Patent ID: 12229902

DETAILED DESCRIPTION

For simplicity and illustrative purposes, the present disclosure is described by referring mainly to examples. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be readily apparent however, that the present disclosure may be practiced without limitation to these specific details. In other instances, some methods and structures have not been described in detail so as not to unnecessarily obscure the present disclosure.

Throughout the present disclosure, the terms “a” and “an” are intended to denote at least one of a particular element. As used herein, the ten “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on.

Temporal impact analysis of cascading events on metaverse-based organization avatar entities apparatuses, methods for temporal impact analysis of cascading events on metaverse-based organization avatar entities, and non-transitory computer readable media having stored thereon machine readable instructions to provide temporal impact analysis of cascading events on metaverse-based organization avatar entities are disclosed herein. The apparatuses, methods, and non-transitory computer readable media disclosed herein provide for an automated technique for simulating propagation of an event as a cascade across semantically connected organization avatar entities (OAEs) in an environmental social and governance (ESG) metaverse, or metaverse generally, so that the event's potential impact on an entity of focus may be assessed before occurrence of the impact. A metaverse may represent a collective virtual shared space. In this regard, the term ESG as utilized herein may refer to the factors of environment, social, and governance that are used to measure sustainability. For the apparatuses, methods, and non-transitory computer readable media disclosed herein, the metaverse may include avatars of organization entities (e.g., “OAEs”) as models of their real-world behaviors, and a system-of-systems model of how these OAEs interact with each other, particularly, with respect to causal chains of events. The apparatuses, methods, and non-transitory computer readable media disclosed herein may further provide for automated selection of an optimal reaction plan with respect to the potential impact from an event as disclosed herein.

For the apparatuses, methods, and non-transitory computer readable media disclosed herein, in the metaverse, once an event occurs, its plausible cascade effect may be simulated using the aforementioned models, and the impact may be analyzed before occurrence, for example, in the real-world. Based on communication of the impact analysis to corresponding real-world ESG organization entities, an optimum mitigation and/or amplification process may be identified for execution as disclosed herein.

The apparatuses, methods, and non-transitory computer readable media disclosed herein may further provide a technical solution to the technical problem of determining the impact of cascading events in a metaverse of organization entities and their organization environments. In this regard, the technical solution provided by the apparatuses, methods, and non-transitory computer readable media disclosed herein may reduce computational resources (e.g., processor time, network bandwidth, and energy) required to mitigate and/or amplify the reaction of the organizations while dealing with the corresponding events in the physical world. Further, the apparatuses, methods, and non-transitory computer readable media disclosed herein provide for the design of a computational process for detecting cascading events in the metaverse. Once an event hypothetically occurs, the apparatuses, methods, and non-transitory computer readable media disclosed herein provide for determining its plausible effect through a simulated propagation across interconnected OAEs using these models, and assessing the event's impact and effect on the execution of mitigation or amplification plans before the event actually occurs.

According to examples disclosed herein, the apparatuses, methods, and non-transitory computer readable media disclosed herein may implement impact analysis as follows.

At the outset, a semantic association graph of OAEs may be generated by inferring associations from the interactions and identities of OAEs. Next, the apparatuses, methods, and non-transitory computer readable media disclosed herein may include receiving a signal from a metaverse interface, and identifying event characteristics. A sequence of causally connected events may be determined along a shortest feasible path. Thereafter, an effect of an event may be determined as a function of a set of state entity transitions, sets of actions taken, and outputs, A semantically closest event may be identified in a knowledge base with a maximally effective reaction plan. Thereafter, plausible effectiveness of the reaction plan may be determined. If the plausible effectiveness of the reaction plan is more than an acceptance level, the reaction plan may be communicated to an external operating environment. Finally, the operating environment may execute the reaction plan per the communication received from an impact analyzer (also designated as “ESG impact analyzer”) as disclosed herein.

The apparatuses, methods, and non-transitory computer readable media disclosed herein may further provide technical improvements such as reduction in computational resources (e.g., processor time, network bandwidth, and energy) that are needed to identify an optimized event-entity interaction in the metaverse. In this regard, the energy savings may be quantified to provide a practical implementation of the apparatuses, methods, and/or non-transitory computer readable media disclosed herein. For example, energy savings may be quantified as follows:

Energy⁢Savings=csubcopEquation⁢(1)

For Equation (1), EnergySavings may estimate the factor by which energy consumption is reduced by executing a system based upon the apparatuses, methods, and/or non-transitory computer readable media disclosed herein, in comparison to a default scenario. For Equation (1), csubmay represent a number of atomic compute steps for executing suboptimal reactions on event occurrence. In this regard, csubmay estimate energy expense of a default scenario (without the system based on the apparatuses, methods, and/or non-transitory computer readable media disclosed herein), where an organization avatar entity executes suboptimal reactions when an event cascades to it. Further, for Equation (1), copmay represent a number of atomic compute steps for executing optimal reactions on event occurrence. In this regard, copmay estimate the energy expense of executing reactions as per a system based on the apparatuses, methods, and/or non-transitory computer eadable media disclosed herein, where an organization avatar entity executes these interactions when an event occurs but has not cascaded to it.

