Description:BACKGROUND
Technical Field
[0001] The present invention generally relates to computer-implemented semantic observation systems and, more particularly, to a persona-aware semantic digital observer and contextual alerting system for generating relevant alerts/notifications by filtering enterprise event noise in distributed computing environments.
Description of the Related Art
[0002] Enterprises rely on alerts, dashboards, and notifications to monitor operations, risks, and opportunities within distributed information environments. Distributed information environments generate massive volumes of heterogeneous activity data from operational systems, analytics pipelines, and machine learning models. Existing alerting systems are typically based on fixed thresholds, predefined rules, static metric subscriptions, or manually curated notification lists. However, such systems are primarily designed for simple event triggering rather than for intelligent semantic observation or contextual alert generation aligned with user responsibilities.
[0003] One major limitation of existing systems is alert fatigue. Users receive excessive alerts that are not relevant to their role or decision authority. Because alerts are generated purely based on thresholds or predefined rules, the same alert is often transmitted to multiple users regardless of their responsibilities, priorities, or decision-making needs. As a result, users are forced to manually filter alerts, which reduces attention to critical notifications and decreases overall operational efficiency. Conventional data retrieval systems based on static storage and search mechanisms are limited to retrieving records corresponding to a query, such as identifying shipments associated with a specified port location. A primary technical limitation of existing systems is computational and network resource exhaustion caused by "broadcast-based" alerting.
[0004] Furthermore, existing systems lack a deterministic semantic reasoning substrate. Most alerting is triggered by isolated numeric deviations rather than by meaningful semantic state changes across temporal sequences. Consequently, these systems fail to provide contextual justification that they may indicate an event occurred, but cannot explain why it matters to a specific persona in the context of enterprise world-model logic. Additionally, recent attempts to use generative AI for alerting are often probabilistic and lack truth monotonicity, making them unsuitable for governed enterprise environments where permanent traces and deterministic admissibility are required. Another limitation of existing alerting systems is the lack of persona awareness. The same alert is frequently sent to executives, operators, analysts, and other users despite their differing responsibilities and priorities.
[0005] Accordingly, there is no existing system that continuously reasons over enterprise semantics, understands persona-specific context and responsibility, dynamically determines alert relevance, and generates explainable contextual notifications aligned with decision-making needs. Therefore, there remains a need for an improved system capable of addressing the above technical limitations in existing alerting technologies.
SUMMARY
[0006] In view of the foregoing, there is provided a computer-implemented system for generating persona-aware semantic and deterministic contextual alerting by filtering of enterprise event noise within distributed information environments. The system comprises a memory that comprises a set of instructions, a domain ontology, persona semantic profiles, and an immutable semantic event space. The system comprises a processor that executes the set of instructions and causes the system to receive activity data from a plurality of distributed information sources through a network, wherein the activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data. The processor records the activity data in the immutable semantic event space as ontology-anchored semantic events, wherein each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events. The processor derives a plurality of semantic state transitions from the immutable semantic event space by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events. The processor accesses persona semantic profiles stored in the memory, wherein each persona semantic profile defines at least one of (i) a corresponding persona entity responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences. The processor dynamically filters the plurality of semantic state transitions to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile, (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level. The processor discards or eliminates the semantic state transition that does not satisfy the persona-specific relevance condition to suppress transmission of a notification for the persona semantic profile. The processor generates persona-specific contextual notifications comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition from the immutable semantic event space for the persona semantic profile and transmits the persona-specific contextual notifications to persona entities associated with the corresponding persona semantic profile, thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.
[0007] In some embodiments, the processor further performs ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of the semantic state transitions before generating the persona-specific contextual notifications, wherein the persona-specific contextual notifications are generated using a generative language model constrained by at least one of (i) the outputs of the reasoning flow graph, (ii) the ontology-anchored semantic events stored in the immutable semantic event space, or (iii) the semantic state transitions that are derived to ensure the notification remains semantically consistent with the domain ontology, wherein the one or more semantic attributes associated with the semantic state transition are validated against an ontology-derived attribute vocabulary, and wherein the one or more semantic attributes that do not belong to a closure-valid subset are rejected before generating the persona-specific contextual notifications.
[0008] In some embodiments, the processor dynamically filters the plurality of semantic state transitions based on at least one of: (i) a convergence of multiple semantic conditions across a plurality of entities, (ii) co-occurrence of the ontology-anchored semantic events within a predefined time window, (iii) accumulation of the one or more semantic attributes, or (iv) decay of the one or more semantic attributes, wherein the persona semantic profiles comprise at least one of risk sensitivity parameters, temporal urgency preferences or levels, preferred notification modality, decision horizon, or subscription intent expressed in terms of ontology concepts, relationships, or event patterns.
[0009] In some embodiments, the processor performs at least one of: (i) implementing a multi-persona semantic filtering logic that processes a same semantic state transition that is derived into distinct alerts or notifications with different semantic justifications for different persona entities, or suppresses the transmission of notification for at least one persona entity while transmitting the notification to another persona entity; (ii) receiving semantic subscriptions defined in natural language as intent-driven conditions referencing ontology concepts and causal chains, rather than fixed numeric thresholds; or (iii) implementing an adaptive feedback loop that refines the persona-specific relevance condition in real-time based on recorded persona actions, including alert acknowledgments, dismissals, or ignored notifications, without requiring retraining of an underlying machine learning model, wherein the processor is further configured to translate the semantic subscriptions defined in a natural language into ontology-grounded subscription conditions referencing ontology concepts, entity relationships, event patterns, or causal chains.
[0010] In some embodiments, The processor refines the persona-specific relevance condition in real-time by: (i) receiving a response action associated with the transmitted persona-specific contextual notifications, wherein the response action comprises at least one of acknowledge, dismiss, or ignore; (ii) generating an ontology-anchored semantic event representing the response action and storing it in the immutable semantic event space ; and (iii) updating at least one relevance weighting parameter associated with a persona semantic profile based on the ontology-anchored semantic event without retraining a predictive model, wherein the response action comprises at least one of acknowledge, dismiss, ignore, escalate, approve, or override.
[0011] In some embodiments, the processor determines the persona-specific relevance condition by matching entities referenced in the plurality of semantic state transitions with "owned entities" associated with the respective persona semantic profile and generating a relevance indicator based on the degree of the match.
[0012] In some embodiments, the processor determines the persona-specific relevance condition by computing a relevance score based on at least one of entity association, state deviation magnitude, or temporal urgency, and comparing the relevance score to a predefined threshold configured within the persona semantic profile.
[0013] In some embodiments, the persona-specific contextual notifications comprise at least one of: (i) a state-transition alert generated upon detection of a change in an ontology-defined entity state; (ii) a risk-propagation alert generated using the propagation of a semantic condition across causally linked entities; (iii) a temporal-threshold alert generated when a semantic state persists beyond a predefined time interval; (iv) a pattern-accumulation alert generated upon detection of a recurring semantic event pattern within a defined time window; or (v) a causal-chain alert generated upon detection of a predefined sequence of causally related semantic events, wherein the persona-specific contextual notifications comprise at least one of an explanation of relevance to the persona entity, an impact assessment, one or more affected entities, one or more recommended actions, a confidence indicator, or evidence identifying the ontology-anchored semantic events.
[0014] In some embodiments, the ontology-anchored semantic events are stored in the immutable semantic event space, such that previously stored semantic events, inferred states, suppression events, generated notifications, delivery events, and recorded response actions are permanently preserved and are never modified or deleted, thereby providing a full permanent semantic trace.
[0015] In some embodiments, the one or more semantic attributes associated with a semantic state transition that are unresolvable to an ontology-derived attribute vocabulary or that violate symbolic grounding requirements are deterministically rejected or constrained to the closure-valid subset before generation of the persona-specific contextual notification.
[0016] In some embodiments, the processor is configured to perform a governance validation before routing the persona-specific contextual notification by checking at least one of authorization rules, a policy constraint, a confidentiality rule, or a delivery constraint associated with the corresponding persona semantic profile or the affected domain ontology-defined entity.
[0017] In another aspect, there is provided a computer-implemented method for generating persona-aware semantic and deterministic contextual alerting by filtering enterprise event noise within distributed information environments. The method includes receiving activity data from a plurality of distributed information sources through a network, wherein the activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data. The method includes, recording the activity data in the immutable semantic event space as ontology-anchored semantic events, wherein each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events. The method includes deriving a plurality of semantic state transitions from the immutable semantic event space by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events. The method includes accessing persona semantic profiles stored in the memory, wherein each persona semantic profile defines at least one of (i) a corresponding persona entity responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences. The method includes dynamically filtering the plurality of semantic state transitions to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile, (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level. The method includes discarding or eliminating the semantic state transition that does not satisfy the persona-specific relevance condition to suppress transmission of a notification for the persona semantic profile. The method includes generating persona-specific contextual notifications comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition from the immutable semantic event space for the persona semantic profile and transmitting the persona-specific contextual notifications to persona entities associated with the corresponding persona semantic profile, thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.
[0018] In some embodiments, the method includes performing ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of the semantic state transitions before generating the persona-specific contextual notifications, wherein the persona-specific contextual notifications are generated using a generative language model constrained by at least one of (i) the outputs of the reasoning flow graph, (ii) the ontology-anchored semantic events stored in the immutable semantic event space, or (iii) the semantic state transitions that are derived to ensure the notification remains semantically consistent with the domain ontology.
[0019] In some embodiments, the one or more semantic attributes associated with the semantic state transition are validated against an ontology-derived attribute vocabulary, and wherein the one or more semantic attributes that do not belong to a closure-valid subset are rejected before generating the persona-specific contextual notifications.
