Methods and systems for generating user-specific content

Abstract

To provide a method and system for generating user-specific content. [Solution] The method includes the step of receiving user-specific input data, base content, and e-commerce data by a processing unit [104]. The method further includes the step of extracting a set of features from the user-specific input data by the processing unit [104]. Furthermore, the method includes the step of modifying the base content based on the e-commerce data and the set of features by the processing unit [104]. Furthermore, the method includes the step of generating user-specific content based on the modification step by the processing unit [104], wherein the user-specific content includes at least one of user-specific visual media and user-specific audiovisual media.

Description

Technical Field

[0001] Embodiments of the present disclosure generally relate to methods of image processing. More particularly, the present disclosure relates to methods and systems for generating user-specific content.

Background Art

[0002] The following description of related art provides background information related to the field of the present disclosure. This section may include some aspects of technologies that may relate to various features of the present disclosure. However, it should be understood that this section is only used to enhance the reader's understanding of the present disclosure and is not used as an admission of prior art.

[0003] When consuming content on a media rendering device such as a television (TV), a user may experience emotions that allow the user to connect with the content and even imagine scenarios. For example, while viewing an album of family holidays, the user may imagine the next family holiday, or while watching the user's favorite team win a game, the user may imagine being present at the moment of victory. The content can be general content such as news, sports, movies, etc., or personal content such as family photos.

[0004] Currently, with the emergence of generative artificial intelligence (AI), there are market-available solutions that transform user images into visuals based on user input prompts. For example, a user can upload their photo and prompt the system to "transform me into a football player." However, conventional technologies rely heavily on explicit user input. Furthermore, these existing concepts are isolated and do not extend to integration into content experiences, which form a crucial part of user engagement in connected devices. These technologies are also absent in devices like connected televisions (TVs). Finally, existing solutions simply work in accordance with user prompts and do not extend to showing users a multitude of future and / or imaginary scenarios.

[0005] To address the aforementioned and other related specific problems of existing technologies, there is an urgent need to provide efficient methods and systems for generating user-specific content. [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] Some of the purposes of this disclosure that are satisfied by at least one embodiment disclosed herein are set forth below.

[0007] One objective of this disclosure is to provide a system and method for generating user-specific content.

[0008] Another purpose of this disclosure is to provide a solution that can deliver a virtual experience of a real moment to the user of a user device.

[0009] Another purpose of this disclosure is to provide a solution that can give users of user devices the option to imagine future scenarios and / or fantasies through generated media content such as images or videos.

[0010] Another purpose of this disclosure is to provide a solution that can offer users of user devices the option to relive past memories in the present through generated media content such as images or videos.

[0011] Another purpose of this disclosure is to provide a solution that enables the user to generate personalized visuals, in which the user is a part, relating to content preferred by the user of the user device.

[0012] Another purpose of this disclosure is to provide a solution that can identify non-human objects in an image, which users of user devices may wish to purchase.

[0013] Another purpose of this disclosure is to provide a solution that allows users to search in an e-commerce catalog for one or more products that match non-human objects present in an image or video.

[0014] Another purpose of this disclosure is to provide a solution that can provide on the user device a link to purchase products that match non-human objects present in media rendered on the user device, the solution being generated on the user device to provide the user of the user device with a virtual experience of a real moment. [Means for solving the problem]

[0015] This section is provided to give a simplified overview of some aspects of the present disclosure, which will be further described below in the embodiments for carrying out the invention. This summary does not identify any essential features or scope of the claimed subject matter.

[0016] One aspect of this disclosure may relate to a method for generating user-specific content. The method includes the step of a processing unit receiving user-specific input data, base content, and e-commerce data. The method further includes the step of a processing unit extracting a set of features from the user-specific input data. The method further includes the step of a processing unit modifying the base content based on the e-commerce data and the set of features. The method further includes the step of a processing unit generating user-specific content based on the modification step, wherein the user-specific content includes at least one of user-specific visual media and user-specific audiovisual media.

[0017] In exemplary embodiments of this disclosure, e-commerce data relates to base content and user-specific input data.

[0018] In exemplary aspects of this disclosure, the base content is at least one of visual media and audiovisual media relating to at least one of the following: trending events, news, sports, geographical areas, weather conditions, entertainment-related events, and images of one or more users on a user device.

[0019] In exemplary embodiments of the Disclosure, user-specific input data is received as at least one of auto-entry and manual entry from one or more users of the user device, and the user-specific input data includes a set of images relating to at least one of people, animals, and objects, and at least one of user-related data, user preference data, content usage pattern information, and content engagement information.

[0020] In exemplary embodiments of the present disclosure, the set of features includes at least one of the following: a set of face parameters, a set of age-related parameters, a set of gender-related parameters, a set of environment-related parameters, a set of non-human object-related parameters, and a set of characteristic-related parameters.

[0021] In exemplary embodiments of this disclosure, a set of features is extracted by a processing unit using a first artificial intelligence-based subsystem.

[0022] In exemplary embodiments of the present disclosure, the steps of modifying base content by a processing unit further include: 1) the processing unit generating one or more suggestions based on user-specific input data using a second artificial intelligence-based subsystem; 2) the processing unit providing one or more suggestions on a user device; 3) the processing unit receiving responses to one or more suggestions from the user device; and 4) the processing unit modifying base content based on the responses.

