DETAILED ACTION
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 .
This action is responsive to the Amendment filed on June 22, 2026. Claims 1, 4, 6, 7, and 9 are amended. Claim 3 is cancelled. Claims 1, 2, and 4-9 are pending in the case. Claim 1 is the independent claims.
This action is final.
Applicant’s Response
In the amendment filed on Juen 22, 2026, Applicant amended the claims and provided arguments in response to the rejection of the claims under 35 USC 102 in the previous office action.
Response to Argument/Amendment
Applicant’s amendments to the claims in response to the rejection of the claims under 35 USC 102 are acknowledged, and Applicant’s associated arguments have been fully considered. Applicant argues that Yang fails to teach or suggest that each of the first and second sub-models has one or more parameters whose values are undetermined, fails to teach or suggest that machine learning function blocks perform machine learning using big data to determine parameter values, and fails to teach or suggest completing a system model by applying the determined parameter values to a hypothetical model as now required by claim 1.
However, Yang teaches:
one or more first function blocks configured to have first data as an input and second data as an output, and having one or more first parameters whose values are undetermined (e.g. page 12, second and third paragraphs, submodel presenting mathematical relationship but does not accurately inform the coefficient or order of the relationships; submodel described by simple linear equation, but values of the coefficients are not known only by theory and must be obtained; page 15 final paragraph through page 16 first full paragraph, obtaining big data by actually operating/observing the target system; learning big data acquired through actual operation and observation of target system to learn information necessary for theory-based primitive model; theory-based primitive model implemented to have structural information; page 16 final paragraph, continuing on page 17, Fig. 10, providing (S1020) first data of acquired data as an input of the first sub-models (i.e. first function block); second data defined as output of first sub-models); and
one or more second function blocks configured to have the second data as an input and third data as an output, and having one or more second parameters whose values are undetermined (e.g. page 12, second and third paragraphs, submodel presenting mathematical relationship but does not accurately inform the coefficient or order of the relationships; submodel described by simple linear equation, but values of the coefficients are not known only by theory and must be obtained; page 16, second paragraph, not all parameters can be measured or observed from the target system; page 16 final paragraph, continuing on page 17, Fig. 10, providing (S1040) second data defined as output of first sub-models as input to second sub-model (i.e. second function block) based on structural information; output of the second sub-model (i.e. third data/output)),
big data obtained by operating and observing the target system (e.g. page 15 final paragraph through page 16 first full paragraph, obtaining big data by actually operating/observing the target system; learning big data acquired through actual operation and observation of target system to learn information necessary for theory-based primitive model; theory-based primitive model implemented to have structural information;);
wherein the one or more processors are configured to:
control a first machine learning function block included in the one or more first function blocks to perform machine learning using the first data as an input and the second data as an output among the big data and thereby determining values of the one or more first parameters, control a second machine learning function block included in the one or more second function blocks so that machine learning is performed by the second machine learning function block using the second data as an input and the third data as an output and thereby determining values of the one or more second parameters (e.g. page 12 second-fourth paragraphs, coefficients or orders of model obtained by data-based learning; values of coefficients of equation describing submodel not known only by theory, and may be obtained through data-based learning; as cited above, a first sub-model (first function block) receives first data as input and second data as output, and a second sub-model (second function block) receives the second data as input and provides third data as output, where one or more (and therefore both in some implementations) of these is implemented as a machine learning model/block, such that the processors control the first machine learning function block and a second machine learning function block to perform machine learning using respective input and output data (including big data) in order to learn determined values for respective undetermined values of parameters for the submodels); and
complete a system model by applying the determined values of the one or more first parameters and the determined values of the one or more second parameters to the hypothetical model, and analyze and predict the operation and performance of the target system using the completed system model (e.g. page 7, second and third paragraphs, embedding machine learning contents using acquired data acquired through operation and observation of target system so that the model itself becomes a verified model in the domain of the acquired data; by embedding machine learning into the system model, achieving effect of model verification (verification with real data); page 14 second paragraph, predicting future operation from current operation of target system; prediction of the system model; page 15 first paragraph, operation of target system predicted; page 15, final paragraph through page 16 second paragraph, constructing complete model (i.e. from hypothetical model) by securing information such as motion functions and parameters necessary for model completion, using big data and a theory-based primitive model generated using the hypothetical model through machine learning for the big data; theory based primitive model for target system generated by applying the learned and verified information based on actual data to a hypothetical model; the theory based primitive model is implemented to have structural information by a number of parameters that can be represented in a target system; editing to implement as the system model 900; page 17 third paragraph, implementing and operating system model describing target system; providing input data to submodels to infer the operation o the target system based on the data; providing output/result to user; page 18 first paragraph, system model of present invention can identify data mapped by each of the sub-systems based on structural information so that target system having complex variable is identified; i.e. after performing learning to determine values for submodel parameters that were previously unknown, the values can be applied to complete the system model based on the hypothetical model, and the system model can be used to predict operation of the target system).
