Dynamic Data Representation Editing with Cascading Weights
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Solution Overview
Problem
Existing data representation editing systems do not allow users to impose specific constraints on how recalculated portions of graphical representations change in response to alterations of other portions, limiting the dynamic editing capabilities.
Innovation Solution
The method involves assigning weights and locks to variables, allowing users to define cascading weights based on iteration or time since the last modification, which constrain recalculations and enable iterative or time-based cascading, thereby controlling how variables change in response to user edits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are permitted to dynamically edit graphical representations by altering variables, then the interactivity and adaptability of the system is improved, but the system lacks the capability to constrain how recalculated variables change in response to user edits
Solution Approach 1:
The patent applies parameter changes by introducing weights as a new parameter that controls the degree to which recalculated variables respond to user edits. The weight parameter (ranging from 0 to 1) modifies the calculation behavior, allowing variables to be partially constrained rather than fully fixed or fully free. This resolves the contradiction by adding a continuous control parameter that enables nuanced constraint management without fundamentally changing the system architecture.
Solution Approach 2:
The patent implements dynamics by making weights time-dependent and iteration-dependent. Weights can change automatically based on the number of iterations since a variable was last edited or based on elapsed time, creating a dynamic constraint system that adapts to user behavior patterns. This allows the system to transition from static constraints to adaptive constraints that evolve during the editing session.
2Productivity
If the system automatically recalculates variables based on functional relationships, then the productivity and responsiveness of the system is improved, but users lose control over the magnitude and direction of variable changes
Solution Approach 1:
The patent implements feedback by having the system calculate the difference between the old and new values of edited variables, then use this feedback to adjust recalculated variables according to their weights. The feedback loop ensures that recalculations respect user intent while maintaining automatic responsiveness. The system monitors user edits and adjusts subsequent automatic recalculations based on this feedback, resolving the contradiction between speed and control.
Solution Approach 2:
The patent uses parameter changes by introducing weight parameters that modulate the impact of automatic recalculations. Instead of binary automatic recalculation (fully on or fully off), the weight parameter creates a spectrum of automaticity, allowing users to control the degree to which the system automatically adjusts variables in response to edits.
3Measurement precision
If weights are assigned to variables to control recalculation, then the precision of control over variable changes is improved, but the complexity of managing multiple weights and iterations increases
Solution Approach 1:
The patent applies periodic action by resetting weights to default values at specific intervals (e.g., after a certain number of iterations or after a time threshold). This periodic reset simplifies weight management by automatically returning the system to a known state, reducing the cognitive load on users who don't need to manually track every weight change. The system handles weight management automatically through these periodic resets.
Solution Approach 2:
The patent implements self-service by having the system automatically assign and manage weights based on iteration count and time elapsed since variable edits. Rather than requiring users to manually configure complex weight relationships, the system self-adjusts weights based on observable patterns in user behavior, such as how frequently variables are edited and how much time has passed between edits.
Data Source
AI summary
A method and system for representing data includes providing a data representation according to defined variables and a functional relationship between the defined variables and receiving an assigned weight assigned to a defined variable. The method includes receiving a modification of a selected defined variable, and providing a further data representation according to a recalculation of an unselected defined variable, based upon the functional relationship, the assigned weight and the modified variable. Assigned weights and a plurality of modifications of the weighted variable are received. A further data representation is provided according to a further recalculation of the weighted variable based upon a weight selected from the assigned weights according to a previous modification of the weighted variable. The recalculating is performed according to a number of modifications performed since the previous modification of the weighted variable and according to a period of time since the previous modification of the weighted variable.