[0023] In exemplary embodiments of this disclosure, user-specific content includes one or more links for purchasing one or more items visible within the user-specific content, one or more links are added during the modification step, and one or more links are identified based on e-commerce data.

[0024] In an exemplary aspect of the present disclosure, user-specific content includes one or more advertisements (Ads), one or more Ads are added during the step of modification, and one or more Ads are identified based on at least one of user-specific input data, base content, and e-commerce data.

[0025] In an exemplary aspect of the present disclosure, a method further includes: 1) providing, by a processing unit, user-specific content generated on a user device; 2) receiving, by the processing unit, user feedback on one or more portions of the generated user-specific content from the user device; and 3) updating, by the processing unit, the generated user-specific content based on the user feedback.

[0026] In an exemplary aspect of the present disclosure, the user device is a smart TV.

[0027] In an exemplary aspect of the present disclosure, a method for generating user-specific content includes performing a compliance check for the user-specific content.

[0028] Another aspect of the present disclosure may relate to a system for generating user-specific content. The system includes a storage unit and a processing unit connected at least to the storage unit. The processing unit is configured to receive user-specific input data, base content, and e-commerce data. The processing unit is further configured to extract a set of features from the user-specific input data. Further, the processing unit is configured to modify the base content based on the e-commerce data and the set of features. Further, the processing unit is configured to generate user-specific content based on the modification, wherein the user-specific content includes at least one of user-specific visual media and user-specific audio-visual media.

[0029] Another object of the present disclosure may relate to a non-transitory computer-readable storage medium storing one or more instructions for generating user-specific content, the instructions including executable code that, when executed by one or more units of the system, causes the processing unit of the system to receive user-specific input data, base content, and e-commerce data. Further, the executable code causes the processing unit to extract a set of features from the user-specific input data when executed. Further, the executable code causes the processing unit to modify the base content based on the e-commerce data and the set of features when executed. Further, the executable code causes the processing unit to generate user-specific content based on the modification, the user-specific content including at least one of user-specific visual media and user-specific audiovisual media.

[0030] The accompanying drawings incorporated herein form a part of the present disclosure. The components in the drawings are not necessarily to scale, and instead, emphasis is placed on clearly showing the principles of the present disclosure. Some of the drawings may use block diagrams to show components and may not represent the internal circuits of each component. It will be understood by those skilled in the art that the disclosure of such drawings may include the disclosure of electrical components or circuits generally used to implement such components. Exemplary connections between sub-components are shown in the accompanying drawings, but it will be understood by those skilled in the art that other connections may be possible without departing from the scope of the present disclosure. Unless otherwise specified, all sub-components within a component may be connected to each other.

Brief Description of the Drawings

[0031] [Figure 1] FIG. is an exemplary block diagram of a system for generating user-specific content according to an exemplary embodiment of the present disclosure. [Figure 2] This is an exemplary flowchart of a method for generating user-specific content according to exemplary embodiments of the present disclosure. [Figure 3] This is an exemplary flowchart for generating user-specific content according to an exemplary embodiment of the present disclosure. [Modes for carrying out the invention]

[0032] The above will become clearer from the more detailed explanation provided in this disclosure below.

[0033] In the following description, various specific details are included for illustrative purposes to provide a complete understanding of the embodiments of this disclosure. However, it will be apparent that embodiments of this disclosure may be carried out without these specific details. Some of the features described below may be used independently of each other or in any combination of other features. Individual features may not address any of the issues described above, or may address only some of the issues described above.

[0034] The following description provides only exemplary embodiments and does not limit the scope, applicability, or configuration of the disclosure. Rather, the following description of exemplary embodiments will provide a description that enables the implementation of the exemplary embodiments for those skilled in the art. It should be understood that various modifications may be made to the function and arrangement of the elements without departing from the spirit and scope of the disclosure described herein.

[0035] Specific details are given in the following description to provide a complete understanding of the embodiments. However, it will be understood by those skilled in the art that embodiments may be carried out without these specific details. For example, circuits, systems, processes, and other components may be shown as components in block diagram form so as not to obscure the embodiments with unnecessary details.

[0036] Furthermore, it should be noted that individual embodiments may be described as processes, represented as flowcharts, flow diagrams, data flow diagrams, structural diagrams, or block diagrams. While flowcharts can describe operations as sequential processes, many of the operations may be performed in parallel or simultaneously. In addition, the order of operations may be rearranged. A process terminates when its operations are completed, but it may have additional steps not shown in the diagram.

[0037] The terms “exemplary” and / or “demonstrative” are used herein to mean that they serve as examples, cases, or illustrations. To avoid 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” should not necessarily be construed as being preferable or advantageous to other aspects or designs, nor is it intended to exclude equivalent exemplary structures and techniques known to those skilled in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the form for carrying out the invention or the claims, such terms are intended to be inclusive—as with the term “comprising” as an open transition word—without excluding any additional or other elements.

[0038] As used herein, “processing unit,” “processor,” or “operating processor” includes one or more processors, where a processor refers to any logic circuit for processing instructions. A processor may be a general-purpose processor, a dedicated processor, a conventional processor, a digital signal processor, a plurality of microprocessors, one or more microprocessors associated with a Digital Signal Processing (DSP) core, a controller, a microcontroller, an application-specific integrated circuit, a field-programmable gate array circuit, or any other type of integrated circuit. A processor may perform signal coding data processing, input / output processing, and / or any other functions that enable the work of the system according to this disclosure. More specifically, a processor or processing unit is a hardware processor. Furthermore, to perform certain operations, a processing unit / processor disclosed herein may include one or more Central Processing Units (CPUs) and one or more Graphics Processing Units (GPUs), selected based on the aforementioned operations. Furthermore, a Graphics Processing Unit (GPU) is a specialized electronic circuit designed to rapidly manipulate and modify memory in order to accelerate the creation of images in a frame buffer for output to a display device.

