Systems and methods for object formulation utilizing artificial intelligence and machine learning techniques

Abstract

A method may receive, by one or more processors, relevant data from a plurality of data sources. A method may input the relevant data, by the one or more processors into a machine learning model, for generating iterations of object models and iterations of object designs. A method may assess, by the one or more processors utilizing an artificial intelligence module, one or more of object performance metrics, user experience indicators, or industry acceptance probabilities based on user feedback and state pattern. A method may cause, by the one or more processors, iterative refinement of the object models and the object designs.

Description

SYSTEMS AND METHODS FOR OBJECT FORMULATION UTILIZING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUESCROSS-REFERENCE TO RELATED APPLICATION(S)

[0001] This patent application claims the benefit of priority to U.S. Provisional Patent Application No. 63 / 637,684, filed on April 23, 2024, the entirety of which is incorporated herein by reference.TECHNICAL FIELD

[0002] This present disclosure relates generally to the field of data analytics and predictive modeling. In particular, the present disclosure relates to employing artificial intelligence (Al) and machine learning (ML) methodologies for accelerating the product ideation, innovation, and development process.BACKGROUND

[0003] Product ideation often grapples with prolonged timelines, and uncertainty regarding feasibility and potential popularity upon launch. The process frequently extends beyond anticipated schedules, hindered by the complexity of predicting consumer trends and market dynamics. In this landscape of ambiguity, service providers encounter considerable challenges in gauging the viability of their ideas and forecasting their sustained appeal in an ever-evolving marketplace. This uncertainty not only compounds the time required for ideation but also exacerbates the risk of investing resources into products that may falter upon release. Thus, there is urgency to streamline the ideation process and incorporate predictive insights to navigate the competitive terrain and deliver innovative solutions that resonate with consumers.SUMMARY

[0004] In some aspects, the techniques described herein relate to a computer- implemented method for accelerating object formulation of an object including: receiving, by one or more processors, relevant data from a plurality of data sources; inputting the relevant data, by the one or more processors into a machine learning model, for generating iterations of object models and iterations of object designs; assessing, by the one or more processors utilizing an artificial intelligence module, one or more of object performance metrics, user experience indicators, or industry acceptance probabilities based on user feedback and state pattern; and causing, bythe one or more processors, iterative refinement of the object models and the object designs.

[0005] In some aspects, the techniques described herein relate to a computer- implemented method, wherein generating the iterations of object models and the iterations of object designs includes: processing, by the one or more processors, the relevant data to identify patterns in consumer preferences and industry patterns; and generating, by the one or more processors utilizing the artificial intelligence module, object ideas and design variations based on the identified patterns.

[0006] In some aspects, the techniques described herein relate to a computer- implemented method, wherein assessing the object performance metrics, the user experience indicators, or the industry acceptance probabilities includes: determining, by the one or more processors, one or more of reliability, durability, or efficiency of the object while assessing the object performance metrics; determining, by the one or more processors, one or more of usability, satisfaction, or engagement while assessing the user experience indicators; or determining, by the one or more processors, one or more of consumer state analysis or industry entity comparison while assessing the industry acceptance probabilities.

[0007] In some aspects, the techniques described herein relate to a computer- implemented method, wherein the iterative refinement of the object models and the object designs includes: processing, by the one or more processors, one or more feedback to determine the object ideas and the design variations match user needs and preferences; and adjusting, by the one or more processors, the object ideas and the design variations based on the one or more feedback.

[0008] In some aspects, the techniques described herein relate to a computer- implemented method, wherein the machine learning model and the artificial intelligence module utilize deep learning techniques for pattern recognition and feature extraction.

[0009] In some aspects, the techniques described herein relate to a computer- implemented method, wherein the machine learning model and the artificial intelligence module utilize reinforcement learning to dynamically adapt the object models and the object designs based on evolving requirement and constraints.

[0010] In some aspects, the techniques described herein relate to a computer- implemented method, wherein the machine learning model and the artificial intelligence module utilize predictive modeling techniques including neural networksfor determining the object performance metrics, the user experience indicators, or the industry acceptance probabilities.

[0011] In some aspects, the techniques described herein relate to a computer- implemented method, wherein the predictive modeling techniques are trained on historical data and continuously updated with real-time feedback.

[0012] In some aspects, the techniques described herein relate to a computer- implemented method, wherein natural language processing (NLP) is used to extract state patterns and identify variables for object enhancement.

[0013] In some aspects, the techniques described herein relate to a computer- implemented method, wherein the relevant data includes one or more of industry patterns, consumer preferences, or industry entity strategies.

[0014] In some aspects, the techniques described herein relate to a system for object model generation and optimization of a object including: one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations including: receiving a plurality of datasets including industry patterns, consumer preferences, and industry entity analyses; processing, by an artificial intelligence module and machine learning model, the plurality of datasets for generating an initial object models; iteratively refining the initial object models through automated simulations and virtual prototyping; incorporating real-time feedbacks from a plurality of parties into a object model generation and optimization process; generating a refined object, based on the object, from the iteratively refining and the incorporating real-time feedback; evaluating, using predictive analytics and probabilistic modeling, one or more of performance, feasibility, or industry potential of the refined object; and generating visual representation of the refined object and results of the evaluating.

[0015] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence module and the machine learning model utilize deep learning techniques for pattern recognition and feature extraction.

[0016] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence module and the machine learning model utilize reinforcement learning to dynamically adapt the object models based on evolving requirement and constraints.

[0017] In some aspects, the techniques described herein relate to a system, wherein the artificial intelligence module and the machine learning model utilize predictive modeling techniques including neural networks for determining object performance, user experience, or industry acceptance.

[0018] In some aspects, the techniques described herein relate to a system, wherein the predictive modeling techniques are trained on historical data and continuously updated with the real-time feedback.

[0019] In some aspects, the techniques described herein relate to a system, wherein natural language processing (NLP) is used to extract state patterns and identify variables for object enhancement.

[0020] In some aspects, the techniques described herein relate to a system for search query analysis including: one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations including: receiving, by a data ingestion module, search queries including one or more of temporal data, location data, or scoring data; causing, by a machine learning model, one or more of embeddings of the search queries, clustering the embeddings into clusters to identify common themes, labeling the clusters based on semantic meaning, and selecting searches with highest scores; and presenting, by an output module, top searches based on the labeled clusters and scores associated with the search queries.

[0021] In some aspects, the techniques described herein relate to a system, wherein the machine learning model utilizes deep learning techniques for pattern recognition and feature extraction.