For the apparatuses, methods, and non-transitory computer readable media disclosed herein, the dements of the apparatuses, methods, and non-transitory computer readable media disclosed herein may be any combination of hardware and programming to implement the functionalities of the respective elements. In some examples described herein, the combinations of hardware and programming may be implemented in a number of different ways. For example, the programming for the elements may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the elements may include a processing resource to execute those instructions. In these examples, a computing device implementing such elements may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separately stored and accessible by the computing device and the processing resource. In some examples, some elements may be implemented in circuitry.

FIG.1illustrates a layout of an example temporal impact analysis of cascading events on metaverse-based organization avatar entities apparatus (hereinafter also referred to as “apparatus100”).FIG.2illustrates an architecture of the apparatus100, in accordance with an example of the present disclosure.

Referring toFIGS.1and2, and particularly toFIG.1, the apparatus100may include a reachability analyzer102that is executed by at least one hardware processor (e.g., the hardware processor1402ofFIG.14, and/or the hardware processor1604ofFIG.16) to generate a semantic association graph104of a plurality of semantically connected organization avatar entities106. The reachability analyzer102may receive a signal108corresponding to an occurrence of a specified event110in a metaverse112. The reachability analyzer102may determine, based on an analysis of the signal108, a metaverse event114in the metaverse112. In this regard, the specified event110in the metaverse112may be the same as the metaverse event114determined by the reachability analyzer102, and may be designated as the metaverse event114once the signal108is analyzed by the reachability analyzer102, The reachability analyzer102may determine, for a specified organization avatar entity116, a feasible path118in the semantic association graph104.

An impact analyzer120that is executed by at least one hardware processor (e.g., the hardware processor1402ofFIG.14, and/or the hardware processor1604ofFIG.16) may determine, based on the feasible path118and for a specified time interval, a temporal impact122of the metaverse event114on the specified organization avatar entity116.

An event similarity reaction analyzer124that is executed by at least one hardware processor (e.g., the hardware processor1402ofFIG.14, and/or the hardware processor1604ofFIG.16) may determine, with respect to the specified organization avatar entity116, a similarity of the metaverse event114in a current temporal context to past events. The event similarity reaction analyzer124may select, from an event database126and based on the determined similarity of the metaverse event114in the current temporal context to past events, a reaction plan128of a plurality of reaction plans130that corresponds to a most similar event within a specified threshold range132. The event similarity reaction analyzer124may determine, based on simulation of the selected reaction plan128, a difference in the temporal impact122with and without the selected reaction plan128. The event similarity reaction analyzer124may forward, based on a determination that the difference in the temporal impact122is greater than a reaction plan threshold value134, the selected reaction plan128to a metaverse operating environment136, Further, the event similarity reaction analyzer124may generate an instruction to execute, by the metaverse operating environment136, the selected reaction plan128.

According to examples disclosed herein, the reachability analyzer102may determine, for the semantic association graph104, a sequence of logically connected properties by applying at least one derivation procedure, Each logically connected property may correspond to a causally connected event.

According to examples disclosed herein, the reachability analyzer102may generate the semantic association graph104of the plurality of semantically connected organization avatar entities106by representing associations between the organization avatar entities106as edges. Further, the reachability analyzer102may represent strengths of the associations between the organization avatar entities106as weights of the edges.

According to examples disclosed herein, the reachability analyzer102may determine, for the specified organization avatar entity116, paths in the semantic association graph104from a set of specified entities to the specified organization avatar entity116. The reachability analyzer102may determine a likelihood of cascading of the metaverse event114along each path of the determined paths. The reachability analyzer102may designate, based on the determined likelihood of cascading, each path of the determined paths for which an event cascade likelihood is greater than a specified event cascade threshold138as a feasible path. Further, the reachability analyzer102may determine, for each feasible path, a reachability140. In this regard, the reachability analyzer102may select, based on the determined reachability for each feasible path, a feasible path that includes a maximum reachability as the feasible path118for the specified organization avatar entity116.

According to examples disclosed herein, the impact analyzer120may determine, for the feasible path that includes the maximum reachability, if there exists a sequence of logically connected properties that are in successive states of entities along the feasible path.

According to examples disclosed herein, the impact analyzer120may determine, based on a determination that the signal108reaches the specified organization avatar entity116, a plurality of state transitions until the specified organization avatar entity116reaches a stationary state.

According to examples disclosed herein, the event database126may include states in which organization avatar entities were before occurrence of past events, states that the organization avatar entities transitioned to due to occurrence of events, the plurality of reaction plans as a set of computable actions for the organization avatar entities, and effectiveness coefficients associated with the plurality of reaction plans.

