Claim Rejections - 35 USC § 112
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated 4/27/2026
Claim amendments under consideration dated 4/27/2026
Claims 1, 4, 5, 67, 10, 11, 12, 13, 16, 17, 18 are amended
No claims cancelled.
Claims 1 – 18 are presented for examination.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 4/27/2026 has been entered.
Response to Arguments
Claim Rejections - 35 USC § 103
The Applicant has amended the independent claims to recite, in relevant part: “… caching a job configuration based on the simulated real-time data received through the set of source operators and from the set of pre-existing virtual models; and changing, a stream system, to the job configuration based on similar conditions to the simulated real-time data.” The Applicant asserts that the art of record does not make such limitations obvious to those of ordinary skill in the art.
In response, the Examiner has reviewed the prior art and finds that Sachs_2019, for example, teaches to use the inferred digital twins 116 as input for management and control apparatus 113 which controls the real-world physical entities as illustrated in Figure 1. Paragraph 34 states: “… the inferred digital twins 116 are made available to a management and control apparatus 114 for analysis and control of the physical entities 100. The inferred digital twin 116 are used to predict behavior of the physical entities 100. The predicted behavior is used by the management and control system 114 to control the physical entities 100 and/or is used by a human operator to control the physical entities. In this way efficiencies in the maintenance and control of the physical entities 100 is achieved.”
Therefore, Sachs_2019 makes obvious “… [predicting behavior of the physical system] based on the simulated real-time data received through the set of source operators and from the set of pre-existing virtual models; and changing, a [real-world] system, based on similar conditions to the simulated real-time data.”
Sachs_2019, however, doesn’t indicate that the behavior of the physical system include job configurations or to cache job configurations.
Accordingly, the previous rejection under 35 USC 103 is withdrawn.
A new search was conducted and it was found the Abdelnur_2010 (US 2010/0042998 A1) teaches makes obvious “caching a job configuration” and also “changing, a stream system, to the job configuration based on similar conditions” (abstract: “… the job request includes a job configuration and a plurality of operations to process the data. The job configuration is extracted… and stored in a configuration cache… this allows information to be obtained from the configuration cache… for processing subsequent job requests with the similar job configuration… operations is executed… the result is provided to the user through at least one of an output stream…”; Fig. 3; Par 63: “… job request is received in an input stream. The first job request includes job configuration the job configuration further includes… runtime configuration… the runtime configuration is used to identify the runtime configuration code required for processing the data in real time…”; par 1 – 4: “a batch system typically processes data in several processing units and combines the results to produce the desired output. In certain cases, data received for processing consists of small volumes of data. In this scenario, processing is accomplished in either of two methods. The first existing method includes processing these small volumes of data using the same batch software… using a batch system to process a small volume of data is inefficient… the second existing method includes writing new software that will perform the same operations of a batch system for small volumes of data on small systems… however, the decision making is not possible as the volume of the data may not be pre-determined in all circumstances…”
EXAMINER NOTE: The above citations from Abdelnur_2010 teaches that batch systems combine data streams and can consist of small volumes of data and when this occurs the processing can be inefficient unless the software that performs the processing operations can be rewritten but the decision to rewrite the software is not possible without pre-determining the volume of data. To address this issue, Abdelnur_2010 teaches to extract job configurations and to store them in a cache for runtime configurations of code used to control the combination of streaming data.)
Sachs_2019 and Abdelnur_2010 are analogous art because they are from the same field of endeavor called data processing. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Sachs_2019 and Abdelnur_2010. The rationale for doing so would have been that Sachs_2019 (par 34) teaches to infer digital twins by observing streaming data of real-world systems and then to use these digital twin models to predictively simulate real-world streaming data and then to use these predictions to manage/control the real-world physical entities. Abdelnur_2010 (par 1 – 4) teaches that batch systems combine data streams and can consist of small volumes of data and when this occurs the processing can be inefficient unless the software that performs the processing operations can be rewritten but the decision to rewrite the software is not possible without pre-determining the volume of data. Therefore, it would have been obvious to combine the digital twin models that predictively simulate streaming data for controlling real-world systems as taught by Sachs_2019 with a real-world batch system that combines streaming data that consists of small volumes of data as taught by Abdelnur_2010 for the benefit of making runtime configuration decision about code requirements (par 63) such as writing new/reconfiguring code to manage/control the performance of batch streaming operations on small volumes of data more efficiently by pre-determining the job configuration and/or volume of data through predictive simulation to obtain the invention as specified in the claims.
