Sensor Subgroup Selection via Utility Functions Without User Preferences
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Solution Overview
Problem
Existing multi-criteria decision-making (MCDM) methods require prior knowledge about decision-maker preferences or interaction to rank options on the Pareto frontier, limiting their applicability when such information is unavailable.
Innovation Solution
A method using hardware processors to compute multiple utility functions, select a subset of decision options that maximize utility values, and apply these options to sensors, employing pseudorandom utility function generators and greedy algorithms to optimize sensor configurations without prior knowledge of user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to collect comprehensive sensory data, then measurement precision and reliability are improved, but device complexity and data processing burden increase
Solution Approach 1:
The patent extracts and selects only the most relevant sensors from a larger sensor network based on utility function evaluation. The system identifies and removes redundant sensors that do not contribute significantly to the decision-making process, thereby reducing device complexity while maintaining measurement precision through selective sensor utilization.
Solution Approach 2:
The patent changes the operational parameters of the sensor network by dynamically adjusting which sensors are active based on computed utility functions. The system modifies the sensor configuration parameters (which sensors are enabled, their sampling rates, etc.) to optimize the balance between measurement precision and device complexity for different operational contexts.
2Reliability
If comprehensive sensory data from all sensors is processed, then decision accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the critical sensory data from the selected subset of sensors that are most relevant to the current decision context. By filtering out data from less important sensors, the system reduces the volume of data requiring processing while maintaining decision reliability through focused analysis of high-value sensory inputs.
Solution Approach 2:
The patent applies partial action by processing only a subset of available sensory data rather than all sensor inputs. The utility function computation identifies which sensors provide marginal benefit, and the system processes only those, achieving acceptable decision reliability with reduced processing time and computational resources.
3Measurement precision
If prior knowledge about user preferences is required for MCDM, then decision accuracy is improved, but ease of operation deteriorates due to additional interaction requirements
Solution Approach 1:
The patent implements self-service by enabling the system to automatically compute utility functions and select optimal sensors without requiring explicit user preference inputs. The system serves itself by generating decision-making parameters through automated utility function computation, eliminating the need for user interaction while maintaining decision accuracy through mathematical optimization.
Solution Approach 2:
The patent performs preliminary action by pre-computing utility functions and sensor importance metrics before the actual decision-making process. This advance computation establishes the basis for accurate decisions without requiring real-time user input, improving ease of operation by eliminating interactive preference elicitation while preserving decision accuracy through pre-established utility models.
Data Source
AI summary
A method comprising using at least one hardware processor for receiving sensory data from at least one physical or virtual sensor. The hardware processor(s) are used for computing a plurality of decision options for configuration of the at least one physical or virtual sensor. The hardware processor(s) are used for computing a plurality of utility functions, and for each utility function: (a) computing a utility value for each decision option, and (b) identifying a first subset of decision options that substantially maximize the computed utility values. The hardware processor(s) are used for selecting at least one cross-function decision option from of the first subsets, wherein the at least one cross-function decision option is included in a substantially maximum number of the first subsets. The hardware processor(s) are used for applying at least one of the at least one cross-function decision options, to at least one physical or virtual sensor.