[0022] In some aspects, the techniques described herein relate to a system, wherein the machine learning model utilizes reinforcement learning to dynamically adapt object models and designs based on evolving requirement and constraints.

[0023] In some aspects, the techniques described herein relate to a system, wherein natural language processing (NLP) is used to extract state patterns and identify variables for object enhancement.

[0024] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosed embodiments, as claimed.BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various example embodiments and together with the description, serve to explain the principles of the disclosed embodiments.

[0026] FIG. 1 is an example computing architecture for implementing Al and ML methodologies for accelerating the product ideation process, according to aspects of the disclosure.

[0027] FIG. 2 is a diagram that illustrates an Al-based approach in facilitating product ideation, according to aspects of the disclosure.

[0028] FIG. 3A illustrates a conceptual framework for Al-based trend forecasting for a product, according to aspects of the disclosure.

[0029] FIG. 3B illustrates a conversational chatbot tailored for knowledge summarization, according to aspects of the disclosure.

[0030] FIG. 4 illustrates a generative Al approach in a product ideation process, according to aspects of the disclosure.

[0031] FIG. 5 illustrates a process for systematic analysis of a plurality of data sources for identifying emerging trends and anticipating their potential impact on market and consumer behavior, according to aspects of the disclosure.

[0032] FIG. 6 illustrates technology readiness levels for a product, according to aspects of the disclosure.

[0033] FIG. 7 shows an example machine learning training flow chart.

[0034] FIG. 8 illustrates an implementation of a computer system that executes techniques presented herein.DETAILED DESCRIPTION OF EMBODIMENTS

[0035] While principles of the present disclosure are described herein with reference to illustrative embodiments for particular applications, it should be understood that the disclosure is not limited thereto. Those having ordinary skill in the art and access to the teachings provided herein will recognize additional modifications, applications, embodiments, and substitution of equivalents all fall within the scope of the embodiments described herein. Accordingly, the embodiments are not to be considered as limited by the foregoing description.

[0036] Various non-limiting embodiments of the present disclosure will now be described to provide an overall understanding of the principles of the structure, function, and use of systems and methods disclosed herein for employing artificialintelligence (Al) and machine learning (ML) methodologies for accelerating the product ideation process.

[0037] Product (also referred to herein as an “object”) ideation presents a multitude of technical challenges, such as the inherent uncertainty surrounding feasibility and market reception. Determining whether a proposed product concept (or model) is technically feasible involves grappling with complexities ranging from technological constraints to resource availability. Moreover, accurately forecasting the popularity of a product by its launch date poses a formidable challenge, as it requires predictive models capable of analyzing evolving consumer preferences, competitive landscapes, and broader market / industry trends or patterns. The dynamic nature of these variables further complicates the task, necessitating sophisticated algorithms and data-driven approaches to provide reliable forecasts. However, traditional product development timelines often struggle to keep pace with this rapid rate of change, presenting significant technical challenges. There is a disconnect between the products being developed and the evolving expectations of consumers, leading to diminished market relevance by the time of the product launch.

[0038] Addressing these challenges requires leveraging advanced methodologies from fields such as data science, predictive analysis, Al, and ML. The integration of Al and ML throughout the new product innovation lifecycle significantly expedites the processes, such as ideation to design, prototyping, testing, and validation. The Al and ML methodologies offer unprecedented capabilities for rapid analysis of market trends, consumer preferences, and competitor strategies, facilitating the identification of opportunities and gaps in the market. The Al and ML driven algorithms streamline design processes by generating and evaluating numerous iterations, while simulations and virtual prototyping tools enable swift testing and validation of product concepts.

[0039] The present disclosure provides embodiments that address the above shortcomings in the field of data analytics and predictive modeling, leading to significant technical improvements in the field. For instance, system 100 discussed in the present disclosure overcomes technical shortcomings of conventional techniques by, for example, developing predictive models that account for the multifaceted nature of the market dynamics and consumer behavior. Developing such predictive models is crucial to mitigating uncertainties in new product ideation.This entails harnessing large datasets encompassing historical market data, consumer insights, and competitor analyses to train models capable of forecasting product viability and popularity over time. Additionally, integrating real-time data streams and feedback mechanisms into the ideation process enables continuous refinement of predictive models, ensuring they remain robust and adaptive in the face of evolving market conditions. Through this technical advancement, service providers can streamline the ideation process, mitigate risks, and increase the likelihood of successfully bringing innovative products to market.

[0040] The above technical improvements, and additional technical improvements, will be described in detail throughout the present disclosure. Also, it should be apparent to a person of ordinary skill in the art that the technical improvements of the embodiments provided by the present disclosure are not limited to those explicitly discussed herein, and that additional technical improvements exist.

[0041] The present disclosure introduces a capability to implement Al and ML methodologies for accelerating the product ideation process. Specifically, FIG. 1 depicts an example computing architecture including a system 100 that comprises an analysis platform 101 and a database 113. In one embodiment, the analysis platform 101 is a platform with multiple interconnected components. The analysis platform 101 includes one or more servers, intelligent networking devices, computing devices, components, and corresponding software for implementing Al and ML for expediting the product ideation process and enhancing decision-making capabilities. At the initial state of ideation, Al and ML methodologies can analyze vast amount of data, including market trends, consumer preferences, and competitor strategies, to identify potential opportunities and gaps in the market more rapidly and accurately than traditional methods. Through advanced data mining techniques and predictive analytics, Al and ML methodologies can uncover hidden patterns and insights that inform the development and formulation of novel product concepts aligned with emerging consumer needs and trends.

[0042] In one instance, during the design and prototyping phase, the analysis platform 101 may utilize the Al and ML techniques to streamline the process by generating and evaluating numerous design iterations based on predefined criteria and constraints. This accelerates the design process and also enhances innovation by exploring a broader range of possibilities. Additionally, Al-powered simulations and virtual prototyping tools enable rapid testing and validation of designs, allowingfor quicker iteration cycles and more efficient refinement of product features and functionalities.

[0043] In one instance, during the testing and validation stage, the analysis platform 101 may implement the Al and ML techniques for efficient and comprehensive evaluation of product performance, user experience, and market acceptance. For example, the analysis platform 101 may implement the Al and ML techniques to assess metrics related to product performance (e.g., retention rate, defect rate, lifespan, shelf life, etc.), indicators related to user experience (e.g., user satisfaction, return rate, ease of assembly, usage frequency, user complaints, user engagement, etc.), and probabilities associated with market acceptance (e.g., adoption rate, market penetration, conversion rate, etc.). The analysis platform 101 may also determine usability, satisfaction, and / or engagement while assessing indicators associated with user experience. In one example, natural language processing (NLP) algorithms may be utilized to analyze customer feedback from various sources (e.g., social media, reviews, surveys, etc.) to identify patterns and sentiment trends, providing valuable insights for product refinement and optimization. Moreover, the analysis platform 101 may utilize the Al and ML methodologies for forecasting the potential success of new products based on historical data and market trends, assisting service providers in making informed decisions.

