Bi-Directional Recommendation Lookup for Personalized Offer Thresholding

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Solution Overview

Problem

Businesses in industries with low customer interaction face negative perceptions leading to customer loss and revenue impact, necessitating the development of high-touch relationships through effective recommender systems that offer personalized and location-specific discounts and benefits.

Innovation Solution

The implementation of recommender systems that aggregate user ratings vectors to generate bi-directional recommendation structures, allowing for periodic updates and efficient storage, enabling the recommendation of offers based on user preferences and behavior, thereby maintaining customer engagement and loyalty.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional recommender systems are used to provide personalized offers, then customer engagement and brand loyalty improve, but storage requirements and computational complexity increase significantly

Engineering Contradiction:

Improvecustomer engagementVSAvoidstorage requirements

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent segments the recommendation system into modular components: profile data module, offer data module, recommendation engine module, and communication module. Each module handles specific data types and functions independently, reducing overall system complexity while maintaining personalized recommendation capabilities

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent uses template-based offer structures where standardized offer templates are created once and then instantiated multiple times with different parameters. This copying approach reduces storage requirements by avoiding redundant data while enabling personalized offer generation

Inventive Principle:

Principle #26Copying

2Measurement precision

If comprehensive user data is collected for personalized recommendations, then recommendation accuracy improves, but data processing time and system complexity increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent implements pre-computed user profiles that aggregate and store user preferences, behavior patterns, and demographic information in advance. This preliminary action eliminates the need for real-time data aggregation during offer generation, significantly reducing processing time while maintaining recommendation accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces complex real-time computational analysis with pre-computed profile data and rule-based matching algorithms. This substitution of mechanical computation with pre-processed information and logical rules reduces processing time while preserving recommendation quality

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If frequent offer updates are provided to maintain customer interest, then customer engagement improves, but system resource consumption and operational complexity increase

Engineering Contradiction:

Improvecustomer engagementVSAvoidsystem resource consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The patent implements periodic offer updates where the recommendation engine generates new offers at scheduled intervals based on user profile changes and engagement metrics. This periodic approach maintains customer interest through regular engagement while avoiding continuous resource consumption associated with real-time updates

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The patent enables the recommendation system to automatically update user profiles and generate offers based on predefined triggers and rules without requiring manual intervention. This self-service capability reduces operational complexity and resource consumption by eliminating the need for continuous human monitoring and manual offer creation

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11127028B1Systems and methods for providing user offers based on efficient iterative recommendation structures

Publication Date: 2021.09.21 STATE FARM MUTAL AUTOMOBILE INSURANCE COMPANY

AI summary

Systems and methods are described for providing user offers based on efficient iterative recommendation structures. In various aspects, a server invokes a bi-directional look-up interface via a lookup request, where the bi-directional look-up interface is exposed via an electronic recommendation structure. The lookup request causes the bi-directional look-up interface to return a bi-directional recommendation value. The bi-directional recommendation value indicates a likelihood of a first user selecting a first offer or a second offer. The bi-directional recommendation value is transmitted via a computer network to a client device associated with the first user upon a determination that the likelihood meets or exceeds a recommendation threshold. The client device is operative to display at least one of the first offer or the second offer.