Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of product recommendation. More particularly, the present disclosure relates to a system and method for recommending products to a user for a personalized shopping experience.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Traditional shopping often shows generic recommendations that may not align with individual user preferences, leading to lower engagement and satisfaction. Products are recommended based on general trends rather than specific user interests, which can result in a disconnect between the offerings and what the user actually wants. Users may feel overwhelmed or frustrated by seeing products that don’t match their tastes or needs, which can negatively impact their overall shopping experience. Users might spend more time searching for what they want, which can lead to a less efficient and enjoyable shopping experience. Without a personalized touch, users may not feel a connection to the shopping platform, reducing their likelihood of returning. Personalized shopping experiences are becoming a standard expectation. A lack of personalization can make a shopping platform less competitive compared to others that offer tailored experiences. Traditional shopping does not leverage the vast amount of user data that can be used to improve recommendations and user satisfaction. Without personalization, insights into user behaviour are less precise, leading to less informed business decisions. A traditional approach can make the shopping experience feel impersonal and less satisfying, impacting overall customer happiness. Thus, traditional shopping lacks the ability to connect with users on an individual level, leading to lower engagement, satisfaction, and sales.
[0004] To address these limitations, the present invention provides a novel system and method that overcomes the shortcomings of the prior art.

OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0006] It is an object of the present disclosure to provide a system for recommending products to a user to tailor product recommendations to individual preferences, making the shopping experience more enjoyable and relevant.
[0007] It is another object of the present disclosure to provide a system for recommending products to a user to proactively present items that match their tastes and needs.
[0008] It is yet another object of the present disclosure to provide a system for recommending products to a user to transform grocery shopping experience for users by making it more efficient, cost-effective, and personalized.
[0009] It is another object of the present disclosure to provide a system for recommending products to a user to analyse user data to uncover insights into shopping behaviours, preferences, and trends.

SUMMARY
[0010] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0011] The present disclosure relates to the field of product recommendation. More particularly, the present disclosure relates to a system and method for recommending products to a user for a personalized shopping experience.
[0012] In an aspect of the present disclosure, a system for recommending products to a user for a personalized shopping experience is disclosed. The system includes a processor and a memory coupled to the processor. The memory includes processor-executable instructions, which on execution, causes the processor to execute a sequence of tasks. The system is configured to receive data from a user. The system is further configured to generate a list of products based on the received data. The system is further configured to recommend one or more stores to the user based on the generated list and a current location of the user. The system is further configured to recommend products to the user available at the one or more stores. The products are recommended based on inventory levels and pricing information of the products and the data received from the user.
[0013] In another aspect of the present disclosure, a method for recommending products to a user for a personalized shopping experience is disclosed. The method begins with receiving, by the processor, data from the user. The method proceeds with generating, by the processor, a list of products based on the received data. The method proceeds with recommending, by the processor, one or more stores to the user based on the generated list and a current location of the user. The method proceeds with recommending, by the processor, products to the user available at the one or more stores. The products are recommended based on inventory levels and pricing information of the products and the data received from the user.

BRIEF DESCRIPTION OF DRAWINGS
[0014] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in, and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure, and together with the description, serve to explain the principles of the present disclosure.
[0015] In the figures, similar components, and/or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description applies to any one of the similar components having the same first reference label irrespective of the second reference label.
[0016] FIG. 1 illustrates an exemplary representation of architecture of the proposed system for recommending products to a user for a personalized shopping experience, in accordance with an embodiment of the present disclosure.
[0017] FIG. 2 illustrates a block diagram representation of the proposed system for recommending products to a user for a personalized shopping experience, in accordance with an embodiment of the present disclosure.
[0018] FIG. 3 illustrates an exemplary view of a flow diagram of the proposed method for recommending products to a user for a personalized shopping experience, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[0019] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[0020] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0021] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[0022] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[0023] The word “exemplary” and/or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and/or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising” as an open transition word without precluding any additional or other elements.
[0024] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[0025] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an”, and “the” are intended to include the plural forms as well, unless the context indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
[0026] The present disclosure relates to the field of product recommendation. More particularly, the present disclosure relates to a system and method for recommending products to a user for a personalized shopping experience.