[0044] As discussed, the current processes in which the research and development (R&D) teams manually collect information from various sources about ingredients, suppliers, and scientific literature to identify solutions to consumer's jobs-to-be done is not only time-consuming but also inefficient. This sequential approach often leads to delays in the ideation process as each department’s input is gathered and analyzed before moving to the next stage. Additionally, this approach may lead to information silos and missed opportunities for cross-functional collaboration, as insights from different teams are not integrated in real-time. As a result, the pace of innovation is hindered, and the ability to respond quickly to evolving consumer needs is compromised. The analysis platform 101 provides a more streamlined and collaborative approach that leverages technologies such as Al and ML to facilitate automated assessment of the technical feasibility of solutions to consumer trends.

[0045] In one instance, the analysis platform 101 may introduce an Al-powered assistant (e.g., chatbot) for product development. The Al-powered assistant mayanswer product discovery questions with the information available from external data sources, supplemented with the internal knowledge base. The Al-powered assistant may also provide trends analytics. In one instance, the initial proof of concept (POC) may support an innovation team with knowledge retrieval and trend prediction. For example, the knowledge retrieval and trend prediction system may:(i) limit consumer trends and technical literature to one area;(ii) create a front-end application that combines a chat interface and intuitive analytics dashboard;(iii) provision a secure sandbox for generative Al experimentation in a private environment (e.g., walled Google cloud platform);(iv) connect external sources of research data available through various sources(e.g., Google) with a selected dataset from R&D;(v) reduce hallucinations in Al answers by grounding the answers to the specified knowledge base;(vi) identify micro and macro chocolate bar trends in publicly available search data and academic data; and(vii) predict the likelihood of trend success for specified time horizons (e.g., 6 and 9 months).

[0046] In one example, the analysis platform 101 combines descriptive and prescriptive analytics with a conversational Al interface, including chat and semantic search, for example:(i) new flavor and new product idea discovery for exploration and development of innovative taste profiles or entirely novel product concepts to meet evolving consumer preferences and market demands;(ii) micro product trends identification for analyzing specific product attributes and consumer preference at a granular level, while macro product trends identification involves examining broader market trends and industry shifts that influence product development and consumer behavior;(iii) trend success forecasting for predicting the likelihood of a trend or innovation being successful in the market;(iv) packaging design ideation for generating creative concepts and ideas for the visual and structural design of product packaging;(v) product design collaterals generation such as concept copies, and options for product images for decision-making and ensuring consistency throughout the design process;(vi) new product modeling for creating computational representations of new product concepts, designs, or systems;(vii) sales and operation (S&OP) readiness simulation to assess an organization’s preparedness for implementing or improving its S&OP process; and(vii) cannibalization simulation to evaluate the potential impact of introducing a new product on the sales of existing offerings within the same product line or portfolio.

[0047] In one embodiment, the analysis platform 101 comprises a data collection module 103, a machine learning (ML) module 105, an artificial intelligence (Al) module 107, a testing and validation module 109, a user interface module 111 , or any combination thereof. As used herein, terms such as “component” or “module” generally encompass hardware and / or software, e.g., that a processor or the like used to implement associated functionality. It is contemplated that the functions of these components are combined in one or more components or performed by other components of equivalent functionality.

[0048] In one embodiment, the data collection module 103 may collect relevant data pertaining to product development, consumer preferences, and market trends through various data collection techniques. In one embodiment, the data collection module 103 may use a web-crawling component to access various databases or information sources to collect the relevant data. In another embodiment, the data collection module 103 may include various software applications (e.g., data mining applications in Extended Meta Language (XML)) that automatically search for and return the relevant data. In one instance, the relevant data may include structured sources like sales data and consumer surveys, providing quantitative insights into purchasing patterns and preferences. In one instance, the relevant data may include unstructured data from various sources such as social media comments and customer reviews, offering quantitative perspectives on consumer sentiments (or, more broadly, consumer states) and emerging trends. The data collection module 103 may perform data standardization and / or data cleansing on the collected data. In one instance, data standardization includes standardizing and unifying data sothat the data are easily processed by other modules. In one instance, the data cleansing includes removing or correcting erroneous data (e.g., redundant, incomplete, or incorrect data) to create high-quality data or validating and correcting values against a known list of entities. The data cleansing technique also includes data enhancement, where data is made more complete by adding related information.

[0049] The collected data is provided to the machine learning module 105 for leveraging ML algorithms to extract actionable insights and make predictions. In one embodiment, the machine learning module 105 may be configured for a supervised machine learning that utilizes training data (e.g., training data 712 illustrated in the training flow chart 700) for training ML models to gain deeper insights into consumer preferences, market trends, and competitive landscapes. The ML algorithms may enable predictive modeling, allowing service providers to forecast product demands, optimize pricing strategies, and identify opportunities for innovations. By automating data analysis, pattern recognition, and decision-making, the machine learning module 105 may streamline the product development cycles and foster the culture of continuous innovation.

[0050] In one example, the machine learning module 105 performs model training using training data, e.g., data from other modules, that contains input and correct output, to allow the model to learn over time. The training is performed based on the deviation of a processed result from a documented result when the inputs are fed into the machine learning model, e.g., an algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. In one embodiment, the machine learning module 105 randomizes the ordering of the training data, visualizes the training data to identify relevant relationships between different variables, identifies any data imbalances, and splits the training data into two parts where one part is for training a model and the other part is for validating the trained model, de-duplicating, normalizing, correcting errors in the training data, and so on. The machine learning module 105 implements various machine learning techniques, e.g., neural network (e.g., recurrent neural networks, graph convolutional neural networks, deep neural networks), regression, inductive programming logic, support vector machines, Bayesian models, Gradient boosted machines (GBM), LightGBM (LGBM), Xtra tree classifier, etc.

