Auto-adjusting App Operations via Keyboard Dynamics Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional automated tools, such as chatbots, struggle to capture and interpret contextual and behavioral cues from users, leading to inefficient interactions and the need for human assistance, as they lack the ability to understand paralinguistic information typically present in face-to-face conversations.
Innovation Solution
A system that includes a keyboard module to detect keyboard dynamics and a throttling module to adjust app operations based on captured paralinguistic cues, such as typing speed, pressure, and device movement, to provide more intuitive and personalized responses, potentially shifting interactions from text-based to voice or human-assisted when urgency is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automated tools like chatbots are used to interact with users, then automation extent is improved, but understanding of user intention and contextual cues deteriorates
Solution Approach 1:
The patent adds a new dimension of data collection by capturing keyboard dynamics (typing speed, pressure, pauses, backspaces) alongside traditional text input. This transforms the interaction from purely text-based to multi-dimensional behavioral data, enabling the automated tool to infer paralinguistic cues that were previously unavailable in digital communication.
Solution Approach 2:
The system introduces an intermediary analysis layer that processes keyboard dynamics between the user and the chatbot. This intermediary module interprets typing behavior patterns to extract emotional state, urgency, and intent information, bridging the gap between automated processing and human-like understanding without requiring direct human intervention.
2Device complexity
If chatbots process only text-based input, then device complexity is reduced, but ability to interpret user intention deteriorates
Solution Approach 1:
The system segments the input analysis into distinct components: text content analysis and keyboard dynamics analysis. By separating these functions, the system can process each type of data through specialized algorithms, maintaining manageable complexity while improving overall interpretation accuracy through the combination of multiple analysis streams.
Solution Approach 2:
The keyboard dynamics detection mechanism serves multiple functions simultaneously: it measures typing speed, detects typing pressure, identifies pauses, and recognizes backspace patterns. This multi-functional approach extracts rich behavioral information from a single detection system, improving reliability without proportionally increasing device complexity.
3Ease of operation
If automated tools lack contextual information, then ease of operation is improved, but productivity in completing complex tasks deteriorates
Solution Approach 1:
The system performs preliminary analysis of keyboard dynamics continuously during user interaction, building a contextual profile before critical decisions are needed. By pre-processing behavioral data and identifying patterns such as urgency or confusion early in the interaction, the system can proactively adjust its responses and provide more efficient assistance, improving task completion speed without complicating the user interface.
Data Source
AI summary
There is much data that is currently not being captured during user interaction with mobile apps that could provide insight into how to effectively address a user concern. Capturing such data may allow auto-adjustments of operational responses provided by mobile apps in response to detecting anomalous user inputs. Such anomalous user inputs may include keyboard dynamics or mobile device movement that deviate from an average or user specific levels. Such anomalous user inputs may indicate that a user concern is particularly urgent. Auto-adjustments to operation of a mobile app may include initiating targeted chatbot or live chat responses.