Mobile Device Intent Inference for Personalized Spatial Representations
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Solution Overview
Problem
Existing technologies fail to effectively create personalized and dynamic representations of physical geographical spaces using mobile devices, as they lack the ability to infer user intent and preferences in real-time, leading to inefficient use of services and data.
Innovation Solution
A system and method that utilizes mobile devices to gather and process signals from broadcasting devices, inferring user movements and preferences through machine-learning techniques and explicit input, to create mediated representations of environments, which can include or exclude objects based on user preferences and device capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing technologies are used to create representations of physical spaces, then basic spatial mapping is achieved, but the representations lack personalization and real-time adaptability to user intent
Solution Approach 1:
The system performs preliminary actions by collecting user preference data and establishing machine learning models in advance. User preferences are gathered through explicit inputs and implicit behaviors, and these preferences are processed beforehand to create personalized representation templates that can be quickly applied when users enter specific environments, eliminating the need for complex real-time analysis during user interaction.
Solution Approach 2:
The patent introduces an intermediary layer consisting of machine learning algorithms and preference processing systems that mediate between raw environmental data and final personalized representations. This intermediary layer translates complex spatial data and user preferences into meaningful personalized views, reducing the complexity burden on the overall system while enabling sophisticated personalization capabilities.
2Measurement precision
If real-time signal processing from broadcasting devices is implemented, then user intent inference capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of user preference data and establishes machine learning models in advance. User preferences are collected through explicit inputs and implicit behaviors, and these preferences are processed beforehand to create personalized representation templates. This allows the system to quickly apply pre-established models to real-time signal data without performing complex computations during user interaction, thus maintaining high inference accuracy while minimizing processing time.
Solution Approach 2:
The patent implements partial processing of signals by focusing on key features and patterns that are most indicative of user intent. Rather than analyzing every aspect of the environmental data in real-time, the system selectively processes the most relevant signals based on pre-established user preferences and context, achieving sufficient inference accuracy with reduced computational overhead and faster processing speeds.
3Loss of information
If mediated representations include all objects in the environment, then completeness of information is achieved, but relevance to user preferences decreases
Solution Approach 1:
The patent applies local quality by customizing the representation content according to specific user preferences and contexts. Instead of uniformly including all objects for all users, the system selectively highlights and emphasizes objects that are relevant to each user's preferences, behaviors, and current context. This allows the representation to maintain sufficient completeness while being highly adapted to individual user needs, with different users receiving different levels and types of information about the same environment.
Solution Approach 2:
The system implements partial inclusion of environmental objects in mediated representations based on user preferences. Rather than displaying all objects equally, the system selectively presents the most relevant objects and information according to pre-established user profiles and real-time context analysis. This partial action approach ensures that representations remain concise and highly relevant to user preferences while retaining sufficient information to be useful, avoiding information overload.
Data Source
AI summary
System and methods are provided to create representations of geographic areas. Such representations enable users to search for items and services of interest and to quickly locate and utilize such items and services. Representations are created using user preferences thus reducing the amount of information presented to a user, i.e., user preferences control the contents of a representation. Control APIs contained within a representation may be used to control devices represented in a representation or to manufacture them using 3-D printing technologies. Methods to learn user preferences via his movements and other actions and impose a user's preferences upon an environment are shown. Some details of the invention are described by applying the invention to problems in retail marketing and figures depicting an implementation illustrate certain aspects of the invention.