{
  "patent_number": "US 10909459",
  "country": "US",
  "title": "Teaching Computers to Understand Document Similarity Using AI",
  "original_title": "Content embedding using deep metric learning algorithms",
  "summary": "This patent describes a way to train a computer program (a neural network) to understand how similar documents are to each other, by showing it examples and teaching it to group similar ones together and separate dissimilar ones.",
  "what_it_does": "This patent explains how to train a computer program, specifically a neural network, to create a 'space' where documents can be placed based on their meaning. Imagine you have a target document (like an article about dogs). You also give the program a 'favored' document (another article about dogs) and several 'unfavored' documents (articles about cats, cars, or anything else). The program learns by trying to make the 'dog' documents closer together in its 'space' and further away from the 'non-dog' documents. It does this by adjusting its internal settings, called parameters, to minimize a 'loss' function. This loss function measures how well it's separating the favored document from the unfavored ones relative to the target document. For instance, a training set might include an article about 'Golden Retrievers' (target), another about 'Labradors' (favored), and articles about 'Siamese Cats' and 'Electric Cars' (unfavored). The system adjusts itself so that the 'Golden Retriever' and 'Labrador' articles are 'close' in its internal representation, while the 'Siamese Cat' and 'Electric Car' articles are 'far' from the 'Golden Retriever' article.",
  "what_it_does_not_cover": [
    "Does not cover methods that do not use a neural network for training.",
    "Does not cover training methods that do not involve a target document, a favored document, and at least two unfavored documents.",
    "Does not cover systems that do not calculate a 'loss' based on the distance between document representations.",
    "Does not cover methods where the computer program is not 'trained' using adjustable parameters.",
    "Does not cover creating an embedding space without using document vectors as input."
  ],
  "filed": "2017-06-09",
  "granted": "2021-02-02",
  "expires": null,
  "status": "active",
  "holder": "Cognizant Technology Solutions US Corp",
  "holder_url": "https://patentbrief.org/company/cognizant-technology-solutions-us-corp",
  "inventors": [
    {
      "name": "Diego Guy M. Legrand",
      "url": "https://patentbrief.org/inventor/diego-guy-m-legrand"
    },
    {
      "name": "Nigel Duffy",
      "url": "https://patentbrief.org/inventor/nigel-duffy"
    },
    {
      "name": "Petr TSATSIN",
      "url": "https://patentbrief.org/inventor/petr-tsatsin"
    },
    {
      "name": "Philip M. Long",
      "url": "https://patentbrief.org/inventor/philip-m-long"
    }
  ],
  "times_cited": 53,
  "tags": [
    "software",
    "ai_ml",
    "telecommunications"
  ],
  "abstract": "The technology disclosed introduces a concept of training a neural network to create an embedding space. The neural network is trained by providing a set of K+2 training documents, each training document being represented by a training vector x, the set including a target document represented by a vector xt, a favored document represented by a vector xs, and K>1 unfavored documents represented by vectors xiu, each of the vectors including input vector elements, passing the vector representing each document set through the neural network to derive an output vectors yt, ys and yiu, each output vector including output vector elements, the neural network including adjustable parameters which dictate an amount of influence imposed on each input vector element to derive each output vector element, adjusting the parameters of the neural network to reduce a loss, which is an average over all of the output vectors yiu of [D(yt,ys)−D(yt, yiu)].",
  "url": "https://patentbrief.org/patent/us/10909459/federated-learning",
  "markdown_url": "https://patentbrief.org/patent/us/10909459/federated-learning/md",
  "google_patents_url": "https://patents.google.com/patent/US10909459",
  "relatedPatents": []
}