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 Application filed on June 26, 2023. Claims 1-9 are pending in the case. Claims 1 and 9 are the independent claims.
This action is non-final.
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 1-9 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 hypothetical model defined on the basis of structural information which is related to a target system for analyzing or predicting operation/performance in the real world and acquirable by acquiring knowledge about 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); and
one or more processors (e.g. page 9 third paragraph, processor 110),
wherein the plurality of function blocks comprise:
one or more first function blocks configured to have first data as an input and second data as an output among big data acquired by actually running and observing the target system on the basis of the structural information (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; 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 among the big data on the basis of the structural information (e.g. 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), and
the one or more processors control a first machine learning function block included in the one or more first function blocks so that machine learning is performed by the first machine learning function block using the first data as an input and the second data as an output among the big data or 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 (e.g. 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 of these is implemented as a machine learning model/block, such that the processors control either a first machine learning function block or a second machine learning function block to perform machine learning using respective input and output data (including big data)).
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 3, Yang teaches all of the limitations of claim 1 as previously discussed, and further teaches wherein, when machine learning is performed by the first machine learning function block, the one or more processors acquire a verified first parameter value and use the first parameter value as information for verifying the first machine learning function block, or when machine learning is performed by the second machine learning function block, the one or more processors acquire a verified second parameter value and use the second parameter value as information for verifying the second machine learning function block (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 9, sixth paragraph, first data as inputs to first submodels, second data as output of first submodel and input of second submodel; page 11, second full paragraph, when sub-models are composed of theory based model, each undergoes verification by actual data; page 13, first data may be obtained from the actual acquired data; page 14 final paragraph, continuing on page 15, performing verification based on actual measured data of second data as intermediate results; i.e. where the first or second function block/sub-model is implemented using machine learning, this includes acquisition of real data acquired through operation of the system (analogous to verified parameter values, such as input data or intermediate/output data) and using this data to verify the block/submodel).
With respect to claim 4, Yang teaches all of the limitations of claim 3 as previously discussed, and further teaches wherein, when the first parameter value and the second parameter value 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 3 as previously discussed, and further teaches wherein, when the first parameter value and the second parameter value 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 3 as previously discussed, and further teaches wherein the first parameter value or the second parameter value 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 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 hypothetical model defined on the basis of structural information which is related to a target system for analyzing or predicting operation/performance in the real world and acquirable by acquiring knowledge about 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 includign 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); and
one or more processors (e.g. page 9 third paragraph, processor 110),
wherein the plurality of function blocks comprise:
one or more first function blocks configured to have first data as an input and second data as an output among big data acquired by actually running and observing the target system on the basis of the structural information (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 hae 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 among the big data on the basis of the structural information (e.g. 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), and
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.
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