Description:Field of Invention
The aim of this invention is to explore the potential of designing a fashion recommendation system for generating full outfit concepts from a single point of input. The system leverages machine learning and data analysis in an effort to produce personalized and consistent fashion recommendations.
Objectives of the Invention
The goal of this invention is to develop an effortless fashion consulting system that offers total outfit ideas from a single input piece. The system aims to improve users' fashion decisions by offering personalized, fashionable, and coherent outfit ideas. It also hopes to facilitate users' selection of outfits, saving users' time and effort. The ultimate goal of the invention is to become a part of users' lives, making fashion coordination effortless and fun.
Background of the Invention
Detection of security-related abnormalities and threats in a computer network environment is the job of a security platform, which uses a vast array of methods and procedures. The security platform is powered by "big data" and uses machine learning to d security analytics. The security platform uses machine learning also. User and entity behavioral analytics (UEBA) are carried out by the security platform to detect security-related abnormalities and threats. Such analysis is carried out whether the anomalies or threats in question were previously recorded. To detect unexpected events and potential dangers, the security platform can use both real-time and batch modes and routes. It is stated in US9609009B2 that network security administrators are allowed to respond to an identified anomaly or threat and to act swiftly by the security platform. This is achieved with the visual presentation of analytic results that are evaluated with risk ratings and supporting information.
In (US11392111B2), A data collection monitoring device for monitoring data collection of a production line of an industrial setting includes a data storage for storing a plurality of data collection templates, each of the plurality of data collection templates including a data collection routine; a data collector for interpreting a plurality of detection values respectively corresponding to a plurality of input channels, wherein the plurality of detection values are received in accordance with a data collection routine of an activated one of the plurality of data collection templates; wherein the data storage is further arranged to store at least a portion of the plurality of detection values; a data analysis circuit for interpreting at least a subset of the detection values to derive a state value corresponding to one of a process or a component of the production line; and an expert system circuit for executing a data collection modification by executing one of: modifying the data collection routine of the activated one of the plurality of data collection templates; or selecting another one of the plurality of data collection templates.
There is a disclosure of systems and methods used to decide on associations and relationships among things. Based on the unstructured text corpora, the tools and methods are able to automatically identify supply chain connections among various organizations. Patent application number US11222052B2 gives the system, which integrates the machine learning models to look for sentences that refer to a supply chain between two firms as evidence. It further contains an aggregation layer that considers the evidence that was gathered and provides a confidence score for the association of the firms.
The revelation of methods and methods of establishing relevance of interactions and associations between entities is available in patent application US103039991. From unstructured corpora of text, the tools and methods can automatically identify supply chain connections between various organizations. The system uses an aggregation layer to take into account the evidence that has been found and give a confidence score to the relationship between the organizations. Machine learning models are used to identify sentences that provide descriptions of supply chain interactions between two firms
. Saving the power of a sensor can be achieved one way by training a model to predict the state of an animal from training data. Deterministic IDs, on the other hand, enable more robust product recommendations, more accurate shopping list generation, as well as more accurate in-store navigation. Deterministic identifiers drive effective and reliable capture of product discovery, purchase, and consumption events. Product identification is pulled from objects, display screens, and ambient sound by a mobile device that is onboard with picture and audio detectors. Aside from a cloud-hosted service, an app installed on a mobile device delivers product data and stores product events for extracted IDs. In-store navigation is enabled by the cloud service, generating recommendations and maps.
The detectors also enable effective and reliable product identification for purchase events and post-shopping product consumption activity. It is available in the document (US20250166035A1).With the help of a prompt, computerized systems and procedures are disclosed in the patent application (US20230252224A1) to use a transformer to create a document.- transformer engineering with a title and a summary to create a description of the document; displaying a set of claims and providing user editing of the set of claims; receiving one or more figures; receiving a part list with a plurality of element names for each figure; creating an expanded description of each element name with prompt engineering from earlier text in the document; choosing one or more boilerplate texts for significant parts of the document; and structuring the document with the title, a background, the summary, a brief description of the drawings, and a detailed description.

Summary of the Invention
This invention is a fashion recommendation system that generates complete, customized outfits from an input piece. Through machine learning and data analysis, it recommends to users stylish and well-balanced clothing combinations, making outfit selection easier and enhancing their fashion lives.
With the pace of the world in the modern world, fashion is an important aspect of personal expression and social interaction. Yet, most people are unable to come up with coordinated sets based on their personal taste and adhering to different forms of social expectation. The emergence of fast fashion and the widespread availability of clothing have also made it more of an issue, making it a more complicated and time-consuming task. The conventional process of choosing ensembles relies on personal judgment and is prone to the impact of limited fashion knowledge, creating ensembles that are not necessarily in harmony or fashionable.