[0020] In some embodiments, the plurality of semantic state transitions is dynamically filtered based on at least one of: (i) a convergence of multiple semantic conditions across a plurality of entities, (ii) co-occurrence of the ontology-anchored semantic events within a predefined time window, (iii) accumulation of the one or more semantic attributes, or (iv) decay of the one or more semantic attributes, wherein the persona semantic profiles comprise at least one of risk sensitivity parameters, temporal urgency preferences or levels, preferred notification modality, decision horizon, or subscription intent expressed in terms of ontology concepts, relationships, or event patterns.
[0021] In some embodiments, the method includes performing at least one of: (i) implementing a multi-persona semantic filtering logic that processes a same semantic state transition that is derived into distinct alerts or notifications with different semantic justifications for different persona entities, or suppresses the transmission of notification for at least one persona entity while transmitting the notification to another persona entity, (ii) receiving semantic subscriptions defined in natural language as intent-driven conditions referencing ontology concepts and causal chains, rather than fixed numeric thresholds, or (iii) implementing an adaptive feedback loop that refines the persona-specific relevance condition in real-time based on recorded persona actions, including alert acknowledgments, dismissals, or ignored notifications, without requiring retraining of an underlying machine learning model, wherein the processor is further configured to translate the semantic subscriptions defined in a natural language into ontology-grounded subscription conditions referencing ontology concepts, entity relationships, event patterns, or causal chains.
[0022] In another aspect, there is provided a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for generating persona-aware semantic and deterministic contextual alerting by filtering of enterprise event noise within distributed information environments. The method comprises receiving activity data from a plurality of distributed information sources through a network, wherein the activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data, The method comprises recording the activity data in the immutable semantic event space as ontology-anchored semantic events, wherein each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events. The method comprises deriving a plurality of semantic state transitions from the immutable semantic event space, by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events. The method comprises accessing persona semantic profiles stored in the memory, wherein each persona semantic profile defines at least one of (i) a corresponding persona entity’s responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences. The method comprises dynamically filtering the plurality of semantic state transitions to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile, (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level. The method comprises discarding or eliminating the semantic state transition that does not satisfy the persona-specific relevance condition to suppress transmission of a notification for the persona semantic profile. The method comprises generating persona-specific contextual notifications comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition from the immutable semantic event space for the persona semantic profile; and transmitting the persona-specific contextual notifications to persona entities associated with the corresponding persona semantic profile, thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.
[0023] In some embodiments, the processors further causes performing ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of the semantic state transitions before generating the persona-specific contextual notifications, wherein the persona-specific contextual notifications are generated using a generative language model constrained by at least one of (i) the outputs of the reasoning flow graph, (ii) the ontology-anchored semantic events stored in the immutable semantic event space, or (iii) the semantic state transitions that are derived to ensure the notification remains semantically consistent with the domain ontology.
[0024] In some embodiments, the one or more semantic attributes associated with a semantic state transition that are unresolvable to an ontology-derived attribute vocabulary or that violate symbolic grounding requirements are deterministically rejected or constrained to the closure-valid subset before generation of the persona-specific contextual notification.
[0025] In some embodiments, the persona-specific relevance condition is refined in real-time by: (i) receiving a response action associated with the transmitted persona-specific contextual notifications, wherein the response action comprises at least one of acknowledge, dismiss, or ignore; (ii) generating an ontology-anchored semantic event representing the response action and storing it in the immutable semantic event space ; and (iii) updating at least one relevance weighting parameter associated with a persona semantic profile based on the ontology-anchored semantic event without retraining a predictive model, wherein the response action comprises at least one of acknowledge, dismiss, ignore, escalate, approve, or override.
[0026] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating preferred embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0028] FIG. 1 is a block diagram that illustrates a computer-implemented system for generating persona-aware semantic and deterministic contextual alerting according to some embodiments herein;
[0029] FIG. 2 illustrates an exemplary layered semantic processing architecture for generating persona-aware semantic and deterministic contextual alerting based on ontolo-gy-driven state transition analysis according to some embodiments herein;
[0030] FIG. 3 illustrates a conceptual representation of a domain ontology and a persona semantic profile used for persona-aware semantic reasoning and contextual notifi-cation generation according to some embodiments herein;
[0031] FIG. 4 illustrates an exemplary flowchart describing a method for filtering semantic state transitions and generating persona-specific contextual notifications based on relevance determination according to some embodiments herein;
[0032] FIG. 5 illustrates an exemplary embodiment demonstrating persona-aware contextual alert generation based on semantic interpretation of an event and evaluation of its impact across multiple persona entities according to some embodiments herein;
[0033] FIG. 6 illustrates an exemplary user interface displaying a persona-specific contextual notification according to some embodiments herein;
[0034] FIG. 7 illustrates an adaptive feedback loop for refining persona-specific rel-evance conditions based on recorded response actions without retraining a machine learning model according to some embodiments;
[0035] FIGS. 8A-8B are flow diagrams that illustrate a method for generating per-sona-aware semantic and deterministic contextual alerting according to some embodiments herein; and
[0036] FIG. 9 is a schematic diagram of a computer architecture in accordance with the embodiments herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0037] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0038] As discussed, there remains a need for an improved computer-implemented system capable of performing persona-aware semantic observation and contextual notification generation within distributed information environments. Existing systems are unable to effectively process large volumes of heterogeneous activity data, dynamically determine relevance based on responsibility context, and generate semantically meaningful alerts aligned with decision-making requirements. Embodiments herein address these limitations by proposing a persona-aware semantic digital observer system configured to record activity data as ontology-anchored semantic events, derive semantic state transitions, and dynamically determine persona-specific relevance conditions for contextual notification/alert generation. The embodiments further disclose a method for generating persona-aware semantic and deterministic persona-specific contextual notifications by filtering enterprise event noise using semantic reasoning and persona-based evaluation. Referring now to the drawings, and more particularly to FIGS. 1 through 9, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0039] Ontology-Anchored Semantic Event: A structured representation of activity data recorded in an immutable semantic event space. Each event comprises at least one of entity references, semantic attributes or predicates, temporal information, provenance metadata, or causal or dependency links.
[0040] Domain Ontology: A structured representation defining enterprise entities, attributes, relationships, constraints, and permissible semantic interpretations used for processing activity data.
[0041] Immutable Semantic Event Space: An append-only storage layer that maintains ontology-anchored semantic events in a time-ordered manner such that previously recorded events are not modified or deleted, thereby preserving an auditable history.
[0042] Semantic State Transition: A detected change in an ontology-defined state of an entity derived by comparing a current state with a previously recorded state across a temporal sequence of ontology-anchored semantic events.
[0043] Reasoning Flow Graph: A graph structure derived from the domain ontology representing relationships between ontology-defined entities and permissible transitions used for deriving semantic state transitions.
[0044] Persona Semantic Profile: A structured definition associated with a persona entity that specifies at least one of responsibility scope, owned entities, decision authority level, or contextual preferences.
[0045] Persona-Specific Relevance Condition: A condition determined for each semantic state transition based on at least one of semantic impact on owned entities, deviation from an expected ontology-defined state, or temporal urgency relative to a decision authority level.
[0046] Semantic Impact: A measure of influence of a semantic state transition on one or more entities associated with a persona semantic profile.
[0047] Temporal Urgency: A time-based parameter indicating the significance of a semantic state transition relative to a temporal interval or decision-making requirement.
[0048] Contextual Notification: A notification generated by the system that includes semantic justification identifying at least one ontology-anchored semantic event or semantic state transition.
[0049] Semantic Justification: Machine-generated explanatory information that links a contextual notification to one or more supporting ontology-anchored semantic events or semantic state transitions that are derived.
[0050] Enterprise Event Noise: Irrelevant or non-actionable semantic state transitions that do not satisfy a persona-specific relevance condition.
[0051] Suppression: The elimination or discarding of semantic state transitions that do not satisfy the persona-specific relevance condition to prevent transmission of contextual notifications.
[0052] Relevance Indicator: A computed representation indicating a degree of association between a semantic state transition and one or more owned entities defined in a persona semantic profile.
[0053] Relevance Score: A computed value representing significance of a semantic state transition based on at least one of entity association, state deviation magnitude, or temporal urgency.
[0054] Governance Validation: A validation process performed prior to transmission of a contextual notification based on at least one of authorization rules, policy constraints, confidentiality rules, or delivery constraints.
[0055] Adaptive Relevance Update: A process of updating one or more relevance weighting parameters associated with a persona semantic profile based on recorded response actions without retraining a predictive model.
[0056] Formal Concept Analysis (FCA): A mathematical framework used to validate semantic admissibility by determining closure-valid attribute sets derived from ontology-anchored semantic events.
[0057] Closure-Valid Subset: A set of semantic attributes that are consistent with an ontology-derived attribute vocabulary and satisfy formal closure conditions.
[0058] Persona-Aware Semantic Filtering: A process of selectively evaluating and filtering semantic state transitions or ontology-anchored semantic events based on persona-specific relevance conditions defined in a persona semantic profile, to determine whether a persona-specific contextual notification is generated, modified, or suppressed.
[0059] Persona entity: An identifiable actor within a distributed information environment, including a user, system role, or automated agent, associated with a persona semantic profile and configured to receive persona-specific contextual notifications.
[0060] FIG. 1 illustrates a computer-implemented system 100 for generating persona-aware semantic and deterministic contextual alerting by filtering enterprise event noise within distributed information environments. The computer-implemented system 100 comprises a plurality of distributed information sources 102A-N, a network 104, a processor 106, a memory 108. The computer-implemented system 100 is communicatively connected to persona entities 118 to transmit a persona-specific contextual notification 116. The network 104 includes one or more wired or wireless communication networks, including the Internet, cellular networks, local area networks, wide area networks, or combinations thereof. The memory 108 includes domain ontology 110, persona semantic profiles 112, and an immutable semantic event space 114. The processor 106 executes instructions stored in the memory 108 to perform the operations described herein. The enterprise event noise refers to semantic state transitions or events that do not satisfy persona-specific relevance conditions and therefore do not result in actionable notifications.
