Description:FIELD OF INVENTION
[0001] The embodiments of the present disclosure generally relate to a field of image processing systems. More particularly, the present disclosure relates to a method and a system for sequencing a plurality of images in an electronic commerce (e-commerce) environment.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Generally, multiple electronic commerce (e-commerce) platforms may rely on a seller to provide multiple images of a product in a desired order and mandatory guidelines. If the images are not as per the guidelines/specifications, a listing of the product on the e-commerce platform may be rejected. Further, on multiple e-commerce platforms, it may be mandatory for sellers to upload one or more product images in order to list the product on the platform. The product images consist of different views, infographics, and other metadata (size chart, list of ingredients, warranty, and the like). On the product page, the sequence of images as well as the quality of the images affects the purchase decision. Because of this, multiple e-commerce platforms may have a set of guidelines/specifications, which sellers are expected to follow while uploading the product images.
[0004] For example, the guidelines/specifications may include, but are not limited to, images must accurately represent the product and show only the product that's for sale, with minimal or no propping, the product and all of its features must be clearly visible, main images must have a pure white background and must be professional photographs of the actual product, must not show excluded accessories, properties that might confuse the customer, text that is not part of the product; or logos, watermarks or inset images, main images must not be multiple views of the same product, images must match the product title, the product must fill at least 85% of the image area, images must have more than e.g., 75 dpi resolution, images should be 1,000 pixels or larger in either height or width, allowed file formats, but JPEG is preferred, and the like. Most of the e-commerce platforms may also have few image guidelines to maintain the aesthetics of a webpage. For example, images should have only white or uniform background, images should not be blurry, the product should cover more than 50% of the area in the image canvas, and the like. Few quality check guidelines are there from a correctness and compliance point of view. For example, non-relevant images should not be there, Not Safe for Work (NSFW) images are not allowed, and the like.
[0005] Further, to automate the sequencing of images or ranking of the images, conventional methods may provide methods for checking a single image to make the decision instead of checking all available images of the product to find a best image. Although, multiple e-commerce platforms have a set of guidelines for uploading images, however, images are uploaded manually by the sellers or by the quality control teams associated with the e-commerce platforms. Currently, the conventional methods may not take into consideration of the user intent (e.g., query, the information buyers are interested in), for the representation of the product images on a product page and a search page. Hence, the conventional methods may not use the user query as a context to reorder the images to select the most relevant first images.
[0006] Therefore, there is a need for a method and a system for solving the shortcomings of the conventional methods, by providing a method and a system for sequencing a plurality of images in an electronic commerce (e-commerce) environment, which automates and consistent in sequencing a plurality of images, reduces the time for onboarding and rejections in listing the products on the e-commerce platforms.

SUMMARY
[0007] This section is provided to introduce certain objects and aspects of the present invention 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. In order to overcome at least a few problems associated with the known solutions as provided in the previous section, an object of the present invention is to provide a technique that may be for sequencing a plurality of images in an electronic commerce (e-commerce) environment.
[0008] It is an object of the present disclosure to provide a method and a system for sequencing a plurality of images in an electronic commerce (e-commerce) environment.
[0009] It is another object of the present disclosure to provide a method and a system for dynamically re-ordering images based on image guidelines, by processing the images.
[0010] It is yet another object of the present disclosure to provide a method and a system for context (i.e., buyers’ query) aware image re-ordering, to display the most relevant images first on the product page and search page of the e-commerce environment.
[0011] It is another object of the present disclosure to provide a method and a system for identifying images to be discarded, which includes non-relevant images, images not meeting guidelines, Not Safe for Work (NSFW), and the like, and then re-order the images based on either set of rules or automatically using ML model trained on existing verified data.
[0012] It is another object of the present disclosure to provide a method and a system for processing one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images.
[0013] It is another object of the present disclosure to provide a method and a system for classifying the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images.