The recent technological advances have opened new avenues to deal with this problem. The integration of machine learning, artificial intelligence, and data analysis across various disciplines has been proved to be useful in offering customized solutions. In the field of fashion, these technologies can revolutionize the manner in which individuals select their outfits. By analyzing vast amounts of fashion data such as colors, fashion styles, trends, and preferences of users, a fashion recommendation system can provide customized outfit recommendations that are in line with the fashion of the times as well as individual preferences. This not only simplifies the selection of outfits but also helps users discover new styles and combinations that they would not select otherwise.
The proposed fashion recommendation system is designed to leverage these technologies to create a cutting-edge solution to fashion coordination. With the entry of a single garment, users have the potential to be shown full outfit ideas that are not only stylish but also suited to their requirements. The system is designed to be simple to use and intuitive, and user-friendly to users of all fashion sophistication, from novices to connoisseurs. Through machine learning algorithms and a vast fashion database, the system can learn and adapt the user's taste on a regular basis, so that the recommended outfits are current and appealing. The invention will be capable of making the overall fashion experience more enjoyable and less daunting for users.
Detailed Description of the Invention
The primary objective of the fashion recommendation system is to help and enhance the process of building outfits that not only look fashionable and trendy but also consistent and suitable for the participants who use it, thereby making the process more efficient. This particular task is accomplished in order to make the whole process easier, making it more accessible and simpler to understand for the participants. During the intricate process of building the system, several high-end and sophisticated machine learning algorithms were employed, which played a very crucial role in the building phase of the system. In handling issues concerning fashion, these algorithms meticulously analyze an immense number of products, prevailing trends, and specific customers' likings to make informed and precise judgments. The algorithms in question are the fundamental building blocks on which the computer system is deliberately constructed; hence, they are the fundamental foundation on which the performance of the whole system is based. This extensive database is packed with a diverse number of clothing items, accessories, and types of footwear, thus a diverse range of choice. It also contains different types of shoes. Style, color, seasonality, and specific occasions are employed to classify and categorize these items into their respective groups. These groups of classifications are employed with the sole objective of classifying the whole collection into a specific and defined group after considering and evaluating all the elements. Periodically, the system is designed to update this extensive database to ensure that it accurately reflects the most current fashion trends, including those still developing and gaining popularity in the fashion world. This updating process is vital in ensuring the system accurately captures and represents prevailing as well as developing trends, hence achieving the precise representation objective. When the user is presented to the system for the first time, the first thing they do is choose one input item from among the options available to them that they own. This is the first thing they do and the start of the entire process they are to undertake. The object they choose could be any of a myriad of objects like a piece of clothing, such as a shirt or a dress, or pants, or even other objects like belts or hats. It is highly likely that this is the very item in question that the user is thinking of right now.
The users have the choice of making their choice from an already existing catalog within the system, with the system showing to them a range of choices from which to make their choice. Or they also have the option of uploading the item directly into the system as either a picture or a description of the item. Both options are within easy grasp of them as they navigate the early processes. All of these options are available to both users, with them having plenty of room for maneuver in how they want to go about things.
With these various options present, they have the option of choosing either of these two options at the same instant, which adds to the user experience. They have the option of choosing which of these two options they would like to use as their chosen option going forward. When the object has been successfully input into the system, the next process is the system doing an in-depth analysis of the item's various features. This process of analysis will be done immediately after the object has been input into the system's database.
The analysis includes primary distinguishing features like the item's color, pattern, style, and material, though it should be noted that this list does not include every one of the important features of the object being analyzed. Individual preferences can also be expressed by the users, and these can be numerous different aspects such as the particular season they want to experience, the event for which they want to utilize the item (for example, it can be something informal, something formal, or even in a corporate environment), and any given style or look that they want to accomplish. Users are also given the ability to properly convey these preferences in the system itself, and this is another facet that is designed to make their experience easier.