[0061] The processor 106 is configured to receive activity data from the plurality of distributed information sources 102A-N through the network 104. The activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data. In some embodiments, the processor 106 is configured to ingest both structured, semi-structured, and unstructured datasets in real-time or batch intervals, including system logs, behavioural activity records, and machine-learning prediction signals. The processor 106 further identifies provenance metadata and temporal information within the received activity data to facilitate the subsequent anchoring of events to the domain ontology 110.
[0062] The processor 106 is configured to record the activity data in the immutable semantic event space 114 stored in the memory 108 as ontology-anchored semantic events. The processor 106 identifies domain ontology-defined entities within the activity data to perform this anchoring. Each ontology-anchored semantic event comprises at least one of: references to domain ontology-defined entities, semantic attributes or predicates, a timestamp or temporal interval, provenance metadata identifying a source system, model, or human actor, or causal or dependency links to prior semantic events. In some embodiments, the processor 106 establishes causal links between newly recorded semantic events and previously stored semantic events to facilitate temporal reasoning. The immutable semantic event space 114 preserves previously recorded semantic events, inferred states, suppression events, AI-generated outputs, generated notifications, delivery events, and recorded response actions without modification or deletion which ensures truth monotonicity and provides a full permanent semantic trace for the replay ability of enterprise state evolution.
[0063] The processor 106 derives a plurality of semantic state transitions from the immutable semantic event space 114. This derivation is performed by: (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events; (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state; and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events. In some embodiments, the processor 106 evaluates these transitions based on complex logical triggers, including a convergence of multiple semantic conditions across a plurality of entities, the co-occurrence of semantic events within a predefined time window, or the accumulation and decay of one or more semantic attributes.
[0064] The processor 106 performs ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of these state transitions to ensure the system functions deterministically. During this process, the semantic attributes associated with a transition are validated against an ontology-derived attribute vocabulary. Any attributes that are unresolvable to the vocabulary, violate symbolic grounding requirements, or do not belong to a closure-valid subset are deterministically rejected or constrained to the closure-valid subset before further processing. This validation substrate ensures that all derived interpretations remain semantically consistent with the domain ontology 110.
[0065] The processor 106 is configured to access persona semantic profiles 112 stored in the memory 108. Each persona semantic profile 112 defines a structured representation of a user’s operational context, including at least one of: a responsibility scope, one or more owned or accountable entities, a decision authority level, or a plurality of contextual preferences. In various embodiments, the persona semantic profiles 112 further comprise advanced parameters such as risk sensitivity parameters (or risk tolerance), temporal urgency preferences or levels, a preferred notification modality, a decision horizon representing the temporal window for authorized action, or subscription intent expressed using ontology concepts, relationships, or event patterns. In some embodiments, the persona entities refer to a computational representation of a role, responsibility scope, or decision authority associated with a user or system within the enterprise.
[0066] The processor 106 receives and manages semantic subscriptions defined in natural language as intent-driven conditions to enable persona-aware observation. The processor 106 translates these natural language intents into ontology-grounded subscription conditions that reference specific ontology concepts, entity relationships, event patterns, or causal chains. This semantic model allows persona entities 118 to subscribe to complex enterprise state transitions rather than relying on fixed numeric thresholds.
[0067] Furthermore, the persona semantic profiles 112 provide the basis for determining a persona-specific relevance condition by matching entities referenced in derived state transitions with the owned entities to generate a relevance indicator based on a degree of the match. The processor 106 may additionally compute a relevance score based on entity association or state deviation magnitude and compare it to a predefined threshold configured within the profile. These profiles are integrated into an adaptive feedback loop. The processor 106 refines relevance weighting parameters in real-time based on recorded persona response actions, including alert acknowledgements, dismissals, or ignored notifications. In some embodiments, the relevance score (R) is computed as a weighted aggregation of multiple factors, such as: R = w1 × Impact + w2 × Deviation + w3 × Urgency, where w1, w2, and w3 represent configurable weighting parameters associated with the persona semantic profile.
[0068] The processor 106 implements a multi-persona semantic filtering logic. The processor 106 processes a single semantic state transition that is derived into a plurality of distinct persona-specific contextual notifications 116, where each featuring different machine-generated semantic justifications is adapted to the unique responsibility scopes and decision authority levels of different persona entities 118. In some embodiments, the processor 106 may determine that a transition is relevant to one persona entity while simultaneously suppressing the transmission of a notification to another persona entity for whom the relevance condition is not satisfied.
[0069] When a relevance condition is met, the processor 106 generates a contextual notification/alert that includes at least one of: a semantic explanation, a persona-specific impact assessment of, an explanation of the state transition and recommended actions aligned with the recipient's authority. In some embodiments, the persona-specific contextual notifications 116 may take several forms, including state-transition alerts, risk-propagation alerts derived from causally linked entities, temporal-threshold alerts, pattern-accumulation alerts, or causal-chain alerts.
[0070] The processor 106 may utilize a generative language model constrained by the outputs of the reasoning flow graphs and the ontology-anchored semantic events. In some embodiments, every suppression decision is recorded as a suppression event within the immutable semantic event space 114. The computer-implemented system 100 provides a full, permanent semantic trace that allows for the complete reconstruction and verification of the system's reasoning at any point in time.
[0071] The processor 106 generates persona-specific contextual notifications 116 comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition. In some embodiments, the notifications are generated using a generative language model constrained by the outputs of the reasoning flow graph, the ontology-anchored semantic events, or the semantic state transitions that are derived to ensure that the natural language explanation remains semantically consistent with the domain ontology 110. This constrained generation serves to certify the alert explanation as a truthful representation of the underlying enterprise state.
[0072] In some embodiments, the persona-specific contextual notifications 116 comprise at least one of: a state-transition alert, a risk-propagation alert (derived from causally linked entities), a temporal-threshold alert, a pattern-accumulation alert, or a caus-al-chain alert. The notifications further include at least one of: an explanation of relevance to the specific persona entity, affected entities, recommended actions aligned with the per-sona’s decision authority, a confidence indicator, or supporting evidence identifying the specific ontology-anchored semantic events. By including these elements, the computer-implemented system 100 ensures that alerts/notifications are explainable by construction, providing a traceable reasoning path from the raw data in the immutable semantic event space 114 to the final recommended action.
[0073] The processor 106 is configured to transmit the persona-specific contextual notifications 116 to the persona entities 118 associated with the corresponding persona se-mantic profiles 112 through the network 104. This targeted transmission filters enterprise event noise through a persona-aware semantic filtering, thereby optimizing network utiliza-tion and the processor 106 attention by reducing the delivery of contextually irrelevant noti-fications. In some embodiments, the processor 106 is further configured to perform a gov-ernance validation before routing or transmitting the notification. This validation involves checking at least one of: an authorization rule, a policy constraint, a confidentiality rule, or a delivery constraint associated with the persona semantic profile 112 or the affected do-main ontology-defined entity to ensure compliance with enterprise security and routing pro-tocols. In some embodiments, the computer-implemented system 100 implements an adap-tive feedback loop to refine the system's accuracy in real-time.
[0074] The processor 106 is configured to receive response actions from the persona entities 118, including, but not limited to, acknowledge, dismiss, ignore, escalate, approve, or override. Upon receipt, the processor 106 generates a new ontology-anchored semantic event representing the response action and stores it in the immutable semantic event space 114. Because these response actions are stored alongside the original alerts/notifications and inferred states, the computer-implemented system 100 maintains a full permanent se-mantic trace of the entire decision lifecycle. Based on these recorded response actions, the processor 106 dynamically updates one or more relevance weighting parameters associated with the persona semantic profile 112. This refinement of the persona-specific relevance condition occurs in real-time without requiring the retraining of an underlying machine learning or predictive model.
[0075] For example, in a distributed enterprise environment, activity data generated from monitoring systems, transaction platforms, and user interaction interfaces is received by the processor 106. The processor 106 records the activity data as ontology-anchored se-mantic events and derives semantic state transitions based on changes in ontology-defined entity states over time. The processor 106 evaluates the semantic state transitions against persona semantic profiles 112 to determine relevance. If a semantic state transition impacts an entity owned by a specific persona and satisfies a relevance condition based on semantic impact, deviation, or temporal urgency relative to decision authority, the processor 106 generates a contextual notification with semantic justification and transmits it to the corre-sponding persona entity. For other persona entities for whom the transition does not satisfy the relevance condition, the processor 106 suppresses transmission of the notification, thereby implementing persona-aware filtering of enterprise event noise.
[0076] In an exemplary embodiment, the computer-implemented system 100 is ap-plied in a logistics scenario involving shipment operations at a port location. While conven-tional systems retrieve shipment records based on static queries, the computer-implemented system 100 records events such as labor strikes or environmental disruptions in an immuta-ble semantic event space 114 and correlates such events with pending shipments using on-tology-based reasoning. The processor 106 derives a semantic state transition indicating a potential delay and evaluates it against persona semantic profiles. Based on persona-specific relevance conditions, differentiated persona-specific contextual notifications are generated, and the operational persona entities receive delay alerts and executive persona entities receive actionable recommendations such as rerouting based on cost or penalty im-plications, thereby enabling proactive and context-aware decision-making.
[0077] FIG. 2 illustrates an exemplary layered semantic processing architecture of the computer-implemented system 100 for generating persona-aware semantic and deter-ministic contextual alerting based on ontology-driven state transition analysis according to some embodiments herein. The architecture is organized into a plurality of processing lev-els including a semantic event input layer 202, a processor 106, and a memory 108-driven persona interpretation layer, which collectively enable the deterministic transformation of raw activity data into persona-specific contextual notifications 116. In Level 1, the seman-tic event input layer 202 is configured to ingest activity data from multiple heterogeneous sources, including operational systems 214, machine learning pipelines 216, analytics sys-tems 220, and human interaction interfaces 218. In some embodiments, the semantic event input layer 202 receives: operational system data including logs and monitoring outputs; outputs from machine learning or artificial intelligence pipelines; analytics-derived signals; and human-generated interaction events. The semantic event input layer 202 normalizes and forwards the activity data to the processor 106 for semantic transformation and anchoring to the domain ontology 110.
