System and method for ai-driven shopping assistance

Abstract

The present invention relates to an AI-powered shopping assistant system (100) designed to enhance the customer experience by providing real-time responses with minimal latency The system leverages dynamic 3D rendering pipeline technology to deliver immersive and interactive visual representations of products, enriching the shopping experience. It features a flexible architecture for seamless deployment within diverse customer environments. Furthermore, the system encompasses applications across multiple domains, including product discovery, finance, insurance, purchasing, pre-sales, sales, and post-sales inquiries.

Description

System and Method for AI-Driven Shopping AssistanceFIELD OF THE INVENTION

[0001] The present invention relates to the fields of artificial intelligence (Al) and machine learning.BACKGROUND OF THE INVENTION

[0002] In the realm of e-commerce, consumers often seek personalized guidance to make informed purchasing decisions. Traditional shopping methods, whether online or in-store, frequently lack the tailored assistance needed to meet individual buyer preferences.

[0003] The Al-powered chatbot for virtual shopping assistance enables organizations to offer a tailored one-on-one consultation to their clients. However, traditional bots mostly rely on rigid, rule-based interactions with limited adaptability, which can frustrate prospective buyers seeking detailed, contextual information.

[0004] Existing solutions often provide basic information but fall short in flexibility, depth, and personalization. This leads to generic and unsatisfactory interactions, leaving users unable to find tailored solutions. The lack of engagement reduces customer satisfaction and limits the overall shopping experience.

[0005] A U.S. document US2021342925A1, provides a method and system for enhancing personalized shopping experiences through the integration of user purchase history, browsing behavior, and inventory tracking across multiple platforms. This method involves the storage of purchase information for items bought by a user from different websites of various merchants. Specifically, the system stores identification information associated with products purchased by the user on both a first and a second merchant's website, where each merchant's website is operated by distinct servers. The data for eachPage No. 1 / 26item is electronically obtained from the respective servers that provide the websites. However, it fails to address the challenge of creating an experience that closely resembles engaging with a knowledgeable salesperson, as opposed to interacting with pre-programmed responses and thereby failing to provide a truly personalized approach.

[0006] A U.S. document US11763365B2, provides an apparatus, system, and method for personalized online product fitting. The method for personalized shopping includes the following steps: an automated shopping assistant system accessing product data, a matchmaking system retrieving user history data, the matchmaking system obtaining user preference data, the matchmaking system acquiring user anatomical data from the automated shopping assistant apparatus, and the automated shopping assistant system integrating user history, preference, and anatomical data to create a personalized matching system. However, it fails to provide real-time engagement in shopping that ensures personalized approaches.

[0007] Therefore, there is a need for a system that can provide personalized customer engagement and simplify the decision-making process in product buying and also which can merge human intelligence with advanced machine learning, which can transform the user experience, making it intuitive, informative, and customer-focused, ultimately supporting buyers in making informed decisions with ease.OBJECTIVE OF THE INVENTION

[0008] The objective of the present invention is to provide an Al powered shopping assistant system that can minimize latency, delivering responses with near real-time efficiency.

[0009] Another objective of the present invention is to provide an Al powered shopping assistant system that leverages existing data pipelining technologies that enhances 3d visualization in a plethora of devices more efficiently. One example of such technology is that provided by Metadome Inc.® to visually showcase products and enhance the customer experience with immersive, interactive visuals.Page No.2 / 26

[0010] Yet another objective of the present invention is to provide an Al powered shopping assistant system with flexible architecture to secure deployment within a customer’s environment.

[0011] Another objective of the present invention is to provide an Al powered shopping assistant system that can guide customers through various product offerings, assists with shortlisting, discovery, decision-making.

[0012] Yet another objective of the present invention is to provide an Al powered shopping assistant system that can support post-sales journeys, acting as a virtual trainer for field engineers on equipment operations or as a virtual mechanic, guiding customers through self-diagnosis and repairs with step-by-step instructions and visual aids.

