Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
This is a Final Office Action of the instant application 17/942,875
(hereinafter the ‘875 application), filed on 9/12/2022, responsive to the Response filed 2-9-2026.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al., U.S. Publication No. 2023/0176557, hereinafter Cella and Saini et al., U.S. Patent No. 11,954,154.
With regard to claim 1, which teaches An energy cost reduction of metaverse operations apparatus comprising: at least one hardware processor; a scenario unification and partitioning analyzer, executed by the at least one hardware processor, to generate a unified model of What-IF scenarios, Cella teaches a system that develops “smart bands” which refer to a processed set of characteristics from a group of inputs that is used to achieve an optimal / correct device configuration (see paragraphs 258 and 272). Cella teaches a system, including program instructions implemented by a processor (see paragraphs 21-24), for determining the combination of components and configuration of components that are optimized to meet a goal, through the iterations of machine learning to converge on a minimal power consumption, optimal energy utilization state (smart band) (see paragraphs 972-973). Cella does this through tracking outcomes of each path through a state machine to determine the scenario (model) with the most favorable outcome (see paragraphs 332-333 and 2181). They system therefor allows for a user to take part in modeling “what if” scenarios to see what would happen with a particular configuration (see paragraph 1152). The system of Cella represents components of the overall system as “digital twins” (avatars) that are models of real world components presented in a digital space, allowing a user to simulation of the real world space in a virtual world through either VR (virtual reality) or AR (augmented reality) (see paragraphs 2177-2181).
With regard to claim 1, which teaches by: unifying logically connected IF scenarios; and identifying, for the unified logically connected IF scenarios, logically independent IF scenarios; Cella further teaches looking at both independent components and maximizing their functionality and the system as a whole comprising multiple independent components that interrelate (see paragraphs 2178, 2181, and 2187).
With regard to claim 1, which teaches a graph generator, executed by the at least one hardware processor, to generate a semantic association graph of organization avatar entities; Cella teaches the generation of a semantic association graph where relations between elements (digital twins), represented by nodes, can be shown in their interconnecting edges (see paragraphs 333-334 and 2177-2180).
With regard to claim 1, which teaches a What-IF sub-metaverse generator, executed by the at least one hardware processor, to determine, for the semantic association graph and for each logically independent IF scenario of the logically independent IF scenarios, a sub-metaverse of semantically connected organization avatar entities; Cella teaches the evaluation of independent sub-component of the system (see paragraphs 2178, 2181, and 2187).
With regard to claim 1, which teaches a scenario simulator, executed by the at least one hardware processor, to: iteratively perform, for the sub-metaverse of semantically connected organization avatar entities and for each logically independent IF scenario of the logically independent IF scenarios, state transitions of the semantically connected organization avatar entities until the sub-metaverse reaches a stationarily stable state or an operating limit; and determine, based on the state transitions of the organization avatar entities and for each logically independent IF scenario of the logically independent IF scenarios, whether a goal condition is met in the sub-metaverse; Cella teaches a system for determining the combination of components and configuration of components that are optimized to meet a goal, through the iterations of machine learning to converge on a minimal power consumption, optimal energy utilization state (smart band) (see paragraphs 972-973). Cella does this through tracking outcomes of each path through a state machine to determine the scenario (model) with the most favorable outcome (see paragraphs 332-333 and 2181).
With regard to claim 1, which teaches a goal analyzer, executed by the at least one hardware processor, to: determine, for each logically independent IF scenario of the logically independent IF scenarios for which the goal condition is met in the sub-metaverse, an overall energy cost; and identify, based on the overall energy cost determined for each logically independent IF scenario of the logically independent IF scenarios for which the goal condition is met, a logically independent IF scenario that includes a minimum energy cost; Cella teaches that the system therefor allows for a user to take part in modeling “what if” scenarios to see what would happen with a particular configuration (see paragraph 1152). The system further uses machine learning to insure the most optimal selection of inputs / configuration to intelligently converge on a goal (see paragraphs 332, 967-968).
With regard to claim 1, which teaches an organization entity controller, executed by the at least one hardware processor, to control, for an organization entity, an operation based on the logically independent IF scenario that includes the minimum energy cost, Cella teaches that smart selection that choses operation based on reaching a goal of minimal energy consumption (see paragraph 973).
Saini teaches a similar system for generating a semantic model of a system from both the perspective of individual system components and the system as a whole (see column 1, lines 15-67 and 16:55-17:5), using state based algorithms and predictive control to advise the best inputs to provide the desired outputs (see 7:40-62), but further specifically is purposed with “minimizing energy costs” (see 11:51-12:27). It would be obvious to one of ordinary skill in the art at the time of the invention to utilize the system of Cella to minimize energy costs as did Saini so as to implement machine learning into the predictive component modeling system to best forecast how changes in inputs will result in desired goal states (energy savings).
