Event Recognition Rule Module (ERRM)
OOF™ Origin Open Foundation™
Independent Methodological Authority
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: TREGA™ — Tax-Relevant Event Governance Architecture™
Parent Standard: Tax-Relevant Event Recognition Standard (TRERS)
Module: ERRM — Event Recognition Rule Module
Operational Layer: Event Recognition Rule Governance Layer
Category: Governance & Enforcement
Subcategory: Tax-Relevant Event Governance
Type: Core Module
Governed Space: Event Recognition Rules
Origin ID: TREGA-TRERS-ERRM-003
Version: 1.0
Status: Canonical · Open Standard
Effective Date: 29 August 2026
AI-Readable: Yes
Authority: OOF®
Protection: MIP® — Methodology Intellectual Property Protection
Canonical Language: English
Canonical Definition
Event Recognition Rule Module (ERRM) is the Core Module withinTax-Relevant Event Recognition Standard (TRERS) that governs the
criteria, logic, conditions, rule structures, decision pathways,
exceptions, and traceable recognition mechanisms used to
determine whether a relevance-screened occurrence should be
formally recognized for continued progression through the TREGA™
event-governance lifecycle.
ERRM receives an occurrence that has passed or remained eligible
after Event Relevance Screening and establishes the governed
recognition logic through which the occurrence can be advanced,
withheld, rejected, conditionally recognized, or referred for
further recognition governance.
Its task is specific:
to govern how recognition decisions are made without converting
recognition logic into jurisdiction-specific tax law, taxability
rules, or legal tax determinations.
Operational Role
ORIM determines whether a distinguishable occurrence exists.ERSM determines whether that occurrence has sufficient potential
relevance to remain within tax-event governance.
ERRM answers the next question:
What governed rules determine whether this relevance-screened
occurrence should be formally recognized?
The distinction is critical.
Potential relevance alone should not automatically create
recognition.
At the same time, recognition should not depend on undocumented,
inconsistent, or opaque logic.
ERRM therefore establishes the rule-governance layer between:
potential relevance
and
formal Event Recognition.
Internal Flow
Relevance-Screened Occurrence↓
Applicable Recognition Rule
Identification
↓
Recognition Condition Evaluation
↓
Rule
Interaction / Priority Resolution
↓
Exception and Uncertainty
Handling
↓
Recognition Rule Result
↓
Recognition Decision
Candidate
The principal output is:
Governed Recognition Rule Result
This output proceeds to ERCM — Event Recognition ConfidenceModule for assessment of the strength and reliability of the
recognition basis.
Operational Space
ERRM governs the space between:an occurrence that has sufficient potential relevance
and
a governed rule-based basis for formal recognition.
Its operational space includes: recognition rule identification;
recognition criteria; recognition conditions; recognition
thresholds where appropriate; rule applicability; rule
precedence; rule combinations; exception handling; conflict
resolution; deterministic recognition logic; probabilistic
recognition logic; AI-assisted recognition logic; hybrid
recognition logic; manual recognition logic; rule versioning;
rule traceability; recognition-rule change; recognition-rule
uncertainty; rule-based rejection; rule-based conditional
recognition.
ERRM governs the mechanism by which recognition logic is applied.
It does not determine whether those rules represent legal tax
obligations.
Operational Scope
ERRM may support recognition methods implemented through:deterministic rules; decision trees; policy logic; threshold
logic; pattern-based rules; statistical criteria; AI-assisted
classification; machine-learning outputs; human review rules;
hybrid human-machine processes; rule engines; workflow systems;
enterprise event processors; public-sector tax infrastructure.
The implementation technology may vary.
The governance requirement remains the same:
the basis for recognition must remain explicit, traceable,
bounded, and separable from jurisdiction-specific tax
determination.
Recognition Rule Principle
ERRM establishes a foundational distinction:Recognition Rule ≠ Tax Rule.
A Recognition Rule determines whether an occurrence should
continue through TREGA™ event governance.
A Tax Rule determines legal consequences under a jurisdiction.
A Recognition Rule may ask:
Does this occurrence satisfy sufficient conditions to be formally
recognized for Event Formation?
A Tax Rule may later ask:
Does this governed event create a taxable obligation under
applicable law?
These are different questions and must remain architecturally
separated.
Rule Applicability
Not every Recognition Rule applies to every occurrence.Rule applicability may depend on governed characteristics such
as: occurrence type; operational environment; economic
characteristics; transaction characteristics; machine or
autonomous execution; value-related attributes;
distributed-system characteristics; available context; evidence
availability; relevance domain.
ERRM must establish which Recognition Rules are applicable before
those rules are evaluated.
Therefore:
Rule Availability ≠ Rule Applicability.
Recognition Conditions
A Recognition Rule may contain one or more conditions that mustbe evaluated.
