Machine learning systems and methods for automated prediction of innovative solutions to targeted problems

A machine learning server is provided for predicting innovations in one or more scenarios. The machine learning server includes a processor and a memory in communication with the processor. The processor is configured to receive a user input, define a scenario profile based on the user input, apply a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable outcome, prompt for a user selection from the target, apply a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the measureable desired outcome, obtain descriptive information related to the at least one candidate innovation, and present the descriptive information related to the at least one candidate innovation.

FIELD OF INVENTION

The field relates to machine learning systems, methods, and architectures for automated prediction and recommendation of innovative solutions to targeted problems identified by the machine learning systems.

BACKGROUND OF THE DISCLOSURE

Identifying innovative solutions within complex fields is a complex, yet necessary activity. In order to improve present technologies, research, development, inventions, processes, supply chains, coordinated activities, and other endeavors, there is a persistent demand to look for ways to improve each and every field. One approach to such innovation is to consult the state-of-the-art and identify deficiencies and problems. However, this approach is extraordinarily labor intensive and requires constant attention to changes in the knowledge base for a given field.

Generally, subjects or topics may be associated with significant corpuses of literature, documents, and other text that describes the state of knowledge within a field. The corpus for each field often entails massive corpus of thousands or millions of texts. In many fields, the corpus is constantly expanding. Because of such complexity and evolution, identifying areas with significant unsolved problems is a complex endeavor. Identifying potential solutions is an even more complex undertaking than identifying problems. Indeed, identifying potential solutions requires knowledge of the state-of-the art in fields, identifying areas for innovation, and determining possible advancements in those areas.

Moreover, many options for solving a problem may not work or will be sub-optimal. Identifying potential solutions requires analysis of the intricacies of each problem set based on known state-of-the art and the literature of each field.

To address the need for improved methods of identifying innovation in fields, disclosed herein are machine learning systems, methods, and architectures for automated prediction of innovative solutions to predicted target problems determined by the machine learning systems.

BRIEF SUMMARY OF THE INVENTION

In one aspect, a machine learning expert system is provided for predicting that innovations will meet target goals in one or more scenarios. The machine learning expert system includes a processor and a memory in communication with the processor and storing processor-executable instructions. When executed by the processor, the instructions configure the processor to receive a user input related to a scenario, define a scenario profile based on the user input, apply a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable desired outcome, prompt for a user selection from the at least one target, apply a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the measureable desired outcome, obtain descriptive information related to the at least one candidate innovation, and present the descriptive information related to the at least one candidate innovation to a user.

In another aspect, a method is provided for predicting that innovations will meet target goals in one or more scenarios. The method includes receiving a user input related to a scenario, defining a scenario profile based on the user input, applying a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable desired outcome, prompting for a user selection from the at least one target, applying a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the measureable desired outcome, obtaining descriptive information related to the at least one candidate innovation, and presenting the descriptive information related to the at least one candidate innovation to a user.

In yet another aspect, a machine learning server is provided for predicting that innovations will meet target goals in one or more scenarios. The machine learning server includes a processor and a memory in communication with the processor. The processor is configured to receive a user input related to a scenario, define a scenario profile based on the user input, apply a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable desired outcome, prompt for a user selection from the at least one target, apply a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the achieve the measureable desired outcome, obtain descriptive information related to the at least one candidate innovation, and present the descriptive information related to the at least one candidate innovation to a user.

DETAILED DESCRIPTION

As discussed above, a known problem in fields of research, development, and technology is identifying areas for innovation, and recommended approaches to such innovation. Described herein are machine learning systems, methods, and architectures for automated prediction of innovative solutions to predicted target problems determined by the machine learning systems. The machine learning systems utilize complex training models, supervised learning, and artificial intelligence models to identify innovation. The systems described also utilize additional technological solutions to provide the benefits described including: (a) custom methods to reinforce artificial intelligence with human computer interfaces (“HCI”); (b) new machine learning protocols in “closed loop” artificial intelligence models; (c) domain-specific algorithms for identifying industrial innovation; (d) domain-specific processes; and (e) value distillers. In particular, the described approaches to machine learning and artificial intelligence represent technological improvements rooted in technology that improves the functioning of the computing systems described, and solves known technological problems.

In an example embodiment, the systems described include a system that may be referred to as an Innovation Intelligence Expert System (“IIES”). The IIES is a digital system that is configured to recommend innovations that, if adopted, improve the chances of achieving a target or goal to address a known problem or challenge. In an example scenario, a user seeking to identify an innovation in a field uses the IIES to consider scenarios for applying innovations to address a problem in the field, obtain a preferred outcome (a “target state”), avoid risks of negative impacts (“risk target state”), or obtain solutions from a target state that have not previously been achieved in the field.

In such examples, a user may access the IIES and provide a preliminary description of a field and context where the user may seek to identify innovation. The field and context may be referred to as a “scenario”. The IIES provides a prompt to the user to add additional information including elaboration of the scenario and classificatory entities. The IIES is an interactive system that processes the scenario information and additional information to provide a set of candidate innovations using the methods described herein. The IIES recommends candidate innovations based on its prediction that such innovations would increase a likelihood of positive target states (or risk target states) in the scenario if the innovations are implemented.

By way of example, the IIES is capable of providing benefits described in the following examples. In a first example, an enterprise with $10 billion in revenues in a business unit seeks to discover disruptive innovations that, if adopted by competitors, would threaten the business unit's core business. These conditions form the basis of the scenario that is provided to or received by the IIES. In response, the IIES recommends an innovation that describes a new method of delivering on a core value proposition of the business unit that is also a core value proposition of another unrelated industry. When such a new method is adopted by the business unit, this method results in the enterprise's business unit continued success and reduced risk target state.

In a second example, a business unit of $100 million annual revenue seeks environmentally sustainable yet profitable improvements in its processes. The conditions of the business are input into a scenario at the IIES. The IIES recommends a technological process or material from within the industry or from an unrelated industry that it predicts, if adopted, results in a more environmentally sustainable process with higher levels of profit, improving the target state.

In a third example, an organization of over one hundred thousand members seeks innovative ways of operating under its charter. The conditions of the organization are input into a scenario at the IIES which predicts that a new method of social interaction will, if adopted, result in the organization functioning better and more efficiently achieving its goals, thus improving the target state.

The systems and methods described, including the IIES, utilize an object-based architecture. Example objects used by the system include the following objects, described below: (a) sources, (b) scenarios, (c) classifiers, (d) targets, (e) innovations, and (f) transformer neural networks using attention-based mechanisms. In some examples, additional objects are utilized by the systems and methods to provide the benefits described. The objects are used within the context of the methods described herein.

As described herein, a “source” is a machine-readable digital object used as an input by the systems and methods. Sources may include machine-readable data of any form including, without limitation, text, audio, video, photograph, graphic design files, or other formats or types. Exemplary sources include, a corpus of text or other literature, schematics, diagrams, audio, and video. In some examples, a machine-learning model may be used as a source as well. Sources may be further associated with a source type or a “source genre”. Source types or source genres may be provided with sources and used to specifically process the source (and, for example, decompose the source) into entities based on the source type. Source types may include, for example, media articles, advertisements, patent literature, research journals, websites, conference sessions, corporate literature, job placements, social media posts, case studies, case law and legal opinions, legal decisions, educational materials, legislative materials, and other suitable source types. Depending on the type and convention of source, different processing methods or approaches may be used.

As used herein, a “scenario” is a description of conditions of significance in the context of the systems and methods, including the IIES, and the users. A scenario may be identified, explained, and/or described using a source or a combination of sources. For example, a source (e.g., an article) may describe a scenario fully or partially, or the IIES may process multiple sources (from multiple source types) to derive the scenario. Scenarios are identified, explained, and/or described to provide context to a particular instance of the systems described and to allow them to recommend candidate innovations that will improve a particular scenario. Such improvement may include, for example, improving a target state, decreasing a target risk state, accessing opportunities, or any other desirable outcome. Scenarios are thus processed by the systems with the objective of achieving certain outcomes (or targets). Thus, the result of system processing is one or more recommended innovations that describe alternative methods, approaches, partnerships, business models, or changes that one may consider in an application for a given scenario.