[0013] Another objective of the present invention is to provide and support diverse applications from product discovery, finance, insurance, and purchasing to managing pre-sales, sales, and post-sales inquiries, including customer complaints. In pre-sales, it facilitates bookings, like test drives for cars or bikes, and assists with reservations.SUMMARY OF THE INVENTION

[0014] The Al-powered shopping assistant system is designed to deliver near real-time responses, minimizing latency and enhancing user experience. It leverages dynamic 3D rendering pipeline technology to provide immersive and interactive visuals, visually showcasing products to elevate the customer experience. With its flexible architecture, the system ensures secure deployment within a customer's specific environment. It guides customers through various product offerings, assisting with shortlisting, discovery, and decision-making processes. Additionally, the system supports post-sales journeys by acting as a virtual trainer for field engineers on equipment operations or as a virtual mechanic, guiding customers through self-diagnosis and repairs with step-by-step instructions and visual aids. Furthermore, the system caters to diverse applications acrossPage No. 3 / 26the sales cycle, including pre-sales activities like facilitating test drive bookings and reservations, as well as managing customer inquiries and complaints during sales and post-sales stages. Its versatility makes it suitable for applications in product discovery, finance, insurance, purchasing, and more, ensuring a seamless and comprehensive shopping experience.

[0015] In an embodiment herein, a virtual shopping assistant system comprises of a client interface configured to receive user request and inputs; a load balancer coupled to the client interface and configured to distribute incoming user requests to plurality of backend servers; and an API server operably connected to a plurality of components including a voice feed infrastructure, an analytics module, and an agent, wherein the system leverages a dynamic 3D rendering pipeline to provide immersive and interactive voice-coordinated 3D visuals of the products.

[0016] Further, in an embodiment, in the virtual shopping assistant system, the user’s request from the client interface is transmitted to the API server that is configured to route the input to a voice feed infrastructure to convert the user input into digital query.

[0017] In another embodiment, the virtual shopping assistant system is configured to utilize the said digital query for dynamic rendering of visual content on the client interface. Furthermore, the system ensures secure deployment within a user specific environment to elevate user experience.

[0018] In yet another embodiment, in the virtual shopping assistant, the agent is operably connected to the API server that may include a Transcriber, a GPT and a Synthesizer to ensure smooth interaction between the user and the system. The analytics module is operably connected to the API server, monitors system performance and user metrics.

[0019] In an embodiment, in the virtual shopping assistant, the user metrics are forwarded to the load balancer for efficient distribution of system resources. Further, the backend servers host one or more application artifacts configured to execute functionalities of the virtualPage No.4 / 26shopping assistant. The system further comprises a recommender system to generate conversation paths.

[0020] In another embodiment, the virtual shopping assistant wherein the system further comprises a path selector to select the appropriate conversation path based on the user's goals.

[0021] In an embodiment, a method for virtual assistance driven shopping, comprises the steps of: capturing user input and transcribing the said input using a transcriber module; achieving dynamic personalisation of the conversation using a user profiler containing a user profile; selecting a response persona based on a persona score obtained due to personalization in the preceding step through a persona module; generating a response in alignment with the chosen persona, coherent query and profiler context; and delivering the response to the user in accordance with the selected persona.

[0022] In another embodiment, in the method for virtual assistance driven shopping, the steps further comprises transmitting user input from a client interface to an API server, operably connected to a voice feed infrastructure, configured to convert user’s input into a digital query. The steps further comprise rerouting the query to a query enrichment module for further refinement. Furthermore, the steps comprise utilizing the digital query for dynamically rendering visuals on the client interface.

[0023] In yet another embodiment, in the method for virtual assistance driven shopping, the user profile is personalized based on the pre-existing user data collected over time by the transcriber. The steps further comprise fetching information from the user profiler to generate and group conversation paths. Furthermore, the steps comprise dynamically selecting the appropriate conversation path to enrich the query.

[0024] In an embodiment, the method for virtual assistance driven shopping, wherein the steps further comprise transmitting the query to a knowledge base to fetch information aboutPage No.5 / 26specific topics from the select conversation path. Further, the transcriber collects the user’s data over time to create a dataset used to enrich the query.

[0025] In another embodiment, for the method for virtual assistance driven shopping, the steps further comprise of capturing user input and transcribing the said input using a transcriber module; structuring the transcribed input into a coherent query using a processing module and forwarding the said query to a cloud load balancing module wherein the cloud load balancing module distributes incoming requests across multiple backend servers; directing the data from backend servers to streaming servers for managing and processing real-time data streams; and transmitting the real time data from the Streaming Server to the Agent service that interacts with multiple submodules including transcriber, GPT Module, Synthesizer.

[0026] Furthermore, the steps comprise of capturing user input and transcribing the said input using a transcriber module; structuring the transcribed input into a coherent query using a processing module and forwarding the said query to a cloud load balancing module; transmitting the incoming user requests to API server followed by dispatching them to a cloud task module; and transmitting the data into the API server stream holder wherein the streamholder manages the data stream.

