AI Agent Personality Creation via Core Meaning Extraction
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Solution Overview
Problem
Existing artificial intelligence systems face challenges such as labor-intensive creation of computer personalities, rigidity in adapting to changing environments, dependence on grammar and punctuation, limited object representation, and lack of flexibility and consistency across devices, along with inadequate personal data protection and authentication methods.
Innovation Solution
A system and method for creating an artificially intelligent agent that processes conversational inputs to derive core meanings, dynamically increasing intelligence by using pre-processor, logic processor, and post-processor components, and enabling roaming personalities with secure data access across devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually creating and editing knowledge base and response templates is used to create computer personality, then the computer personality can achieve high level of artificial intelligence, but the process becomes very labor intensive and time consuming
Solution Approach 1:
The system performs preliminary actions by automatically generating a large number of question-response pairs and training data before the actual personality creation process. This pre-computation and pre-generation of training materials significantly reduces the manual effort and time required during the actual personality development phase.
Solution Approach 2:
The system enables self-service by allowing the computer personality to automatically learn and improve through interaction with the knowledge base and training data without requiring continuous manual intervention. The personality can autonomously process information and generate responses, reducing the need for ongoing manual editing and maintenance.
2Adaptability or versatility
If discrete computer personalities are created for different contexts, then each personality can be optimized for its specific context, but the creation process requires unique sets of QR pairs for each context
Solution Approach 1:
The system implements universality by creating a single unified knowledge base that can serve multiple different contexts and personality types. Instead of maintaining separate knowledge bases for each context, the system allows one knowledge base to be dynamically utilized across various contexts, reducing overall system complexity while maintaining context-specific optimization.
3Stability of the object's composition
If standardized object representation formats are used, then objects can be consistently represented across systems, but the representation is limited to pre-determined classification sets
Solution Approach 1:
The system applies dynamics by making the object representation system adaptable and flexible rather than static and rigid. The knowledge base can dynamically incorporate new objects, concepts, and classifications beyond pre-determined sets, allowing the system to evolve and adapt to new contexts while maintaining consistent representation through standardized formats.
4Stability of the object's composition
If prior art systems are issued as standards, then they provide standardized operation, but they become rigid and difficult to adapt to changing technological environments
Solution Approach 1:
The system implements dynamics by designing an architecture that can adapt to changing technological environments. The knowledge base and processing systems can be updated and modified without requiring complete system replacement, allowing the system to evolve with changing technologies while maintaining operational stability through its core standardized framework.
Data Source
AI summary
A system and associated methods for creating and implementing an artificially intelligent agent or system are disclosed. In at least one embodiment, a target personality is implemented in memory on an at least one computing device and configured for responding to an at least one conversational input received from an at least one communicating entity. An at least one conversational personality is configured for conversing with the target personality as needed in order to provide the target personality with appropriate knowledge and responses. For each conversational input received by the target personality, it is first processed to derive an at least one core meaning associated therewith. An appropriate raw response is determined then formatted before being transmitted to the communicating entity. Thus, the target personality is capable of carrying on a conversation, even if some responses provided by the target personality are obtained from the at least one conversational personality.