Autonomous prediction of actions for interacting with computing devices

Abstract

The present disclosure relates to methods and computing devices for leveraging Large Action Models and Machine Learning Models to optimize decision-making, enhance user experience, and facilitate personalized interactions of users with one or more computing devices, which may be implemented across various enterprise-based applications which are based on digital- based or machine-based methods, products, or a combination thereof.

Description

AUTONOMOUS PREDICTION OF ACTIONS FOR INTERACTING WITH COMPUTING DEVICESTECHNICAL FIELD

[0001] The present subject matter relates to the field of artificial intelligence and, more particularly to methods and computing devices to autonomously generate, in real-time, accurate predictions of prospective actions to be undertaken by the users to interact with one or more computing or electronic devices or actions undertaken by one or more computing or electronic devices with itself.BACKGROUND

[0002] The rapid enhancement of artificial intelligence and machine learning has lead to methods and computing devices that can simulate human / user behaviour and preferences while interacting with computing or electric devices, such as smartphones, mobile phones, personal computers, laptops, tablet computers, desktop computers. It may be understood that the user behaviour and preferences while interacting with a computing device may include actions undertaken intrinsically by the computing device with itself or one or more external computing devices, including servers, virtual machines, cloud-based electronic devices, etc.BRIEF DESCRIPTION OF FIGURES

[0003] Fig. 1 illustrates a block diagram of a computing device of the present subject matter, in accordance with an example.DETAILED DESCRIPTION

[0004] With the rapid advancement of artificial intelligence and machine learning, there is a growing need for systems that can simulate human / user behavior and preferences and, particularly, predict user actions indicative of user’s interactions with systems or computing devices to achieve a desired objective. The systems or the computing devices include, but are not limited to, smartphones, mobile phones, personal computers, laptops, tablet computers, desktop computers. The desired objective, for example, may include applying for a job, applying for an academic course, tailoring a training course for a user, interacting with social media platforms and online shopping platforms, etc.

[0005] Traditional systems, such as Sequential Recommender Systems (SRSs), often struggle to capture the evolving nature of user preferences over time and therefore fall short in accurately predicting user actions and decisions while the users are interacting with such systems. The inaccuracy in predicting user actions and decisions: (a) makes the users vulnerable to undertaking incorrect actions or decisions while interacting with the traditional systems; and (b) increases the time spent by the users in interacting with such systems, either or both of which cause increase in the amount of processing resources and power utilization (and, therefore, battery drainage depending on the type of computing device) required or spent in achieving the desired objectives.

[0006] In contrast, the methods and the computing devices described in the present disclosure, may be based on SRSs designed to account for sequential dependencies in user interactions, enabling them to learn and adapt how user’ s preferences change dynamically, i.e., in real-time, such that the SRSs generate, autonomously and in real-time, more accurate recommendations of user actions to be undertaken with the computing devices than the traditional SRSs. The computing device includes, but are not limited to, smartphones, mobile phones, personal computers, laptops, tablet computers, desktop computers, servers, virtual machines, cloud-based computing devices, and such.

[0007] The subject matter described in the present disclosure aims to optimize decisionmaking which facilitates enhancing user experience and in personalized interactions of the users with one or more computing or electronic devices across various industries.

[0008] The methods and computing devices described in the present disclosure leverages large action models and involves mechanisms to learn, to receive user feedback and to adapt, in real-time, to provide or generate, in real-time, accurate predictions of future actions, activities, and interactions that a user may perform on the computing device or a computing device may perform with itself. The ability to receive user feedback and to adapt is indicative of capturing the evolving nature of the user behaviour and / or preferences over time.

[0009] In other words, the methods and the computing devices of the present disclosure, for example, enable creation of dynamic and responsive computing devices that: (a) reflect, in realtime, user behaviors and preferences while users are interacting with the created computing devices and / or with one or more external computing devices; and / or (b) mimic, in real-time,interactions that users may have with the created computing devices and / or with one or more external computing devices; and / or (c) mimic, in real-time, interactions that the computing devices with itself and / or with one or more external computing devices. The dynamic and responsive computing devices, created by the methods and the computing devices of the present disclosure, function like virtual representations of users and can function or operate in a user-like manner.

[0010] The methods and the computing devices of the present subject matter enable optimization of decision-making, enhancement of user experience, and facilitation of personalized interactions of users with one or more computing devices across various industries.

[0011] The methods and the computing devices of the preset subject matter generate, autonomously and in real-time, more accurate recommendations of user actions than the traditional systems. Since, the accurate recommendations of user actions are generated autonomously and in real-time, the utilization of processing resources and the power (and internal battery) by the computing devices is reduced significantly, thereby improving the computing devices.

[0012] The methods and the computing devices described in the present disclosure enable prediction of user needs, personalize services, and enhance user engagement by creating a virtual counterpart for each user. The methods and the computing devices of the present subject matter utilizes advanced mechanisms to analyze data from various sources, for example, one or more computer-executable applications, one or more external computing devices and such, and to allow the mechanisms to adapt and evolve based on changing user preferences.

[0013] Exemplary Features and / or Functionalities of the methods and the computing devices of the present subject matter:

[0014] 1. Dynamic Behavior Modeling: Dynamic Behavior Modeling refers to the process through which the methods and the computing devices of the present subject matter utilize advanced algorithms and machine learning techniques to analyze user data, i.e., data associated with user interactions with one or more computing devices, continuously in real-time. Unlike the traditional systems that utilize static customer profiles relying solely on historical data, i.e.,data associated with historical user interactions with one or more computing devices, the methods and the computing devices of the present subject matter are modeled to evolve based on real-time interactions, making them highly responsive to shifts or changes in user’s behavior associated with interactions with one or more computing devices. The methods and the computing devices are modeled to continuously learns from user interactions, adapting their behavior to reflect real-time changes in preferences and needs.

