and / or other synthetic operator communication interaction, the user may request a base shoe sole design for a forefoot sprint / extended toe space running shoe for the US market 334 (only the base sole design is required before further industry design, color, and decorative material considerations are made, but ultimately the sole design should be capable of conforming to the Nike design vocabulary). The synthesis operator may be configured to interface with available resources (full AWS and in-house computing access, full web access, solid modeling capabilities, electronic access to specific facts) and load specific facts (Nike historical design documentation and all available design documentation for sole and composite material configurations, modulus data, and testing information, a library of mechanical performance and wear information for injection-moldable polymers, regulatory information for safety and hazardous materials, full access to anthropometric data) and process configurations (assuming the assembly of one injection-molded cushioning material and one structural / traction sole element bonded thereto, and presenting the working sole design and associated geometry to the user along with estimated performance data for wear and local / overall modulus) (336). The synthesis operator may be configured to progress through an execution plan based on all inputs, including the process configuration, and, for example, compile a list of candidate shoe sole configurations utilizing available resources in light of all requirements, specific inputs, and process configurations (338). Finally, the compositing operator may be configured to return the results to the user (340).

[0056] Referring to FIG. 18G, a composition operator ("SO") central flow is illustrated for a challenge. Once all inputs for a particular challenge are gathered, the SO may be configured to have some system-level problem-solving capabilities (352). The SO may be configured to first record the requirements / objectives at a very basic level (e.g., the objective is to find a shoe sole shape featuring two materials) and, using the inputs and resources, develop a basic paradigm for proceeding and reaching the objective based on a specified process (e.g., understand the requirements, use available information to find candidate solutions, analyze the candidate solutions, and present the results) (354). The SO may be configured to search to determine the toe space contents within the shoe and a geometric shape that would fit 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 (trademarked) (358). The SO may be configured to search to determine that the controlling factor in a shoe's sole design is cushioning performance, and that the controlling factors in cushioning performance relate to material modulus, shape, and structural content (360). The SO may be configured to determine that the sole ground contact profile is similar to the Nike React Infinity Run v2 (trademarked), that the Nike design language provides a surface configuration, generally open-cell foam, on the side of the shoe, and that the key variables in the challenge are the cushioning foam material, its thickness, and the area / shape of the toe space (dictated by anthropometric data) (362). The SO may be configured to analyze variations / combinations / lists of sole assemblies using various cushioning materials and thicknesses (again, working within the sole ground contact profile and anthropometric data of the Nike React Infinity Run v2) (364). Finally, the synthesis operator may be configured to present the results to the user (366).

[0057] In various embodiments, it may be useful to have a composite operator capability configured to address multithreaded challenges, such as the simulated involvement of multiple players, multiple subprocesses, and the like, as in many human-scale challenges. Referring to FIG. 19A , for example, a composite operator (212) configuration is illustrated, and a composite artificial intelligence configuration, such as one utilizing a convolutional neural network (“CNN”) (376), may be employed. For example, referring to FIG. 19A , the CNN driving the functionality of the composite operator (212) may be informed by a supervised learning configuration, and interviews with appropriate subject matter experts may be utilized, along with iterative and varied scenario presentations and case studies from past processes (368). For example, to build a composite operator capability similar to that of David Packard, the renowned engineering manager and founder of Hewlett Packard Inc., interviews, scenarios, and case studies of what David Packard actually did in various situations may be studied. Decision nodes and associated decisions may be labeled based on such research and input for a supervised learning model for these decision nodes and decisions (370) so that a CNN can be created and operated (376). Using a recorded audit trail of labeled data from actual outcomes utilizing the associated CNN-based synthesis operator, further feedback refinement and evolution of the synthesis operator is facilitated over time and through experience using the synthesis operator with actual outcome data. Additionally, synthetic scenarios with decision nodes, decisions, and outcomes may be created. For example, simulated scenarios may be created for the situations and reasoning regarding what David Packard did in a particular engineering management situation, along with details about the synthetic scenario, such as decision nodes, decisions, and outcomes.To increase the amount of synthetic data from such configurations, simulated variation techniques for various variables in such processes or subprocesses may be utilized to generate more synthetic data, which may be automatically labeled and utilized to further train the CNN in a supervised learning configuration (374).

[0058] Referring to Figure 19B, in various complex composite operator improvement processes, it may be desirable to have hybrid functionality, and two different composite operator configurations (380, 382) may be utilized together to address specific challenges. The configuration of Figure 19B illustrates two different composite operators utilizing the same input (384) in a parallel configuration, whereby the system may be configured to receive each of the independent results (386, 388), weight and / or combine them based on user preferences, and present a combined or hybrid result (392).