Scenarios may or may not include the primary stakeholders who conceived, operate and describe the scenario. Such identification may assist with determining whether target states are achieved for such stakeholders and/or whether they vary for particular stakeholders. Scenarios may also include identification and enumeration of deficits of scenario either explicitly or implicitly including, for example, identifying known problems or challenges. Scenarios may also include contextual information for the scenario including details of where, when, and how the scenario occurs and environmental conditions including resources. Scenarios may also include the significance of the scenario and factors that establish the scenario as important. Scenarios may further include the impact of the scenario on stakeholders, populations, and/or bystanders. Scenarios may also include possible downsides and its associated adverse effects. Scenarios may additionally include potential and/or known benefits from eliminating any problems or characteristics inherent in the scenario. Scenarios may also include descriptions of previous attempts at innovating on the scenario including known reasons for failure. Scenarios may include any background conditions where innovations may be provided including, for example, descriptions of industrial processes, descriptions of scientific research in a field, descriptions of environmental conditions, descriptions of public institutions, and descriptions of corporate ecosystems.

As used herein, a “target” is a result that may be predicted or determined when a particular innovation is applied in the context of a scenario. As explained, targets may refer broadly to positive improvements to conditions, mitigations of risk, or any other desirable outcome that can be quantified. A target may apply to one quantifiable metric (e.g., revenue or cost of a particular project) or include a function or index that incorporates multiple underlying metrics. The systems and methods described are configured to make predictions of the targets associated with innovations. As a result, the targets serve as guiding metrics and are applied by the system to identify relevant candidate innovations for recommending, and for sorting and/or filtering from candidate innovations to identify those that provide maximized predicted utility for targets. After identifying targets, the system makes predictions for target outcomes for each candidate innovation. Thus, target values for each innovation are assessed as to their efficacy, and filtered and sorted to provide relevant innovations with desired targets for each scenario. In an example embodiment, information to define a target contains one or more of the following characteristics (whether explicit or implicit), which are either part of the target or derived from the target: (a) definitions of boundary values or conditions, (b) constraints and conditions such as strategies or activities that are applied to control the steps performed by the system including Processes (defined below), further including both how such steps are applied and how results are assessed; (c) one or more operational guidelines for identifying a candidate innovation (also referred to as a “Recommendation”); (d) tests to apply to a candidate innovation; (e) identification of issues of primary concern to the candidate innovation; (f) metrics relevant to assessing to what degree an candidate innovation, if adopted, will make a scenario improve with respect to a target; (g) procedures to select candidate innovations; (h) objective or subjective criteria for decision-making on selecting candidate innovations; and (i) suggested metrics, objectives, or outcomes that are to be avoided or deprecated in a possible candidate innovation. Targets may include, for example, the aggregate amount of environmental pollution from a process, profit or revenue from a project or product, market penetration, and metrics related to disruption in a field. Targets may also include parameters to define the direction of maximization (e.g., more revenue or profit is desirable, but less environmental pollution is desirable) and minimum satisfactory metrics for an innovation (defined by, e.g., break-even points).

As described herein, an “innovation” is a source, or a set of sources that describe a scenario deemed to have some innovative characteristics. Innovations, in the context of the systems and methods described, have been derived from publicly available resources, open source content, and/or recommended into the system by the innovator himself. In some examples, innovations are defined or extrapolated by the systems whether based on source input or otherwise. As described herein, the system recommends candidate innovations to one or more users based on predicted targets and a candidate innovation is adopted to achieve a target.

In the example embodiment, innovations are processed and determined by the system in accordance with the targets. Thus, candidate innovations are recommended based on their impact to targets in the scenario(s). The candidate innovations include descriptions for the particular recommended innovations. In some examples, the candidate innovations may also include some or all of the following: (a) the primary stakeholders who conceived, operated, and described each innovation; (b) the context relevant to the innovation; (c) the significance of the innovation; (d) the impact of the innovation on stakeholders, populations, and/or bystanders; (e) anticipated and/or known costs and benefits of each candidate innovation; (f) factors that establish the candidate innovation as significant; (g) comparisons of other scenarios where other innovations were applied to achieve targets similar to what the candidate innovation is intended to accomplish and the results of such other innovations in other scenarios; (h) details of where, when, and how the candidate was conceived; (i) alternative methods of implementing the candidate innovation; (j) steps to be performed (including any prescribed order) in order to deploy the candidate innovation; and (k) known costs and logistics for the candidate innovation.

As described herein, “innovation characteristics” are a set of data derived innovation data representing a distillation of descriptions of innovations into machine-readable texts that may be stored in the system and used as an innovation source. The systems and methods described may utilize suitable information processing algorithms to derive innovation characteristics from innovations (including candidate innovations). Examples of innovation characteristics include functionality that brings increased efficiency in an innovation, or a more sustainable way of processing a product after applying an innovation. Innovation characteristics may also include new capacities based on new technical approaches. By way of an example, where an innovation includes use of carbon fibers instead of steel in a product manufacturing process, innovation characteristics may include the impacts of such a change to cost, sustainability, and revenue. Where an innovation includes hosting plastic recycling bins on mobile buses, innovation characteristics may similarly include the impacts of such a change to recycling throughput, adoption, cost, sustainability, and revenue.

As used herein, an “innovation-seeker” is a user of the systems and methods, including of the IIES. An innovation-seeker may engage with the IIES platform (or other similar systems) to engage in one or more of the following innovation-centered activities: (a) to seek an innovation in order to solve a problem associated with a scenario; (b) to seek an innovation in order to capitalize on new potential opportunities; (c) to seek inspiration on looking at problem-solving differently; and (d) to maximize opportunities within a given scenario. An innovation-seeker may include a representative, participant, member, manager, or leader from a company, governmental entity, academic institution, or non-profit organization.

As described herein, a “supplementary source” is a source that can be used to enrich or enhance the sources for improved processing by the methods described herein, including machine learning, human-in-the-loop processing, natural language processing (“NLP”), artificial intelligence, and other related processes. Supplementary sources may relate to sources (which may also be referred to as “primary sources”) in one or more ways: (a) supplementary sources may further explain information from primary sources; (b) supplementary sources may elaborate on information from primary sources; (c) supplementary sources may define information from primary sources; (d) supplementary sources may illustrate information from primary sources and use; (e) supplementary sources may define contexts for the primary sources; (f) supplementary sources may explain or amplify the significance of information from the primary sources or the impact of the primary sources; (g) supplementary sources may explain the environmental impact of information from the primary sources; (h) supplementary sources may describe the source from other perspectives; and (i) supplementary sources may provide alternative media for information from the primary sources.

As described herein, a “risk target” is a target associated with one or more negative outcomes (e.g., potential threats) that an innovation-seeking user may seek to avoid. An innovation seeker may seek a candidate innovation to accomplish a target state and/or to avoid a risk target state. In some examples, the risk target may include one or more of the following characteristics: (a) an enumeration of stakeholder roles who will be adversely impacted by the negative outcomes; (b) identification and enumeration of adverse effects, quantitatively and/or qualitatively, that may occur if the risk target occurs; (c) possible external benefits that could accrue to any party should the risk target occur without mitigation; (d) details of where, when, and how the risk target occurs; (e) the context of the risk target; (f) the significance of the risk target and factors that establish the risk target as important; (g) the impact of the risk target on stakeholders, populations, and/or bystanders; (h) the potential benefits and costs of eliminating the risk target; and (i) descriptions of previous attempts at mitigating the risk target and the results of such attempts. Risk targets may include, for example, negative outcomes from climate change such as flooding a manufacturer's factories or customer sites, impact on a pandemic to supply chains, a competitor disrupting a business, changing to sourcing materials that could threaten production, and the impact of variable pricing on profits.