[0015] 2. Real-Time Adaptation: In accordance with an example, as a user engages with a brand — whether through browsing products, making purchases, or interacting on social media on a computing device of the present subject matter — the computing device, and the method executed by the computing device, captures these interactions and updates its model accordingly. This means that the recommendations and insights generated by the methods and the computing devices of the present subject matter are accurately aligned with the users' current / latest interests and needs, in real-time.

[0016] 3. Contextual Understanding: By analyzing user interaction data and the associated computational data from multiple touchpoints, such as the online behavior, the purchase history, and even the external factors like the seasonal trends and / or the economic changes, the methods and the computing devices of the present subject matter can develop a nuanced understanding of what drives a user's decisions at any given moment, in real-time.

[0017] 4. Predictive Capabilities: The ability to learn from ongoing user interactions with one or more computing devices, allows the methods and the computing devices of the present subject matter to accurately predict, in real-time, future behaviors of the users.

[0018] 5. Personalization: By analyzing a user’s data, the methods and the computing devices of the present subject matter provides tailored recommendations and services that enhance user satisfaction with respect to user’s interactions with the computing devices.

[0019] As described above, the methods and the computing devices of the present subject matter involve dynamic behavior modeling that is an adaption of dynamic representation learning models, particularly through Sequential Recommender Systems (SRSs). The SRSs involved in the methods and the computing devices of the present subject matter treat useritem interactions as a sequence, recognizing that a user's next action is influenced not only bytheir long-term preferences but also by their immediate context and recent interactions. This allows for more accurate predictions of future behaviors of the user with respect to his / her interaction with one or more computing devices.

[0020] Further, by incorporating temporal elements into the modeling process, SRSs can adapt recommendations based on the time of day, the seasonality, or even the trending items within a specific timeframe. This adaptability enhances the relevance of recommendations provided to users.

[0021] Another approach to model the methods and the computing devices of the present subject matter is adapting in dynamic behavior modeling is the development of session-based recommendation models. These models focus on providing recommendations based on a single session's interactions rather than relying solely on the historical data associate with the user’s interaction with one or more computing devices.

[0022] Session-Aware Recommendations: By analyzing real-time data from a user's current session with one or more computing devices, the proposed models can offer personalized suggestions that reflect immediate interests of the users with respect to his / her prospective interactions with one or more computing devices. This is particularly useful in environments where users may have different intents during each session with the computing device(s).

[0023] Short-Term Interest Modeling: Session-based models can effectively capture shortterm interests that may not align with long-term preferences, allowing the methods and the computing devices of the present subject matter to respond dynamically to changing user behaviors during individual experiences across the product. The product herein may refer to one or more computer-executable applications, one or more computing devices, or a combination thereof.

[0024] The concept proposed in the present disclosure, powered by Large Action Models (LAMs), involves creating a dynamic digital representation of individual user that continuously learns and adapts based on real-time interactions. The proposed concept involves a unique mathematical framework for implementing the methods and the computing devices using LAMs, focusing on action representation, state modeling, and learning algorithms, as described below in detail.

[0025] 1. Action Space Representation: An action space A for a user accessing or operating a computing device is defined as:A = {a^,...,an],. (1) where each action a£is represented as:a£= (s£,p£,r£),. (2) wheres£: State of the product before executing action a£,Pt'. Parameters associated with action a£(e.g., user’s preferences for achieving a desired objective), andr£: Actual reward received after executing action a£.

[0026] The action space A is a set of actions or activities which a user undertakes on a computing device, or which a computing device inherently undertakes with itself or with one or more other computing devices, where the actions undertaken are related to a particular objective or intent that a user wishes to achieve or fulfill over one or more products, or relate to a particular context over one or more products. The action space A, in an example, can be defined by data associated with a digital footprint of a user performing certain tasks on one or more computing devices in order to achieve or fulfill a particular objective or intent, or relate to a particular context. The data that defines an action space may be in the form of text, numbers, symbols, images, audio, videos, etc., or in the form of computer-readable digital value thereof. The objective or intent or context, for example, may be defined as a purpose for which a user uses / accesses his / her one or more computing devices. It may be understood that the computing device(s) may perform certain tasks inherently, with itself, that relate to the purpose for which the user accesses the computing device(s). The product herein may be defined as one or more computer-executable applications that are in the computing device(s) and accessible by the user for achieving a particular objective. In an example, the objective or intent or context may be to create a user profile, or apply for a job, or release a job offer, on a product, such as one or more of the social media applications, the job applying applications, the educational service applications, etc. The product may include a free-to-use computerexecutable application or a paid-per-use computer-executable application. In an example, each product may have multiple action spaces, and each action space may include multiple actions related to a particular objective or intent or context. The action spaces associated with a productand contextually related to each other for a particular objective of the user are utilized by the methods and the computing devices of the present subject matter. In an example, the product can be a computing device.

[0027] Further, each actionis represented as a set of: (1) stateof the product before executing action ap, (2) predefined parameters pt associated with action(e.g., user’s preferences for achieving a desired objective); and (3) an actual rewardreceived after executing action at. In an example, different statesinclude: an idle state, an active state, a background state, or a quit mode state. In an example, the predefined parameters associated with each actionand a predefined reward mechanism for each actionor objective is prestored stored in a memory of, or communicatively coupled to, the computing device. The actual reward for eachare determined from the predefined reward mechanism.

[0028] In an example, each activityin the action space A is a one-dimensional data representation and each action space A is a two-dimensional data representation, each of the representations including data that defines that activity, and such data may be in the form of text, numbers, symbols, images, audio, videos, etc., or in the form of computer-readable digital value thereof.

[0029] It may be noted that the activities that define an action space are recorded or determined by the computing device in a continuum with respect to time. That is, the activities that define an action space are built by the computing device over a period of time which, for example, may end as the desired objective of the user is met or the state of the product, for example, becomes an idle state or a quit mode state.

