Artificial Solutions Receives US Patent for Network of Knowledge Using a Common Communication Protocol
Artificial Solutions, www.teneo.ai, a leading innovator in the field of artificial intelligence (AI), is proud to announce that it has been granted US Patent No. US20220019904A1 for its groundbreaking invention, "Network of Knowledge Using a Common Communication Protocol."
The patented technology is a network of knowledge that combines different AI bots using a common communication protocol called NOK. A designated bot acts as a gateway through which all queries are passed while using the NOK protocol to draw information and capabilities from member bots transparently to the user. The invention also includes eagerness scores, derived for each member and each query, indicating a given bot's ability to respond well to the query. Eagerness may be used to provide recommendations to the gateway for a specific member to handle a given query.
Per Ottosson, CEO at Artificial Solutions, said: "We are thrilled to receive this patent, which recognizes the innovative nature of our work and underscores our commitment to advancing the field of AI. Our network of knowledge technology using a common communication protocol has the potential to revolutionize how AI bots communicate and collaborate, making them more effective and efficient."
The patented technology adds to the already impressive portfolio of AI technologies developed by Artificial Solutions. The company is dedicated to advancing the field of AI and is committed to excellence in research, development, and innovation.
Other patents include:
- Systems and methods for delivering advanced natural language interaction applications.
Description: A system for delivering advanced natural language interaction applications, comprising a dialog interface module, a natural language interaction engine, a solution data repository component operating comprising at least one domain model, at least one language model, and a plurality of flow elements and rules for managing interactions with users.
- Systems and methods for semi-automatic generation and tuning of natural language interaction applications.
Description: A system for supervised automatic code generation and tuning for natural language interaction applications, comprising a build environment comprising a developer user interface, automated coding tools, automated testing tools, and automated optimization tools, and an analytics framework software module.
- Systems and methods for semi-automatic generation and tuning of natural language interaction applications.
Description: A system for supervised automatic code generation and tuning for natural language interaction applications, comprising a build environment comprising a developer user interface, automated coding tools, automated testing tools, and automated optimization tools, and an analytics framework software module.
- System and methods for virtual assistant networks.
Description: A system for the operation of a virtual assistant (VA) network, comprising a plurality of VAs and related brokers operating on network-connected devices, wherein the VAs receive user requests and send these to VA brokers which delegate these to other VAs for fulfilment.
- Natural language data analytics platform.
Description: A system for natural language analytics, comprising a natural language application data importer, a natural language application data augmenter that enriches the data and an analytics component which provides a means of querying structured as well as unstructured data.
- Hybrid approach for developing, optimizing and executing conversational interaction applications.
Description: A system for hybrid approach to natural language understanding is provided, comprising a machine learning configured to create a new natural language understanding model from user-provided examples, and provide a natural language understanding model dataset to other components of the system; an integrated development environment configured to retrieve the natural language understanding model dataset, and publish a hybrid runtime solution and generate a log dataset; wherein, the log dataset may be used to retrain and improve existing models.
- Conversational system for recognizing, understanding, and acting on multiple intents and hypotheses.
Description: A conversational system that recognizes, understands, and acts on multiple intents that may be explicit or implicit during conversations with humans. During a conversation, one or more utterances are received and processed through a plurality of machine learning algorithms to establish precise meanings, additional intentions, and alternative hypothesis.