[0027] In an embodiment, the method for virtual assistance driven shopping, wherein the steps further comprise of capturing user input and transcribing the said input using a transcriber module; structuring the transcribed input into a coherent query using a processing module and forwarding the said query to a cloud load balancing module; transmitting the real time data from the Streaming Server to the Agent service; and transmitting the processed data from the agent service to API server wherein the API server interacts with a database module to store and fetch essential information. Furthermore, the step comprises dynamically rendering the interactive visual representations of the products to a user.

[0028] In one embodiment, a non-transitory computer-readable medium is disclosed that is configured to store the instructions which when executed by a processor, cause aPage No.6 / 26computing device to perform capturing user input and transcribing the said input using a transcriber module; structuring the transcribed input into a coherent query using a processing module; achieving dynamic personalisation of the conversation using a user profiler containing a user’s profile; selecting a response persona based on a persona score obtained due to personalization in above step through a persona module; generating a response in alignment with the chosen persona, coherent query and profiler context; and delivering the response to the user in accordance with the selected persona.BRIEF DESCRIPTION OF THE DRAWINGS

[0029] These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

[0030] Fig. 1 : illustrates a block diagram depicting a system designed for facilitating operation of the Al based virtual shopping assistant using voice feed infrastructure as per embodiment herein.

[0031] Fig. 2 : illustrates a block diagram depicting the process of an Al assisted shopping system designed to enable dynamic, human-like conversational interactions through personalized query processing and response generation as per embodiment herein.

[0032] Fig. 3 : illustrates a block diagram of heterogeneous infrastructure deployment of an Al assisted shopping assistant as per embodiment herein.

[0033] Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, the drawings may show only those specific details that are pertinent to understanding the embodimentsPage No. 7 / 26of the present invention so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.DETAILED DESCRIPTION OF THE INVENTION

[0034] Fig. 1 : illustrates a block diagram depicting a system designed for facilitating operation of the Al based virtual shopping assistant using voice feed infrastructure as per embodiment herein. Fig. 2: illustrates a block diagram depicting the process of an Al assisted shopping system designed to enable dynamic, human-like conversational interactions through personalized query processing and response generation as per embodiment herein. Fig- 3: illustrates a block diagram of heterogeneous infrastructure deployment of an Al assisted shopping assistant as per embodiment herein.

[0035] The following description is set forth for the purpose of explanation in order to provide an understanding of the invention. However, it is apparent that one skilled in the art will recognize that embodiments of the present invention, some of which are described below, may be incorporated into a number of different shopping systems and devices. Structures shown below in the diagram are illustrative of exemplary embodiments of the invention and are meant to avoid obscuring the invention. Furthermore, connections between components within the figures are not intended to be limited to direct connections. Rather, connection / s between these components may be modified, re-arranged or otherwise changed by intermediary components.

[0036] Reference in the specification to “one embodiment”, “in one embodiment” or “an embodiment” etc. means that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.Page No. 8 / 26

[0037] In one embodiment, the present invention may be implemented using a computing device comprising one or more processors and a memory storing computer-readable instructions. The computer-readable instructions, when executed by the processor, cause the computing device to perform the various methods described herein for virtual assistance driven shopping. The instructions may be stored on a non-transitory computer-readable medium including, but not limited to, memory devices, storage devices, or any suitable tangible medium capable of storing program code. The computing device may be configured to receive user input through a client interface, process the input using one or more software modules, and generate outputs in the form of audio, visual, or combined responses. The described functionalities may be implemented as software, firmware, or a combination thereof, and may operate in a standalone manner or in a distributed computing environment, without departing from the scope of the present disclosure.

[0038] The Al-powered shopping assistant system is designed to revolutionize the customer experience by providing near real-time responses, minimizing latency, and ensuring seamless interaction. Powered by dynamic 3D rendering pipeline technology, the system delivers immersive and interactive visual representations of products, enhancing the shopping journey by visually showcasing features and benefits to customers. This not only elevates user engagement but also aids in making informed decisions.