According to examples disclosed herein, the event similarity reaction analyzer124may generate, based on a determination that the difference in the temporal impact122is less than or equal to the reaction plan threshold value134, an indication of no known feasible action.

An organization entity controller142that is executed by at least one hardware processor (e.g., the hardware processor1402ofFIG.14, and/or the hardware processor1604ofFIG.16) may control, for the organization avatar entity116, an operation144based on the selected reaction plan128. With respect to operations that are performed by the organization avatar entity116, and also operations that are performed by the metaverse operating environment136, based on the selected reaction plan128, the organization avatar entity116may interact with other organization avatar entities that are connected to it as per the semantic association graph104. Such an interaction may enable state transitions in these entities. The metaverse operating environment136may communicate with the entities in the physical universe with information containing the states of the organizational avatar entities as a result of these state transitions.

Operation of the apparatus100is described in further detail with reference toFIGS.1-13.

Referring toFIGS.1and2, the metaverse112may represent a computational process with sub-processes specified as follows:
ESGmeta=[{α1,α1(t),act(·),env1, . . . ,αn,αn(t),act(·),envαn},envG]  Equation (2)

For Equation (2), χ=α1, . . . , αn, may represent a set of models emulating organization entities—referred to hereinafter as organization avatar entities (OAEs). Further, for Equation (2), αi(t), αct(αi,t), envαi, and envGmay be described as follows:αi(t): State of OAE aiat time point tαct(ai,t): Set of actions, which OAE αimay execute in state ai(t) at timepoint tenvαi: Operating environment of OAE aiincluding its semantically connected neighboring entities and plausible actions which the semantically connected neighboring entities can perform in association with αienvG: Global environment consisting of external entities, which can interact with entities in χ and plausible actions which the external entities can perform in association with entities in χ

With respect to semantic association graphs (e.g., also referred to as “state transition graphs”) as models of organization entities, computationally, each OAE in the metaverse may be modelled as the semantic association graph104. The semantic association graph104may specify in which state an entity currently is, and would transition from a current state to when transition conditions are enabled.

With respect to nodes representing states of OAEs, states of OAEs may be characterized by input state variables that hold values of observable characteristics of OAEs, and output state variables that hold values of outputs produced by OAEs while performing transitions. A directed edge u→cv may represent that if an OAE is in state u, the OAE would transit to state v if condition c holds in state u. Each transition condition c may represent a Boolean logic formula over input state variables and specify which states an OAE may transit to from a current state.

As shown inFIG.2, an ESG event analyzer200may determine the potential temporal impact of a cascading event and selecting an optimum reaction plan for mitigating or amplifying the impact. The ESG event analyzer200may encompass the reachability analyzer102, the impact analyzer120, and the event similarity reaction analyzer124.

Referring again toFIG.1, as disclosed herein, the reachability analyzer102may generate the semantic association graph104of a plurality of semantically connected oraanization avatar entities106. In this regard,FIG.3illustrates organization avatar entities to illustrate operation of the apparatus100, in accordance with an example of the present disclosure. Further,FIG.4illustrates a semantic association graph104of the organization avatar entities ofFIG.3to illustrate operation of the apparatus100, in accordance with an example of the present disclosure.

Referring toFIG.3, examples of the organization avatar entities106may be specified as follows:Avatar Entity #1: Company A (e.g., at300) executing production operations using electricity. Company A is located close to a river.Avatar Entity #2: There is a bridge over the river with water level sensor S (e.g., at302).Avatar Entity #3: Power transformer (e.g., at304) providing electrical supply to Company A

The semantic association graph104(e.g., state transition graph) for the organization avatar entities106ofFIG.3is shown inFIG.4.

FIG.5illustrates state transitions of semantically connected organization avatar entities to illustrate operation of the apparatus100, in accordance with an example of the present disclosure. For example, for goal condition IF (Sales≥μ−σ), this goal condition specifies if sale of the product would decrease by one standard deviation from the average sale of the previous years.

In this regard, with reference toFIG.5, the state transitions may be reached by the following enabling conditions:1. At500, IF (RainDepth>8 mm)=True for entity Eorg1at time-point t, transition it to state=LowSupply2. At502, IF (SupplyRaw<Expected)=True for entity Eorg2at time-point t+1, transition it to state=LowProduction3. At504, IF (SupplyPro<Expected)=True for entity Eorg3Aat time-point t+2, transition it to state=LowSales

In state LowSales, goal condition IF (Sales≥μ−σ) is True.