[0014] In an aspect, the present disclosure provides a method for sequencing a plurality of images in an electronic commerce (e-commerce) environment. The method includes sequencing a plurality of images in an electronic commerce (e-commerce) environment. The method includes receiving a plurality of images from a seller associated with an e-commerce environment. Further, the method extracting one or more features in the plurality of images using a feature extraction network model. Furthermore, the method includes processing the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images. Additionally, the method includes classifying the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images. Further, the method includes sequencing the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification. The sequenced plurality of images is stored in a database, to display to a buyer associated with the e-commerce environment. Furthermore, the method includes reordering dynamically the sequenced plurality of images stored in the database, based on a context of the one or more queries, when the buyer inputs one or more queries in the e-commerce environment.
[0015] In an embodiment, for the transformer encoder model, the method includes providing additional learnable embeddings with the plurality of images according to the sequence of the plurality of images, to predict the class of the plurality of images after updating the generated multi-head attention map.
[0016] In an embodiment, the MLP model classifies the plurality of images by sharing weight parameters between the plurality of images.
[0017] In another embodiment, the received plurality of images corresponds to at least one of non-relevant images of a product, images not meeting guidelines of the e-commerce environment, and Not Safe for Work (NSFW) images.
[0018] In another aspect, the present disclosure provides a system for sequencing a plurality of images in an electronic commerce (e-commerce) environment. The system receives a plurality of images from a seller associated with an e-commerce environment. Further, the system extracts one or more features in the plurality of images using a feature extraction network model. Furthermore, the system processes the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images. Additionally, the system classifies the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images. Further, the system sequences the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification. The sequenced plurality of images is stored in a database, to display to a buyer associated with the e-commerce environment. Furthermore, the system reorders dynamically, the sequenced plurality of images stored in the database based on a context of the one or more queries, when the buyer inputs one or more queries in the e-commerce environment.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0019] The accompanying drawings, which are incorporated herein, and constitute a part of this invention, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present invention. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry/sub-components of each component. It will be appreciated by those skilled in the art that the invention of such drawings includes the invention of electrical components, electronic components, or circuitry commonly used to implement such components.
[0020] FIG. 1 illustrates an exemplary block diagram representation of a network architecture implementing a proposed system for sequencing a plurality of images in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure.
[0021] FIG. 2 illustrates an exemplary detailed block diagram representation of the proposed system, according to embodiments of the present disclosure.
[0022] FIG. 3A illustrates an exemplary flow diagram representation of a method for filtering and re-ordering plurality of images using static rules, upon uploading by a seller, according to embodiments of the present disclosure.
[0023] FIG. 3B illustrates an exemplary flow diagram representation of a method for dynamically filtering and re-ordering plurality of images, upon uploading by a seller, according to embodiments of the present disclosure.
[0024] FIG. 3C illustrates an exemplary flow diagram representation of a method for dynamically re-ordering plurality of images based on a context of a user query, according to embodiments of the present disclosure.
[0025] FIG. 3D illustrates a plurality of exemplary flow diagram representations of image classification methods, according to embodiments of the present disclosure.
[0026] FIG. 3E illustrates an exemplary flow diagram representation of image classification during a dynamically re-ordering of the plurality of images based on a context of a user query, according to embodiments of the present disclosure.
[0027] FIG. 3F illustrates an exemplary schematic representation of a seller uploaded sequence and model corrected sequence, according to embodiments of the present disclosure.
[0028] FIG. 4 illustrates a flow chart depicting a method of sequencing a plurality of images in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure.
[0029] FIG. 5 illustrates a hardware platform for the implementation of the disclosed system according to embodiments of the present disclosure.
[0030] The foregoing shall be more apparent from the following more detailed description of the invention.

DETAILED DESCRIPTION OF INVENTION
[0031] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0032] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that, various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.
[0033] 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 in order to avoid obscuring the embodiments.
[0034] Also, it is noted that individual embodiments may be described as a process which 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.
[0035] 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.