[0039] Built with a flexible and secure architecture, the system can be deployed in various customer-specific environments, ensuring adaptability to unique operational requirements. It acts as a comprehensive guide for customers, streamlining the discovery and shortlisting processes, and assisting in making confident purchasing decisions. Beyond the initial purchase, the system plays a vital role in the post-sales journey, functioning as a virtual trainer for field engineers by providing detailed instructions for equipment operations. It also acts as a virtual mechanic, enabling customers to diagnose and repair issues independently through clear, step-by-step guidance accompanied by visual aids.Page No.9 / 26

[0040] The system’s capabilities extend across the entire sales lifecycle, supporting pre-sales activities such as facilitating test drive bookings and managing reservations. During sales and post-sales stages, it handles customer inquiries and complaints efficiently, ensuring satisfaction at every touchpoint. Its versatility makes it a valuable tool in various domains, including product discovery, finance, insurance, and purchasing, offering tailored solutions to diverse customer needs. By integrating these features, the Al-powered assistant guarantees a seamless, engaging, and comprehensive shopping experience, redefining how customers interact with products and services.

[0041] In various embodiments herein, Al-powered Virtual Shopping system comprises an agent pipeline configured to execute across a plurality of infrastructure types, including but not limited to cloud-based platforms and on-premises environments. This flexibility helps the pipeline to be adaptable and not tied to a single type of infrastructure and can help the applications to be segregated at different blocks. More specifically, different parts (or blocks) of the application can run independently on different infrastructure environments. In one embodiment the application data is stored within the customer’s network infrastructure. In another embodiment application data is stored on a provider cloud. In yet another embodiment of the invention application data is stored on a private on-premise cloud infrastructure. This deployment architecture allows it to serve enterprise customers by running the application blocks that manage customer data within the customer's own infrastructure, giving them complete control over the data flow from end to end.

[0042] In one embodiment, the system for capturing and processing user input may include a user interface that is configured to receive text input from the user. In another embodiment, the user interface may be configured to voice recognition and process the received input in real time. In another embodiment, the system may utilize a dedicated service that subscribes to said data feeds and stores them securely at the preferred location which may include customer's infrastructure, which helps in enhancing bot performance and tweak as required by the customer.Page No. 10 / 26

[0043] In other embodiment, a user request received through a client interface is transmitted to an API server, which functions as a central communication layer for managing incoming user requests / input. The API server routes the user request to a voice feed infrastructure for processing, wherein voice feed-based inputs are converted into digital information representing a query. The generated digital query is subsequently made available to the system for further processing and may be utilized to determine how the visual content is generated and presented on the client interface.

[0044] Reference is now made to Fig. 1, which is a schematic diagram depicting system 100 for facilitating operation of the Al based virtual shopping assistant using voice feed infrastructure, according to an embodiment. System (100) enables a cohesive integration of mobile client browsers, load balancers, application artifacts, and advanced analytics to optimize user experience and system performance. These components are configured to function in an interconnected manner to provide a seamless and efficient user experience.

[0045] As shown, system (100) may include a mobile client browser (101) which may facilitate user with the system via a web-based or application-based interface, and communicates with load balancer (102), which may configured to distribute incoming requests form the client browser (101) to plurality of backend servers hosting the application artifacts (103). In one embodiment, the system (100) comprises API server (104) that functions as a central communication hub for various components. The API server (104) is operatively connected to multiple components, including a voice feed infrastructure (105), an analytics module (106), and an Agent (107).

[0046] In one embodiment, the Agent (107) may connect to the API server (104) and collect user input data. The agents (107) involved in this process may include, but are not limited to, a Transcriber, GPT and Synthesizer. These agents work together to ensure a smooth, tailored interaction between the user and the system.

[0047] In one embodiment, API server (104) which may connect to the voice feed infrastructure (105), which serves for handling voice data. The voice feed infrastructure (105) receivesPage No. 11 / 26voice signals from the API server (104) and processes them to convert the spoken input into digital information.

[0048] In one embodiment, the API server (104) may communicate with the analytics module (107), which monitors system performance and gathers usage metrics. The analytic module may be configured to collect data associated with the system operation, including but not limited to request handling, performances, response times, component utilization, and interaction patterns generated during user engagement with the system. The collected metrics are communicated to the API server and may be used to assess system behaviour, optimize resource allocation, and improve overall system behaviour. These metrics are subsequently forwarded to a load balancer (102) to ensure the efficient distribution of system resources.

[0049] Reference is now made to Fig- 2, this is a block diagram illustrating an example, non-limiting embodiment of a system (200) configured to enable human-like conversational abilities in accordance with various aspects described herein. In this example, system (200) may process user queries dynamically and interactively, leveraging multiple interconnected modules to generate contextually relevant, personalized, and enriched responses, and thus the system ensures accurate assistance, keeping responses precise and focused without unnecessary digression or incorrect information.