To clarify, Yang appears to teach that the various described submodels may include parameters, such as of corresponding mathematical functions/relationships in the models, whose values are not known (i.e. undetermined parameter values of first and second function blocks), and that these unknown parameter values are obtained through data-based learning, including using big data (i.e. performing machine learning using big data to determine parameter values); further Yang appears to teach generating a complete system model by applying learned and verified information to a hypothetical model to generate a theory based primitive model, and further editing/modifying this theory based primitive model to generate the completed system model (i.e. applying determined parameter values to a hypothetical model to complete the system model), and using the system model to predict operation of the target system. Therefore, Applicant’s arguments are not persuasive, and the rejection is maintained below.
Claim Rejections – 35 USC § 102
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 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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1,2, and 4-9 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al. (WO 2021075927 A1) (citations below provided with reference to machine translation provided with this office action).
With respect to claim 1, Yang teaches a computing system for implementing a system model using big data machine learning (e.g. page 15, final paragraph, using hypothetical model through machine learning for acquired big data), the computing system comprising:
a target system to be modeled (e.g. page 9 third and fourth paragraphs, target system);
a hypothetical model configured to analyze and predict an operation and performance of the target system in the real world, the hypothetical model being defined on the basis of structural information and knowledge regarding the target system, and including a plurality of function blocks therein (e.g. page 9 third and fourth paragraphs, system model 120 inferring/predicting operation of target system; system model 120 including theory-driven model and data-driven model; page 11 final paragraph, continuing on page 12, model combining theory-based model (white box) and data-based model (black box), referred to as a gray box mode, including a sub-model block which is a black box and sub-model block which is a white box; page 12, fifth through eighth paragraphs, determining structure of system model based on structural information; structural information defining internal structure of system model obtained based on theory-based primitive model obtained using knowledge of target system; page 13, seventh-eighth paragraphs, Fig. 5, sub-model 112a of Fig. 5 includes data-based model 510 and theory-based model 520 therein, and logic module 530 is any one or data-based model and theory-based model; page 15, final paragraph, hypothetical model for target system defined in form of gray box by first grasping domain knowledge/experience and theories acquired related to target system; i.e. a hypothetical model is defined as a gray box model defined based on structural information of a target system, where the gray box hypothetical model further includes a plurality of sub-model (i.e. function) blocks including at least a data-based model and a theory-based model), the plurality of function blocks comprising:
one or more first function blocks configured to have first data as an input and second data as an output, and having one or more first parameters whose values are undetermined (e.g. page 12, second and third paragraphs, submodel presenting mathematical relationship but does not accurately inform the coefficient or order of the relationships; submodel described by simple linear equation, but values of the coefficients are not known only by theory and must be obtained; page 15 final paragraph through page 16 first full paragraph, obtaining big data by actually operating/observing the target system; learning big data acquired through actual operation and observation of target system to learn information necessary for theory-based primitive model; theory-based primitive model implemented to have structural information; page 16 final paragraph, continuing on page 17, Fig. 10, providing (S1020) first data of acquired data as an input of the first sub-models (i.e. first function block); second data defined as output of first sub-models); and
one or more second function blocks configured to have the second data as an input and third data as an output, and having one or more second parameters whose values are undetermined (e.g. page 12, second and third paragraphs, submodel presenting mathematical relationship but does not accurately inform the coefficient or order of the relationships; submodel described by simple linear equation, but values of the coefficients are not known only by theory and must be obtained; page 16, second paragraph, not all parameters can be measured or observed from the target system; page 16 final paragraph, continuing on page 17, Fig. 10, providing (S1040) second data defined as output of first sub-models as input to second sub-model (i.e. second function block) based on structural information; output of the second sub-model (i.e. third data/output)),