[0030] 2. State Space Representation: The state space Stof the user accessing or operating the computing device at a particular time t is expressed as:St=. (3) whereXt: Historical interaction data at time I, andYtContextual information at time t.

[0031] The historical interaction data Xtincludes the data related to the interactions of user with the computing device at and prior to time t. This includes any input provided by the user while accessing any computer-executable application on the computing device and / or data accessed by and output generated by the computing device for achieving the objective described earlier.

[0032] For the contextual information Ytat time t, the methods and the computing devices of the present subject matter utilize a large action model and / or a machine learning model, based on the learning mechanism (as described below), to deduce the contextual information from the activities undertaken by the user on the computing device over a period of time.

[0033] It may be understood that the state space Stis captured or built by the computing device with passage of time, with a time interval of, for example, 1 femtosecond to 1 microsecond. In an example, the time interval may be about 1 nanosecond. It may also be understood that the contextual data at a particular time t will get added to, or become a part of, the historical data at the next time instance or interval, with the time interval in a range of 1 femtosecond to 1 microsecond. The 2-dimensional state space representation is built by the computing device over a period of time which, for example, may end as the desired objective of the user is met or the state of the product, for example, becomes an idle state or a quit mode state. The computing device starts building the state space for the user as soon as the state of the product becomes an active state and continues building the state space even when the state of the product is a background state.

[0034] In an example, the state space Stat a particular time t is a one-dimensional data representation and state space built over a period of time is a two-dimensional data representation, each of the representations including data that defines that activity, and such data may be in the form of text, numbers, symbols, images, audio, videos, etc., or in the form of computer-readable digital value thereof.

[0035] 3. Dynamic Behavior Modeling: To model user preferences dynamically over a time series, the methods and the computing devices of the present subject matter utilize the following equation:P t) = f(X(t), F(t)),. (4) whereP(t) Preferences data for the user up till time series t determined by the computing device, and-):Function, based on a large action model and / or a machine learning model, that integrates / combines the historical data X(t) and the contextual data F(t) built up till time series t to generate data associated with future or prospective actions to be undertaken by the user or by the computing device over the product to achieve the desired objective of the user, i.e., to match the user’s preference data.

[0036] For the integration / combining of the data, the function-) takes the historical data X(t) and the contextual data F(t) as inputs, with or without suitable dynamically changing weights (for example, as per a known weight mechanism), and generates a machine learning mechanism-based compute or data that is indicative of future or prospective actions to be undertaken by the user or by the computing device over the product to achieve the desired objective of the user, i.e., to match the user’s preference data.

[0037] 4. Feedback Loop and Adaptation: To ensure continuous adaptation to the changing user behavior with time, the methods and the computing devices of the present subject matter implement the following feedback loop equation, which kicks in if the d\i(X(t),is not equal to P(t). where t / is a dynamically changing (with time) constant between 0.01 to 0.999...P(t + 1) = P(t) + k [F - P(t)],. (5) whereP(t+1)-. User’s preference data at up till time series Z+l, where 1 is figuratively indicative of the next time interval or instance with a time increment in a range of 1 femtosecond to 1 microsecond.k'. Constant determining how quickly preferences adapt. This value will be between 0 < k < 1 (not 0 or 1), depending on the false positive.F: Feedback provided by the user from his / her recent interactions with the computing device.

[0038] The value of d is directly proportional to time. Thus, as the time progresses, P(t) and [f(X(t), Y(t))] in equation (4) become equal or substantially equal and, therefore, highly accurate future or prospective actions, to be undertaken by the user or by the computing device over the product, are generated by the computing device to achieve the desired objective of the user.

[0039] 5. Learning Mechanism: The methods and the computing devices of the present subject matter update the expected utility of actions of the user, on the computing device, using reinforcement learning principles as per the following equation:Q(St,at) Q(St,at)+a[rt+1+ya'maxQ(St+1+a') - Q(5t,at)],. (6) whereQ St,at) Expected utility data of taking action atin state space Stat time t,rt+1Prospective reward received after executing action at, where in t+1, 1 is figuratively indicative of the next time interval or instance with a time increment in a range of 1 femtosecond to 1 microsecond.a: Learning rate at time t, where the value of a is one from 0.01 to 0.999.... and it increases with time, i.e., as time increases or proceeds.y: Discount factor for future rewards at time Z+l, where the value of y is a positive value based on reward rt+i. anda' saturation level of the learning of the computing device from 0.01 to 0.999.... For example, a' can be approximately or substantially close to 0.96.

[0040] It may be noted that if the action is repeated then the reward should not be repeated, which is incorporated in the proposed mathematical framework by this discount factor y. Further, the value of y becomes null and void when the learning reaches its saturation level, i.e., a’ reaching the max predefined value, for example, 0.999.... Further, the value of a’ is directly proportional to the learning rate a.

[0041] The valueof is incorporated, as the learning mechanism, in the contextual information Ytat time t, as defined in equation (3), which helps in building X(t) and K(t) over a period of time, which further, as per equation (4), enables the methods and the computing devices of the present subject matter generate highly accurate future or prospective actions,which if undertaken by the user or by the computing device over the product, achieve the desired objective of the user much accurately, helps in achieving the desired objective to the satisfaction of the user much faster, which enables reduction in utilization of the processing resources and the power (energy spent) at the computing device end.

[0042] 6. Simulation Environment: Mirror World: The above-described mathematical framework to generate future actions by the computing device enables gaining insights, at any given point of time, towards user’s interactions with the computing device as per the following equation:I = g(P(t),S_t), (7)whereI: Insights gained from user’s interactions at time t,g( ) Function that processes the user preferences and the state of the product at time t to generate actionable insights, andS t: Product’s state at time t, where S t is one from amongst an idle state, an active state, a background state, and a quit mode state.

[0043] For the processing of the data, the function g( ) takes the user preferences and the state of the product at time t as inputs, with or without suitable dynamically changing weights (for example, as per a known weight mechanism), and generates a machine learning mechanismbased compute or data that is indicative of actionable insights.

