Methods and systems of provisioning a conversation based on a context

Abstract

The present disclosure provides a method of provisioning a conversation based on a context. Further, the method may include receiving a user request data. Further, the method may include analyzing the user request data based on a first AI model. Further, the method may include identifying a context data based on the analyzing. Further, the method may include identifying a team of models based on the context data. Further, the two or more AI models includes the second AI model. Further, the identifying of the second AI model may be further based on one or more of a functional characteristic and a non-functional characteristic of the two or more AI models. Further, the method may include generating a response data based on each of the user request data and the second AI model. Further, the method may include transmitting the response data to the user device.

Description

METHODS AND SYSTEMS OF PROVISIONING A CONVERSATION BASED ON A CONTEXTRELATED APPLICATIONS

[0001] The present application claims the benefit of United States provisional patent application No. 63552185 titled “METHODS AND SYSTEMS FOR FACILITATING DYNAMIC AND CONTEXT-AWARE INTERACTIONS USING MULTIPLE COMPUTATIONAL MODELS”, filed on Feb 11 , 2024, the entirety of which has been incorporated by reference herein.FIELD OF DISCLOSURE

[0002] The present disclosure generally relates to data processing. More specifically, the present disclosure relates to methods and systems of provisioning a conversation based on a context.BACKGROUND

[0003] In the rapidly evolving field of artificial intelligence, conversational Al has emerged as a key area of innovation, significantly impacting how individuals and businesses interact with digital systems. The existing conversational system have relied heavily on single model architecture, often resulting in interactions that lack depth, context sensitivity, consistency and emotional intelligence, and are relatively unskilled. This limitation becomes especially pronounced in difficult applications like psychotherapy, where understanding nuanced human emotions and maintaining coherent, goal-oriented dialogue is paramount, but must also be combined with the absolute reliability of portions of the conversation, to satisfy regulators requirements for risk and safety.

[0004] Therefore, there is a need for methods and systems for provisioning a conversation based on a context, that may overcome one or more of the above-mentioned problems and / or limitations.SUMMARY OF DISCLOSURE

[0005] This summary is provided to introduce a selection of concepts in a simplified form, that are further described below in the Detailed Description. This summary is not intended to identifykey features or essential features of the claimed subject matter. Nor is this summary intended to be used to limit the claimed subject matter’s scope.

[0006] The present disclosure provides a method of provisioning a conversation based on a context. Further, the method may include receiving, using a communication device, a user request data from a user device. Further, the user device may be associated with a user. Further, the method may include analyzing, using a processing device, the user request data based on a first Al model. Further, the method may include identifying, using the processing device, a context data based on the analyzing. Further, the context data corresponds to a context of the user request data. Further, the method may include identifying, using the processing device, a second Al model from two or more Al models based on the context data. Further, the two or more Al models includes the second Al model. Further, the identifying of the second Al model may be further based on one or more of a functional characteristic and a non-functional characteristic of the two or more Al models. Further, the method may include generating, using the processing device, a response data based on each of the user request data and the second Al model. Further, the method may include transmitting, using the communication device, the response data to the user device.

[0007] The present disclosure provides a system of provisioning a conversation based on a context. Further, the system may include a communication device. Further, the communication device may be configured for receiving a user request data from a user device. Further, the user device may be associated with a user. Further, the communication device may be configured for transmitting a response data to the user device. Further, the system may include a processing device. Further, the processing device may be configured for analyzing the user request data based on a first Al model. Further, the processing device may be configured for identifying a context data based on the analyzing. Further, the context data corresponds to a context of the user request data. Further, the processing device may be configured for identifying a second Al model from two or more Al models based on the context data. Further, the two or more Al models includes the second Al model. Further, the identifying of the second Al model may be further based on one or more of a functional characteristic and a non-functional characteristic of the two or more Al models. Further, the processing device may be configured for generating the response data based on each of the user request data and the second Al model.

[0008] Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.BRIEF DESCRIPTIONS OF DRAWINGS

[0009] The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the applicants. The applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.

[0010] Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure.

[0011] Fig. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure.

[0012] Fig. 2 is a block diagram of a computing device 200 for implementing the methods disclosed herein, in accordance with some embodiments.

[0013] Fig. 3A illustrates a flowchart of a method 300 of provisioning a conversation based on a context, in accordance with some embodiments.

[0014] Fig. 3B illustrates a continuation of the flowchart of the method 300 of provisioning a conversation based on a context, in accordance with some embodiments.

[0015] Fig. 4 illustrates a flowchart of a method 400 of provisioning a conversation based on a context including generating, using the processing device 704, a reinforcement learning data, in accordance with some embodiments.

[0016] Fig. 5 illustrates a flowchart of a method 500 of provisioning a conversation based on a context including generating, using the processing device 704, an insight data, in accordance with some embodiments.

[0017] Fig. 6 illustrates a flowchart of a method 600 of provisioning a conversation based on a context including generating, using the processing device 704, an alert data, in accordance with some embodiments.

[0018] Fig. 7 illustrates a block diagram of a system 700 of provisioning a conversation based on a context, in accordance with some embodiments.

[0019] Fig. 8 illustrates a flowchart of a method 800 of provisioning a conversation based on a context including generating, using the processing device 704, a script language feedback data, in accordance with some embodiments.DETAILED DESCRIPTION OF DISCLOSURE

[0020] As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

[0021] Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enablingdisclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim limitation found herein and / or issuing here from that does not explicitly appear in the claim itself.

[0022] Thus, for example, any sequence(s) and / or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.

[0023] Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein — as understood by the ordinary artisan based on the contextual use of such term — differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

[0024] Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”

[0025] The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example,substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and / or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

[0026] The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of the disclosed use cases, embodiments of the present disclosure are not limited to use only in this context.

[0027] In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the Internet. In some other embodiments, the method may be performed by one or more of at least one server computer, at least one client device, at least one network device, at least one sensor and at least one actuator. Examples of the one or more client devices and / or the server computer may include, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a portable electronic device, a wearable computer, a smart phone, an Internet of Things (loT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, mini-computer, micro-computer, a storage server, an application server (e.g. a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS server etc.), a quantum computer, and so on.Further, one or more client devices and / or the server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g. Windows, Mac OS, Unix, Einux, Android, etc.) in order to provide a user interface (e.g. GUI, touch-screen based interface, voice based interface, gesture based interface etc.) for use by the one or more users and / or a network interface for communicating with other devices over a communication network. Accordingly, the server computer may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying, determining, generating, transforming, calculating, computing, compressing, decompressing,encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. Further, the server computer may include a communication device configured for communicating with one or more external devices. The one or more external devices may include, for example, but are not limited to, a client device, a third party database, public database, a private database and so on. Further, the communication device may be configured for communicating with the one or more external devices over one or more communication channels. Further, the one or more communication channels may include a wireless communication channel and / or a wired communication channel. Accordingly, the communication device may be configured for performing one or more of transmitting and receiving of information in electronic form. Further, the server computer may include a storage device configured for performing data storage and / or data retrieval operations. In general, the storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, the storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role based access control, and so on.

[0028] Further, one or more steps of the method disclosed herein may be initiated, maintained, controlled and / or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end user, an admin, a service provider, a service consumer, an agent, a broker and a representative thereof. Further, the user as defined herein may refer to a human, an animal or an artificially intelligent being in any state of existence, unless stated otherwise, elsewhere in the present disclosure. Further, in some embodiments, the one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human readable secret data (e.g. username, password, passphrase, PIN, secret question, secret answer etc.) and / or possession of a machine readable secret data (e.g. encryption key, decryption key, bar codes, etc.) and / or or possession of one or more embodied characteristics unique to the user (e.g. biometric variables such as, but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves, and so on) and / or possession of a unique device (e.g. a device with a unique physical and / or chemical and / or biological characteristic, a hardware device with a unique serial number, anetwork device with a unique IP / MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon, etc.). Accordingly, the one or more steps of the method may include communicating (e.g. transmitting and / or receiving) with one or more sensor devices and / or one or more actuators in order to perform authentication. For example, the one or more steps may include receiving, using the communication device, the secret human readable data from an input device such as, for example, a keyboard, a keypad, a touch-screen, a microphone, a camera and so on. Likewise, the one or more steps may include receiving, using the communication device, the one or more embodied characteristics from one or more biometric sensors.

[0029] Further, one or more steps of the method may be automatically initiated, maintained and / or terminated based on one or more predefined conditions. In an instance, the one or more predefined conditions may be based on one or more contextual variables. In general, the one or more contextual variables may represent a condition relevant to the performance of the one or more steps of the method. The one or more contextual variables may include, for example, but are not limited to, location, time, identity of a user associated with a device (e.g. the server computer, a client device etc.) corresponding to the performance of the one or more steps, environmental variables (e.g. temperature, humidity, pressure, wind speed, lighting, sound, etc.) associated with a device corresponding to the performance of the one or more steps, physical state and / or physiological state and / or psychological state of the user, physical state (e.g. motion, direction of motion, orientation, speed, velocity, acceleration, trajectory, etc.) of the device corresponding to the performance of the one or more steps and / or semantic content of data associated with the one or more users. Accordingly, the one or more steps may include communicating with one or more sensors and / or one or more actuators associated with the one or more contextual variables. For example, the one or more sensors may include, but are not limited to, a timing device (e.g. a real-time clock), a location sensor (e.g. a GPS receiver, a GLONASS receiver, an indoor location sensor etc.), a biometric sensor (e.g. a fingerprint sensor), an environmental variable sensor (e.g. temperature sensor, humidity sensor, pressure sensor, etc.) and a device state sensor (e.g. a power sensor, a voltage / current sensor, a switch-state sensor, a usage sensor, etc. associated with the device corresponding to performance of the or more steps).

[0030] Further, the one or more steps of the method may be performed one or more number of times. Additionally, the one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise, elsewhere in the present disclosure. Further, two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. Further, in some embodiments, there may be one or more time gaps between performance of any two steps of the one or more steps.

[0031] Further, in some embodiments, the one or more predefined conditions may be specified by the one or more users. Accordingly, the one or more steps may include receiving, using the communication device, the one or more predefined conditions from one or more and devices operated by the one or more users. Further, the one or more predefined conditions may be stored in the storage device. Alternatively, and / or additionally, in some embodiments, the one or more predefined conditions may be automatically determined, using the processing device, based on historical data corresponding to performance of the one or more steps. For example, the historical data may be collected, using the storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g. initiating, maintaining, interrupting, terminating, etc.) of the one or more steps and / or the one or more contextual variables associated therewith. Further, machine learning may be performed on the historical data in order to determine the one or more predefined conditions. For instance, machine learning on the historical data may determine a correlation between one or more contextual variables and performance of the one or more steps of the method. Accordingly, the one or more predefined conditions may be generated, using the processing device, based on the correlation.

[0032] Further, one or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing betweentwo or more devices. Another objective may be to restrict a location of one or more of an input data, an output data and any intermediate data there between corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to the server computer. Accordingly, one or more steps of the method operating on the sensitive data and / or a derivative thereof may be performed at the client device.Overview:

[0033] The present disclosure may relate to an innovative Orchestrated Conversational Framework designed to transform the capabilities of automated dialogue systems. The present disclosed system encompasses an integration of multiple computational models and other conventionally programmed deterministic models, each specialized for distinct skills and aspects of conversation, such as natural language understanding, context retention, reasoning, conversation routing, data manipulation, user simulation, code generation, reliability, and emotional intelligence. This multi-model orchestration may enable the framework to conduct highly adaptive, nuanced, and contextually aware conversations across various applications, and select a computational model that meets the required skills and regulatory requirements of the particular interaction.

[0034] This framework may feature a proprietary scripting language, allowing for the flexible creation and management of conversation modules. These modules can be dynamically arranged and tailored to specific interaction contexts, ranging from general customer service to specialized fields like psychotherapy. The system may prioritize user privacy and data security, employing state-of-the-art encryption and ethical, regulatory-compliant data handling practices.

[0035] A significant aspect of this disclosed system is its application in psychotherapy, where it leverages its advanced conversational abilities to provide empathetic, humanistic, context sensitive, and therapeutically aligned interactions. In direct contrast with this, the framework can also integrate deterministic conversational abilities that are capable of being certified to comply with relevant regulatory standards. The adaptability of the framework is further enhanced by reinforcement learning algorithms, which enable continuous improvement and personalization based on user interactions and feedback.

[0036] This Orchestrated Conversational Framework may represent a significant advancement in the field of artificial intelligence, offering a versatile, secure, and intelligent platform for enhancing human-computer communication. Further, the disclosed system may be associated with the field of artificial intelligence (Al), specifically focusing on advancements in conversational Al systems. Further, the disclosed system may involve the innovative integration and orchestration of multiple computational models to enhance the capabilities of automated dialogue systems. This disclosed system may be situated at the intersection of natural language processing (NLP), machine learning, and human-computer interaction leveraging cutting edge Al technology to create sophisticated, dynamic, and contextually aware conversational experiences.

[0037] The disclosed system may find relevance in a range of applications, from customer service automation and personal virtual assistants to more specialized, regulated fields such as psychotherapy, mental health support, and educational tools. Further, the disclosed system may address the need for more natural, intuitive, and effective interactions between Al systems and users, transcending traditional boundaries in Al communication and setting a new standard in the realm of conversational Al.

[0038] Further, the disclosed Orchestrated Conversational Framework may provide a groundbreaking solution in the realm of artificial intelligence and human-computer interaction. This framework may distinguish by its unique integration and orchestration of multiple computational models, each specifically tailored to enhance different facets of conversational intelligence.