[0036] As used herein, "connect", "configure", "couple" and its cognate terms, such as "connects", "connected", "configured", and "coupled" may include a physical connection (such as a wired/wireless connection), a logical connection (such as through logical gates of semiconducting device), other suitable connections, or a combination of such connections, as may be obvious to a skilled person.
[0037] As used herein, "send", "transfer", "transmit", and their cognate terms like "sending", "sent", "transferring", "transmitting", "transferred", "transmitted", etc. include sending or transporting data or information from one unit or component to another unit or component, wherein the content may or may not be modified before or after sending, transferring, transmitting.
[0038] 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 invention. 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.
[0039] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly 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.
[0040] Various embodiments of the present disclosure provide a method and a system for sequencing a plurality of images in an electronic commerce (e-commerce) environment. The present disclosure provides a method and a system for dynamically re-ordering images based on image guidelines, by processing the images. The present disclosure provides a method and a system for context (i.e., buyers’ query) aware image re-ordering, to display most of the relevant images first on the product page and search page of the e-commerce environment. The present disclosure provides a method and a system for identifying images to be discarded, which includes non-relevant images, images not meeting guidelines, Not Safe for Work (NSFW), and the like, and then re-order the images based on either set of rules or automatically using ML model trained on existing verified data. The present disclosure provides a method and a system for processing one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images. The present disclosure provides a method and a system for classifying the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images.
[0041] FIG. 1 illustrates an exemplary block diagram representation of a network architecture 100 implementing a proposed system 110 (i.e., an image sequencing system) for sequencing a plurality of images in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure. The network architecture 100 may include the system 110 (interchangeably referred to as the image sequencing system 110 or the system 110), an electronic device 108, and a centralized server 118. The system 110 may be connected to the centralized server 118 via a communication network 106. The centralized server 118 may include, but is not limited to, a stand-alone server, a remote server, a cloud computing server, a dedicated server, a rack server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof, and the like. The communication network 106 may be a wired communication network or a wireless communication network. The wireless communication network may be any wireless communication network capable of transferring data between entities of that network such as, but is not limited to, a Bluetooth, a Zigbee, a Near Field Communication (NFC), a Wireless-Fidelity (Wi-Fi), a Light Fidelity (Li-FI), a carrier network including a circuit-switched network, a public switched network, a Content Delivery Network (CDN) network, a Long-Term Evolution (LTE) network, a New Radio (NR), a Narrow-Band (NB), an Internet of Things (IoT) network, a Global System for Mobile Communications (GSM) network and a Universal Mobile Telecommunications System (UMTS) network, an Internet, intranets, Local Area Networks (LANs), Wide Area Networks (WANs), mobile communication networks, combinations thereof, and the like.
[0042] The system 110 may be implemented by way of a single device or a combination of multiple devices that may be operatively connected or networked together. For example, the system 110 may be implemented by way of a standalone device such as the centralized server 118, and the like, and may be communicatively coupled to the electronic device 108. In another example, the system 110 may be implemented in/ associated with the electronic device 108. In yet another example, the system 110 may be implemented in/ associated with respective computing device 104-1, 104-2, …..., 104-N (individually referred to as the computing device 104, and collectively referred to as the computing devices 104), associated with one or more user 102-1, 102-2, …..., 102-N (individually referred to as the user 102, and collectively referred to as the users 102). In such a scenario, the system 110 may be replicated in each of the computing devices 104. The users 102 may be a user of, but are not limited to, an electronic commerce (e-commerce) platform, a hyperlocal platform, a super-mart platform, a media platform, a service providing platform, a social networking platform, a messaging platform, a bot processing platform, a virtual assistance platform, an Artificial Intelligence (AI) based platform, a blockchain platform, a Non-Fungible Token (NFT) marketplace, and the like. In some instances, the user 102 may correspond to an entity/administrator of platforms/services.