[0044] The insights gained at any given point in time facilitates the methods and the computing devices of the present subject matter: (1) generate data indicative of any course corrections, if required, from the perspective of user’s interactions with one or more computing devices; (2) generate data indicative of enhancing user’s experience towards his / her interactions with one or more computing devices; (3) generate date indicative of enhancing user’s engagement, preparing questionnaires, providing weblinks, and such, in possibly a correct way to achieve a desirable objective; (4) generate reports of user’s experience and / or interactions with one or more computing devices; (5) generate prompts for the user to provide feedback of his / her experience with respect to actions generated by the computing device and with respect to user’s interactions with one or more computing devices.

[0045] It may be noted that the benefits of the insights (also referred to as actionable insights) are not limited to the list provided above. More benefits may be achieved and drawn as may be described in the exemplary use cases provided below.

[0046] Fig. 1 illustrates a block diagram of a computing device 100 of the present subject matter, in accordance with an example. The computing device 100 executes the method(s) of the present subject matter to autonomously generate, in real-time, accurate predictions of prospective actions to be undertaken by the users for interacting with the computing device(s) 100 or actions undertaken by the computing device(s) 100 with itself for the achieving the objective of the present subject matter. In an example, the computing device 100 executes a method based the mathematical framework to autonomously generate, in real-time, predictions of prospective actions accurately.

[0047] As shown in Fig. 1, the computing device 100 includes processor(s) 102. The processor(s) 102 may be implemented as microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and / or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) 102 fetch and execute computer-readable instructions stored in a memory. The functions of the various elements shown in Fig. 1, including any functional blocks labeled as "processor(s)", may be provided through the use of dedicated hardware as well as hardware capable of executing machine-readable instructions.

[0048] In an example, the computing device 100 also interface(s) (not shown). The interface(s) may include a variety of machine-readable instruction-based and hardware interfaces that allow the computing device 100 to interact with one or more external computing devices, such as network entities, web servers and other external repositories / databases, for the purposes executing the method(s) of the present subject matter.

[0049] Further, in an example, the computing device 100 includes a memory (not shown). The memory is coupled to the processor(s) 102. The memory may include any computer-readable medium including, for example, volatile memory (e. g., RAM), and / or non-volatile memory (e.g., EPROM, flash memory, NVRAM, memristor, etc.). In an example, the processor(s) 102 may fetch computer-readable instructions, indicative of the functionalities, as described in the present disclosure, from the memory.

[0050] Further, the computing device 100 includes module(s) 104 coupled to the processor(s) 102. The module(s) 104, amongst other things, include routines, programs, objects, components, data structures, and the like, which perform particular tasks. The module(s) 104 further include modules that supplement applications and fucntions on the computing device 100, for example, modules of an operating system.

[0051] In an example, the module(s) 104 includes a user-actions prediction module (not shown) and other module(s) 210. The user-action prediction module performs the method(s) to autonomously generate, in real-time, accurate predictions of prospective actions to be undertaken by the users for interacting with the computing device(s) 100 or actions undertaken by the computing device(s) 100 with itself for the achieving the objective of the present subject matter. In an example, the user-actions prediction module executes a method based the mathematical framework to autonomously generate, in real-time, predictions of prospective actions accurately. The other module(s) 210 may include programs or coded instructions that supplement applications and functions, for example, programs in the operating system of the computing device 100.

[0052] Exemplary Use Cases of the present subject matter:

[0053] 1. Personalized Learning Experiences: One of the most compelling applications of the methods and the computing devices of the present subject matter in education is the ability to create personalized learning experiences tailored to individual students' needs and preferences.• Adaptive Learning Platforms: The methods and the computing devices of the present subject matter can analyze students’ learning styles, strengths, and weaknesses by collecting data on their interactions with educational content on one or more computing devices. For instance, an online learning platform can adjust the difficulty level of quizzes or suggest additional resources based on a student’s performance. This adaptive approach ensures that students receive instruction that is aligned with their unique learning paths.• Customized Curriculum Development: By utilizing insights from the methods and the computing devices of the present subject matter, the educators can design curriculathat cater to diverse student needs. For example, if a significant number of students struggle with a particular topic, educators can modify lesson plans to include more comprehensive explanations or supplementary materials.

[0054] 2. Enhanced Student Engagement: The methods and the computing devices of the present subject matter can play a pivotal role in increasing student engagement through interactive and immersive learning environments.• Virtual Classrooms: The methods and the computing devices of the present subject matter enable the creation of virtual classrooms where students can interact with 3D models and simulations on one or more computing devices. For instance, in a science class, students can explore the human body through virtual reality (VR) applications, on VR devices, that allow them to visualize anatomical structures in real-time. This hands-on experience enhances understanding and retention of complex concepts.• Gamification of Learning: Integrating gamification techniques into educational platforms through the methods and the computing devices of the present subject matter can make learning more engaging. Features such as rewards, challenges, and leaderboards motivate students to participate actively in their learning journey. For example, a language learning application might use gamified elements to encourage daily practice and track progress through points and badges on one or more computing devices.

[0055] 3. Data-Driven Insights for Educators: The methods and the computing devices of the present subject matter provide educators with valuable data-driven insights that can inform instructional strategies and improve overall teaching effectiveness.• Performance Analytics: By analyzing data collected from the methods and the computing devices of the present subject matter, educators can gain insights into student performance trends over time. For instance, if certain students consistently perform poorly in assessments, teachers can identify, based on the methods and the computing devices of the present subject matter, underlying issues — such as gaps in foundational knowledge — and intervene accordingly.• Predictive Analytics for Student Success: Educational institutions can use predictive analytics powered by the methods and the computing devices of the present subject matter to identify at-risk students early on. By analyzing patterns in attendance, participation, and academic performance, institutions can implement targeted support measures before students fall too far behind.

