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technology

Vertical Machine Learning

A Vertical Machine Learning approach provides a competitive moat around your business

VERTICAL MACHINE LEARNING IN FASHION

We at the Fashion Taste API follow a Vertical Machine Learning approach to fashion. We believe in the unique advantage of having a profound understanding of the consumer problems, end to end.

With a focus on the consumers, we’ve applied machine learning to automate the two processes that we’ve considered game-changing:

  • Capturing shopper’s taste data (including what clothes they have in their closet);
  • Building the data infrastructure that allows us to understand input automatically, and to deliver personalized output also automatically.

Read our Manifesto on the Future of Fashion Retail.

THE ADVANTAGE OF FASHION RETAILERS

We believe that fashion retailers have a clear advantage over newcomers: they are the domain experts. The specific benefits, however, need to be captured:

  • Fashion retailers have a profound understanding of the fashion consumer;
  • Shoppers generate a unique body of data around the retailers properties. Retailers can choose to start capturing that intelligence and build their moat;
  • Physical stores are a unique layer of experimentation to test omnichannel solutions and receive fast feedback.

NEWCOMERS WILL SOON CATCH UP

At some point, newcomers will offer In-Bedroom Fashion Stylist or Smart Mirrors to end consumers.

Apart from the increase in market share that this new category will generate, In-Bedroom Fashion Stylists will have two very relevant consequences:

  • Data: The amount of incoming taste data will grow exponentially;
  • Learning: The speed of learning will accelerate thanks to better shopper feedback.

And the current competitive moat around traditional fashion retailers will be gone.

THE VIRTUOUS CYCLE OF DATA

The more digitally powered experiences you offer, the more taste data you will capture. The more data you capture, the better you can automatically serve your shoppers. The better you serve your shoppers, the more adrenaline you generate in your teams, the more ambitious they become.

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technology

Foundational patents in capturing and understanding fashion taste

Patents expected to provide a competitive advantage

PATENTS WITH RELEVANT MARKET ADOPTION

We’ve been analyzing fashion taste for more than a decade.

As a result, we own foundational patents in the online fashion market. An independent review of the Fashion Taste API portfolio uncovered relevant market adoption related to some of our patents.

This intellectual property powers our Digital Closet Technology, our In-Store Outfit Recommender, our In-Bedroom Fashion Stylist and other solutions.

A SYSTEM TO TAG FASHION IMAGES WITH SHOPPABLE PRODUCTS

We are all starting to see fashion images with dots on top of them, these dots enabling you to find and buy the products in the picture. This system is gaining popularity since 2016, and it was patented by us in 2013.

A SYSTEM TO CAPTURE AND UNDERSTAND HOW CLOTHES IN OUTFITS AND CLOSETS ARE MIXED, MATCHED AND RELATED

In our prior work with music, we extracted relations among songs by analyzing the sequence in which people would listen to music, and by analyzing our interpretation of items. Apple later bought the systems and patents for Apple Music. We then built similar systems for personal finance, and for content-agnostic input.

With this background, we thought that an outfit was like a playlist of songs: a way to communicate that it makes sense to consume several items together. This simple concept allowed us to conceptualize the first version of our Social Fashion Graph. In 2013, we protected a system to capture and understand how people mix and match clothes in outfits and closets.

Today, the technology has evolved significantly. It captures any input in our our Ontology, which includes any concept relevant to fashion, and assigns descriptors to products and to people. We can extract all type of relations among analyzed items.

SEARCH ENGINE FOR FASHION IMAGES AND OUTFIT IDEAS

Our initial interest was related to social tagging of images. Then, we invented more complex mechanisms to assign descriptors to clothes, outfits and people. Our IP protects a system to search outfit ideas and fashion images.

It could be a simple query such as “give me ideas to wear jeans” or a more complex one such as “give me ideas to wear my pair of red pants with my floral shirt, to go to the office in winter”, or “give me clothes that are “similar” grouped by your concepts of similarity”. This is strongly related to our Fashion Taste Graph Technology.

The Fashion Taste API patents are expected to provide a competitive advantage.

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technology

Our fashion ontology

Our tool to understand products AND people

THE ORIGINS OF OUR ONTOLOGY

As we wrote in our Taste Graph Personalization Technology, via our consumer apps, we have received millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear. We originally built our Ontology to understand this input.

WHAT IS OUR FASHION ONTOLOGY?