[0027] In an embodiment of the present disclosure, a system for recommending products to a user for a personalized shopping experience is disclosed. The system includes a processor and a memory coupled to the processor. The memory includes processor-executable instructions, which on execution, causes the processor to execute a sequence of tasks. The system is configured to receive data from a user. The system is further configured to generate a list of products based on the received data. The system is further configured to recommend one or more stores to the user based on the generated list and a current location of the user. The system is further configured to recommend products to the user available at the one or more stores. The products are recommended based on inventory levels and pricing information of the products and the data received from the user
[0028] In an embodiment, the processor is configured to receive current location, personal shopping preferences, and grocery lists from the user to generate the list of products.
[0029] In an embodiment, the processor is configured to apply natural language processing techniques to interpret voice commands received from the user pertaining to product requirements to generate the list of products.
[0030] In an embodiment, the processor is configured to analyse the received data by applying predictive analytics to generate the list of products.
[0031] In an embodiment, the processor is configured to create the list of products by applying image recognition techniques and Optical Character Recognition (OCR) techniques to capture and digitize information from physical documents.
[0032] In an embodiment, the processor is configured to train a machine learning model on datasets of purchasing patterns and preferences of the user to recommend products.
[0033] In an embodiment, the processor is configured to apply conversational AI techniques to engage with the user in a dialogue to refine and optimize the list of products.
[0034] In an embodiment, the processor is configured to apply generative AI techniques to provide interactive voice responses comprising confirmation messages, alternative suggestions, and additional information to the user about the list of products.
[0035] In an embodiment of the present disclosure, a method for recommending products to a user for a personalized shopping experience is disclosed. The method begins with receiving, by the processor, data from the user. The method proceeds with generating, by the processor, a list of products based on the received data. The method proceeds with recommending, by the processor, one or more stores to the user based on the generated list and a current location of the user. The method proceeds with recommending, by the processor, products to the user available at the one or more stores. The products are recommended based on inventory levels and pricing information of the products and the data received from the user.
[0036] The advent of digital technology has revolutionized many aspects of consumer behaviour, with a significant impact on how shopping is conducted. In the grocery sector, challenges such as store selection, budget management, and time optimization are predominant among consumers seeking efficiency and convenience in their shopping experiences. Addressing these needs, the proposed system not only simplifies creation and management of grocery lists but also enhances the decision-making process for choosing the most suitable store based on various criteria.
[0037] This system commences operation with user authentication and proceeds with facilitating accepting manual, audio and scanned input from the authenticated user. Further, the system performs a dynamic search operation for store selection, classification of stores nearby based on size of shops like ration shops, medium-sized stores, and shopping malls. The system incorporates a host of integrated filters to customize the search results. The varied features of the system are designed to provide users with a tailored shopping experience that minimizes time and financial expenditure while maximizing convenience and accessibility.
[0038] The various embodiments throughout the disclosure will be explained in more detail with reference to Figs. 1-3.
[0039] FIG. 1 illustrates an exemplary representation of architecture of the proposed system for recommending products to a user for a personalized shopping experience, in accordance with an embodiment of the present disclosure.
[0040] Referring to FIG.1, a system 102 automatically detects presence of the at least one user to access a computing device. The system 102 comprises a network 104, one or more computing devices 106-1, 106-2…,106-N (individually referred to as one or more computing devices 106), one or more users 108-1, 108-2…,108-N (individually referred to as one or more users 108), and a centralized server 110. The computing device 106 comprising a processor 202 and a memory 204. The memory 204 may comprise a set of instructions, which when executed, causes the processor 202 to manage personal security of a user. The one or more user transactions are received via one or more computing devices 106.