As described herein, a “recommendation” is a recommended innovation (or a candidate innovation) that is returned by the system because it is predicted to improve a likelihood of achieving desired targets in a scenario (whether avoiding risk targets or achieving positive targets). An example of a recommendation or a “candidate innovation” may be an innovation of an algae-grown textile that is predicted to produce low environmental impact and high revenue, while being relevant to a textile manufacturer's customers and production processes.

As described herein, a “match” occurs when a user selects a candidate innovation or recommendation that is provided by the system (including the IIES) as satisfactory. Selection of a match causes the connection of sources and scenarios with a recommendation (or candidate innovation) selected in the match. In one aspect, a match expresses the expectation that the application of the recommendation to the scenario will achieve desired target states (or avoid risk target states). In some examples, stakeholders who may create, distribute, codify, promote or operate the candidate innovation, may use the system to identify new stakeholders interested in their candidate innovation based on the match. Such identification could be used to find new business opportunities, markets and growth potential for the candidate innovation. The system may also include scoring regarding the degree to which the match satisfies the target state or mitigates the risk target state. An example of a match may include where an end user, having entered into the system the scenario of improving plastic recycling, is recommended a candidate innovation for a new way of recycling, wherein the user declares the innovation to be a match and indicates that the candidate innovation is satisfactory.

In some examples, the systems and methods determine a value (referred to as a “naked value”) for each innovation. A “naked value” represents the value that a candidate innovation represents when isolating for benefits delivered to users. In this respect, innovations may be regarded as delivery mechanisms for creating benefits. As such, naked value metrics isolate the value that is most significant to a stakeholder, multiple stakeholders, or a group. In one example, naked value is a ratio between the number of benefits relevant stakeholders receive and the amount of resources required to achieve those benefits. This approach allows for optimizing of the most benefit with the lowest amount of resources.

As described herein, a “classifier” is an object used to classify sources (whether they are primary or supplementary sources). Classifiers include sub-objects for (1) type; (2) class; (3) entity; (4) chain; (5) process; (6) hypothesis; and (7) negative/positive statements. In one respect, classifiers provide an organizational hierarchy for classifying sources and information.

As described herein, “transformer” may be described as a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. Transformers are used by the expert machine learning system to, for example, correlate content that was previously unrelated based on classification. As such, transformers are used for predicting targets, hypotheses, candidate innovations, and performing other tasks including content enrichment.

A type has one or more classes as members that, by virtue of the type definition, are contained within the type. Classes in a given type can be correlated in accordance with the characteristics of the type, with such correlation deriving its relevance, meaning and function depending on the nature of the type itself. A type may include all industrial classifications, all functional classifications, or all organizational classifications.

A class has one or more entities as members that, by virtue of the class membership rules, are contained within the class. Entities in a given class can be correlated in accordance with the semantics of the membership rules, with the characteristics and effects of such correlation deriving their relevance, meaning and function from the class itself. Classes may enforce on their members correlations such as (a) continuous, discrete; (b) inclusion and exclusion; and (c) hierarchical or heterarchical. As used herein, “heterarchical” refers to organizations where members of the class have unknown rules governing membership in the class and is distinct from “hierarchical” which refers to organizations where members of the class have known rules governing membership in the class. Thus, classes can encompass elements with no known ordering or organizational rules. Classes may be defined by lists or arrays, conditional data (e.g., geographical or temporal restrictions), or language rules. Examples of a class may include, for example, industrial classification codes (e.g., the North American Industrial Classification Scheme (“NAICS”)), geographical regional definitions, industry groupings such as the Fortune 500, United Nations Sustainability Goals, and heterarchical classes.

An entity is a text label used as a classifier. An entity may include a numerical metric, a unit of measurement, a dictionary definition, or any relevant similar discrete unit that may be derived from and/or associated with a source. An entity may or may not be associated by a predicate that expresses the semantics of the association to the source. An entity may be associated with one or more sources in order to expand the corpus associated with the entity. An entity may also be associated with a source in order to classify the source. An entity may additionally be associated with a supplementary source to explain, elaborate upon, more narrowly define, or illustrate the entity. Some entities may be curated into classes, while others may be standalone. An entity may include a label or name for an industry, a product, a manufacturing step, or a location.

A chain refers to a relationship between parts and a whole. A chain may include multiple chained events, chained dependencies, or formal mathematical models such as mereological analytical models that describe part-to-whole relationships and part-to-part relationships within a whole. A chain may include, for example, a supply chain for a product or service, a manufacturing process for a product, or a product life cycle. A part/whole relationship is a series of parts—classes, entities, or mixtures of the two—that when actioned, enacted, assembled, or otherwise implemented in their appropriate process and order reach a state of completion. The relationship of parts to whole may or may not be physical, cognitive, or both. In some examples, details of where, when and how the part/whole relationship operates may be specified to great detail or more generally. In some examples, a clear definition of the relationship between parts both to one another and to the whole may be present. Such relationship information may or may not have the following characteristics: (a) collectively possessing a clear consistency and similarity across the entire part/whole relationship, or having inconsistent and dissimilar qualities across the entire part/whole relationship; (b) the parts may be separated, or not, in any time, space, energy or suitable qualitative metric; (c) parts may be simple units or whole organizations of people involved in complex activities; (d) the parts may relate to one another in a relation of mutual dependence or independence; (e) steps in the part/whole relationship may pass through all parts, or some of the parts, or vary each time the part/whole relationship is enacted; (f) the part/whole relationship may or may not specify stakeholders who conceive, operate and assess the part/whole relationship; (g) the context of the scenario and its environment; (h) the significance of the part/whole relationship's various parts and steps, and factors that establish certain parts as important and others as less important; (i) the potential failure modes of the part/whole relationship or any of its parts; and (j) descriptions of alternative part/whole relationships that can achieve the same result.

A process is a set of operations or steps that take as input objects including sources, entities, and classes, and output processing results. Processes may extract, transform or re-render texts as needed to obtain results useful to the system. Processes may perform deep semantic analysis, natural language processing, rich multi-faceted analysis, transformer neural networks using attention-based mechanisms, and simulations, as well as generate hypotheses and perform graph inference. Processes may also assess class inclusion versus exclusion for entities, classes, types, and other elements. Some processors use mature NLP techniques such as entity extraction, term frequency-inverse document frequency (TF-IDF) and vector processes (e.g., word2vec).

Other processors may work via the human computer interface (“HCI”), eliciting responses from the innovation-seeker. The major processes include hypothesis and domain-specific processes, described below. Other processes are more complex and are discussed in detail below. Some examples of processes include running an entity recognition process to extract entity names, identify a label for entities, and label a source. An exemplary process described herein includes processing a corpus of literature to assist a user in identifying a scenario in which they seek innovation, recommending an innovation for the scenario, and receiving a match.

As used herein, a “hypothesis” is an output of a process referred to as a “hypothesis generation process”. A hypothesis generation process is an algorithm that generates hypotheses for candidate innovations as being suitable to respond to a scenario to achieve a target state. Hypotheses are assessed based on the target and other criteria described herein. In some examples, the hypothesis generation process may be trained based on matches. If a user selects a particular hypothesis (i.e., an output candidate innovation), that hypothesis is determined to be relevant and used to train the algorithm. If the user does not select a particular hypothesis, that hypothesis is determined to not be relevant and used to similarly train the algorithm. An example of a hypothesis is that a material product be created that avoids use of processes and precursors associated with a risk target and instead uses certain substitute processes and precursors. In some examples, the hypothesis generator process may create precursor hypothesis to identify possibilities within given confidence intervals.

