Pre-cognitive In-context Information Delivery System
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Solution Overview
Problem
In networked environments, readers may not find embedded links useful as they are pre-configured by editors who may not represent the actual readers' interests, leading to repeated look-ups of the same terms and lack of dynamic information relevant to their curiosity and confusion.
Innovation Solution
A system that tracks readers' interactions and interests to automatically provide in-context related information dynamically, allowing community-based information to supplement human editor-designated content, making the content more comprehensible and useful over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If editors pre-configure embedded links in a page, then the page can provide additional information to readers, but the embedded links may not match actual readers' interests and require repeated look-ups
Solution Approach 1:
The system performs preliminary actions by tracking and analyzing reader interactions with the page in advance. Before a reader needs information, the system has already identified which terms and concepts are most frequently looked up by monitoring previous readers' behavior. This allows the system to pre-prepare and deliver relevant information proactively, rather than waiting for each individual reader to request it.
Solution Approach 2:
The system implements feedback loops by continuously monitoring reader interactions (look-ups, clicks, time spent) and using this data to improve future information delivery. The tracking mechanism collects feedback about which terms readers find confusing or interesting, and this feedback is fed back into the system to dynamically adjust which additional information is provided to subsequent readers, creating a self-improving cycle.
2Adaptability or versatility
If the system tracks and analyzes reader interactions to provide dynamic information, then information relevance improves, but processing requirements and system complexity increase
Solution Approach 1:
The system applies partial action by focusing tracking and analysis only on specific, high-value interactions rather than monitoring all reader behaviors equally. It prioritizes tracking key metrics such as term look-ups, link clicks, and time spent on specific sections, rather than attempting to analyze every pixel movement or mouse hover. This selective approach provides sufficient adaptability while conserving processing energy.
Solution Approach 2:
The system enables self-service by allowing reader interactions to automatically generate the data needed for improving information delivery. Each reader's look-ups and interactions automatically contribute to the collective understanding of what information is valuable, without requiring manual curation or intensive external analysis. The system serves itself by converting user behavior into actionable insights automatically.
3Productivity
If pre-selected terms are chosen by a single editor or computer, then the page can be quickly prepared, but the terms may not represent diverse reader perspectives and interests
Solution Approach 1:
The system achieves universality by combining multiple functions into a unified approach: editors can quickly pre-configure initial links while the system simultaneously tracks reader interactions and dynamically adapts the information provided. This multi-functional system serves both the need for rapid page preparation and the need for diverse reader representation by aggregating perspectives from multiple readers into the information delivery process.
Solution Approach 2:
The system introduces dynamics by making the embedded links and additional information adaptive rather than static. While editors can quickly set up initial links, the system continuously evolves the information provided based on real-time reader interactions. The same page can dynamically serve different readers with different sets of additional information based on their individual needs and the collective patterns observed from previous readers.
Data Source
AI summary
An apparatus and method for providing pre-cognitive delivery of in-context related information is disclosed herein. A user's expressed interest in a particular portion of a requested page and his/her requests for additional information relating to the particular portion are tracked. The tracked data permits determination of the content of in-context related information. For each of the particular portions of the requested page deemed to be of sufficient popularity, in-context information relating to each such particular portion is automatically provided to users along with the requested page.