[0041] In an embodiment of the present disclosure, the system 102 may be configured to enable the user to manually create a list of products. The system 102 may also be configured to receive voice-to-text input from the user for generation of lists and recommendation of products. Further, the system 102 is configured to scan physically prepared grocery lists in order to generate a digital list of products. In an aspect, the system 102 may be configured to suggest convenient periods of time and locations for shopping by taking into account factors including historical data pertaining to shopping patterns of the user, predictions of crowd at one or more stores in which the products may be available, and typical inventory levels. The system 102 implements proactive planning technologies to help the user with optimizing their shopping schedule, avoiding peak hours and ensuring that desired products are in stock. Moreover, the system 102 is configured to provide interactive voice responses and real-time collaboration features, enhancing the overall efficiency and personalization of shopping experience for the user.
[0042] In an embodiment of the present disclosure, the system 102 is configured to recommend one or more stores based on the generated list of products. In order to enhance store selection, the system 102 may be configured to evaluate multiple factors to recommend the most suitable shopping destinations. The system 102 is configured to consider the current location of the user, personal shopping preferences of the user, grocery lists of the user, and real-time data pertaining to the products including product availability, current pricing, promotional offers, and even store ratings. Furthermore, the system 102 is configured to incorporate proximity considerations to recommend the one or more stores in order to minimize travel time while maximizing shopping convenience and cost-effectiveness. This personalized approach adopted by the system 102 not only simplifies the decision-making process but also tailors the shopping experience to meet individual needs precisely.
[0043] In an embodiment of the present disclosure, the system 102 may be configured to provide the user with real-time access to inventory levels and pricing information, enabling the user to make informed decisions without physically visiting stores. Thus, the system 102 is configured to support cost-effective shopping by allowing price comparisons of products across multiple retailers within the vicinity of the user. The system 102 may be further configured to apply enhanced map integration and navigation technology to guide the user directly to the stores, showing the most efficient routes and even a layout of the stores for quicker in-store navigation. By equipping shoppers with this information, the system 102 significantly reduces time spent on searching for the products and waiting at checkout lines, thus streamlining the entire grocery shopping process. By integrating these features, the system 102 may be configured to streamline the shopping process, reducing costs for the user, and improving overall satisfaction by addressing the specific needs and preferences of each user. This approach of the system 102 not only enhances the shopping experience but also utilizes the data obtained from the user to continuously improve the efficiency and effectiveness of consumer retail interactions.
[0044] In an embodiment, the system 102 for recommending products to a user for a personalized shopping experience comprises a processor 202 operatively coupled to a memory 204 that comprises a set of instructions, which upon being executed, causes the processor 202 to recommend products to a user for a personalized shopping experience.
[0045] FIG. 2 illustrates a block diagram representation of the proposed system for recommending products to a user for a personalized shopping experience, in accordance with an embodiment of the present disclosure.
[0046] Referring to FIG. 2, an exemplary architecture of the proposed system 102 is disclosed. The system 102 comprises one or more processor(s) 202. The one or more processor(s) 202 are implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in the memory 204 of the device. The memory 204 stores one or more computer-readable instructions or routines, which are fetched and executed to create or share the data units over a network service. The memory 204 comprises any non-transitory storage device comprising, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0047] In an embodiment, the system 102 also comprises an interface(s) 206. The interface(s) 206 comprises a variety of interfaces, for example, interfaces for data input and output devices referred to as I/O devices, storage devices, and the like. The interface(s) 206 facilitates communication of the user device 102 with various devices or servers coupled to the user device. The interface(s) 206 also provides a communication pathway for one or more components of the user device 108.
[0048] In an embodiment, the processing engine(s) 208 are implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. The system 102 further includes the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system 102 and the processing resource. In other examples, the processing engine(s) 208 is implemented by electronic circuitry. Database 224 comprises data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208.
[0049] In an embodiment, the processing engine(s) 208 can include a data receiving module 210, a list generation module 212, a store recommendation module 214, a product recommendation module 216, and other module(s) 218, but not limited to the likes. The other module(s) 218 implements functionalities that supplement applications or functions performed by the system 102 or the processing engine(s) 208. The data (or database 220) serves, amongst other things, as a repository for storing data processed, received, and generated by one or more of the modules.
[0050] In an embodiment, the processor 202 may be configured to receive data from a user via the data receiving module 210.