[0078] In Level 2, the processor 106 implements a semantic observer engine 222 to perform semantic interpretation and reasoning over the ingested data. The semantic observ-er engine 222 utilizes the domain ontology 110 to map incoming activity data into ontolo-gy-anchored semantic events and constructs a reasoning flow graph 210representing rela-tionships between ontology-defined entities. The processor 106 derives semantic state tran-sitions 212 by: (i) traversing the reasoning flow graph, (ii) comparing current and previous ontology-defined states, and (iii) detecting temporal changes across sequences of semantic events. In some embodiments, the processor 106 applies semantic filtering logic based on a convergence of multiple semantic conditions, co-occurrence of events within a defined time window, or the accumulation and decay of semantic attributes. To ensure truth monotonici-ty, the processor 106 performs ontology-based validation using Formal Concept Analysis (FCA) to ensure the semantic admissibility of derived state transitions. In some embodi-ments, the reasoning flow graph is represented as a directed semantic dependency graph, where nodes correspond to ontology-defined entities and edges represent causal or depend-ency relationships between the entities. The processor 106 traverses the directed edges to infer state transitions and propagate semantic impact across related entities.
[0079] The processor 106 is configured to perform ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of derived state transitions before any notification is generated. This logic serves as a validation substrate to ensure that all interpretations remain semantically consistent with the domain ontology 110. In some embodiments the FCA logic involves attribute vocabulary validation where the processor 106 validates the one or more semantic attributes associated with a state tran-sition against an ontology-derived attribute vocabulary.
[0080] To ensure the semantic admissibility of derived state transitions, the processor 106 implements a deterministic validation substrate based on Formal Concept Analysis (FCA). The system defines a Formal Context K=(G,M,I), where G represents a set of ontology-anchored semantic events (objects), M represents the ontology-derived attribute vocabulary (attributes), and I⊆G×M is a binary relation indicating that a semantic event possesses a specific attribute.
[0081] For illustrative purposes, consider a sample set of semantic events G={e1,e2,e3} and a set of attributes from the vocabulary M={a1,a2,a3,a4}, where:
a1: ShipmentDelay
a2: CriticalInventoryImpact
a3: HighRiskSupplier
a4: ExpeditedLogisticsRequired.
[0082] Assume the following incidence relation I based on the Domain Ontology 110:
Event e1 is characterized by attributes {a1,a2}.
Event e2 is characterized by attributes {a1,a2,a3}.
Event e3 is characterized by attributes {a1,a4}.
[0083] The closure operator (⋅)′′ is applied to a set of observed attributes A⊆M to determine the closure-valid subset.
[0084] If the system identifies a candidate state transition with a partial attribute set A={a3}(HighRiskSupplier), the system identifies all events in G possessing a3 (which is {e2}).
[0085] The set of all attributes shared by all events possessing a3 is {a1,a2,a3}.
[0086] Therefore, the closure A′′ = {a1,a2,a3}.
[0087] If a semantic state transition that is derived attempts to assert {a3} without the logically entailed {a1,a2}, the processor 106 identifies this as a violation of symbolic grounding and either rejects the transition or constrains it to the closure-valid subset {a1,a2,a3}before generating a persona-specific contextual notification 116.
[0088] The ontology-derived attribute vocabulary is maintained in a well defined format, such as JSON-LD, to ensure deterministic resolution during the derivation of state transitions. A representative schema for the attributes is provided below:
{
"@context": {
"enterprise": "https://schema.formcept.com/ontology/",
"attr": "https://schema.formcept.com/attributes/",
"rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
"label": "http://www.w3.org/2000/01/rdf-schema#label"
},
"@graph": [
{
"@id": "attr:ShipmentDelay",
"@type": "enterprise:SemanticAttribute",
"label": "Shipment Delay detected",
"enterprise:appliesTo": "enterprise:ShipmentEntity",
"enterprise:closureConstraint": "attr:TemporalThreshold"
},
{
"@id": "attr:HighRiskSupplier",
"@type": "enterprise:SemanticAttribute",
"label": "Supplier Audit Failure",
"enterprise:appliesTo": "enterprise:SupplierEntity",
"enterprise:implies": "attr:CriticalInventoryImpact"
}
]
}
[0089] This schema ensures that every attribute associated with a semantic state transition is resolvable to the domain ontology 110, enabling the deterministic rejection of unresolvable or non-conformant predicates
[0090] Closure-Valid Subsets: The system determines closure-valid subsets, which are defined as sets of semantic attributes that are consistent with the ontology and satisfy formal closure conditions. Any attributes that do not belong to these valid subsets are de-terministically rejected or constrained before the generation of a contextual notification
[0091] Symbolic Grounding Requirements: Attributes that are unresolvable or that violate symbolic grounding requirements are rejected. This ensures that every reasoned output is rooted in the ground truth of the immutable semantic event space 114.
[0092] In level 3: The memory 108 stores persona semantic profiles 112 and contex-tual parameters used for persona-aware interpretation. Each persona semantic profile 112 includes a responsibility scope 204, an authority level 206, owned entities, and a risk toler-ance or sensitivity 208. In some embodiments, the persona semantic profiles 112 further define: temporal urgency preferences, decision horizons, notification modality preferences, and semantic subscription conditions. The processor 106 evaluates the semantic state tran-sitions that are derived against these profiles to determine persona-specific relevance con-ditions based on semantic impact, state deviation, and urgency. The layered architecture enables upward semantic abstraction, where raw activity data is transformed into semantic events at the input layer, semantic events are processed into state transitions at the proces-sor layer, and state transitions are interpreted relative to persona context at the memory layer. In some embodiments, a same semantic state transition is interpreted differently across multiple persona semantic profiles 112, implementing a multi-persona semantic that results in differentiated notification outcomes or suppression for selected persona entities.
[0093] Based on the persona-specific relevance condition determined at the memory 108, the processor 106 generates persona-specific contextual notifications 116 comprising machine-generated semantic justification and transmits the notifications to the correspond-ing persona entity. In some embodiments, feedback signals generated from persona entity responses including acknowledgments or dismissals are recorded as new semantic events in the immutable semantic event space 114, thereby enabling adaptive refinement of relevance conditions without requiring the retraining of underlying machine learning models
[0094] By implementing a multi-persona semantic filtering logic, the computer-implemented system 100 transforms a singular enterprise event into divergent, contextually relevant signals while selectively suppressing irrelevant data paths. This directed pro-cessing architecture reduces the cumulative computational overhead and network band-width consumption typically associated with broadcast-based enterprise monitoring sys-tems, providing a technical improvement in distributed information environment perfor-mance
[0095] FIG. 3 illustrates a conceptual representation of a domain ontology 110 and a persona semantic profile 112 used for persona-aware semantic reasoning and contextual notification generation according to some embodiments herein. The domain ontology 110 represents a structured world model defining domain knowledge used for semantic interpretation. The domain ontology 110 includes a plurality of ontology components, including concepts 302, causal chains 304, event relationships 306, and business rules 308. The concepts 302 define domain entities and their associated attributes. The causal chains 304 define cause-and-effect relationships between events or entity states. The event relationships 306 define associations, dependencies, or interactions between ontology-defined entities. The business rules 308 define constraints, policies, or logical conditions governing permissible states and transitions within the domain.
[0096] In some embodiments, the domain ontology 110 is used to construct a reasoning flow graph to derive semantic state transitions and to validate the semantic consistency of ontology-anchored semantic events.
[0097] The persona semantic profile 112 represents a user-specific contextual model used to determine the relevance of semantic state transitions. The persona semantic profile 112 includes owned entities 310, decision authority 312, risk sensitivity 314, and notification preferences 316. The owned entities 310 represent ontology-defined entities associated with the responsibility of a persona. The decision authority 312 defines a level of control or decision-making capability associated with the persona entity. The risk sensitivity 314 defines tolerance thresholds for deviations or risk conditions. The notification preferences 316 define preferred modalities, timing, or formats for receiving contextual notifications. In some embodiments, the persona semantic profile 112 further includes responsibility scope, temporal urgency preferences, decision horizon, and semantic subscription conditions defined using ontology concepts, relationships, or causal chains.
[0098] The domain ontology 110 and the persona semantic profile 112 operate in conjunction to enable persona-aware filtering of semantic state transitions. The domain ontology 110 provides a semantic interpretation framework for activity data, while the persona semantic profile 112 provides a contextual relevance framework for evaluating the interpreted semantic state transitions. In some embodiments, the processor 106 maps ontology-defined entities referenced in semantic state transitions to owned entities defined in the persona semantic profile 112 to determine relevance. The processor 106 further evaluates semantic impact, deviation from expected ontology-defined states, and temporal urgency relative to the decision authority to determine whether a notification should be generated.
[0099] In some embodiments, the same semantic state transition derived using the domain ontology 110 is interpreted differently for different persona semantic profiles 112, resulting in the generation of distinct persona-specific contextual notifications or suppres-sion of notifications for selected persona entities based on respective relevance conditions.
[00100] FIG. 4 illustrates an exemplary flowchart describing a method for filtering semantic state transitions and generating persona-specific contextual notifications 116 based on relevance determination according to some embodiments herein. The dynamic filtering may be performed by the logic/logic modules as described in the figure and executed by the relevance engine. At step 402, an event stream is received, and the event stream comprises ontology-anchored semantic events derived from activity data generated by distributed information sources 102A-N. At step 404, the processor 106 evaluates whether a semantic state transition associated with the event stream satisfies a persona-specific relevance condition. The relevance condition is determined based on multiple factors, including semantic impact, state deviation, and urgency relative to persona entity’s authority. As part of the relevance evaluation, the processor 106 determines a semantic impact 406 of the semantic state transition on one or more owned entities associated with a persona semantic profile 112. The processor 106 further determines whether the semantic state transition represents a deviation from an expected ontology-defined state 408. In some embodiments, the deviation is identified by comparing current and previous states of an ontology-defined entity across a temporal sequence of semantic events. The processor 106 evaluates whether the semantic state transition satisfies a temporal urgency condition 410 relative to a decision authority level associated with the persona entity, thereby determining whether the transition is actionable. If the semantic state transition does not satisfy the persona-specific relevance condition (FALSE branch), the processor 106 discards or suppresses the transition at step 414, thereby preventing the generation of contextually irrelevant notifications and reducing alert fatigue. If the semantic state transition satisfies the persona-specific relevance condition (TRUE branch), the processor 106 proceeds to step 412. In some embodiments, the method operates as a continuous event processing loop, where subsequent event streams are iteratively evaluated following notification generation or suppression.
[00101] At step 412, the processor 106 generates a persona-specific contextual notification comprising semantic justification derived from one or more ontology-anchored semantic events or semantic state transitions. The persona-specific contextual notification/alert enables actionable decision-making for the corresponding persona entity 118
[00102] In an exemplary embodiment, multiple evaluation conditions, including semantic impact, deviation, and urgency, are combined to determine the relevance condition. A semantic state transition affecting an owned entity with high deviation and high urgency relative to the persona entity’s authority level results in the generation of a contextual notification/alert, whereas transitions lacking sufficient relevance are suppressed. In some embodiments, the relevance is computed using a relevance score and compared against a threshold. Multiple semantic conditions across entities are aggregated before evaluation, and co-occurring events within a time window contribute to relevance determination. Feedback from prior alerts influences future relevance evaluation without retraining a machine learning model.