FIG.6illustrates avatar entity details to illustrate operation of the apparatus100, in accordance with an example of the present disclosure. InFIG.6, both of the graphs depict semantic association graphs of different organization avatar entities106. Both of the semantic association graphs differ in their states s0versus s′0, s1,kversus s′1,h, etc. In each state, different organization avatar entities106have a different number of state transitions. For example, for entity α1state s0has k transitions whereas for entity αn, state s′0has h transitions. Enabling conditions for transitions for both entities are different, for example, from state s0, entity α1has enabling conditions as c1, . . . , ck, whereas from state s′0, entity αnhas enabling conditions as c′1, . . . , c′h.

Referring again toFIG.1, with respect to a model of the metaverse event114(e.g., also referred to herein as “ESG event”), an event in the metaverse112may be identified by a simultaneous state transition of multiple OAEs such that a specific property that characterizes the event holds true for all these entities after state transitions as follows:
e(Ae,t)⇒∀αi∈Ae:Pe(s(αi,t+1))=Truee is an event occurring at timepoint tPeis the property characterizing event e represented as a formula in 1storder logic or a programAe={α1, . . . , αn} is the set of organization avaatar entities associated with the event e.State function s(α, t) returns state of an entity α at timepoint t Occurrence of an event e at time point t involving entities in Aemay imply that property Peholds for all transited states of the entities in Aeat time point t+1.

With respect to a model of a cascading ESG event, a cascading ESG event may represent an ESG event (e.g., the metaverse event114) that causally transmits across semantically related ESG avatar entities (e.g, the organization avatar entities106) in the metaverse. The cascade effect of an event may be assessed by event properties (P*), which are causally associated with original event property (Pe), such that P* hold true in future time points whenever Peholds true at current timepoint t as follows:
Pe(s(αi,t))⇒P*(s(x,t+))  Equation (3)

For Equation (3), entity x may relate to some αi∈Aethrough one or more semantic associations, and a timeline may be specified as 0→1→ . . . →t→t+→ . . . .

With respect to the model of the cascading ESG event (e.g., the metaverse event114), every event may represent a degree-0 cascade event. An event e may represent a degree-1 cascade event if for more than α∈[0,1] fraction of OAEs affected by e (e.g., more than [α*n] fraction of {α1, . . . , αn}), at least one of the immediately neighboring OAEs also transit to new states (e.g., at time point greater than t+1), where event property Peor its causally connected property P* holds.

With respect to the model of the cascading ESG event, A*e⊆Aemay be specified to be the fraction of OAEs affected by event e such that:
|Ae+|≥α|Ae|

With respect to the model of the cascading ESG event, for each OAE α∈A*e:Sαmay be specified to be the set of OAEs semantically directly connected with α. Further, S*α⇒Sαmay be specified to be the subset of semantic neighbors α such that:

❘"\[LeftBracketingBar]"Sa*❘"\[RightBracketingBar]"❘"\[LeftBracketingBar]"Sa❘"\[RightBracketingBar]"≥β1∈(0,1]β1∈(0,1] is the minimum fraction of semantic neighbors of a such that, with respect to the model of the cascading ESG event, for an event e to be a degree-1 cascade event, event property Peor one of its causally connected properties may hold true in all these semantic neighbors in S*αat future timepoints t+bounded by l1≥l2, that is, t<t+≤t+l1as follows:For each b ∈∪α∈A*eS*α:
(Zt+=True)∧∀t′<t+:(Zt′=False)
Zt+≡Pe(s(b,t+))∨P*(s(b,t+))
Zt′≡Pe(s(b,t′)∨P*(s(b,t′))  Equation (4)
For Equation (5), ∪α∈A*eS*αmay represent the union of all of the subsets of semantic neighbors of entities in A*e. Expressed differently,
∪α∈A*eS*α=S*α∪ . . . ∪S*αnEquation (5)
For Equation (5),
Ae+={a1,a2, . . . ,a_n}

With respect to the model of the cascading ESG event, by extending the semantically connected neighboring sets to the next levels of adjacencies, a degree-k cascade event may be determined. In this regard, Sa,kmay be specified to be the set of OAEs semantically connected with a by following a chain of k intermediate entities, that is, for each x ∈Sa,kthere exist x1, . . . xk−1such that a is semantically connected to x1, which is semantically connected to x2, x . . . , xk−1, which is semantically connected to xk, and xkis semantically connected to x as follows:
α→x1→x2→ . . . →xk→x

With respect to the model of the cascading ESG event, Sα,k(t+)⊆Sα,kmay be specified to be the subset of k-semantic neighbors α (e.g., those at distance k from α) such that:

❘"\[LeftBracketingBar]"Sa,k(t+)❘"\[RightBracketingBar]"❘"\[LeftBracketingBar]"Sa,k❘"\[RightBracketingBar]"≥βk∈(0,1]

With respect to the model of the cascading ESG event, for event e to be a degree-k cascade event, event property Peor one of its causally connected properties may need to hold true in at least one of the k-semantic neighbors, Sα(t+) at some future timepoint t+bounded by lk≥1 as follows:
t+Σj∈[1,k−1]lj<t+≤t+Σj∈[1,k]lj

In this regard, for each OAE b, b may be specified as follows:
b∈∪α∈AeSa,k(t+)

The impact analyzer120may determine if the following constraint holds:
∧=(Zt+=True)∧∀t′<t+:(Zt′=False)
Zt+≡Pe(s(b,t+))∨P*(s(b,t+))
Zt′≡Pe(s(b,t′))∨P*(s(b,t′))If ∧ holds for b, the impact analyzer120may add ESG entity b to the list rImpactedk.