A new ground of rejection is presented in the body of the Office action below.
End Response to Arguments
Claim Rejections - 35 USC § 103
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 - 18 are rejected under 35 U.S.C. 103 as being unpatentable over Sachs_2019 (US 2019/0294978) in view of Cella_2021 (US 2021/0248514 A1) in view of Self-Modifying-Code_2021 (Self-Modifying Code Downloaded from Wikipedia Archive dated December 2021) in view of Abdelnur_2010 (US 2010/0042998 A1)
Claim 1. Sachs_2019 makes obvious “A method(FIG. 8; par 90: “FIG. 8 illustrates various components of an exemplary computing-based device 800 which are implemented as any form of a computing and/or electronic device, and in which embodiments of a digital twin are implemented in some examples.”; par 91: “computing-based device 800 comprises one or more processors 802 which are microprocessors, controllers or any other suitable type of processors for processing computer executable instructions to control the operation of the device in order to receive and process event stream data and gossiped schema from other digital twins, in order to infer digital twins and relationships between the digital twins. In some examples, for example where a system on a chip architecture is used, the processor 802 includes one or more fixed function blocks (also referred to as accelerators) which implement a part of the method of any of FIGS. 3A, 3C, 4A, 4B, 5, 6A to 6D and 7…”; par 92: “… computer storage media, such as memory 812… RAM… ROM… EPROM… EEPROM… CD-ROM… DVD…”; par 95: “the methods described herein are performed, in some examples, by software in machine readable form on a tangible storage medium e.g., in the form of a computer program comprising computer program code means adapted to perform all the operations of one or more of the method described herein…”) comprising: receiving a streaming analytics application (par 92: “the computer executable instructions are provided using any computer-readable media that is accessible by computing based device 800… for storage of information such as computer readable instructions…”; par 97: “those skilled in the art will realize that storage devices utilized to store program instructions are optionally distributed across a network… the local computer may download pieces of the software as needed, or execute some software instructions at the local terminal and some of the remote computer (or computer network)…”; par 9: “FIG. 2a is a schematic diagram of primitive digital twins at the start of a process of analyzing edge data…”; par 28: “… capture and analyze data about the behavior of the physical entities 100… such as… a router in a telecommunications network…”; par 39: “FIG. 3A is a flow diagram of an example of a method performed at the data ingestion component 106 of FIG. 1. The data ingestion component 106 receives packets of event data in the event data stream from at least one capture apparatus 102… the data ingestion component 106 decodes 300 the data payloads… and is then able to analyze the content…” NOTE: the above citations teach a computer receiving software that is downloaded from a network or read from a media that analyzes data streamed from physical plant such as telecommunication networks) that is configured to receive a plurality of data streams respectively from a set of source operators; connecting, in data communication, a set ofvirtual model(s) respectively to the set of source operators (par 33: “the computing device 118 has a component for distributed inference 112. The distributed inference component 112 sends and receives data about the dynamic schemas and/or the event data, with other ones of the computing device 118. The distributed inference component 112 makes comparisons and aggregates digital twins or establishes peer relationships between digital twins, according to the comparison results. The comparisons are between the sent and received data as explained in more detail with reference to FIGS. 5 and 6A to 6D. The data ingestion component 106, dynamic schema computation 108 and distributed inference 112 operate continually and at any point in time the current inferred digital twins 116 are available as output…”); receiving, by the streaming analytics application, a set of input data streams respectively from the set of virtual models as simulated real-time data in the streaming analytics application (abstract: “… a distributed inference process by sending information about the schema or the received event stream to at least one other digital twin in the communications network and receiving information about the schemas or received event streams from the other digital twin…”;
par 91: “…computer executable instructions to control the operation of the device in order to receive and process event stream data and gossiped schema from other digital twins, in order to infer digital twins and relationships between the digital twins…”);
[predicting behavior of the physical system] based on the simulated real-time data received through the set of source operators and from the set of pre-existing virtual models; and changing, a [real-world] system, based on similar conditions to the simulated real-time data” (Figure 1 teaches to use the inferred digital twins 116 as input for management and control apparatus 113 which controls the real-world physical entities. Paragraph 34: “… the inferred digital twins 116 are made available to a management and control apparatus 114 for analysis and control of the physical entities 100. The inferred digital twin 116 are used to predict behavior of the physical entities 100. The predicted behavior is used by the management and control system 114 to control the physical entities 100 and/or is used by a human operator to control the physical entities. In this way efficiencies in the maintenance and control of the physical entities 100 is achieved.”)
