User Preference Adaptation via Statistical Analysis in UI Records

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Solution Overview

Problem

Service providers face challenges in accurately presenting user-preferred attributes in user interfaces, as users have varying interests in different attributes of records, leading to inefficient presentation of information.

Innovation Solution

A system comprising a frontend and backend server that determines user-preferred attributes through analysis of variance (ANOVA) and f-tests, allowing for personalized presentation of records and attributes based on user interactions and profiles.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If service providers present the same associated attributes for all records, then the user interface is simple and consistent, but users cannot see their preferred attributes in the initial presentation

Engineering Contradiction:

Improveuser preference adaptationVSAvoiduser interface complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by determining user-preferred attributes before presenting records. The backend server analyzes user interactions and profiles to identify preferred attributes in advance, then incorporates these into the initial user interface presentation, eliminating the need for users to navigate through multiple interfaces to find preferred information.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses feedback mechanisms where user interactions with records and attributes are continuously monitored and fed back to the backend server. This feedback loop enables the system to learn and update user preferences dynamically, allowing the user interface to adapt to individual user needs while maintaining consistency.

Inventive Principle:

Principle #23Feedback

2Loss of information

If service providers present multiple user interfaces to show different attributes, then users can access their preferred attributes, but users must navigate through multiple interfaces which is inefficient

Engineering Contradiction:

Improveattribute accessibilityVSAvoidtime to access preferred attribute

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system determines user-preferred attributes in advance through analysis of user profiles and interactions. This preliminary determination allows the system to pre-configure the user interface to display preferred attributes prominently or by default, eliminating the need for users to navigate through multiple interfaces to access their preferred information.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system provides self-service functionality by automatically learning and adapting to user preferences without requiring explicit user input or manual configuration. Users simply interact with the system naturally, and the system autonomously determines and presents their preferred attributes, saving time and effort.

Inventive Principle:

Principle #25Self-service

3Measurement precision

If service providers use analysis of variance and f-tests to determine user-preferred attributes, then attribute presentation becomes personalized and accurate, but the system complexity increases

Engineering Contradiction:

Improveuser preference detection accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The backend server acts as an intermediary component that handles the complex statistical analysis separately from the user interface. This intermediary layer performs the analysis of variance and f-tests to determine user preferences, then translates these results into simplified user interface decisions, allowing high measurement precision without exposing system complexity to users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual or simple attribute selection mechanisms with automated statistical analysis. Instead of relying on basic user selections or fixed presentation rules, the system uses sophisticated statistical methods (analysis of variance and f-tests) to objectively determine user preferences, increasing measurement precision through data-driven decision making.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11822608B2Learning affinities through design variations

Publication Date: 2023.11.21 SALESFORCE INC

AI summary

Disclosed herein are system, method, and computer program product embodiments for determining a user-preferred feature type. An embodiment operates by maintaining user-presented features associated with user-presented records, wherein the user-presented features comprise one or more user-presented feature types. After receiving a user-desired feature of the user-presented features, a user-preferred feature type of the user-presented feature types is determined based on the user-presented features and the user-desired feature. Thereafter, a new record and associated feature are to be presented with the new feature being of the user-preferred type.