With respect to a model of an ESG event analyzer200, the ESG event analyzer200may perform steps [1]-[18] as described below. The steps [1]-[18] described below are specified to facilitate a description of operation of the apparatus100, and not to limit the scope of operation of the apparatus100to the specified steps, which may be different than the order specified for the steps described below, or which may eliminate one or more of the steps described below. The steps [1]-[18] may specify the model of the ESG event analyzer200for determining the potential temporal impact of a cascading event and selecting an optimum reaction process for mitigating or amplifying the impact. The model for the ESG event analyzer200may include models of the reachability analyzer102(e.g., steps [1]-[9]), the impact analyzer120(e.g., (steps [10]-[12]), and the event similarity reaction analyzer124(e.g., steps 13-[18])

FIG.7illustrates a graphical illustration of event cascading from source entity to target entity to illustrate operation of the apparatus100, in accordance with an example of the present disclosure. Referring toFIGS.1and7, a first step (e.g., step MD executed by the reachability analyzer102may include generating the semantic association graph104of a plurality of semantically connected organization avatar entities106. In this regard, the reachability analyzer102may generate a weighted directed graph G=(V,E,w) of semantically connected OAEs in the metaverse112by inferring associations among OAEs from their interactions and digital identities. Edges in E may represent associations between entities along specific ESG dimensions or as specified by the design environment. Edges may represent directions that specify that events cascade from the source entity to the target entity (e.g., along the directions of the edges). The weight w(e) of an edge e may represent the strength of the semantic association between entities connected by e. Edge weights may determine how likely is it that an event may cascade through the edge (e.g., from the source entity of an edge to its target entity). ForFIG.7, as shown at700, edge weights (e.g., 073) may determine a likelihood of an event cascading from source entity (α1) to target entity (α3).

For a next step (e.g., step [2]) executed (e.g., in the metaverse) by the reachability analyzer102the reachability analyzer102may receive the signal108corresponding to an occurrence of the specified event110in the metaverse112. In this regard, the reachability analyzer102may receive a signal from a r etaverse interface on occurrence of an event ereal.

For a next step (e.g., step [3]) executed by the reachability analyzer102, the reachability analyzer102may determine, based on an analysis of the signal108, the metaverse event114in the metaverse112. In this regard, the reachability analyzer102may execute a model of an event (e.g., e(Ae, t)⇒∀αi∈Ae: Pe(s(αi, t+1))=True) to determine event emetain the metaverse112corresponding to the received event signal. The reachability analyzer102may populate set Aemeta={α1, αn} as the set of OAEs associated with the event emeta.

For a next step (e.g., step [4]) executed by the reachability analyzer102, the reachability analyzer102may determine, for the semantic association graph104, a sequence of logically connected properties by applying at least one derivation procedure. Each logically connected property may correspond to a causally connected event. In this regard, the reachability analyzer102may derive the sequence of all logically connected properties P* with Pemetaby applying derivation procedures of 1storder logic. Each derived property may correspond to a causally connected event.

For a next step (e.g., step [5]) executed by the reachability analyzer102, the reachability analyzer102may determine, for the specified organization avatar entity116, paths in the semantic association graph104from a set of specified entities to the specified organization avatar entity116. In this regard, for ESG OAE a of interest, the reachability analyzer102may determine all the paths in semantic association graph G from a set of entities Aemetato α.

For a next step (e.g., step [6]) executed by the reachability analyzer102, the reachability analyzer102may determine a likelihood of cascading of the metaverse event114along each path of the determined paths. In this regard, the reachability analyzer102may estimate a likelihood of cascading of the event emetaalong each such path using Equation (6) below, where such a path is >δ. In this regard, the path through the entities may be specified as follows;
T≡αi→p1b1→p3. . . →pkbk→pαα  Equation (6)

For Equation (6), piis the likelihood of cascading of the event emetathrough edge αi-1→αi. With respect to p1, . . . , pk, pα, the likelihood of event emetacascading to entity α along path T may be specified as follows:
EventCascadeLikelihood(eneta,T,Aemeta,α)=minipi

For a next step (e.g., step [7]) executed by the reachability analyzer102, the reachability analyzer102may designate, based on the determined likelihood of cascading, each path of the determined paths for which an event cascade likelihood is greater than the specified event cascade threshold138as a feasible path. In this regard, the reachability analyzer102may label Path T as feasible if
EventCascadeLikelihood(emeta,T,Aemeta,α)≥δ  Equation (7)