[0039] As used herein, “storage unit” or “memory unit” means a machine-readable medium or computer-readable medium that includes any mechanism for storing information in a form readable by a computer or similar machine. For example, computer-readable medium includes read-only memory (ROM), random access memory (RAM), magnetic disk storage, optical storage, flash memory devices, or other types of machine-accessible storage. A storage unit can be any type of storage unit, such as cloud or content delivery network (CDN) storage, public, shared, private, or telecommunications carrier-based storage, or any other type of storage that may be obvious to a person skilled in the art, is known in the art, or may be developed in the future, in order to implement the features of this disclosure. A storage unit stores data that may be required by one or more units of a server / system / user device to perform their respective functions.

[0040] "Smart computing device" or "user device" means any electrical, electronic, electromechanical device, or combination thereof. A smart computing device may include, but is not limited to, mobile phones, smartphones, pagers, laptops, general-purpose computers, desktops, personal digital assistants, tablet computers, mainframe computers, smart televisions, gaming consoles, media streaming devices, or any other computing device that may be obvious to a person skilled in the art for implementing the features disclosed herein. Generally, a smart computing device is a digital, user-configured, autonomously operating, computer-networked device. A smart computing device is one of several suitable systems for storing data.

[0041] This subject matter will be further explained with reference to the attached figures. Wherever possible, the same reference numerals are used in the figures and the following description to refer to the same or similar parts. Note that the description and figures are merely illustrative of the principles of this subject matter. Therefore, it should be understood that various arrangements encompassing the principles of this subject matter may be devised, even if they are not expressly described or illustrated herein. Furthermore, all descriptions herein describing the principles, embodiments, and examples of this subject matter, as well as specific examples thereof, are intended to encompass their equivalents.

[0042] The method by which user-specific content is generated is described in detail with respect to Figures 1 to 3. Please note that the diagrams of this subject shown herein are for illustrative purposes only and should not be construed as limiting the scope of the claimed subject.

[0043] Referring to Figure 1, an exemplary block diagram of a system

[0100] for generating user-specific content according to an exemplary embodiment of the present disclosure is shown. The system

[0100] comprises at least one storage unit

[0102] and at least one processing unit

[0104] . It is assumed that all components / units of the system

[0100] are connected to one another unless otherwise specified below. Although only a few units are shown in Figure 1, the system

[0100] may comprise multiple such units, or the system

[0100] may comprise any number of such units as required to implement the features of the present disclosure. In one implementation, the system

[0100] may reside in a server connected to a user device. In another implementation, the system

[0100] may reside in a user device. In yet another implementation, the system

[0100] may reside partially in a server and / or a user device in a manner that will be understood by those skilled in the art in light of the present disclosure. In another implementation, the system

[0100] may be connected to a server and a user device in a manner that would be understood by a person skilled in the art in light of this disclosure.

[0044] In the operation for generating user-specific content, processing unit

[0104] receives user-specific input data, base content, and e-commerce data. In one implementation, user-specific input data is received as at least one of the following: automatic input and manual input from one or more users of the user device. Automatic input is user-specific input data automatically received from cloud storage by processing unit

[0104] . The user may integrate cloud storage with system

[0100] for automatic retrieval of input from cloud storage (e.g., user-specific input data). Furthermore, manual input is user-specific input data manually shared by the user, for example, through a quick response (QR) code. The user may scan a QR code (registered trademark) to share user-specific data with system

[0100] . If the QR code scan is successful, processing unit

[0104] may receive user-specific input data. It is appropriate to note that other means of providing manual input, which can be understood by those skilled in the art, may also be conceivable for implementing the features disclosed herein.

[0045] In one implementation, user-specific input data comprises a set of images relating to at least one of people, animals, and objects, and at least one of user-related data, user preference data, content usage pattern information, and content engagement information. In one implementation, the processing unit

[0104] may also receive text input data. Text input data may include one or more text strings that are essentially descriptive. In one implementation, text input data may be automatically generated based on user-specific input data and base content. In another implementation, text input data may be manually provided by the user of the user device based on one or more specific parameters. One or more specific parameters may include, but are not limited to, clothing, background scenes in images, etc. Furthermore, text input data may be provided to the processing unit

[0104] at any time, such as at the start of the process of generating user-specific content, and / or as a recommendation or feedback after user-specific content has been generated. Furthermore, user-specific content is generated by converting the received user-specific input data and base content into user-specific content.

[0046] Furthermore, the base content is at least one of visual media and audiovisual media relating to at least one of the following: trending events, news, sports, geographical areas, weather conditions, entertainment-related events, images of one or more users of a user device, and other information that would be understood by a person skilled in the art in light of this disclosure.

[0047] Furthermore, e-commerce data relates to base content and user-specific input data. E-commerce data may be received from the e-commerce catalog of the e-commerce platform to generate user-specific content. The e-commerce catalog contains data relating to multiple products available on the e-commerce platform. Thus, based on user-specific data and base content, e-commerce data containing product details related to the base content and user-specific input data is received from the e-commerce catalog to generate user-specific content.