While Saches_2019 teaches to refine code and by both reducing its size and aggregating it (FIG. 3C block 3034 “simplify”; par 54: “… the inferred type is simplified 2024 in order to reduce its size…”; par 73: “… digital twins are aggregated…”), the refined code is not explicitly recited to be the streaming analytics application itself. Therefore, Saches_2019 does not teach “and refining code and configuration data of the streaming analytics application based on the simulated real-time data received through the set of source operators and from the set of pre-existing set of virtual models, wherein the streaming analytics application comprises the code and the configuration data.”
While Sachs_2019 teaches continually update digital twins, see par 62, 45, and thereby implies that at least some of the inferred digital twins were previously inferred and therefore preexisting; Sachs_2019 does not explicitly recite “pre-existing”. Therefore, Sachs_2019 does not explicitly teach that the set of digital twin virtual model(s) are a set of “pre-existing” models.
Sachs_2019, doesn’t indicate that the behavior of the physical system include “to cache job configurations” or changing, a “stream” system, “to the job configuration”
Cella_2021, however, makes obvious that the set of digital twin virtual model(s) are a set of “pre-existing” models. (Par 2403: “in some embodiments… the digital twin system 11120 may process sensor data and create a digital replica of a set of transaction entities of the plurality of transaction entities to facilitate design, real-time simulation, predictive simulation, and/or hypothetical simulation of a related group of transaction entities…”; Par 3009: “In embodiments, the one or more processors are further configured to detect objects within the mapping information and, for each detected object within the mapping information, determine whether the detected object corresponds to an existing real-world-element digital twin, add, in response to determining that the detected object does not correspond to an existing real-world-element digital twin, a detected-object digital twin to the real-world-element digital twins within the digital twin datastore using a digital twin generation system, and update, in response to determining that the detected object corresponds to an existing real-world-element digital twin, the real-world-element digital twin to include new information detected by the simultaneous location and mapping sensor.” NOTE: the above citations teach to add a digital twin element to the set of pre-existing digital twins and also teaches to update pre-existing digital twin models).
Sachs_2019 and Cella_2021 are analogous art because they are from the same field of endeavor called digital twins. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Sachs_2019 and Cella_2021. The rationale for doing so would have been that Sachs_2019 teaches to create a digital twin that represents a real-world device. Sachs_2019 also teaches to use digital twins to facilitate management and control of physical object. See paragraph 1 - 2. Cella_2021 teaches that when a device is detected to determine if the device has a digital twin among the set of pre-existing digital twins. If the detected device does not have a digital twin then make a digital twin but if the device already has a digital twin then to update the pre-existing digital twin model. Therefore; it would have been obvious to combine Sachs_2019 and Cella_2021 for the benefit of creating new digital twins and updating digital twins during the on-going management and control of facilities which have physical objects that are added/removed/modified due to facility design changes and maintenance to obtain the invention as specified in the claims.
Sachs_2019 and Cella_2021 does not explicitly teach “and refining code and configuration data of the streaming analytics application based on the simulated real-time data received through the set of source operators and from the set of pre-existing set of virtual models, wherein the streaming analytics application comprises the code and the configuration data.”