[0039] The present disclosure encompasses the following key features:1. Goal-Directed conversations with Agency:• While traditional approaches to employing LLM's for conversational interfaces use the LLM as a better processor of very granular, natural language utterances and matching intents, the framework pursues much large, complex goals and directs / orchestrates the free-flowing conversation to achieve those goals, by giving the agent more agency which is the freedom to achieve the stated goal that is within the skills of the model.2. Multi-Model Orchestration:• At the heart of the disclosed system is the orchestration of diverse LLMs, traditional Al models, and purely deterministic models, each specializing in aspects such as natural language understanding, monitoring to ensure alignment, conversational routing, data manipulation, API management, code generation, emotional intelligence, absolute predictability / reliability, contextual continuity, and domain-specific knowledge.• This orchestration may enable the system to conduct highly adaptive and contextually nuanced conversations, surpassing the capabilities of traditional single-model conversational systems. amic Model Selection:• The system may pick the optimal model(s) required to complete an interaction, by analyzing, for each interaction, the requested interaction and the session’s accumulated context. Further, the process of determining an optimal model, may include not just the model’s ability to complete the interaction according to requirements, but may also include other dimensions such as cost, speed, observed service levels and response times, or other characteristics. r Preferences:• The user may influence model selection by specifying a desired performance level for an interaction, for example specifying that a highly skilled model with longer latency and higher cost be used for certain conversational tasks requiring the strongest available level of reasoning and deepest insight, while allowing lower skill and cost models to be used for other tasks. rietary Scripting Language for Conversational Modules:• The framework may include a specially designed scripting language that enables the creation of dynamic, modular conversational flows. This language allows for the flexible scripting of conversation scenarios, adaptable to various user interactions and objectives. A script may assign a model or a team of models to any level of an interactions, such as an individual atomic step in the process, a grouping of steps, a complete conversational module, or a complete session that utilizes multiple modules. satile Application across Domains:• While universally applicable, the framework may show exceptional promise in specialized applications like psychotherapy, where it can engage in empathetic, sensitive, and therapeutically aligned dialogues, while also addressing the intricacies and regulatory concerns of mental health conversations.7. Transforms natural language conversations into structured data:• Bridges the gap between diverse, free-flowing speech and the structured data required by enterprise-grade business applications such as Healthcare, Finance, Customer Service and other others.8. Journalizing and auditing of the entire interaction:• It may provide both an audit trail and interaction history of the entire interaction, but also a log (similar to a "database log") to allow a user to resume an interaction that the user has previously terminated, by "rolling forward" the log of the previous session, without loss of the information content the user provided. This session may be resumed an unlimited number of times, with zero loss of context, allowing the system to manage conversations of much greater length.9. Privacy and Data Security Emphasis:• Recognizing the paramount importance of user privacy, the framework may incorporate advanced data encryption and ethical handling practices, ensuring the utmost security and confidentiality of user interactions.10. Adaptive and Personalized User Experience:• Leveraging reinforcement learning, the system may continuously evolve based on user feedback and interaction patterns, leading to progressively personalized and effective conversational experiences.11. Seamless Multi-Platform Integration:• The framework is adeptly designed to integrate with various communication platforms, ensuring a consistent and engaging user experience across different mediums, from mobile apps to voice interfaces.

[0040] The Orchestrated Conversational Framework may mark a significant advancement in conversational Al technology, offering a level of interaction that is deeply intuitive, empathetic, and contextually aware. Further, the disclosed systems may bridge the gap between human-likeconversational capabilities and the efficiency of Al, creating a platform that is not only technologically advanced but also user-centric and ethically responsible.

[0041] The Orchestrated Conversational Framework is a groundbreaking platform designed to revolutionize the way automated systems engage in goal-directed conversations. At its core, the framework may integrate a suite of computational models, including advanced Large Language Models (LLMs), conventional Al models and deterministic algorithms, each tailored to excel in various facets of conversation processing, from understanding user intent to generating contextually relevant responses. This integration may allow for a seamless and dynamic conversational experience, unmatched by conventional single-model systems.

[0042] The method and system may encompass the following architectural components:1. Multiple Computational Model Integration:• The framework incorporates an array of Al models, and purely deterministic models, each specializing in aspects such as natural language understanding, monitoring to ensure alignment, conversational routing, data manipulation, API management, code generation, emotional intelligence, absolute predictability / reliability, contextual continuity, and domain-specific knowledge. These models work in concert, orchestrated by a central processing unit that efficiently delegates tasks to an optimal selection of models based on the conversation's current needs and goals.2. Communication Channel Adapters:• To ensure broad applicability, the framework is equipped with adapters for various communication channels, including but not limited to web interfaces, mobile applications, voice interfaces, and messaging platforms. This multichannel adaptability may allow the framework to operate in diverse environments and cater to users' preferences in communication modes.3. Multi-Modal and Hybrid Interaction System:• Recognizing the multifaceted nature of human communication, the framework features a hybrid interaction system. This system seamlessly integrates text, voice, and visual inputs and outputs, thus enriching the conversational context and enhancing user engagement.4. Orchestration:• An orchestration engine that executes the structured conversation, conversing with the user in a structured conversation designed to achieve a complex goal, while continuously monitoring the conversation for alignment and safety, and fulfillment of conversational objectives.5. Data Management and Integration:• Model-driven processing of the ongoing conversation, to supply, extract and update data associated with the conversation's objectives and overall goal.• The system may exchange this data with enterprise business applications, integrating external system API’s to exchange data.

[0043] The method and system may encompass the following workflow:1. Input Processing:• Upon receiving input from a user through any integrated communication channel, the system employs specialized computational models to interpret the input, taking into account the user's language, tone, and context.2. Context Management:• A dedicated module maintains the continuity of the conversation and its structured data, tracking the dialog's history and evolving context. This module ensures that responses are not only relevant to the immediate step of the conversational process but also aligned with the broader conversation trajectory and goal..3. Model Selection:• Using the user’s stated preferences and requirements, and the system context and the requirements of the current interaction, the framework may select and utilize appropriate computational models to generate responses.4. Response Generation:• Responses are tailored to advance the conversation towards predefined goals while maintaining a natural and engaging dialog flow. The responses are tailored to the computing device the user is using, and the literacy level of the user.5. Feedback and Learning Loop:• The framework continuously learns from each interaction, employing reinforcement learning algorithms to refine its understanding and response mechanisms. User feedback and conversational analytics are integral to this learning process, ensuring perpetual improvement of the system's performance.

[0044] Scalability and Integration of the disclosed methods and system:

[0045] The architecture is designed for extensibility, allowing easy integration of additional computational models as they become available and as the needs of the system evolve.Furthermore, the modular nature of the framework facilitates its expansion and customization for specific applications, including but not limited to psychotherapy, sales customer service, education, entertainment, data acquisition, and personal virtual assistance.

[0046] Computational Model Orchestration of the disclosed methods and system:1. The following are the concept of computational model orchestration:• The Orchestrated Conversational Framework may introduce a pioneering approach in utilizing multiple computational models for comprehensive and nuanced conversation management. The orchestration of these computational models is a cornerstone of the system, enabling a level of conversational intelligence and adaptability previously unattainable in singular model systems.• The system may use user preferences and goals, and the session context including conversation and data, and current interaction requirements to select an optimal set of models, with the requisite skills for the current interaction.• The framework and proprietary scripting language decomposes a large, complex goal into a curated multi-step conversational process that generates intermediate outcomes and work products / data in attainment of that goal.2. Selection and Integration of Computational Models:• Criteria for Computational Model Selection:Each computational model in the framework is chosen based on specific skills, strengths, capabilities, and non-functional characteristics (reliability / certifiability, size / footprint, cost, scalability, performance, etc.) Criteria for selection include proficiency in specific skills including language understanding, reasoning, dataanalysis and conversational routing, context retention, data management, API management, emotional intelligence, and domain- specific knowledge. This selection may ensure that each aspect of a conversation, from technical queries to empathetic responses, is handled by the most capable model.• Dynamic Model Assignment:During conversations, the system dynamically assigns tasks to different computational models based on the conversational context and objectives. For instance, one model may be adept at understanding technical jargon, while another excels at gauging emotional undertones. Or a deterministic model that is strictly executing a question and answer (Q&A) protocol might require the assistance of a language model if a user's response extends beyond the conversation boundaries allowed for the deterministic model. The system intelligently routes tasks to leverage these strengths.

[0047] Orchestration Mechanics of the disclosed method sand systems:1. Centralized Orchestration Logic:• At the heart of the system lies a sophisticated orchestration logic. This central processor evaluates each conversational input and determines the most suitable computational model or combination of computational models to handle the response. This decision-making process considers factors such as the current conversation stage, user sentiment, and specific goals.2. Seamless Interaction Among Computational Models:• The computational models are interconnected in a manner that allows for seamless exchange of information and context. This interplay ensures that the transition between different models is invisible to the user, maintaining a fluid and cohesive conversational experience.• Alignment: when a language model such as LLM is engaged, the framework uses one or more "monitor" language models to monitor the behavior of each "actor" language model, to ensure alignment with the conversations goals at various degrees of granularity, and compliance with overall requirements for safety, factuality, security and privacy.3. Feedback and Optimization:• The orchestration system is not static; it continuously evolves through machine learning algorithms that analyze past interactions. By understanding which computational model combinations yield the most effective outcomes, the system optimizes future decision-making for computational model assignments.

[0048] The orchestrated use of multiple computational models presents several advantages:1. Mixture of Experts: Use the most expert model for each particular task the framework performs. Instead of relying on one model that has a range of skill levels for a variety of tasks, each task can leverage the best tool for the job.2. Enhanced Contextual Understanding: With different models focusing on various conversation aspects, the system achieves a deeper understanding of both the explicit and implicit elements of the dialogue.3. Broader Perspective: With multiple computational models playing multiple roles in analyzing user input and generating conversational output, multiple perspectives can be considered and combined, to achieve greater insights and to generate higher quality, more comprehensively thought out responses to the user.4. Deeper reasoning and insight: The use case or the user’s preference may require the system to select a model with a higher degree of reasoning skill to complete a critical analytical task, while sacrificing speed and economy.5. Adaptive Conversational Style: Depending on the user's style and the conversation's tone, the system can adapt its response style, ranging from formal to casual, technical to empathetic.6. Robustness in Complexity: The framework is particularly adept at handling complex conversational scenarios, seamlessly managing transitions between different topics or emotional states.7. Cost-Effectiveness: The system may select the least-cost model with the level of a particular skill needed for the current interaction.8. Performance: The system may select the most performant model with the service level needed for the current interaction. Dimensions of service level may include performance, historical reliability, transaction quotas, and other similar attributes.9. Certifiability, Consistency and Reliability: Components of the framework can be certified as being 100% predictable and reliable, to meet regulatory requirements.

[0049] Application- Specific Computational Model Customization:

[0050] The architecture allows for customization and integration of specialized models tailored to specific applications, such as psychotherapy or technical support. This flexibility ensures that the framework can be adapted to meet diverse industry needs while maintaining high conversation quality.

[0051] The disclosed system and method may base on Retrieval Augmented Generation (RAG):• The Orchestrated Conversational Framework incorporates Retrieval Augmented Generation as a key feature within its suite of LLMs. RAG represents a significant advancement in conversational Al, enabling the system to enhance its responses with information retrieved from a vast corpus of data.

[0052] Functionality and Benefits of RAG:• Contextual Data Retrieval: RAG allows the system to access and utilize external information sources during conversations. This capability is vital for generating responses that are not only contextually appropriate but also rich in content and accuracy.• Enhanced Response Quality: By augmenting generative models with retrieved, curated data, RAG significantly improves the factuality, quality, relevance, and in formativeness of the system's responses, especially in complex subject matters, specialized domains, or where an organization provides its own specific, private knowledge.

[0053] Integration of RAG with LLMs:Seamless Operation with LLMs: RAG works in conjunction with the orchestrated LLMs, enhancing their natural language processing and generation capabilities with additional context and data.• Dynamic Information Integration: During a conversation, RAG dynamically integrates relevant information, ensuring that the system's responses are both accurate and up-to-date.

[0054] Applications of RAG in Conversational Al:• Educational and Informative Dialogues: In educational contexts or scenarios requiring detailed information dissemination RAG provides the system with the ability to source and incorporate educational content or factual data into its responses.• Business and Technical Queries: For business or technical queries, RAG aids in retrieving specific industry-related information, offering more precise and informed responses.• Specification-Driven conversations: Where a conversation requires a very specific process, protocol, industry framework, or other specification to be applied, RAG enables the framework to provide the exact guidance required, in context.

[0055] Operational Mechanics of RAG:• Data Source Connectivity: The framework connects to a variety of structured and unstructured data sources, from which RAG retrieves relevant information.• Real-Time Retrieval and Processing: The retrieval process is conducted in real-time, with sophisticated algorithms determining the relevance and credibility of the information sourced.

[0056] The disclosed system and the method encompasses the intent management, the following are the overview of the intent management:• The Orchestrated Conversational Framework incorporates a sophisticated Conversation Intent Management system, integral to monitoring the conversation to detect conditions that require immediate attention, and to understanding and directing the flow of dialogue. This system is pivotal in deciphering the underlying purposes and goals of user interactions.

[0057] Mechanics of Intent Interpretation:• Intent Recognition: The system employs advanced algorithms to interpret and analyze user input, recognizing not just the explicit content but also the implicit intents and objectives. It uses the LLM's ability to apply common sense and general knowledge to the processing of the user input and the overall conversational context, to explore the meaning of the input in the context of the intermediate and final goals the framework is driving to achieve.• Contextual Understanding: Leveraging the power of LLMs, the framework may interpret user intents within the broader context of the conversation, ensuring responses are relevant and aligned with the user's expectations and conversational history.• Continual Improvement: The intent processing algorithms provide the rationale for decisions made and conclusions reached, providing valuable feedback that is used to continually improve the instructions for the conversation.

[0058] Dynamic Intent Routing:• Orchestration Based on Intent: Upon recognizing whether the user has satisfied a conversational intent, the system dynamically routes the conversation to appropriate steps and models. This routing is based on the nature of the intent, whether it requires factual information, emotional support, or decision-making assistance.• Adaptation to Evolving Intents: As the conversation progresses and user intents evolve, the system adapts in real-time, recalibrating its responses and strategies to maintain alignment with user needs.

[0059] Intent Management in Complex Scenarios:• Handling Ambiguity and Conflicts: The framework is adept at managing ambiguous or conflicting intents, employing logic and context to disambiguate and prioritize intents effectively.• Escalation Protocols: In scenarios where user intents indicate urgent or sensitive matters, the system may globally monitor the entire conversation and if a condition is detected activate escalation protocols, ensuring appropriate handling, which may include alerting or transferring to a human operator.

[0060] Integration with Data Management:• Utilizing User Data: Conversation Intent Management is enhanced by integrating user data, allowing the system to personalize responses and predict user intents based on historical interactions.• Privacy Compliance: All operations involving user data are conducted in strict compliance with data privacy standards, ensuring user trust and system integrity.

[0061] Benefits of Advanced Intent Management:• Permits Model Agency: The conversational framework gives a skilled model the agency to conduct an element of the conversation in the way that it sees fit to accomplish a stated conversational goal, while monitoring the conversation to ensure the conversational intents are satisfied• Enhanced User Experience: By accurately interpreting and managing user intents, the framework provides more meaningful and satisfying conversational experiences.• Efficiency in Conversational Flow: This management system may contribute to the efficiency and effectiveness of dialogues, minimizing misunderstandings and streamlining interactions.

[0062] The disclosed system and method may encompass proprietary scripting language for conversation modules:

[0063] A key innovation in the Orchestrated Conversational Framework is its proprietary scripting language. This language is designed specifically for creating and managing sophisticated conversation modules, enabling precise control over the flow and content of dialogues. The language allows developers to script conversations in a way that closely mimics natural human interaction, while also leveraging the power of the orchestrated models.