The systems and methods described also utilize negative and positive statements to process source, scenario, target, hypotheses, and other objects. Negative statements include gaps, lacks, holes in capability, negative language convert to positive assertions. The systems and methods described have the capacity to transform negative statements into positive statements and vice versa. This feature is significant because it allows the systems and methods to match across a broader corpus and to, for example, identify sources with information that respond to a negative requirement. Thus, the system may map negative to positive statements to identify candidate innovations. Where a requirement for a scenario is to reduce output of carbon, for example, a statement in a source may state “carbon output is produced in excess in this process”, and a supplementary source used to identify a candidate innovation may reference a similar but modified process and note “excess carbon is not produced by applying this approach.” In such an example, the system may associate the supplementary and primary source and identify the referenced modified process as a candidate innovation.

Described below are specific implementations of the methods that may be performed by the systems described, including a machine learning expert system that may be referred to as the Innovation Intelligence Expert System (“IIES”). In an example embodiment, the IIES includes a machine learning server including a processor and a memory. In the example embodiments, the machine learning server is in communication with a database. In some examples, the machine learning server also is in communication with a secondary database and/or external systems including third-party data. The machine learning server includes a machine learning layer configured to perform the processes described herein. The machine learning layer may further include components including an entity extraction and classification component, a calculator component, and a pattern association component. The machine learning server also provides a user interface to provide output to a user and receive input. The user interface may be provided directly at the machine learning server through suitable displays and input/output devices. In some examples, the user interface is provided through a secondary user computing device that is in communication with the machine learning server. This description is intentionally brief, and a more detailed discussion of the system architecture is provided below.

In an example embodiment, the machine learning server is configured to a) train machine learning models for i) generating targets based on a particular scenario, and ii) generating hypotheses to identify candidate innovations predicted to provide a positive target state for the particular scenario; and b) apply the trained machine learning models for i) generating targets based on a particular scenario, and ii) generating hypotheses to identify candidate innovations predicted to provide a positive target state for the particular scenario. In some examples, the machine learning server is further configured to a) train machine learning models for i) enriching and refining scenarios provided by users based on source information; ii) enriching scenarios based on part/whole analysis, mereological analysis, and chain analysis, and iii) textually processing statements for sentiments to apply to improve hypothesis generation; and b) identify trends based on source information to enrich and refine scenarios based on trend analysis.

The machine learning server is in communication with other devices that may provide it with relevant datasets including, without limitation, sources (including primary and supplementary sources). As described herein, sources include a broad corpus of information that may be associated with a large variety of source types and source classes. Sources may be originally derived from public information, subject matter expert information, proprietary information related to the IIES, and third-party information that is not public. In the example embodiments, sources can be processed to extract a variety of relevant objects and features from the sources including, without limitation, targets, hypotheses, candidate innovations, statement enrichment, trend characteristics, part/whole datasets, and scenarios. The machine learning server also has access to methods including scraping, user interfaces, human computer interface (“HCI”), and data migration and loading tools including extract, transformation, and loading (“ETL”) tools which may be used to migrate or move data described including sources.

In an example embodiment, the machine learning server receives a user input that relates to a scenario. In one example, the user input is provided in text. In other alternatives, the user input may be provided using structured data, audio, video, images, or any suitable media. The machine learning server processes the user input to define a scenario profile. A scenario profile refers to an expanded, elaborated, or defined version of a scenario. In many examples described herein, a user input describes a scenario at a lower level of detail. A scenario profile includes elements that may further define the scenario, and therefore constitute a collection or profile. A scenario profile may also be referred to as a scenario collection.

Defining the scenario profile (or collection) may include parsing statements from the user input and classifying the parsed statement(s) with relevant objects including, without limitation, statement type, statement class, statement entities. In an example embodiment, the machine learning server assesses the vocabulary of each source by applying classifiers to identify the type (or genre), class, and/or entity for each source. In some embodiments, the machine learning server initially decomposes each source into component elements (such as, for example, sentence fragments from a textual source) before classifying the component elements into types, classes, or entities. The classification models described may utilize natural language processing methods to identify relevant attributes of each source (or component) used for further classification including, without limitation, mature NLP techniques such as entity extraction, term frequency-inverse document frequency (TF-IDF), vector processes (e.g., word2vec), and transformer predictions based on attention mechanisms. Sources with common vocabulary may be grouped into common types or classes where they have sufficient overlapping attributes. Entities may be determined by applying similar techniques to find a label or label most likely to associate with a source or component.

In the example embodiment, classification tools and related classes, types, and entities are available to the machine learning server. Additionally, the machine learning server is configured to utilize networked models that utilize an attention mechanism to provide transformations and provide dynamic relationships between previously unrelated information. Likewise, all objects and processors described above are available to the machine learning server.

As described below, in some examples the scenario profile is defined by the machine learning server performing at least one statement enrichment process, typically using a machine learning model, to add details to the scenario indicated by the user input. In one example, the machine learning server identifies a statement type associated with the user input and applies a trained content enrichment machine learning model to identify enrichment content (e.g., statements from one or a plurality of sources that may elaborate on the statement(s) in the user input describing the scenario, or may predict related and logical terms that elaborate on the statement(s) in the user input describing the scenario).

In one example, the content enrichment machine learning model is trained using the following approach. A corpus of sources (e.g., sources or supplementary sources) are provided to or received by the machine learning server (using, for example, the database). The corpus includes documents which each include components (e.g., sentences and sentence fragments). In some examples, each component is classified using classifier algorithms into classes, types, and entities. The machine learning server decomposes each item of the corpus. The machine learning server performs training cycles wherein a user provides a training input related to a training scenario. The training input is used to define a training scenario profile. In some examples, the training input is decomposed into component parts. (In some examples, the machine learning server also applies the classifier to the training input and identifies the at least one component based on overlapping classification between the at least one component and the training scenario.) The machine learning server provides at least one component of the corpus to a user as training enrichment content, to determine whether the at least one component relates to the training scenario. The machine learning server receives an enrichment training response indicating whether the selection (the at least one component) of the set of training enrichment content relates to the training scenario. In some examples, the enrichment training response is a binary or Boolean where the user may identify the at least one component as either relevant or not relevant. In other examples, the user may provide a relevancy score or ranking to indicate a relative relevancy. In further examples, the user may rank or score the at least one component based on context, such that the machine learning model may train for relationships between scenario context and enrichment context. The enrichment training response is used to train the content enrichment machine learning model. In operation, when the machine learning server applies the trained content enrichment machine learning model, the machine learning server may continue to re-train the model based on feedback. As such, a user may provide enrichment training responses in response to a trained content enrichment machine learning model to allow the model to continue to be refined.

In some examples, the machine learning server also applies a trained negative-to-positive machine learning model to process statements in the system. The negative-to-positive machine learning model is used to enrich content with information that is relevant, while appropriately controlling for content that is contrary to an assertion. For example, a scenario may be provided as a negative limitation such as, “it is desirable to reduce the output of a particular hazardous chemical during the manufacture of a particular product.” The corpus available to the machine learning server may contain information related to the hazardous chemical and/or the particular product, but the machine learning server is used to provide hypotheses and candidate innovations in positive expressions. Thus, the machine learning server attempts to identify information that would identify the steps that could be taken to meet the above negative limitation, although they are described positively. Processed statements may include components or the entirety of any statements provided to or received by the machine learning server including, without limitation, sources (e.g., primary and supplementary), scenarios, targets, candidate innovations, trends, and hypotheses. The negative-to-positive machine learning model is trained as follows. A training assertion input is received by the machine learning server. As above, and in all examples of training, a corpus of sources (e.g., sources or supplementary sources) are provided to or received by the machine learning server (using, for example, the database). The corpus includes documents which each include components (e.g., sentences and sentence fragments). In some examples, each component is classified using classifier algorithms into classes, types, and entities. Based on the corpus above, the machine learning server receives a corpus of training assertion information (or training assertion content) which is classified and processed in a manner similar to how the corpus for enrichment is processed above. The training assertion input is processed by a language processing algorithm (such as an NLP) which extracts sentiment related to the training assertion input. The training assertion input is also decomposed and classified in a manner similar to the approach of the content enrichment machine learning model. Thus, in some examples, the machine learning server also applies the classifier to the training assertion input. The machine learning server identifies a component of the corpus that matches the training assertion input (and the training assertion scenario) based on overlapping classification between the component and the training assertion scenario. The machine learning server provides the component of the corpus to a user as training assertion content, to determine whether the at least one component relates to the training assertion scenario. The machine learning server receives a training assertion response indicating whether the selection (the component) of the training assertion content relates to the training scenario. The training assertion response is used to train the negative-to-positive machine learning model. In operation, when the machine learning server applies the negative-to-positive machine learning model, the machine learning server may continue to re-train the model based on further feedback. As such, a user may provide training assertion responses in response to a trained negative-to-positive machine learning model to allow the model to continue to be refined.