[0051] In one embodiment, the artificial intelligence module 107 may build upon the foundation laid by the machine learning module 105. In one instance, through advanced neural networks and deep learning algorithms, the artificial intelligence module 107 may analyze vast datasets including market trends, consumer behavior, and competitor strategies, for discerning latent correlations and predicting future trends to augment the decision-making process. In one instance, the artificial intelligence module 107 may incorporate NLP techniques to extract insights from textual data sources (e.g., customer feedback, social media, and market reports) for real-time sentiment analysis and actionable insights for product refinement. In one instance, the artificial intelligence module 107 may facilitate iterative design optimization through generative adversarial networks (GANs) and reinforcement learning algorithms. By generating and evaluating multiple design iterations based on predefined criteria and constraints, it expedites prototyping and testing phases, enabling faster iteration cycles and more efficient refinement of product features and functionalities. In one instance, the artificial intelligence module 107 may utilize clustering algorithms and advanced analytics to categorize customers into homogenous groups (e.g., identifying distinct consumer segments based on behavior, demographics, and preferences), enabling personalized product recommendations. In one instance, the artificial intelligence module 107 may utilize regression analysis and reinforcement learning techniques to identify optimal prices for point and dynamic pricing strategies that maximize revenue and profitability.

[0052] In one embodiment, the testing and validation module 109 may assess the viability of the product ideation process generated by the artificial intelligence module 107. In one instance, the testing and validation module 109 may subject the product ideation process to rigorous testing with real-world data to evaluate its capabilities in generating innovative and pertinent product ideas. In one instance, the testing and validation module 109 may leverage advanced Al and ML technologies to comprehensively assess product performance, user experience, and market acceptance. In one instance, the testing and validation module 109 may incorporate an automated testing framework that enables the systematic and repeatable evaluation of the performance of the product ideation process across various scenarios and datasets.

[0053] In one embodiment, the user interface module 111 may facilitate the users to interact with the various functionalities and capabilities of the Al-powered tools,enabling intuitive input of criteria, exploration of generated ideas, and provision of feedback. In one instance, the user interface module 111 may enable users to access and leverage Al-driven features such as data analysis, concept generation, design evaluation, and sentiment analysis. In one instance, the user interface module 111 may act as a conduit for translating complex Al-driven insights into actionable decisions, empowering users to make informed choices and iterate on product concepts efficiently. In one example, the user interface module 111 may generate a presentation of a graphical user interface (GUI) in a user device to visualize the insights derived from Al-driven analysis in real-time through various application programming interfaces (APIs) or other function calls, enabling the display of graphics primitives such as icons, bar graphs, menus, buttons, data entry fields, etc. In another example, the user interface module 111 may cause interfacing of guidance information to include, at least in part, one or more annotations, audio messages, video messages, or a combination thereof pertaining to product ideation.

[0054] In one embodiment, the database 113 is any type of database, such as relational, hierarchical, object-oriented, and / or the like, wherein data are organized in any suitable manner, including data tables or lookup tables. In one instance, the database 113 includes any suitable data for aiding in the content provisioning and sharing process during data analytics for product ideation. By centralizing and organizing vast amounts of data, database 113 facilitates efficient data retrieval, management, and analysis. In one embodiment, the database 113 includes a machine-learning based training database with a pre-defined mapping defining a relationship between various input parameters and output parameters based on various statistical methods. In one instance, the training database includes a dataset that includes data collections that are not subject-specific (e.g., data collections based on population-wide observations, local, regional, or super-regional observations, and the like). The training database is routinely updated and / or supplemented based on machine learning methods.

[0055] FIG. 2 is a diagram that illustrates an Al-based approach 200 for facilitating product ideation, according to aspects of the disclosure. In step 201 , the analysis platform 101 via artificial intelligence module 107 may accelerate the new product development process for meeting consumer needs in the areas of interest. In one example, the artificial intelligence module 107, via topic modeling or timeseries forecasting, may facilitate the identification of topics within the areas ofinterest by analyzing vast amounts of data, including market trends, consumer behavior, and competitor strategies. By discerning patterns and uncovering emerging trends, the artificial intelligence module 107 may enable service providers to focus their efforts on developing products that align closely with current consumer demands. In step 203, the predictive capabilities of the artificial intelligence module 107 may enable service providers to forecast the growth trajectory of potential product concepts, allowing for more informed decision-making and resource allocation. In step 205, the artificial intelligence module 107 may streamline the product development process by automating tasks, generating insights, and facilitating collaboration among cross-function teams, thereby reducing cycle times and expediting the product ideation process. In step 207, the artificial intelligence module 107 is grounded in scientifically reliable sources, leveraging data from reputable sources and employing robust analytical methodologies to ensure the accuracy and validity of insights generated.

[0056] FIG. 3A illustrates a conceptual framework for Al-based trend forecasting for a product, according to aspects of the disclosure. As discussed, in the dynamic landscape of product innovation, service providers are increasingly turning to advanced Al-driven methodologies for decision-making to stay ahead of evolving consumer preferences. As illustrated in trend forecasting box 300, the analysis platform 101 may perform trend forecasting by delving into both macro and micro trends using a combination of data sources 303 (e g., reviews, keyword analysis, and trends) to identify emerging patterns and shifts in consumer behavior. A neural network 305 (e.g., the analysis platform 101) may utilize social listening and sentiment analysis to enhance this understanding by capturing real-time insights from social media platforms to gauge public sentiments and anticipate market demands. Moreover, the analysis platform 101 may emphasize sustainability and health-conscious choices prompting a shift towards products with certain attributes 307 (e.g., reduced sugar content, personalized offerings, functional ingredients, or premium products). In one embodiment, the neural network 305 of analysis platform 101 may leverage entity extraction techniques to extract valuable insights from unstructured data, tailoring product offerings to meet evolving consumer preferences while ensuring relevance and resonance. Through this comprehensive approach, the analysis platform 101 may anticipate consumer needs and also deliver innovative solutions that align with sustainability goals and cater to diverse consumerpreferences (e.g., attributes 307). In one instance, the analysis platform 101 may integrate trend forecast with feasibility assessment.

[0057] FIG. 3B illustrates a conversational chatbot tailored for knowledge summarization, according to aspects of the disclosure. In one instance, by curating trends 309 spanning flavor preferences, consumption habits, and emerging market dynamics, the analysis platform 101 may equip chatbot (e.g., “RAG”) 301 with a comprehensive understanding of the evolving landscape within an industry (e.g., the chocolate industry). The chatbot 301 may utilize information from a variety of sources 313, such as Google Patent, Google Scholar, Trade Press, and Industry Newsletter, etc. In one example, the chocolate industry may be witnessing an increased demand for health and wellness (H&W) chocolate, reduced sugar options, or premium chocolate. In one instance, by leveraging large language models (LLMs) 315, the chatbot 301 may serve as a virtual assistant, adept at distilling vast quantities of information from various sources into concise, digestible summaries. The LLMs 315 may receive the information derived from variety of sources 313. In one example, the chatbot 301 may aggregate and distill insights 311 related to information on ingredients, packaging, claims, and suppliers within these trend categories 309. In one example, the chatbot 301 may notify that ingredients such as non-dairy proteins are increasingly popular among Gen Z consumers, due to their increasing focus on health, sustainability, and ethical consumption. In one example, the chatbot 301 may notify that among the seven suppliers providing ingredients for chocolates within the selected trends, the aspect of taste remains unaddressed in their offerings. In one example, the chatbot 301 may notify the users of model recipes for making chocolates, for example. In such a manner, the chatbot 301 may provide concise summaries and actionable insights 311 , enabling users to stay informed about the latest developments and make informed decisions in product development.