[0050] In one embodiment, the transcriber (201) captures and transcribes user input. The transcriber may receive user input in the form of voice signals and may further convert them into textual data. This conversion may be essential in several embodiments for subsequent computational processing of the user input which may allow the system to operate independently. The query processing module (202) then analyzes and structures this input into a coherent query. The user profiler (203) achieves real time hyper personalisation of the conversation, which is then used by the persona (204) to select an appropriate response persona based on the persona score. The response module (206) generates the response in alignment with the chosen persona (205), considering the queryPage No. 12 / 26and profiler context. Finally, the text-to-speech module (207) converts the generated text into speech, delivering the response audibly in a natural, fluent manner consistent with the selected persona.

[0051] In the above embodiment, the system converts a user’s raw input received via client’s interface into a coherent query. The coherent query may be a structured, standardized, and contextually meaningful representation of a user input that may be efficiently processed by the system. Unlike raw input, which may include casual phrasing, filler words, incomplete statements, or ambiguities, the coherent query provides a machine-readable representation of the user’s intent, relevant entities, and contextual information. The said query may be used by various modules of the system, including user profiler and persona selection module.

[0052] In the above embodiment, the personas are employed to tailor responses to individual users. Each persona defines a specific style, tone, behaviour mimicking that of humans. These decide the system generated response, including the choice of words, level of details, and conversational manner. For example, a persona may be configured to provide responses in a friendly and casual tone, a professional and formal tone, or an enthusiastic and energetic tone, depending on the interaction context or the user’s profile. The system may select an appropriate persona based on information derived from the user profiler. Once a persona is selected, the response module generates textual responses consistent with the chosen persona. By using appropriate personas, the system provides contextually relevant coherent interactions, enabling the virtual shopping assistant to adapt dynamically to user preferences.

[0053] In another embodiment, the transcriber (201) captures and transcribes the user input. The query processing module (202) then analyzes and structures this input into a coherent query. Simultaneously, the query (202) may be routed to a query enrichment module (208) for further refinement. In the query enrichment module (208), the enriched query is developed by leveraging insights from the call history (209), where the data is collected over time by the transcriber (201) and submitted periodically, creating a detailed callPage No. 13 / 26history record for further use. Recommender system (210) which fetches information from the User Profiler, and generates conversation paths, which are groups of related topics and questions that belong to the same category or “family”. The path selector (211) dynamically chooses the most relevant conversation path based on the user’s short term and long term goals. The selected path (212) is then used to enhance the query, to create enriched query (208), which is a more refined and informative version of the original query.

[0054] In another embodiment, enriched query may be sent to the knowledge base (213), where it helps to fetch relevant information for the specific resolution topic from the chosen conversation path. From there, the enriched query is passed to the chosen persona (206), which generates a tailored response, which is finalized and synthesized into speech through text-to- speech module (207).

[0055] The Reference is now made to Fig. 3, this is a block diagram illustrating heterogeneous infrastructure deployment of an Al assisted shopping assistant system as per embodiment herein. The architecture divides responsibilities across three primary domains: the customer network, the provider cloud and the on-prem cloud. Each domain is specialized for its role, working in unison to ensure seamless user interactions and efficient data processing.

[0056] In one embodiment, the infrastructure (300) begins with the customer browser (301), which serves as the primary interface for user interactions. The browser processes user input and forwards it to a cloud load balancing module (302), which efficiently distributes incoming requests across multiple servers.

[0057] In yet another embodiment, the data received from the customer browser (301) and loaded to the cloud load balancing module (302) is then directed to Streaming Server (303), which may be used for managing and processing real-time data streams. This server handles real-time communication, such as voice or text input from users, and transmits it to the Agent Service (304) for further processing. In yet another embodiment,Page No. 14 / 26Agent Service (304) receives real-time data from the Streaming Server (303) and interacts with multiple submodules which include but not limited to transcriber (305), GPT Module (306), Synthesizer (307).

[0058] In one embodiment, the API server (308) processes incoming user requests and dispatches them to a dedicated cloud task module (309). From the cloud task (309) the data flows into the API server stream holder (310), which acts as a conduit for managing data streams. In another embodiment API server receives processed data from the Agent Service (304) and interacts with a MongoDB (309) database to store and fetch essential information.