[0043] The electronic device 108 may be at least one of, an electrical, an electronic, an electromechanical, and a computing device. The electronic device 108 may include, but is not limited to, a mobile device, a smart-phone, a Personal Digital Assistant (PDA), a tablet computer, a phablet computer, a wearable computing device, a Virtual Reality/Augment Reality (VR/AR) device, a laptop, a desktop, a server, and the like. The system 110 may be implemented in hardware or a suitable combination of hardware and software. The system 110 or the centralized server 118 may be associated with entities (not shown). The entities may include, but are not limited to, an e-commerce company, a company, an outlet, a manufacturing unit, an enterprise, a facility, an organization, an educational institution, a secured facility, a warehouse facility, a supply chain facility, and the like.
[0044] Further, the system 110 may include a processor 112, an Input/Output (I/O) interface 114, and a memory 116. The Input/Output (I/O) interface 114 of the system 110 may be used to receive user inputs, from the computing devices 104 associated with the users 102. Further, system 110 may also include other units such as a display unit, an input unit, an output unit, and the like, however the same are not shown in FIG. 1, for the purpose of clarity. Also, in FIG. 1 only a few units are shown, however, the system 110 or the network architecture 100 may include multiple such units or the system 110/ network architecture 100 may include any such numbers of the units, obvious to a person skilled in the art or as required to implement the features of the present disclosure. The system 110 may be a hardware device including the processor 112 executing machine-readable program instructions to sequence a plurality of images in an electronic commerce (e-commerce) environment.
[0045] Execution of the machine-readable program instructions by the processor 112 may enable the proposed system 110 to sequence a plurality of images in an electronic commerce (e-commerce) environment. The “hardware” may comprise a combination of discrete components, an integrated circuit, an application-specific integrated circuit, a field-programmable gate array, a digital signal processor, or other suitable hardware. The “software” may comprise one or more objects, agents, threads, lines of code, subroutines, separate software applications, two or more lines of code, or other suitable software structures operating in one or more software applications or on one or more processors. The processor 112 may include, for example, but is not limited to, microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and any devices that manipulate data or signals based on operational instructions, and the like. Among other capabilities, the processor 112 may fetch and execute computer-readable instructions in the memory 116 operationally coupled with the system 110 for performing tasks such as data processing, input/output processing, feature extraction, and/or any other functions. Any reference to a task in the present disclosure may refer to an operation being or that may be performed on data.
[0046] In the example that follows, assume that a user 102 of the system 110 desires to improve/add additional features for sequencing a plurality of images in an electronic commerce (e-commerce) environment. In this instance, the user 102 may include an administrator of a website, an administrator of an e-commerce site, an administrator of a social media site, an administrator of an e-commerce application/ social media application/other applications, an administrator of media content (e.g., television content, video-on-demand content, online video content, graphical content, image content, augmented/virtual reality content, metaverse content), an administrator of supply chain platform, an administrator of Non-Fungible Token (NFT) marketplace, among other examples, and the like. The system 110 when associated with the electronic device 108 or the centralized server 118 may include, but is not limited to, a touch panel, a soft keypad, a hard keypad (including buttons), and the like.
[0047] In an embodiment, the system 110 may receive a plurality of images from a seller (i.e., user 102) associated with an e-commerce environment. In an embodiment, the received plurality of images corresponds to, but is not limited to, non-relevant images of a product, images not meeting guidelines of the e-commerce environment, Not Safe for Work (NSFW) images, and the like.
[0048] In an embodiment, the system 110 may extract one or more features in the plurality of images using a feature extraction network model.
[0049] In an embodiment, the system 110 may process the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images. In an embodiment, the system 110 may generate a multi-head attention map for the linearly embedded plurality of images. For the transformer encoder model, the system 110 may provide an additional learnable embedding(s) with the plurality of images according to the sequence of the plurality of images, to predict the class of the plurality of images after updating the generated multi-head attention map.
[0050] In an embodiment, the system 110 may classify the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images. In an embodiment, the system 110 executes the MLP model to classify the plurality of images by sharing weight parameters between the plurality of images.