[0048] Furthermore, the processing unit

[0104] is also configured to extract a set of features from user-specific input data. The set of features comprises at least one of the following: a set of face parameters, a set of age-related parameters, a set of gender-related parameters, a set of environment-related parameters, a set of non-human object-related parameters, and a set of characteristic-related parameters. The set of face parameters comprises one or more parameters that indicate the details of one or more faces related to an object, such as one or more users of a user device. The set of age-related parameters comprises one or more parameters that indicate age-related information. The set of gender-related parameters comprises one or more parameters that indicate gender-related information. The set of environment-related parameters comprises one or more parameters that indicate environment-related information. The set of non-human object-related parameters comprises one or more parameters that indicate information about non-human objects. The set of characteristic-related parameters comprises one or more parameters that indicate information related to the characteristics of an object. The set of features is extracted by the processing unit

[0104] using a first artificial intelligence-based subsystem.

[0049] In one implementation, the first artificial intelligence-based subsystem may be a personal reference image analyzer. The personal reference image analyzer may employ an advanced visual model to analyze visual data, such as a set of images provided by the user of the user device. The set of images may include, but are not limited to, images of one or more individuals, such as the user, the user's family, the user's friends, and / or one or more animals. The personal reference image analyzer may extract important features related to one or more individuals and / or one or more animals present in the set of images provided by the user. Important features may include, but are not limited to, one or more facial features, age, and gender of one or more individuals and / or one or more animals present in the set of images provided by the user of the user device.

[0050] Consider an example where the set of images includes images of User A, User B, User C, and User D. In such an example, the Personal Reference Image Analyzer may extract facial features that provide details such as User A having a beard, User B having a round face, and User C having a cut or mole on their face. Furthermore, the Personal Reference Image Analyzer may identify the age and gender of one or more individuals present in the set of images, such as User A being identified as 30 years old and male, User B being identified as 29 years old and male, User C being identified as 32 years old and female, and User D being identified as 19 years old and male.

[0051] Therefore, in one implementation, the first artificial intelligence-based subsystem is an age and gender classifier. The age and gender classifier can clearly identify the age and gender of one or more individuals in a set of images provided by the user of the user device, thereby improving accuracy and personalization when generating user-specific content.

[0052] Further, the processing unit

[0104] is configured to modify the base content based on e-commerce data and a set of features. The received base content is converted into output media, such as an output visual, based on the set of features and the e-commerce data. For example, user-specific input data is received, which includes base content, e.g., audio-video media related to a geographical area including a waterfall in a forest, and a set of images. The received set of images includes images of one or more individuals, including the user of the user device and the user's family / friends. In this example, one or more text inputs from the user of the user device are also received to generate user-specific content. Upon receiving the set of images, a set of features, including but not limited to age, gender, and facial features, is extracted along with the e-commerce data. The received base content (e.g., an audio-video media file) is then converted into a visual that includes one or more individuals present in the set of images, i.e., the visual includes, but not limited to, one or more individuals present at a waterfall in a forest. For example, the text input includes data indicating that one or more individuals to be added to the visual may be wearing tracking clothing. Therefore, the converted visual may include one or more individuals wearing tracking clothing at a waterfall in a forest. Furthermore, the visual may also include e-commerce data related to the base content and user-specific input data. The e-commerce data may include, but is not limited to, details such as links to purchase tracking clothing.

[0053] Furthermore, in order to modify the base content, the processing unit

[0104] is configured to generate one or more suggestions based on user-specific input data using a second artificial intelligence-based subsystem. The processing unit

[0104] is then configured to provide one or more suggestions on the user device. In one implementation, the user device is a smart television.

[0054] In one implementation, the second artificial intelligence-based subsystem is the Gen AI Prompt Generator. The Gen AI Prompt Generator can generate one or more prompts based on user-specific input. The generated prompts can act as instructions for generating one or more user-specific content. Furthermore, in one implementation, one or more suggestions are one or more reverse prompts that can suggest one or more modifications to the base content based on user-specific content that indicates the user's interests. Thus, by suggesting user-interest-based modifications that will be added to the base content, one or more reverse prompts show the user one or more new possibilities to improve the user's interactive experience.

[0055] Furthermore, it should be noted that the one or more of the aforementioned suggestions are illustrative only and do not in any way limit the scope of this disclosure.

[0056] Further, the processing unit

[0104] is configured to receive responses to one or more suggestions from the user device, and based on the responses received from the user device, the processing unit

[0104] then modifies the base content. Based on the modification of the base content, the processing unit

[0104] generates user-specific content. The user-specific content comprises at least one of user-specific visual media and user-specific audiovisual media. In one implementation, the user-specific content comprises one or more links for purchasing one or more items visible within the user-specific content. The one or more links are added during the modification of the base content, and the one or more links are identified based on e-commerce data. For example, the processing unit

[0104] may generate one or more suggestions (i.e., one or more reverse prompts), such as "Would you like to see memories in an autumn setting?" or "Imagine scenarios in a future city landscape." Then, based on the user's selection of such reverse prompts, the corresponding base content is modified by the processing unit

[0104] according to the reverse prompts in order to generate user-specific content. The generated user-specific content may also include one or more links to purchase one or more items visible within the user-specific content, such as clothing, shoes, musical instruments, etc.