wherein at least one of the one or more first function blocks and the one or more second function blocks is a machine learning function block for machine learning (e.g. page 3 first full paragraph, machine learning based model is representative method of data modeling; data model built through machine learning; page 7 second and third paragraphs, system model with built-in data-based model embeds machine learning contents using acquired data acquired through operation and observation of target system; page 15 final paragraph continuing on page 16, using machine learning algorithm such as artificial neural network; page 16, second paragraph, not all parameters can be measured or observed from the target system;),
one or more processors (e.g. page 9 third paragraph, processor 110), and
big data obtained by operating and observing the target system (e.g. page 15 final paragraph through page 16 first full paragraph, obtaining big data by actually operating/observing the target system; learning big data acquired through actual operation and observation of target system to learn information necessary for theory-based primitive model; theory-based primitive model implemented to have structural information;);
wherein the one or more processors are configured to:
control a first machine learning function block included in the one or more first function blocks to perform machine learning using the first data as an input and the second data as an output among the big data and thereby determining values of the one or more first parameters, control a second machine learning function block included in the one or more second function blocks so that machine learning is performed by the second machine learning function block using the second data as an input and the third data as an output and thereby determining values of the one or more second parameters (e.g. page 12 second-fourth paragraphs, coefficients or orders of model obtained by data-based learning; values of coefficients of equation describing submodel not known only by theory, and may be obtained through data-based learning; as cited above, a first sub-model (first function block) receives first data as input and second data as output, and a second sub-model (second function block) receives the second data as input and provides third data as output, where one or more (and therefore both in some implementations) of these is implemented as a machine learning model/block, such that the processors control the first machine learning function block and a second machine learning function block to perform machine learning using respective input and output data (including big data) in order to learn determined values for respective undetermined values of parameters for the submodels); and
complete a system model by applying the determined values of the one or more first parameters and the determined values of the one or more second parameters to the hypothetical model, and analyze and predict the operation and performance of the target system using the completed system model (e.g. page 7, second and third paragraphs, embedding machine learning contents using acquired data acquired through operation and observation of target system so that the model itself becomes a verified model in the domain of the acquired data; by embedding machine learning into the system model, achieving effect of model verification (verification with real data); page 14 second paragraph, predicting future operation from current operation of target system; prediction of the system model; page 15 first paragraph, operation of target system predicted; page 15, final paragraph through page 16 second paragraph, constructing complete model (i.e. from hypothetical model) by securing information such as motion functions and parameters necessary for model completion, using big data and a theory-based primitive model generated using the hypothetical model through machine learning for the big data; theory based primitive model for target system generated by applying the learned and verified information based on actual data to a hypothetical model; the theory based primitive model is implemented to have structural information by a number of parameters that can be represented in a target system; editing to implement as the system model 900; page 17 third paragraph, implementing and operating system model describing target system; providing input data to submodels to infer the operation o the target system based on the data; providing output/result to user; page 18 first paragraph, system model of present invention can identify data mapped by each of the sub-systems based on structural information so that target system having complex variable is identified; i.e. after performing learning to determine values for submodel parameters that were previously unknown, the values can be applied to complete the system model based on the hypothetical model, and the system model can be used to predict operation of the target system).
With respect to claim 2, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein the hypothetical model and the plurality of function blocks are defined on the basis of domain knowledge, experience, and a theory which are acquirable regarding the target system (e.g. page 10 seventh paragraph, each of the sub-models defined based on obtainable domain knowledge, experience, and theory related to the target system; theory-based model capable of deductive inference or data learned based on acquired data).