[0058] FIG. 4 illustrates a generative Al approach in a product ideation process, according to aspects of the disclosure. In step 401 , the analysis platform 101 may anticipate evolving consumer preferences and market trends with accuracy through trend prediction, social media analysis, product review feedback analysis, and user persona articulation. By harnessing vast datasets and sophisticated algorithms, generative Al provides actionable insights that facilitate strategic decision-making and product development strategies. In step 403, the analysis platform 101 mayleverage an automated concept generator to facilitate the rapid generation of dynamic product concepts tailored to meet specific consumer needs and preferences. Whether crafting claims language, creating dynamic concept variation, or visual rendering of concepts, the analysis platform 101 enables iterative ideation at scale, accelerating the exploration of innovative product ideas. In step 405, the analysis platform 101 may integrate a product creation assistant chatbot, such as the Response-Attention-Generation (RAG) model, enhancing collaboration and efficiency throughout the ideation and development process. Such Al-powered assistant provides real-time guidance and support, assisting teams in brainstorming sessions and refining concepts, thereby streamlining the path from ideation to market launch.

[0059] FIG. 5 illustrates a process for systematic analysis of a plurality of data sources for identifying emerging trends and anticipating their potential impact on market and consumer behavior, according to aspects of the disclosure. Using process 501 , the analysis platform 101 may analyze online searches (e.g., Google searches), which may serve as a valuable indicator of shifting interests and evolving preferences. By monitoring the frequency and volume of searches related to specific topics and keywords, the analysis platform 101 may gain insights into the emerging trends and patterns of consumer behavior. Additionally, advanced analytics techniques, such as machine learning algorithms may be employed to predict the likelihood of these trends materializing within specific time horizons. In one instance, the analysis platform 101 may analyze historical search data to identify patterns and forecast the trajectory of emerging trends for proactively adapting strategies. As will be further described below, steps 503-513 may describe the process 501 from input to output.

[0060] In step 503, the analysis platform 101 may harness the power of search queries to extract meaningful insights. The structured data associated with search queries, comprising elements such as data, search term, location, and score, serves as a rich repository of user intent and preferences. In step 505, the analysis platform 101 may ingest and transform the data into embeddings, enabling the representation of search queries in a high-dimensional space. In step 507, the analysis platform 101 may apply clustering algorithms to group similar queries, facilitating the identification of common themes and trends. In step 509, the analysis platform 101 may label the queries based on their semantic meaning or intent, enhancing theinterpretability of the results. In step 511 , the analysis platform 101 may select searches with the highest score, indicative of their relevance and significance. In step 513, the analysis platform 101 may generate an output that comprises the top searches, offering valuable insights into emerging topics, user interest, and trending keywords. In one example, the analysis platform 101 may aggregate and analyze the frequency of search terms to identify popular searches, providing actionable intelligence for product development.

[0061] FIG. 6 illustrates technology readiness levels for a product, according to aspects of the disclosure. In step 601 , the analysis platform 101 may frame the opportunity and translate business potential into concrete technical objectives. The analysis platform may define the scope of the opportunity for establishing clear boundaries and quantifying the business potential. The analysis platform 101 may draw from past experiences and may conduct a comprehensive literature search and landscape review. The analysis platform may synthesize insights from previous endeavors and examine existing knowledge and trends to gain a deeper understanding of the problem space and potential solutions. The analysis platform 101 may translate business objectives into technical goals to lay the foundation for subsequent stage development.

[0062] In step 603, the analysis platform 101 may align timelines, budgets, and resources to propel the project forward. This entails crafting a research plan and designing experiments, which undergo peer review to ensure validity. The analysis platform 101 may establish a critical success factor (CSF) and agree upon with handover partners, delineating deliverables essential for project progression. The analysis platform 101 may conduct risk assessments to identify and mitigate potential challenges. The analysis platform 101 may generate a preliminary information file (PIF) consolidating essential project details and insights during the initial stages. The analysis platform 101 may define the business case to outline the strategic rationale, market opportunities, and projected outcomes. Step 601 and step 603 may be considered ideation and scoping steps where the product is determined.

[0063] In step 605, the analysis platform 101 may focus on demonstrating that the experiment can effectively address the underlying hypothesis. At this stage, the emphasis is on validating the feasibility of the proposed concept through initial experimentation and prototyping. The analysis platform 101 may also developmethods to guide subsequent stages of experimentation and development, laying the groundwork for further refinement.

[0064] In step 607, the analysis platform 101 may expand the experiments to validate the viability of the concept. The analysis platform 101 may indicate that the proposed hypothesis cannot be refuted based on promising results from initial tests and experiments. The emphasis lies on establishing proof of principle, demonstrating that the technology or concept can feasibly achieve its intended objectives. The analysis platform 101 may select the most suitable technology or approach from the options explored based on performance and potential for further development. The analysis platform 101 may define elements of the model or framework for failure analysis to identify potential risks and challenges.

[0065] In step 609, the analysis platform 101 may conduct initial feasibility studies to determine whether the product has achieved consumer acceptance and desirability. The analysis platform 101 may also confirm the viability of the product and align the business case and lead region. The analysis platform 101 may compile the final science report, subject to peer and expert review to validate the technical aspects of the product. Additionally, the analysis platform 101 may confirm a comprehensive equipment feasibility plan to ensure that necessary resources are in place for further development. In one embodiment, the analysis platform 101 may determine equipment specifications to assist with the feasibility of production. In one example, the analysis platform 101 may determine the capacity of the equipment to handle production volume within the desired timeframe. In one example, the analysis platform 101 may determine the accuracy, durability, reliability, scalability, and cost considerations of the equipment. The analysis platform 101 may evaluate CSFs and update KQA to reflect the evolving requirements. Moreover, the analysis platform 101 may draft specifications to outline the initial product package. Step 605, step 607, and step 609 may be considered learning and demonstrating steps where the feasibility of the product is determined.

