Computational Knowledge Rules for Proactive Device State Detection

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Solution Overview

Problem

Current methods for determining customer and device states regarding computing devices are non-proactive, often detecting negative states only after customer frustration, and fail to effectively gauge positive states, leading to missed opportunities for proactive action and increased usage or sales.

Innovation Solution

A system and method for remotely acquiring and processing device data using computational knowledge represented by a set of rules, such as forward chaining rules, to infer device usage patterns and propose actions, including remediation for negative states and propagation of positive states, enabling proactive customer state determination on a continuous basis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional periodic or sporadic customer contact methods are used, then customer service coverage is maintained, but customer state determination is not proactive and negative states are detected only after customer frustration

Engineering Contradiction:

Improvecustomer state determination reliabilityVSAvoidtime to detect customer state

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by continuously monitoring device data and applying computational knowledge rules proactively, before customers experience frustration or contact support. This enables early detection of negative customer states and allows preventive actions to be taken, resolving the contradiction between reliable state determination and timely detection.

Inventive Principle:

Principle #10Preliminary action

2Loss of information

If conventional methods focus on detecting negative customer states, then customer dissatisfaction can be identified, but positive customer states are not gauged and opportunities for increased usage or sales are missed

Engineering Contradiction:

Improveinformation about customer stateVSAvoiddevice utilization

Core Design Contradiction:

Loss of informationVSProductivity

Solution Approach 1:

The computational knowledge rule set is designed to be universal, handling both negative and positive customer state determinations through the same system infrastructure. This multi-functional approach enables the system to not only detect problems but also identify opportunities for increased device utilization and cross-selling, resolving the contradiction between complete information gathering and productivity enhancement.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Ease of operation

If manual customer service methods are used, then human judgment can be applied, but the process is non-proactive and requires significant human resources for periodic contact

Engineering Contradiction:

Improvecustomer service operationVSAvoidautomated state determination

Core Design Contradiction:

Ease of operationVSExtent of automation

Solution Approach 1:

The system implements self-service by enabling automated device data collection, processing, and customer state determination without requiring human intervention for each customer interaction. The computational knowledge rules automatically analyze device data and propose actions, significantly reducing manual effort while maintaining or improving service quality, thus resolving the contradiction between ease of operation and automation extent.

Inventive Principle:

Principle #25Self-service

4Reliability

If proactive determination of customer state is implemented, then timely actions can be proposed, but the system complexity increases due to computational knowledge processing

Engineering Contradiction:

Improvecustomer state determination reliabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system introduces an intermediary layer consisting of computational knowledge rules that mediate between raw device data and customer state determination. This rule-based intermediary simplifies the complexity by providing structured, pre-defined logic for analyzing device data and proposing actions, making the system more manageable while maintaining high reliability in state determination.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS8965949B2System and method for applying computational knowledge to device data

Publication Date: 2015.02.24 XEROX CORP

AI summary

A system and method are provided for acquiring and processing device usage data and applying a computational knowledge thereto for proactively determining customer state, including inferring device usage patterns, and accordingly proposing at least one action, if any, to be undertaken. In particular, computational knowledge represented by a set of rules is applied to the processed device data for analyzing the processed data and describing at least one feature or characteristic relating to the processed data using keywords. A rules database is accessed and at least one rule is selected from a set of rules. The at least one selected rule includes keywords which substantially match the keywords used to describe the feature or characteristic of the processed data. The at least one selected rule is then correlated to at least one proposed action using the rules database. The at least one determined rule and/or at least one proposed action are then outputted.