[0059] In accordance with the present invention, a method that provides a virtual assistant is disclosed that captures and transcribes user input, processes the transcribed input into a coherent query, and performs real time personalization of the interaction using the user;s profile. Based on this personalization, the system selects an appropriate response persona, generates a response consistent with the coherent query and persona context, and delivers the response to the user in a manner aligned with the selected persona. This approach enables contextually relevant and personalized virtual shopping experiences.

[0060] In one embodiment, the method for virtual assistance-driven shopping further comprises transmitting a user input from a client interface to an API server, which is operably connected to a voice feed infrastructure configured to convert the user input into a digital query. The digital query may then be rerouted to a query enrichment module for further refinement to include additional contextual information. The enriched query can be utilized to dynamically render interactive visuals on the client interface, enhancing the user experience. The user profile may be personalized based on pre-existing user data collected over time by the transcriber, and information from the user profiler can be used to generate and organize conversation paths. The system dynamically selects an appropriate conversation path to further enrich the query, and the enriched query may be transmitted to a knowledge base to fetch relevant information for specific topics associated with the selected conversation path. Additionally, the transcriber collects and maintains user data over time to create a dataset that can be leveraged to improve queryPage No. 15 / 26enrichment and personalization, enabling the system to provide contextually relevant, dynamic, and personalized interactions throughout the virtual shopping experience.

[0061] In one embodiment, the method for virtual assistance-driven shopping includes capturing user input and transcribing the input using a transcriber module, followed by structuring the transcribed input into a coherent query using a processing module. The coherent query is forwarded to a cloud load balancing module, which is configured to distribute incoming requests across multiple backend servers to ensure scalability and efficient resource utilization. Data processed by the backend servers is directed to one or more streaming servers responsible for managing and processing real-time data streams. The real-time data from the streaming server is then transmitted to an agent service, which interacts with multiple submodules, including a transcriber, a language processing module, and a synthesizer, to enable real-time understanding, response generation, and output delivery within the virtual assistance-driven shopping system.

[0062] In another embodiment, the method includes capturing user input and transcribing the input using a transcriber module, followed by structuring the transcribed input into a coherent query using a processing module. The coherent query may be forwarded to a cloud load balancing module for managing incoming requests, after which the user requests are transmitted to an API server. The API server dispatches the requests to a cloud task module configured to handle task execution and processing workflows. The processed data is transmitted into an API server stream holder, which manages and maintains the data stream to support continuous, real-time communication and coordinated processing across system components during the virtual assistance-driven shopping interaction.

[0063] In one embodiment, the method further comprises leveraging a dynamic 3D rendering pipeline that is operably coordinated with voice-based interactions to present immersive and interactive three-dimensional visuals of a product. The system synchronizes the rendered visual elements with the user's spoken input and system-generated voice responses, such that changes in product orientation, features, or attributes are dynamicallyPage No. 16 / 26reflected in the rendered 3D visuals in response to voice-driven queries or commands. The voice coordination enables real-time alignment between audible guidance and corresponding visual transitions, allowing the product visualization to adapt as the conversation progresses. This coordinated interaction between voice output and 3D rendering facilitates a more intuitive and engaging shopping experience by ensuring that the visual representation of the product remains contextually aligned with the ongoing voice-based interaction.

[0064] In one embodiment, a system is disclosed that integrates an Al-powered Virtual Shopping Assistant (AI-VSA) with dynamic 3D rendering pipeline technology to transform the way products are presented and understood. The system leverages advanced capabilities to automatically generate dynamic and visually compelling content in diverse media formats, including audio narrations, descriptive text, high-quality images, animated GIFs, and videos. The generated content is specifically tailored for products showcased by the AI-VSA, ensuring that every feature and value proposition is conveyed with clarity and impact. Audio narrations and descriptive text provide detailed insights into the product’s features, functionality, and value, while high-quality images and videos offer vivid visual representations to enhance understanding. Animated GIFs add an element of interactivity and simplicity, enabling even complex features to be communicated effectively.

[0065] The system addresses the limitations of existing solutions, which often lack the capability to generate tailored content or provide fragmented and inconsistent visuals. By contrast, the present technology seamlessly combines various media formats to create a cohesive and immersive product demonstration experience, significantly improving user engagement and comprehension.

[0066] In one embodiment, a rendering pipeline is disclosed that facilitates exceptional content fidelity and an effortless content generation process. The rendering pipeline is configured to generate diverse media assets, such as GIFs, images, and videos, using a single input, which may include a 2D or 3D representation of the desired content. In another embodiment, the system may generate highly optimized content by minimizing customerPage No. 17 / 26bandwidth usage and delivering rapid rendering performance, all while preserving high-quality visual outputs.

