Compromised System Scoring via Encoded Key Translation
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Solution Overview
Problem
Current patch testing for multiple sclerosis (MS) faces challenges due to high variability in patient symptoms and progression, making it difficult to accurately assess patch efficacy and match client groups for effective testing.
Innovation Solution
The use of encoded keyed data processed through multiple codebooks to generate higher-level encodings that characterize client conditions and determine eligibility for patch testing, allowing for more precise client grouping and accurate efficacy assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If patch testing includes clients with high variability in EDSS scores and system faults, then the patch can be approved for broader MS types, but the efficacy assessment becomes unreliable due to heterogeneous patient populations
Solution Approach 1:
The patent segments the heterogeneous MS patient population into distinct subgroups based on encoded keyed data from multiple codebooks (ICD, CPT, etc.). This segmentation creates matched disability client groups with similar characteristics, allowing reliable efficacy assessment within each subgroup while maintaining the ability to evaluate the patch across multiple MS types through aggregation of subgroup results.
2Measurement precision
If the EDSS scale is used to assess client disability, then a standardized metric is available, but the scoring becomes unreliable due to heavy influence from self-reporting and professional observations that vary across clients
Solution Approach 1:
The patent introduces an intermediary encoding system that translates subjective EDSS components (self-reporting and professional observations) into standardized coded data from multiple codebooks. This intermediary layer processes the influential but variable human assessments through structured coding, reducing their direct impact on the final efficacy assessment while preserving the essential disability information.
3Quantity of substance
If patch testing includes more clients with mid-range EDSS scores, then the sample size increases, but the corruption-progression results may underestimate patch efficacy due to faster score progression in this group
Solution Approach 1:
The patent applies local quality by creating matched disability client groups with specific EDSS score ranges rather than using a homogeneous overall sample. Each subgroup is characterized by similar progression rates and disease characteristics, allowing accurate efficacy estimation within each local group. The results from multiple local groups are then aggregated to provide both sufficient sample size and accurate efficacy measurement.
Data Source
AI summary
Method and systems are provided for generating compromised system scores based on encryption codes. Support-system authentication request data associated with a first client is received. A set of keys may be extracted with each key characterizing a system fault or a root cause determination of a set of a system faults. Each key may be allocated to a class based on fault severity. A compromised system score may be identified based at least in part on the fault severity of each code. The compromised system score indicating one or more processes of the client that are disabled or that have modified functionality. A result based on the compromised system score or system fault identifier may be output.