[0064] Design and Functionality of Scripting Language:• User-Friendly Syntax: The scripting language features a syntax that is intuitive and easy to learn, even for those without extensive programming experience.• Machine readable: A script is stored in a standards-compliant data format that is easily stored and exchanged. Further, the system uses this same data format to manage business data, allowing the system to implement modules that function on its own interaction scripts to implement tools for script management.• Instructions are in natural language: It prioritizes readability and ease of use, ensuring that developers can focus on crafting effective conversations rather than navigating complex code.• Modular Conversation Building Blocks: The language is structured to facilitate modular conversation design, consistency and re-use. Developers can create individual modules representing different parts of a conversation, such as greeting, information gathering, problem-solving, and closing. These modules can be easily rearranged or modified, providing flexibility in conversation design.• Integration with models: The scripting language is seamlessly integrated with the underlying models. Script commands can dictate which model should handle a particular part of the conversation, how to interpret user inputs, and how to generate responses. This integration allows for sophisticated conversations that are both contextually aware and goal-directed.• Authoring Automation: A script is stored in a standards-compliant data format that is easily stored and exchanged. Further, the system uses this same data format to manage business data, so that a script for a module is understandable to an LLM and interpretable by the data management framework. Its instructions to a model are in natural language. Therefore, another module can implement an automated agent that is a model author, editor, analyst and tester.

[0065] Examples and Application:• Example Scripts: To illustrate, a script for a customer service interaction might include modules for user greeting, issue identification, solution suggestion and feedback collection. Each module would be scripted to guide the conversation through these stages smoothly, with commands embedded to leverage specific computational models for understanding technical terms, empathizing with user frustration, or suggesting solutions.• Dynamic Scripting Capabilities: The language also supports dynamic scripting, where parts of the conversation can change in real-time based on user inputs or context. For instance, if a user expresses urgency, the script can adapt to provide quicker responses or escalate the issue.

[0066] Customization of Scripting Language for Specific Domains:• Domain- Specific Scripting: The scripting language allows for the creation of domainspecific conversation modules. For psychotherapy applications, scripts can include modules for empathy expression, therapeutic questioning, and progress tracking. Each module would be tailored to the nuances of therapeutic dialogue.• Scripting for Emotional Intelligence: In domains like psychotherapy, the language supports scripting for emotional intelligence, allowing the system to recognize and appropriately respond to a wide range of emotional cues.

[0067] The proprietary scripting language offers several advantages:• Enhanced Control over Conversations: It provides developers with precise control over how conversations unfold, ensuring that dialogues align with specific objectives.• Scalability and Reusability: Scripts can be easily scaled or repurposed for different applications, making the system versatile and efficient.• Improved User Experience: By enabling the creation of more natural and context- sensitive conversations, the language significantly enhances the overall user experience.• Scripts Can Manipulate Scripts: The script for a module is just structured data, handled by the Data Management framework, and interpretable by an LLM. A script can author, edit, analyze and test other scripts.

[0068] Deterministic Plugin for Model Certifiability and Consistency:

[0069] In the Orchestrated Conversational Framework, a pivotal component ensuring the reliability and consistency of conversations is the "Deterministic Plugin for Model Consistency". This plugin plays a crucial role in compensating for the inherent variabilities and uncertainties in responses generated by Large Language Models (LLMs).

[0070] Purpose and Functionality of Deterministic Plugin:• Ensuring Consistency: The primary function of the deterministic plugin is to provide a layer of consistency and reliability in the conversational output, especially in scenarios where absolute precision and reliability are paramount, and / or are regulatory requirements.• Compensating for LLM Variabilities: While LLMs offer remarkable capabilities in natural language understanding and generation, they can sometimes produce varied responses to similar inputs. The deterministic plugin mitigates this by enforcing a consistent response pattern where needed.• Fallback Mechanism: In situations where LLMs may not offer the desired level of reliability (e.g., critical customer service scenarios or sensitive psychotherapy sessions), the deterministic plugin acts as a fallback mechanism, ensuring that the conversation strictly adheres to predefined standards and protocols.

[0071] Integration and Operation of Deterministic Plugin:• Seamless Integration with LLMs: The plugin is seamlessly integrated into the framework's architecture, operating in conjunction with multiple LLMs. It activates based on specific triggers or criteria defined within the conversation modules.• Operation in Procedural Code: Unlike LLMs that generate responses based on probabilistic models, the deterministic plugin operates through procedural code , ensuring predictable and uniform responses. This code can be inspected and proven to be 100% reliable, for regulated applications.• Runtime Interpretation: During a conversation, the plugin is invoked at runtime by the orchestration engine, interpreting the conversation's context and objectives to determine when a deterministic response is necessary.

[0072] Application Scenarios:• Critical Communication Instances: In applications where accuracy and consistency are critical, such as clinical assessment, legal advisories or medical informationdissemination, the deterministic plugin ensures that the provided information is precise and unambiguous.• Guaranteed Inputs: The deterministic plugin ensures that an exact request is made to the user, and the user response matches an exact list of allowable responses. It eliminates all non-determinism and variability inherent in an LLM.• Standardized Responses: For scenarios requiring standardized responses, like regulatory compliance or brand messaging in customer service, the plugin guarantees that the communication aligns with the set standards.

[0073] Benefits of Deterministic Plugin:• Certifiability, Reliability and Trust: By ensuring consistency and reliability in responses, the deterministic plugin fosters user trust in the system, particularly important in sensitive applications.• Balancing Flexibility and Control: The plugin strikes a balance between the flexibility of Al-driven conversational models and the control needed in specific interaction scenarios.

[0074] Technical Specifications:• Customizable Triggers and Rules: The plugin can be customized with specific triggers and rules, aligning its activation and responses with the unique requirements of different conversation modules.• Integration with Module Editor: The plugin's parameters and operational rules can be managed and edited through the framework's visual module editor, allowing for easy adjustments and updates.

[0075] The disclosed methods and system encompass modular design of conversations:

[0076] The Orchestrated Conversational Framework introduces a modular approach to conversation design, which is central to its functionality and flexibility. This modular design involves breaking down conversations into discrete, manageable components, or modules, each handling a specific part of the interaction.• Modular Components: Each module is designed to accomplish a specific goal within the conversation, such as introducing the conversation topic, gathering information, providing responses, dealing with an emergency scenario, or concluding the dialogue. These modules can be customized rearranged, or reused across different conversations, offering significant versatility.• Well Defined Interfaces: A Module defines its required input data, and the output data it is responsible for generating. This data is provided by, and consumed by, a business application that uses the framework.

[0077] Implementation and Flexibility:• Dynamic Module Sequencing: The framework allows for dynamic sequencing of modules based on the conversation's context and user responses. This means that the pathway through the modules can vary, adapting to the user's needs and the conversation's direction.• Logical Flow and Continuity: The conversation flows logically from one module to another, maintaining continuity and context throughout the interaction. This design ensures that even in complex conversations spanning multiple sessions or topics, coherence is maintained.• Customization for Various Use Cases: Modules can be specifically designed for different scenarios or user groups. For example, in a psychotherapy context, modules may include therapeutic techniques, mood assessments, or crisis management protocols.

[0078] Integration with Computational Models:• Each module is closely integrated with the computational models. Depending on the module's purpose, different computational models can be called upon to process user inputs, generate appropriate responses, or analyze conversation sentiment. This integration ensures that each part of the conversation is handled by the most suitable model.

[0079] Advantages of Modular Design:• Ease of Development and Maintenance: Modular design simplifies the process of conversation development and maintenance. New modules can be added or existing ones updated without affecting the entire conversation structure.• Personalization and Adaptability: Modules can be tailored to individual user preferences and behaviors, enabling a high degree of personalization in conversations. The system can also adapt in real-time, switching between modules as needed to address the evolving conversation.• Scalability and Reusability: Modules designed for specific purposes or industries can be reused or adapted for different applications, enhancing the framework's scalability across various domains. Modules define their data interfaces but are otherwise black boxes that exhibit the required behavior and accomplish their stated objective. This modularity enables re-use.

[0080] Application- Specific Modules:• Domain- Specific Modules: The framework supports the creation of domain-specific modules. For instance, modules for customer service interactions might focus on problem resolution and customer satisfaction, while those for healthcare could prioritize information accuracy and empathy.• Therapeutic Conversation Modules: In psychotherapy applications, modules are crafted to facilitate therapeutic conversations, integrating techniques like reflective listening, open-ended questioning, and therapeutic alliance building.

[0081] Integration with Communication Platforms:

[0082] The Orchestrated Conversational Framework is designed to seamlessly integrate with a wide array of communication platforms. This integration is crucial for ensuring that the system is accessible and functional across various user interfaces, ranging from web browsers and mobile apps to voice calls and instant messaging services.

[0083] Omni-Channel Compatibility:1. Diverse Platform Support: The framework is compatible with multiple communication channels, including but not limited to web browsers, mobile applications, voiceinterfaces, SMS, email, and social media platforms. This wide-ranging compatibility ensures that users can engage with the Al system through their preferred medium.2. Channel-Specific Adaptations: Each communication channel has unique characteristics and limitations. The framework adapts to these specificities, ensuring optimal interaction regardless of the medium. For instance, voice interactions involve natural language processing and speech recognition, while text-based channels may leverage typing pattern analysis for additional context.3. Device- Specific Adaptations: Each device that a user uses has unique characteristics and limitations. The framework adapts to these specificities, ensuring optimal interaction regardless of the device being used. For instance, a smartphone has a limited screen size and by default an interaction should be more concise than if a tablet, smart TV or desktop browser is being used. The framework tailors the interaction to the user's device.

[0084] Hybrid Interaction Capabilities:1. Blending Modes of Communication: Recognizing the multimodal nature of human communication the framework supports hybrid interaction models. This includes the ability to handle and respond to a combination of text, touch (button click, hyperlink invocation, list selection), voice, image, and video inputs, offering a rich and immersive conversational experience.2. Dynamic Content Presentation: Depending on the capabilities of the communication platform, the framework dynamically adjusts how content is presented. For example, visual aids may be used in web interfaces, while voice modulation can be employed in audio channels to convey emotions or emphasize points.

[0085] Seamless Integration Mechanism:1. APIs and Middleware: The framework utilizes a set of APIs and middleware solutions to integrate with various platforms. These tools handle the translation of data and commands between the framework and the external interfaces, ensuring smooth and efficient communication.2. Customizable Interface Adapters: To cater to specific platform requirements or functionalities, the framework includes customizable adapters. These adapters can beconfigured to match the interaction style and features of different platforms, enhancing user experience and engagement.

[0086] Data Exchange with Enterprise Systems:

[0087] Key Functionality:• The Orchestrated Conversational Framework is designed to not only interface seamlessly with diverse communication platforms but also to facilitate robust data exchange with enterprise systems. This integration plays a crucial role in enhancing the framework's applicability in business and industrial environments. It intermediates between the latent information in a free-flowing conversation and the structured data that drive enterprise applications.

[0088] Mechanisms of Data Exchange:• API Integration: The framework utilizes advanced APIs (Application Programming Interfaces) to connect with various enterprise systems, enabling the smooth transfer of data back and forth. This integration allows the framework to access, retrieve, and update information in real-time within enterprise databases and applications.• Data Synchronization: It ensures real-time data synchronization between the conversational Al system and enterprise systems. This synchronization is critical for maintaining up-to-date information across platforms, essential for tasks such as customer relationship management, inventory tracking, and workflow automation.

[0089] Security and Compliance:• Secure Data Transfer Protocols: The data exchange process adheres to stringent security protocols, ensuring that all data transfers are encrypted and secure. This aspect is vital for maintaining data integrity and confidentiality, especially when handling sensitive corporate information.• Compliance with Industry Standards: The framework's data exchange mechanisms comply with relevant industry standards and regulations, ensuring that data handling meets legal and corporate policy requirements.

[0090] Enhanced Functionality through Enterprise Integration:• Automated Workflows: By exchanging data with enterprise systems, the framework can automate and streamline various business processes, enhancing efficiency and reducing manual effort.• Data-Driven Insights: Integration allows the framework to leverage enterprise data to provide insights, make informed decisions, and offer personalized responses based on the gathered business intelligence.

[0091] Use Cases:• Customer Service Enhancement: In customer service scenarios, the framework can access customer data from enterprise systems to provide personalized service and support.• Internal Business Operations: For internal operations, the framework can integrate with HR CRM, and ERP systems to automate and assist in various organizational functions.

[0092] Benefits of Platform Integration:1. Transform Conversations to Data: Translates conversational content and context, with the application of natural language processing, reasoning and general knowledge, into the data it is communicating.2. Hyper-personalization: Able to use all data available about the user’s past history, preferences, cognitive skills, language skills, and other characteristics to highly personalize the user conversation for maximum functional performance and effectiveness. For example, the system may tailor the communication with a user to match the user’s literacy level, preferred interpersonal style, ability to use cognitive models for understanding, or to provide specific accommodations to the user (for example to communicate effectively with an ADHD patient).3. User Accessibility and Convenience: By being accessible over multiple platforms, the framework ensures a broader reach and convenience for users, accommodating their preferences and technological access.4. Consistent User Experience: Despite the diversity of platforms, the framework maintains a consistent conversational experience, with adjustments made only to complement the specific features of each platform.5. Real-Time Data Synchronization: The system ensures real-time synchronization of conversation data across platforms, enabling users to switch between them without losing the context or continuity of the conversation.

[0093] Application in Varied Contexts:1. Adaptation to Different Domains: The flexible integration with various platforms makes the framework suitable for different domains, from customer service in ecommerce to patient interactions in telehealth.2. Specialized Platform Functions: For specialized applications, such as psychotherapy, the framework can leverage specific platform features, like secure messaging or encrypted voice calls, to ensure privacy and confidentiality.

[0094] Application in Psychotherapy:

[0095] The Orchestrated Conversational Framework is particularly adept for applications in the field of psychotherapy. It leverages its advanced conversational capabilities and sensitivity to emotional cues to provide support and engagement in therapeutic contexts.

[0096] Tailoring Conversations for Therapeutic Needs:1. Understanding Emotional States: The system is equipped with EEMs specifically trained to recognize and interpret a wide range of emotional expressions and cues. This capability is crucial in psychotherapy, where understanding the client's emotional state is key to effective communication and support.2. Therapeutic Conversation Techniques: The framework includes modules that incorporate therapeutic conversation techniques, such as Socratic interviewing, reflective listening, motivational interviewing, and cognitive-behavioral interventions. These modules are scripted to guide the conversation in a manner that is supportive, empathetic, and conducive to therapeutic goals.3. Accommodation: tailoring the conversation to facilitate more effective interaction and understanding, ensuring that the communication is accessible and effective for an individual user with particular needs.4. Privacy and Confidentiality: Recognizing the sensitive nature of psychotherapeutic conversations, the system is designed with robust privacy and confidentiality protocols. This includes secure data handling and encryption to protect client information.

[0097] Adaptive and Personalized Therapy Sessions:• Dynamic Session Management: The framework adapts the flow and content of therapy sessions based on real-time analysis of the client's responses and emotional state. This adaptability ensures that each session is tailored to the client's current needs.• Personalization Over Time: By leveraging reinforcement learning and session analytics, the system continuously refines its understanding of the client's therapeutic journey, allowing for increasingly personalized and effective sessions.

[0098] Integration with Clinical Tools and Protocols:• Clinical Data Integration: The framework can integrate with clinical tools and databases, allowing therapists to input and access relevant client information, treatment plans, and progress notes. This integration ensures that the Al system aligns with the client's overall therapeutic plan.• Crisis Management Protocols: In situations where the client exhibits sign of severe distress or risk, the system can activate crisis management protocols. These protocols might include alerting a human therapist, providing emergency contact information, or offering immediate coping strategies.