Conditions may examine whether: the occurrence is sufficiently
distinguishable; the occurrence passed relevance screening;
required contextual elements exist; material relationships are
available; duplicate occurrence risk is sufficiently controlled;
minimum evidence conditions are satisfied; unresolved
contradictions remain within acceptable limits; required
governance references exist.
These conditions remain methodological recognition conditions.
They are not statutory taxation conditions.
Recognition Thresholds
Some implementations may use thresholds.For example, a threshold may determine whether: sufficient
information exists; a probabilistic model has adequate support;
duplicate risk is below an acceptable governance level;
contextual coverage is sufficient; rule confidence is adequate
for automatic progression.
ERRM allows such thresholds where appropriate.
However:
Recognition Threshold ≠ Statutory Threshold.
A governance threshold should not be confused with a monetary tax
threshold, filing threshold, exemption threshold, or other legal
threshold.
Rule Interaction
Several Recognition Rules may apply to the same occurrence.ERRM must govern their interaction.
Rules may be: complementary; cumulative; alternative;
conditional; hierarchical; mutually exclusive; conflicting.
The existence of several applicable rules must not automatically
create several recognition decisions.
ERRM requires a governed method for resolving rule interaction.
Rule Priority
Where multiple rules conflict, precedence must not be arbitrary.Rule priority may be established through: architecture-defined
precedence; implementation policy; rule specificity; evidence
quality; system authority; approved governance hierarchy.
ERRM must preserve the reason why one rule prevailed over another
where the decision is materially significant.
Therefore:
Rule Priority ≠ Hidden System Preference.
Rule Conflict
Recognition rules may produce contradictory results.For example:
one rule may support recognition;
another may reject;
a probabilistic model may indicate uncertainty;
a human-review rule may require escalation.
ERRM must represent the conflict rather than suppress it.
Possible outcomes may include:
Recognition Rule Conflict
Conditional Recognition
Recognition Withheld
Escalation Required
Rule conflict is a governance state, not a system failure.Deterministic Recognition Rules
A deterministic rule produces a defined outcome where itsconditions are satisfied.
Example logic may take the form:
If Condition A + Condition B + Condition C are established
↓
Recognition Rule Satisfied.
Deterministic logic can improve consistency.
But deterministic logic does not guarantee correctness if its
inputs, scope, or assumptions are wrong.
Therefore:
Deterministic Rule ≠ Correct Recognition by Default.
Probabilistic Recognition Rules
Some occurrence types may require probabilistic assessment.Examples may include: pattern recognition; transaction
clustering; machine-generated activity classification;
distributed occurrence association; anomaly-driven recognition.
ERRM permits probabilistic methods where governed.
However:
Model Probability ≠ Recognition Truth.
A probability score must remain distinct from the final
recognition outcome.
Its use must be subject to governed confidence, uncertainty, and
escalation mechanisms.
AI-Assisted Recognition
AI systems may assist in determining whether an occurrencesatisfies Recognition Rules.
They may: classify occurrences; detect patterns; identify
duplicates; assess rule applicability; surface missing
information; detect contradictions; recommend recognition
outcomes.
But AI output cannot become an ungoverned substitute for
Recognition Rules.
Therefore:
AI Recommendation ≠ Governed Recognition Decision.
Where AI participates, ERRM requires the applicable rule basis to
remain sufficiently interpretable and governable for the
implementation context.
Human Recognition
Manual review may also form part of recognition governance.Human reviewers may resolve: unusual activity; incomplete
evidence; rule conflicts; novel machine-generated occurrences;
exceptional economic arrangements.
But manual involvement does not remove the need for governance.
Therefore:
Human Decision ≠ Governed Decision by Default.
The decision basis must remain traceable where materially
required.
Hybrid Recognition
Many operational systems will combine:Automated Rules + AI Analysis + Human
Escalation
ERRM supports hybrid recognition where the division ofresponsibilities remains explicit.
For example:
Rule Engine
↓
AI-Assisted Assessment
↓
Confidence Evaluation
↓
Human Escalation
The architecture remains technology-neutral.
Recognition Rule Versioning
Recognition Rules may evolve.Operational systems change.
New activity types emerge.
AI and machine economies create new patterns.
Governance methodologies improve.
ERRM therefore requires that materially relevant rule changes
remain distinguishable.
A historical occurrence should not silently be represented as if
a Recognition Rule created years later had been applied
originally.
Therefore:
Current Recognition Rule ≠ Historical Recognition Rule. Rule
Change ≠ Occurrence Change
A Recognition Rule may change while the underlying occurrence
remains identical.
This is especially important where organizations reprocess
historical data.
A newly revised Recognition Rule may lead to a different
governance result.
But:
Rule Change ≠ Change in Operational Reality.
The historical occurrence must remain separable from the
recognition logic later applied to it.