[0051] In an embodiment, the system 110 may sequence the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification. The sequenced plurality of images is stored in a database, to display to a buyer (i.e., user 102) associated with the e-commerce environment.
[0052] In an embodiment, the system 110 may reorder dynamically, the sequenced plurality of images stored in the database based on a context of the one or more queries, when the buyer (i.e., user 102) inputs one or more queries in the e-commerce environment.
[0053] FIG. 2 illustrates an exemplary detailed block diagram representation of the proposed system 110, according to embodiments of the present disclosure. The system 110 may include the processor 112, the Input/Output (I/O) interface 114, and the memory 116. In some implementations, the system 110 may include data 202, and modules 204. As an example, the data 202 may be stored in the memory 116 configured in the system 110 as shown in FIG. 2.
[0054] In an embodiment, the data 202 may include image data 206, features data 208, embedded data 210, multi-head attention map data 212, sequenced data 214, re-ordered data 216, and other data 218. In an embodiment, the data 202 may be stored in the memory 116 in the form of various data structures. Additionally, the data 202 can be organized using data models, such as relational or hierarchical data models. The other data 218 may store data, including temporary data and temporary files, generated by the modules 204 for performing the various functions of the system 110.
[0055] In an embodiment, the modules 204, may include a receiving module 222, an extracting module 224, a processing module 226, a classifying module 228, a sequencing module 230, a re-ordering module 232, and other modules 234.
[0056] In an embodiment, the data 202 stored in the memory 116 may be processed by the modules 204 of the system 110. The modules 204 may be stored within the memory 116. In an example, the modules 204 communicatively coupled to the processor 112 configured in the system 110, may also be present outside the memory 116, as shown in FIG. 2, and implemented as hardware. As used herein, the term modules refer to an Application-Specific Integrated Circuit (ASIC), an electronic circuit, a processor (shared, dedicated, or group) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
[0057] In an embodiment, the receiving module 222 may receive a plurality of images from a seller (i.e., user 102) associated with an e-commerce environment. In an embodiment, the received plurality of images corresponds to, but is not limited to, non-relevant images of a product, images not meeting guidelines of the e-commerce environment, Not Safe for Work (NSFW) images, and the like. The received plurality of images from the seller may be stored as the image data 206.
[0058] In an embodiment, the extracting module 224 may extract one or more features in the plurality of images using a feature extraction network model. The extracted one or more features may be stored as the features data 208.
[0059] In an embodiment, the processing module 226 may process the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images. The linearly embedded plurality of images may be stored as the embedded data 210. In an embodiment, the system 110 may generate a multi-head attention map for the linearly embedded plurality of images. The generated multi-head attention map of the plurality of images may be stored as the multi-head attention map data 212. For the transformer encoder model, the system 110 may provide an additional learnable embedding(s) with the plurality of images according to the sequence of the plurality of images, to predict the class of the plurality of images after updating the generated multi-head attention map.
[0060] In an embodiment, the classifying module 228 may classify the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images. In an embodiment, the system 110 executes the MLP model to classify the plurality of images by sharing weight parameters between the plurality of images.
[0061] In an embodiment, the system 110 may sequence the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification. The sequenced plurality of images is stored in a database, to display to a buyer (i.e., user 102) associated with the e-commerce environment. The sequence plurality of images in the pre-defined pattern may be stored as the sequenced data 214.
[0062] In an embodiment, the system 110 may re-order dynamically, the sequenced plurality of images stored in the database based on a context of the one or more queries, when the buyer (i.e., user 102) inputs one or more queries in the e-commerce environment. The dynamically re-ordered plurality of images may be stored as the re-ordered data 216.