[0067] In one embodiment, an Al-powered Virtual Shopping Assistant (Al- VS A) is disclosed, which seamlessly integrates a rendering pipeline with advanced Al capabilities to enhance user interaction and content delivery. The integration leverages real-time conversation analysis to understand the user's context and intent based on their queries and preferences. Using the contextual understanding derived from real-time conversation analysis, the Al- VS A intelligently fetches relevant content from a media library powered by dynamic 3D rendering pipeline technology. The media library comprises diverse media assets, including images, GIFs, and videos, which are dynamically selected to align with the user's specific needs and inquiries. The integration ensures that the Al- VS A dynamically presents the most appropriate media assets in real-time.

[0068] In various embodiments, Al-powered Virtual Shopping Assistants (Al- VS As) may utilized across diverse industries to provide personalized and efficient assistance to users:

[0069] It may be utilized to assist customers in selecting jewelry pieces based on their preferences for style, material, occasion, and budget. By offering virtual try-ons, detailed product descriptions, and expert recommendations, the Al -VS A ensures a personalized shopping experience.

[0070] It may be utilized to assist customers through vast product catalogs used in various Platforms like Myntra® by Flipkart Pvt. Ltd., AJIO® by Reliance Retail Ltd. and Flipkart® by Flipkart Pvt. Ltd. The assistant can suggest products based on user behavior, preferences, and purchase history, and answer specific questions about product features, warranties, and return policies.

[0071] It may be utilized in the travel industry to help users find the perfect accommodation by filtering options based on location, amenities, budget, and travel preferences. They canPage No. 18 / 26provide recommendations for family-friendly stays, business accommodations, or unique travel experiences, along with real-time booking assistance.

[0072] It may be utilized by Online fashion retailers like H&M® by Hennes & Mauritz AB and Zara® by Inditex, to recommend clothing items based on the customer’s size, style preferences, and current trends.

[0073] It may be utilized by the healthcare sector to guide patients in selecting medical equipment, health insurance plans, or wellness products. They can also assist in booking appointments or providing information about symptoms and treatments (e.g., Al chatbots on pharmacy websites).

[0074] It may be utilized by Platforms like IKEA® by Inter IKEA Systems B. V or Wayfair® by Wayfair Inc. to utilize Al- VS As to help customers choose furniture, home decor, or renovation products. Augmented Reality (AR) features integrated with the AI-VSA can let users visualize products in their spaces before making a purchase.

[0075] Platforms like Udemy® by Udemy, Inc., Coursera® by Coursera Inc. or edX® by edX Inc., can use Al- VS As to recommend courses based on a user’s career goals, interests, and skill level. They can also provide insights into course difficulty, prerequisites, and potential career opportunities.Page No. 19 / 26

Claims

CLAIMS:We claim,1. A virtual shopping assistant system (100), comprising:a client interface (101) configured to receive user request and inputs; a load balancer (102) coupled to the client interface and configured to distribute incoming user requests to plurality of backend servers; andan API server (104) operably connected to a plurality of components including a voice feed infrastructure (105), an analytics module (106), and an agent (107);wherein the system (100) is configured to leverage a dynamic 3D rendering pipeline to provide immersive and interactive, voice-coordinated 3D visuals of a product.

2. The virtual shopping assistant system (100) as claimed in claim 1, wherein the user’s request from the client interface (101) is transmitted to the API server (104) that is configured to route the input to a voice feed infrastructure (105) to convert the user input into digital query.

3. The virtual shopping assistant system (100) as claimed in claim 1, wherein the system (100) is configured to utilize the said digital query for dynamic rendering of visual content on the client interface (101).

4. The virtual shopping assistant system (100) as claimed in claim 1, wherein the system (100) ensures secure deployment within a user specific environment to elevate user experience.

5. The virtual shopping assistant system (100) as claimed in claim 1, wherein the agent (107) operably connected to the API server (104) comprises a Transcriber (305), GPT (306) and Synthesizer (307) to ensure smooth interaction between the user and the system (100).Page No. 20 / 266. The virtual shopping assistant system (100) as claimed in claim 1, wherein the analytics module (106) operably connected to the API server (104) monitors system’s performance and user metrics.

7. The virtual shopping assistant system (100) as claimed in claim 1, wherein the user metrics are forwarded to the load balancer (102) for efficient distribution of system resources.