In another example, the user input is processed with a trained machine learning model for component enrichment and a trained machine learning model for business impact enrichment. In this example, the user input is converted into a scenario profile and the trained machine learning model for component enrichment identifies parts that may correspond to the scenario profile. The trained machine learning model for component enrichment is trained to find, for example, component parts, chains, bills of material, and other aspects of the scenario profile that may assist in defining the scenario profile further. Such additional definition may improve the quality of targets, hypotheses, and scenario enrichment provided by the machine learning server. Similarly, the trained business impact enrichment model is trained to identify, for example, business impacts associated with a particular scenario including, for example, supply chain risks, environmental risks, and pricing risks.

The machine learning model for component enrichment is trained as follows. As above, and in all examples of training, a corpus of sources (e.g., sources or supplementary sources) are provided to or received by the machine learning server (using, for example, the database). The corpus includes documents which each include components (e.g., sentences and sentence fragments). In some examples, each component is classified using classifier algorithms into classes, types, and entities. The corpus may also be classified using whole-to-part or similar part analysis techniques. As such, the corpus may be processed using language processing algorithms to identify documents or texts containing references to compositions, manufacture, supply chains, lifecycle assessments (“LCA”), bill of materials (“BOM”), parts, and other processes and products defining a multi-step or multi-component process or product, or similar information. The machine learning server decomposes each item of the corpus including decomposition into segments related to any identified parts or segments. The machine learning server performs training cycles wherein a user provides a component training input related to a training scenario involving compositions, manufacture, supply chains, lifecycle assessments (“LCA”), bill of materials (“BOM”), parts, components, and other processes and products defining a multi-step or multi-component process or product, or similar products or processes. (Each identified and decomposed part of the sources and input may be further classified using the classifiers described, and including classification by component.) Input may be further classified using the classifiers described, and including classification by component. Alternately, input may be enriched by words predicted by an attention-based process trained on the relevant corpus that predicts relevant terms using a transformer process. The component training input is used to define a component scenario profile. In many examples, the component training input is decomposed into component parts. (In many examples, the machine learning server also applies the classifier to the component training input and identifies the at least one component based on overlapping classification between the at least one component and the component training scenario.) The machine learning server provides at least one component of the corpus to a user as component content, to determine whether the at least one component relates to the component training scenario. In most examples, the component of the corpus selected includes information related to the fact that the component training input and component scenario profile relate to part-to-whole or similar concepts. The machine learning server receives a training component response indicating whether the selection (the component) of the component content relates to the training scenario. The training component response is used to train the machine learning model for component enrichment. In operation, when the machine learning server applies the component enrichment machine learning model, the machine learning server may continue to re-train the model based on further feedback. As such, a user may provide training component responses in response to a trained component enrichment machine learning model to allow the model to continue to be refined.

The machine learning model for business impact enrichment is trained as follows. As above, and in all examples of training, a corpus of sources (e.g., sources or supplementary sources) are provided to or received by the machine learning server (using, for example, the database). The corpus includes documents which each include components (e.g., sentences and sentence fragments). In some examples, each component is classified using classifier algorithms into classes, types, and entities. The corpus may also be classified based on relationship to business impacts including, for example, supply chain risk, environmental impact, competitive risks, and pricing risks. As such, the corpus may be processed using language processing algorithms to identify documents or texts containing references to business impacts or similar information. The machine learning server decomposes each item of the corpus including decomposition into segments related to any identified business impacts. The machine learning server performs training cycles wherein a user provides a business impact training input related to a training scenario involving business impact. (Each identified and decomposed part of the sources and input may be further classified using the classifiers described, and including classification by business impact.) The business impact training input is used to define a business impact scenario profile. In many examples, the business impact training input is decomposed into parts based on any related issues. The machine learning server provides at least one component of the corpus, as business impact content, to a user to determine whether the at least one component relates to the business impact training scenario. (In most examples, the machine learning server also applies the classifier to the business impact training input and identifies the at least one component based on overlapping classification between the at least one component and the business impact training scenario.) In most examples, the component of the corpus selected includes information related to the fact that the business impact training input and business impact scenario profile relate to business impacts such as those described herein. The machine learning server receives a training business impact response indicating whether the selection (the component) of the business impact content relates to the training scenario. The training business impact response is used to train the machine learning model for business impact. In operation, when the machine learning server applies the business impact enrichment machine learning model, the machine learning server may continue to re-train the model based on further feedback. As such, a user may provide training business impact responses in response to a trained business impact enrichment machine learning model to allow the model to continue to be refined. Business impacts that may be addressed using the business impact enrichment machine learning model include appending content related to environmental impact (e.g., footprint calculations and waste), externalities, and exposure to risks in supply chains.

In further examples, the machine learning server defines and uses a trend prediction algorithm to supplement the functions provided. In the example embodiment, the machine learning server receives a corpus of source information. Generally, each item of the corpus is associated with a time period such as a date or a time. The machine learning server further parses each item of the corpus of source information to identify sentence fragments (or other component elements) along with an associated time period. The machine learning server further submits sentence fragments (or other component elements) to an attention-based transformer trained on a relevant corpus or multiple corpus, to predict associated relevant content such as sentence fragments. In many examples, the transformer functions in real-time or near real-time. The machine learning server additionally identifies a context associated with each of the sentence fragments (or other component elements). The machine learning server also applies a sentiment analysis algorithm to determine whether the content of the sentence fragments (or component elements) is being discussed in a positive or in a negative manner. Thus, for each item of the corpus of source material (including primary and supplementary sources, as described herein), the machine learning server obtains: (a) component element content (e.g., strings of text, pages of text, or portions of audio or video); (b) a related time period; (c) a related context, and (d) a related sentiment. The machine learning server processes these elements together to determine trends that specify how particular phrases, terms, words, names, or other content become (a) more popular; (b) less popular; (c) well-known; and/or (d) less referenced in contexts. The machine learning server then defines a model for the trend prediction algorithm to provide relevant content in conjunction with the hypotheses, targets, scenarios, and other content described herein that is provided directly or through enrichment by the machine learning server. In one embodiment, the machine learning server trains the trend prediction algorithm using a machine learning model. In this approach, the machine learning server receives training inputs, decomposes and classifies the training inputs, identifies trend content based on the context of the training input, provides the trend content to a user, receives a user response indicating whether the trend content was relevant or not (whether by score, ranking, or a binary yes/no), and trains a machine learning model based on such input. In operation, when the machine learning server applies the trend machine learning model, the machine learning server may continue to re-train the trend machine learning model based on further feedback. As such, a user may provide further trend responses in response to a trained trend machine learning model to allow the model to continue to be refined.

For each of the machine learning models described herein, the machine learning server iterates repeatedly to improve the predictive quality. In some examples, for each of the machine learning models, entities, classes, and types are organized within a hierarchy such that distinct entities may be related (or “nearer”) to one another, as are distinct classes, and distinct types. In such examples, the machine learning server may provide content to a user via the machine learning models (whether in training or in operation) for “adjacent” entities within a particular entity within a class or classes within a type. In a similar manner, the machine learning model for component enrichment may organize parts and wholes and provide content to a user based on a related or adjacent part or whole that may not be present in the scenario input.

