Patentability assessments

Identifies documents containing references to patentability assessments in international contexts. This information type is classified as personally identifiable information under applicable data protection regulations.

Type

regex

Engine

boost_regex

Confidence

medium

Confidence justification

category-aware structural regex with anchor and context constraints replaces phrase-only detection. Added context gating and exclusion rules improve precision and reduce incidental matches.

Detection quality

Not detected

Jurisdictions

global

Regulations

GDPR

Data categories

pii

Scope

wide

Platform compatibility

Purview: Compatible, GCP DLP: Compatible, Macie: Compatible, Zscaler: Compatible, Palo Alto: Degraded, Netskope: Unsupported

Pattern

(?is)\b(?:patentability\s+assessment|patentability\s+opinion|freedom\s+to\s+operate|prior\s+art\s+search|novelty\s+assessment|patent\s+landscape|infringement\s+analysis|patent\s+validity|patent\s+review|inventive\s+step)\b

Corroborative evidence keywords

patentability assessments, patentability, assessments, intellectual, property, trade, secrets

Proximity: 300 characters

Should match

• patentability assessment — Primary topic phrase match
• patentability opinion — Case-insensitive topic phrase match
• freedom to operate — Alternative topic phrase match
• prior art search — Additional topic phrase match

Should not match

• unrelated generic text without domain phrases — No relevant topic phrases present
• placeholder value 12345 — Random text should not match topic-specific regex
• algorithm schematic — Generic word pair from old broad template should not match

Known false positives

• Common words and phrases related to patentability assessments appearing in policy documents, training materials, HR templates, or compliance guidelines without actual personal data. Mitigation: Require corroborative evidence keywords within the proximity window to confirm sensitive data context rather than general discussion.
• In English (as the primary international business language), similar terminology used in formal or administrative contexts (education, professional documentation) that does not constitute sensitive data collection. Mitigation: Layer with additional contextual signals such as structured identifiers, form fields, or database column headers to distinguish sensitive records from general references.
• High-frequency pattern matches in large document corpora due to broad regex anchors. Expected match rate is significantly higher than specific identifier patterns. Mitigation: Tune confidence thresholds for bulk scanning. Consider using this pattern primarily as a pre-filter with secondary validation.

References

• https://www.ipaustralia.gov.au/patents/how-to-apply-for-a-standard-patent/what-to-consider-before-applying-for-a-patent
• https://www.ipaustralia.gov.au/patents/what-are-patents