Bi-Directional Recommendation Structure for Low-Overhead Offer Personalization

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Solution Overview

Problem

Businesses in industries with high customer interaction sensitivity, such as insurance, face negative customer perceptions leading to low engagement and potential loss of customers, necessitating the development of high-touch relationships to foster brand loyalty and maintain customer interaction.

Innovation Solution

Implementing recommender systems that generate efficient iterative electronic recommendation structures using processors to aggregate ratings vectors, generate similarity pairing values, and create bi-directional recommendation structures, allowing for periodic updates and reduced storage requirements, enabling effective offer recommendations and customer engagement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

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

Engineering Contradiction:

Improvepersonalization capabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the recommendation system into modular components: user profile module, offer database, recommendation engine with configurable algorithms, and delivery system. This segmentation allows the system to provide personalized recommendations while maintaining manageable complexity through independent, loosely-coupled modules that can be developed and maintained separately

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The recommendation system is designed as a universal platform that can serve multiple business objectives simultaneously - cross-selling, up-selling, customer retention, and promotional distribution. The system handles diverse offer types (discounts, coupons, loyalty rewards) and can adapt to different industry requirements, reducing the need for separate specialized systems

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Measurement precision

If comprehensive user data is collected and processed to generate personalized recommendations, then recommendation accuracy improves, but data processing time and computational costs increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoiddata processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing user data during off-peak hours to build user profiles, segment user groups, and pre-calculate recommendation candidates. User profiles are maintained and updated incrementally as new data arrives, so that when recommendation requests occur, the system can quickly retrieve and refine pre-computed results rather than processing raw data from scratch

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial processing by focusing computational resources on the most relevant user attributes and offer features for each recommendation query. Rather than analyzing all possible user data points equally, the system identifies and processes only the critical subset needed for accurate recommendations, reducing overall processing time while maintaining accuracy

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If real-time personalized offers are delivered to users, then customer retention and brand loyalty improve, but system computational load and infrastructure requirements increase

Engineering Contradiction:

Improvecustomer retentionVSAvoidcomputational load

Core Design Contradiction:

ReliabilityVSUse of energy by moving object

Solution Approach 1:

The system merges multiple functions into unified processes: user authentication and profile retrieval are combined, recommendation generation and offer selection are integrated, and delivery tracking is merged with engagement monitoring. This consolidation reduces redundant computations and infrastructure requirements while maintaining real-time personalization capabilities

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system implements self-service mechanisms where user profiles automatically update based on interaction history, recommendation preferences are inferred from behavior patterns without manual input, and offer effectiveness is self-measured through engagement metrics. This reduces the computational burden of continuous manual data collection and processing while maintaining high personalization accuracy

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11734706B2Systems and methods for providing user offers based on efficient iterative recommendation structures

Publication Date: 2023.08.22 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.