[0059] Referring to FIG. 19C, after process decomposition, a configuration is illustrated in which nodes of the process are determined to be handled by two or more composition operators to be applied sequentially, with the sequential operation occurring (393) such that a first (394) composition operator handles a first portion of the problem, followed by a handoff to a second (396) composition operator to handle the remainder of the problem and present a hybrid result.

[0060] 19D, a hybrid configuration featuring both serial and parallel compositing operator activity is illustrated, where a first line of compositing operator configurations (590, 382, 592 for compositing operators 7 (414), 2 (396), and 5 (412)) are operated in parallel with a second line featuring a single compositing operator configuration (594) for compositing operator 3 (408), and a third line featuring two compositing operator configurations (596, 598) in series for compositing operator 9 (416) and compositing operator 4 (410). The results (402, 404, 406) may be weighted and / or combined (390) as predefined by the user, and the results presented (392).

[0061] Thus, various configurations are illustrated in Figures 19A-19D, and various types of compositing operator configurations may be utilized to address complex challenges, and a human user or operator may be enabled through a user interface to select between single compositing operators, multiple compositing operators, and hybrid operator configurations (e.g., multiple compositing operators with hybrid or process relaxation, in which a single compositing operator is configured to have various properties of two other separate compositing operators, as described herein). Accordingly, various embodiments may be directed to a synthetic engagement system for process-based problem solving, comprising: a computing system having one or more operably coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator according to a predetermined process configuration toward established requirements based, at least in part, on one or more concrete facts, the user interface being configured to enable the human operator to select, interactively engage with, and collaboratively progress through the predetermined process configuration with two or more synthetic operators operated by the computing system, and return results to the human operator that are selected, at least in part, to satisfy the established requirements, the two or more synthetic operators each being informed, at least in part, by a convolutional neural network informed by the historical actions of a particular actual human operator. The one or more concrete facts may be selected from the group consisting of text information, numerical data, audio information, video information, emotional state information, analog chaotic input selection, activity disturbance selection, curiosity selection, memory configuration, learning model, filtration configuration, and encryption configuration. The one or more specific facts may include text information about specific background information from a history store. The one or more specific facts may include text information about an actual operator.The one or more concrete facts may include textual information about the composite operator. The concrete facts may include a predetermined profile of concrete facts developed as a starting module for the concrete composite operator profile. The one or more operably coupled computing resources may comprise 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 comprise an embedded computing resource selected from the group consisting of an embedded microcontroller, an embedded microprocessor, and an embedded gate array. The one or more operably coupled computing resources may comprise 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 further comprise a location element operably coupled to the computing system and configured to determine the location of the human operator relative to a global coordinate system. The location element may be selected from the group consisting of a GPS sensor, an IP address detector, a connectivity triangulation detector, an electromagnetic location sensor, and an optical location sensor. One or more operably coupled computing resources may be activated based on the determined location of the human operator. The user interface may comprise a graphical user interface. The user interface may comprise an audio user interface. The graphical user interface may be configured to engage the human operator using elements selected from the group consisting of a computer graphic engagement display, a video graphic engagement display, and audio engagement with a graphical display.The graphical user interface may comprise a video graphic engagement display configured to present real-time or near-real-time graphical representations of video interface engagement characters with which a human operator may interact. The video interface engagement characters may be selected from the group consisting of humanoid characters, animal characters, and cartoon characters. The user interface may be configured to enable the human operator to select the visual presentation of the video interface engagement characters. The user interface may be configured to enable the human operator to select visual presentation characteristics of the video interface engagement characters 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 engagement characters may be modeled after a selected actual human. The user interface may be configured to enable the human operator to select one or more audio presentation aspects of the video interface engagement characters. The user interface may be configured to enable the human operator to select one or more audio presentation aspects of the video interface engagement characters selected from the group consisting of character voice intonation, character voice volume, character speaking language, character speaking dialect, and character voice dynamic range. One or more audio presentation aspects of the video interface participation characters may be modeled from a selected actual human. The predetermined process configuration may include a finite set of steps through which participation will proceed in furtherance of the established requirements. The predetermined process configuration may include process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, process sequential paradigms, process cyclical / iterative paradigms, and AI utilization and configuration settings.The finite set of steps may include steps selected from the group consisting of problem definition, potential solution overview, preliminary design, and detailed design. The predetermined process configuration may include element selection by a human operator. Element selection by the human operator may include selecting a composite operator resource allocation for one or more aspects of the predetermined process configuration. The system may be configured to enable the human operator to specify a specific resource