[0057] Furthermore, in one implementation, the system

[0100] may use an enhanced object detection technique to add one or more links in the base content for purchasing one or more items visible within the user-specific content. The enhanced object detection technique can detect and analyze not only human subjects but also non-human objects in a set of images relating to at least one of people, animals, and objects. Non-human objects may include, but are not limited to, the surroundings, clothing, and household furniture. Analysis of both human subjects and non-human objects may generate more comprehensive and contextually accurate user-specific content.

[0058] Next, using an enhanced object detection technique, if a non-human object is identified, the system

[0100] may search an e-commerce catalog to find one or more products that match the identified one or more non-human objects. Subsequently, links for purchasing one or more products that match the identified one or more non-human objects are added to the base content during the base content modification for generating user-specific content. In one implementation, a quick response (QR) code may also be added for the link along with one or more matched products during the base content modification for purchasing one or more matched products via user-specific content.

[0059] Furthermore, in one implementation, user-specific content includes one or more advertisements (Ads). One or more Ads are added during changes to the base content, and one or more Ads are identified based on at least one of the following: user-specific input data, base content, and e-commerce data.

[0060] In one implementation, the processing unit

[0104] is configured to provide generated user-specific content on the user device, and then to receive user feedback from the user device regarding one or more parts of the generated user-specific content. For example, the user may provide feedback to improve the generated user-specific content based on one or more specific parameters. One or more specific parameters may include, but are not limited to, body shape, image style, objects, etc., in the generated user-specific content. The processing unit

[0104] is then configured to update the generated user-specific content based on the user feedback. The generated user-specific content may also be updated in real time based on the user feedback and regenerated to revert to its original state.

[0061] Furthermore, in one implementation, in order to generate user-specific content, the processing unit

[0104] is configured to perform suitability checks for user-specific content. The suitability checks are performed by checking whether any information relating to the user-specific input data and base content maps to a predefined set of critical issues in order to confirm that the user-specific input data and base content are eligible for conversion to user-specific content. For example, base content, such as news relating to an issue that maps to a critical issue in a predefined set of critical issues, or any sensitive statement given by a politician that maps to a critical issue in a predefined set of critical issues, may lead to critical issues and therefore would not be considered suitable for conversion to user-specific content.

[0062] Referring to Figure 2, an exemplary flowchart of a method

[0200] for generating user-specific content according to an exemplary embodiment of the present disclosure is shown. In one implementation, method

[0200] is performed by system

[0100] . Method

[0200] shown in Figure 2 begins in step

[0202] .

[0063] In step

[0204] , method

[0200] includes receiving user-specific input data, base content, and e-commerce data by processing unit

[0104] . In one implementation, user-specific input data is received as at least one of auto-input and manual input from one or more users of the user device. Auto-input is user-specific input data automatically received from cloud storage by processing unit

[0104] . The user may integrate cloud storage with system

[0100] for automatic retrieval of input from cloud storage (e.g., user-specific input data). Furthermore, manual input is user-specific input data manually shared by the user, for example, through a quick response (QR) code. The user may scan a QR code to share user-specific data with system

[0100] . If the QR code scan is successful, processing unit

[0104] may receive the user-specific input data. It is appropriate to note that other means of providing manual input, which can be understood by those skilled in the art, may also be conceivable for implementing the features disclosed herein.

[0064] In one implementation, user-specific input data comprises a set of images relating to at least one of people, animals, and objects, and at least one of user-related data, user preference data, content usage pattern information, and content engagement information. In one implementation, the processing unit

[0104] may also receive text input data. Text input data may include one or more text strings that are essentially descriptive. In one implementation, text input data may be automatically generated based on user-specific input data and base content. In another implementation, text input data may be manually provided by the user of the user device based on one or more specific parameters. One or more specific parameters may include, but are not limited to, clothing, background scenes in images, etc. Furthermore, text input data may be provided to the processing unit

[0104] at any time, such as at the start of the process of generating user-specific content, and / or as a recommendation or feedback after user-specific content has been generated. Furthermore, user-specific content is generated by converting the received user-specific input data and base content into user-specific content.

[0065] Furthermore, the base content is at least one of visual media and audiovisual media relating to at least one of the following: trending events, news, sports, geographical areas, weather conditions, entertainment-related events, and images of one or more users of a user device, as well as other information that would be understood by a person skilled in the art in light of this disclosure.

[0066] Furthermore, e-commerce data relates to base content and user-specific input data. E-commerce data may be received from the e-commerce catalog of the e-commerce platform to generate user-specific content. The e-commerce catalog contains data relating to multiple products available on the e-commerce platform. Thus, based on user-specific data and base content, e-commerce data containing product details related to the base content and user-specific input data is received from the e-commerce catalog to generate user-specific content.

[0067] Next, in step

[0206] , method

[0200] includes extracting a set of features from user-specific input data by processing unit

[0104] . The set of features comprises at least one of the following: a set of face parameters, a set of age-related parameters, a set of gender-related parameters, a set of environment-related parameters, a set of non-human object-related parameters, and a set of characteristic-related parameters. The set of face parameters comprises one or more parameters that indicate the details of one or more faces related to an object, such as one or more users of a user device. The set of age-related parameters comprises one or more parameters that indicate age-related information. The set of gender-related parameters comprises one or more parameters that indicate gender-related information. The set of environment-related parameters comprises one or more parameters that indicate environment-related information. The set of non-human object-related parameters comprises one or more parameters that indicate information about non-human objects. The set of characteristic-related parameters comprises one or more parameters that indicate information related to the characteristics of an object. The set of features is extracted by processing unit

[0104] using a first artificial intelligence-based subsystem.