For Equation (7), δ∈[0,1] may represent an event cascade threshold (default=0.3)

For a next step (e.g., step [8]) executed by the reachability analyzer102, for each of the feasible paths, the reachability analyzer102may determine, for each feasible path, a reachability140. In this regard, the reachability analyzer102may estimate reachability as follows:
Reachability(emeta,T,Aemeta,α)=p1×p2× . . . ×pk×pα

For a next step (e.g., step [9]) executed by the reachability analyzer102, the reachability analyzer102may select, based on the determined reachability for each feasible path, a feasible path that includes a maximum reachability as the feasible path118for the specified organization avatar entity116. In this regard, the reachability analyzer102may select the feasible path T that has a maximum Reachability( ). This path T may represent the path along which the cascade event is most likely to impact entity α before other feasible paths.

FIG.8illustrates a table for a sequence of logically connected properties to illustrate operation of the apparatus100, in accordance with an example of the present disclosure. Referring toFIG.8, for a next step (e.g., step [10]; denoted propagation of cascading event) executed by the impact analyzer120, the impact analyzer120may determine, for the feasible path that includes the maximum reachability, if there exists a sequence of logically connected properties that are in successive states of entities along the feasible path. In this regard, for the maximally likely feasible path identified in step [9], the impact analyzer120may determine if there exists a sequence of logically connected properties holding in the successive states of the entities along the path. In this regard, table800illustrates paths at802, time points at804, states at806, and properties at808.

The impact analyzer120may iteratively simulate propagation of an event across semantically related entities. At each time point, for all of the entities that had transited during a previous time point, the impact analyzer120may send event property and edge weights to their immediate semantically associated entities along edge directions. Each receiving entity may accept the message if the incoming edge strength is more than a specified threshold (≥δ), and the event property is a triggering property. If both conditions are met, the entity may transit to a new state where some other event property in the causal chain may hold.

FIG.9illustrates a graphical illustration of event simulation across organization avatar entities to illustrate operation of the apparatus100, in accordance with an example of the present disclosure.

With respect toFIG.9, the ESG event analyzer200may execute steps to perform event simulation across organization avatar entities by executing enabling conditions starting at900with the entity for which property Peholds and thereafter successively selecting a fraction of neighboring entities that are connected with the currently selected entity with an edge strength greater than a threshold. Further, the ESG event analyzer200may execute enabling conditions for those entities such that properties P1, P2, P3, P4, P5, . . . ,P10, . . . , P15, . . . and finally P* hold for these selected entities and their enabling conditions.

FIG.10illustrates state transitions to illustrate operation of the apparatus100, in accordance with an example of the present disclosure. For a next step (e.g., step [11]) executed by the impact analyzer120, the impact analyzer120may determine, based on a determination that the signal108reaches the specified organization avatar entity116, a plurality of state transitions until the specified organization avatar entity116reaches a stationary state. In this regard, when an event signal reaches the entity α, the impact analyzer120may observe, as shown at1000, its one or more state transitions until it reaches a stationary state:
a(t+)→t1α(t++1)→t2. . . →tkα(t++k)→tk+1. . . →tk+rα(t++k+r)
such that
a(t++k)=α(t++k+1)= . . . =α(t++k+r)
In this regard, the set of all the actions that a performs during these state transitions may be specified as follows:
{τ1,τk, . . . τk+r}

Further, the set of all the effects (e.g., outputs) that are emitted by entity α during these state transitions may be specified as:
{01, . . . 0k, . . . 0k+r}

For a next step (e.g., step [12]) executed by the impact analyzer120, the impact analyzer120may determine, based on the feasible path118and for a specified time interval, the temporal impact122of the metaverse event114on the specified organization avatar entity116. In this regard, the impact analyzer120may determine the temporal impact of event e on entity α during time interval T=[t+, t++k+r] as follows:
impact(e,α,T)=(Ψ,T,O)
Ψ={s(α,t+),s(α,t++1), . . . ,s(α,t++k)}
T={τ1, . . . τk, . . . τk+r}
O={01, . . . 0k, . . . 0k+r}

For a next step (e.g., step [13]) executed by the event similarity reaction analyzer124, the event similarity reaction analyzer124may perform automated selection of an optimum reaction plan. In this regard, an event database may include a state in which an OAE was before occurrence of past events (original or cascading), a state the OAE transitioned to due to the occurrence of the event, a reaction plan as set of computable actions, and an effectiveness coefficient of the reaction plan. With respect to the reaction plan as set of computable actions, the reaction plan may include a mitigation plan in case an impact is negative, or an amplification plan in case an impact is positive.

