Contextual Cookies Using Gaze-Based Intent Tracking
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Solution Overview
Problem
Conventional cookies do not consider gaze information to assess user intent or interest, requiring rigid front-end code changes for parameter adjustments, limiting flexibility and efficiency in generating and updating user interaction data.
Innovation Solution
A system that tracks user engagement information, including gaze points, to dynamically generate and update contextual cookies based on machine learning algorithms, allowing real-time adjustment of user intent and interest data without requiring front-end code changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cookies are used to store user information, then user preferences and interactions can be tracked, but the system requires rigid front-end code changes to adjust cookie parameters, reducing flexibility and efficiency
Solution Approach 1:
The patent introduces a back-end service as an intermediary between the front-end web application and cookie management. This back-end service receives requests from the front-end, dynamically generates or modifies cookie parameters, and returns the configured cookies. This mediator eliminates the need for front-end code changes when adjusting cookie parameters, as all modifications are handled through back-end API calls. The front-end simply interacts with the back-end service using standard interfaces, while the back-end service manages the complexity of cookie configuration and generation.
2Ease of operation
If front-end code is modified to change cookie parameters, then cookie behavior can be adjusted, but the deployment process becomes more complex and time-consuming
Solution Approach 1:
The patent implements dynamic cookie parameter adjustment through a back-end service that can modify cookie configurations in real-time without requiring front-end code changes or redeployment. The back-end service accepts dynamic inputs (such as user segments, campaign parameters, or behavioral data) and generates or modifies cookie parameters accordingly. This dynamic approach allows operators to adjust cookie behavior by simply updating back-end configuration files or databases, which takes effect immediately without involving the front-end deployment pipeline.
Solution Approach 2:
The back-end service pre-generates cookie templates and parameter configurations based on anticipated needs. When the front-end application needs cookies, it requests them from the back-end service, which has already prepared the necessary cookie structures and parameter sets. This preliminary preparation eliminates the need for last-minute code changes and deployments when cookie adjustments are needed, as the back-end service can instantly provide pre-configured cookies or generate new ones on-demand.
3Measurement precision
If contextual cookies are generated dynamically based on user engagement, then user intent analysis improves, but the system complexity increases without automated generation mechanisms
Solution Approach 1:
The back-end service implements self-service mechanisms for contextual cookie generation by automatically analyzing user engagement data (such as click streams, time-on-page, scroll depth, and interaction patterns) and generating appropriate cookie parameters without manual intervention. The system uses predefined analysis rules and machine learning models to interpret user behavior and automatically configure cookies that reflect user intent, interests, and preferences. This automation eliminates the need for manual cookie configuration while maintaining high precision in user intent assessment.
Solution Approach 2:
The system implements feedback loops where cookie performance and user interaction data are continuously monitored and fed back into the back-end service. This feedback informs dynamic adjustments to cookie generation parameters, allowing the system to refine its understanding of user intent over time. The back-end service uses this feedback to automatically optimize cookie configurations, improving measurement precision while the automation handles the complexity of continuous analysis and adjustment.
Data Source
AI summary
An illustrative system includes a memory that stores instructions and a processor communicatively coupled to the memory and configured to execute the instructions to perform a process. The process may comprise tracking engagement information of a user while the user views user interface content, determining, based on the engagement information, a gaze point of the user with respect to the user interface content, mapping the gaze point of the user to a user interface element associated with the user interface content, and dynamically generating or updating, based on the engagement information and the mapping of the gaze point of the user to the user interface element, a contextual cookie that is configured to store user intent information associated with the user's engagement with the user interface content.