[0099] Supporting Therapist-Client Collaboration:• Augmenting Therapist Efforts: The framework can be used as a tool to augment therapists' efforts, providing them with insights from session analytics or suggesting intervention strategies based on Al analysis.• Client Engagement Outside Sessions: The system can also engage clients outside of traditional therapy sessions, offering supportive messages, reminders for therapeutic exercises, or check-ins to maintain engagement and support.

[0100] Ethical Considerations and Compliance:• Ethical Framework: The application of Al in psychotherapy is governed by an ethical framework that prioritizes client welfare informed consent, and non-maleficence.• Regulatory Compliance: The system is designed to comply with healthcare regulations and standards, ensuring that its use in therapeutic settings meets legal and ethical requirements.

[0101] Data Management and Privacy:

[0102] Overview of Data Management:• Effective data management is crucial for the Orchestrated Conversational Framework, particularly given its applications in sensitive areas such as psychotherapy. The system transforms free-flowing, natural language conversation to the data that is relevant to the context of the conversation and its goals. The system is designed to handle, store, and process large volumes of conversational data with utmost integrity and efficiency.

[0103] Data Interpretation and Extraction:• Natural Language Interpretation: The framework uses specialized LLM's to interpret the meaning of natural language conversation, examine the meaning, and satisfy directives to extract specific data from the conversation.• Transforming to Context: The framework uses an LLM's reasoning abilities to transform the information within a conversation to its meaning within the context of the goal of a conversation. For example, in order to determine the passenger seating requirements for a vehicle purchase the framework transforms "We have one son and my wife is expecting" to a requirement for two adults and two children.

[0104] Data Collection and Storage:• Secure Data Collection: The framework collects data from user interactions across various communication channels. This data includes textual inputs, voice recordings, user preferences, and interaction history.• Encrypted Storage: All collected data is securely encrypted and stored in compliance with industry-standard protocols. The framework employs advanced encryption methods to protect data both at rest and in transit.

[0105] Data Processing and Usage:• Contextual Analysis: The system processes conversational data to understand context, user sentiment, and conversation dynamics. This analysis is crucial for generating appropriate responses and for the system's learning mechanisms.• User Data Privacy: User data is processed with a strong emphasis on privacy. The system ensures that personal information is used only to enhance the conversational experience and not for any unauthorized purposes.

[0106] Privacy Policies and User Consent:• Transparent Privacy Policies: The framework operates with transparent privacy policies, clearly communicating to users how their data is used. Users are informed about the data collection processes, storage practices, and usage purposes.• Informed Consent: User consent is a cornerstone of the system's operation. Users are required to provide informed consent regarding data collection and usage, with options to opt-out or request data deletion in accordance with privacy laws and regulations.

[0107] Regulatory Compliance:• Adherence to Privacy Regulations: The system is designed to comply with global data protection and privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR) and the Personal Information Protection and Electronic Documents Act (PIPED A) ensuring legal compliance and user trust.• Certifiability: the framework is designed to be certified to be in compliance with required regulatory standards such as those of medical devices.• Regular Audits and Updates: The framework undergoes regular audits to ensure ongoing compliance with evolving data protection laws. Privacy policies and practices are updated as necessary to maintain the highest standards of data security and user privacy.

[0108] Data Security Measures:• Robust Security Protocols: The system implements robust security protocols, including firewalls, intrusion detection systems, and regular security assessments, to safeguard against unauthorized access and data breaches.• Employee Training and Awareness: Employees and developers are regularly trained in data security and privacy best practices, ensuring that everyone involved in the system's operation contributes to the overall security posture.

[0109] The disclosed methods and the system encompass reinforcement learning and system improvement:

[0110] Overview of Reinforcement Learning in Al:

[0111] The Orchestrated Conversational Framework employs reinforcement learning (RL) as a key mechanism for ongoing system improvement and adaptation. Reinforcement learning allows the system to learn and evolve based on interactions, feedback, and outcomes, continually enhancing its conversational capabilities and effectiveness.

[0112] Implementation of Reinforcement Learning:• Feedback Loop Integration: The framework integrates a feedback loop where user interactions, responses, and outcomes are used as input data for RL algorithms. This feedback includes direct user input, engagement metrics, and conversational analytics.• Adaptive Learning Algorithms: Utilizing advanced RL algorithms, the system adapts and refines its response strategies, conversation flows, and LLM orchestration. Thisadaptive learning ensures that the system becomes more proficient and personalized in handling user interactions over time.

[0113] Continuous System Improvement:• Conversation Quality Enhancement: Through RL, the framework continually enhances the quality of conversations. This includes improving context understanding, response relevance, and emotional intelligence in dialogues.• User Experience Optimization: The system learns to better match user preferences and styles, leading to a more personalized and engaging user experience. This is particularly important in applications like psychotherapy, where user comfort and trust are crucial.

[0114] User Feedback and Analytics:• Leveraging User Feedback: User feedback is a valuable component of the learning process. The system solicits and incorporates user ratings, suggestions, and critiques to guide its learning and improvement.• Analyzing Conversational Data: The framework journalizes and analyzes detailed conversational data, identifying patterns, strengths, and areas for improvement. This analysis covers aspects such as conversation flow, topic handling, and user engagement levels.

[0115] Ethical Considerations in Learning:• Bias Monitoring and Correction: The system is designed to monitor and correct for any biases in responses or learning processes. This includes biases related to language, culture, or user demographics.• Transparent Learning Processes: The framework maintains transparency in its learning processes, ensuring that changes and adaptations are made with ethical considerations and user welfare in mind.

[0116] Integration with Clinical and Therapeutic Insights:• Incorporating Clinical Expertise: In psychotherapy applications, the system integrates clinical insights and therapeutic best practices into its learning process. This ensures that its evolution aligns with therapeutic goals and standards.• Custom Learning for Therapeutic Contexts: The RL algorithms are tailored to understand and adapt to the nuances of therapeutic conversations, ensuring sensitivity and appropriateness in responses and interventions.

[0117] User Interface and Accessibility:• Designing for User Engagement and Ease of Use: The multi-channel, Orchestrated Conversational Framework prioritizes a user-centric design in its interface, ensuring that interactions are intuitive, engaging, and accessible to a diverse user base.

[0118] Interface Design Principles:• Simplicity and Clarity: The user interface (UI) is designed for simplicity and ease of navigation. Clear visual cues, minimalistic design elements, and straightforward navigation paths are employed to enhance user comfort and reduce complexity.• Responsive Design: The UI is responsive and adaptable to various devices and screen sizes, ensuring a consistent and seamless experience across desktops, tablets ,and mobile devices.

[0119] Accessibility Features:1. Inclusive Design: The framework is built with inclusive design principles, ensuring accessibility for users with disabilities. This includes features like screen reader compatibility, voice navigation, and adjustable text sizes.2. Accommodation: Dynamically tailoring the conversation to facilitate more effective interaction and understanding, ensuring that the communication is accessible and effective for an individual with particular needs.3. Multimodal Interaction: To accommodate different user preferences and needs, the system supports multimodal interactions, including text, touch, voice commands, and visual inputs.

[0120] Personalization and User Preferences:1. Customizable UI: Users can personalize the interface according to their preferences. Options for customization include theme changes, font adjustments, and layout modifications.2. Adaptive Content Presentation: The system dynamically adapts content presentation based on user behavior and preferences, optimizing the interface for individual preferences, interaction patterns and abilities.3. Cognitive alignment: The system analyzes a corpus of a user’s conversations to determine the user’s language processing abilities, emotional intelligence, emotional triggers, social cognitive skills, problem solving skills, creativity, attention span, adaptability, likes and dislikes, preferences. The system uses this psychological profile to hyper-personalize conversation to build a deeper relationship with the user and provide more effective interactions and service.

[0121] Visual Editor for Module Composition:

[0122] Introduction to the Visual Editor:

[0123] A key component of the Orchestrated Conversational Framework is its Visual Editor, designed specifically for the composition and management of conversation modules. This editor plays a vital role in enabling users, including those without extensive programming expertise, to craft and customize conversational flows effectively.

[0124] Functionality and User Experience:• Intuitive Design: The Visual Editor boasts an intuitive, drag-and-drop interface that simplifies the process of creating and arranging conversation modules.• Real-Time Module Composition: Users can visually construct and modify the flow of conversation, seeing the effects of their changes in real-time. This feature enhances the user’s ability to create complex conversational architectures with ease.• Customization Capabilities: The editor allows for extensive customization of modules, including setting triggers, responses, and linking different computational models to specific parts of the conversation for targeted functionality.

[0125] Integration with Proprietary Scripting Language:• Seamless Scripting Integration: The Visual Editor is seamlessly integrated with the framework’s proprietary scripting language, allowing users to define and edit scripts visually without needing to write code manually.• Accessibility for Non-Technical Users: This integration makes the advanced capabilities of the scripting language accessible to non-technical users, democratizing the process of conversation design.

[0126] Collaborative Features:• Shared Module Editing: The editor supports collaborative features, enabling multiple users to work on the same conversation modules, fostering teamwork and shared project management.• Version Control: It includes version control mechanisms, allowing users to track changes, revert to previous versions, and maintain the integrity of conversation designs.

[0127] Enhanced Development Efficiency:• Rapid Prototyping: With the Visual Editor, users can rapidly prototype and test conversational flows, speeding up the development process and enabling quick iterations.• Immediate Feedback: The editor provides immediate visual and functional feedback, helping users to understand how their design choices impact the conversation's flow and user experience.

[0128] User Support and Guidance:• Onboarding Experience: The framework includes an intuitive onboarding process to acquaint users with the system's features and functionalities, enhancing their comfort and confidence in using the platform.• Help and Support Features: Integrated help options, such as FAQs, tutorial videos, and live support, are readily available to assist users in navigating and utilizing the system effectively.

[0129] Ethical Design Considerations:• User Autonomy and Control: The design ensures that users maintain autonomy and control over their interactions. Users can easily access privacy settings, data preferences, and opt-out options.• Transparency in Design: The UI design maintains transparency, particularly in areas like data usage and system capabilities, helping users make informed decisions while interacting with the Al.

[0130] Design for Specialized Applications:• Psychotherapy-Specific UI Features: For psychotherapy applications, the interface incorporates elements that promote a calming and safe environment. Subtle color schemes, non-intrusive notifications, and discretion in content display are key aspects.• Culturally Sensitive Design: The UI is culturally sensitive, offering language options and culturally appropriate content, ensuring relevance and respect for diverse user backgrounds.

[0131] Technical Specifications and Requirements:

[0132] System Architecture and Components:1. Processing Power: The framework requires robust processing capabilities to efficiently orchestrate multiple computational models and handle real-time data analysis. This includes high-performance CPUs, GPUs, or specialized Al accelerators.2. Memory and Storage: Adequate memory (RAM) is essential for the smooth functioning of computational models and storing temporary data. Additionally, scalable storage solutions are required for persisting conversation histories, user profiles, and system logs.3. Network Requirements: A high-speed and reliable network connection is crucial for cloud-based data processing and real-time updates. This ensures seamless interaction and data synchronization across various platforms.

[0133] Software and Platform Compatibility:1. Operating System Compatibility: The system is compatible with major operating systems including Windows, macOS, Linux, iOS, and Android, ensuring broad accessibility.2. Integration with External APIs: The framework is designed to integrate with various external APIs, including those for communication platforms, data storage, and specialized services like sentiment analysis or translation.

[0134] Security Specifications:• Encryption Standards: State-of-the-art encryption standards are employed for data security, including TLS / SSL for data in transit and AES for data at rest.• Compliance with Security Protocols: The framework adheres to industry standard security protocols and best practices, including regular security audits and vulnerability assessments.

[0135] Scalability and Reliability:• Scalable Architecture: The system architecture supports scalability to handle varying loads, from individual users to large-scale deployments.• High Availability and Disaster Recovery: Measures for high availability such as load balancing and redundancy, are in place. Disaster recovery plans ensure data integrity and system functionality in case of failures.• Model Resiliency and Succession: The system is able to identity multiple models that possess a required skill, and use backup models if a selected model is not complying with a required service level.

[0136] User Data Management:• Data Backup and Recovery: Regular data backups are performed, and robust recovery mechanisms are in place to prevent data loss.• Data Retention Policies: Clear data retention policies are established, complying with legal and regulatory requirements, and ensuring responsible data management.

[0137] Hardware and Software Maintenance:• Regular Updates and Patches: The system undergoes regular software updates and patches to enhance features, fix bugs, and improve security.• Hardware Maintenance: For deployed hardware components, regular maintenance schedules are established to ensure optimal performance and longevity.

[0138] Performance Metrics:• System Response Time: The framework is optimized for quick response times, ensuring fluid and natural conversations.• Load Handling and Stress Testing: The system is rigorously tested for load handling and stress conditions, ensuring reliability under various operational scenarios.

[0139] Environmental and Operational Considerations:• Energy Efficiency: The system is designed with energy efficiency in mind, reducing the environmental impact of its operation.• Operational Temperature and Conditions: Hardware components are rated for a range of operational temperatures and environmental conditions, ensuring reliability and durability.

[0140] General Use Case - Customer Service Interaction:1. Input Processing: When a customer initiates a conversation, perhaps through a chat interface on a website, the system first processes the input text. An LLM dedicated to understanding natural language processes the customer's query, identifying key elements like intent, sentiment, embedded data, and specific requests or questions.2. Contextual Analysis and Response Generation: The system then analyzes the context of the conversation, using historical data and the current dialogue's flow. Another LLM, specialized in determining whether global or local intents have been satisfied, determines whether an intermediate goal has been achieved, and routes the conversation accordingly. If none of the immediate goals have been achieved yet, an expert conversational LLM crafts an appropriate response to steer the conversation towards the goal. This might involve collecting specific information, providing information, resolving a complaint, or escalating the query to a human agent.3. Dynamic Interaction and Learning: Throughout the interaction, the system dynamically adapts to the conversation's tone and direction. Feedback mechanisms and reinforcement learning algorithms allow the system to learn from each interaction ’improving its responses over time.

[0141] Specific Scenario - Psychotherapy Session:1. Emotional State Analysis: In a psychotherapy session, the agent's introductory conversation to "break the ice" enables it to analyze the client's emotional state through their verbal cues and language use. An LLM trained in emotional intelligence assesses sentiment and emotional indicators to understand the client's current state, to guide the remainder of the session.2. Hyper-personalization and cognitive alignment: The framework is capable of assessing the ongoing conversation with the user and adapting its output to align with the user's communication profile. The dimensions assessed include but are not limited to:• Language proficiency and vocabulary skills.• Attention and Focus: Attention span and the ability to maintain focus without distraction.• Tone and Formality: The level of professionalism, formality, and seriousness preferred.• Emotional Intelligence: Ability to manage one's emotions and respond appropriately to emotional cues.• Emotional Expressiveness: Comfort level with emotional discussions and expressing emotions during conversations.• Cognitive Schema Ability: Ability to form and use cognitive schemas to organize and process information.3. Therapeutic Dialogue Management: Based on this guidance, the system navigates through different therapeutic dialogue modules, perhaps starting with open-ended questions to encourage the client to speak more about their feelings. The conversation modules are scripted using the proprietary language, incorporating therapeutic techniques and principles.4. Adaptive Responses and Intervention: As the session progresses, the system continually adapts, tailoring its responses to support therapeutic goals. If the conversation indicates a heightened emotional state or a potential crisis, the system can trigger specific intervention protocols, such as providing crisis resources or alerting a human therapist.