With regard to claim 2, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the goal analyzer is executed by the at least one hardware processor to determine, for each logically independent IF scenario of the logically independent IF scenarios for which the goal condition is met in the sub-metaverse, a number of state transitions to reach a goal state, the claims only notes to determine “a number of state transitions to reach a goal state” not actual utilization of what is determined in anyway. Nevertheless Cella teaches the use of various statistical operations for use in data mining (see paragraph 328). Clearly a number of state transitions are determined to meet a goal (see paragraphs 333-334).
With regard to claim 3, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the goal analyzer is executed by the at least one hardware processor to determine, for each logically independent IF scenario of the logically independent IF scenarios for which the goal condition is met in the sub-metaverse, sizes of encodings of transition enabling conditions, the claims only notes to determine “sizes of encodings of transition enabling conditions” not actual utilization of what is determined in anyway. Nevertheless Cella teaches the use of various statistical operations for use in data mining (see paragraph 328). Clearly a size of encodings of transition enabling conditions are determined to meet a goal (see paragraphs 333-334).
With regard to claim 4, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the goal analyzer is executed by the at least one hardware processor to determine, for each logically independent IF scenario of the logically independent IF scenarios for which the goal condition is met in the sub-metaverse, a number of operations that involve state variables during transitions to reach a goal state, the claims only notes to determine “a number of operations that involve state variables” not actual utilization of what is determined in anyway. Nevertheless Cella teaches the use of various statistical operations for use in data mining (see paragraph 328). Clearly a number of operations that involve state variables are determined in order to meet a goal (see paragraphs 333-334).
With regard to claim 5, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the scenario unification and partitioning analyzer is executed by the at least one hardware processor to identify, for the unified logically connected IF scenarios, logically independent IF scenarios by: retaining, from each cluster of a plurality of clusters of the unified logically connected IF scenarios, a single IF scenario, Cella teaches performing regression analysis to determine the best set of input the provide the best outputs in each iteration of the system (see paragraph 2374 and 2935). Retaining the best scenario of each iteration.
With regard to claim 6, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the goal analyzer is executed by the at least one hardware processor to determine, for each logically independent IF scenario of the logically independent IF scenarios for which the goal condition is met in the sub-metaverse, the overall energy cost as a function of at least one of: energy emission of executing a state transition; energy emission of determining logical validity of a state transition enabling condition; or energy emission of assigning a value to an output state variable, Cella teaches interpreting the energy cost as a function of total energy utilized as the states are transitioned through each component in the state diagram (see paragraphs 1152), determining the combination of components and configuration of components that are optimized to meet a goal, through the iterations of machine learning to converge on a minimal power consumption, optimal energy utilization state (smart band) (see paragraphs 972-973). Saini further teaches the overall energy cost being a function of energy emission as a combination of state transition trough the state based algorithm (see 11:51-12:27 and 16:37-17:5).
With regard to claim 7, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the goal analyzer is executed by the at least one hardware processor to: compare the minimum energy cost to a bounding limit; and based on a determination that the minimum energy cost is less than the bounding limit, identify the logically independent IF scenario as including the minimum energy cost that is less than the bounding limit, Cella teaches use of the system to determine when one of the data points (power usage) is outside of an acceptable range, and when it is outside of that range identifying the condition as a problem (see paragraphs 1168 and 1193). Cella further teaches comparting aspects against upper and lower limits to identify problem conditions (see paragraph 2050). Problem conditions are evaluated as shown above to find scenarios / smart bands where the issue is alleviated (supra). Saini teaches a similar identification of a parameter outside of a range and mitigation (see column 34, lines 3-33).
With regard to claim 8, which teaches the energy cost reduction of metaverse operations apparatus according to claim 1, wherein the goal analyzer is executed by the at least one hardware processor to: compare the minimum energy cost to a bounding limit; and based on a determination that the minimum energy cost is greater than the bounding limit, identify the logically independent IF scenario as including the minimum energy cost that is greater than the bounding limit, Cella teaches use of the system to determine when one of the data points (power usage) is outside of an acceptable range, and when it is outside of that range identifying the condition as a problem (see paragraphs 1168 and 1193). Cella further teaches comparting aspects against upper and lower limits to identify problem conditions (see paragraph 2050). Problem conditions are evaluated as shown above to find scenarios / smart bands where the issue is alleviated (supra). Saini teaches a similar identification of a parameter outside of a range and mitigation (see column 34, lines 3-33).