In addition to this, the sophisticated algorithm not only keeps in view the specific item which is being presented to the user, but also takes into consideration the personal choices of the user, which are derived from their personal tastes and inclinations. As an immediate consequence of this comprehensive analysis, a myriad number of distinct costume proposals are generated as a result of the user input. There is a very precise matching developed between the specific item which is being inputted and various pieces of clothing, accessories, and footwear that have already been saved in the database, such that these pieces harmonize with the one that is being received at the time. This precise matching process is conducted with the specific focus of ensuring that the item seamlessly fits into the overall paradigm of the database. The primary motivation behind the generation of these thoughtful proposals is to bring greater satisfaction to the user; hence, this precise approach is undertaken. The algorithms which are used for machine learning very strictly take into account an exhaustive set of factors, such as color science, style coordination, and prevailing fashion design trends, while making their calculated decisions. In order to ensure that the recommendations provided are not only in fashion but also harmonious with one another, this kind of precise evaluation is conducted to ensure their harmony. Each user is presented with a visual presentation of each item, accompanied by elaborate and comprehensive descriptions of each item, in relation to the various outfits which are being proposed to them. This elaborate approach is conducted with the purpose of categorically painting the picture of the relationship between each individual item of clothing and the corresponding outfits which they relate to. In the event the concerned items are indeed available to be bought from the vendor, it is noteworthy that purchase links to the items are likewise available as part of the package. Aside from this, the package itself includes key links that enable users to buy the items directly. One of the greatest boons that this new technology has to offer is the incredible ability to learn and adapt to the individual user's unique preferences over a period of time. This is a unique feature that is one of the greatest boons that it has to provide to its users. The ability of owning this feature of adaptation is one of the great technological aspects that it has to boast with pride. Users who actively use the system are provided with the option to provide their feedback on the recommendations provided by providing ratings to certain ensembles, which can be positive or negative in nature. This precious feedback could in fact be provided through the medium of ratings. Through this interactive use, users are provided with an option to provide their feedback on the recommendations provided. It is the explicit intention of the system to use this feedback in such a way so that it gets a better understanding of each user's preferences in terms of the design of the interface. Through this ongoing process, the system will ultimately attain the ability of making recommendations that are more and more relevant to the individual user, as well as being designed to meet their individual requirements in the future. This phenomenal advancement will be made possible due to the ongoing improvement and fine-tuning of the system itself. In addition to other things, customers can also save their favorite ensembles, which ultimately results in the development of a customized and personalized lookbook that can be accessed later. This carefully selected collection is a point of reference for the decisions they make about their wardrobe options and fashion choices. Furthermore, it is an easily available lookbook that can be provided to customers at any given moment, and it is simple for them to access it whenever they need to use it again. In addition to that, it is technically feasible to make this new system compatible with any diverse e-commerce website and retail outlet involved in the sale of clothing, thus making the entire shopping process entertaining.
The capabilities of the system are designed specifically to be adaptable and versatile, such that it can handle a wide range of requirements, and making this feasible is not just a possibility but also a highly desirable requirement for its effectiveness. Another feature that differentiates the system is that it has been specifically designed to showcase flexibility and versatility in terms of the various applications it can perform, thereby allowing it to be more beneficial in various environments. Due to this interaction with the users, it becomes feasible for the customers to make direct purchases of goods that have been highly recommended to them in accordance with their preferences. Due to this, it increases the development of the system's overall ease of use and functionality, which automatically leads to a significant boost in the overall performance of the system and satisfaction of users. In addition, it becomes very feasible to design the system in such a way as to make it compatible with the smooth integration of new fashion accessories and trends as they become accessible. Due to this flexibility, adaptability will ensure that the system remains relevant and encompasses the latest fashion trends that are accessible on the market, thereby providing the users with updated recommendations. This will ensure that the procedure will not just remain relevant in the years that are yet to come but will also continue to evolve and improve according to future developments in the fashion industry. Not only does our fashion recommendation system provide a smart and innovative solution to the users, but it also provides a solution that is highly user-friendly, thereby making it accessible to a wide variety of people. Under these circumstances, it is possible to design new concepts for outfits that are specifically tailored to the individual, something that would otherwise not be feasible under any other conditions. By providing the ability for the user to have access to a much wider variety of trendy items, the system significantly increases the user's overall interaction with fashion, making it more enjoyable and interactive. The realization of this particular goal is enabled by making use of powerful and sophisticated machine learning algorithms and a large and varied library of fashion. Through the application of these sophisticated tools, this goal can be adequately realized by exposing the user to a much larger number of options from which to choose when deciding on their outfit. As a direct and immediate consequence of the application of this particular feature, outfit selection is going to be much less daunting and much more pleasurable for the individual user. This upgrade is just one of the many advantages that the addition has to offer to the overall setting.
Brief description of Drawing
Figure 1, Data flow diagram
The provided diagram represents the workflow of the fashion recommendation system, showcasing the key components and their interactions. , Claims:Claim:
The scope of the invention is defined by the following claims:
1. A system/method to the recommend the fashion based on the previous data, said system/method comprising the steps of:
a) The system starts up, and the training the data from the previous data (1). The data is feed the neural systems model (2).
b) The developed system for deployment on a cloud-based platform accepts data feeds from inventory data (3). The system is trained from the developed model (4), its divide into 70 for training and 30 for testing (5).
c) The proposed invention analyze with past data as well as images (6). The inputs are analyzed with the different algorithms to find out the annoy (7). Finally, the system will come up with the proposed fashion recommendation system with respect to inventory data (8).
2. As mentioned in claim 1, the system features neural networks trained to extract meaningful patterns and embeddings from fashion data. These embeddings capture essential characteristics of fashion items in vector format.
3. According to claim 1, the system processes inventory data to create embeddings for each fashion item in the catalog. These embeddings represent the features of available fashion items and are stored in a database.
4. According to claim 1, based on the nearest neighbor search results, the system generates personalized outfit recommendations and Recommendations include complementary fashion items that create a cohesive and stylish outfit.
5. According to claim 1, the system adapts to user feedback and preferences over time and Machine learning algorithms refine their understanding of user preferences for more accurate future recommendations.