allocation for a first concrete portion of the predetermined process configuration. The system may be configured to enable the human operator to specify a specific resource allocation for a second concrete portion of the predetermined process configuration that differs from the specific resource allocation for the first concrete portion of the predetermined process configuration. The system may be configured to enable the human operator to specify a specific resource allocation for the first concrete portion of the predetermined process configuration based on multiple composite operator characters. Each of the multiple composite operator characters may be applied to the first concrete portion sequentially. Each of the multiple composite operator characters may be applied to the first concrete portion simultaneously. The system may be configured to enable a human operator to define a specific resource allocation for a first concrete portion of a predetermined process configuration based on one or more hybrid composite operator characters. The one or more hybrid composite operator characters may comprise a combination of otherwise distinct composite operator characters that may be applied to the first concrete portion simultaneously. The convolutional neural network may be informed using input from a training dataset that includes data regarding the historical actions of specific actual human operators. The convolutional neural network may be informed using input from the training dataset using a supervised learning model. The convolutional neural network may be informed using input from the training dataset along with an analysis of established requirements using a reinforcement learning model.Each of the two or more composite operators may be informed by a convolutional neural network that is informed, at least in part, by a curated selection of composite action records of composite actions of actual human operators. Each of the two or more composite operators may be informed by a convolutional neural network that is informed, at least in part, by a curated selection of composite action records of the composite operators' composite actions. The computing system may be configured to separate each of the finite sets of steps with an execution step, during which the two or more composite operators are configured to progress toward 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. The computing system may be configured to divide the execution steps into multiple tasks that can be addressed by available resources in furtherance of the established requirements based, at least in part, on the at least one execution behavior that is based on project leadership influence. The computing system may be further configured to project manage the performance of the plurality of tasks toward one or more milestones in pursuit of the established requirements based at least in part on the at least one execution behavior based on the influence of project leadership. The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks in one or more stages of execution based at least in part on the at least one execution behavior based on the influence of project leadership.The computing system may be further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for review at each of the finite steps within the process configuration based at least in part on the at least one execution behavior based on the influence of project leadership. The computing system may be further configured to functionally present the updates for review by a human operator utilizing a user interface operated by the computing system based at least in part on the at least one execution behavior based on the influence of project leadership. The computing system may functionally present the updates for review by a human operator utilizing a user interface operated by the computing system as the finite steps of the process configuration continue based at least in part on the at least one execution behavior based on the influence of project leadership. The system may further be configured to incorporate instructions from a human operator regarding proposed updates to be used in the process. The user interface may be configured to allow the human operator to pause the computing system while proceeding through an otherwise predetermined process configuration so that one or more intermediate results may be inspected by the human operator with respect to established requirements. The user interface may be configured to allow the human operator to modify one or more aspects of one or more specific facts during the pause of the computing system and to facilitate future execution based on the modifications. The user interface may be configured to provide the human operator with a calculated resource allocation cost based, at least in part, on utilization of 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 composite operators are different. The system may be configured to allow the human operator to specify that two or more composite operators are identical and may be configured to cooperatively expand their productivity as they proceed through the predetermined process configuration. Two or more composite operators may be configured to automatically optimize their application as resources as they proceed through the predetermined process configuration. The system may be configured to generate a set of initial decision nodes associated with the established requirements utilizing two or more composition operators based at least in part on characteristics of the two or more composition operators. The system may be further configured to create a set of intermediary decision nodes based on the set of initial decision nodes. The system may be further configured to create a set of operational decision nodes based on the set of intermediary decision nodes.Two or more composite operators may be operated by a computing system to cooperatively advance through a predetermined process configuration by sequentially progressing through operational decision nodes in furtherance of established requirements. The two or more composite operators may have a plurality, limited only by the computing resources to which they are operatively coupled.

[0062] Referring to FIG. 20A, for example, a configuration (396) for creating and updating a mechanical engineer composite operator "2" is illustrated, where a continuously updated CNN can be utilized to generate an optimized set of decision nodes (422) for this particular composite operator, Mechanical Engineer 2 (i.e., somewhat similar to the process for how this engineer addresses and works through a challenge).

[0063] Referring to FIG. 20B, for example, a configuration (418) for creating and updating an accountant composite operator "11" is illustrated, where a continuously updated CNN can be utilized to generate optimized decision nodes (420) for this particular composite operator accountant 11 (i.e., somewhat similar to the process for how this accountant addresses and works through challenges).