[0051] In an embodiment, the processor 202 may be configured to generate a list of products based on the received data via the list generation module 212.
[0052] In an embodiment, the processor 202 may be configured to recommend one or more stores to the user based on the generated list and a current location of the user via the store recommendation module 214.
[0053] In an embodiment, the processor 202 may be configured to recommend products to the user available at the one or more stores via the product recommendation module 216. The products may be recommended based on inventory levels and pricing information of the products and the data received from the user.
[0054] In an embodiment of the present disclosure, the system 102 may be configured to leverage computer vision techniques to scan bills and handwritten documents for generation of the list of products. The system 102 is further configured to apply advanced image recognition and optical character recognition (OCR) techniques to accurately capture and digitize information from physical documents to generate the list of products. The system 102 is further configured to interpret various formats of bills and handwritten notes and extract relevant data to generate the list of products. Thus, the system 102 is configured to enable the user to quickly convert physical records and documents into organized, actionable lists for a convenient and personalized shopping experience.
[0055] In an embodiment of the present disclosure, the system 102 may be configured to create and train state-of-the-art speech recognition models to accurately transcribe user speech into text. The system 102 is configured to receive voice commands from the user in order to create the list of products based on the voice commands. The voice commands are interpreted by the system 102 by applying natural language understanding (NLU) techniques to identify relevant information and recognize context-specific phrases in the received voice commands. The system 102 is further configured to accept and interpret multilingual voice commands and automatically translate the received voice commands in different languages into a preferred language of the user for generation of the list of products. The system 102 may be configured to apply generative AI techniques to understand a context of the voice commands of the user. In an example, if the user utters the command, "add the usual snacks", the system 102 may infer and add specific items based on historical shopping data of the data.
[0056] In an embodiment of the present disclosure, the system 102 may be further configured to apply generative AI techniques to analyse current shopping lists and provide proactive recommendations for products that might be missing based on typical purchase patterns. In an example, the system 102 may be configured to suggest eggs if milk is added to the list by the user by applying generative AI techniques. The system 102 may be further configured to apply predictive analytics techniques to suggest products that are likely to be needed soon, based on consumption patterns, seasonal trends, or upcoming events. In an example, the system 102 may be configured to suggest sunscreen in summer season or extra groceries during Holiday season. The system 102 may be further configured to apply generative AI techniques to generate interactive voice responses that confirm actions, suggest alternatives, and offer additional information about the products. The system 102 may be further configured to apply conversational AI techniques to engage the user in a dialogue to refine and optimize the list of products, ensuring all necessary products are included.
[0057] FIG. 3 illustrates an exemplary view of a flow diagram of the proposed method for recommending products to a user for a personalized shopping experience, in accordance with an embodiment of the present disclosure.
[0058] In an embodiment, the proposed method 300 for recommending products to a user for a personalized shopping experience is disclosed. At step 302, receiving, by the processor 202, the data from the user. At step 304, generating, by the processor 202, the list of products based on the received data. At step 306, recommending, by the processor 202, the one or more stores to the user based on the generated list and a current location of the user. At step 308, recommending, by the processor 202, the products to the user available at the one or more stores. The products are recommended based on inventory levels and pricing information of the products and the data received from the user.
[0059] In an example embodiment, the user may issue a voice command "Hey, add milk, bread, and my usual snacks to the shopping list.". The system 102 may be configured to receive the command, analyse and interpret the command by applying natural language processing techniques, and then inform the user that milk, bread, and the usual snacks including chips and cookies have been added to the list pf products created by the system 102. The system 102 may be configured to apply conversational AI techniques to engage the user in a conversation by informing the user "I've added milk and bread. For your usual snacks, should I add chips and cookies?". The system 102 may be configured to confirm the action of adding the products including milk, bread, chips, and cookies to the product. The system 102 may now apply predictive analytics techniques to suggest products that the user may not have suggested at the moment but may have suggested previously. The system 102 may now suggest to the user to add eggs and butter to the list of products based on the purchasing history of the user. The user may accept the suggestion and issue another command to confirm the action. Upon receiving the instruction of confirmation, the system 102 updates the list of products. The system 102 may now ask if there are more products to be added to the list of products to which the user may reply in negative. The system 102 may now be configured to provide the updated list of products to the user. Thereafter, the system 102 may now locate one or more stores within the vicinity of the user in which milk, bread, chips, and cookies are available. The system 102 may also suggest stores in which milk, bread, chips, and cookies are available at minimum prices.