[0142] Data Analysis and Reporting:• After each interaction, the system performs a comprehensive analysis of the conversation. This includes evaluating the effectiveness of responses, user satisfaction (where applicable), and any areas for improvement. In a psychotherapy context, this might also involve identifying key mental health concepts, states, triggers or other factors. It might also provide insight and analysis by summarizing key themes or progress indicators for review by a human therapist.

[0143] Continuous Improvement:• Feedback from users and the analytical data are fed back into the system, contributing to the reinforcement learning process. This continuous cycle of interaction, analysis, and adaptation ensures that the system becomes more efficient and effective over time, providing more personalized and contextually relevant interactions.

[0144] Technical Specifications:• Diverse LLM Suite: The system employs a variety of LLMs, each selected for specific strengths such as advanced natural language understanding, emotional intelligence, context retention, and specific domain knowledge (e.g., psychotherapy, customer service).• Custom-Built LLMs: Alongside standard models the framework includes custom- built LLMs tailored for specific functions, such as interpreting technical jargon in IT support or empathetic responses in therapeutic settings.• Continuous LLM Model Updates: The LLMs are regularly updated with the latest advancements in Al and NLP, ensuring the system remains at the forefront of conversational Al technology.• Deterministic Models: The model suite includes deterministic models that are custom-built to provide guaranteed consistency and reliability.

[0145] Integration with Communication Channels:• Multi-Platform Compatibility: The framework is designed to integrate seamlessly with various communication platforms, including web browsers, mobile applications, voice interfaces, instant messaging services, and social media platforms.• Adaptive Interface Technology: Interface adapters allow the system to adapt its conversational style and content presentation based on the capabilities and limitations of each communication channel.• Real-Time Synchronization: The system ensures real-time data synchronization across platforms, enabling users to switch between different communication channels without losing the context or continuity of the conversation.

[0146] Data Handling and Processing:• Data Encryption and Security: All user data is encrypted using advanced encryption standards. The system employs robust security protocols for data transmission and storage.• Efficient Data Processing: The framework utilizes high-performance computing resources for real-time data processing. This includes cloud-based solutions for scalability and on premise options for sensitive applications.• Data Analytics Engine: A sophisticated data analytics engine processes conversational data for insights, system improvement, and, where applicable, therapeutic analysis. This engine respects user privacy and confidentiality standards.

[0147] System Architecture and Maintenance:• Scalable and Modular Architecture: The system is built on a scalable and modular architecture, allowing for easy integration of new features, computational models -or communication channels.• Regular System Updates and Maintenance: The framework undergoes regular updates for feature enhancements, security improvements, and performance optimization. Maintenance protocols ensure high system availability and reliability.• Energy Efficiency and Environmental Considerations: The system's design includes energy-efficient computing practices, minimizing its environmental impact.

[0148] Compliance and Standards:• Regulatory Compliance: The framework is designed to comply with relevant data protection and privacy regulations (like HIPAA, GDPR, and PIPEDA) , medical device regulations, as well as industry-specific standards.• Accessibility Standards: The system adheres to accessibility standards ensuring it is usable by people with a wide range of abilities and disabilities.

[0149] Description of the Preferred Embodiments:

[0150] The following are some preferred embodiments and potential applications of theOrchestrated Conversational Framework:

[0151] Embodiment 1 : Customer Service Application1. Natural Language Understanding: The system interprets customer queries in natural language, identifying key issues and intentions.2. Automated Response Generation: Dynamically selects appropriate conversation modules based on the query's context to provide accurate and relevant responses.3. Integration with Customer Databases: Accesses customer histories and product databases for personalized assistance.4. Hyper-Personalization: Analyzes a corpus of a user's conversations to determine the user's language processing abilities likes and dislikes and preferences. Uses this profile to personalize conversation to deliver better service to the customer.5. Feedback Collection: Collects customer feedback post-interaction for continual performance improvement.6. Application in E-Commerce: Assists with product selection recommendations, and purchasing process support in e-commerce settings.

[0152] Embodiment 2: Psychotherapy Assistant1. Emotionally Intelligent Interactions: Recognizes and responds to emotional cues with empathetic dialogue.2. Cognitive Alignment: Analyzes a corpus of a user's conversations to determine the user's language processing abilities, emotional intelligence, emotional triggers, social cognitive skills, problem solving skills, creativity, attention span, adaptability, likes and dislikes, preferences. Uses this psychological profile to hyper personalize conversation to build a deeper relationship with the user and provide more effective interactions.3. Therapeutic Conversation Management: Incorporates therapeutic techniques for mental well-being and progress tracking.4. Crisis Management Protocols: Activates protocols for severe distress situations to provide immediate assistance or escalate to human therapists.5. Data Privacy and Confidentiality: Maintains the confidentiality and security of sensitive personal information.6. Therapeutic Progress Tracking: Monitors and tracks therapeutic progress, offering insights to therapists and clients.

[0153] Embodiment 3: Educational Tutor1. Interactive Learning Modules: Employs modules to interview a student, teach concepts, answer questions, and provide exercises.2. Adaptive Learning Paths: Adapts teaching methods and content based on student responses and progress.3. Language Translation and Support: Offers real-time translation and multilingual support for diverse linguistic needs.4. Engagement and Motivation Tools: Integrates gamification elements and motivational messages to enhance student engagement.

[0154] General Features Across Embodiments:• Multi-Model Orchestration. Maintains nuanced conversation handling across all applications.• Data Management: Transforms abstract conversations to actionable data.• User-Friendly Interface: Ensures accessibility and ease of use.• Continuous Learning: Employs reinforcement learning for performance improvement.• Adaptability and User-Centricity: Tailors functionalities to specific applications while upholding the framework's core principles.• Conversational Analysis: Analyzes conversation to build a psychological digital twin of the user, to optimize interactions and service.• Hyper-Personalization: To build deeper relationships.

[0155] Additional Potential Applications:• General Applications: Includes customer service support, e-commerce assistance, educational and training tools, and enterprise resource management.• Specialized Applications in Psychotherapy: Covers therapeutic conversations, mental health support, crisis intervention and progress tracking.• Other Healthcare Applications: Encompasses patient engagement, diagnosis, care, and telehealth services.• Accessibility and Inclusion: Offers assistance for individuals with disabilities and language learning and translation tools.• Business and Marketing: Utilized for market research, consumer insights, personalized marketing, and customer engagement.

[0156] The Orchestrated Conversational Framework represents a groundbreaking innovation in the field of artificial intelligence and human-computer interaction. By seamlessly integrating and orchestrating multiple computational models, each specialized for various aspects of conversational intelligence, this framework sets a new standard in creating natural, context- aware, and emotionally intelligent interactions. This approach marks a significant advancement beyond the capabilities of traditional conversational Al systems.

[0157] The framework's use of multiple specialized models ensures unparalleled conversational depth, adaptability, and precision, offering a more nuanced understanding than single-model systems. Further, the disclosed system may be configured for dynamic personalization and continuous learning. Powered by advanced reinforcement learning mechanisms, the frameworkcontinually evolves, personalizing interactions to make each conversation more engaging and relevant to the user. Further the disclosed system may be configured for performing hyperpersonalization using a psychological profile built by natural language analysis of the user's conversation history. With its modular design and ability to integrate across multiple platforms, the framework is versatile, suitable for a range of applications including customer service, e- commerce, education, and notably in psychotherapy. The framework prioritizes data security and ethical Al use, addressing critical concerns in digital interactions, particularly in sensitive fields like mental health support. Emphasizing accessibility and ease of use, the framework is designed to be inclusive, appealing to a broad spectrum of users.

[0158] The Orchestrated Conversational Framework is more than an incremental improvement over existing technologies; it is a transformative solution that redefines Al assisted communication. Its proficiency in understanding and responding to human emotions, adapting to varied conversational contexts, and learning from each interaction positions it as a pioneering tool in advancing empathetic and intelligent Al.

[0159] The framework's potential in psychotherapy and mental health underscores its importance in societal well-being. It not only supports and augments human capabilities in critical areas but also stands as a testament to the positive impact of technological advancement.

[0160] In summary, this Orchestrated Conversational Framework culminates from cutting-edge Al research, user-centric design, and ethical consideration. It is poised not just to enhance the efficiency and effectiveness of various services and applications but also to forge deeper, more meaningful connections between humans and the digital world. This framework is a significant leap towards empathetic, intelligent, and user-focused Al systems, with the potential to profoundly enhance human interactions with digital systems, making these exchanges more natural, intuitive, and effective.

[0161] The problem addressed by the invention pertains to the limitations in current conversational Al systems. Specifically, these limitations include:1. Eack of ability to carry on a goal-directed conversation. Traditional Al approaches, including applications of EEM's, focus on better natural language processing ofindividual utterances. They do not give an agent the responsibility and agency for solving a bigger problem, in the way that the LLM sees fit. Lack of model flexibility. Traditional systems allow a single model to be selected for a complete interaction. They do not allow specific models to be used for specific atomic elements of an interaction, or to use a team of models that possess the optimal skills required for the interaction. Lack of Deep Contextual Understanding: Traditional conversational Al systems often struggle to fully grasp and retain the context of extended dialogues, especially in complex or evolving conversation scenarios. This leads to interactions that can feel disjointed or irrelevant. Limited Emotional Intelligence: Many existing systems have a limited ability to recognize and appropriately respond to emotional cues in human communication. This is particularly problematic in applications requiring empathy and understanding, such as psychotherapy or customer service. Rigid and Inflexible Conversation Flows: Conventional Al conversational systems are often restricted by rigid dialogue structures, lacking the flexibility to dynamically adapt conversations based on real-time user inputs or changes in the conversation's direction. Inadequate Personalization: There is a gap in the ability of current systems to personalize interactions based on individual user preferences, histories, and interaction styles, leading to a one-size-fits-all approach in conversations. Inadequate Cognitive Alignment: There is a gap in the ability of current systems to personalize interactions based on the user's cognitive abilities, emotional intelligence, personality traits, values and beliefs, mental health indicators, and social skills, leading to misalignment of the Al agent's behavior with the user's thought processes. Privacy and Data Security Concerns: With increasing awareness and concern about digital privacy and data security, existing conversational Al systems often fall short in providing robust and transparent data handling and privacy assurance. Inability to transform conversation to actionable data. Traditional approaches use simple entity extraction from catalogued utterances to fill data slots, with no ability to interpret and analyze conversation to infer the needed data.10. Integration Challenges Across Platforms: Many conversational Al systems face challenges in seamlessly integrating and maintaining consistent functionality across various communication platforms (like mobile apps, web interfaces, and voice systems).

[0162] These limitations collectively result in conversational Al experiences that can be unsatisfactory for users, especially in scenarios requiring nuanced understanding, empathy, personalization, and privacy assurance.

[0163] While there are existing inventions in the field of conversational Al that attempt to address similar problems, they often fall short in fully resolving the issues due to several limitations. These limitations highlight how the Orchestrated Conversational Framework offers significant advantages and improvements over existing technologies.

[0164] Limitations of Existing Conversational Al Systems:• Single-Model Limitations: Many systems rely on a single Al model, leading to gaps in understanding complex contexts and handling nuanced emotional cues.• Lack the specialized capabilities needed for diverse conversational requirements, affecting personalized responses.• Surface-Level Emotional Intelligence: Existing systems may incorporate basic emotional intelligence but struggle with deeper emotional understanding and empathetic responses, crucial in applications like psychotherapy.• Rigid Dialogue Structures: Predefined dialogue paths and structures limit flexibility, leading to scripted or irrelevant responses during unexpected conversational turns.• Generic Personalization: A generalized approach to personalization, relying on broad user data, fails to account for individual user nuances, diminishing the personalization quality.• Data Privacy and Security Concerns: Some systems lack robust privacy and security measures, especially crucial in handling sensitive personal information in healthcare and other sensitive domains.• Limited Cross-Platform Integration: Challenges in seamless operation across multiple communication platforms can result in fragmented user experiences.• Lack of Continuous Learning and Adaptation: Many systems do not continuously learn and adapt from each specific interaction, hindering long-term effectiveness and personalization.

[0165] Advantages of the Orchestrated Conversational Framework Over Existing Technologies:• Enhanced Conversational Intelligence: Orchestration of multiple computational models leads to more natural contextually rich, and emotionally intelligent conversations.• Advanced Personalization and Adaptability: Uses reinforcement learning for dynamic personalization and flexible response strategies, capable of handling a wide variety of conversational scenarios.• Robust Data Management and Privacy: Superior data security and ethical data usage, fostering user trust and regulatory compliance.• User-Centric Design and Accessibility: Inclusive, accessible interface design, and intuitive interaction, which are often lacking in current technologies.• Versatility and Scalability: Demonstrates capability to be customized and scaled across different industries, seamlessly integrating with various platforms.• Continuous Improvement and Innovation: A self-improving system that remains effective and up-to-date, designed with provisions for integrating emerging technologies.

[0166] The Orchestrated Conversational Framework addresses the aforementioned problems in existing conversational Al systems through several innovative approaches and technologies, making it a more effective solution compared to existing systems:1. Multi-Model Orchestration:• Unlike single-model systems, this framework integrates multiple computational models, each specialized in different aspects of conversational intelligence such as natural language understanding, emotional intelligence, and context retention.• The system allows for dynamic selection of an optimal team of models that possess the individual skills required for an element of an interaction.• This orchestration allows for a more nuanced understanding and handling of conversations, as each aspect of the interaction is managed by the most suitable LLM, leading to more coherent, contextually relevant, and emotionally resonant responses. anced Emotional Intelligence:• The system includes computational models specifically trained to recognize and interpret a wide range of emotional cues and subtleties, allowing for empathetic and emotionally appropriate responses.• This capability is especially crucial in fields like psychotherapy, where understanding and responding to emotional states are key. amic and Flexible Conversation Flows.• The proprietary scripting language enables the creation of modular conversation components that can be dynamically arranged and adapted in real-time based on the user's responses and conversation flow.• This flexibility ensures that conversations can deviate from predefined paths as needed, allowing for more natural and user-responsive interactions.onalized User Experience:• The framework uses reinforcement learning algorithms to continually adapt and personalize its interactions based on individual user feedback and interaction patterns.• Over time, this leads to increasingly personalized conversations that better align with individual user preferences and styles. ust Data Privacy and Security:• The invention prioritizes data privacy and security, employing advanced encryption methods and ethical data handling practices to ensure user data protection.• This approach addresses growing concerns about digital privacy, especially in handling sensitive conversations in areas such as healthcare or mental wellness.mless Multi-Platform Integration:• The system is designed to integrate seamlessly across various communication channels while maintaining a consistent and engaging user experience.• This cross-platform capability addresses the fragmentation issue in current systems, ensuring smooth transitions and coherent interactions across different user interfaces.7. Continuous Learning and Improvement:• The framework's use of reinforcement learning allows it to continuously learn and improve from each interaction.• Unlike systems with static learning mechanisms, this framework evolves and becomes more efficient and user-aligned over time, based on specific user interactions and feedback.