Self-Modifying-Code_2021 makes obvious “and refining code and configuration data of the streaming analytics application based on the simulated real-time data received through the set of source operators and from the set of pre-existing set of virtual models, wherein the streaming analytics application comprises the code and the configuration data” (page 1: “… self-modifying code (SMC) is code that alters its own instructions while it is executing… to reduce the instruction path length and improve performance or simply to reduce otherwise repetitively similar code… the modifications may be performed… during initialization… when the process is more commonly described as software ‘configuration’… alteration of program entry pointers… through execution (“on the fly”) – based on particular program states… in either case, the modifications may be performed directly on the machine code instructions themselves…” EXAMINER NOTE: the above teaches modifying program entry pointer (i.e., configuration data) and also to modify the “code instructions themselves”.)
Sachs_2019 and Self-Modifying-Code_2021 are analogous art because they are from the same field of endeavor called software. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Sachs_2019 and Self-Modifying-Code_2021. The rationale for doing so would have been that Sachs_2019 teaches computer implemented software method that use program instructions (Sachs_2019 par 95: “… computer program comprising computer program code means…”) and Self-Modifying-Code_2021 teaches to implement self-modifying computer code for the benefit of reducing instruction path length and improving software performance. Therefore, it would have been obvious to combine Sachs_2019 and Self-Modifying-Code_2021 for the benefit of reducing instruction path length and improving software performance to obtain the invention as specified in the claims.
Abdelnur_2010 makes obvious “caching a job configuration” and also “changing, a stream system, to the job configuration based on similar conditions” (abstract: “… the job request includes a job configuration and a plurality of operations to process the data. The job configuration is extracted… and stored in a configuration cache… this allows information to be obtained from the configuration cache… for processing subsequent job requests with the similar job configuration… operations is executed… the result is provided to the user through at least one of an output stream…”; Fig. 3; Par 63: “… job request is received in an input stream. The first job request includes job configuration the job configuration further includes… runtime configuration… the runtime configuration is used to identify the runtime configuration code required for processing the data in real time…”; par 1 – 4: “a batch system typically processes data in several processing units and combines the results to produce the desired output. In certain cases, data received for processing consists of small volumes of data. In this scenario, processing is accomplished in either of two methods. The first existing method includes processing these small volumes of data using the same batch software… using a batch system to process a small volume of data is inefficient… the second existing method includes writing new software that will perform the same operations of a batch system for small volumes of data on small systems… however, the decision making is not possible as the volume of the data may not be pre-determined in all circumstances…”
EXAMINER NOTE: The above citations from Abdelnur_2010 teaches that batch systems combine data streams and can consist of small volumes of data and when this occurs the processing can be inefficient unless the software that performs the processing operations can be rewritten but the decision to rewrite the software is not possible without pre-determining the volume of data. To address this issue, Abdelnur_2010 teaches to extract job configurations and to store them in a cache for runtime configurations of code used to control the combination of streaming data.)
Sachs_2019 and Abdelnur_2010 are analogous art because they are from the same field of endeavor called data processing. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Sachs_2019 and Abdelnur_2010. The rationale for doing so would have been that Sachs_2019 (par 34) teaches to infer digital twins by observing streaming data of real-world systems and then to use these digital twin models to predictively simulate real-world streaming data and then to use these predictions to manage/control the real-world physical entities. Abdelnur_2010 (par 1 – 4) teaches that batch systems combine data streams and can consist of small volumes of data and when this occurs the processing can be inefficient unless the software that performs the processing operations can be rewritten but the decision to rewrite the software is not possible without pre-determining the volume of data. Therefore, it would have been obvious to combine the digital twin models that predictively simulate streaming data for controlling real-world systems as taught by Sachs_2019 with a real-world batch system that combines streaming data that consists of small volumes of data as taught by Abdelnur_2010 for the benefit of making runtime configuration decision about code requirements (par 63) such as writing new/reconfiguring code to manage/control the performance of batch streaming operations on small volumes of data more efficiently by pre-determining the job configuration and/or volume of data through predictive simulation to obtain the invention as specified in the claims.