[0064] Referring to Figure 20C, to work two different composite operators through specific process steps and together arrive at an outcome (i.e., as opposed to independent parallel or sequential actions followed by a final combination of the results), as is often the case in complex human teams, it may be useful to develop a CNN (428) informed by optimized decision nodes for each composite operator (422, 420 in the illustrative examples of Mechanical Engineer 2 and Accountant 11 in Figures 20A and 20B) and actual (424) and composite (426) data regarding how these decision nodes should be combined and mediated. Such a CNN may be utilized to create operational decision nodes for the composite operator Mechanical Engineer 2, which works with the composite operator Accountant 11 throughout a given process. In other words, decision nodes are now available for collaboration based on a previously heterogeneous set of decision nodes, and now a composite operator configuration (436) (i.e., in this particular illustrative scenario, relating to mechanical engineer 2 and accountant 11 as in Figures 20A and 20B) may be executed at runtime (432) and utilized to generate results (434).

[0065] Referring to Figure 21A, with two composition operators (such as the Mechanical Engineer composition operator configuration 438 and the Accountant composition operator configuration 440), there is essentially one relationship (442) between the two and one process intermediation to address both to make them a consistent process. Referring to Figure 21B, by incorporating an additional composition operator, such as the Product Marketing composition operator configuration (444), each composition operator theoretically has two different relationships (442, 446, 448), and the process intermediation becomes more complex as a result. Referring to Figure 21C, with five composition operator configurations (438, 440, 452, 454, 444), a configuration is illustrated to show the multiplication of relationships (442, 456, 462, 468, 446, 448, 458, 464, 460, 466) for process intermediation.

[0066] Referring to Figure 22, such complexity may be addressed in various configurations. After defining the problem (470) and determining the functional groups of expertise to capture a particular process (472), the user or supervisor may determine a model for the interoperation of the process (474). For example, it may be determined that all relationships are modeled 1:1 per composite operator, that each composite operator is modeled only to the rest of the group as a whole ("1:(G-1)"), or that the user or supervisor will direct the process interoperation for the group as a unified whole ("G-integration") (i.e., "This is the process we all will perform"). Using action decision nodes (476) that determine how the functional groups collaborate together in the process, the composite operator configuration (436) can be utilized to execute at runtime (432) and produce results (434).

[0067] Referring to Figure 23A, as an example, a problem for sole design for a Nike® shoe is defined 478. A simplified grouping of mechanical engineering composition operators is to be combined with an accounting composition operator 480. By using only two composition operators, one relationship and process intermediation is required 474, which may be dictated by a user or supervisor, for example, as illustrated in Figure 23B, and the accounting composition operator occurs in only two places in the process, primarily the engineering process.

[0068] Thus, referring to FIG. 23B , once the Mechanical Engineer (“ME”) SO and the Accounting SO have all the inputs for a challenge, the Synthesis Operator may be configured to have a systems-level problem-solving capability (482), and the Accounting SO may be configured to provide a range of cost of goods sold (“COGS”) that may exist for a material and discuss supply chain issues (484). The ME SO may be configured to first record the requirements / objectives at a very basic level (e.g., the objective is to find a shoe sole shape featuring two materials) and, using the inputs and resources, develop a basic paradigm for proceeding and reaching the objective based on a specified process (e.g., understand the requirements, use available information to find candidate solutions, analyze the candidate solutions, and present the results) (486). The ME SO may be configured to search to determine the toe space contents within the shoe and a geometry that would fit 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 (trademarked) (490). The ME SO may be configured to search to determine that the controlling factor in the shoe sole design is cushioning performance, and that the controlling factors in cushioning performance relate to material modulus, shape, and structural content (492). The ME SO may be configured to determine that the sole ground contact profile is determined to be similar to the Nike React Infinity Run v2 (trademarked), that the Nike design language provides a surface configuration, generally open-cell foam, on the side of the shoe, and that the key variables in the challenge are the cushioning foam material, its thickness, and the area / shape of the toe well (driven by anthropometric data) (494). The accounting SO may be configured to provide reminders of COGS ranges and supply chain issues that may exist for a material (496).The ME SO may be configured to analyze variations / combinations / lists of sole assemblies using various cushioning materials and thicknesses (again, working within the sole ground contact profile and anthropometric data of the Nike React Infinity Run v2) (498). The results of this complex process configuration may be presented to the user (500).