[0066] In step 611 , the analysis platform 101 may determine that each unit of the product demonstrates capability. The analysis platform 101 may finalize products and packaging specifications, with drafts undergoing review and validation. The analysis platform 101 may determine the anticipated launch date for initial release (I R) for coordinating marketing strategies and production schedules. The analysis platform 101 may perform market prioritization to identify critical success factors(CSF) to guide market entry. The analysis platform 101 may assess platform capability to ensure that the product aligns seamlessly with the technological infrastructure necessary for successful deployment.

[0067] In step 613, the technology is tested at scale in a pilot plant or temporary system within the factory, typically in a single unit, often in partnership with the technology development team (TDT). The TDT charter may be approved signifying a formalized commitment and alignment of stakeholders towards the project’s goal. Moreover, there is proof of the full technology’s capabilities, validated through rigorous KQA assessments. Final drafts of both product and packaging specifications have been completed, indicating a solidification of the product design and requirements. While technical specifications are still in the initial draft stage, the viability of the product has been confirmed through comprehensive testing and analysis. Furthermore, the primary capital project estimates have been developed to provide insight into the anticipated investment required for full-scale implementation.

[0068] In step 615, the analysis platform 101 may prove full line capability at full scale for product development with KQA. The product and packaging specifications are vetted and approved, signifying their readiness to be actively integrated into the operational system. The process specification advances to its final draft, marking a pivotal juncture in refining the operational intricacies of production. While the technical specification undergoes review and validation, the foundational technology concept lays the groundwork for subsequent phases, initiating the blueprinting process. As the groundwork solidifies, a capital project proposal is crafted for approval. Step 611 , step 613, and step 615 may be considered scaling up steps where the scalability of the product is determined.

[0069] In step 617, the analysis platform 101 may complete the handover. At this stage, the validation period begins (e.g., 3-6 months), during which the product undergoes testing and scrutiny to ensure its functionality, reliability, and compliance with specifications. The process specifications are approved, outlining methodologies and standards by which the product will be manufactured and delivered. With the technical specifications finalized and documented, the draft phase commences, where the blueprint is drafted and executed.

[0070] In step 619, the product has reached a notable level of maturity. Having been successfully implemented and validated at another location, the product demonstrates its capability to function reliably in real-world settings. With itstechnical specifications approved, the design and functionality of the product are confirmed to align with project requirements. Furthermore, the validation of the blueprint confirms that the product design is robust and ready for production or deployment across the network. Step 617 and step 619 may be considered deployment steps where the economic feasibility (e.g., the ability to efficiently deploy the product) of the product is determined.

[0071] One or more implementations disclosed herein include and / or are implemented using a machine learning model. For example, one or more of the modules of the analysis platform 101 are implemented using a machine learning model and / or are used to train the machine learning model. A given machine learning model is trained using the training flow chart 700 of FIG. 7. Training data 712 includes one or more of stage inputs 714 and known outcomes 718 related to the machine learning model to be trained. Stage inputs 714 are from any applicable source including text, visual representations, data, values, comparisons, and stage outputs, e.g., one or more outputs from one or more steps discussed in this disclosure. The known outcomes 718 are included for the machine learning models generated based on supervised or semi-supervised training. An unsupervised machine learning model is not trained using known outcomes 718. Known outcomes 718 includes known or desired outputs for future inputs similar to or in the same category as stage inputs 714 that do not have corresponding known outputs.

[0072] The training data 712 and a training algorithm 720, e.g., one or more of the modules implemented using the machine learning model and / or are used to train the machine learning model, is provided to a training component 730 that applies the training data 712 to the training algorithm 720 to generate the machine learning model. According to an implementation, the training component 730 is provided comparison results 716 that compare a previous output of the corresponding machine learning model to apply the previous result to re-train the machine learning model. The comparison results 716 are used by training component 730 to update the corresponding machine learning model. The training algorithm 720 utilizes machine learning networks and / or models including, but not limited to a deep learning network such as Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Fully Convolutional Networks (FCN) and Recurrent Neural Networks (RCN), probabilistic models such as Bayesian Networks and Graphical Models, classifiers such as K-Nearest Neighbors, and / or discriminative models suchas Decision Forests and maximum margin methods, the model specifically discussed herein, or the like.

[0073] The machine learning model used herein is trained and / or used by adjusting one or more weights and / or one or more layers of the machine learning model. For example, during training, a given weight is adjusted (e.g., increased, decreased, removed) based on training data or input data. Similarly, a layer is updated, added, or removed based on training data / and or input data. The resulting outputs are adjusted based on the adjusted weights and / or layers.

[0074] In general, any process or operation discussed in this disclosure is understood to be computer-implementable, such as, for example, the processes illustrated in FIG. 1 are performed by one or more processors of a computer system as described herein. A process or process step performed by one or more processors is also referred to as an operation. The one or more processors are configured to perform such processes by having access to instructions (e.g., software or computer-readable code) that, when executed by one or more processors, cause one or more processors to perform the processes. The instructions are stored in a memory of the computer system. A processor is a central processing unit (CPU), a graphics processing unit (GPU), or any suitable type of processing unit.

[0075] A computer system, such as a system or device implementing a process or operation in the examples above, includes one or more computing devices. One or more processors of a computer system are included in a single computing device or distributed among a plurality of computing devices. One or more processors of a computer system are connected to a data storage device. A memory of the computer system includes the respective memory of each computing device of the plurality of computing devices.

[0076] FIG. 8 illustrates an implementation of a computer system that executes techniques presented herein. The computer system 800 includes a set of instructions that are executed to cause the computer system 800 to perform any one or more of the methods or computer based functions disclosed herein. The computer system 800 operates as a standalone device or is connected, e.g., using a network, to other computer systems or peripheral devices.

[0077] Unless specifically stated otherwise, as apparent from the following discussions, it is appreciated that throughout the specification, discussions utilizingterms such as "processing," "computing," "calculating," “determining”, analyzing” or the like, refer to the action and / or processes of a computer or computing system, or similar electronic computing device, that manipulate and / or transform data represented as physical, such as electronic, quantities into other data similarly represented as physical quantities.

[0078] In a similar manner, the term "processor" refers to any device or portion of a device that processes electronic data, e.g., from registers and / or memory to transform that electronic data into other electronic data that, e.g., is stored in registers and / or memory. A “computer,” a “computing machine,” a "computing platform," a “computing device,” or a “server” includes one or more processors.