With respect to claim 4, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein, when the determined values of the one or more first parameters and the determined values of the one or more second parameters are applied to the hypothetical model, the one or more processors complete a system model and provide a control or optimization module,
wherein the control or optimization module collects simulation data for analyzing and predicting the target system through simulation of the completed system model, uses the collected simulation data and actual system collection data of the target system in analysis and prediction of artificial intelligence (Al), statistics, and engineering, and provides visualization tools required for analysis and prediction of Al, statistics, and engineering,
collects failure state data of the target system through simulation of the completed system model and uses the collected failure state data to detect a failure of the target system by comparing the collected failure state data with normal state simulation data of the target system or uses the collected failure state data to diagnose a cause of the failure of the target system by comparing the failure state data with forced failure simulation data, or
collects sensor data or simulation prediction values of the target system through simulation of the completed system model and controls or optimizes the target system using the sensor data or the simulation prediction values (e.g. e.g. page 2 second through fifth paragraphs, digital twin is replica of physical object/system which maintains properties/states of target object elements through their lifecycle and describes the dynamic nature of how they behave; digital twin implemented in computing system is interlocked with target object/physical asset to reflect the real situation and predict the situation that may occur in the real world or inform the conditions for optimizing operation; IoT technology and digital twin technology are closely related, enabling smart services such as ML/AI prediction, etc. after collecting sensor data of operating system in real time; page 9, target system may be digital twin of a smart city; collected data linked using IoT infrastructure in the city; using such data it is possible to simulate changes in behavior, activities, etc.; page 14 eighth full paragraph through page 15 second full paragraph, uses conditional/control/design variables to conduct normative analysis, plan establishment, and design optimization for the target system; to improve reliability, stability, performance, and quality of target system, varying set of condition variables and repeating inference/prediction repeatedly to obtain simulation results corresponding to the set of various condition values; page 20 fifth full paragraph, representing state information in nodes, based on state information providing service models such as digital twin model or virtual sensor model; i.e. collected sensor data (such as in a digital twin model/IoT embodiment) and/or simulated prediction values of the target system are collected/obtained and are used to optimize the target system).
With respect to claim 5, Yang teaches all of the limitations of claim 4 as previously discussed, and further teaches wherein at least two of the processors exchange information or share situational awareness in communication with each other through a machine- to-machine (M2M) or Internet of things (loT) platform, analyze and predict target systems each corresponding thereto through simulation of the completed system model, detect a failure of the target systems corresponding thereto and diagnose a cause of the failure, or provide a control or optimization module for the target systems each corresponding thereto (e.g. page 2 second through fifth paragraphs, digital twin is replica of physical object/system which maintains properties/states of target object elements through their lifecycle and describes the dynamic nature of how they behave; digital twin implemented in computing system is interlocked with target object/physical asset to reflect the real situation and predict the situation that may occur in the real world or inform the conditions for optimizing operation; IoT technology and digital twin technology are closely related, enabling smart services such as ML/AI prediction, etc. after collecting sensor data of operating system in real time; page 9, target system may be digital twin of a smart city; collected data linked using IoT infrastructure in the city; using such data it is possible to simulate changes in behavior, activities, etc.; page 20, fourth through sixth full paragraphs, each node defined according to spatial distribution and state information of node updated over time; based on state information, service models including digital twin and virtual sensor models can be derived; when used in industrial site nodes in each production facility, etc.; determining spread of fires, flooding status, inundation status, traffic jam, abnormalities in industrial site, etc.; providing optimized route for current situation, etc.; i.e. the derived model of the target system may be utilized to provide a service combining IoT and digital twin technology which is capable of sharing situational awareness between two processors, such as a processor of the real system which is modeled, and a processor of a computing system implementing the digital twin of the system which is interlocked with the physical/real-world system, including detection/determination of an abnormality or other issue (i.e. a failure), providing optimizations, etc.).