Exemplary scenario 1:
[0063] Consider a scenario, where the seller is uploading a plurality of images of the product on an e-commerce platform. For example, the seller uploads images of a dress to the e-commerce platform as shown in FIG. 3A (i.e., seller path with static rules). The system 110 may automatically sort and select the uploaded plurality of images, based on ordering the images using static rules or re-ordering the images based on existing data. The system 110 may output the sorted and selected images based on ordering/ re-ordering of the images. Further, the system 110 may store the output images in a database.
[0064] Consider, another scenario where the seller uploads a plurality of images of a product and the user queries a product. For example, the seller uploads images of a dress to the e-commerce platform as shown in FIG. 3B (i.e., seller path with dynamic rules). In this flow, images are filtered and reordered right after the images are uploaded by the sellers and then stored in the database. Further, the system 110 may re-order the stored images, when a user queries the product (e.g., sleeveless floral dress), and whenever a product is selected to be displayed as a response to the search query, as shown in FIG. 3C (i.e., user path with dynamic re-ordering rules). In this second re-ordering, context from the user query is considered to re-order the images in such a way that the best image is displayed first according to the user query.
Exemplary scenario 2:
[0065] Currently, the uploaded images are classified using a feature extraction network model, and MLP model as shown in FIG. 3D (a), (b). The output from the MLP model is the classes of the images. The classes may include, but are not limited to, primary view, secondary view, another view, unrelated image, and the like. For example, the embedding layer may be a keyword for example, “gown”, to the MLP model, as shown in FIG. 3D (b).
[0066] In another example, consider a scenario where the seller uploads a plurality of images to the e-commerce platform, as shown in FIG. 3D (c), (d), and (e). The proposed system 110 may receive a plurality of images from a seller (i.e., user 102) associated with the e-commerce environment. The received plurality of images corresponds to, but is not limited to, non-relevant images of a product, images not meeting guidelines of the e-commerce environment, Not Safe for Work (NSFW) images, and the like. Further, the system 110 may extract one or more features in the plurality of images using a feature extraction network model.
[0067] Further, the system 110 may process the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images. Further, the system 110 may generate a multi-head attention map for the linearly embedded plurality of images, as shown in FIG. 3D (c), (d), and (e). For the transformer encoder model, the system 110 may provide an additional learnable embedding(s) with the plurality of images according to the sequence of the plurality of images, to predict the class of the plurality of images after updating the generated multi-head attention map.
[0068] Further, the system 110 may classify the plurality of images using a Multi-Layer Perceptron (MLP) model (i.e., MLP head), based on the multi-head attention map of the plurality of images. Further, the system 110 executes the MLP model to classify the plurality of images by sharing weight parameters between the plurality of images. The MLP model may output the classified plurality of images as shown in FIG. 3F.
[0069] In another example, consider a user who has inputted a query to the e-commerce environment, as shown in FIG. 3E. The system 110 may sequence the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification. The sequenced plurality of images is stored in a database, to display to a buyer (i.e., user 102) associated with the e-commerce environment. Further, the system 110 may re-order dynamically, the sequenced plurality of images stored in the database based on a context of the one or more queries (i.e., sentence embedding), when the buyer (i.e., user 102) inputs one or more queries in the e-commerce environment.
[0070] FIG. 4 illustrates a flow chart depicting a method 400 of sequencing images in an electronic commerce (e-commerce) environment, according to embodiments of the present disclosure.
[0071] At block 402, the method 400 includes, receiving, by the processor 112 associated with the image sequencing system 110, a plurality of images from a seller (i.e., user 102) associated with the e-commerce environment.
[0072] At block 404, the method 400 includes extracting, by the processor 112, one or more features in the plurality of images using the feature extraction network model.
[0073] At block 406, the method 400 includes processing, by the processor 112, the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images.
[0074] At block 408, the method 400 includes classifying, by the processor 112, the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images.
[0075] At block 410, the method 400 includes sequencing, by the processor 112, the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification. The sequenced plurality of images is stored in a database, to display to a buyer (i.e., user 102) associated with the e-commerce environment.