[0167] In summary, the Orchestrated Conversational Framework presents a comprehensive solution that addresses the limitations of current conversational Al systems by offering deeper contextual understanding, advanced emotional intelligence, dynamic conversation flows, personalized interactions, strict data privacy, seamless multi-platform operation, and continuous adaptation and learning. This holistic approach makes it a superior choice for a wide range of applications where nuanced and intelligent conversational capabilities are essential.

[0168] Here is a list outlining the individual components (elements) that make up best version of the Orchestrated Conversational Framework:1. Multi-Model: A suite of specialized models, each tailored for different aspects of conversational processing such as natural language understanding, emotional intelligence, and context retention.2. Dynamic model selection: Just-in-time selection of the set of optimal models for an interaction element, based on the requirements of the interaction element.3. Orchestration Engine: The central processing unit that dynamically coordinates and assigns tasks to the appropriate computational models based on the context and objectives of the conversation.4. Intent Recognition Function. Analyses context and user speech / actions to determine when objectives have been attained. Monitoring and Alignment Function. Multiple, fit- for-purpose "monitor" models ensure an "actor" model ' s alignment with objectives and standards.Proprietary Scripting Language: A specially designed language for creating and managing modular conversational flows, enabling dynamic and flexible conversation scripting. Modular Conversation Components: Modular units of conversation, crafted using the proprietary scripting language, that can be arranged and adapted dynamically for various conversational scenarios. Communication Channel Integrators: Interface adapters and integrators that enable the framework to connect and operate seamlessly across various communication platforms, such as web browsers, mobile apps, and voice interfaces. Data Management and Encryption System: A system for managing, storing, and encrypting user data, ensuring robust data privacy and security. User Interface (UI): An adaptive and user-friendly interface that facilitates easy interaction with the system and is designed for accessibility and inclusivity. Reinforcement Learning Mechanism: A set of algorithms that enable the system to learn and adapt from user interactions, enhancing personalization and effectiveness over time. Analytics Engine: A component for analyzing conversational data to extract insights, gauge user satisfaction, and identify areas for improvement. Feedback Collection and Processing Module: A module designed to collect and process user feedback, feeding into the reinforcement learning mechanism for system improvement. Sentiment Analysis Module: A specialized module for detecting and interpreting emotional cues and sentiments expressed in conversations. Multi-Platform Synchronization System: A system to ensure real-time data synchronization and consistent user experience across different communication platforms. Crisis Management Protocols: Specific protocols designed to handle situations of heightened emotional distress or risk, particularly relevant in psychotherapy application. Privacy and Compliance Framework: A set of policies and protocols ensuring that the system adheres to data privacy laws and ethical standards, particularly in handling sensitive personal information.17. Customization and Personalization Settings: Features that allow users to customize and personalize their interaction experience with the system.

[0169] These components collectively form the best version of the Orchestrated Conversational Framework, providing a sophisticated, versatile, and user-centric solution in conversational Al and non- Al technology.

[0170] The interaction among the components of the Orchestrated Conversational Framework is designed to create a seamless, intelligent, and adaptive conversational experience. Here's the way of the components interaction:1. Interaction Initiation via Communication Channels:• Users initiate interactions through various communication channels integrated with the framework, such as web browsers, mobile apps, or voice interfaces.• The Communication Channel Integrators facilitate this interaction, ensuring that user inputs are accurately captured and conveyed to the system.2. Input Processing by Computational Models:• The incoming user inputs are first processed by the relevant selected computational models. For instance, a natural language understanding LLM interprets the query, while another specialized in sentiment analysis gauges the emotional tone.3. Dynamic Orchestration of Responses:• The Orchestration Engine, upon receiving processed input, dynamically determines which computational model or combination of computational models is best suited to handle the response, considering the context and user's emotional state.• It coordinates the computational models, ensuring that each part of the user's query is addressed by the most appropriate model.4. Utilization of the Scripting Language and Modular Components:• The response generation is guided by scripts created with the Proprietary Scripting Language. These scripts define the structure and flow of the conversation using Modular Conversation Components.• Depending on the user's input and conversation context, different modules are selected and sequenced to formulate a coherent and contextually appropriate response. a Management and Encryption:• Throughout the interaction, the Data Management and Encryption System securely handles any user data involved, maintaining privacy and security.• This includes encrypting sensitive information and ensuring compliance with privacy standards. r Interface Interaction:• The User Interface presents the generated response to the user in an accessible, user-friendly format.• It allows for user inputs to be continuously fed back into the system maintaining an interactive dialogue. back Collection and Reinforcement Learning:• Post-interaction, or at certain intervals, the Feedback Collection and Processing Module gathers user feedback.• This feedback, along with data from the Analytics Engine, feeds into the Reinforcement Learning Mechanism. This mechanism analyzes the information to adapt and improve future interactions, enhancing personalization and effectiveness. timent Analysis and Crisis Management:• In parallel, the Sentiment Analysis Module continuously evaluates the emotional content of the conversation, providing insights that guide response generation.• In scenarios like psychotherapy, if heightened emotional distress or risk is detected, Crisis Management Protocols are activated to provide appropriate support or escalation. ti-Platform Synchronization:• The Multi-Platform Synchronization System ensures that conversations remain coherent and consistent across different platforms, allowing users to switch between devices or modes without losing context.

[0171] In summary, the components of the Orchestrated Conversational Framework interact in a coordinated and intelligent manner, ensuring that each conversation is handled with contextawareness, emotional intelligence, and user-centricity. This orchestration of technology and processes allows the system to deliver advanced and personalized conversational experiences.

[0172] Each component of the Orchestrated Conversational Framework serves a distinct function, and their collaboration is essential for the system to perform its desired function effectively. Here's how they work individually and together:1. Computational Models.• Individually: Each computational model specializes in a specific aspect of conversation, such as understanding natural language, analyzing emotional cues, or maintaining context.• Together: In unison, these computational models provide a comprehensive understanding and response capability, covering the various nuances of human conversation.2. Orchestration Engine:• Individually: The engine acts as the central coordinator, analyzing conversation requirements and deciding which computational models to engage at different conversation stages.• Together with computational models: It ensures that the right computational model is used at the right time, facilitating a seamless and effective conversation flow.3. Proprietary Scripting Language and Modular Conversation Components.• Individually: The scripting language allows for the creation of diverse and dynamic conversational modules that can be easily modified or rearranged.• Together with computational models: The language and modules guide the computational models in structuring responses, ensuring that conversations are coherent and aligned with the user's intent.4. Communication Channel Integrators:Individually: These integrators enable the system to function across different platforms, like web browsers or mobile apps, capturing user inputs accurately.• Together with UI: They ensure that the user experience is consistent and seamless, regardless of the communication channel used. a Management and Encryption System:• Individually: This system manages and secures user data, ensuring privacy and compliance with data protection regulations.• Together with the entire system: It underpins the framework's operations, ensuring that all interactions are conducted with respect for user privacy. r Interface (UI):• Individually: The UI presents information to the user in an accessible engaging, and clear manner.• Together with other components: It facilitates user interaction with the system, collecting inputs and displaying responses, forming the front-end of the conversational experience. forcement Learning Mechanism:• Individually: This mechanism allows the system to learn from interactions and evolve, enhancing its conversational abilities over time.• Together with feedback and analytics. It uses user feedback and conversational analytics to continuously improve the system's performance and user experience.timent Analysis Module and Crisis Management Protocols:• Individually: The sentiment analysis module interprets emotional cues, while the crisis protocols provide specific responses in sensitive situations.• Together: They ensure that the system responds empathetically and appropriately, especially in emotionally charged or critical situations like psychotherapy sessions. ti-Platform Synchronization System:• Individually: This system maintains coherence and continuity of conversations across different platforms.• Together with the entire framework: It ensures a unified user experience, allowing users to switch between devices or platforms without losing the context of the conversation.

[0173] Together these components enable the Orchestrated Conversational Framework to function as a cohesive system. They ensure that the framework can handle complex nuanced, and personalized conversations, adapting to the user's needs and preferences, while maintaining privacy, security, and cross-platform consistency.

[0174] Creating the Orchestrated Conversational Framework involves a series of detailed steps that integrate various technologies and components. Here's a breakdown of the process:

[0175] Step 1: Designing the System Architecture:• Define the Core Objectives: Determine the primary goals of the conversational Al system, such as enhancing user engagement, providing emotional intelligence, or specific applications like customer service or psychotherapy.• Architectural Blueprint: Develop a detailed architectural blueprint of the system, outlining how the different components will interact. This includes the integration of multiple computational models, the orchestration engine, data management systems, and user interfaces.

[0176] Step 2: Selection and Integration of Computational Models:• Choose Suitable Computational Models: Select a range of computational models, each with specialized capabilities in areas like natural language processing, emotional analysis, and contextual understanding.• Custom Model Development: If necessary, develop custom models tailored to specific needs of the system such as domain-specific language comprehension.• Integration Process: Integrate these models into the system, ensuring they can communicate and operate cohesively under the orchestration engine.

[0177] Step 3: Developing the Proprietary Scripting Language and Conversation Modules:• Design the Scripting Language: Create a proprietary scripting language that allows for the flexible and dynamic construction of conversation modules.• Develop Modular Components: Using this language, develop a library of modular conversational components that can be assembled into various conversation flows.

[0178] Step 4: Building the Orchestration Engine:• Engine Development: Develop the orchestration engine capable of dynamically assigning conversation segments to appropriate models based on the context and user input.• Algorithm Optimization: Optimize algorithms within the engine for efficient decision-making and seamless model switching.

[0179] Step 5: Integration with Communication Channels:• Identify Key Platforms: Identify the main communication channels for integration, such as web interfaces, mobile apps, voice systems, etc.• Develop Channel Integrators: Create integrators or APIs that allow the system to connect with and operate seamlessly across these platforms.

[0180] Step 6: Implementing Data Management and Security:• Data Handling Protocols: Establish robust data handling protocols, including data collection, storage, and encryption methods.• Compliance and Security Measures: Implement security measures and ensure compliance with data protection regulations like HIPAA, GDPR and PIPEDA.

[0181] Step 7: Designing the User Interface:• UI / UX Design: Design user interfaces that are intuitive, accessible, and adaptable to different user needs and platforms.• Testing and Iteration: Test the UI for usability, making iterative improvements based on user feedback.

[0182] Step 8: Incorporating Learning and Adaptation Mechanisms:• Implement Learning Algorithms: Integrate reinforcement learning algorithms to enable the system to learn from interactions and improve over time.• Feedback Loop Setup: Establish mechanisms for collecting and processing user feedback to inform system adaptations.

[0183] Step 9: Testing and Refinement:• System Testing: Conduct extensive testing of the system, including stress tests, performance evaluations, and user acceptance testing.• Iterative Refinement: Refine the system based on test results, user feedback and performance data.

[0184] Step 10: Deployment and Ongoing Maintenance:• Deployment: Deploy the system in the chosen application environment, ensuring all components are functioning as intended.• Maintenance and Updates: Establish a schedule for regular maintenance, updates, and continuous improvement based on user interaction and technological advancements.

[0185] This process outlines the creation of the Orchestrated Conversational Framework, detailing the technical and developmental steps required to build a sophisticated, multifaceted conversational Al system. Each step involves careful planning, development, integration, and testing to ensure the system achieves its intended functionality and user experience goals.

[0186] This disclosed system is designed to provide users with intelligent, dynamic, and empathetic conversational experiences. The following guide will help a user to navigate and utilize the system effectively:

[0187] Step 1: System Setup:• Installation: Follow the installation guide to set up the software on your desired platform(s), such as a web interface, mobile app, or other integrated communication channels.• Model Recruiting: Find, evaluate, benchmark and select additional conversational models required for a specific use case, building on the roster of models already available on the platform.• Configuration: Use the configuration tool to customize the initial settings, including language preferences, user interface options, and initial conversation modules.

[0188] Step 2: Understanding the Interface:• Dashboard Overview: Familiarize yourself with the main dashboard, which provides access to various features such as conversation history, settings, and analytics.• Navigation: Learn to navigate through different sections like module library, user feedback, and system reports.

[0189] Using the Framework:

[0190] Step 3: Conversation Module Management:• Creating Conversations: Utilize the proprietary scripting language to create or modify conversational modules. These modules can range from greetings and information inquiries to complex therapeutic dialogues.• Arranging Modules: Assemble and sequence these modules to form complete conversation flows suitable for your specific application.

[0191] Step 4: Engaging in Conversations:• Initiating Dialogues: Start conversations through your chosen platform. The system will handle user inputs using its orchestrated computational models, providing relevant and context-aware responses.• Interactive Experience: Engage in dynamic interactions. The system adapts its responses based on the ongoing conversation, ensuring a fluid and natural experience.

[0192] Step 5: Monitoring and Feedback:• User Feedback: Regularly provide feedback on the system's performance through the provided feedback tools. This can include ratings, comments, or suggestions for improvement.• Analytics Review: Utilize the analytics section to review conversation metrics, user satisfaction scores, and other relevant data.

[0193] Advanced Features:

[0194] Step 6: Personalization and Learning:• Customization: Explore personalization settings to tailor the system's conversational style, response strategies, and interface layout according to your preferences.• Learning from Interactions: The system continuously learns from each interaction. Observe how it adapts and improves its responses and suggestions over time.

[0195] Step 7 : Data Security and Privacy:• Data Management: Understand the data management protocols, including how user data is stored, encrypted, and used.• Privacy Settings: Review and adjust privacy settings to ensure your data handling preferences are accurately reflected.

[0196] Maintenance and Support

[0197] Step 8: System Maintenance:• Regular Updates: Keep the system up-to-date with regular updates and patches for enhanced features and improved security.• Maintenance Schedules: Follow the recommended maintenance schedules to ensure optimal performance.

[0198] Step 9: Getting Support:• Accessing Help: Utilize the help and support section for troubleshooting, FAQs, and contact information for technical support.• Community and Resources: Engage with the user community and access additional resources for tips, best practices, and advanced usage scenarios.

[0199] FIG. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform 100 may be hosted on a centralized server 102, such as, for example, a cloud computing service. The centralized server 102 may communicate with other network entities, such as, for example, a mobile device 106 (such as a smartphone, a laptop, a tablet computer etc.), other electronic devices 110 (such as desktop computers, server computers etc.), databases 114, and sensors 116 over a communication network 104, such as, but not limited to, the Internet. Further, users of the onlineplatform 100 may include relevant parties such as, but not limited to, end-users, administrators, service providers, service consumers and so on. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the platform.

