Inference Processor for Predictive Collateral Object Generation
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Solution Overview
Problem
Legacy approaches in cloud-based collaboration systems require manual user intervention to reformat and present content effectively, failing to consider the totality of user environments and needs, leading to inefficiencies and wasted productivity.
Innovation Solution
The system predicts user intent by inferring user needs for collateral object representations through an inference processor, using predictive heuristics and learning models to automatically generate and present derivative representations tailored to the user's context and environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual user commands are used to reformat and re-present collaboration objects, then users have control over content presentation, but user productivity decreases and manual effort increases
Solution Approach 1:
The system performs self-service by automatically generating collateral object representations without requiring user commands. The inference processor analyzes user context, device characteristics, and collaboration scenarios to autonomously determine and generate appropriate content formats, eliminating manual reformatting efforts while maintaining user needs
Solution Approach 2:
The system performs preliminary action by proactively generating multiple collateral object representations in advance based on predicted user needs. The inference processor anticipates what formats users will need based on their context and device characteristics, preparing these representations before users actually request them, thus eliminating wait time and manual effort
2Device complexity
If legacy approaches are used for content reformatting, then system complexity remains low, but adaptability to different user environments and needs deteriorates
Solution Approach 1:
The system applies parameter changes by dynamically adjusting content representation parameters based on user context, device characteristics, and collaboration scenarios. The inference processor modifies parameters such as format type, resolution, and content excerpt selection to adapt to different user environments, transforming a static system into a dynamically adaptable one
Solution Approach 2:
The system implements dynamics by making the content generation process adaptive and responsive to changing user needs and environments. The inference processor continuously monitors user context and device characteristics, dynamically selecting and generating appropriate collateral object representations rather than using fixed, static formatting rules
3Productivity
If automatic prediction of user intent is implemented, then productivity increases and manual effort decreases, but system complexity and computational requirements increase
Solution Approach 1:
The system applies segmentation by dividing the complex inference task into distinct functional components: context analysis module, device characteristic analysis module, prediction module, and generation module. This modular architecture manages system complexity by organizing computational tasks into separate, manageable segments that can operate independently yet cooperatively
Data Source
AI summary
Systems for online collaboration. Exemplary embodiments are implemented within cloud-based service platforms. User actions that are performed by a first user over collaboration object are observed. Other users collaborate over the same collaboration object and their actions are observed as well. Rather than responding to an explicit user request for conversions into collateral object representations in particular forms or formats, the system uses a predictor to determine the forms and formats of collateral object representations to generate on behalf of the first user. Based on then-current conditions and any sets of data collected that pertain to the first user and/or the other users, collateral object representations are formed and presented in particular forms or formats that are applicable to the then current conditions. When conditions change, the determined collateral object representations are then re-formed and re-presented in as many different forms or formats as are applicable to the changed conditions.