Recognition Rule Independence from Tax-Law
Change
Jurisdiction-specific tax laws may change frequently.TREGA™ Recognition Rules should not need to be reconstructed
solely because a tax rate, exemption, legal test, or filing
obligation changes downstream.
Where a legal change alters what occurrences need to reach tax
systems, implementation-level recognition rules may be updated.
But the architecture itself preserves the separation:
Tax-Law Change ≠ Recognition Rule Change by Default.
Rule Transparency
A Recognition Rule should be sufficiently explainable toestablish: what rule applied; why it applied; what conditions
were evaluated; what result it produced; what exceptions or
conflicts occurred; what version was used.
Transparency does not require disclosure of every implementation
detail to every actor.
It requires enough governance traceability to reconstruct
materially important recognition decisions.
Rule Traceability
Where materially required, ERRM should preserve:Occurrence Reference
↓
Applied Rule
↓
Rule Version
↓
Evaluated
Conditions
↓
Rule Result
This creates a traceable bridge between potential event relevance
and formal recognition.
Recognition Exceptions
Some occurrences may fall outside ordinary rule logic.Exceptions may arise where: a novel economic model appears;
autonomous agents create unfamiliar activity; existing rules do
not cover a new platform structure; available information is
incomplete; a transaction structure is materially unusual.
ERRM must allow exceptions to be governed rather than forcing
them into inappropriate rule categories.
Possible handling includes:
Exception Review
Conditional Recognition
Recognition Withheld
Escalation Recognition Rule Failure
A Recognition Rule may fail because: required inputs areunavailable; conditions cannot be evaluated; the rule conflicts
with another rule; the rule is technically unavailable; the
occurrence falls outside the rule's valid scope.
Such failure should not automatically become rejection.
Therefore:
Rule Failure ≠ Occurrence Rejection.
The appropriate result may instead be unresolved recognition or
escalation.
Rule Coverage
A governance system should understand whether the relevantoccurrence is covered by available Recognition Rules.
Possible states may include:
Rule Coverage Established
Partial Rule Coverage
Rule Coverage Gap
Rule Applicability Unresolved
A coverage gap is important in new economic environments.It may reveal that the methodology does not yet adequately govern
a type of occurrence.
Governance Requirements
ERRM requires that: Recognition Rules remain distinct from TaxRules; rule applicability is established; rule conditions are
explicit; rule outputs are governable; rule conflicts can be
represented; rule precedence is governed; thresholds remain
distinguishable from legal thresholds; deterministic outputs are
not treated as truth by default; probabilistic outputs remain
distinguishable from recognition outcomes; AI-assisted
recommendations remain governed; manual decisions remain
traceable where materially required; rule versions are
distinguishable; historical recognition remains reconstructable;
rule failure does not automatically become occurrence rejection;
exceptions can be governed; rule coverage gaps remain visible;
recognition logic does not establish legal taxability.
Governance Logic
ERRM applies five principal questions:Which Recognition Rule or Rules legitimately apply to this
occurrence?
What recognition conditions must be evaluated?
What happens if several rules interact or conflict?
What governed result does the applicable rule logic produce?
Is the resulting rule basis suitable for confidence assessment or
must it remain unresolved?
The transformation is:
Relevant Occurrence
→ Applicable Recognition Rules→ Condition Evaluation
→ Rule Resolution
→ Governed Recognition Rule Result
Minimum Implementation Framework
A minimum ERRM implementation must be capable of:1. receiving a relevance-screened occurrence from ERSM; 2.
identifying applicable Recognition Rules; 3. evaluating
Recognition Conditions; 4. representing rule applicability; 5.
resolving or representing multiple-rule interactions; 6.
preserving rule conflicts; 7. distinguishing governance
thresholds from statutory thresholds; 8. recording rule versions
where materially required; 9. representing exceptions and rule
failures; 10. producing a Governed Recognition Rule Result for
ERCM; 11. preserving enough traceability to reconstruct the
recognition logic; 12. preventing Recognition Rules from becoming
jurisdiction-specific Tax Determinations.
ERRM does not prescribe a specific rules engine, programming
language, machine-learning model, AI system, tax platform, or
workflow technology.
Governance Outputs
ERRM may produce:Governed Recognition Rule Result — the result produced by
applicable recognition logic.
Applicable Recognition Rule Set — the rules determined to apply
to the occurrence.
Recognition Condition Result — the outcome of individual or
combined rule conditions.
Recognition Rule Conflict Record — representation of
contradictory rule outcomes.
Recognition Exception Record — representation of an occurrence
requiring exception handling.
Recognition Rule Version Reference — identification of the rule
version used.
Rule Coverage Status — whether adequate recognition-rule coverage
exists.
Rule Failure Indicator — representation of a recognition rule
that could not be legitimately executed.