[0076] At block 412, the method 400 includes reordering dynamically, by the processor 112, the sequenced plurality of images stored in the database, based on a context of the one or more queries, when the buyer inputs one or more queries in the e-commerce environment.
[0077] The order in which the method 400 is described is not intended to be construed as a limitation, and any number of the described method blocks may be combined or otherwise performed in any order to implement the method 400 or an alternate method. Additionally, individual blocks may be deleted from the method 400 without departing from the spirit and scope of the present disclosure described herein. Furthermore, the method 400 may be implemented in any suitable hardware, software, firmware, or a combination thereof, that exists in the related art or that is later developed. The method 400 describes, without limitation, the implementation of the system 110. A person of skill in the art will understand that method 400 may be modified appropriately for implementation in various manners without departing from the scope and spirit of the disclosure.
[0078] FIG. 5 illustrates a hardware platform 500 for implementation of the disclosed system 110, according to an example embodiment of the present disclosure. For the sake of brevity, the construction, and operational features of the system 110 which are explained in detail above are not explained in detail herein. Particularly, computing machines such as but not limited to internal/external server clusters, quantum computers, desktops, laptops, smartphones, tablets, and wearables which may be used to execute the system 110 or may include the structure of the hardware platform 500. As illustrated, the hardware platform 500 may include additional components not shown, and that some of the components described may be removed and/or modified. For example, a computer system with multiple GPUs may be located on external-cloud platforms including Amazon® Web Services, or internal corporate cloud computing clusters, or organizational computing resources, and the like.
[0079] The hardware platform 500 may be a computer system such as the system 110 that may be used with the embodiments described herein. The computer system may represent a computational platform that includes components that may be in a server or another computer system. The computer system may execute, by the processor 505 (e.g., a single or multiple processors) or other hardware processing circuit, the methods, functions, and other processes described herein. These methods, functions, and other processes may be embodied as machine-readable instructions stored on a computer-readable medium, which may be non-transitory, such as hardware storage devices (e.g., RAM (random access memory), ROM (read-only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory). The computer system may include the processor 505 that executes software instructions or code stored on a non-transitory computer-readable storage medium 510 to perform methods of the present disclosure. The software code includes, for example, instructions to gather data and documents and analyze documents. In an example, the modules 204, may be software codes or components performing these steps.
[0080] The instructions on the computer-readable storage medium 510 are read and stored the instructions in storage 515 or in random access memory (RAM). The storage 515 may provide a space for keeping static data where at least some instructions could be stored for later execution. The stored instructions may be further compiled to generate other representations of the instructions and dynamically stored in the RAM such as RAM 520. The processor 505 may read instructions from the RAM 520 and perform actions as instructed.
[0081] The computer system may further include the output device 525 to provide at least some of the results of the execution as output including, but not limited to, visual information to users, such as external agents. The output device 525 may include a display on computing devices and virtual reality glasses. For example, the display may be a mobile phone screen or a laptop screen. GUIs and/or text may be presented as an output on the display screen. The computer system may further include an input device 530 to provide a user or another device with mechanisms for entering data and/or otherwise interacting with the computer system. The input device 530 may include, for example, a keyboard, a keypad, a mouse, or a touchscreen. Each of these output devices 525 and input device 530 may be joined by one or more additional peripherals. For example, the output device 525 may be used to display the results such as bot responses by the executable chatbot.
[0082] A network communicator 535 may be provided to connect the computer system to a network and in turn to other devices connected to the network including other clients, servers, data stores, and interfaces, for instance. A network communicator 535 may include, for example, a network adapter such as a LAN adapter or a wireless adapter. The computer system may include a data sources interface 540 to access the data source 545. The data source 545 may be an information resource. As an example, a database of exceptions and rules may be provided as the data source 545. Moreover, knowledge repositories and curated data may be other examples of the data source 545.
[0083] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the invention. These and other changes in the preferred embodiments of the invention will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the invention and not as a limitation.