The machine learning server also includes a target machine learning model used to identify targets in response to a scenario. The machine learning server receives a corpus and decomposes each item of the corpus in underlying fragments. In the example embodiment, the machine learning server identifies elements with candidate metrics from the corpus including content that describes a particular thing, process, compound, technology, field, issue, or topic in conjunction with a particular metric. The metric may include any discernible measurement including numerical measures, currency amounts, percentages, ratings, or any other measurement. The machine learning server also classifies each component of the corpus and each decomposed portion of the corpus using the classification approaches described above. The machine learning server also identifies a context for each item of the corpus and each fragment of the corpus. Thus, the machine learning server obtains a pool of classified targets, each associated with a context. The machine learning server receives a training input for a scenario to train target prediction, decomposes and classifies the training input. The machine learning server also identifies at least one responsive target with a context and classification corresponding to or related to the context and classification of the training input. The machine learning server provides at least one responsive target to a user, and receives a user response indicating whether the responsive target relevant to the scenario or not (whether by score, ranking, or a binary yes/no). The machine learning server trains the target machine learning model based on such user response. In operation, when the machine learning server applies the target machine learning model, the machine learning server may continue to re-train the model based on further feedback. As such, a user may provide responses in response to a trained target machine learning model to allow the model to continue to be refined.

The machine learning server also includes a hypothesis machine learning model used to identify candidate innovations in response to a scenario and in response to targets. The machine learning server receives a corpus and decomposes each item of the corpus in underlying fragments. In the example embodiment, the machine learning server identifies elements with candidate innovations from the corpus including content that describes a particular thing, process, compound, technology, field, issue, or topic in conjunction with a particular innovative concept. The innovation may include any improvement in an analogous or related field, any hypothesized or predicted improvement, scientific data and/or analysis indicating a new possible opportunity for design or development, business data and/or analysis indicating a new possible opportunity for design or development, or similar information. The machine learning server also classifies each component of the corpus and each decomposed portion of the corpus using the classification approaches described above. The machine learning server also identifies a context for each item of the corpus and each fragment of the corpus. As described above, the machine learning server may associate targets from the pool of targets with classifications and contexts. The machine learning server applies the target machine learning model to identify targets relevant to each hypothesis, based on related or corresponding context and classification. Thus, the machine learning server obtains a pool of classified hypotheses, each associated with a context and at least one target. The machine learning server receives a training input for a scenario and target to train hypothesis prediction, and decomposes and classifies the training input. The machine learning server also identifies at least one responsive hypothesis with a context and classification corresponding to or related to the context and classification of the training input. The machine learning server provides at least one responsive hypothesis to a user, and receives a user response indicating whether the responsive hypothesis relevant to the scenario and target, or not (whether by score, ranking, or a binary yes/no). The machine learning server trains the hypothesis machine learning model based on such user response. In operation, when the machine learning server applies the hypothesis machine learning model, the machine learning server may continue to re-train the model based on further feedback. As such, a user may provide responses in response to a trained hypothesis machine learning model to allow the model to continue to be refined.

In operation the machine learning models described aggregate the learning derived and continually iterate to improve their learning. In one example, a multi-dimensional matrix is maintained for each machine learning model, whereby matches that occur (i.e., wherein a user selects a particular output as relevant) are weighted (based in part on any relevancy score or rating). The multi-dimensional matrix forms vectors for each match and generates a score for each component of a corpus across relevant scenarios, targets, and other objects.

The machine learning server applies the machine learning models described to perform the functions described herein.

In some examples, the machine learning models may be defined specific to a domain or a group of domains of information. In such examples, the machine learning models may be trained to provide specific functionality within a context, and to predict targets and hypotheses for scenarios specific to the context of that domain.

In one aspect, the systems described may be referred to as providing a “human-in-the-loop” method of machine learning because the machine learning models continually learn from human intelligence and use matches to accelerate the learning of the system. However, the underlying architecture of each machine learning model depends upon relationships derived based on classification data, context data, language processing, and object relationships, as described herein.

Below are examples of output provided by the machine learning server applying the methods described. In a first example, a user provides the input, “battery materials are toxic” to define a scenario. The machine learning server applies at least the content enrichment machine learning model, parses the statement to identify the terms “battery”, “materials”, and “toxic”, and identifies the classification and context for each and obtains new content to append and revise the statement. In some examples, the machine learning server submits the text to a transformer based on attention-based mechanisms, before appending and revising the statement. The input is appended and revised to state: “Batteries use toxic materials, are difficult or inconvenient to recharge and/or recycle, and have limited electrical capacity. They are in more and more demand, however. Portable devices, and small electronic units are multiplying. More people use their devices on the go, and need convenient portable power that they can easily swap and replace.”

In some examples, the machine learning server and machine learning expert system may apply custom algorithms and metrics specific to a domain. In one example related to environmental impact, an algorithm for volumetric mass and energy may be determined. The system receives its data input required for assessing the material and energy usage of a given process by means of user input, or derivation from the text by a system of number annotation. The resulting mathematical information is extracted (number, ordinal, cardinal, units) and used to calculate. The calculation is completed. Similar volumetric mass and energy values from other domains are correlated, and the volumetric mass/energy optimization is made clear.

Generally, the systems and methods described herein are configured to perform at least the following steps: receiving a user input related to a scenario; defining a scenario profile based on the user input; applying a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable desired outcome; prompting for a user selection from the at least one target; applying a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the achieve the measureable desired outcome; obtaining descriptive information related to the at least one candidate innovation; presenting the descriptive information related to the at least one candidate innovation to a user; obtaining a pool of targets; receiving a training user input related to a training scenario; defining a training scenario profile based on the training user input; applying a first machine learning algorithm to the scenario to identify a selection of the pool of targets predicted to be responsive to the training scenario profile based on semantic correlation or attention-mechanism based transformation; receiving a target training response indicating whether the selection of the pool of targets relates to the training scenario profile; training a first machine learning model as the first trained machine learning model based on the target training response defining relationships between the training response and the training scenario; obtaining a pool of hypotheses; receiving a training user input related to a training scenario and a training target; defining a training hypothesis model based on the training user input; applying a second machine learning algorithm to the scenario to identify a selection of the pool of hypotheses predicted to be responsive to the training scenario and the training target, based on semantic correlation or attention-mechanism based transformation; receiving a hypothesis training response indicating whether the selection of the pool of hypotheses relates to the training scenario and the training target; training a second machine learning model as the second first trained machine learning model based on the hypothesis training response defining relationships between the hypothesis training response, the training scenario, and the training target; receiving the user input related to a scenario; identifying a statement type associated with the user input; applying a third trained machine learning model to the user input to identify a set of enrichment content associated with the scenario and the statement type; appending the user input with the set of enrichment content; defining the scenario profile based on the appended user input; obtaining a set of training enrichment content; receiving a training user input related to a training scenario; applying a third machine learning algorithm to the training scenario to identify a selection of the set of training enrichment content predicted to be responsive to the training scenario, based on application of a natural language processing algorithm; receiving an enrichment training response indicating whether the selection of the set of training enrichment content relates to the training scenario; training a third machine learning model as the second first trained machine learning model based on the enrichment training response defining relationships between the enrichment training response and the training scenario; applying the second trained machine learning model and a trend prediction algorithm to the scenario profile and the user selection from the at least one target to generate the at least one candidate innovation predicted to achieve the achieve the measureable desired outcome, wherein the at least one candidate innovation is related to a trend predicted by the trend prediction algorithm; receiving a corpus of source information, wherein each of the corpus is associated with a time period; parsing each of the corpus of source information to identify a plurality of sentence fragments and the associated time period; identifying a context associate with each of the sentence fragments; applying a sentiment analysis algorithm to determine a related sentiment associated with each of the sentence fragment; defining a model for the trend prediction algorithm based on the sentiment and time period associated with each of the sentence fragments, wherein the model describes trend in opinion of the sentence fragment for each context over time; receiving the user input related to a scenario; applying a fourth trained machine learning model to the user input to identify a set of parts associated with the scenario; defining the scenario profile based at least partially on the set of parts.