[0056] 4. Streamlined Administrative Processes: The methods and the computing devices of the present subject matter can enhance administrative efficiency within educational institutions by automating routine tasks and improving decision-making processes.• Enrollment Management: Institutions can leverage the methods and the computing devices of the present subject matter to streamline enrollment processes by predicting student demand for specific programs based on historical data. This predictive capability allows schools to allocate resources effectively and plan for future growth.• Resource Allocation: By analyzing data on classroom utilization and student enrollment patterns, based on the methods and the computing devices of the present subject matter, administrators can optimize resource allocation — ensuring that facilities and staff are deployed where they are most needed. For example, if data indicates that certain classes are consistently oversubscribed, administrators can consider adding additional sections or adjusting scheduling.

[0057] 5. Continuous Feedback Mechanisms: The methods and the computing devices of the present subject matter enable continuous feedback mechanisms that enhance the learning process for both students and educators.• Instant Feedback on Assessments: Educational platforms powered by the methods and the computing devices of the present subject matter can provide instant feedback on quizzes and assignments. This immediate response from the methods and the computing devices of the present subject matter allows students to understand their mistakes and learn from them right away rather than waiting for graded assignments.• Teacher Feedback Loops: Educators can receive feedback on their teaching methods based on student performance data collected through the methods and the computing devices of the present subject matter. For example, if a particular teaching strategy doesnot yield positive results across multiple classes, educators can adapt their approaches based on this feedback.

[0058] 6. Career Pathways and Guidance: The methods and the computing devices of the present subject matter can assist students in exploring career pathways by analyzing their skills, interests, and academic performance.• Personalized Career Counseling: By assessing a student's strengths and weaknesses through the methods and the computing devices of the present subject matter, career counselors can provide tailored advice on potential career paths that align with their skills and aspirations. For instance, if a student excels in mathematics but struggles with writing, counselors might suggest careers in engineering or data analysis over those requiring strong verbal skills.• Skill Development Programs: Institutions can use insights from the methods and the computing devices of the present subject matter to design skill development programs that align with industry demands. By analyzing labor market trends alongside student performance data, based on the methods and the computing devices of the present subject matter, schools can prepare students with the skills needed for future job opportunities.

[0059] 7. Personalized Job Recommendations: The methods and the computing devices of the present subj ect matter can analyze a j ob seeker’ s profile, including their skills, work history, and preferences, to deliver tailored job recommendations.• Data Aggregation: By aggregating data from resumes, LinkedIn™ profiles, and previous applications, the methods and the computing devices of the present subject matter can create a comprehensive understanding of the candidate’s qualifications and interests.• Algorithmic Matching: Using machine learning algorithms, the methods and the computing devices of the present subject matter can match candidates with job openings that align closely with their profiles. For example, if a candidate has experience indigital marketing and expresses interest in remote work, the system can prioritize remote digital marketing roles.• Dynamic Updates: As the job seeker interacts with different platforms (e.g., applying for j obs or updating their resume), the methods and the computing devices of the present subject matter continuously learns and refines its recommendations based on new data inputs.

[0060] 8. Streamlined Application Processes: The application process often involves repetitive tasks that can be tedious for job seekers. The methods and the computing devices of the present subject matter can automate many aspects of this process.• Autofill Capabilities: The methods and the computing devices of the present subject matter can automatically fill out application forms by pulling relevant information from the candidate's profile. This includes basic details like name and contact information as well as more complex fields such as employment history and skills.• Consistent Messaging: By maintaining a consistent tone and messaging across applications, the methods and the computing devices of the present subject matter help ensure that candidates present themselves effectively to potential employers. This reduces the risk of errors or conflicting information across different applications.• Answer Generation for Open-Ended Questions: For open-ended application questions that require detailed responses (e.g., “Describe a challenging project you worked on”), the methods and the computing devices of the present subject matter can generate tailored answers based on the candidate’s experiences as recorded in their profile.• Preparing for interviews is crucial for job seekers, and the methods and the computing devices of the present subject matter can provide valuable support in this area.• Mock Interviews: Using AI-driven simulations, the methods and the computing devices of the present subject matter can conduct mock interviews with candidates.These simulations can adapt based on the candidate's responses, providing real-time feedback and suggestions for improvement.• Tailored Resources: Based on the specific roles candidates are applying for, the methods and the computing devices of the present subject matter can recommend relevant resources such as articles, videos, or courses that help candidates prepare effectively for interviews.• Feedback Analysis: After interviews, candidates can input feedback into one or more computing devices of the present subject matter. This data helps refine future interview preparation strategies and identify areas for improvement.

[0061] 9. Networking Opportunities: Networking is an essential aspect of job hunting that can often be overlooked. The methods and the computing devices of the present subject matter can facilitate connections between job seekers and industry professionals.• Professional Networking Suggestions: By analyzing a candidate’s background and career goals, the methods and the computing devices of the present subject matter can recommend relevant professional organizations or networking events to attend. For example, if a candidate is pursuing a career in software development, the system might suggest tech meetups or coding boot camps.• Social Media Integration: The methods and the computing devices of the present subject matter can assist candidates in optimizing their LinkedIn™ profiles to attract recruiters. It may suggest keywords to include based on current job market trends or recommend connections with industry leaders who could provide valuable insights or referrals.

[0062] 10. Job Market Insights: The methods and the computing devices of the present subject matter can provide candidates with valuable insights into current job market trends and salary expectations.• Salary Benchmarking: By analyzing data from various sources (e.g., Glassdoor™, PayScale™), the methods and the computing devices of the present subject matter caninform candidates about typical salary ranges for specific roles within their industry and geographic location. This information empowers candidates to negotiate better compensation packages.• Market Demand Analysis: The methods and the computing devices of the present subject matter can analyze trends in job postings to identify which skills are currently in high demand. Candidates can then focus on developing these skills through online courses or certifications to enhance their employability.