Recognition Decision Candidate — the rule-supported candidate
result transferred to confidence assessment.
Critical Distinctions
Recognition Rule ≠ Tax Rule.Recognition governs architecture progression. Tax law governs
legal consequence.
Rule Availability ≠ Rule Applicability.
A rule existing in the system does not mean it applies to the
occurrence.
Recognition Threshold ≠ Statutory Threshold.
Governance thresholds must not become legal tax thresholds.
Deterministic Rule ≠ Correct Recognition by Default.
Predictability of logic is not proof of validity.
Model Probability ≠ Recognition Truth.
A probability score is an input, not a legal or governance fact.
AI Recommendation ≠ Governed Recognition Decision.
AI assistance remains subject to rule governance.
Human Decision ≠ Governed Decision by Default.
Human review also requires a legitimate basis.
Rule Priority ≠ Hidden System Preference.
Precedence must remain governed.
Rule Conflict ≠ Recognition Failure.
Conflict may legitimately require additional governance.
Rule Failure ≠ Occurrence Rejection.
Failure of logic execution does not prove non-relevance.
Current Recognition Rule ≠ Historical Recognition Rule.
Rule evolution must not rewrite past governance.
Rule Change ≠ Occurrence Change.
Methodology may evolve while operational reality remains the
same.
Recognition Rule Result ≠ Recognition Confidence.
ERRM determines what the rule logic says. ERCM determines how
strong the basis for relying on that result is.
Recognition Rule Result ≠ Taxability Determination.
No recognition rule establishes that tax is legally due.
Enterprise Application
ERRM addresses a major governance weakness in largetransaction-processing environments:
recognition logic often exists, but is fragmented across systems,
undocumented, embedded in code, mixed with tax logic, or changed
without preserving historical meaning.
A multinational enterprise may process the same activity through:
Commerce Rules
↓
Payment Rules
↓
ERP Logic
↓
Tax Engine Filters
↓
Reporting Rules
while an autonomous platform may add:
AI Classification
↓
Machine Rules
↓
Dynamic Policies
↓
Human
Exception Review.
Without ERRM, different systems can decide independently what
counts as an event candidate.
The result may be inconsistent recognition across systems,
duplication, missing events, non-reconstructable historical
logic, and premature legal assumptions.
ERRM creates a governed recognition-rule layer that can sit
before jurisdiction-specific Tax Engines. Use CasesUse Case 1 —
Historical Tax Review After Recognition Logic Has Changed
Repeatedly
A multinational digital platform processes hundreds of millions
of transactions each year.
Over six years, it changes: payment infrastructure; transaction
classification logic; ERP workflows; event filters; AI models;
tax engines; internal recognition thresholds.
A tax authority later asks why a particular historical
transaction entered one reporting pathway while another
apparently similar transaction did not.
The current recognition logic cannot answer the question because
the rules have changed several times.
ERRM preserves the applicable Recognition Rule, rule version,
conditions, exceptions, and resulting rule outcome associated
with the historical occurrence.
The organization can reconstruct:
what recognition logic existed when the decision was made
without pretending that today's rules governed yesterday's
activity.
This preserves a critical institutional distinction:
historical economic reality and historical recognition
methodology must remain independently reconstructable. Use Case 2
— AI Recognition System Changes Behavior Across a
Machine-to-Machine Marketplace
A machine-to-machine marketplace processes continuous exchanges
of computing capacity, energy, digital services, data access, and
autonomous resource reservations.
An AI model assists in identifying which occurrences should enter
full Event Formation.
As the marketplace evolves, the model is retrained and begins
treating certain resource reservations differently.
Two operationally similar machine interactions processed six
months apart now produce different recognition recommendations.
Without governed Recognition Rules, the organization may be
unable to explain whether the difference resulted from: changed
economic activity; changed model behavior; changed thresholds;
changed policy; changed evidence; or genuine methodological
improvement.
ERRM separates the underlying occurrence from the recognition
logic applied to it.
AI output becomes one governed input within a traceable
Recognition Rule structure rather than the invisible definition
of what constitutes a Tax-Relevant Event candidate.
This allows machine-scale recognition to evolve without
surrendering methodological control to the model producing the
recommendation.
Canonical Boundary
ERRM governs the criteria, rules, conditions, logic,interactions, exceptions, and traceable mechanisms through which
a relevance-screened occurrence receives a governed
recognition-rule result. It does not determine the confidence
that should be placed in that result, resolve unresolved
recognition through escalation, form the Tax-Relevant Event, or
determine legal taxability.
The TRERS internal progression remains:
ORIM — Did a distinguishable occurrence occur?
→ ERSM — Does it possess sufficient potential relevance?
→ ERRM — What governed Recognition Rules apply and what result do
they produce?
→ ERCM — How strong and reliable is the recognition basis?
→ EREM — Does unresolved recognition require escalation?