FIG. 1is a functional block diagram of an example computing device that may be used in the machine learning expert system described, and may represent the machine learning server, the source database, and any other systems described herein (all shown inFIGS. 2-5). Specifically, computing device10illustrates an exemplary configuration of a computing device for the systems shown herein, and particularly inFIGS. 2-5. Computing device10illustrates an exemplary configuration of a computing device operated by a user5in accordance with one embodiment of the present invention. Computing device10may include, but is not limited to, the machine learning server, the database, and any other systems described herein (all shown inFIGS. 2-5), other user systems, and other server systems. Computing device10may also include servers, desktops, laptops, mobile computing devices, stationary computing devices, computing peripheral devices, smart phones, wearable computing devices, medical computing devices, and vehicular computing devices. Alternatively, computing device10may be any computing device capable of the described methods for machine learning and for predicting innovations that will achieve one or more target goals in a scenario. In some variations, the characteristics of the described components may be more or less advanced, primitive, or non-functional.

In the exemplary embodiment, computing device10includes a processor11for executing instructions. In some embodiments, executable instructions are stored in a memory area12. Processor11may include one or more processing units, for example, a multi-core configuration. Memory area12is any device allowing information such as executable instructions and/or written works to be stored and received. Memory area12may include one or more computer readable media.

Computing device10also includes at least one input/output component13for receiving information from and providing information to user5. In some examples, input/output component13may be of limited functionality or non-functional as in the case of some wearable computing devices. In other examples, input/output component13is any component capable of conveying information to or receiving information from user5. In some embodiments, input/output component13includes an output adapter such as a video adapter and/or an audio adapter. Input/output component13may alternatively include an output device such as a display device, a liquid crystal display (LCD), organic light emitting diode (OLED) display, or “electronic ink” display, or an audio output device, a speaker or headphones. Input/output component13may also include any devices, modules, or structures for receiving input from user5. Input/output component13may therefore include, for example, a keyboard, a pointing device, a mouse, a stylus, a touch sensitive panel, a touch pad, a touch screen, a gyroscope, an accelerometer, a position detector, or an audio input device. A single component such as a touch screen may function as both an output and input device of input/output component13. Input/output component13may further include multiple sub-components for carrying out input and output functions.

Computing device10may also include a communications interface14, which may be communicatively coupleable to a remote device such as a remote computing device, a remote server, or any other suitable system. Communication interface14may include, for example, a wired or wireless network adapter or a wireless data transceiver for use with a mobile phone network, Global System for Mobile communications (GSM), 3G, 4G, or other mobile data network or Worldwide Interoperability for Microwave Access (WIMAX). Communications interface14is configured to allow computing device10to interface with any other computing device or network using an appropriate wireless or wired communications protocol such as, without limitation, BLUETOOTH®, Ethernet, or IEE 802.11. Communications interface14allows computing device10to communicate with any other computing devices with which it is in communication or connection.

FIGS. 2-5illustrate block diagrams of the machine learning expert system according to four embodiments. Referring toFIG. 2, a first illustration of the machine learning expert system100is provided. The systems described in machine learning expert systems inFIGS. 2-5may include the features of computing device10(shown inFIG. 1) although not all parallel features are shown due to brevity. The machine learning expert system100includes a user interaction component101, a processor component110, a store and query component130, and a machine learning component120. In some embodiments, store and query component130is performed using a memory like memory12(shown inFIG. 1). User interaction component101is configured to provide interaction with a user such as user5via an input/output such as input/output component13(both shown inFIG. 1). User interaction component101includes a component to receive input text102, a component to allow a user to view text103, a component to evaluate and give feedback104, and a component to receive enhanced feedback105such as text additions, edits, gestures, or rankings. Processor component110is configured with configuration component111, receives user interaction with interaction component112, applies expert system processing with component113, and presents results to a user with component114. In examples described processor repeatedly iterates through the use of components111-114. Store and query component130includes a database131and 3rdparty data sources137. Database131includes semantic tuples store133, key value store134, relational database store135, and document store136. 3rdparty data sources137include data suppliers138and sensors139.

Machine learning component120includes components used to create, define, train, update, and apply the machine learning models described herein, including an entity extraction and classification component141, a calculator component151, and a pattern associating component161. Entity extraction and classification component141includes a clean source component142, an algorithm selection component143, a model creation and training component144, and a classification, parsing, and transforming component145. Calculator component151includes components used to perform the computations described herein including relevance scores152, relative relevance scores153, and domain specific values such as volumetric matter-energy values154. Calculator151also is configured to compute the multi-dimensional matrix used to define and update some machine learning models.

Pattern association component161includes a classifier-to-classifier component162, a graph traversal component163, and calculation derived relationship component164.

Referring toFIGS. 3-5, alternative versions of the expert machine learning system illustrated inFIG. 2are provided that may be deployed using the computing device shown inFIG. 1. The expert machine learning systems200,300, and400, provide functionality similar to that described in expert machine learning system100(shown inFIG. 2), using subsets of the components described therein.

FIG. 6is a conceptual block diagram600illustrating a first example classification approach provided by the machine learning server ofFIGS. 2-5. In diagram600, a given scenario610and an innovation670classified in relation to classifying entities620,630,640,650, and660. As indicated, scenario610is classified within both type A620and type B630while innovation670is classified in type D650and type B650. (Note that types B630and650correspond to the same classification but are depicted separately for clarity with regard to their relationships to scenario610and innovation670.) Thus, scenario610is classified with entities620,630,640,650, and660. Since entities620,630,640,650, and660are parts (or components) of classes and types, semantic graph queries can “walk the graph” via a hypothesis and find an innovation680. The distance walked is weighted and scored, and stored in an aggregator. At the conclusion of running several hypotheses, the highest scoring innovations are returned as recommendations.

FIG. 7is a conceptual block diagram700illustrating a second example classification approach provided by the machine learning server ofFIGS. 2-5. In diagram700, a class710is shown with hierarchical entities720,730,740,750, and760. The level1entities720is associated hierarchically with multiple level2entities730and740, which each have sub-entities. The level2entities730and740are associated with multiple level3entities750and760. All entities720,730,740,750, and760are visible to the aggregator770, which can accumulate weights and scores.

FIG. 8is a conceptual block diagram800illustrating a third example classification approach provided by the machine learning server ofFIGS. 2-5. Diagram800provides a closer view of walking the graph via a hypothesis. The hypothesis indicated is that related entities820of class B830will return relevant innovations for scenario810. Entity Y820is classified to the scenario810. Since it is in class B830, and the hypothesis states that class B830related entities will return relevant innovations, entity X840of class B850(which corresponds to class B830as entity Y820and860correspond) is queried and returns the innovation870. The innovation870is passed to the aggregator with the relevant hypothesis name, which accumulate weights and updates scores.

FIG. 9is a conceptual block diagram900illustrating a fourth example classification approach provided by the machine learning server ofFIGS. 2-5. Diagram900provides a resource processing view. The scenario910is processed for resources, classified by resource risk classes into desired functionalities920and undesired functionalities930which may correspond to target state and risk target state respectively. These are then graph-queried to discover where a given resource is associated with waste940or benefit950.

FIG. 10is a conceptual block diagram1000illustrating a fifth example classification approach provided by the machine learning server ofFIGS. 2-5. The resource type1010includes classes of ephemeralization1020and risk reduction1030, which in turn are associated with entities of geo-Political risk1040and ecological risk1050. By traversing the semantic relationships, the process can link each resource in a part/whole relationship with risks and by inverse, targets.

FIG. 11is an example user interface1100for configuring aspects of the methods described provided by the machine learning server ofFIGS. 2-5. In interface1100, a user may prioritize interests to allow human preference to influence machine learning.

