Analytics Command Recommendation Engine Using Goal Orientation Scoring

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Solution Overview

Problem

Conventional analytics systems lack effective guidance and recommendation systems, leading to inefficiencies and ineffectiveness in data analysis due to the vast number of commands and complexity, particularly for novice users and those with unclear objectives.

Innovation Solution

An analytics system that uses a goal engine and command engine to generate command recommendations based on analysis-goal information, incorporating machine-learning models and a loss function fine-tuning framework to provide relevant data command suggestions with a goal orientation score, aligning commands with user objectives.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional analytics systems provide many commands for data analysis, then the system's capability to perform various analytics tasks is improved, but the complexity of operation and difficulty of selecting relevant commands increases

Engineering Contradiction:

Improveanalytics task capabilityVSAvoidcommand selection difficulty

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system implements feedback by continuously monitoring user interactions, selected commands, and analysis goals to dynamically generate recommendations. The recommendation engine uses feedback from user behavior patterns and goal completion status to refine command suggestions, making the system adaptive to user needs while reducing selection difficulty.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system provides self-service through automated command recommendations that adapt to user goals without requiring manual configuration. The recommendation engine automatically analyzes user intent and provides relevant command suggestions, allowing users to benefit from intelligent guidance without additional effort or complexity.

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If analytics systems provide comprehensive command options, then the system's functionality is improved, but the time required for users to make decisions and complete analysis tasks increases

Engineering Contradiction:

Improvecommand functionalityVSAvoiddecision making time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The system extracts and prioritizes only the most relevant commands based on user goals and context, presenting a filtered subset rather than the complete command set. This extraction principle reduces decision time by focusing users on essential commands while maintaining access to comprehensive functionality when needed.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The recommendation engine provides partial action by suggesting only the top N most relevant commands rather than all possible commands. This partial presentation reduces cognitive load and decision time while maintaining system versatility, as users can access additional commands if needed.

Inventive Principle:

Principle #16Partial or excessive action

3Productivity

If analytics systems provide guidance and recommendation features, then user productivity is improved, but the system complexity and computational resources required increase

Engineering Contradiction:

Improveuser productivityVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the recommendation functionality into separate modular components: goal analysis module, pattern recognition module, recommendation generation module, and feedback processing module. This segmentation reduces overall system complexity by allowing independent development, testing, and optimization of each component while maintaining high user productivity.

Inventive Principle:

Principle #1Segmentation

4Productivity

If analytics systems provide intelligent command recommendations, then data analysis efficiency is improved, but the computational resources and processing time required increase

Engineering Contradiction:

Improvedata analysis efficiencyVSAvoidcomputational resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary action by pre-processing user goals and analyzing historical interaction patterns before generating recommendations. This advance preparation reduces real-time computational requirements, as the system has already identified relevant patterns and filtered potential commands before the user needs suggestions.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20220019909A1Intent-based command recommendation generation in an analytics system

Publication Date: 2022.01.20 ADOBE INC

AI summary

Methods, systems, and computer storage media for providing command recommendations for an analysis-goal, using analytics system operations in an analytics systems. In operation, an analytics client is configured to provide an analytics interface for receiving a selection of analysis-goal information that corresponds to an analysis-goal model. A goal engine selects an analysis-goal based on the analysis-goal information. A command engine is configured to use the analysis-goal and goal-driven models to predict probable commands for the analysis goal. The command engine also selects a next command recommendation from the probable commands. The command engine generates additional command recommendation data based on a loss function fine tuner. The additional command recommendation data can include a goal orientation score that quantifies a degree to which a command aligns with the analysis-goal. The next command recommendation and additional command recommendation output data are communicated and caused to be displayed on the analytics interface.