Best New Algorithm for AI

Best New Algorithm for AI

AI’s Next Generation Natural Language Understanding API


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Language Based AI

Pat Inc. is teaching language to machines. We deliver meaning-as-a-service: connecting natural language with structured information that creates meaning and understanding. Pat Inc. is building the world’s most powerful natural language service for developers to build intelligent agents and applications that you can talk to or text.

Linguistics not Statistics

The difference with Natural Language Understanding (NLU) is not in how it translates words or guesses the intent of a question. It understands the true meaning of sentences and conversations. Pat Inc. takes a linguistic approach to NLU, not statistical or machine learning based. After all, humans don’t learn language reading thousands of books and memorising colocation patterns and probabilities.

Training Data Not Required

Rather than training data or annotated corpora, Pat builds knowledge on language just like a human by progressively learning the way words are combined, regarding real objects, people, processes, and events in context. Pat is not trying to learn patterns about language meaning from big data, an approach that is still struggling to beat the language capabilities of a 3-year-old.


Pat Inc.’s approach to NLU is language independent and delivers one solution across all languages. The meaning of a sentence or conversation is mapped regardless of the language. In fact, multiple languages can even be mixed up within a single sentence or conversation and still connect to meaning.

  • "The amazing John Ball of @PatisNLU has been a trailblazer for decades. His work forms the path of the next frontier of the #VoiceFirst revolution. There’s a fork in the road ahead for #VoiceFirst technology:
      Brute force machine learning of words
      Words in sentences surmised to meaning and intent
    I only work with protocols that are on road #2. Protocols are the only way to full conversations. Only conversations matter."
    Brian Roemmele
    Recognized global authority on voice AI, President of Multiplex,
    Scientist, Researcher, Analyst, Highly regarded futurist and expert contributor
  • “Pat Inc. offers next generation Natural Language Understanding technology, that is capable of being the conversational user interface of the future.”
    Dr. Hossein Eslambolchi
    Open Lab Technical Advisor at Facebook, Chairman & CEO at 2020 Venture Partners,
    Top 10 Influential Leader of the 21st Century, 1000+ patents BS, MS, Ph.D, UCSD
  • “Statistical systems can accomplish NLP to a considerable degree, but they can never achieve NLU, which involves meaning. The answer lies in linguistics. Pat Inc. solves that.”
    Professor Robert D. Van Valin, Jr
    Global linguist expert, University of Dusseldorf, University of Buffalo and State University of New York,
    Author of 7+ books and 100+ publications
  • "To teach computers conversational skills and language, we need to connect language to some form of meaning representation in the computer, like Pat Inc. is doing."
    Chris Lonsdale
    Global technology advisor, linguist and educator,
    TEDx speaker with 13+M views, Author of 2+ books

Pat Inc. bridges five decades of functional linguistic studies in Role and Reference Grammar with three decades of cognitive science studies in Patom Theory. This miraculous combination would shape the future of the next generation of humanized machines that can exactly understand the human languages. Pat’s meaning-matcher engine makes this dream a tangible reality.

Natural Language for Developers

Pat Inc. offers next generation NLU for developers to build language-based applications and devices for users to engage, talk and text. Our vision is to humanize conversations with machines. Pat doesn’t need corpora to train on a new domain. We’re teaching machines language, not data. In functional linguistics the words and phrases connect to meaning or as we say, Form follows Meaning™.

Question Answering

Search engines put the world’s wealth of information at our fingertips, but imagine the difference if you could search with natural language? Google knows firsthand the frustration todays state-of-the-art causes users, who often need to try a number of different search results to find the answer they are looking for.

  • Human-Machine Conversation
    Language Learning
    Customer Service intelligent assistant

Information Extraction

Key decisions in the financial markets are increasingly moving away from human oversight and control. Algorithmic trading is becoming more standard, a form of financial investing that is entirely controlled by technology. But, many of these financial decisions are impacted by news and by journalism, which is still presented predominantly in English. 

  • Sentiment analysis
  • Text analysis

IoT and Robotics

Today we have started talking to our smart homes and home robots. How much better will this be with natural language that understands when we make corrections to our spoken words and its response more closely mimics a human? When ambiguity is handled or clarified. We are only at the beginning of the social robot  and home automation revolution.

  • Home Automation
  • Conversational User Interface

How does it work?

Pat maps words to meaning by storing only certainties, not probabilities. 885+ competitors in NLP using state-of-the-art statistical methodologies can only approximate meaning. This can be seen with digital assistant technology, which sometimes gets our requests right and sometimes wrong, because they don’t really understand us unless we provide recognized commands or familiar text based on the machine’s corpora training.

Counting words and tracking word order, or even parsing by syntax results in probability—or guesswork, not meaning. Pat, the meaning matcher, links straight to meanings combining the linguistic model Role and Reference Grammar (RRG) with Patom Theory. Pat matches every word to the correct meaning based on the meanings of the other words in the sentence or story, just like a 3-year-old does without guesswork. As a result, language is broken down by meaning, storing only certainties like a human does, not probabilities.

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