[0200] A user 112, such as the one or more relevant parties, may access online platform 100 through a web based software application or browser. The web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 200.

[0201] With reference to FIG. 2, a system consistent with an embodiment of the disclosure may include a computing device or cloud service, such as computing device 200. In a basic configuration, computing device 200 may include at least one processing unit 202 and a system memory 204. Depending on the configuration and type of computing device, system memory 204 may comprise, but is not limited to, volatile (e.g. random-access memory (RAM)), nonvolatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 204 may include operating system 205, one or more programming modules 206, and may include a program data 207. Operating system 205, for example, may be suitable for controlling computing device 200’ s operation. In one embodiment, programming modules 206 may include image -processing module, machine learning module. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 2 by those components within a dashed line 208.

[0202] Computing device 200 may have additional features or functionality. For example, computing device 200 may also include additional data storage devices (removable and / or nonremovable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 2 by a removable storage 209 and a non-removable storage 210. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules, or other data. System memory 204, removable storage 209, and non-removable storage 210 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 200. Any such computer storage media may be part of device 200. Computing device 200 may also have input device(s) 212 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, a location sensor, a camera, a biometric sensor, etc. Output device(s) 214 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.

[0203] Computing device 200 may also contain a communication connection 216 that may allow device 200 to communicate with other computing devices 218, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 216 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.

[0204] As stated above, a number of program modules and data files may be stored in system memory 204, including operating system 205. While executing on processing unit 202, programming modules 206 (e.g., application 220 such as a media player) may perform processes including, for example, one or more stages of methods, algorithms, systems, applications, servers, databases as described above. The aforementioned process is an example, and processing unit 202 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include machine learning applications.

[0205] Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may performparticular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, general purpose graphics processor-based systems, multiprocessor systems, microprocessor-based or programmable consumer electronics, application specific integrated circuit-based electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0206] Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general-purpose computer or in any other circuits or systems.

[0207] Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and / or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0208] The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random-access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

[0209] Embodiments of the present disclosure, for example, are described above with reference to block diagrams and / or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions / acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality / acts involved.

[0210] While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods’ stages may be modified in any manner, including by reordering stages and / or inserting or deleting stages, without departing from the disclosure.

[0211] Fig. 3 A and Fig. 3B illustrate a flowchart of a method 300 of provisioning a conversation based on a context, in accordance with some embodiments.

[0212] Accordingly, the method 300 may include a step 302 of receiving, using a communication device 702, a user request data from a user device. Further, the user device may be associated with a user. Further, the method 300 may include a step 304 of analyzing, using a processing device 704, the user request data based on a first Al model. Further, the method 300 may include a step 306 of identifying, using the processing device 704, a context data based on the analyzing. Further, the context data corresponds to a context of the user request data. Further, the method 300 may include a step 308 of identifying, using the processing device 704, a second Al model from two or more Al models based on the context data. Further, the two or more Al models includes the second Al model. Further, the identifying of the second Al model may be further based on one or more of a functional characteristic and a non-functional characteristic of the two or more Al models. Further, the method 300 may include a step 310 of generating, using the processing device 704, a response data based on each of the user request data and the second Al model. Further, the method 300 may include a step 312 of transmitting, using the communication device 702, the response data to the user device.

[0213] In some embodiments, the method 300 may further include storing, using the processing device 704, each of the user request data and the response data in a database. Further, the database corresponds to the user.

[0214] In some embodiments, the database includes each of two or more user request data and two or more response data. Further, the two or more user request data includes a first user request data associated with a first time instance and a second user request data associated with a second time instance. Further, the second time instance occurs later the first time instance. Further, the two or more response data includes a first response data corresponding to the first user request data and a second response data corresponding to the second user request data. Further, the generating of the second response data may be based on each of the first user request data and the first response data.

[0215] Fig. 4 illustrates a flowchart of a method 400 of provisioning a conversation based on a context including generating, using the processing device 704, a reinforcement learning data, in accordance with some embodiments.

[0216] Further, in some embodiments, the method 400 further may include a step 402 of receiving, using the communication device 702, a user feedback data from the user device. Further, the user feedback data corresponds to a feedback based on the response data. Further, in some embodiments, the method 400 further may include a step 404 of generating, using the processing device 704, a reinforcement learning data based on each of the user feedback data, the user request data and the response data. Further, the reinforcement learning data represents a relevance of the response data in relation to the user request data. Further, the reinforcement learning data may be used for performing a training of the second Al model. Further, the generating of the response data may be further based on the training.

[0217] Fig. 5 illustrates a flowchart of a method 500 of provisioning a conversation based on a context including generating, using the processing device 704, an insight data, in accordance with some embodiments.

[0218] Further, in some embodiments, the method 500 further may include a step 502 of analyzing, using the processing device 704, each of the two or more user request data and the two or more response data. Further, in some embodiments, the method 500 further may include a step 504 of generating, using the processing device 704, an insight data based on the analyzing of each of the two or more user request data and the two or more response data. Further, the insight data corresponds to an insight. Further, in some embodiments, the method 500 further may include a step 506 of transmitting, using the communication device 702, the insight data to a second user device. Further, the second user device may be associated with a second user.

[0219] In some embodiments, the context may be based on a domain. Further, the domain corresponds to a psychotherapy, further includes generating, using the processing device 704, a disorder level data based on the analyzing of the user request data. Further, the disorder level data corresponds to one or more of two or more disorder levels. Further, the two or more disorder levels represent a level of a metal disorder, may. Further, the user may be associatedwith the metal disorder. Further, the generating of the response data may be further based on the disorder level data.

[0220] Fig. 6 illustrates a flowchart of a method 600 of provisioning a conversation based on a context including generating, using the processing device 704, an alert data, in accordance with some embodiments.

[0221] Further, in some embodiments, the two or more disorder levels may include a first disorder level representing a first value and a second disorder level representing a second value. Further, the second value may be greater than the first value. Further, the second disorder level corresponds to an extreme mental disorder associated with the user, further may include a step 602 of generating, using the processing device 704, an alert data based on the disorder level data. Further, the alert data corresponds to an alert. Further, the disorder level data corresponds to the second disorder level. Further, the two or more disorder levels may include a first disorder level representing a first value and a second disorder level representing a second value. Further, the second disorder level corresponds to an extreme mental disorder associated with the user, further may include a step 604 of transmitting, using the communication device 702, the alert data to a second user device associated with a second user. Further, the second user corresponds to a therapist.

[0222] In some embodiments, the method 300 may further include analyzing, using the processing device 704, each of the two or more user request data and the two or more response data. Further, the generating of the response data may be based on the analyzing of each of the two or more user request data and the two or more response data.

[0223] In some embodiments, the analyzing of each of the two or more user request data and the two or more response data includes determining a user ability. Further, the user ability corresponds to one or more of a user language processing ability, a social cognitive skill, an emotional intelligence, a problem solving skill, an emotional trigger and a user adaptability.

[0224] In some embodiments, the method 300 may further include retrieving, using the processing device 704, a contextual data from an external database based on the analyzing of the user request data. Further, the generating of the response data may be further based on the contextual data. Further, the contextual data corresponds an information relevant to user requestdata. Further, the external database includes two or more contextual data corresponding to a domain. Further, the user request data may be associated with the domain.

[0225] Fig. 7 illustrates a block diagram of a system 700 of provisioning a conversation based on a context, in accordance with some embodiments.

[0226] Accordingly, the system 700 may include a communication device 702. Further, the communication device 702 may be configured for receiving a user request data from a user device. Further, the user device may be associated with a user. Further, the communication device 702 may be configured for transmitting a response data to the user device. Further, the system 700 may include a processing device 704. Further, the processing device 704 may be configured for analyzing the user request data based on a first Al model. Further, the processing device 704 may be configured for identifying a context data based on the analyzing. Further, the context data corresponds to a context of the user request data. Further, the processing device 704 may be configured for identifying a second Al model from two or more Al models based on the context data. Further, the two or more Al models includes the second Al model. Further, the identifying of the second Al model may be further based on one or more of a functional characteristic and a non-functional characteristic of the two or more Al models. Further, the processing device 704 may be configured for generating the response data based on each of the user request data and the second Al model.

[0227] In some embodiments, the processing device 704 may be further configured for storing each of the user request data and the response data in a database. Further, the database corresponds to the user.

[0228] In some embodiments, the database includes each of two or more user request data and two or more response data. Further, the two or more user request data includes a first user request data associated with a first time instance and a second user request data associated with a second time instance. Further, the second time instance occurs later the first time instance. Further, the two or more response data includes a first response data corresponding to the first user request data and a second response data corresponding to the second user request data. Further, the generating of the second response data may be based on each of the first user request data and the first response data.

[0229] In some embodiments, the communication device 702 may be further configured for receiving a user feedback data from the user device. Further, the user feedback data corresponds to a feedback based on the response data. Further, the processing device 704 may be further configured for generating a reinforcement learning data based on each of the user feedback data, the user request data and the response data. Further, the reinforcement learning data represents a relevance of the response data in relation to the user request data. Further, the reinforcement learning data may be used for performing a training of the second Al model. Further, the generating of the response data may be further based on the training.

[0230] Further, in some embodiments, the processing device 704 may be further configured for analyzing each of the two or more user request data and the two or more response data. Further, the processing device 704 may be further configured for generating an insight data based on the analyzing of each of the two or more user request data and the two or more response data. Further, the insight data corresponds to an insight. Further, the communication device 702 may be further configured for transmitting the user insight data to a second user device. Further, the second user device may be associated with a second user.

[0231] In some embodiments, the context may be based on a domain. Further, the domain corresponds to a psychotherapy. Further, the processing device 704 may be further configured for generating a disorder level data based on the analyzing of the user request data. Further, the disorder level data corresponds to one or more of two or more disorder levels. Further, the two or more disorder levels represent a level of a metal disorder, may. Further, the user may be associated with the metal disorder, may. Further, the generating of the response data may be further based on the disorder level data.

[0232] In some embodiments, the two or more disorder levels includes a first disorder level representing a first value and a second disorder level representing a second value. Further, the second value may be greater than the first value. Further, the second disorder level corresponds to an extreme mental disorder associated with the user. Further, the processing device 704 may be further configured for generating an alert data based on the disorder level data. Further, the alert data corresponds to an alert. Further, the disorder level data corresponds to the second disorder level. Further, the communication device 702 may be further configured for transmittingthe alert data to a second user device associated with a second user. Further, the second user corresponds to a therapist.

[0233] In some embodiments, the processing device 704 may be further configured for analyzing each of the two or more user request data and the two or more response data. Further, the generating of the response data may be based on the analyzing of each of the two or more user request data and the two or more response data.

[0234] In some embodiments, the analyzing of each of the two or more user request data and the two or more response data includes determining a user ability. Further, the user ability corresponds to one or more of a user language processing ability, a social cognitive skill, an emotional intelligence, a problem solving skill, an emotional trigger and a user adaptability.

[0235] In some embodiments, the processing device 704 may be further configured for retrieving a contextual data from an external database based on the analyzing of the user request data. Further, the generating of the response data may be further based on the contextual data. Further, the contextual data corresponds an information relevant to user request data. Further, the external database includes two or more contextual data corresponding to a domain. Further, the user request data may be associated with the domain.

[0236] In some embodiments, the processing device 704 may be configured for identifying a set of Al models from the two or more Al models based on the context data.

[0237] In some embodiments, the identifying of the context data is further based on a two or more of user request data.

[0238] In some embodiments, the processing device 704 may be configured for identifying a set of Al models from the two or more Al models based on a two or more of context data.

[0239] In some embodiments, the user request data includes one or more of a query and a request.

[0240] In some embodiments, the user request data includes an audio data.

[0241] In some embodiments, the user request data includes a text data.

[0242] In some embodiments, the response data includes a text data.

[0243] In some embodiments, the response data includes an audio data.

[0244] In some embodiments, the context may be based on a domain.

[0245] In some embodiments, the domain corresponds to one or more of a psychotherapy, a customer service, a medicine and an educational.

[0246] In some embodiments, the context corresponds to one or more of a user literacy level and a tone. Further, the user request data is characterized by the tone.

[0247] In some embodiments, the context corresponds to a cognitive skill fo the user and a language skill of the user.

[0248] In some embodiments, the domain corresponds to a psychotherapy. Further, the patient corresponds to an attention deficit hyperactivity disorder patient.

[0249] In some embodiments, the two or more Al models comprises a third Al model. Further, one or more of the functional characteristic and the non-functional characteristic of the second Al model is different from the one or more of the functional characteristic and the non-functional characteristic of the third Al model.

[0250] In some embodiments, the context data corresponds to a requirement of the conversation. Further, each of the user request data and the response data is a part of the conversation.

[0251] In some embodiments, the non-functional characteristic of the two or more Al models corresponds to one or more of a reliability, a certifiability, a size, a footprint, a cost, a scalability, a speed, a response time, a service level and a performance.

[0252] In some embodiments, the functional characteristic of the two or more Al models corresponds to one or more of a language understanding, a data analysis, a conversation routing, a context retention, a data management, an API management, an emotional intelligence, a user simulation, a code generation, a reasoning ability, an emotional intelligence and a domainspecific knowledge.

[0253] In some embodiments, the non-functional characteristic of the two or more Al models corresponds to one or more of a historical reliability and a limit of data processed by the two or more Al models.

[0254] In some embodiments, the context corresponds to a psychotherapy. Further, the generating of the response data may be further based on two or more therapeutic techniques.

[0255] In some embodiments, the two or more therapeutic techniques corresponds to one or more of a reflective listening, a Socratic questioning and a motivational interviewing.

[0256] In some embodiments, the response data is characterized by a two or more response characteristics. Further, the two or more response characteristics corresponds to one or more of a humanistic response, an empathetic response and a therapeutic response.

[0257] In some embodiments, the database further includes a user personal data corresponding to the user. Further, the generating of the response data may be further based on the user personal data.

[0258] In some embodiments, the user personal data corresponds to a personal detail of the user. Further, the personal detail corresponds to one or more of a name of the user, an age of the user and a gender of the user.

[0259] In some embodiments, the conversation corresponds to each of two or more user request data and a plurality response data occurs over a time interval. Further, the conversation may be associated with two or more conversation parts. Further, the conversation may be further based on a sequence of two or more conversation parts. Further, the two or more response data may be associated with the two or more conversation parts. Further, the generating of the response data may be based on the sequence. Further, the training of the second Al model corresponds to a modification of the sequence.

[0260] In some embodiments, the two or more conversation parts corresponds at least one a conversation topic introduction, a user detail gathering and a dialogue conclusion.

[0261] In some embodiments, the two or more conversation parts may be based on the two or more Al models.

[0262] In some embodiments, the two or more conversation parts may be based on a one or more of the two or more Al models.

[0263] In some embodiments, the two or more response data includes a first response data corresponding to a first conversation part and a second response data corresponding to a second conversation part. Further, the two or more conversation parts includes each of the first conversation part and the second conversation part. Further, the first response data and the second response data may be based on the second Al model and a third Al model respectively. Further, the two or more Al models includes the third Al model.