[0063] 11. Application Tracking and Management: Managing multiple job applications simultaneously can be overwhelming. The methods and the computing devices of the present subject matter simplify this process through effective tracking systems.• Application Tracking Systems (ATS): The methods and the computing devices of the present subject matter can integrate with ATS platforms to help candidates keep track of where they have applied, interview dates, and follow-up reminders. This ensures that no opportunities are missed during the application process.• Automated Follow-Ups: After submitting applications or attending interviews, the methods and the computing devices of the present subject matter can remind candidates to send follow-up emails to express gratitude or inquire about application status. Automated templates may also be generated based on previous communications.

[0064] 12. Continuous Learning and Skill Development: The methods and the computing devices of the present subject matter encourage ongoing personal development by identifying skill gaps and recommending resources for improvement.• Skill Gap Analysis: By comparing a candidate’s current skills against those required for desired positions, the methods and the computing devices of the present subject matter identifies areas needing improvement. For instance, if a candidate aims for a project management role but lacks certification in Agile methodologies, the system will highlight this gap.• Learning Pathways: The methods and the computing devices of the present subject matter can suggest tailored learning pathways that include online courses, workshops,or certifications relevant to the candidate’s career goals. This proactive approach helps candidates remain competitive in their fields.

[0065] 13. Personalized Sales Strategies: The methods and the computing devices of the present subject matter enable organizations to develop highly personalized sales strategies by analyzing individual customer data and preferences.• Customer Profiling: By aggregating data from various sources, including purchase history, browsing behavior, and demographic information, by the methods and the computing devices of the present subject matter, businesses can create detailed profiles for each customer. This enables sales teams to tailor their approach based on specific customer needs and preferences.• Targeted Recommendations: Using insights derived from the methods and the computing devices of the present subject matter, sales representatives can provide personalized product recommendations. For example, if a customer frequently purchases fitness equipment, the methods and the computing devices of the present subject matter can suggest related products such as workout apparel or supplements, increasing the likelihood of additional sales.

[0066] 14. Enhanced Customer Engagement: The methods and the computing devices of the present subject matter facilitate improved customer engagement by enabling more meaningful interactions between sales teams and customers.• Real-Time Interaction Tracking: By monitoring customer interactions in real-time, using the methods and the computing devices of the present subject matter, businesses can adapt their sales strategies dynamically. For instance, if a customer shows interest in a particular product category during a conversation, the sales representative can pivot the discussion to highlight relevant products or promotions.• Proactive Outreach: The methods and the computing devices of the present subject matter can predict when customers are likely to make a purchase based on their behavior patterns. This allows sales teams to reach out proactively with tailored offers or reminders, enhancing the chances of conversion.

[0067] 15. Data-Driven Decision Making: The insights generated by the methods and the computing devices of the present subject matter empower sales teams to make informed decisions based on data rather than intuition.• Sales Forecasting: By analyzing historical data and trends from the methods and the computing devices of the present subject matter, businesses can improve their sales forecasting accuracy. This capability allows organizations to anticipate demand fluctuations and adjust inventory levels accordingly.• Performance Analytics: Sales teams can utilize data from the methods and the computing devices of the present subject matter to evaluate their performance metrics. For example, if certain representatives consistently achieve higher conversion rates with specific customer segments, organizations can analyze what strategies are working and replicate those tactics across the team.

[0068] 16. Optimizing Sales Processes: The methods and the computing devices of the present subject matter streamline various aspects of the sales process, making it more efficient and effective.• Automated Follow-Ups: After initial interactions or meetings with potential customers, the methods and the computing devices of the present subject matter can automate follow-up communications based on established timelines or customer preferences. This ensures that no opportunities are missed while maintaining a consistent outreach strategy.• Lead Scoring: By analyzing user behavior patterns and engagement levels, the methods and the computing devices of the present subject matter can assign lead scores to potential customers. This scoring framework helps prioritize leads that are more likely to convert, allowing sales teams to focus their efforts where they will have the greatest impact.

[0069] 17. Improved Customer Support: Incorporating the methods and the computing devices of the present subject matter into the sales process enhances customer supportcapabilities by providing representatives with valuable insights into customer history and preferences.• Contextual Support: When customers reach out for assistance, having access to one or more computing devices of the present subject matter allows support agents to provide contextually relevant solutions quickly. For example, if a customer previously expressed dissatisfaction with a product feature, the agent can address this concern directly during the interaction.• Feedback Loop for Continuous Improvement: Organizations can use feedback collected from the methods and the computing devices of the present subject matter to identify common pain points among customers. By addressing these issues proactively through improved products or services, businesses can enhance overall customer satisfaction and loyalty.

[0070] 18. Sales Training and Development: The methods and the computing devices of the present subject matter can also play a role in training sales teams by providing insights into effective selling techniques and strategies.• Performance Benchmarking: By analyzing successful interactions recorded in the methods and the computing devices of the present subject matter, organizations can identify best practices among top-performing sales representatives. These insights can be used to develop training programs that equip other team members with effective techniques for engaging customers.• Role-playing Simulations: Utilizing data from the methods and the computing devices of the present subject matter, companies can create realistic role-playing scenarios for training purposes. Sales representatives can practice handling different customer situations based on real-world data, improving their skills and confidence when interacting with actual clients.

[0071] 19. Market Trend Analysis: The methods and the computing devices of the present subject matter enable organizations to stay ahead of market trends by analyzing aggregated user data over time.• Identifying Emerging Trends: By continuously monitoring user behavior through the methods and the computing devices of the present subject matter, businesses can identify shifts in preferences or emerging trends within specific demographics. This information allows organizations to adapt their product offerings or marketing strategies accordingly.• Competitive Analysis: Insights gained from the methods and the computing devices of the present subject matter can also be used to analyze competitor performance and market positioning. By understanding how different segments respond to various marketing tactics or product features, businesses can refine their own strategies to gain a competitive edge.