[00103] FIG. 5 illustrates an exemplary embodiment demonstrating persona-aware contextual alert generation based on the semantic interpretation of an event and evaluation of its impact across multiple persona entities according to some embodiments herein. FIG. 5 depicts an event 500 corresponding to a supplier shipment delay is received and represented as an ontology-anchored semantic event. The event 500 indicates that a supplier shipment is delayed by a plurality of days, which may impact multiple operational and strategic functions within an enterprise. The processor 106 derives one or more semantic state transitions based on the event 500, including impacts on logistics operations, procurement planning, and other related enterprise functions. The semantic state transition is evaluated across multiple persona semantic profiles to determine persona-specific relevance conditions. For a first persona entity, represented as a logistics manager (User A 502), the processor 106 determines that the event has a direct operational impact. Based on the semantic impact and temporal urgency associated with shipment delays, the processor 106 determines that the relevance condition is satisfied and generates a contextual notification indicating that dock labor should be rescheduled immediately. For a second persona entity, represented as a chief procurement officer (User B 504), the processor 106 determines that the event 500 has a strategic impact on procurement targets and compliance metrics. Accordingly, the processor 106 generates a different contextual notification indicating a compliance risk on quarterly spend targets. For a third persona entity, represented as an IT director (User C 506), the processor 106 determines that the event 500 has no domain relevance with respect to the persona semantic profile. Accordingly, the relevance condition is not satisfied, and the processor 106 suppresses the generation of a notification, resulting in no alert. FIG. 5 demonstrates a semantic filtering mechanism, where the same semantic event or the semantic state transition that is derived results in distinct persona-specific contextual notifications for different persona entities or suppression of notifications based on respective relevance conditions. In some embodiments, the persona-specific contextual notifications 116 generated for each persona entity comprise semantic justification, including identification of relevant ontology-anchored semantic events, affected entities, impact assessment, and recommended actions tailored to the corresponding persona entity. In some embodiments, response actions associated with the generated notifications, including acknowledgement, dismissal, or escalation, are recorded as ontology-anchored semantic events are utilized to refine persona-specific relevance conditions for subsequent evaluations without retraining a machine learning model.
[00104] FIG. 6 illustrates an exemplary user interface displaying a persona-specific contextual notification according to some embodiments herein. The contextual notification comprises a semantic state transition detected at 602, a semantic explanation portion 606, a persona-aligned impact portion that is indicated at 610, a recommendation action 614, and an actionable control element that is indicated at 618. The notification further includes a semantic explanation layer that is indicated at 604, a persona alignment and impact layer that is indicated at 608, an authority-based action 612, and an integrated workflow 616. The detected semantic state transition represents the detection of a change in an ontology-defined state of an entity derived from ontology-anchored semantic events. The semantic explanation portion that is indicated at 606, corresponding to the semantic explanation layer 604, describes “what happened.” The supplier entity has entered a risk state due to a shipment delay. The persona-aligned impact portion that is indicated at 610, corresponding to the persona alignment and impact layer that is indicated at 608, provides a contextual explanation of “why it matters” to the persona entities by identifying one or more affected entities associated with the persona entity. The recommendation action 614, corresponding to the authority-based action 612, provides a suggested action within a decision authority of the persona entities. The actionable control element that is indicated at 618, corresponding to the integrated workflow 616, enables the persona entities to perform an action within the notification interface, where the action is integrated with an underlying workflow system. In some embodiments, the persona-specific contextual notification further includes semantic justification, urgency indicators, affected entities, and supporting evidence derived from ontology-anchored semantic events. In some embodiments, response actions performed via the actionable control element approve that is indicated at 618, are recorded as ontology-anchored semantic events and used to refine persona-specific relevance conditions without retraining a machine learning model.
[00105] FIG. 7 illustrates an adaptive feedback loop for refining persona-specific relevance conditions based on recorded response actions without retraining a machine learning model, according to some embodiments. The centre of the feedback loop, a condition indicating that no model retraining is required 702. The adaptation of the system is achieved through semantic updates rather than retraining of predictive models. The process begins with the generation and transmission of a persona-specific contextual notification alert 704 to a corresponding persona entity based on a determined relevance condition. Following transmission of the contextual notification, one or more response user actions 706 are received from the persona entity. The response action 706 includes at least one of acknowledge, dismiss, or ignore. The response action 706 that is received is recorded as ontology-anchored semantic records 708 within an immutable semantic event space. The semantic records 708 capture user interaction behaviour associated with contextual notifications. Based on the recorded semantic events, the system performs refinement of persona-specific relevance conditions. The profile refinement 710 includes updating one or more relevance weighting parameters associated with a persona semantic profile. The refined relevance conditions influence subsequent determination of persona-specific relevance for future semantic state transitions. The process iteratively continues cyclically, enabling continuous improvement in contextual notification relevance without retraining a machine learning model. In some embodiments, the adaptive feedback loop improves the precision of alert generation, reduces notification noise, and enhances alignment between generated notifications and persona-specific decision requirements.
[00106] FIGS. 8A-8B are flow diagrams that illustrate a method for generat-ing persona-aware semantic and deterministic contextual alerting according to some em-bodiments herein. At step 802, activity data is received from a plurality of distributed in-formation sources 102A-N through a network. The activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data
[00107] At step 804, the activity data is recorded in the immutable semantic event space as ontology-anchored semantic events. Each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events.
[00108] At step 806, deriving a plurality of semantic state transitions from the semantic event space by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events. At step 808, persona semantic profiles stored in the memory are accessed. Each persona semantic profile defines at least one of (i) a corresponding persona entity responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences.
[00109] At step 810, the plurality of semantic state transitions are dynamically filtered to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile, (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level. At step 812, the semantic state transition that does not satisfy the persona-specific relevance condition is discarded or eliminated to suppress transmission of a notification for the persona semantic profile. At step 814, persona-specific contextual notifications comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition are generated from the immutable semantic event space for the persona semantic profile.
[00110] At step 816, the persona-specific contextual notifications are transmitted to persona entities associated with the corresponding persona semantic profile, thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.
[00111] The computer-implemented system 100 and method provide a significant technical advancement over traditional monitoring systems by transforming enterprise reality from static, metric-driven noise into proactive, persona-aware cognitive signals. By implementing a multi-persona semantic filtering logic rooted in a formal domain ontology, the computer-implemented system 100 enables a single enterprise event to be processed into divergent, contextually relevant notifications or suppressed entirely based on the specific responsibility scope, owned entities, and decision authority defined in a persona’s semantic profile. This targeted filtering deterministically reduces alert fatigue and optimizes network utilization and processor 106 attention by ensuring that only high-signal, actionable notifications are transmitted. Furthermore, the computer-implemented system 100 utilizes an immutable semantic event space 114 to record not only observations but also inferred states and suppression events, thereby providing a full permanent semantic trace that ensures truth monotonicity and enables temporal reasoning over the evolution of enterprise state. Unlike existing art reliant on fixed numeric thresholds, the present invention delivers alerts that are explainable by construction, featuring machine-generated semantic justifications that detail "what happened," "why it matters," and "what changed" relative to the recipient's unique operational context. Finally, the system's adaptive feedback loop allows for the real-time refinement of relevance conditions based on user interaction such as acknowledgements or dismissals, without requiring the retraining of underlying machine learning models, providing a continuously evolving and scalable observation substrate.
[00112] A representative hardware environment for practicing the embodiments herein is depicted in FIG. 9, with reference to FIGS. 1 through 8A and 8B. This schematic drawing illustrates a hardware configuration of a computer system in accordance with the embodiments herein. The computer includes at least one processing device 10 and a cryptographic processor 11. The special-purpose CPU 10 and the cryptographic processor (CP) 11 may be interconnected via system bus 14 to various devices such as a random-access memory (RAM) 15, read-only memory (ROM) 16, and an input/output (I/O) adapter 17. The I/O adapter 17 can connect to peripheral devices, such as disk units 12 and tape drives 13, or other program storage devices that are readable by the system. The computer can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein. The system further includes a user interface adapter 20 that connects a keyboard 18, mouse 19, speaker 25, microphone 23, and/or other user interface devices such as a touch screen device (not shown) to the bus 14 to gather user input. Additionally, a communication adapter 21 connects the bus 14 to a data processing network 26, and a display adapter 22 connects the bus 14 to a display device 24, which provides a graphical user interface (GUI) 30 of the output data in accordance with the embodiments herein, or which may be embodied as an output device such as a monitor, printer, or transmitter, for example. Further, a transceiver 27, a signal comparator 28, and a signal converter 29 may be connected with the bus 14 for processing, transmission, receipt, comparison, and conversion of electric or electronic signals.
[00113] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope.
, Claims:I/WE CLAIM:
1. A computer-implemented system (100) for generating persona-aware semantic and deterministic contextual alerting by filtering of enterprise event noise within distributed information environments, comprising:
a memory (108) that comprises a set of instructions, a domain ontology (110), persona semantic profiles (112), and an immutable semantic event space (114);
a processor (106) that executes the set of instructions and is configured to:
receive activity data from a plurality of distributed information sources (102A-N) through a network (104), wherein the activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data;
record the activity data in the immutable semantic event space (114) as ontology-anchored semantic events, wherein each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events;
derive a plurality of semantic state transitions from the immutable semantic event space (114), by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events;
access persona semantic profiles (112) stored in the memory (108), wherein each persona semantic profile (112) defines at least one of (i) a corresponding persona entity’s responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences;
dynamically filter the plurality of semantic state transitions to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile (112), (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level;
discard or eliminate the semantic state transition that does not satisfy the persona-specific relevance condition to suppress transmission of a notification for the persona semantic profile (112);
generate persona-specific contextual notifications (116) comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition from the immutable semantic event space (114) for the persona semantic profile (112); and
transmit the persona-specific contextual notifications (116) to persona entities (118) associated with the corresponding persona semantic profile (112), thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.

2. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) is further configured to perform ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of the semantic state transitions before generating the persona-specific contextual notifications (116), wherein the persona-specific contextual notifications (116) are generated using a generative language model constrained by at least one of (i) the outputs of the reasoning flow graph, (ii) the ontology-anchored semantic events stored in the immutable semantic event space (114), or (iii) the semantic state transitions that are derived to ensure the notification remains semantically consistent with the domain ontology (110), wherein the one or more semantic attributes associated with the semantic state transition are validated against an ontology-derived attribute vocabulary, and wherein the one or more semantic attributes that do not belong to a closure-valid subset are rejected before generating the persona-specific contextual notifications (116).

3. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) dynamically filters the plurality of semantic state transitions based on at least one of: (i) a convergence of multiple semantic conditions across a plurality of entities, (ii) co-occurrence of the ontology-anchored semantic events within a predefined time window, (iii) accumulation of the one or more semantic attributes, or (iv) decay of the one or more semantic attributes, wherein the persona semantic profiles (112) comprise at least one of risk sensitivity parameters, temporal urgency preferences or levels, preferred notification modality, decision horizon, or subscription intent expressed in terms of ontology concepts, relationships, or event patterns.

4. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) is configured to perform at least one of:
implementing a multi-persona semantic filtering logic that processes a same semantic state transition that is derived into distinct alerts or notifications with different semantic justifications for different persona entities (118), or suppresses the transmission of notification for at least one persona entity while transmitting the notification to another persona entity;
receiving semantic subscriptions defined in natural language as intent-driven conditions referencing ontology concepts and causal chains, rather than fixed numeric thresholds; or
implementing an adaptive feedback loop that refines the persona-specific relevance condition in real-time based on recorded persona actions, including alert acknowledgments, dismissals, or ignored notifications, without requiring retraining of an underlying machine learning model, wherein the processor (106) is further configured to translate the semantic subscriptions defined in a natural language into ontology-grounded subscription conditions referencing ontology concepts, entity relationships, event patterns, or causal chains.

5. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) is further configured to refine the persona-specific relevance condition in real-time by:
(i) receiving a response action associated with the transmitted persona-specific contextual notifications (116), wherein the response action comprises at least one of acknowledge, dismiss, or ignore;
(ii) generating an ontology-anchored semantic event representing the response action and storing it in the immutable semantic event space (114); and
(iii) updating at least one relevance weighting parameter associated with a persona semantic profile (112) based on the ontology-anchored semantic event without retraining a predictive model, wherein the response action comprises at least one of acknowledge, dismiss, ignore, escalate, approve, or override.

6. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) determines the persona-specific relevance condition by matching entities referenced in the plurality of semantic state transitions with "owned entities" associated with the respective persona semantic profile (112) and generating a relevance indicator based on the degree of the match.

7. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) determines the persona-specific relevance condition by computing a relevance score based on at least one of entity association, state deviation magnitude, or temporal urgency, and comparing the relevance score to a predefined threshold configured within the persona semantic profile (112).

8. The computer-implemented system (100) as claimed in claim 1, wherein the persona-specific contextual notifications (116) comprises at least one of:
(i) a state-transition alert generated upon detection of a change in an ontology-defined entity state;
(ii) a risk-propagation alert generated using the propagation of a semantic condition across causally linked entities;
(iii) a temporal-threshold alert generated when a semantic state persists beyond a predefined time interval;
(iv) a pattern-accumulation alert generated upon detection of a recurring semantic event pattern within a defined time window; or
(v) a causal-chain alert generated upon detection of a predefined sequence of causally related semantic events, wherein the persona-specific contextual notifications (116) comprises at least one of an explanation of relevance to the persona entity (118), an impact assessment, one or more affected entities, one or more recommended actions, a confidence indicator, or evidence identifying the ontology-anchored semantic events.

9. The computer-implemented system (100) as claimed in claim 1, wherein the ontology-anchored semantic events are stored in the immutable semantic event space (114) such that previously stored semantic events, inferred states, suppression events, generated notifications, delivery events, and recorded response actions are permanently preserved and are never modified or deleted, thereby providing a full permanent semantic trace.