FIG. 12is an example flowchart1200of a feedback mechanism provided by the machine learning models described herein in the machine learning server ofFIGS. 2-5. The feedback mechanism allows input of humans using the expert machine learning system to influence the prediction of candidate innovations and targets. The feedback mechanism (like the machine learning models described herein) allows the machine learning system to achieve subject matter expertise in the human re-enforced learning results. Specifically, the machine learning system obtains results from recommender results1210which are processed through a filtered view1220and sent to review. A user may receive results1210and determine whether they are relevant or not, partially relevant, or relevant but implemented already or relevant but unfeasible. As such, user may indicate approval with selection1225and send data1226back to the machine learning system to train that the results were relevant. User may also indicate disapproval with selection dismissal1230and further indicate the reasons as being relevant but implemented1240, relevant but not feasible1250, and not relevant1260. When the user selects such reasons1240,1250,1260, the user may trigger an alert1242,1252, and1262and transmit training data1244,1254, and1264to the machine learning system.

FIG. 13is an example method1300for prediction and recommendation of innovative solutions to targetted problems for scenarios performed by the machine learning server ofFIGS. 2-5. The method is performed by the machine learning server and includes receiving1310a user input related to a scenario, defining1320a scenario profile based on the user input, applying1330a first trained machine learning model to the scenario profile to generate at least one target associated with the scenario, wherein the at least one target includes a measurable desired outcome, prompting1340for a user selection from the at least one target, applying1350a second trained machine learning model to the scenario profile and the user selection from the at least one target to generate at least one candidate innovation predicted to achieve the achieve the measureable desired outcome, obtaining1360descriptive information related to the at least one candidate innovation, and presenting1370the descriptive information related to the at least one candidate innovation to a user.

FIG. 14is an illustration of a machine learning process1400performed by the machine learning server ofFIGS. 2-5in accordance with a first embodiment. Process1400depicts an example embodiment wherein the machine learning server receives1410a scenario input and creates1420a scenario profile. In some examples, the scenario profile is created1420by refining and enriching1415the scenario input in accordance with the methods described herein. The machine learning server also generates targets1428based on a target machine learning model1426trained by target training set1424. As described below, the target machine learning model1426is also trained using feedback from a user in a target supplementary training set1442. Based on such target generation, the machine learning server proposes1430targets to a user. The machine learning server receives1440an input from a user in the form of a target selection (or rejection) of the targets provided. Based on such selection or rejection, the machine learning server trains on user input which becomes target supplementary training set1442. The machine learning server also generates hypotheses1448based on a hypothesis machine learning model1446trained by hypothesis training set1444. As described below, the hypothesis machine learning model1446is also trained using feedback from a user in a hypothesis supplementary training set1462. Based on such hypothesis generation, and on the targets that resulted from the above processes, the machine learning server1450applies the hypotheses to recommend candidate innovations to a user. The machine learning server receives1460an input from a user in the form of a hypothesis selection (or rejection) of the hypotheses provided. Based on such selection or rejection, the machine learning server trains on user input which becomes target supplementary training set1462. If the user rejects all of the hypotheses provided, the machine learning server restarts the1400process.

FIG. 15is an illustration of a machine learning process1500performed by the machine learning server ofFIGS. 2-5in accordance with a second embodiment. Specifically, process1500depicts content enrichment of statements1510using a content enrichment learning model1526. Machine learning server receives statement input1510. In some examples, statement input1510is obtained after parsing statement input from user input1505. This approach may be appropriate if the input is complex, voluminous, re-ordered or pre-processed for more efficient processing. Machine learning server determines1520a statement type based on statement input1510, using classification, entity-extraction, a natural language processing similarity system, or other type-determination subroutine; Machine learning server appends1528statement input1510with statement enrichment. Statement enrichment may be provided by static content enrichment1527which may represent standardized content that is not changing and may relate to statement input1510and any classification thereof. Statement enrichment may also be provided by dynamic content enrichment provided by content enrichment machine learning model1526which trains on a content training set1524and, in some examples, on content supplementary training set1542, described below. Machine learning server proposes1530enriched statements to a user and receives1540a response from the user indicating whether the user accepts or rejects such enrichment. The user response may be used to create content supplementary training set1542. The output of process1500may be applied by the system to content (such as text, media, or source input of any kind) that when enriched improve the ability of the expert machine learning system to predict targets and predict candidate innovations. For example, such enrichment can be provided in the enrichment of scenario input1410(shown inFIG. 14) or applied using a transformer in the machine learning systems ofFIGS. 2-5.

FIG. 16is an illustration of a machine learning process1600performed by the machine learning server ofFIGS. 2-5in accordance with a third embodiment. Specifically, process1600depicts sentiment enrichment (or negative-to positive enrichment) of statements1610using an opposing content enrichment learning model1626or a negative-to-positive content enrichment learning model. Machine learning server receives assertion statement input1610. In some examples, assertion statement input1610is obtained after parsing assertion statement input from user input1605. This approach may be appropriate if the input is complex, voluminous, re-ordered or pre-processed for more efficient processing. Machine learning server performs sentiment analysis1620, or alternatively performs sentiment analysis on statements1610, and determines a sentiment type indicating whether an assertion is positive or negative. Machine learning server appends1628assertion statement input1620with opposing content enrichment. For example, the machine learning system may identify an assertion statement phrased negatively and identifying a problem, and identify content that is responsive and positive, and indicates a solution. Opposing statement enrichment (or negative-to-positive enrichment) may be provided by static opposing content enrichment1627which may represent standardized opposing content that is not changing and may relate to assertion statement input1610and any classification thereof. Opposing statement enrichment may also be provided by dynamic opposing content enrichment provided by opposing content enrichment machine learning model1626(or negative-to-positive machine learning model) which trains on an opposing content training set1624and, in some examples, on opposing content supplementary training set1642, described below. Machine learning server proposes1630enriched assertion statements to a user and receives1640a response from the user indicating whether the user accepts or rejects such opposing enrichment. The user response may be used to create opposing content supplementary training set1642. The output of process1600may be applied by the system to any sources that, when supplied with negative-to-positive statement enrichment, improve the ability of the system to predict candidate innovations or targets. For example, such enrichment can be provided in the enrichment of scenario input1410(shown inFIG. 14) or applied using a transformer in the machine learning systems ofFIGS. 2-5.

FIG. 17is an illustration of a trend prediction and fine-tuning process1700performed by the machine learning server ofFIGS. 2-5in accordance with a first embodiment. Specifically, process1700describes a trend prediction approach of the machine learning server. Machine learning server receives scenario input1710and parses1720scenario input to obtain components such as sentence fragments and terms. Machine learning server submits1730the parsed elements of the scenario to the trend prediction algorithm1735. Trend prediction algorithm1735is created and trained by receiving a corpus of information and processing fragments or components from each item of the corpus to determine at least (a) context data1731; (b) time data1732; and (c) sentiment data1733. Additionally, each fragment or component may be classified using the tools described herein. As such, the trend prediction algorithm1735develops a constantly evolving model of trends related to particular terms, concepts, ideas, structures, methods, names, phrases, and other text with respect to sentiment in a particular industry. Trend prediction algorithm1735determines whether particular components are (a) more popular; (b) less popular; (c) well-known; and/or (d) less referenced in contexts, over a time period. Trend prediction algorithm1735may also detect the ability of a component to evolve or grow outside a context into a second context, such as when a term moves from being used in one field to its being used in a second field. Machine learning server applies trend prediction algorithm1735to provide1740trend predictions relevant to a scenario, using the classification approaches described herein. Machine learning server also receives1750a response to the trend prediction from the user that may be used to create training data1752to refine trend prediction algorithm1735.

The module may include one or more interface circuits. In some examples, the interface circuit(s) may implement wired or wireless interfaces that connect to a local area network (LAN) or a wireless personal area network (WPAN). Examples of a LAN are Institute of Electrical and Electronics Engineers (IEEE) Standard 802.11-2016 (also known as the WIFI wireless networking standard) and IEEE Standard 802.3-2015 (also known as the ETHERNET wired networking standard). Examples of a WPAN are the BLUETOOTH wireless networking standard from the Bluetooth Special Interest Group and IEEE Standard 802.15.4.