Entity-Specific Visual Bot Generation for Real-Time Query Response
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Solution Overview
Problem
Conventional technologies lack the ability to facilitate virtual multimedia contact through user interfaces for invoking virtual multimedia bots, restricting real-time visual interaction and effective response to user queries, and require external expertise for bot creation, which is costly and language-limited.
Innovation Solution
A system and method for self-generation of entity-specific bots using a machine learning architecture, enabling entities to record and customize automated visual responses without external help, by processing potential queries and intents through a machine learning model to generate a prediction engine for real-time responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional technologies are used for bot creation, then external expertise is required, but this increases cost and limits language capability
Solution Approach 1:
The system enables entities to self-generate their own bots by providing a user-friendly interface where users can input their content, select languages, and configure bot parameters without requiring external technical expertise. The machine learning model automatically processes this input and generates the bot, eliminating the need for professional bot creation services.
Solution Approach 2:
The machine learning model acts as an intermediary between the entity's content and the final bot generation. It processes the input data, translates it into appropriate bot responses, and generates the functional bot application, thereby mediating the complex technical processes behind bot creation.
2Ease of operation
If textual responses are used for user queries, then simplicity is maintained, but information completeness and effectiveness are reduced
Solution Approach 1:
The system transitions from one-dimensional textual responses to multi-dimensional responses that include video frames, images, and other visual media. This dimensional expansion allows the bot to convey information more effectively while maintaining ease of interaction through automated response generation.
Solution Approach 2:
The system changes the parameter of response modality from purely textual to multimodal (video, image, text). By adjusting this fundamental parameter, the bot can provide rich visual information while maintaining automated operation and user-friendly interaction.
3Productivity
If video-based responses are implemented, then user engagement and information effectiveness are improved, but system complexity and resource requirements increase
Solution Approach 1:
The system pre-processes and stores video frames and visual content in a database during the training phase. When a user query arrives, the machine learning model retrieves pre-prepared visual responses rather than generating them in real-time, significantly reducing the computational complexity during operation while maintaining high user engagement.
Solution Approach 2:
The system dynamically adjusts the type and amount of visual content returned based on the user query and context. The machine learning model determines which video frames or images are most relevant for each specific query, optimizing the balance between information richness and system resource utilization.
Data Source
AI summary
The present disclosure relates to a system and method for generating an executable bot application specific to an entity. In an exemplary implementation, the proposed system receives a knowledgebase comprising a set of potential queries associated with the entity, and receives video frame responses corresponding to the potential queries, wherein each potential query is mapped to an intent. The system processes, through a machine learning model, training data comprising the set of potential queries, the video frame responses, and the intent mapped to each potential query to generate a trained model, based on which a prediction engine is configured to process an end-user query and predict an intent associated with the end-user query, and facilitate response to the end-user query based on video frame response that is mapped with the predicted intent. Using the prediction engine, the proposed system auto-generates executable bot application by the entity.