[0060] In an embodiment of the present disclosure, the system 102 is configured to receive information about user behaviour, including browsing history, past purchases, and purchasing preferences. The system 102 is also configured to take into account details about the products such as categories, descriptions, prices, and features and information about the time, location, and device used by the user. The data is cleaned and transforming into a usable format by the system 102. The system 102 is then configured to extract key features from the data that can help in making recommendations to the user such as user preferences and product attributes. The system 102 creates detailed profiles for the user based on their data by taking into account the current context to tailor the recommendations. The system 102 is further configured to use historical data of the user to train machine learning models and neural networks and advanced algorithms to analyse complex patterns in data. Further, the recommendations are updated by the system 102 in real-time as new data is collected while ensuring that the system 102 can handle large amounts of data and provide quick responses. The recommendations are displayed to the user in a user-friendly manner by the system 102 that is also configured to accept feedback from the user to fine tune the recommendations. Thus, the system 102 is configured to continuously learn from user interactions and preferences to provide increasingly accurate and personalized product suggestions, enhancing the shopping experience and driving sales.
[0061] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are comprised to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[0062] The present disclosure provides a system for recommending products to a user to tailor product recommendations to individual preferences, making the shopping experience more enjoyable and relevant.
[0063] The present disclosure provides a system for recommending products to a user to proactively present items that match their tastes and needs.
[0064] The present disclosure provides a system for recommending products to a user to transform grocery shopping experience for users by making it more efficient, cost-effective, and personalized.
[0065] The present disclosure provides a system for recommending products to a user to analyse user data to uncover insights into shopping behaviours, preferences, and trends.
, Claims:1. A system for recommending products to a user (102), the system (102) comprising:
a processor (202); and
a memory (204) coupled to the processor (202), wherein the memory (204) comprises processor-executable instructions, which on execution, causes the processor (202) to:
receive data from a user;
generate a list of products based on the received data;
recommend one or more stores to the user based on the generated list and a current location of the user; and
recommend products to the user available at the one or more stores, wherein the products are recommended based on inventory levels and pricing information of the products and the data received from the user.
2. The system (102) as claimed in claim 1, wherein the processor (202) is configured to receive current location, personal shopping preferences, and grocery lists from the user to generate the list of products.
3. The system (102) as claimed in claim 1, wherein the processor (202) is configured to apply natural language processing techniques to interpret voice commands received from the user pertaining to product requirements to generate the list of products.
4. The system (102) as claimed in claim 1, wherein the processor (202) is configured to analyse the received data by applying predictive analytics to generate the list of products.
5. The system (102) as claimed in claim 1, wherein the processor (202) is configured to create the list of products by applying image recognition techniques and Optical Character Recognition (OCR) techniques to capture and digitize information from physical documents.
6. The system (102) as claimed in claim 1, wherein the processor (202) is configured to train a machine learning model on datasets of purchasing patterns and preferences of the user to recommend products.
7. The system (102) as claimed in claim 1, wherein the processor (202) is configured to apply conversational AI techniques to engage with the user in a dialogue to refine and optimize the list of products.
8. The system (102) as claimed in claim 1, wherein the processor (202) is configured to apply generative AI techniques to provide interactive voice responses comprising confirmation messages, alternative suggestions, and additional information to the user about the list of products.
9. A method (300) for recommending products to a user, the method (300) comprising steps of:
receiving (302), by a processor (202), data from a user;
generating (304), by the processor (202), a list of products based on the received data;
recommending (306), by the processor (202), one or more stores to the user based on the generated list and a current location of the user; and
recommending (308), by the processor (202), products to the user available at the one or more stores, wherein the products are recommended based on inventory levels and pricing information of the products and the data received from the user.