ADVANTAGES OF THE PRESENT DISCLOSURE
[0084] The present disclosure provides a method and a system for sequencing a plurality of images in an electronic commerce (e-commerce) environment.
[0085] The present disclosure provides a method and a system for dynamically re-ordering images based on image guidelines, by processing the images.
[0086] The present disclosure provides a method and a system for context (i.e., buyers’ query) aware image re-ordering, to display the most relevant images first on the product page and search page of the e-commerce environment.
[0087] The present disclosure provides a method and a system for identifying images to be discarded, which includes non-relevant images, images not meeting guidelines, Not Safe for Work (NSFW), and the like, and then re-order the images based on either set of rules or automatically using ML model trained on existing verified data.
[0088] The present disclosure provides a method and a system for processing one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images.
[0089] The present disclosure provides a method and a system for classifying the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images.
, Claims:1. A method for sequencing a plurality of images in an electronic commerce (e-commerce) environment, the method comprising:
receiving, by a processor (112) associated with an image sequencing system (110), a plurality of images from a seller (102) associated with an e-commerce environment;
extracting, by the processor (112), one or more features in the plurality of images using a feature extraction network model;
processing, by the processor (112), the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generating a multi-head attention map for the linearly embedded plurality of images;
classifying, by the processor (112), the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images;
sequencing, by the processor (112), the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification, wherein the sequenced plurality of images are stored in a database, to display to a buyer (102) associated with the e-commerce environment; and
reordering dynamically, by the processor (112), the sequenced plurality of images stored in the database, based on a context of the one or more queries, when the buyer (102) inputs one or more queries in the e-commerce environment.
2. The method as claimed in claim 1, wherein, for the transformer encoder model, providing, by the processor (112), an additional learnable embedding with the plurality of images according to the sequence of the plurality of images, to predict the class of the plurality of images after updating the generated multi-head attention map.
3. The method as claimed in claim 1, wherein the MLP model classifies the plurality of images by sharing weight parameters between the plurality of images.
4. The method as claimed in claim 1, wherein the received plurality of images corresponds to at least one of non-relevant images of a product, images not meeting guidelines of the e-commerce environment, and Not Safe For Work (NSFW) images.
5. An image sequencing system (110) for sequencing a plurality of images in an electronic commerce (e-commerce) environment, the image sequencing system (110) comprising:
a processor (112); and
a memory (116) coupled to the processor (112), wherein the memory (116) comprises processor-executable instructions, which on execution, cause the processor (112) to:
receive a plurality of images from a seller (102) associated with an e-commerce environment;
extract one or more features in the plurality of images using a feature extraction network model;
process the extracted one or more features using a transformer encoder model, by linearly embedding the plurality of images, and generate a multi-head attention map for the linearly embedded plurality of images;
classify the plurality of images using a Multi-Layer Perceptron (MLP) model, based on the multi-head attention map of the plurality of images;
sequence the received plurality of images in a pre-defined pattern, using one or more static rules and historical data for sequencing the plurality of images, based on the classification, wherein the sequenced plurality of images is stored in a database, to display to a buyer (102) associated with the e-commerce environment; and
reorder dynamically, the sequenced plurality of images stored in the database based on a context of the one or more queries, when the buyer (102) inputs one or more queries in the e-commerce environment.
6. The image sequencing system (110) as claimed in claim 5, wherein, for the transformer encoder model, the processor (112) is configured to provide an additional learnable embedding with the plurality of images according to the sequence of the plurality of images, to predict the class of the plurality of images after updating the generated multi-head attention map.
7. The image sequencing system (110) as claimed in claim 5, wherein the processor (112) executes the MLP model to classify the plurality of images by sharing weight parameters between the plurality of images.
8. The image sequencing system (110) as claimed in claim 5, wherein the received plurality of images corresponds to at least one of non-relevant images of a product, images not meeting guidelines of the e-commerce environment, and Not Safe for Work (NSFW) images.