With respect to claim 6, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein, when the determined values of the one or more first parameters and the determined values of the one or more second parameters are applied to the hypothetical model, the one or more processors complete a system model, and
at least two of the processors create a service in which the target system is combined with machine-to-machine (M2M) or Internet of things (loT) through simulation of the system model completed when the at least two processors communicate with each other through the M2M or IoT platform and exchange information or share situational awareness (e.g. page 2 second through fifth paragraphs, digital twin is replica of physical object/system which maintains properties/states of target object elements through their lifecycle and describes the dynamic nature of how they behave; digital twin implemented in computing system is interlocked with target object/physical asset to reflect the real situation and predict the situation that may occur in the real world or inform the conditions for optimizing operation; IoT technology and digital twin technology are closely related, enabling smart services such as ML/AI prediction, etc. after collecting sensor data of operating system in real time; page 9, target system may be digital twin of a smart city; collected data linked using IoT infrastructure in the city; using such data it is possible to simulate changes in behavior, activities, etc.; page 20, fourth through sixth full paragraphs, each node defined according to spatial distribution and state information of node updated over time; based on state information, service models including digital twin and virtual sensor models can be derived; when used in industrial site nodes in each production facility, etc.; i.e. the derived model of the target system may be utilized to provide a service combining IoT and digital twin technology which is capable of sharing situational awareness between two processors, such as a processor of the real system which is modeled, and a processor of a computing system implementing the digital twin of the system which is interlocked with the physical/real-world system).
With respect to claim 7, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein the determined values of the one or more first parameters or the determined values of the one or more second parameters includes a variable value, a probability, a function, or a graph which is input to the first machine learning function block or the second machine learning function block (e.g. page 9, first full paragraph, many variables affecting the target system, which can be combined and implemented as the target system and the elements are analyzed and abstracted; page 11, first and second full paragraphs, performing verification by actual measurement data; page 14, seventh full paragraph through page 15, first paragraph, models having condition/control/design variables C1, C2, C3; conducting normative analysis, plan establishment, and design optimization using variables; variables of target system corresponding to associations of first, second, third data of the sub-models, and verifying validity, reliability, and stability based on actual measured data corresponding to the data, such as second data; page 15, second full paragraph, varying set of condition variables repeatedly to obtain simulation results corresponding to sets of various condition variables; i.e. parameters of the submodels may include a variable value (such as inputs, outputs, intermediate values and/or other variables of the target system)).
With respect to claim 8, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein the first data is data represented as an input to the hypothetical model on the basis of the structural information (e.g. page 16 final paragraph, continuing on page 17, Fig. 10, providing (S1020) first data of acquired data as an input of the first sub-models (i.e. first function block),
the second data is data represented as an internal variable of the hypothetical model on the basis of the structural information (e.g. page 16 final paragraph, continuing on page 17, Fig. 10, second data defined as output of first sub-models; providing (S1040) second data defined as output of first sub-models as input to second sub-model (i.e. second function block) based on structural information), and
the third data is data represented as an output of the hypothetical model on the basis of the structural information (e.g. page 16 final paragraph, continuing on page 17, Fig. 10, output of the second sub-model (i.e. third data/output)).
With respect to claim 9, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein the one or more processors receive new input data for the target system, input the new input data to the hypothetical model, control the hypothetical model so that an inference process of the hypothetical model is performed, and provide an output of the hypothetical model as a result of the hypothetical model inferring an output of the target system from the new input (e.g. page 9 second full paragraph, when new input for the target system is given after the learning of the system model is finished, responding to the new input to predict the action to be taken by the target system; inferring/predicting operation of the target system).
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. “The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain,” In re Heck, 699 F.2d 1331, 1332-33, 216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting in re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (GCPA 1968)). Further, a reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments. Merck & Co, v. Biocraft Laboratories, 874 F.2d 804, 10 USPQ2d 1843 (Fed. Cir.), cert, denied, 493 U.S. 975 (1989). See also Upsher-Smith Labs. v. Pamlab, LLC, 412 F,3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir, 2005): Celeritas Technologies Ltd. v. Rockwell International Corp., 150 F.3d 1354, 1361, 47 USPQ2d 1516, 1522-23 (Fed. Cir. 1998).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JEREMY L STANLEY whose telephone number is (469)295-9105. The examiner can normally be reached on Monday-Friday from 9:00 AM to 5:00 PM CST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Al Kawsar, can be reached at telephone number (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEREMY L STANLEY/
Primary Examiner, Art Unit 2127
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