8. The virtual shopping assistant system (100) as claimed in claim 1, wherein the backend servers host one or more application artifacts (103) configured to execute functionalities of virtual shopping assistant.

9. The virtual shopping assistant system (100) as claimed in claim 1, wherein the system further comprises a recommender system (210) to generate conversation paths.

10. The virtual shopping assistant system (100) as claimed in claim 1, wherein the system further comprises a path selector (211) to select the appropriate conversation path based on the user's goals.

11. A method for virtual assistance driven shopping, comprising the steps of:a. Capturing user input and transcribing the said input using a transcriber module (305);b. Structuring the transcribed input into a coherent query using a processing module (202);c. Achieving dynamic personalisation of the conversation using a user profiler (203) containing a user’s profile;d. Selecting a response persona (204) based on a persona score obtained due to personalization in step (c) through a persona module;e. Generating a response in alignment with the chosen persona (205), coherent query and profiler context; andPage No. 21 / 26f. Delivering the response to the user in accordance with the selected persona.

12. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the method further comprises transmitting user input from a client interface (101) to an API server (104), operably connected to a voice feed infrastructure (105), configured to convert user’s input into a digital query.

13. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the method further comprises rerouting the query to a query enrichment module (208) for further refinement.

14. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the method further comprises utilizing the digital query for dynamically rendering visuals on the client interface.

15. The method for virtual assistance driven shopping as claimed in claim 11, wherein the user profile is personalized based on the pre-existing user data collected over time by the transcriber (305).

16. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the steps further comprises fetching information from the user profiler to generate and group conversation paths.

17. The method for virtual assistance driven shopping as claimed in claim 11, wherein the steps further comprises dynamically selecting the appropriate conversation path to enrich the query.

18. The method for virtual assistance driven shopping as claimed in claim 11, wherein the steps further comprises transmitting the query to a knowledge base (213) to fetch information about specific topics from the select conversation path.Page No.22 / 2619. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the transcriber (305) collects the user’s data over time to create a dataset used to enrich the query.

20. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the steps further comprises:a. Capturing user input and transcribing the said input using a transcriber module (305);b. Structuring the transcribed input into a coherent query using a processing module (202) and forwarding the said query to a cloud load balancing module (302) wherein the cloud load balancing module (302) distributes incoming requests across multiple backend servers;c. Directing the data from backend servers to streaming servers (303) for managing and processing real-time data streams; andd. Transmitting the real time data from the Streaming Server (303) to the Agent service (304) that interacts with multiple submodules including transcriber (305), GPT Module (306), Synthesizer (307).

21. The method for virtual assistance driven shopping as claimed in claim 11, wherein the steps further comprises:a. Capturing user input and transcribing the said input using a transcriber module (305);b. Structuring the transcribed input into a coherent query using a processing module (202) and forwarding the said query to a cloud load balancing module (302); c. Transmitting the incoming user requests to API server (308) followed by dispatching them to a cloud task module (309); andd. Transmitting the data into the API server stream holder (310) wherein the streamholder manages the data stream.

22. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the steps further comprises:Page No. 23 / 26a. Capturing user input and transcribing the said input using a transcriber module (305);b. Structuring the transcribed input into a coherent query using a processing module (202) and forwarding the said query to a cloud load balancing module (302); c. Transmitting the real time data from the Streaming Server (303) to the Agent service (304); andd. Transmitting the processed data from the agent service (304) to API server (308) wherein the API server interacts with a database module (309) to store and fetch essential information.

23. The method for virtual assistance driven shopping as claimed in claim 11 , wherein the step further comprises leveraging a dynamic 3D rendering pipeline to provide immersive and interactive, voice coordinated 3D visuals of a product.

24. A non-transitory computer-readable medium storing instructions which, when executed by a processor, cause a computing device to perform the steps comprising:a) Capturing user input and transcribing the said input using a transcriber module (305);b) Structuring the transcribed input into a coherent query using a processing module (202);c) Achieving dynamic personalisation of the conversation using a user profiler (203) containing a user’s profile;d) Selecting a response persona (204) based on a persona score obtained due to personalization in the preceding step through a persona module;e) Generating a response in alignment with the chosen persona (205), coherent query and profiler context; andf) Delivering the response to the user in accordance with the selected persona.

25. The non-transitory computer-readable medium as claimed in claim 24, wherein the instructions further cause the computing device to leverage a dynamic 3D renderingPage No. 24 / 26pipeline coordinated with voice input and output to render interactive 3D visual representations of products on a client interface (101).Page No. 25 / 26

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