[0068] In one implementation, the first artificial intelligence-based subsystem may be a personal reference image analyzer. The personal reference image analyzer may employ an advanced visual model to analyze visual data, such as a set of images provided by the user of the user device. The set of images may include, but are not limited to, images of one or more individuals, such as the user, the user's family, the user's friends, and / or one or more animals. The personal reference image analyzer may extract important features related to one or more individuals and / or one or more animals present in the set of images provided by the user. Important features may include, but are not limited to, one or more facial features, age, and gender of one or more individuals and / or one or more animals present in the set of images provided by the user of the user device.

[0069] Furthermore, in step

[0208] , method

[0200] includes modifying the base content based on the e-commerce data and set of features by the processing unit

[0104] . The received base content is converted into output media such as output visuals based on the set of features and e-commerce data.

[0070] Furthermore, in order to modify the base content, the processing unit

[0104] uses a second artificial intelligence-based subsystem to generate one or more suggestions based on user-specific input data. The processing unit

[0104] then provides one or more suggestions on the user device. In one implementation, the user device is a smart television.

[0071] In one implementation, the second artificial intelligence-based subsystem is the Gen AI Prompt Generator. The Gen AI Prompt Generator can generate one or more prompts based on user-specific input. The generated prompts can act as instructions for generating one or more user-specific content. Furthermore, in one implementation, one or more suggestions are one or more reverse prompts that can suggest one or more modifications to the base content based on user-specific content that indicates the user's interests. Thus, by suggesting user-interest-based modifications that will be added to the base content, one or more reverse prompts show the user one or more new possibilities to improve the user's interactive experience.

[0072] To continue, in one implementation, the processing unit

[0104] receives a response to one or more suggestions from the user device, and based on the response received from the user device, the processing unit

[0104] then modifies the base content.

[0073] Furthermore, in step

[0210] , method

[0200] includes generating user-specific content based on modifications by processing unit

[0104] , wherein the user-specific content includes at least one of user-specific visual media and user-specific audiovisual media. In one implementation, the user-specific content includes one or more links for purchasing one or more items visible within the user-specific content. The one or more links are added during modifications to the base content, and the one or more links are identified based on e-commerce data.

[0074] Furthermore, in one implementation, the method may include using an enhanced object detection technique to add one or more links to purchase one or more items visible within the user-specific content to the base content. The enhanced object detection technique can detect and analyze not only human subjects but also non-human objects in a set of images relating to at least one of people, animals, and objects. Non-human objects may include, but are not limited to, the surroundings, clothing, and household furniture. Analysis of both human subjects and non-human objects can generate more comprehensive and contextually accurate user-specific content.

[0075] Next, using an enhanced object detection technique, if a non-human object is identified, the system

[0100] may search an e-commerce catalog to find one or more products that match the identified one or more non-human objects. Subsequently, links for purchasing one or more products that match the identified one or more non-human objects are added to the base content during the base content modification for generating user-specific content. In one implementation, a quick response (QR) code may also be added for the link along with one or more matched products during the base content modification for purchasing one or more matched products via user-specific content.

[0076] Furthermore, in one implementation, user-specific content includes one or more advertisements (Ads). One or more Ads are added during changes to the base content, and one or more Ads are identified based on at least one of the following: user-specific input data, base content, and e-commerce data.

[0077] In one implementation, the processing unit

[0104] provides generated user-specific content on the user device and then receives user feedback from the user device regarding one or more parts of the generated user-specific content. For example, the user may provide feedback to improve the generated user-specific content based on one or more specific parameters. One or more specific parameters may include, but are not limited to, body shape, image style, objects, etc., in the generated user-specific content. The processing unit

[0104] then updates the generated user-specific content based on the user feedback. The generated user-specific content may also be updated in real time based on the user feedback and regenerated to revert to its original state.

[0078] Furthermore, in one implementation, in order to generate user-specific content, the method includes performing a suitability check for the user-specific content by the processing unit

[0104] . The suitability check is performed on the basis of checking whether any information relating to the user-specific input data and base content maps to a predefined set of critical issues in order to confirm that the user-specific input data and base content are eligible for conversion to user-specific content. For example, base content, such as news relating to an issue that maps to a critical issue in a predefined set of critical issues, or any sensitive statement given by a politician that maps to a critical issue in a predefined set of critical issues, may lead to critical issues and is therefore not considered suitable for conversion to user-specific content.

[0079] Subsequently, in step

[0212] , method

[0200] may be terminated.

[0080] Referring to Figure 3, an exemplary flowchart for generating user-specific content according to an exemplary embodiment of the present disclosure is shown.

[0081] In step

[0302] , the user uploads a set of images to the system

[0100] that may serve as a basis for generating one or more personalized visuals (i.e., one or more user-specific content). The set of images may be uploaded manually by the user by scanning a quick response (QR) code, or it may be automatically received from cloud storage by integrating cloud storage.

