FASHION MATCHING TECHNOLOGY PATENTS

PATENTED AI ALGORITHM SOLUTIONS FOR PERSONALIZED STYLING

US Patent #8,103,551 and #8,249,941

This IP Asset Portfolio Provides Broad Claims, Market Ownership & Competitive Leadership Positions in E-Commerce, Personalized Fashion Matching, & Fashion Recommendations. Providing Strong Leadership for Fashion Matching with Neural Networks and Artificial Intelligence.

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PATENTED AI ALGORITHM SOLUTIONS FOR PERSONALIZED STYLING

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FASHION MATCHING TECHNOLOGY PATENTS

The patented systems, methods and technologies are directly correlated and relevant for any company operating in the business of personally matching fashion styles for users over the Internet, through private servers, or via other electronic methods that include style-matrix, or a style-matrix supported by machine learning.

A system and method is shown for fashion matching that interprets a user's style or fashion and finds a match given a set of fashion items. A fashion is a style of dress, while a fashion item includes an article of clothing, jewelry, or anything that is used to denote a style of dress. A user includes a customer of a business that transacts in fashion items. Further, a user may include an expert such as a fashion professional, where this fashion professional includes a fashion designer, a personal shopper, a personal stylist, a journalist who reports on fashion, or some other suitable person.

Matching is based, in part, upon an attribute of a fashion item. Attributes include color, fabric, cut, designer, size, time of creation, and other suitable attributes used in denoting a fashion. In one example embodiment, matching includes receiving input in the form of a particular fashion item and based upon this fashion item suggesting an additional fashion item based upon matching attributes.

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Patent #1 - 8,103,551

A fashion matching computer system comprising:

  • a receiver to receive a fashion item choice;
  • a memory storing a first fashion item, attribute-association matrix and a second fashion item, attribute-association matrix;
  • a matching engine to match the fashion item with an additional fashion item based on an attribute of the fashion item and
  • a style matrix generated through a threshold function that finds a difference between the first attribute association matrix and the second attribute association matrix, where this difference is less than or equal to a threshold value, wherein the style matrix includes items that are selectable using the matching engine;
  • and a transmitter to output the additional fashion item as part of an outfit.

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Patent #2 - 8,249,941

A computer implemented method that is implemented on a server comprising:

  • receiving an item choice, using a receiver, that includes a fashion item;
  • storing a style matrix from a neural network computer system, the style matrix generated through a threshold function that finds a difference between a first attribute association matrix and a second attribute association matrix, where this difference is less than or equal to a threshold value, and the neural network computer system to include a processor to train and a first memory coupled to the processor to train;
  • Matching the fashion item with an additional fashion item, using a matching engine including a processor to match and a second memory coupled to the processor to match, the additional fashion item selected from the style matrix, and the matching based upon an attribute of the fashion item; and
  • transmitting, using a transmitter, the additional fashion item as part of an outfit match set.

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Fashion Matching Markets, Sectors & Patent Categories

Fashion Matching Markets, Sectors & Patent Categories

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Apparel matching for home, in-store, and e-commerce

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Apparel Matching

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The recent McKinsey Global Fashion Index (MGFI) forecasts that due to Covid-19, the global fashion industry growth will slow slightly. McKinsey offers that to counter this slowdown and increase sales; Fashion companies need to become digital-first and fully leverage new technologies, to improve diversity across their assortments and organizations, and to address growing demand for the industry….

The “Fashion Matching” patent portfolio offered provides fashion companies with the exclusive rights, broad claims & technology roadmap to implement a transformational digital solution. This solution uses Artificial Intelligence & Machine Learning to provide end customers with rapid matching of apparel to their preferences quickly and efficiently. This enables accelerated identification and selection of clothing and accessories across an inventory of hundreds of thousands of items accelerating your sales. When combined with the patented style matrix that makes the perfect suggestion based on customer attributes, this increases product sales up to 40% or more.

McKinsey “The State of Fashion 2021” Website
https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion
McKinsey “The State of Fashion 2021” PDF 128 Page Report
https://www.mckinsey.com/~/media/McKinsey/Industries/Retail/Our%20Insights/State%20of%20fashion/2021/The-State-of-Fashion-2021-vF.pdf

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Personal Styling Services and Automation

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Personal Styling

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In today’s world, there are many personal styling companies utilizing automated technologies on the Internet. These companies include Stitch Fix, Amazon, Trunk Club, Walmart, The Daily Look, Target, Le Tote, E-Bay, Wantable, Thread, Alibaba, Microsoft, ThredUp, Nordstrom , Sparc and more.

Most of these companies when selling apparel online utilize a business model whereby consumers input their clothing desires on the website, the request is provided to the company and then employees in the background provide suggestions back to the buyer for selection and or

Although many of these companies discuss a 100% automated process, research indicates these companies need additional technologies to achieve a fully automated, intelligent solution that works seamlessly and correctly to what the end customer requires. The patents and related technologies are the first solution that truly provides a comprehensive suite of technologies that achieve the goals.

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Household goods, furniture, and interior design

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Household Goods

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Fashion takes many forms. Although most people initially think of clothing and apparel; the household items in our home, offices, and stores can also be considered “fashion”. Furniture is one of the items referenced that can considered to be fashion.

In this context, the patented technology offers interior designers and similar professionals the ability to pick out a piece of furniture and by utilizing the style matrix have other pieces of complementary furniture be suggested.

Suggestions can be based on varying types of fabric, colors, materials and more. The fashion style matrix is constantly trained using AI and ML algorithms.

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Personal Shopping Assistant Services for fashion or decorating

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Personal Shopping

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A personal shopper is someone who helps others shop by giving advice, making suggestions or that take their direction from an employer.

Most personal shoppers are employed by department stores, boutiques and related. The focus is usually on clothes, but furniture is also a large and growing market.

The available patent portfolio and related technology easily allows personal shoppers to buy for their customers – whether in a department store or elsewhere.

A client can simply answer key questions posed from the personal shopping assistant or service. Afterwards, the fashion matching system instantly identifies, selects and in some cases integrates with systems that support the delivery of the products efficiently and seamlessly.

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Valuable for Data Processing; Electronic Shopping, Shopping Interface, Graphical Representation of Item and Shopper, Item Recommendation, Artificial Intelligence, E-Commerce

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