18336920. CROWD WISDOM BASED UNIVERSAL CONVERSATIONAL SYSTEM simplified abstract (Tata Consultancy Services Limited)
Contents
- 1 CROWD WISDOM BASED UNIVERSAL CONVERSATIONAL SYSTEM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CROWD WISDOM BASED UNIVERSAL CONVERSATIONAL SYSTEM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 How does the system handle privacy and data security concerns in conversational interactions?
- 1.11 What are the potential challenges in integrating this universal conversational system with existing software or platforms?
- 1.12 Original Abstract Submitted
CROWD WISDOM BASED UNIVERSAL CONVERSATIONAL SYSTEM
Organization Name
Tata Consultancy Services Limited
Inventor(s)
NARENDRAN Sivakumar of London (GB)
SANKARANARAYANAN Viswanathan of Chennai (IN)
CROWD WISDOM BASED UNIVERSAL CONVERSATIONAL SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18336920 titled 'CROWD WISDOM BASED UNIVERSAL CONVERSATIONAL SYSTEM
Simplified Explanation
A conversational system patent application describes a method for creating a universal conversational system using an ensemble of chatbots. The system combines responses from named entity recognition (NER) and rule-based conversational models, as well as generative knowledge chatbots that rely on pre-trained models. The system is designed to learn from human responses and from each other, creating a virtuous automated learning loop.
- Ensemble of chatbots combining NER, rule-based, and generative knowledge models
- Pre-trained models supplemented by domain-specific training
- Universal conversational system that learns from human responses and other models
- Wisdom of crowd approach for improving conversational capabilities
Potential Applications
The technology could be applied in customer service chatbots, virtual assistants, educational chatbots, and healthcare information systems.
Problems Solved
The system addresses the limitations of individual chatbots by combining different models to provide more accurate and diverse responses to user queries.
Benefits
Improved conversational capabilities, enhanced user experience, increased efficiency in handling customer queries, and continuous learning and improvement of the system.
Potential Commercial Applications
The technology could be used in various industries such as e-commerce, healthcare, education, and customer service to provide automated conversational interfaces for users.
Possible Prior Art
Prior art in this field may include existing conversational systems, chatbot technologies, and machine learning models used for natural language processing and dialogue generation.
Unanswered Questions
How does the system handle privacy and data security concerns in conversational interactions?
The article does not address the specific measures or protocols in place to ensure user data privacy and security during conversational interactions.
What are the potential challenges in integrating this universal conversational system with existing software or platforms?
The article does not discuss the potential obstacles or complexities that may arise when integrating this technology with different software systems or platforms.
Original Abstract Submitted
Conversational systems are intelligent machines that can understand language and conversing with a customer in writing or verbally. Embodiments herein provide a method for generating a universal conversational system using an ensemble of chatbots and a universal conversational system that adopts wisdom of crowd manifesting as an ensemble of chatbots. The ensemble of chatbots takes responses from NER and rule based conversational models. The knowledge based conversation models where complex queries that require question and answer, and the ensemble of generative knowledge chatbots are relying on a pre-trained models. The pre-trained models are complemented by domain specific training to answer queries that fall outside rule-based chatbot or knowledge graph-based conversation bot capability. The universal conversational system capable of building online virtuous automated learning loop where the models learn from each other and also from human response as wisdom of crowd.