[0082] Next, in step

[0304] , the set of images is sent to a personal reference image analyzer. The personal reference image analyzer employs an advanced visual model to analyze the set of images and, in one implementation, extracts important features from the set of images, such as facial features, age, and gender. In one implementation, this analysis and / or extraction in this step generates a dynamic template that guides subsequent processing steps.

[0083] Next, in step

[0306] , the set of images and / or dynamic templates are sent to the age and gender classifier model. The age and gender classifier model clearly identifies the age and gender of individuals in the uploaded images, improving the accuracy and personalization of the generated visuals.

[0084] Furthermore, in step

[0308] , the set of images and / or a dynamic template are sent to the Gen AI prompt generator. The Gen AI prompt generator creates detailed prompts based on the output received from the personal reference image analyzer and / or the age and gender classifier model. The generated prompts are derived from the set of images and one or more descriptive text inputs. The generated prompts act as one or more instructions for the generative model.

[0085] Furthermore, in step

[0310] , the set of images and the generated prompts are sent to one or more generative models. One or more generative models generate the final visual content. One or more generative models use the prompts provided by the Gen AI prompt generator to produce highly personalized, contextually relevant images and videos.

[0086] Furthermore, in step

[0312] , the generated final visual content is sent to one or more embedding models and a vector database. The one or more embedding models convert the images and templates corresponding to the final visual content into one or more vector embeddings, enabling an efficient retrieval and matching process. In addition, the vector database stores one or more vector embeddings, enabling fast access to one or more associated templates and one or more embeddings required for content generation (i.e., to generate user-specific content).

[0087] Next, in step

[0314] , the user provides one or more prompts in the form of one or more descriptive text inputs outlining their desired scenario or modifications in the final generated visual content. The one or more user prompts guide the generative model in creating the personalized visual. For example, the one or more user prompts may include, but are not limited to, "Imagine yourself and your wife getting married on the moon," or "Show me yourself celebrating your birthday with your grandparents."

[0088] Furthermore, in step

[0316] , the system for correcting the final generated visual content employs an enhanced object detection technique to detect and analyze not only human subjects but also non-human objects in the set of images. Non-human objects include, but are not limited to, the surroundings, clothing, household furniture, and other objects present in the set of images.

[0089] Furthermore, in step

[0318] , one or more reverse prompts are generated based on user interaction and preferences. One or more reverse prompts suggest further modifications or scenarios that the user may find interesting. One or more reverse prompts are designed to expose the user to new possibilities and enhance the interactive experience.

[0090] Furthermore, in step

[0320] , real-time corrections are added to the final generated visual content. Thus, the user can interact with the system

[0100] in real time through the user's connected TV. The user can provide new prompts or correct existing final visual content and, accordingly, prompt the system

[0100] to adjust the generated visuals. User interaction ensures that the user has continuous control over the personalization of their content.

[0091] Furthermore, in step

[0322] , the user may enhance user-generated content using one or more services provided by system

[0100] . One or more services may include multiple functions, such as story generation, personalized image editing, teleporting to a virtual location, commerce templates (i.e., integrating product catalogs to enable e-commerce functionality), and different artificial intelligence (AI) based art formats.

[0092] Furthermore, in step

[0324] , when the user selects an ad-based service provided by system

[0100] , the Ads catalogue in-painter integrates the ads in the relevant locations into the generated visual / final visual content.

[0093] Subsequently, in step

[0326] , the client-facing service ensures that the final visual content, such as the final generated images and videos, is delivered to the user. The client-facing service utilizes cloud storage and one or more delivery mechanisms to make the content accessible on the user's device.

[0094] Furthermore, in step

[0328] , user possibilities (e.g., the user's virtual presence) in one or more generated visual contents as the final output are experienced by the user through their connected TV or other device. One or more possibilities showcase AI-generated visuals and scenarios, providing an interactive and immersive experience.

[0095] Another object of this disclosure may relate to a non-temporary computer-readable storage medium storing one or more instructions for generating user-specific content, the instructions including executable code that, when executed by one or more units of the system

[0100] , causes a processing unit

[0104] of the system

[0100] to receive user-specific input data, base content, and e-commerce data. Furthermore, when executed, the executable code causes the processing unit

[0104] to extract a set of features from the user-specific input data. Furthermore, when executed, the executable code causes the processing unit

[0104] to modify the base content based on the e-commerce data and the set of features. Furthermore, when executed, the executable code causes the processing unit

[0104] to generate user-specific content based on the modification, wherein the user-specific content includes at least one of user-specific visual media and user-specific audiovisual media.

[0096] Therefore, this disclosure provides a technical solution for generating user-specific content. More specifically, this disclosure overcomes existing problems in the technology field by generating user-specific content. Furthermore, this disclosure provides a solution for generating user-specific content through which users can virtually experience real-world moments and imagine future scenarios and / or fantasies. This disclosure also provides a solution for generating user-specific content that provides users with experiences to relive past memories in the present. Furthermore, this disclosure provides a solution for generating personalized visuals related to content preferred by the user, in which the user is part of the visual. Furthermore, this disclosure provides a solution for generating user-specific content that includes shopping links for non-human objects in the user-specific content that the user may wish to purchase.

