AI Agent Personality Creation via Template Segmentation
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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, lack of flexibility, and inadequate personal data protection, especially in mobile devices, leading to inconsistent user experiences and decreased effectiveness of advertising.
Innovation Solution
A system and method for creating an artificially intelligent agent that processes conversational inputs to derive core meanings, dynamically increases intelligence by obtaining real-time responses, and securely personalizes interactions across devices, using a target personality, conversational personalities, and a teacher personality to enhance knowledge and responses, while ensuring secure data access and consistent user experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manually creating and editing knowledge base and response templates, then the computer personality can be tailored to specific contexts, but the process becomes very labor intensive and time consuming
Solution Approach 1:
The patent uses template-based copying where pre-defined response templates can be replicated and reused across different contexts. The system copies successful response patterns and adapts them to new situations, reducing the need to create responses from scratch and significantly cutting creation time while maintaining context-specific relevance.
Solution Approach 2:
The knowledge base is segmented into reusable components such as response templates, conversation categories, and knowledge objects. This segmentation allows the system to assemble context-specific personalities by combining pre-defined segments rather than creating everything manually, reducing labor intensity and time requirements.
2Ease of manufacture
If using standardized AI systems, then implementation is simplified, but the systems become rigid and difficult to adapt to changing environments
Solution Approach 1:
The patent implements dynamic adaptability by allowing the AI system to learn from user interactions and update its knowledge base in real-time. The system dynamically adjusts its responses based on contextual cues and user feedback, transforming from a static standardized system into a flexible adaptive system that evolves with changing environments while maintaining ease of initial implementation through standardized templates.
Solution Approach 2:
The system changes parameters such as response tone, knowledge depth, and conversation style based on detected context and user preferences. This parameter adjustment allows standardized AI systems to adapt to different environments and user needs without requiring complete system redesign, maintaining implementation simplicity while achieving environmental adaptability.
3Measurement precision
If relying on grammar and punctuation for recognition, then text processing is accurate, but the system cannot adapt to voice recognition inputs
Solution Approach 1:
The patent creates a universal processing framework that handles multiple input types including text, voice, and other modalities through a common knowledge base and response generation system. The same knowledge objects and response templates serve all input types, allowing the system to maintain text processing accuracy while adding voice recognition capability without requiring separate specialized systems.
Solution Approach 2:
The system uses an intermediary processing layer that converts various input formats into a unified internal representation. This intermediary layer translates voice commands, text inputs, and other modalities into a common knowledge base format, enabling the system to leverage existing text-based knowledge structures while accommodating diverse input devices and maintaining processing accuracy across different modalities.
4Reliability
If creating unique computer personalities for each context, then the AI can be optimized for specific domains, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent merges common knowledge and response patterns into shared knowledge objects that can be reused across multiple domains. Instead of creating separate personalities for each context, the system combines domain-specific information with universal response templates, reducing overall system complexity while maintaining domain-specific performance through selective combination of relevant knowledge objects.
Solution Approach 2:
The system applies local quality by selectively activating different knowledge objects and response templates based on the specific context and domain requirements. Rather than loading entire personalized systems for each domain, the system dynamically assembles only the relevant local knowledge components needed for the current interaction, reducing complexity while maintaining domain-specific optimization where required.
5Reliability
If implementing upgrades by issuing new versions, then the system can incorporate improvements, but versioning problems arise and systems must come offline
Solution Approach 1:
The patent implements dynamic updates where the knowledge base can be modified and upgraded without issuing complete new versions. The system dynamically incorporates improvements by updating individual knowledge objects and response templates in place, allowing continuous improvement while maintaining system availability and eliminating the need for offline upgrades and version management.
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.