[0079] In a networked deployment, the computer system 800 operates in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system 800 is also implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a wireless telephone, a land-line telephone, a control system, a camera, a scanner, a facsimile machine, a printer, a pager, a personal trusted device, a web appliance, a network router, switch or bridge, or any other machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. In a particular implementation, the computer system 800 is implemented using electronic devices that provide voice, video, or data communication. Further, while the computer system 800 is illustrated as a single system, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

[0080] As illustrated in FIG. 8, the computer system 800 includes a processor 802, e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both. The processor 802 is a component in a variety of systems. For example, the processor 802 is part of a standard personal computer or a workstation. The processor 802 is one or more processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, servers, networks, digital circuits, analog circuits, combinations thereof, or other now known or later developeddevices for analyzing and processing data. The processor 802 implements a software program, such as code generated manually (i.e. , programmed).

[0081] The computer system 800 includes a memory 804 that communicates via bus 808. Memory 804 is a main memory, a static memory, or a dynamic memory. Memory 804 includes, but is not limited to, computer-readable storage media such as various types of volatile and non-volatile storage media, including but not limited to random access memory, read-only memory, programmable read-only memory, electrically programmable read-only memory, electrically erasable read-only memory, flash memory, magnetic tape or disk, optical media and the like. In one implementation, the memory 804 includes a cache or random-access memory for the processor 802. In alternative implementations, the memory 804 is separate from the processor 802, such as a cache memory of a processor, the system memory, or other memory. Memory 804 is an external storage device or database for storing data. Examples include a hard drive, compact disc (“CD”), digital video disc (“DVD”), memory card, memory stick, floppy disc, universal serial bus (“USB”) memory device, or any other device operative to store data. The memory 804 is operable to store instructions executable by the processor 802. The functions, acts, or tasks illustrated in the figures or described herein are performed by processor 802 executing the instructions stored in memory 804. The functions, acts, or tasks are independent of the particular type of instruction set, storage media, processor, or processing strategy and are performed by software, hardware, integrated circuits, firmware, micro-code, and the like, operating alone or in combination. Likewise, processing strategies include multiprocessing, multitasking, parallel processing, and the like.

[0082] As shown, the computer system 800 further includes a display 810, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid-state display, a cathode ray tube (CRT), a projector, a printer or other now known or later developed display device for outputting determined information. The display 810 acts as an interface for the user to see the functioning of the processor 802, or specifically as an interface with the software stored in the memory 804 or in the drive unit 806.

[0083] Additionally or alternatively, the computer system 800 includes an input / output device 812 configured to allow a user to interact with any of the components of the computer system 800. The input / output device 812 is a numberpad, a keyboard, a cursor control device, such as a mouse, a joystick, touch screen display, remote control, or any other device operative to interact with the computer system 800.

[0084] The computer system 800 also includes the drive unit 806 implemented as a disk or optical drive. The drive unit 806 includes a computer-readable medium 822 in which one or more sets of instructions 824, e.g. software, is embedded. Further, the sets of instructions 824 embodies one or more of the methods or logic as described herein. Instructions 824 resides completely or partially within memory 804 and / or within processor 802 during execution by the computer system 800. The memory 804 and the processor 802 also include computer-readable media as discussed above.

[0085] In some systems, computer-readable medium 822 includes the set of instructions 824 or receives and executes the set of instructions 824 responsive to a propagated signal so that a device connected to network 830 communicates voice, video, audio, images, or any other data over network 830. Further, the sets of instructions 824 are transmitted or received over the network 830 via the communication port or interface 820, and / or using the bus 808. The communication port or interface 820 is a part of the processor 802 or is a separate component. The communication port or interface 820 is created in software or is a physical connection in hardware. The communication port or interface 820 is configured to connect with the network 830, external media, display 810, or any other components in the computer system 800, or combinations thereof. The connection with network 830 is a physical connection, such as a wired Ethernet connection, or is established wirelessly as discussed below. Likewise, the additional connections with other components of the computer system 800 are physical connections or are established wirelessly. Network 830 alternatively be directly connected to the bus 808.

[0086] While the computer-readable medium 822 is shown to be a single medium, the term "computer-readable medium" includes a single medium or multiple media, such as a centralized or distributed database, and / or associated caches and servers that store one or more sets of instructions. The term "computer-readable medium" also includes any medium that is capable of storing, encoding, or carrying a set of instructions for execution by a processor or that causes a computer system to perform any one or more of the methods or operations disclosed herein. The computer-readable medium 822 is non-transitory, and may be tangible.

[0087] The computer-readable medium 822 includes a solid-state memory such as a memory card or other package that houses one or more non-volatile read-only memories. The computer-readable medium 822 is a random-access memory or other volatile re-writable memory. Additionally or alternatively, the computer- readable medium 822 includes a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals such as a signal communicated over a transmission medium. A digital file attachment to an e-mail or other self-contained information archive or set of archives is considered a distribution medium that is a tangible storage medium. Accordingly, the disclosure is considered to include any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions are stored.

[0088] In an alternative implementation, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays, and other hardware devices, is constructed to implement one or more of the methods described herein. Applications that include the apparatus and systems of various implementations broadly include a variety of electronic and computer systems. One or more implementations described herein implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that are communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.

[0089] Computer system 800 is connected to network 830. Network 830 defines one or more networks including wired or wireless networks. The wireless network is a cellular telephone network, an 802.10, 802.16, 802.20, or WiMAX network.Further, such networks include a public network, such as the Internet, a private network, such as an intranet, or combinations thereof, and utilizes a variety of networking protocols now available or later developed including, but not limited to TCP / IP based networking protocols. Network 830 includes wide area networks (WAN), such as the Internet, local area networks (LAN), campus area networks, metropolitan area networks, a direct connection such as through a Universal Serial Bus (USB) port, or any other networks that allows for data communication. Network 830 is configured to couple one computing device to another computing device to enable communication of data between the devices. Network 830 is generally enabled to employ any form of machine-readable media for communicatinginformation from one device to another. Network 830 includes communication methods by which information travels between computing devices. Network 830 is divided into sub-networks. The sub-networks allow access to all of the other components connected thereto or the sub-networks restrict access between the components. Network 830 is regarded as a public or private network connection and includes, for example, a virtual private network or an encryption or other security mechanism employed over the public Internet, or the like.

[0090] In accordance with various implementations of the present disclosure, the methods described herein are implemented by software programs executable by a computer system. Further, in an example, non-limited implementation, implementations can include distributed processing, component / object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.

[0091] Although the present specification describes components and functions that are implemented in particular implementations with reference to particular standards and protocols, the disclosure is not limited to such standards and protocols. For example, standards for Internet and other packet switched network transmission (e.g., TCP / IP, LIDP / IP, HTML, HTTP) represent examples of the state of the art. Such standards are periodically superseded by faster or more efficient equivalents having essentially the same functions. Accordingly, replacement standards and protocols having the same or similar functions as those disclosed herein are considered equivalents thereof.