With regard to claim 9, which teaches a method for energy cost reduction of metaverse operations, the method comprising: determining, by at least one hardware processor, for a semantic association graph of organization avatar entities and for each logically independent IF scenario of a plurality of logically independent IF scenarios, a sub-metaverse of semantically connected organization avatar entities; iteratively performing, by the at least one hardware processor, for the sub-metaverse of semantically connected organization avatar entities and for each logically independent IF scenario of the plurality of logically independent IF scenarios, state transitions of the semantically connected organization avatar entities until the sub-metaverse reaches a stationarily stable state or an operating limit; determining, by the at least one hardware processor, based on the state transitions of the organization avatar entities and for each logically independent IF scenario of the plurality of logically independent IF scenarios, whether a goal condition is met in the sub-metaverse; determining, by the at least one hardware processor, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, an overall energy cost; and identifying, by the at least one hardware processor, based on the overall energy cost determined for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met, a logically independent IF scenario that includes a minimum energy cost, << SEE REJECTION TO CLAIM 1 ABOVE >>
With regard to claim 10, which teaches the method according to claim 9, further comprising controlling, by the at least one hardware processor, for an organization entity, an operation based on the logically independent IF scenario that includes the minimum energy cost, << SEE REJECTION TO CLAIM 1 ABOVE >>
With regard to claim 11, which teaches the method according to claim 9, further comprising determining, by the at least one hardware processor, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, a number of state transitions to reach a goal state, << SEE REJECTION TO CLAIM 2 ABOVE >>
With regard to claim 12, which teaches the method according to claim 9, further comprising determining, by the at least one hardware processor, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, sizes of encodings of transition enabling conditions, << SEE REJECTION TO CLAIM 3 ABOVE >>
With regard to claim 13, which teaches the method according to claim 9, further comprising determining, by the at least one hardware processor, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, a number of operations that involve state variables during transitions to reach a goal state, << SEE REJECTION TO CLAIM 4 ABOVE >>
With regard to claim 14, which teaches the method according to claim 9, further comprising determining, by the at least one hardware processor, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, the overall energy cost as a function of at least one of: energy emission of executing a state transition; energy emission of determining logical validity of a state transition enabling condition; or energy emission of assigning a value to an output state variable, << SEE REJECTION TO CLAIM 6 ABOVE >>
With regard to claim 15, which teaches the method according to claim 9, further comprising: comparing, by the at least one hardware processor, the minimum energy cost to a bounding limit; and based on a determination that the minimum energy cost is less than the bounding limit, identifying, by the at least one hardware processor, the logically independent IF scenario as including the minimum energy cost that is less than the bounding limit, << SEE REJECTION TO CLAIM 7 ABOVE >>
With regard to claim 16 which teaches, a non-transitory computer readable medium having stored thereon machine readable instructions, the machine readable instructions, when executed by at least one hardware processor, cause the at least one hardware processor to: determine for a semantic association graph of organization avatar entities and for each logically independent IF scenario of a plurality of logically independent IF scenarios, a sub-metaverse of semantically connected organization avatar entities; iteratively performing, by the at least one hardware processor, for the sub-metaverse of semantically connected organization avatar entities and for each logically independent IF scenario of the plurality of logically independent IF scenarios, state transitions of the semantically connected organization avatar entities until the sub-metaverse reaches a stationarily stable state or an operating limit; determining, by the at least one hardware processor, based on the state transitions of the organization avatar entities and for each logically independent IF scenario of the plurality of logically independent IF scenarios, whether a goal condition is met in the sub-metaverse; determining, by the at least one hardware processor, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, an overall energy cost; and identifying, by the at least one hardware processor, based on the overall energy cost determined for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met, a logically independent IF scenario that includes a minimum energy cost, << SEE REJECTION TO CLAIM 1 ABOVE >>
With regard to claim 17 which teaches, the non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: control, for an organization entity, an operation based on the IF scenario that includes the minimum energy cost, Cella implements a control based upon the identified scenario with minimum energy usage (see paragraphs 239 and 624).
With regard to claim 18 which teaches, the non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: determine for each logically independent IF scenario of the plurality of logically independent IF scenarios, for which the goal condition is met in the sub metaverse, number of state transitions to reach a goal, Cella teaches the generation of a semantic association graph where relations between elements (digital twins), represented by nodes, can be shown in their interconnecting edges (see paragraphs 333-334 and 2177-2180). Cella further teaches looking at both independent components and maximizing their functionality and the system as a whole comprising multiple independent components that interrelate (see paragraphs 2178, 2181, and 2187).
