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Find What’s Relevant

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Risk and Opportunity Detection

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How it works

Semantic Visions is an actionable analytics company based in Prague that specializes in using AI and machine learning technology to analyze a huge portion of global news in 12 languages. Our solution includes source collection, source analysis, natural language processing, and proprietary artificial intelligence.

We monitor over 270,000 unique domains approximating 900,000 sources, collecting and analyzing over 1.1 million news articles a day from online global media in 12 languages. Our source analysis performs automated source discovery, classification, ranking, and evaluation. Through natural language processing, we extract events, entities, topics, sentiment, and themes; effectively mimicking how the human brain reads text. Our proprietary artificial intelligence connects companies or entities to risk events and analyzes hundreds of signals to calculate relevance for the client.

No matter how large or small your portfolio or distributed your ecosystem, Semantic Visions’ solutions can help keep you risk-free and ahead of the curve, in real time.

1
Source analysis and metadata extraction
  • Performs automated source discovery, classification, ranking and assesses source value
  • Transforms unstructured textual content from the article into structured metadata
2
Named entity recognition
  • Extracts and disambiguates companies (IBM, Amazon, Microsoft, Oracle, Daedalus Blue), with further capability to separate foreign subsidiaries and ambiguous company names
  • Recognises the location, extracting Texas and recognising its ‘parental’ location is the USA
3
Event type recognition
  • Detects Lawsuit and Patent Infringement as two of the event types in the article
  • Determines that Daedalus Blue sues Microsoft and not vice versa
  • Understands that the other companies are not suing or being sued
  • Concludes that this particular Lawsuit case is current and did not occur in the past
4
Scenario calculation and generation
  • Quantifies parameters of the signal, such as scope and intensity
  • Detects the sentiment relating to each company and the overall article sentiment
  • Clusters articles and combines them into the scenario across all 12 languages
  • Decides whether to generate the scenario for a client, using ≈ 50 semantic and statistical parameters
Example article

On Wednesday, Daedalus Blue,LLC filed a complaint in the Western District of Texas against Microsoft, claiming that Microsoft infringed the patents-in-suit with its Microsoft Azure cloud computing product.

According to the complaint, the patents-in-suit “were invented by International Business Machines Corporation (IBM),” and relate to cloud computing.

The plaintiff noted that Amazon Web Services and Oracle Corporation, competitors in the cloud computing space, have licensed the asserted patents. Daedalus Blue is the current owner and assignee of these asserted patents.

Daedalus Blue is represented by Bunsow De Mory LLP and George Brothers Kincaid & Horton, L.L.P.

Example article

On Wednesday, Daedalus Blue,LLC filed a complaint in the Western District of Texas against Microsoft, claiming that Microsoft infringed the patents-in-suit with its Microsoft Azure cloud computing product.

According to the complaint, the patents-in-suit “were invented by International Business Machines Corporation (IBM),” and relate to cloud computing.

The plaintiff noted that Amazon Web Services and Oracle Corporation, competitors in the cloud computing space, have licensed the asserted patents. Daedalus Blue is the current owner and assignee of these asserted patents.

Daedalus Blue is represented by Bunsow De Mory LLP and George Brothers Kincaid & Horton, L.L.P.

Example article

On Wednesday, Daedalus Blue,LLC filed a complaint in the Western District of Texas against Microsoft, claiming that Microsoft infringed the patents-in-suit with its Microsoft Azure cloud computing product.

According to the complaint, the patents-in-suit “were invented by International Business Machines Corporation (IBM),” and relate to cloud computing.

Example of differences between languages

In Italian the meaning of “discontinuity” (discontinuità) in the institutional context is closer to change than to interruption, Japanese use the same word “警告” for warning and alarming, the French “parfait/parfaite” additionally to perfect can have the meaning of finished or adult and French word “issue” rather means outcome, than problem

AI+Ontology AI+Ontology AI+Ontology Client Client Daedalus Blue, LLC Lawsuit

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