[0069] As discussed above, with reference to Figures 20C and 22, etc., both the composition operator configuration (436) and the decision node process intermediation (430, 476) for determining operational decision nodes for functional groups to collaborate on play important roles at runtime (432). With reference to Figures 23A and 23B, and with reference to Figure 23C, ME composition operator configuration may be initiated (502), and user, management, and / or supervisor discussion or input may be similar to, "This is an important product, we need to get it right the first time, engineer Bob Smith always gets these things right, apply Bob Smith here" (504). Accounting composition operator configuration may be initiated (506), and user, management, and / or supervisor discussion or input may be similar to, "Let's get out of the way of engineering first, always apply friendly / effective accountant Sally Jones first, but use accountant Eeyore Johnson at the end to make sure we hit our COGS numbers" (508). The system may be configured to initiate analysis and selection of an operational decision node for functional groups (ME, accounting) to collaborate together (510), with user, management, and / or supervisor discussion or input similar to, "This is primarily about engineering, let them control the process, but they need to get COGS and supply chain input first, and then finally COGS will be the control filter." Using such input, an operational decision node may be developed as discussed from the process intermediary (430), along with associated composite operator configuration (436), runtime (432), and results (434).

[0070] With reference to Figures 24A-24C, a complex configuration is illustrated in which synthesis operators relating to the four Beatles™, their producer, and their manager may be utilized to create an add-on to a previous album. With reference to Figure 24A, as noted above, the number of relationships (526) is significant when a significant number of synthesis operators (514, 516, 524, 520, 518, 522) are involved. With reference to Figure 24B, the problem of developing aligned verses, choruses, bridges, and solos for a mid-tempo rock and roll song by the Beatles that may be added to the "Sgt Peppers" album may be defined (530). A decision may be made regarding functional expertise groups to incorporate into the process, i.e., synthesis operator models for six individuals (Ringo, McCartney, Lennon, Harrison, George Martin, and Brian Epstein), each developed based on historical / anecdotal information (532). To model the interoperation of functions, a decision may be made regarding the technique for arriving at an intermediary decision node for this large set of composition operators (e.g., 1:1 analysis, 1:(G1) analysis, G-synthesis), which in this case may be determined to be G-synthesis (534) based on historical / anecdotal information about how they worked together on the "Sgt Peppers" album. Using such a decision and configuration, an action decision node (476) may be utilized along with the composition operator configurations (436) created for these particular characters, which may be utilized at runtime (432) to deliver a result (434) as further illustrated in FIG. 24C.

[0071] Referring to Figure 24C, process intermediation is determined by the user within the boxes (536, 536, 540, 542, 544, 546, 548, 550) shown on the right. SO Harrison and SO McCartney experimentally develop a bass and guitar "rif" combination that can function as a chorus (552). SO Lennon and SO Ringo provide input, but control initially remains with So Harrison and SO McCartney (554). SO Lennon and SO Ringo develop multiple related verses that work with the chorus (556). SO Lennon and SO Ringo provide further input, but control initially remains with SO Harrison and SO McCartney (558). SO Lennon and SO Ringo develop a bridge to work with the verse and chorus material (560). The basics of the song are coming together and can now be played through verse-chorus-verse-chorus-bridge, with SO Harrison playing lead guitar on the verses, chorus, and bridge, SO McCartney playing bass on the verses, chorus, and bridge, SO Ringo playing drums throughout, and SO Lennon playing rhythm guitar throughout, all continuing to provide input and mutual contributions to the overall structure (562). Epstein begins recording and operates the mixing board as the song unfolds, with George Martin providing very minimal input (564). SO Harrison, with minimal input from SO McCartney and SO Lennon, develops the basic guitar solo to be positioned sequentially after the bridge (566). The result may be completed and presented (568).

[0072] Referring to FIG. 25, an example user interface is presented in which a user may be presented with a representation of an event sequence (570) and may be able to click or right-click on a particular event such that a sub-presentation (such as a box or speech bubble) (572) is further presented with further information regarding the composition operator enhanced computing operations and status.

[0073] Referring to FIG. 26, a calculator portion (574) is shown illustrating that various business models can be utilized to offer significant value to users / customers while also creating opportunities for positive operating profits depending on costs such as those related to computing resources.

[0074] 27A, as noted above, many human processes are complex and varied, and to address various challenges of complexity, it may be useful to bring together many different types of composite operators (576, 578, 580, 582, 584, 586). Indeed, in various embodiments, it is preferable for various system instantiations to utilize composite operator resources in a consistent and connected manner (588), somewhat similar to actual human processes in which highly skilled people combine to address complex challenges.

