AI Sales Call Simulation With Predictive Training Feedback
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Solution Overview
Problem
Existing sales training systems lack the ability to simulate live interactions and provide comprehensive analytics, failing to utilize fine-tuned models based on pre-trained language models for accurate sales call evaluation.
Innovation Solution
An AI-based automated system that uses a Training Support Processing Server (TSPS) with an AI/ML module to analyze target profile data, generate conversation recommendation parameters, and simulate sales calls with a chatbot, integrating blockchain for secure and transparent transaction recording.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional sales training methods (live role play, recorded calls with supervisors) are used, then personal interaction and guidance are provided, but the training lacks realistic simulation challenges and comprehensive analytics
Solution Approach 1:
The patent creates a simulated sales call environment that copies real sales call dynamics through AI chatbots and voice synthesis. Instead of using actual customers or supervisors, the system generates virtual representations that replicate the complexity and unpredictability of real interactions, allowing trainees to practice in a safe but realistic setting.
Solution Approach 2:
The patent replaces the mechanical system of human supervisors and live customers with an AI-based automated evaluation system. The chatbot with voice synthesis and the automated analytics engine substitute for human interaction and assessment, providing scalable, consistent, and comprehensive feedback without requiring human trainers for each call.
2Ease of operation
If basic Sales Simulator solutions are used, then sales representatives can practice their sales, but the solutions lack challenging live interactions and comprehensive analytics
Solution Approach 1:
The patent implements a comprehensive feedback system that automatically analyzes sales calls using AI and generates detailed performance reports. The system provides immediate feedback on multiple dimensions including communication skills, product knowledge, and closing techniques, with suggestions for improvement based on comparison with top performers and industry benchmarks.
Solution Approach 2:
The patent creates dynamic, adaptive training scenarios where the AI chatbot adjusts its responses based on the trainee's performance in real-time. The simulation evolves during the call, presenting unexpected objections and scenarios that require adaptive thinking, rather than following a predetermined script.
3Productivity
If automated AI/ML processing is implemented, then comprehensive analytics and predictive models are generated, but data security and transparency requirements increase
Solution Approach 1:
The patent segments the data architecture into distinct layers: raw call data, processed analytics, and blockchain-verified results. This segmentation allows different security protocols to be applied at each level, with the blockchain layer providing immutable verification of the analytics process while the raw data remains protected through access controls and encryption.
Solution Approach 2:
The patent introduces blockchain technology as an intermediary layer between the AI/ML processing system and the final training outcomes. The blockchain verifies and records the analytics generation process, providing transparency and auditability without requiring direct access to the underlying proprietary AI models or raw customer data, thus maintaining security while enabling trust.
Data Source
AI summary
A system for an automated sales training call processing based on call-related data including a processor of a call training support processing (TSPS) server node configured to host a machine learning (ML) module and connected to at least one user-entity node and to at least one manager-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: acquire target profile data comprising a list of characteristics of a sale target person from the at least one manager-entity node; parse the target profile data to extract a plurality of key classifying features; query a call training database to retrieve local historical calls'-related data based on the plurality of key classifying features; generate at least one classifier vector based on the plurality of key classifying features and the local historical calls'-related data; and provide the at least one classifier feature vector to the ML module configured to generate a predictive model for producing a set of conversation recommendation parameters for a chatbot executed by the TSPS, wherein the chatbot represents the sale target person in communication with the user-entity node over a call generating call data.