10. The computer-implemented system (100) as claimed in claim 2, wherein the one or more semantic attributes associated with the semantic state transition that are unresolvable to an ontology-derived attribute vocabulary or that violate symbolic grounding requirements are deterministically rejected or constrained to the closure-valid subset before generation of the persona-specific contextual notification (116).

11. The computer-implemented system (100) as claimed in claim 1, wherein the processor (106) is further configured to perform a governance validation before routing the persona-specific contextual notification (116) by checking at least one of an authorization rule, a policy constraint, a confidentiality rule, or a delivery constraint associated with the corresponding persona semantic profile (112) or the affected domain ontology-defined entity.

12. A computer-implemented method for generating persona-aware semantic and deterministic contextual alerting by filtering of enterprise event noise within distributed information environments, comprising:
receiving activity data from a plurality of distributed information sources (102A-N) through a network (104), wherein the activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data;
recording the activity data in the immutable semantic event space (114) as ontology-anchored semantic events, wherein each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events;
deriving a plurality of semantic state transitions from the immutable semantic event space (114), by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events;
accessing persona semantic profiles (112) stored in the memory (108), wherein each persona semantic profile (112) defines at least one of (i) a corresponding persona entity’s responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences;
dynamically filtering the plurality of semantic state transitions to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile, (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level;
discarding or eliminating the semantic state transition that does not satisfy the persona-specific relevance condition to suppress transmission of a notification for the persona semantic profile;
generating persona-specific contextual notifications (116) comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or semantic state transition that satisfies the persona-specific relevance condition from the immutable semantic event space (114) for the persona semantic profile; and
transmitting the persona-specific contextual notifications (116) to persona entities (118) associated with the corresponding persona semantic profile, thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.

13. The computer-implemented method as claimed in claim 12, wherein the method comprises performing ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of the semantic state transitions before generating the persona-specific contextual notifications (116), wherein the persona-specific contextual notifications (116) are generated using a generative language model constrained by at least one of (i) the outputs of the reasoning flow graph, (ii) the ontology-anchored semantic events stored in the immutable semantic event space (114), or (iii) the semantic state transitions that are derived to ensure the notification remains semantically consistent with the domain ontology (110).

14. The computer-implemented method as claimed in claim 13, wherein the one or more semantic attributes associated with the semantic state transition are validated against an ontology-derived attribute vocabulary, and wherein the one or more semantic attributes that do not belong to a closure-valid subset are rejected before generating the persona-specific contextual notifications (116).

15. The computer-implemented method as claimed in claim 12, wherein the plurality of semantic state transitions is dynamically filtered based on at least one of: (i) a convergence of multiple semantic conditions across a plurality of entities, (ii) co-occurrence of the ontology-anchored semantic events within a predefined time window, (iii) accumulation of the one or more semantic attributes, or (iv) decay of the one or more semantic attributes, wherein the persona semantic profiles comprise at least one of risk sensitivity parameters, temporal urgency preferences or levels, preferred notification modality, decision horizon, or subscription intent expressed in terms of ontology concepts, relationships, or event patterns.

16. The computer-implemented method as claimed in claim 12, wherein the method comprises performing at least one of:
implementing a multi-persona semantic filtering logic that processes a same semantic state transition that is derived into distinct alerts or notifications with different semantic justifications for different persona entities, or suppresses the transmission of notification for at least one persona entity while transmitting the notification to another persona entity;
receiving semantic subscriptions defined in natural language as intent-driven conditions referencing ontology concepts and causal chains, rather than fixed numeric thresholds; and
implementing an adaptive feedback loop that refines the persona-specific relevance condition in real-time based on recorded persona actions, including alert acknowledgments, dismissals, or ignored notifications, without requiring retraining of an underlying machine learning model, wherein the processor (106) is further configured to translate the semantic subscriptions defined in a natural language into ontology-grounded subscription conditions referencing ontology concepts, entity relationships, event patterns, or causal chains.

17. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for generating persona-aware semantic and deterministic contextual alerting by filtering of enterprise event noise within distributed information environments, comprising:
receiving activity data from a plurality of distributed information sources (102A-N) through a network (104), wherein the activity data comprises at least one of machine-generated data, monitoring outputs, model-generated outputs, or user-generated interaction data;
recording the activity data in the immutable semantic event space (114) as ontology-anchored semantic events, wherein each ontology-anchored semantic event comprises at least one of (i) one or more references to domain ontology-defined entities, (ii) one or more semantic attributes or predicates, (iii) a timestamp or temporal interval, (iv) provenance metadata identifying a source system, model, or human actor or (v) one or more causal or dependency links to prior semantic events;
deriving a plurality of semantic state transitions from the immutable semantic event space (114), by (i) traversing a reasoning flow graph derived from the domain ontology-defined entities referenced in the ontology-anchored semantic events, (ii) comparing a current ontology-defined state associated with an entity with a previously recorded ontology-defined state, and (iii) detecting a change in the ontology-defined state across a temporal sequence of the ontology-anchored semantic events;
accessing persona semantic profiles stored in the memory (108), wherein each persona semantic profile (112) defines at least one of (i) a corresponding persona entity’s responsibility scope, (ii) one or more owned entities, (iii) a decision authority level or (iv) a plurality of contextual preferences;
dynamically filtering the plurality of semantic state transitions to determine a persona-specific relevance condition by at least one of (i) evaluating a semantic impact of each semantic state transition on at least one owned entity defined in a persona semantic profile, (ii) determining a deviation from an expected ontology-defined state or (iii) determining if the semantic state transition satisfies the persona-specific relevance condition based on a temporal urgency level relative to the persona entity’s decision authority level;
discarding or eliminating the semantic state transition that does not satisfy the persona-specific relevance condition to suppress transmission of a notification for the persona semantic profile;
generating persona-specific contextual notifications (116) comprising machine-generated semantic justification that identifies at least one ontology-anchored semantic event or the semantic state transition that satisfies the persona-specific relevance condition from the immutable semantic event space (114) for the persona semantic profile; and
transmitting the persona-specific contextual notifications (116) to persona entities (118) associated with the corresponding persona semantic profile, thereby filtering the enterprise event noise through a persona-aware semantic filtering to reduce redundant event transmissions across distributed information environment and reducing the generation of contextually irrelevant notifications.

18. The non-transitory computer-readable medium as claimed in claim 17, wherein the one or more processors further causes performing ontology-based reasoning using Formal Concept Analysis (FCA) to validate the semantic admissibility of the semantic state transitions before generating the persona-specific contextual notifications (116), wherein the persona-specific contextual notifications (116) are generated using a generative language model constrained by at least one of (i) the outputs of the reasoning flow graph, (ii) the ontology-anchored semantic events stored in the immutable semantic event space (114), or (iii) the semantic state transitions that are derived to ensure the notification remains semantically consistent with the domain ontology (110).

19. The non-transitory computer-readable medium as claimed in claim 18, wherein the one or more semantic attributes associated with the semantic state transition that are unresolvable to an ontology-derived attribute vocabulary or that violate symbolic grounding requirements are deterministically rejected or constrained to the closure-valid subset before generation of the persona-specific contextual notification (116).

20. The non-transitory computer-readable medium as claimed in claim 17, wherein the persona-specific relevance condition is refined in real-time by:
(i) receiving a response action associated with the transmitted persona-specific contextual notifications (116), wherein the response action comprises at least one of acknowledge, dismiss, or ignore;
(ii) generating an ontology-anchored semantic event representing the response action and storing it in the immutable semantic event space (114); and
(iii) updating at least one relevance weighting parameter associated with a persona semantic profile (112) based on the ontology-anchored semantic event without retraining a predictive model, wherein the response action comprises at least one of acknowledge, dismiss, ignore, escalate, approve, or override.

Dated this 27th April 2026

Arjun Karthik Bala
(IN/PA 1021)
Agent for Applicant