[0097] While considerable emphasis is placed on the implementations disclosed herein, it should be understood that numerous implementations are possible and numerous modifications can be made to these implementations without departing from the principles of this disclosure. These and other modifications in the implementations of this disclosure will be apparent to those skilled in the art, and it should be understood that the above-mentioned descriptive matters to be implemented are illustrative and not limiting. [Explanation of Symbols]

[0098] 100 Systems 102 Memory Units 104 Processing Units

Claims

1. A method for generating user-specific content, wherein the method is - A processing unit [104] receives user-specific input data, base content, and e-commerce data. - The processing unit [104] performs the step of extracting a set of features from the user-specific input data, - The processing unit [104] modifies the base content based on the e-commerce data and the set of features, - A step of generating user-specific content based on the step of modification by the processing unit [104], wherein the user-specific content includes at least one of user-specific visual media and user-specific audiovisual media. Methods that include...

2. The method according to claim 1, wherein the e-commerce data relates to the base content and the user-specific input data.

3. The method according to claim 1, wherein the base content is at least one of visual media and audiovisual media relating to at least one of the following: trending events, news, sports, geographical areas, weather conditions, entertainment-related events, and images of one or more users of a user device.

4. The method according to claim 1, wherein the user-specific input data is received as at least one of automatic input and manual input from one or more users of the user device, and the user-specific input data includes a set of images relating to at least one of people, animals, and objects, and at least one of user-related data, user preference data, content usage pattern information, and content engagement information.

5. The method according to claim 1, wherein the set of features includes at least one of the following: a set of face parameters, a set of age-related parameters, a set of gender-related parameters, a set of environment-related parameters, a set of non-human object-related parameters, and a set of characteristic-related parameters.

6. The method according to claim 1, wherein the set of features is extracted by the processing unit [104] using a first artificial intelligence-based subsystem.

7. The processing unit [104] performs the step of changing the base content, - The processing unit [104] uses a second artificial intelligence-based subsystem to generate one or more suggestions based on the user-specific input data, - The processing unit [104] provides the one or more suggestions on the user device, - The processing unit [104] receives a response from the user device to one or more proposals, - The processing unit [104] modifies the base content based on the response. The method according to claim 1, further comprising:

8. The method according to claim 1, wherein the user-specific content includes one or more links for purchasing one or more items visible within the user-specific content, the one or more links are added during the step of modification, and the one or more links are identified based on the e-commerce data.

9. The method according to claim 1, wherein the user-specific content includes one or more advertisements (Ads), the one or more Ads are added during the step of modification, and the one or more Ads are identified based on at least one of the user-specific input data, the base content, and the e-commerce data.

10. - The processing unit [104] provides the generated user-specific content on the user device, - The processing unit [104] receives user feedback from the user device regarding one or more portions of the generated user-specific content, - The processing unit [104] updates the generated user-specific content based on the user feedback. The method according to claim 1, including the method described in claim 1.

11. The method according to claim 10, wherein the user device is a smart television.

12. The method according to claim 1, wherein the method includes the step of performing a suitability check for the user-specific content in order to generate the user-specific content.

13. A system for generating user-specific content, wherein the system - Memory unit [102] and, - A processing unit [104] connected to at least the storage unit [102] and The processing unit [104] is equipped with, 〇Receiving user-specific input data, base content, and e-commerce data, ○ Extracting a set of features from the user-specific input data, ○ Modifying the base content based on the e-commerce data and the set of features, ○ To generate user-specific content based on the above modification, wherein the user-specific content includes at least one of user-specific visual media and user-specific audiovisual media. A system configured to perform the following actions.

14. The system according to claim 13, wherein the e-commerce data relates to the base content and the user-specific input data.

15. The system according to claim 13, wherein the base content is at least one of visual media and audiovisual media relating to at least one of trending events, news, sports, geographical areas, weather conditions, entertainment-related events, and images of one or more users of a user device.

16. The system according to claim 13, wherein the user-specific input data is received as at least one of automatic input and manual input from one or more users of the user device, and the user-specific input data includes a set of images relating to at least one of people, animals, and objects, and at least one of user-related data, user preference data, content usage pattern information, and content engagement information.

17. The system according to claim 13, wherein the set of features includes at least one of the following: a set of face parameters, a set of age-related parameters, a set of gender-related parameters, a set of environment-related parameters, a set of non-human object-related parameters, and a set of characteristic-related parameters.

18. The system according to claim 13, wherein the set of features is extracted by the processing unit [104] using a first artificial intelligence-based subsystem.

19. The processing unit [104] for changing the base content, - Using a second artificial intelligence-based subsystem, generate one or more suggestions based on the user-specific input data, - To provide one or more of the above proposals on a user device, - Receiving a response to one or more proposals from the user device, - Modify the base content based on the response. The system according to claim 13, configured to perform the following:

20. The system according to claim 13, wherein the user-specific content includes one or more links for purchasing one or more items visible within the user-specific content, the one or more links are added during the modification step, and the one or more links are identified based on the e-commerce data.

21. The system according to claim 13, wherein the user-specific content includes one or more advertisements (Ads), the one or more Ads are added during the modification step, and the one or more Ads are identified based on at least one of the user-specific input data, the base content, and the e-commerce data.

22. The processing unit [104] - To provide the generated user-specific content on the user's device, - Receiving user feedback from the user device regarding one or more portions of the generated user-specific content, - Update the generated user-specific content based on the user feedback. The system according to claim 13, further configured to perform the following:

23. The system according to claim 22, wherein the user device is a smart television.

24. The system according to claim 13, wherein the processing unit [104] is configured to perform suitability checks for the user-specific content in order to generate the user-specific content.

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