[0075] Referring to FIG. 28A, a synthesis operator (212) configuration (380) is illustrated with additional details regarding how continuous learning and evolution can be accomplished using various factors. For example, as described above, the neural network configured to operate aspects of the synthesis operator may be informed by actual historical data, synthetic data, and audit data regarding usage. A learning model (614) may be configured to help filter, protect, and encrypt inputs to the process that constantly adjusts the neural network. For example, in various embodiments, a user may be presented with a control or control panel that allows configuration of mood / emotional state (such as via selection of an area on an emotional state chart) (602), access to various experiences and teachings of others (604), analog chaotic input selection (606), activity disturbance selection (608), curiosity selection (610), and memory configuration (612). For example, with a positive emotional state selected, the synthesis operator may be configured to engage more positive information and approaches. More access to teaching and experience can broaden the potential of composite operator construction. Additional chaos in the composite operator process can be good or bad; for example, it can keep activity very active or lead to wasted cycles. Activity perturbations at high levels can help keep processes, learning, and other activity at a high level. Curiosity at high levels can improve learning and be incorporated as input into the neural network. Memory construction, with significant long-term and short-term memory, can aid in the development of neural networks.

[0076] Referring to FIG. 28B, various aspects of the learning model configuration may be informed by real human teachings and experiences (616), real experiential input from real human scenarios (618), synthetic facts and scenario teachings (620) (such as a synthetic scenario about how the Cyberdine Systems took over the world as seen in the movie "Terminator"™), and other synthetic experiential input (622) (such as how the war between the Cyberdine Systems and humans occurred).

[0077] Referring to FIG. 28C , various aspects of the learning model configuration may be further informed by the interaction of possible composition relationships (624) between composition operators, as influenced by, for example, user settings for the current learning model configuration, and a composition environment (626) that may be configured to help the composition operators engage in various composition experiences, teachings, and encounters. For example, synthetic worlds (624, 626, 628) are illustrated in FIGS. 29A-29C . The system may be configured to utilize the composition operator configurations, along with the learning model settings, to help a given composition operator synthetically navigate such worlds and engage in relevant experiences and learning. For example, if SO #27 is a heavy metal guitarist and has an emotional state setting in the associated learning model that is set to black for a period of time, then that SO #27 may be drawn toward the darker, heavier aspects of the associated synthetic world, which may be correlated with darker, heavier information and experiences, such as a “dark cave filled with scorpions.” In contrast, a yoga instructor SO with a very positive emotional state preference will be drawn to the brighter, more cheerful, and more positive aspects of the composite world and may have more positive information and experiences at that stage of evolution.

[0078] 30A-30D, the system may be configured to help users compose various sequences and customize the results based on sequence and time-domain considerations. For example, FIG. 30A illustrates a process depiction of a 10-step process involving four musicians, a producer, and a manager. The depicted composition includes members of The Beatles for the entire 10-step process. Referring to the composition (632) illustrated in FIG. 30B, Eddie Van Halen is replaced on lead guitar in stages 6 and 7, and Alex Van Halen is replaced on drums in stages 8, 9, and 10, with Jimi Hendrix at the mixing board and as producer for stages 8, 9, and 10. If the net result of the configuration in FIG. 30B is unsatisfactory, a time domain selector (636) can be utilized to return the process to the beginning of stage 8, as shown in FIG. 30C, and then the process can move forward from there again, as shown in FIG. 30D, with Ringo returning to drums for stages 8, 9, and 10, but Jimi Hendrix still playing the producer role at the mixing board for stages 8, 9, and 10, to see how that affects the outcome.

[0079] Referring to FIG. 31 , a process configuration is illustrated, and a computing system is provided to a user (the computing system including operatively coupled resources, such as local and / or remote computing systems and subsystems) (702). The computing system may be configured to present a user interface (graphical, audio, video, etc.) so that a human operator may be engaged in working through a predetermined process configuration toward established requirements (i.e., goals or objectives, etc.), and concrete facts may be utilized to inform the process and computing configuration (704). The user interface may be configured to enable the human operator to select and interact with one or more composition operators operated by the computing system to progress through the predetermined process configuration, and to return to the human operator, via the user interface, etc., partial or complete results that are selected to at least partially satisfy the established requirements (706). In embodiments where two or more composition operators are utilized, they may be configured to collaborate together through the process configuration toward the established requirements, according to configurations such as decision node intermediation (708).

[0080] Various exemplary embodiments of the present invention are described herein. Reference is made to these examples in a non-limiting sense, and they are provided to illustrate the more broadly 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 invention. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, process acts, or steps to the objective, spirit, or scope of the present invention. Moreover, as will be understood by those skilled in the art, each of the individual variations described and illustrated herein has discrete components and features that can be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. All such modifications are intended to be within the scope of the claims associated with this disclosure.

