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Custom SIT patterns and corroborating keywords recognize organization-specific text formats. That would detect proprietary claim references; the question instead requires a way to detect standard card-number formats.

Document fingerprinting detects documents resembling an approved text template, including configured partial matches. That would detect templated engagement letters; the question instead requires a way to detect standard card-number formats.

EDM checks one-way-hashed source data values rather than accepting every syntactically matching number. That would match only registered personnel values; the question instead requires a way to detect standard card-number formats.

Document fingerprinting detects documents resembling an approved text template, including configured partial matches. That would detect templated engagement letters; the question instead requires a way to detect proprietary claim references.

Microsoft's built-in sensitive information type evaluates card-number patterns with its defined supporting evidence. That would detect standard card-number formats; the question instead requires a way to detect proprietary claim references.

EDM checks one-way-hashed source data values rather than accepting every syntactically matching number. That would match only registered personnel values; the question instead requires a way to detect proprietary claim references.

Microsoft's built-in sensitive information type evaluates card-number patterns with its defined supporting evidence. That would detect standard card-number formats; the question instead requires a way to match only registered personnel values.

Document fingerprinting detects documents resembling an approved text template, including configured partial matches. That would detect templated engagement letters; the question instead requires a way to match only registered personnel values.

Custom SIT patterns and corroborating keywords recognize organization-specific text formats. That would detect proprietary claim references; the question instead requires a way to match only registered personnel values.

Custom SIT patterns and corroborating keywords recognize organization-specific text formats. That would detect proprietary claim references; the question instead requires a way to detect templated engagement letters.

EDM checks one-way-hashed source data values rather than accepting every syntactically matching number. That would match only registered personnel values; the question instead requires a way to detect templated engagement letters.

Microsoft's built-in sensitive information type evaluates card-number patterns with its defined supporting evidence. That would detect standard card-number formats; the question instead requires a way to detect templated engagement letters.

Higher confidence depends on stronger supporting evidence and typically reduces weak detections. That would require corroborated high-confidence matches; the question instead requires a way to reduce context-free numeric matches.

Lower confidence matching can detect items with less supporting evidence and may increase false positives. That would favor recall when context is missing; the question instead requires a way to reduce context-free numeric matches.

A validated primary pattern rejects values that match the shape but fail the check. That would validate a structured code; the question instead requires a way to reduce context-free numeric matches.

Supporting evidence and proximity can distinguish relevant identifiers from unrelated numeric strings. That would reduce context-free numeric matches; the question instead requires a way to validate a structured code.

Lower confidence matching can detect items with less supporting evidence and may increase false positives. That would favor recall when context is missing; the question instead requires a way to validate a structured code.

Higher confidence depends on stronger supporting evidence and typically reduces weak detections. That would require corroborated high-confidence matches; the question instead requires a way to validate a structured code.

A validated primary pattern rejects values that match the shape but fail the check. That would validate a structured code; the question instead requires a way to favor recall when context is missing.

Supporting evidence and proximity can distinguish relevant identifiers from unrelated numeric strings. That would reduce context-free numeric matches; the question instead requires a way to favor recall when context is missing.

Higher confidence depends on stronger supporting evidence and typically reduces weak detections. That would require corroborated high-confidence matches; the question instead requires a way to favor recall when context is missing.

A validated primary pattern rejects values that match the shape but fail the check. That would validate a structured code; the question instead requires a way to require corroborated high-confidence matches.

Lower confidence matching can detect items with less supporting evidence and may increase false positives. That would favor recall when context is missing; the question instead requires a way to require corroborated high-confidence matches.

Supporting evidence and proximity can distinguish relevant identifiers from unrelated numeric strings. That would reduce context-free numeric matches; the question instead requires a way to require corroborated high-confidence matches.

A keyword dictionary centrally maintains many exact terms used as supporting evidence or patterns. That would maintain a term-list detector; the question instead requires a way to recognize example-based complaint language.

Built-in trainable classifiers identify semantic document types without a custom training project when suitable. That would classify document type by learned content; the question instead requires a way to recognize example-based complaint language.

A predefined SIT captures an established identifier without requiring new supervised examples. That would classify common identification numbers; the question instead requires a way to recognize example-based complaint language.

Built-in trainable classifiers identify semantic document types without a custom training project when suitable. That would classify document type by learned content; the question instead requires a way to classify common identification numbers.

A trainable classifier learns content characteristics from examples where deterministic expressions are inadequate. That would recognize example-based complaint language; the question instead requires a way to classify common identification numbers.

A keyword dictionary centrally maintains many exact terms used as supporting evidence or patterns. That would maintain a term-list detector; the question instead requires a way to classify common identification numbers.