[0264] In some embodiments, the second user corresponds to a therapist. Further, the user corresponds to a patient.

[0265] In some embodiments, the insight corresponds to one or more of a progress and a regress of a mental disorder, may. Further, the user may be associated with the mental disorder, may. Further, the insight corresponds a therapeutic technique. Further, one or more of the two or more response data may be based on the therapeutic technique.

[0266] In some embodiments, the response data corresponds to an emergency contact detail. Further, the emergency contact detail corresponds to a therapist.

[0267] In some embodiments, the response data corresponds to an immediate coping strategies. Further, the immediate coping strategies corresponds to a set of instructions representing a set of actions. Further, the set of actions reduces the level of the metal disorder.

[0268] In some embodiments, the two or more Al models includes a large language processing model.

[0269] In some embodiments, the two or more Al models may be based on a machine learning algorithm.

[0270] In some embodiments, the two or more Al models includes a deterministic model.

[0271] In some embodiments, the two or more Al models includes a non-deterministic model.

[0272] In some embodiments, the deterministic model may be based on a set of rules. Further, the deterministic model may be associated with consistency in two or more response data.

[0273] In some embodiments, the non-deterministic model corresponds to a probabilistic model. Further, the probabilistic model may be associated with randomness in two or more response data.

[0274] In some embodiments, the conversation may be complied with a healthcare regulation.

[0275] In some embodiments, the healthcare regulation corresponds to a Health Insurance Portability and Accountability Act.

[0276] In some embodiments, the healthcare regulation corresponds to a General Data Protection Regulation Act.

[0277] In some embodiments, the healthcare regulation corresponds to a Personal Information Protection and Electronic Documents Act.

[0278] In some embodiments, the method 300 may further include generating, using the processing device 704, each of an encrypted user request data and an encrypted response data based on an encryption protocol. Further, the database includes each of the encrypted user request data and the encrypted response data.

[0279] In some embodiments, the user feedback data includes one or more of a text data and an audio data.

[0280] In some embodiments, the user feedback data corresponds to a rating. Further, the rating represents a level of user satisfaction on the response data.

[0281] In some embodiments, the user feedback data corresponds to one or more of a suggestion and a critique.

[0282] In some embodiments, the method 300 may further include receiving, using the communication data, a user preference data from the user device. Further, the user preference data corresponds to a preference of the user. Further, the generating of the response data may be further based on the user preference data.

[0283] In some embodiments, the user preference data corresponds to a type of conversation.

[0284] In some embodiments, the preference corresponds to a performance level of an Al model associated with the conversation.

[0285] In some embodiments, the response data corresponds to a critical analytical response. Further, the second Al model is characterized by a longer latency, a high skill and a high cost.

[0286] In some embodiments, the critical analytical response corresponds to a deep insight.

[0287] In some embodiments, the response data includes a Graphical User Interface data.Further, the Graphical User Interface data corresponds to a Graphical User Interface. Further, the user device may be associated with a presentation device. Further, the presentation device may be configured to present the Graphical User Interface. Further, the preference corresponds to two or more elements associated with the Graphical User Interface.

[0288] In some embodiments, the two or more elements corresponds to one or more of a theme, a font size and a layout of the Graphical User Interface.

[0289] In some embodiments, the generating of the response data may be further based on the user ability.

[0290] In some embodiments, the two or more Al models may be based on a scripting language. Further, the scripting language corresponds to a programming language. Further, the conversation may be based on the scripting language. Further, the scripting language may be based on a domain

[0291] In some embodiments, a second user device may be associated with a second user presentation device. Further, the presentation device may be configured to present a Graphical User Interface. Further, the second user device corresponds to a second user.

[0292] In some embodiments, the method 300 may further include generating, using the processing device 704, a script language data based on an interaction between the second user and the Graphical User Interface. Further, the script language data includes a modified scripting language. Further, the interaction corresponds to a modification of the scripting language.Further, the generating of the response data may be further based on the modified scripting language.

[0293] In some embodiments, the identifying of the response data may be further based on the modified scripting language.

[0294] In some embodiments, the method 300 may further include storing, using the processing device 704, a script language data in the script language database. Further, the script language database includes two or more script language data corresponding to a two or more scripting languages.

[0295] In some embodiments, the script language data is associated with a standard-complaint format.

[0296] In some embodiments, the two or more script language data includes a first script language data modified at a first time instance and a second script language data modified at a second time instance. Further, the second time instance occurs later the first time instance. Further, the first script language data and the second script language data includes a first scripting language and a second scripting language respectively.

[0297] In some embodiments, the two or more script language data corresponds to a proprietary scripting language.

[0298] Fig. 8 illustrates a flowchart of a method 800 of provisioning a conversation based on a context including generating, using the processing device 704, a script language feedback data, in accordance with some embodiments.

[0299] Further, in some embodiments, the method 800 further may include a step 802 of analyzing, using the processing device 704, a script language data. Further, in some embodiments, the method 800 further may include a step 804 of generating, using the processing device 704, a script language feedback data based on the analyzing of the script language data. Further, the script language feedback data corresponds a feedback corresponding to the modified scripting language. Further, in some embodiments, the method 800 further may include a step 806 of transmitting, using the communication device 702, the script language feedback data to the second user device.

[0300] In some embodiments, the feedback represents an impact of the modified scripting language in the conversation.

[0301] In some embodiments, the response data includes a video data.

[0302] In some embodiments, the video data corresponds to a tutorial video.

[0303] In some embodiments, the user preference data corresponds to a culture of the user.

[0304] In some embodiments, the encryption protocol corresponds to an Advanced Encryption Standard.

[0305] In some embodiments, the encryption protocol corresponds to one or more of a Transport Layer Security and a Secure Socket Layer.

[0306] In some embodiments, the method 300 may be the two or more Al models may be associated with two or more functional characteristics.

[0307] In some embodiments, the two or more Al models includes each of the second Al model, a third Al model and a fourth Al model. Eurther, the two or more functional characteristics includes a first characteristic, a second characteristic, and a third characteristic. Eurther, the second Al model, the third Al model and the fourth Al model may be associated with the first characteristic, the second characteristic and the third characteristic respectively.

[0308] In some embodiments, one or more of the two or more Al models may be associated with a natural language understanding.

[0309] In some embodiments, one or more of the two or more Al models may be associated with an emotional intelligence.

[0310] In some embodiments, one or more of the two or more Al models may be associated with a technical jargon understanding. Further, the technical jargon may be based on a domain of the conversation. Further, the technical jargon corresponds to a terminology of the domain.

[0311] In some embodiments, the user device may be device may be configured to execute one or more of a browser and an application. Further, the one or more of the browser and the application may be configured to present the response data.

[0312] In some embodiments, the domain corresponds to an e-commerce. Further, the conversation corresponds to a recommendation for product selection.

[0313] In some embodiments, the domain corresponds to an e-commerce. Further, the conversation corresponds to a process of product purchase.

[0314] In some embodiments, the domain corresponds to an education. Further, the user corresponds to a student. Further, the response data corresponds to the teaching of a concept.

[0315] In some embodiments, the domain corresponds to an education. Further, the user corresponds to a student. Further, the conversation corresponds to a practice exercise. Further, the exercise includes two or more questions for student practice.

[0316] In some embodiments, the domain corresponds to an education. Further, the user corresponds to a student. Further, the conversation corresponds to an interview with the student.

[0317] In some embodiments, the domain corresponds to a psychotherapy. Further, the user corresponds to a patient. Further, the conversation corresponds to providing a therapy to the patient.

[0318] In some embodiments, the domain corresponds to a customer service.

[0319] In some embodiments, the domain corresponds to a finance.

[0320] Although the invention has been explained in relation to its preferred embodiment, it is to be understood that many other possible modifications and variations can be made without departing from the spirit and scope of the invention as hereinafter claimed.

Claims

CLAIMS1. A method of provisioning a conversation based on a context, the method comprising: receiving, using a communication device, a user request data from a user device, wherein the user device is associated with a user; analyzing, using a processing device, the user request data based on a first Al model; identifying, using the processing device, a context data based on the analyzing, wherein the context data corresponds to a context of the user request data; identifying, using the processing device, a second Al model from a plurality of Al models based on the context data, wherein the plurality of Al models comprises the second Al model, wherein the identifying of the second Al model is further based on at least one of a functional characteristic and a non-functional characteristic of the plurality of Al models; generating, using the processing device, a response data based on each of the user request data and the second Al model; and transmitting, using the communication device, the response data to the user device.

2. The method of claim 1 further comprises storing, using the processing device, each of the user request data and the response data in a database, wherein the database corresponds to the user.

3. The method of claim 2, wherein the database comprises each of a plurality of user request data and a plurality of response data, wherein the plurality of user request data comprises a first user request data associated with a first time instance and a second user request data associated with a second time instance, wherein the second time instance occurs later the first time instance, wherein the plurality of response data comprises a first response data corresponding to the first user request data and a second response data corresponding to the second user request data, wherein the generating of the second response data is based on each of the first user request data and the first response data.

4. The method of claim 1 further comprises:receiving, using the communication device, a user feedback data from the user device, wherein the user feedback data corresponds to a feedback based on the response data; and generating, using the processing device, a reinforcement learning data based on each of the user feedback data, the user request data and the response data, wherein the reinforcement learning data represents a relevance of the response data in relation to the user request data, wherein the reinforcement learning data is used for performing a training of the second Al model, wherein the generating of the response data is further based on the training.

5. The method of claim 3 further comprises: analyzing, using the processing device, each of the plurality of user request data and the plurality of response data; generating, using the processing device, an insight data based on the analyzing of each of the plurality of user request data and the plurality of response data, wherein the insight data corresponds to an insight; and transmitting, using the communication device, the insight data to a second user device, wherein the second user device is associated with a second user.

6. The method of claim 1, wherein the context is based on a domain, wherein the domain corresponds to a psychotherapy, further comprises generating, using the processing device, a disorder level data based on the analyzing of the user request data, wherein the disorder level data corresponds to at least one of a plurality of disorder levels, wherein the plurality of disorder levels represent a level of a metal disorder, wherein the user is associated with the metal disorder, wherein the generating of the response data is further based on the disorder level data.

7. The method of claim 6, wherein the plurality of disorder levels comprises a first disorder level representing a first value and a second disorder level representing a second value, wherein the second value is greater than the first value, wherein the second disorder level corresponds to an extreme mental disorder associated with the user, further comprises:generating, using the processing device, an alert data based on the disorder level data, wherein the alert data corresponds to an alert, wherein the disorder level data corresponds to the second disorder level; and transmitting, using the communication device, the alert data to a second user device associated with a second user, wherein the second user corresponds to a therapist.

8. The method of claim 3 further comprises analyzing, using the processing device, each of the plurality of user request data and the plurality of response data, wherein the generating of the response data is based on the analyzing of each of the plurality of user request data and the plurality of response data.

9. The method of claim 8, wherein the analyzing of each of the plurality of user request data and the plurality of response data comprises determining a user ability, wherein the user ability corresponds to at least one of a user language processing ability, a social cognitive skill, an emotional intelligence, a problem solving skill, an emotional trigger and a user adaptability.

10. The method of claim 1 further comprises retrieving, using the processing device, a contextual data from an external database based on the analyzing of the user request data, wherein the generating of the response data is further based on the contextual data, wherein the contextual data corresponds an information relevant to user request data, wherein the external database comprises a plurality of contextual data corresponding to a domain, wherein the user request data is associated with the domain.

11. A system of provisioning a conversation based on a context, the system comprising: a communication device configured for: receiving a user request data from a user device, wherein the user device is associated with a user; transmitting a response data to the user device; a processing device configured for: analyzing the user request data based on a first Al model;identifying a context data based on the analyzing, wherein the context data corresponds to a context of the user request data; identifying a second Al model from a plurality of Al models based on the context data, wherein the plurality of Al models comprises the second Al model, wherein the identifying of the second Al model is further based on at least one of a functional characteristic and a nonfunctional characteristic of the plurality of Al models; and generating the response data based on each of the user request data and the second Al model.

12. The system of claim 11, wherein the processing device is further configured for storing each of the user request data and the response data in a database, wherein the database corresponds to the user.

13. The system of claim 12, wherein the database comprises each of a plurality of user request data and a plurality of response data, wherein the plurality of user request data comprises a first user request data associated with a first time instance and a second user request data associated with a second time instance, wherein the second time instance occurs later the first time instance, wherein the plurality of response data comprises a first response data corresponding to the first user request data and a second response data corresponding to the second user request data, wherein the generating of the second response data is based on each of the first user request data and the first response data.

14. The system of claim 11, wherein the communication device is further configured for receiving a user feedback data from the user device, wherein the user feedback data corresponds to a feedback based on the response data, wherein the processing device is further configured for generating a reinforcement learning data based on each of the user feedback data, the user request data and the response data, wherein the reinforcement learning data represents a relevance of the response data in relation to the user request data, wherein the reinforcement learning data is used for performing a training of the second Al model, wherein the generating of the response data is further based on the training.

15. The system of claim 13, wherein the processing device is further configured for:analyzing each of the plurality of user request data and the plurality of response data; and generating an insight data based on the analyzing of each of the plurality of user request data and the plurality of response data, wherein the insight data corresponds to an insight, wherein the communication device is further configured for transmitting the user insight data to a second user device, wherein the second user device is associated with a second user.

16. The system of claim 11, wherein the context is based on a domain, wherein the domain corresponds to a psychotherapy, wherein the processing device is further configured for generating a disorder level data based on the analyzing of the user request data, wherein the disorder level data corresponds to at least one of a plurality of disorder levels, wherein the plurality of disorder levels represent a level of a metal disorder, wherein the user is associated with the metal disorder, wherein the generating of the response data is further based on the disorder level data.

17. The system of claim 16, wherein the plurality of disorder levels comprises a first disorder level representing a first value and a second disorder level representing a second value, wherein the second value is greater than the first value, wherein the second disorder level corresponds to an extreme mental disorder associated with the user, wherein the processing device is further configured for generating an alert data based on the disorder level data, wherein the alert data corresponds to an alert, wherein the disorder level data corresponds to the second disorder level, wherein the communication device is further configured for transmitting the alert data to a second user device associated with a second user, wherein the second user corresponds to a therapist.

18. The system of claim 13, wherein the processing device is further configured for analyzing each of the plurality of user request data and the plurality of response data, wherein the generating of the response data is based on the analyzing of each of the plurality of user request data and the plurality of response data.

19. The system of claim 18, wherein the analyzing of each of the plurality of user request data and the plurality of response data comprises determining a user ability, wherein the user ability corresponds to at least one of a user language processing ability, a social cognitive skill, an emotional intelligence, a problem solving skill, an emotional trigger and a user adaptability.

20. The system of claim 11 , wherein the processing device is further configured for retrieving a contextual data from an external database based on the analyzing of the user request data, wherein the generating of the response data is further based on the contextual data, wherein the contextual data corresponds an information relevant to user request data, wherein the external database comprises a plurality of contextual data corresponding to a domain, wherein the user request data is associated with the domain.

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