[0072] The following description encompass various aspects of the methods and the computing devices of the present subject matter, including their structure, functionality, and applications in different domains:1. Functionality of the methods and the computing devices of the present subject matter comprises:o aggregating user data from multiple sources, such as one or more computing devices, for example, accessible to users or storing data associated with users or their computing devices; ando processing the data to generate a digital or virtual representation of the user.2. The user data includes historical interactions, browsing history, and demographic information of the user with respect to one or more computing devices.3. The methods and the computing devices of the present subject matter are updated dynamically based on real-time interactions of the user with one or more computing devices. In an example, the computing device of the present subject matter comprises:o a data collection module; ando an analysis engine that processes incoming data to adjust the attributes associated with the user.4. The analysis engine utilizes machine learning algorithms to identify patterns in user behavior.5. In an example, the methods and the computing devices of the present subject matter generate personalized marketing strategies for the user by:o analyzing the attributes of the user, based on the interactions of the user with one or more computing devices; ando generating targeted marketing content based on the analysis.. Further, in an example, the methods and the computing devices of the present subject matter dynamically adjust marketing content based on real-time user interactions with the content using one or more computing devices.7. In an example, the methods and the computing devices of the present subject matter enhances customer support for a user by:o providing support agents with access to the historical interaction data of the user; ando enabling the support agents to offer personalized solutions based on past behaviors and preferences of the user.8. Further, the support agents receive real-time alerts when the method and the computing device of the present subject matter exhibits signs of potential dissatisfaction or disengagement of the user.9. In an example, the methods and the computing devices of the present subject matter generates data indicative of predictions of user needs by:o a predictive analytics module that forecasts future user actions based on historical user data.10. Further, the predictive analytics module employs reinforcement learning techniques to improve, over time, accuracy of the predictions of the user needs.11. In an example, the methods and the computing devices of the present subject matter optimize product recommendations by:o analyzing user preferences and behaviors; ando generating personalized product suggestions based on the analysis.12. Further, the effectiveness of product recommendations is evaluated and the recommendations are refined based on user feedback.13. In an example, the methods and the computing devices of the present subject matter enhances user onboarding experiences by:o tailoring onboarding processes based on individual user profiles generated from one or more computing devices of the present subject matter.14. Further, the onboarding materials are dynamically adjusted based on user engagement levels during the onboarding process.15. In an example, the methods and the computing devices of the present subject matter integrate user (lead) data into sales processes by:o a lead scoring module that prioritizes leads based on their data generated by one or more computing devices that are accessed by the leads.16. Further, sales representatives receive insights from the methods and the computing devices of the present subject matter to tailor their pitches effectively during sales interactions.17. In an example, the methods and the computing devices of the present subject matter facilitate continuous learning and skill development of the methods and the computing devices by:o identifying skill gaps in users based on their profiles;o recommending relevant training resources or courses.18. Further, user progress in skill development is tracked and recommendations accordingly adjusted.19. In an example, the methods and the computing devices of the present subject matter analyze market trends using aggregated data from the proposed computing devices associated with the multiple users by:o identifying shifts in user preferences across different demographics;o generating actionable insights for product development and marketing strategies.20. The market trend analysis is performed using advanced statistical techniques and machine learning algorithms to ensure accuracy.21. In an example, the methods and the computing devices of the present subject matter automate follow-up communications with users utilizing their respective computing devices as proposed by:o an automated messaging module that sends personalized follow-ups based on predefined triggers.22. The follow-up messages are tailored according to previous interactions recorded in the user profile.23. In an example, the methods and the computing devices of the present subject matter enhance collaborative learning environments for users (students) in educational settings by:o creating digital representations of students that adapt based on their learning styles and progress.24. Further, the methods and the computing devices of the present subject matter facilitate peer-to-peer interactions by matching students with similar interests or challenges based on their data in one or more computing devices.25. In an example, the methods and the computing devices of the present subject matter integrate IoT devices with the proposed computing devices to enhance real-time data collection and analysis regarding user behavior across various platforms and environments.

Claims

WE CLAIM:

1. A computer-implemented method for generating and operating an autonomous Consumer Twin, comprising:aggregating, via one or more processors, multi-source consumer interaction data from a plurality of digital touchpoints;processing said data to instantiate a dynamic digital twin representation of a consumer within a simulation environment, said representation defined by a state vector S t = (X_t, Y_t), where X_t is historical interaction data and Y_t is real-time contextual information;modeling consumer behavior using a Large Action Model (LAM) framework, wherein the LAM defines an action space A = {a l, a_2,..., a n} for the Consumer Twin, each action a_i represented as a tuple (s i, p i, r_i), where s i is a pre- action state, p i are action parameters representing consumer preferences, and r_i is a simulated reward;dynamically updating a consumer preference function P(t) = f(X(t), Y(t)) using a reinforcement learning feedback loop, wherein an expected utility Q(S_t, a t) for actions is iteratively refined based on a reward signal r_(t+ 1 ); andgenerating, by the autonomous Consumer Twin operating within the simulation environment, at least one of: a predictive behavioral output, a personalized recommendation, or an automated action in a physical or digital channel.

2. The method of claim 1, wherein the reinforcement learning feedback loop implements a Q-learning update rule: Q(S_t, a_t) <— Q(S_t, a_t) + a [ r_(t+l) + y max_a' Q(S_(t+l), a') - Q(S_t, a t)], where a is a learning rate and y is a discount factor for future rewards, and wherein the reward r_(t+ 1) is derived from observed consumer engagement metrics.

3. The method of claim 1, wherein the action space A includes composite actions representing multi-step consumer journeys, and wherein the LAM decomposes and simulates each sub-action within the Mirror World prior to output.

4. The method of claim 1, wherein the state vector Y_t includes exogenous contextual data selected from: local economic indicators, real-time social media sentiment, weather data, and inventory levels.