[0081] Any of the devices described for performing the subject diagnostic or interventional procedures may be provided in packaged combinations for use in performing such interventions. These supply "kits" may further include instructions for use and may be packaged in sterile trays or containers such as those commonly employed for such purposes.

[0082] The present invention includes methods that may be implemented using the subject devices. The methods may include the act of providing such a suitable device. Such provisioning may be performed by an end user. In other words, the act of "providing" merely requires the end user to take an action to obtain, access, approach, locate, configure, activate, power on, or otherwise provide the device required in the subject methods. The methods recited herein may occur in any order of the recited events, and in the recited order of events, that is logically possible.

[0083] Exemplary aspects of the invention, along with details regarding material selection and manufacturing, are described above. As for other details of the invention, these may be understood in connection with the above-referenced patents and publications and are generally known or may be understood by those skilled in the art. The same may be true with respect to method-based aspects of the invention in terms of additional acts as commonly or logically adopted.

[0084] Additionally, while the present invention has been described with reference to several embodiments, optionally incorporating various features, the present invention should not be limited to those described or shown as each variation of the invention is considered. Various modifications may be made to the invention as described, and equivalents (whether recited herein or not included for purposes of brevity to some extent) may be substituted without departing from the true spirit and scope of the invention. Additionally, when a range of values is provided, it is understood that all intervening values between the upper and lower limits of that range, and any other stated or intervening values within the stated range, are also encompassed within the invention.

[0085] It is also contemplated that any optional features of the described inventive variations may be set forth and claimed independently or in combination with any one or more of the features described herein. Reference to a singular item includes the possibility that plurals of the same items are present. More specifically, as used in this specification and the claims associated therewith, the singular forms "a," "an," "said," and "the" include plural references unless specifically stated otherwise. In other words, the use of articles allows for "at least one" of the items of the present subject matter in the claims associated with the above description and this disclosure. Furthermore, it should be noted that such claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a predicate for the use of exclusive terminology such as "solely," "only," and the like in connection with the recitation of claim elements or the use of a "negative" limitation.

[0086] Absent the use of such exclusive terminology, the term "comprising" in claims associated with this disclosure shall permit the inclusion of any additional elements, regardless of whether a given number of elements are recited in such claim or whether the addition of features can be considered to transform the nature of the elements recited in such claim. Except as specifically defined herein, all technical and scientific terms used herein should be given the broadest possible commonly understood meaning while maintaining the validity of the claims.

[0087] The scope of the present invention should not be limited by the examples and / or specification provided, but rather only by the scope of the claim terms associated with this disclosure.

Claims

1. A synthetic involvement system for process-based problem solving, a. A computing system comprising one or more operablely coupled computing resources, b. A user interface, which is operated by the computing system and is configured to engage a human operator in accordance with a predetermined process configuration toward established requirements, at least in part based on one or more specific facts. Equipped with, The user interface is configured to allow the human operator to select one or more synthesis operators to be operated by the computing system, to engage with the one or more synthesis operators bidirectionally, to proceed through the predetermined process configuration, and to return to the human operator results selected to at least partially satisfy the established requirements. A system in which each of the one or more synthetic operators is provided with information by a convolutional neural network that is at least partially provided with information by the 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, numerical data, audio information, video information, emotional state information, analog disorder input selection, activity disturbance selection, curiosity selection, memory 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 relating to specific background information from a history storage device.

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

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

7. The system according to claim 1, wherein the one or more operablely coupled computing resources include local computing resources.

8. The system according to claim 7, wherein the local computing resources are selected from the group consisting of mobile computing resources, desktop computing resources, laptop computing resources, and built-in computing resources.

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

10. The system according to claim 1, wherein the one or more operablely coupled computing resources comprises resources selected from the group consisting of remote data centers, remote servers, remote computing clusters, and assemblies of computing systems at remote locations.

11. The system according to claim 1, further comprising a localization element operably coupled to the computing system and configured to determine the location 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 connectivity triangulation detector, an electromagnetic positioning sensor, and an optical positioning sensor.

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

14. The system according to claim 1, wherein the user interface comprises a graphical user interface.

15. The system according to claim 1, wherein the user interface comprises an audio user interface.

16. The system according to claim 14, wherein the graphical user interface is configured to engage the human operator using elements selected from the group consisting of a computer graphics-engineered display, a video graphics-engineered display, and audio engagement with graphic display.

