EML Patent Landscape for Email Security Innovation

Overview of Technical Issues:

The query requests a patent landscape analysis for email security rather than describing a specific technical problem with identifiable components, functional defects, or harmful effects; to conduct TRIZ-based functional analysis and problem extraction, please provide details about a concrete email security system issue — such as which security mechanism is failing, what harmful effect or insufficient function is occurring, and what consequences result from this defect.

Solution directions generated for this problem

Problem Direction 1 :

ImproveProblem definition completeness
VS
ConstraintUser communication effort

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Contextually disambiguating queries
Innovative Solution Refine solution

Progressive multi-stage problem intake form with auto-populated domain templates

Divide problem intake into three sequential micro-forms to reduce cognitive load
How to solve :
  • Stage 1: User selects from 5 pre-defined email security domains (spam filtering, phishing detection, encryption, authentication, DLP) via single-click dropdown — 10 seconds
  • Stage 2: System auto-populates domain-specific problem templates with example defects (e.g., "spam filter allows 15% phishing emails, target <2%") — user modifies only numerical values and component names, 60 seconds
  • Stage 3: System extracts measurable parameters from modified template and presents confirmation checklist (component, defect, current performance, target performance) — user verifies with checkboxes, 20 seconds
Expected Effect : Total input time reduced from 5–10 min to 90 sec; problem completeness rate ≥95%
Risk Control :
  • template coverage insufficient for edge cases
  • user misinterprets pre-filled examples
  • domain classification ambiguity

Problem Direction 2 :

ImproveInput specification clarity
VS
ConstraintProblem scope definition

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Connection processing method between mobile station and base station, mobile station and base station
Innovative Solution Refine solution

Modular problem intake framework with domain-specific templates

Divide intake into independent modular stages
How to solve :
  • Segment intake into three independent modules: domain classifier (5-second selection from 8 predefined email security categories), defect descriptor (user selects from 12 common failure patterns per domain with pre-filled metrics), performance quantifier (auto-populated target ranges based on industry benchmarks, user adjusts ±20%)
  • Implement progressive disclosure interface where each module activates only after prior completion, reducing cognitive load from 5-minute open description to three 20-second selections with 85% auto-completion
  • Deploy domain-specific template library containing 96 pre-structured problem statements (8 domains × 12 defect patterns) with component lists, functional relationships, and measurable parameters following ISO/IEC 27001 email security taxonomy, user modifies rather than creates
Expected Effect : Input clarity +90%, scope coverage maintained across 8 domains, completion time reduced from 5min to 1.2min
Risk Control :
  • template coverage gaps for emerging threats
  • user confusion during module transitions
  • auto-populated metrics misalignment with specific systems

Problem Direction 3 :

ImproveProblem scope definition
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Tool for machine-learning data analysis
Innovative Solution Refine solution

Pre-structured problem intake framework with domain-specific templates for email security analysis

Pre-build domain taxonomy before user input
How to solve :
  • Establish five-tier email security taxonomy (spam filtering, phishing detection, encryption, authentication, DLP) with 3–5 pre-structured problem templates per tier before user interaction — each template contains component fields, defect type checklist, and metric placeholders (current/target performance)
  • Deploy two-phase intake workflow: Phase 1 (0–15 seconds) accepts any input format and auto-maps to taxonomy tier using keyword matching (accuracy ≥85%)
  • Phase 2 (15–60 seconds) loads tier-specific template with pre-filled examples, user completes missing fields via dropdown menus and numeric input boxes
  • Implement template validation logic: system checks completeness score (component identified=25%, defect described=25%, current metric=25%, target metric=25%) and blocks analysis submission until ≥90% completeness, providing real-time prompts for missing fields
Expected Effect : Problem definition time reduced 60%; completeness score ≥90% in 95% of submissions; analysis-ready input rate increased from 20% to 85%
Risk Control :
  • taxonomy coverage gaps for emerging threats
  • keyword mapping misclassification rate
  • user resistance to structured input format
Patsnap Eureka Solution