Our fashion ontology is our tool to understand products and people: it goes well beyond assigning descriptors to clothing. It has a very strong emphasis on describing people.

Our ontology today is the classification of descriptors needed to define a fashion product and a shopper’s need, in terms that are relevant for shoppers, for retailers and for data scientists. It contains the vocabulary used by people and by fashion retailers, with an understanding of how these descriptors are used together. Our ontology covers all aspects related to fashion purchase drivers: product properties, occasions, brands or influencers, or trends: It is formed by +2500 unique concepts and a few hundreds of thousands of descriptors.

We care about::

  • The ontology contains clothing and non-clothing descriptors. Products and people;
  • The ontology contains descriptors used by your organization, and by your shoppers;
  • The ontology has several levels, descriptors are classified in categories, types, properties and more;
  • The ontology contains the correlations among your descriptors as defined by your products and your shoppers.

THE BACKBONE OF OUR OPERATIONS

The ontology is the first component required to enable the automated personalization carried out by our our Taste Graph Personalization Technology.

WHAT SERVICES DO WE OFFER?

We build your fashion ontology, which will help you automate lots of internal processes related to your catalogue, and then it will allow you to understand your shoppers.

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technology

Our Fashion Taste Graph Technology for Omnichannel Personalization

HUNDREDS OF MILLIONS OF CORRELATED DATA POINTS

Through our consumer apps, we have received millions of described outfits, the described clothes in millions of closets, and hundreds of millions of what-to-wear queries from people trying to decide what to wear.

In 2013, with so much consumer data we built a Taste Profile for each person, and a Taste Graph to serve the right products to each individual based on her specific interests. It is important to note that a profile in fashion is not only driven by taste, but also by other triggers. You can read about it in the Taste Profiles section.

  • Described outfits: We capture this type of data from millions of described outfits and we also capture how other people interact with those outfit creating more relations among items and allowing the Taste Graph to assign further descriptors to the outfits and the Taste Profiles.
  • Described clothes in millions of closets: We also receive the described clothes in millions of closets from where we can extract a lot of meaning in terms of correlations and in terms of our own interpretation of the products.
  • Queries: We also receive hundreds of millions of what-to-wear queries from people trying to decide what to wear and what clothes to buy, for any occasion you can think of. Each of this queries informs the Ontology and the Taste Graph.

That’s the origin of our technology. The Taste Graph Technology receives data and correlations. It also assigns the correct descriptors to clothes, outfits and to people. It is the tool on top of which teams can effectively work in personalization. Delivered via a website, in a physical store, or delivered via Alexa in a bedroom or closet.

TASTE GRAPH TECHNOLOGY TO DESCRIBE SHOPPERS

Our Fashion Taste Graph Technology for Omnichannel Personalization is the engine that allows us to assign descriptors to shoppers. It allows us to understand the taste of each shopper, and provide them with personalized omnichannel recommendations.

It is the infrastructure and data that contains, manages and understands the taste of each individual shopper, what clothes she has in her closet, and what are the drivers behind her purchases. The behaviour of each individual, the aggregated behaviour of everyone, and the behaviour of any subset we’d want to target.

The backbone of our Taste Graph is our fashion ontology, which you can read about here.

WHAT SERVICES DO WE OFFER?

We build your Taste Graph, your tool to understand the taste of each individual shopper.

Read about it here.

OUR PREVIOUS GRAPHS

During the last 20 years, we’ve solved specific problems in car manufacturing, music and finance by introducing technologies to understand specific behaviours and automating very concrete processes.

  • In music, Apple acquired the technology and intellectual property for Apple Music.
  • In finance, Barclays, Deutsche Bank, ING, BBVA and other 700 banks still use the technology that our team built before we moved to fashion.
  • Since 2010, we are exclusively focused on fashion.

OUR CONSUMER FASHION APPS

Our consumer fashion apps bring unique impact to our learning process.

They allow us to see the future with more clarity, by solving the problem end to end and receiving direct feedback from the end-consumer. Iterating a consumer app is a unique learning experience, and it contributes in a great way to the definition of the Taste Graph and of course of the Ontology. You can read about or approach to growth in our consumer product in this Medium essay.

Then, we from Using that data to build an automated personalization engine lead us to build our Taste Graph, and patent it in 2013. It has proven to be a unique personalization omnichannel technology, and a great internal tool for classification and categorization of clothes.

Read our Manifesto on the Future of Fashion Retail.