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

18. The system according to claim 17, wherein the video interface participating character is selected from the group consisting of humanoid characters, animal characters, and cartoon characters.

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

20. The system according to claim 19, wherein the user interface is configured to enable the human operator to select visual presentation characteristics of the video interface-involved character 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 according to claim 19, wherein the visual presentation of the video interface-involved character can be modeled from a selected real person.

22. The system according to 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 according to claim 22, wherein the user interface is configured to enable the human operator to select one or more audio presentation aspects of the video interface-involved character, selected from the group consisting of character voice intonation, character voice volume, character speech language, character speech dialect, and character voice dynamic range.

24. The system according to claim 23, wherein one or more audio presentation aspects of the video interface-involved character can be modeled from a selected real person.

25. The system according to claim 1, wherein the predetermined process configuration comprises a finite set of steps, and through the finite set of steps, the involvement will be carried out in facilitating the established requirements.

26. The system according to claim 1, wherein the predetermined process configuration includes process elements selected from the group consisting of one or more generalized operating parameters, one or more resource / input recognition and utilization settings, domain expertise modules, process sequential progression paradigms, process cycle / iterative paradigms, and AI utilization and configuration settings.

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

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

29. The system according to claim 28, wherein the selection of elements by the human operator includes selecting a synthetic operator resource allocation for one or more aspects of the predetermined process configuration.

30. The system according to claim 29, wherein the system is configured to enable the human operator to specify a particular resource allocation for a first specific part of the predetermined process configuration.

31. The system according to claim 30, wherein the system is configured to enable the human operator to specify a particular resource allocation for a second specific part of the predetermined process configuration that is different from the particular resource allocation for a first specific part of the predetermined process configuration.

32. The system according to claim 30, wherein the system is configured to enable the human operator to define a specific resource allocation for a first specific part of the predetermined process configuration based on a plurality of synthetic operator characters.

33. The system according to claim 32, wherein each of the plurality of composite operator characters is applied sequentially to the first specific part.

34. The system according to claim 32, wherein each of the plurality of composite operator characters is simultaneously applied to the first specific part.

35. The system according to claim 30, wherein the system is configured to enable the human operator to define a specific resource allocation for a first specific part of the predetermined process configuration based on one or more hybrid synthetic operator characters.

36. The system according to claim 30, wherein the one or more hybrid composite operator characters comprise a combination of otherwise separate composite operator characters that can be applied simultaneously to the first specific part.

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

38. The system according to claim 37, wherein the convolutional neural network is provided with information using input from a training dataset using a supervised learning model.

39. The system according to claim 37, wherein the convolutional neural network is informed using input from a training dataset, along with an analysis of the established requirements for using a reinforcement learning model.

40. The system according to claim 1, wherein each of the one or more synthesis operators is provided with information by a convolutional neural network which is at least partially provided with information by a curated selection of synthesis action records relating to the synthesis actions of actual human operators.

41. The system according to claim 1, wherein each of the one or more synthesis operators is provided with information by a convolutional neural network which is at least partially provided with information by a curated selection of synthesis action records relating to the synthesis actions of the synthesis operators.

42. The computing system according to claim 25, wherein each of the finite set of steps, which involves an execution step, is configured to separate them, and during the execution step, the one or more synthesis operators proceed toward the established requirements according to one or more execution behaviors associated with the associated convolutional neural network.

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

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

45. The computing system according to claim 44, further configured to project-manage the execution of the plurality of tasks toward one or more milestones in the pursuit of the established requirements, at least in part on the execution behavior based on the influence of project leadership.

46. The computing system according to claim 44, further configured to functionally provide updates regarding the performance of the plurality of tasks at one or more stages of the execution step, at least in part, based on the influence of project leadership on the at least one execution behavior.

47. The computing system according to claim 46, further configured to functionally provide updates regarding the performance of the plurality of tasks at the end of each execution step for consideration in each of the finite set of steps in the process configuration, at least in part based on the influence of project leadership on the at least one execution behavior.

48. The system according to claim 47, wherein the computing system is further configured to functionally present the updates for review by the human operator using the user interface operated by the computing system, at least in part on the influence of the project leadership.

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

50. The system according to claim 1, wherein the user interface is configured to allow the computing system to be paused so that one or more intermediate results can be examined by the human operator with respect to the established requirements, while the human operator otherwise proceeds through the predetermined process configuration.

51. The system according to claim 50, wherein the user interface is configured to enable the human operator to modify one or more aspects of the one or more specific facts during a pause of the computing system and to facilitate future execution based on the modification.

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

Images