Claim 7. The limitations of claim 7 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claim 1. Sachs_2019 makes also makes obvious “And creating, by the streaming analytics application, a newly-created virtual model, based on the simulated real-time data received through the set of source operator(s) and from the pre-existing set of virtual model(s)” (FIG. 1 116 Inferred Digital Twin; FIG. 2B Digital Twin A; par 33: “… the current inferred digital twin 116 are available as output… the inferred digital twin 116 are made available to a management and control apparatus 114…”)
Claim 13. The limitations of claim 13 are substantially the same as those of claim 1 and are rejected due to the same reasons as outlined above for claim 1. Sachs_2019 makes also makes obvious “… creating, by the streaming analytics application, a newly-created virtual model, based on the simulated real-time data received through the set of source operator(s) and from the pre-existing set of virtual model(s)” ((FIG. 1 116 Inferred Digital Twin; FIG. 2B Digital Twin A; par 33: “… the current inferred digital twin 116 are available as output… the inferred digital twin 116 are made available to a management and control apparatus 114…”)
Claim 2, 8, 14. Sachs_2019 makes obvious “wherein the set of pre-existing virtual model(s) includes a first virtual model corresponding to an Internet of Things (IoT) device” (par 1: “… digital twins are digital representations of physical objects or processes. Digital twins are used in many application domains including product and process engineering, internet of things, logistics, asset management, and others. The digital twin provides a model of the behavior of the physical object and once such digital representations are available it is possible for automated computing systems to use the digital twin to facilitate management and control of the physical object.”).
Claim 3, 9, 15. Sachs_2019 makes obvious “wherein the first virtual model is a digital twin of a real world instantiation of an IoT device” (Fig. 1 “physical entity A”; par 1: “… digital twins are digital representations of physical objects or processes. Digital twins are used in many application domains including product and process engineering, internet of things, logistics, asset management, and others. The digital twin provides a model of the behavior of the physical object and once such digital representations are available it is possible for automated computing systems to use the digital twin to facilitate management and control of the physical object.”).
Claim 4, 10, 16. Sachs_2019 makes obvious “wherein each virtual model of the set of pre-exiting virtual model(s) is a digital twin matching a real world instantiation of a physical asset” (Fig. 1 “physical entity A”; par 1: “… digital twins are digital representations of physical objects or processes. Digital twins are used in many application domains including product and process engineering, internet of things, logistics, asset management, and others. The digital twin provides a model of the behavior of the physical object and once such digital representations are available it is possible for automated computing systems to use the digital twin to facilitate management and control of the physical object.”).
Claim 5, 11, 17. Sachs_2019 makes obvious “wherein each virtual model of the set of pre-existing virtual model(s) is a digital twin matching a real world instantiation of a process” (Fig. 1 “physical entity A”; par 1: “… digital twins are digital representations of physical objects or processes. Digital twins are used in many application domains including product and process engineering, internet of things, logistics, asset management, and others. The digital twin provides a model of the behavior of the physical object and once such digital representations are available it is possible for automated computing systems to use the digital twin to facilitate management and control of the physical object.”).
Claim 6, 12, 18. Sachs_2019 makes obvious “wherein each virtual model of the set of pre-existing virtual model(s) is a digital twin matching a real world instantiation of a computer system” (Fig. 1 “physical entity A”; par 1: “… digital twins are digital representations of physical objects or processes. Digital twins are used in many application domains including product and process engineering, internet of things, logistics, asset management, and others. The digital twin provides a model of the behavior of the physical object and once such digital representations are available it is possible for automated computing systems to use the digital twin to facilitate management and control of the physical object.” Par 26: “… the physical entities 100 are any physical objects or processes where it is required to capture and analyze data about the behavior of the physical entities 100. In the case that the physical entity 100 comprises a process the physical entity 100 is something which is able to carry out a process, such as a manufacturing apparatus, a router in a telecommunications network, a traffic light. A non-exhaustive list of examples of physical entities 100 is: street light, traffic signal installation, domestic appliances, automotive vehicle, logistics asset, power distribution network equipment…” NOTE: “a router in a telecommunications network” makes obvious a real-world instantiation of computer system because, for example, an network router is computer equipment in a computer network system).
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.
/BRIAN S COOK/Primary Examiner, Art Unit 2187
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