[0092] It will be understood that the steps of methods discussed are performed in one embodiment by an appropriate processor (or processors) of a processing (i.e. , computer) system executing instructions (computer-readable code) stored in storage. It will also be understood that the disclosure is not limited to any particular implementation or programming technique and that the disclosure is implemented using any appropriate techniques for implementing the functionality described herein. The disclosure is not limited to any particular programming language or operating system.

[0093] It should be appreciated that in the above description of example embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. This method of the present disclosure, however, is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of the present disclosure.

[0094] Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the present disclosure, and form different embodiments, as would be understood by those skilled in the art. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0095] Furthermore, some of the embodiments are described herein as a method or combination of elements of a method that can be implemented by a processor of a computer system or by other means of carrying out the function. Thus, a processor with the necessary instructions for carrying out such a method or element of a method forms a means for carrying out the method or element of a method. Furthermore, an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the present disclosure.

[0096] In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the present disclosure are practiced without these specific details. In other instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

[0097] Thus, while there has been described what are believed to be the preferred embodiments of the present disclosure, those skilled in the art will recognize that other and further modifications are made thereto without departing from the spirit of the present disclosure, and it is intended to claim all such changes and modifications as falling within the scope of the present disclosure. For example, any formulas given above are merely representative of procedures that may be used. Functionality may be added or deleted from the block diagrams andoperations may be interchanged among functional blocks. Steps may be added or deleted to methods described within the scope of the present disclosure.

[0098] The above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other implementations, which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents, and shall not be restricted or limited by the foregoing detailed description. While various implementations of the disclosure have been described, it will be apparent to those of ordinary skill in the art that many more implementations and implementations are possible within the scope of the disclosure. Accordingly, the disclosure is not to be restricted except in light of the attached claims and their equivalents.

Claims

CLAIMSWhat is claimed is:1 . A computer-implemented method for accelerating object formulation of an object comprising: receiving, by one or more processors, relevant data from a plurality of data sources; inputting the relevant data, by the one or more processors into a machine learning model, for generating iterations of object models and iterations of object designs; assessing, by the one or more processors utilizing an artificial intelligence module, one or more of object performance metrics, user experience indicators, or industry acceptance probabilities based on user feedback and state pattern; and causing, by the one or more processors, iterative refinement of the object models and the object designs.

2. The computer-implemented method of claim 1 , wherein generating the iterations of object models and the iterations of object designs comprises: processing, by the one or more processors, the relevant data to identify patterns in consumer preferences and industry patterns; and generating, by the one or more processors utilizing the artificial intelligence module, object ideas and design variations based on the identified patterns.

3. The computer-implemented method of claim 1 , wherein assessing the object performance metrics, the user experience indicators, or the industry acceptance probabilities comprises: determining, by the one or more processors, one or more of reliability, durability, or efficiency of the object while assessing the object performance metrics; determining, by the one or more processors, one or more of usability, satisfaction, or engagement while assessing the user experience indicators; ordetermining, by the one or more processors, one or more of consumer state analysis or industry entity comparison while assessing the industry acceptance probabilities.

4. The computer-implemented method of claim 2, wherein the iterative refinement of the object models and the object designs comprises: processing, by the one or more processors, one or more feedback to determine the object ideas and the design variations match user needs and preferences; and adjusting, by the one or more processors, the object ideas and the design variations based on the one or more feedback.

5. The computer-implemented method of claim 1 , wherein the machine learning model and the artificial intelligence module utilize deep learning techniques for pattern recognition and feature extraction.

6. The computer-implemented method of claim 1 , wherein the machine learning model and the artificial intelligence module utilize reinforcement learning to dynamically adapt the object models and the object designs based on evolving requirement and constraints.

7. The computer-implemented method of claim 1 , wherein the machine learning model and the artificial intelligence module utilize predictive modeling techniques including neural networks for determining the object performance metrics, the user experience indicators, or the industry acceptance probabilities.

8. The computer-implemented method of claim 7, wherein the predictive modeling techniques are trained on historical data and continuously updated with real-time feedback.

9. The computer-implemented method of claim 1 , wherein natural language processing (NLP) is used to extract state patterns and identify variables for object enhancement.

10. The computer-implemented method of claim 1 , wherein the relevant data includes one or more of industry patterns, consumer preferences, or industry entity strategies.11 . A system for object model generation and optimization of a object comprising: one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving a plurality of datasets including industry patterns, consumer preferences, and industry entity analyses; processing, by an artificial intelligence module and machine learning model, the plurality of datasets for generating an initial object models; iteratively refining the initial object models through automated simulations and virtual prototyping; incorporating real-time feedbacks from a plurality of parties into a object model generation and optimization process; generating a refined object, based on the object, from the iteratively refining and the incorporating real-time feedback; evaluating, using predictive analytics and probabilistic modeling, one or more of performance, feasibility, or industry potential of the refined object; and generating visual representation of the refined object and results of the evaluating.

12. The system of claim 11 , wherein the artificial intelligence module and the machine learning model utilize deep learning techniques for pattern recognition and feature extraction.

13. The system of claim 11 , wherein the artificial intelligence module and the machine learning model utilize reinforcement learning to dynamically adapt the object models based on evolving requirement and constraints.

14. The system of claim 11 , wherein the artificial intelligence module and the machine learning model utilize predictive modeling techniques including neuralnetworks for determining object performance, user experience, or industry acceptance.

15. The system of claim 14, wherein the predictive modeling techniques are trained on historical data and continuously updated with the real-time feedback.

16. The system of claim 11 , wherein natural language processing (NLP) is used to extract state patterns and identify variables for object enhancement.

17. A system for search query analysis comprising: one or more processors of a computing system; and at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving, by a data ingestion module, search queries including one or more of temporal data, location data, or scoring data; causing, by a machine learning model, one or more of embeddings of the search queries, clustering the embeddings into clusters to identify common themes, labeling the clusters based on semantic meaning, and selecting searches with highest scores; and presenting, by an output module, top searches based on the labeled clusters and scores associated with the search queries.

18. The system of claim 17, wherein the machine learning model utilizes deep learning techniques for pattern recognition and feature extraction.

19. The system of claim 17, wherein the machine learning model utilizes reinforcement learning to dynamically adapt object models and designs based on evolving requirement and constraints.

20. The system of claim 17, wherein natural language processing (NLP) is used to extract state patterns and identify variables for object enhancement.

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