For a next step (e.g., step [14]) executed by the event similarity reaction analyzer124, the event similarity reaction analyzer124may determine, with respect to the specified organization avatar entity116, a similarity of the metaverse event114in a current temporal context to past events. In this regard, the event similarity reaction analyzer124may determine similarity of a current event in the current temporal context of the entity with past events. In this regard, similarity may be determined as a function of states before and after events.

For a next step (e.g., step [15]) executed by the event similarity reaction analyzer124, the event similarity reaction analyzer124may select, from the event database126and based on the determined similarity of the metaverse event114in the current temporal context to past events, the reaction plan128of the plurality of reaction plans130that corresponds to a most similar event within the specified threshold range132. In this regard, the event similarity reaction analyzer124may select a reaction plan corresponding to the most similar event within a specified threshold range (e.g., default [85, 1.00]). If multiple events are within this range, an event with a highest effectiveness coefficient may be selected.

For a next step (e.g., step [16]) executed by the event similarity reaction analyzer124the event similarity reaction analyzer124may determine, based on simulation of the selected reaction plan128, a difference in the temporal impact122with and without the selected reaction plan128.

For a next step (e.g., step [17]) executed by the event similarity reaction analyzer124, the event similarity reaction analyzer124may forward, based on a determination that the difference in the temporal impact122is greater than the reaction plan threshold value134, the selected reaction plan128to the metaverse operating environment136. In this regard, if the difference in the impact with or without the plan is more than a specified threshold (e.g., default 10%), the event similarity reaction analyzer124may communicate this difference back to the metaverse operating environment136. The event similarity reaction analyzer124may generate, based on a determination that the difference in the temporal impact122is less than or equal to the reaction plan threshold value134, an indication of no known feasible action. That is, the event similarity reaction analyzer124may generate a signal to the metaverse operating environment136that NO_FEASIBLE_ACTION_KNOWN.

For a next step (e.g., step [18]) executed by the event similarity reaction analyzer124, the event similarity reaction analyzer124may generate an instruction to execute, by the metaverse operating environment136, the selected reaction plan128. In this regard, the metaverse operating environment136may execute the reaction plan128as per the communication received from the ESG event analyzer200.

FIG.11illustrates a semantic association graph104to illustrate operation of the apparatus100, in accordance with an example of the present disclosure.FIGS.12A-12Dillustrate models of entities to illustrate operation of the apparatus100, in accordance with an example of the present disclosure.

For the example ofFIG.11, an organization A at1100may include manufacturing unit F at1108, which is 7 Km away from a river R at1102. River R may flow close to a hill H1104and during rainy season, water from the hill may flow into river R. There is a bridge B at1106over the river with depth sensor d. For the last 3 days, rain fall has been above average. For this case, the ESG event analyzer200may determine the impact of rain on organization A. Further, the ESG event analyzer200may determine how to best minimize the impact. Models associated with the bridge B at1106, the river R at1102, hill H at1104, and the other entities shown inFIG.11are shown inFIGS.12A-12D.

FIG.13illustrates cascade simulation for the example ofFIGS.11and12A-12Dto illustrate operation of the apparatus100, in accordance with an example of the present disclosure,

Referring toFIG.13, cascade simulation may be specified as follows:
erain({Hill,River},t=0)→[excessWater(Hill(t=1))=True] AND [excessWater(River(t=1))=True]
Hill(t=1)=wet,River(t=1)=OverFlow

Referring toFIG.13, (Entity) X=(State) S may indicate that Entity X at the current time point is in State S. For example, at1300, Hill=Wet may indicate that Hill at the current time point is in a Wet state.

For the impact analyzer120, an impact may be specified as follows:
impact=|μ−prodVolF|*α  Equation (8)

For Equation (8),

μ=1n⁢∑t∈{t1,t2,…,tn}⁢prod⁢Volt
is the average production of the manufacturing unit F during previous years {t1, t2, . . . , tn}, prodVolFis the current total production from various sections of manufacturing facility F, and α is a sales factor that estimates impact on sales for each unit of production from the factory. In the context ofFIGS.11-13, an impact may estimate the sales impact of reduction in production for manufacturing unit F due to event of rain.

The reaction process may be specified as follows:Select Facility Fj≠F and its increase production of Facility Fjby factor β≥1 in order to compensate for the impact due to decreased production in manufacturing unit F as follows:
pVolFj→β*pVolFjIn the context ofFIGS.11-13, Company A communicates to Facility Fj(different from the manufacturing unit F) so that Facility Fjincreases its production by factor β such that Facility Fjminimizes the impact from the loss of production from manufacturing unit F.