<< ADDITIONALLY SEE REJECTION TO CLAIM 2 ABOVE >>
With regard to claim 19 which teaches, the non-transitory computer readable medium according to claim 18, wherein the machine readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: determine for each logically independent IF scenario of the plurality of logically independent IF scenarios for which goal condition is met in the sub-metaverse sizes of encodings of transition enabling conditions, Cella teaches a system for determining the combination of components and configuration of components that are optimized to meet a goal, through the iterations of machine learning to converge on a minimal power consumption, optimal energy utilization state (smart band) (see paragraphs 972-973). Cella does this through tracking outcomes of each path through a state machine to determine the scenario (model) with the most favorable outcome (see paragraphs 332-333 and 2181).
<< ADDITIONALLY SEE REJECTION TO CLAIM 3 ABOVE >>
With regard to claim 20 which teaches, the non-transitory computer readable medium according to claim 16, wherein the machine readable instructions, when executed by the at least one hardware processor, further cause the at least one hardware processor to: determine, for each logically independent IF scenario of the plurality of logically independent IF scenarios for which the goal condition is met in the sub-metaverse, a number of operation that involve state variables during transitions to reach a goal state, Cella teaches that the system therefor allows for a user to take part in modeling “what if” scenarios to see what would happen with a particular configuration (see paragraph 1152). The system further uses machine learning to insure the most optimal selection of inputs / configuration to intelligently converge on a goal (see paragraphs 332, 967-968). Cella further teaches that smart selection that choses operation based on reaching a goal of minimal energy consumption (see paragraph 973).
<< ADDITIONALLY SEE REJECTION TO CLAIM 4 ABOVE >>
Response to Arguments
Applicant's arguments filed 2-9-2023 have been fully considered but they are not persuasive.
Applicant argues that paragraphs 2178, 2181, and 2187 of Cella do not cover the rejection of the sub-metaverse generator. That “Cella does not disclose, teach, suggest or shows the determination of a sub- metaverse of semantically connected organization avatar entities, accordingly, Cella also fails to teach or suggest a What-IF sub-metaverse generator, executed by the at least one hardware processor, to determine, for the semantic association graph and for each logically independent IF scenario of the logically independent IF scenarios, a sub-metaverse of semantically connected organization avatar entities, as recited in independent claim 1.”
In response, the Examiner respectfully submits that:
Cella at para [2178] generating “a digital twin of a manufacturing environment” … “representing various systems within the manufacturing environment, such as nodes representing an HVAC system, a lighting system, a manufacturing system, and the like”. This shows representation of the metaverse, including the metaverse as a whole, as well as individual representation and evaluation at a sub-metaverse level. Here “the HVAC system may connect to a subsystem node representing a cooling system of the facility, a second subsystem node representing a heating system of the facility, a third subsystem node representing the fan system of the facility, and one or more nodes representing a thermostat of the facility (or multiple thermostats)”. Where “the subsystem nodes and/or component nodes may connect to lower-level nodes”
Cella at para [2181] breaks down use of the “digital twin system” in “dynamic modeling, simulations, machine learning”. Here “the digital twin system 40000 may traverse a graph database and may determine a configuration of the environment to be depicted based on the nodes in the graph database that are related (either directly or through a lower level node) to the environment node of the environment and the edges that define the relationships between the related nodes.”
Cella at para [2187] describes how multiple “IF” scenarios are tried to arrive at a successful solution. Here the system of Cella “iteratively adjust(s) one or more parameters of a digital twin and/or one or more embedded digital twins.”… “for each set of parameters, executes a simulation based on the set of parameters and may collect the simulation outcome data resulting from the simulation.” Put another way, the digital twin simulation system 40006 may collect the properties of the digital twin and the digital twins within or containing the digital twin used during the simulation as well as any outcomes stemming from the simulation. “In some embodiments, the operating parameters may be varied to evaluate the effectiveness of the machine.”
Applicant argues that “Thus, Cella merely teaches about a manufacturing environment having various systems within the manufacturing environment, the various systems are represented by a node of graph database.”
“However, Applicant's claim 1 recites a sub-metaverse generator to determine a sub- metaverse of semantically connected organization avatar entities, which are not at all associated with subsystems and/or components of the system of the manufacturing environment as disclosed in Cella.”
In response, the Examiner respectfully submits that Cella teaches generation and evaluation of sub-metaverses. The system further evaluated these individual portions of the entire metaverse, simulating different input sets to fully evaluate the effectiveness of a solution before implementing it (see paragraphs 9, 13, 2181, and 2187), as further laid out above.
Summary
Claims 1-20 are REJECTED.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS G BONSHOCK whose telephone number is (571)272-4047. The examiner can normally be reached M-F 7:15 - 4:45.
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/DENNIS G BONSHOCK/Primary Examiner, Art Unit 3992