5. The method of claim 1, wherein the Consumer Twin is capable of autonomousnegotiation, engaging with other automated agents (e.g., pricing bots, inventory systems) within the Mirror World to secure optimal terms, with the resulting strategy being executed in the real world.

6. The method of claim 1, wherein the Consumer Twin employs a neuro-symbolic LAM, combining neural network-based pattern recognition with a symbolic reasoning engine that explains its recommended actions in human-interpretable rules.

7. A system for real-time preference adaptation in a Consumer Twin, comprising:a memory storing a consumer state model S t;one or more processors configured to execute a preference evolution engine that implements:a dynamic preference function P(t), representing a consumer's multidimensional preferences at time t;a feedback assimilation module that calculates an updated preference state P(t+1) according to: P(t+1) = P(t) + k[F - P(t)], where k is an adaptation constant governing learning rate and F is a feedback vector derived from real-time consumer interactions; andan action policy module that maps the updated preference state P(t+1) to a predicted next-best-action a_(t+l) from the LAM's action space A, wherein said mapping optimizes for a simulated cumulative reward.

8. The system of claim 7, wherein the feedback vector F is weighted by a confidence score derived from the source and recency of the interaction data, such that feedback from high-fidelity, recent interactions accelerates adaptation.

9. The system of claim 7, further comprising a divergence detection module that triggers a model recalibration process when the rate of change of P(t) exceeds a predefined threshold, indicating a fundamental preference shift.

10. The system of claim 7, further comprising a federated learning layer that allows Consumer Twins to learn from population-level patterns without sharing raw individual data, preserving privacy while improving model accuracy.

11. The system of claim 7, integrated with a plurality of IoT devices, wherein sensory data (e.g., in-store foot traffic, product handling time) serves as real-time contextual input Y_t to refine the Consumer Twin's state during physical -world interactions.

12. A simulated interaction environment, termed a Mirror World, fortraining and deploying an autonomous Consumer Twin, the environment comprising:a simulation engine that generates a digital ecosystem replicating a plurality of marketplace conditions and consumer decision pathways;an embedding layer that projects the Consumer Twin's state S t and preference function P(t) into the Mirror World;an insight generation function I = g(P(t), S t), executable by the one or more processors, that processes interactions within the Mirror World to produce actionable insights, wherein said insights are used to refine marketing strategies, product configurations, or service personalization's in a corresponding physical -world system; anda validation gateway that compares the Consumer Twin's simulated behavior against real-world consumer actions to calibrate model fidelity.

13. The simulated interaction environment of claim 12, wherein the Mirror World is used for stress-testing product launches by deploying a cohort of heterogeneous Consumer Twins to simulate market adoption curves and identify potential failure points.

14. The simulated interaction environment of claim 12, wherein the insight generation function g(-) employs counterfactual reasoning, asking "what-if scenarios to determine the causal impact of different marketing stimuli on the Consumer Twin's behaviour.

15. A method for dynamic behavior modeling within a Consumer Twin, comprising:processing, via a Sequential Recommender System (SRS), a temporal sequence of useritem interactions as a time- ordered vector;extracting, via the SRS, sequential dependencies wherein a probability of a future consumer action is conditioned on both long-term preference embeddings and a short-term interaction context window;incorporating temporal factors, including time-of-day and seasonality, directly into the SRS's attention mechanisms to weight recent interactions relative to historical patterns; andoutputting, from the integrated SRS-LAM architecture, a session-aware recommendation, wherein the Consumer Twin's proposed action a_i is predicated on a real-time session context, distinct from and capable of overriding long-term historical preference models.

16. The method of claim 15, wherein the SRS is a session-based recommender system that models short-term interest dynamics, and wherein the Consumer Twin maintains a dual preference model: a stable long-term model P_L(t) and a volatile session-specific model P_S(t), the final action being a weighted combination.

17. A personalized career pathway system comprising the autonomous Consumer Twin of claim 1, wherein:the action space A comprises career-related actions (e.g., "apply to job," "enroll in course," "network with professional");the reward r_i is a function of projected career advancement and skillset alignment; and the system generates a sequenced, personalized plan of career actions, dynamically adjusted based on simulated labor market changes within the Mirror World.

18. An automated job application system utilizing the Consumer Twin of claim 1, comprising:a profile parser that extracts skills and experiences to populate the Consumer Twin's state parameters p_i;an application autofill module that uses the Consumer Twin to generate context-aware responses to open-ended application questions, consistent with the individual's historical profile; andan ATS (Application Tracking System) integrator that allows the Consumer Twin to manage follow-ups and interview scheduling as autonomous actions.

19. An educational platform comprising the autonomous Consumer Twin of claim 1, wherein:the Consumer Twin models a student's learning state, including knowledge gaps and engagement level;the Mirror World simulates different pedagogical approaches; andthe platform dynamically adjusts curriculum difficulty, content format, and intervention timing in real-time based on the Twin's predicted frustration or mastery.

20. A sales orchestration system integrating the Consumer Twin of claim 1, comprising: a lead scoring module that prioritizes leads based on the predicted Q-value of a "purchase" action within their respective Consumer Twin's model;a real-time guidance interface for sales representatives that displays the Consumer Twin's predicted next-best-offer and potential objections during a live interaction; anda feedback loop that records the outcome of the sales interaction to update the Twin's reward structure.

21. A system for market trend genesis prediction, comprising:a plurality of autonomous Consumer Twins according to claim 1, representing a statistically significant market segment;an aggregation engine that analyzes emergent action patterns across the cohort within the Mirror World; anda trend forecasting module that identifies nascent preferences or behaviors before they achieve significant expression in real-world market data.

22. A method for preventing consumer churn, wherein the Consumer Twin's internal state metrics are monitored for early warning signals of disengagement (e.g., declining Q-values for core engagement actions), triggering pre-emptive retention interventions.

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