FIGS.14-16respectively illustrate an example block diagram1400, a flowchart of an example method1500, and a further example block diagram1600for temporal impact analysis of cascading events on metaverse-based organization avatar entities, according to examples. The block diagram1400, the method1500, and the block diagram1600may be implemented on the apparatus100described above with reference toFIG.1by way of example and not of limitation. The block diagram1400, the method1500, and the block diagram1600may be practiced in other apparatus. In addition to showing the block diagram1400,FIG.14shows hardware of the apparatus100that may execute the instructions of the block diagram1400. The hardware may include a processor1402, and a memory1404storing machine readable instructions that when executed by the processor cause the processor to perform the instructions of the block diagram1400. The memory1404may represent a non-transitory computer readable medium.FIG.15may represent an example method for temporal impact analysis of cascading events on metaverse-based organization avatar entities, and the steps of the method.FIG.16may represent a non-transitory computer readable medium1602having stored thereon machine readable instructions to provide temporal impact analysis of cascading events on metaverse-based organization avatar entities according to an example. The machine readable instructions, when executed, cause a processor1604to perform the instructions of the block diagram1600also shown inFIG.16.

The processor1402ofFIG.14and/or the processor1604ofFIG.16may include a single or multiple processors or other hardware processing circuit, to execute the methods, functions and other processes described herein. These methods, functions and other processes may be embodied as machine readable instructions stored on a computer readable medium, which may be non-transitory (e.g., the non-transitory computer readable medium1602ofFIG.16), such as hardware storage devices (e.g., RAM (random access memory), ROM (read only memory), EPROM (erasable, programmable ROM), EEPROM (electrically erasable, programmable ROM), hard drives, and flash memory). The memory1404may include a RAM, where the machine readable instructions and data for a processor may reside during runtime.

Referring toFIGS.1-14, and particularly to the block diagram1400shown inFIG.14, the memory1404may include instructions1406to generate a semantic association graph104of a plurality of semantically connected organization avatar entities106.

The processor1402may fetch, decode, and execute the instructions1408to receive a signal108corresponding to an occurrence of a specified event110in a metaverse112.

The processor1402may fetch, decode, and execute the instructions1410to determine, based on an analysis of the signal108, a metaverse event114in the metaverse112.

The processor1402may fetch, decode, and execute the instructions1412to determine, for a specified organization avatar entity116, a feasible path118in the semantic association graph104.

The processor1402may fetch, decode, and execute the instructions1414to determine, based on the feasible path118and for a specified time interval, a temporal impact122of the metaverse event114on the specified organization avatar entity116.

The processor1402may fetch, decode, and execute the instructions1416to determine, with respect to the specified organization avatar entity116, a similarity of the metaverse event114in a current temporal context to past events.

The processor1402may fetch, decode, and execute the instructions1418to select, from an event database126and based on the determined similarity of the metaverse event114in the current temporal context to past events, a reaction plan128of a plurality of reaction plans130that corresponds to a most similar event within a specified threshold range132.

The processor1402may fetch, decode, and execute the instructions1420to determine, based on simulation of the selected reaction plan128, a difference in the temporal impact122with and without the selected reaction plan128.

The processor1402may fetch, decode, and execute the instructions1422to based on a determination that the difference in the temporal impact122is greater than a reaction plan threshold value134, forward the selected reaction plan128to a metaverse operating environment136, and generate an instruction to execute, by the metaverse operating environment136, the selected reaction plan128.

Referring toFIGS.1-13and15, and particularlyFIG.15, for the method1500, at block1502, the method may include determining, for a specified time interval, a temporal impact122of a metaverse event114on a specified organization avatar entity116.

At block1504, the method may include determining, with respect to the specified organization avatar entity116, a similarity of the metaverse event114in a current temporal context to past events,

At block1506, the method may include selecting, from an event database126and based on the determined similarity of the metaverse event114in the current temporal context to past events, a reaction plan128of a plurality of reaction plans that corresponds to a most similar event within a specified threshold range132.

At block1508, the method may include determining, based on a simulation of the selected reaction plan128, a difference in the temporal impact122with and without the selected reaction plan128.

At block1510, the method may include generating, based on a determination that the difference in the temporal impact122is greater than a reaction plan threshold value134, instructions to execute the selected reaction plan128by a metaverse operating environment136.

Referring toFIGS.1-13and16, and particularlyFIG.16, for the block diagram1600, the non-transitory computer readable medium1602may include instructions1606to determine a temporal impact122of a metaverse event114on a specified organization avatar entity116.

The processor1604may fetch, decode, and execute the instructions1608to determine, with respect to the specified organization avatar entity116, a similarity of the metaverse event114in a current temporal context to past events.

The processor1604may fetch, decode, and execute the instructions1610to select, from an event database126and based on the determined similarity, a reaction plan128of a plurality of reaction plans.

The processor1604may fetch, decode, and execute the instructions1612to generate, based on an analysis of the temporal impact122with respect to the selected reaction plan128, instructions to execute the selected reaction plan128by a metaverse operating environment136.

What has been described and illustrated herein is an example along with some of its variations. The terms, descriptions and figures used herein are set forth by way of illustration only and are not meant as limitations. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims and their equivalents in which all terms are meant in their broadest reasonable sense unless ether vise indicated.