DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, 17/662,470, was filed on May 9, 2022, and does not claim domestic, foreign, or international priority to any other application.
The effective filing date is after the AIA date of March 16, 2013, and so the application is being examined under the “first inventor to file” provisions of the AIA .
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.
Status of the Application
This Final Office Action is in response to Applicant’s communication of Feb. 24, 2026.
Claims 1, 3-11, and 13-20 are pending, of which claims 1 and 11 are independent.
In the current response, independent claims 1 and 11 and dependent claims 6 and 16 have been amended, and dependent claims 2 and 12 have been cancelled.
All pending claims have been examined on the merits.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-11, and 13-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to an abstract idea, without “significantly more”.
Based on the flowchart in MPEP § 2106, Step 1 of the Alice/Mayo analysis is: “Is the claim to a process, machine, manufacture or composition of matter?”
In regards to Step 1 of the Alice/Mayo analysis, independent claim 1 is a method claim, and claim 11 is an article of manufacture claim or product by process claim (“non-transitory computer readable medium”).
For the sake of compact prosecution, we continue with the Alice/Mayo “abstract idea” analysis.
Step 2A, prong 1 of the Alice/Mayo analysis is: “Does the claim recite a law of nature, a natural phenomenon (product of nature), or an abstract idea?”
In regards to Step 2A, prongs 1 and 2 of the Alice/Mayo analysis, the abstract idea elements recited in independent claim 11 are shown in italic font. (The “additional elements” and “extra solution steps” are shown in italic and underlined font):
11. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:
collecting data regarding operations of equipment in an operating environment, wherein the data includes sensor data collected from one or more sensors operating on the equipment or in the operating environment, and wherein the sensor data reflects an actual mechanical operation of the equipment;
causing a simulator to use the collected data to create simulated data, wherein the simulated data represents simulations of actual mechanical operations of the equipment, and wherein the simulator comprises a physical or numerical modeling formulation that guides the simulator when generating the simulated data to ensure the simulation properly represents the actual mechanical operations;
using the simulated data to build a model that is operable to detect an event of interest that relates to the equipment wherein the event of interest comprises an anomalous mechanical operation of the equipment in which the equipment is simulated to operate in a manner that deviates away from a non-anomalous mechanical operation, the anomalous mechanical operation being included among the actual mechanical operations that are represented by the simulations of the simulator;
using the collected data to refine the model, resulting in a reduction in inaccuracy arising from mismatches between the simulations of the actual mechanical operations, as represented by the simulated data, and the actual equipment behavior, as represented by the sensor data; and
applying the model in a target environment, wherein applying the model comprises deploying the model to a given equipment operating in the target environment.
Moreover, claims 1, 3-11, and 13-20 recite “Mathematical Concepts", specifically “Mathematical Relationships”, “Mathematical Formulas or Equations”, and “Mathematical Calculations”, as discussed in MPEP §2106.04(a)(2) Part (IV), and in the 2019 Revised Patent Subject Matter Eligibility Guidance.
The mathematic elements include:
The Examiner interprets that the “simulator”, “model”, and “physical or numerical modeling formulation” claimed in the following limitations refer to a mathematical model comprising mathematical formulas or equations.
“causing a simulator to use the collected data to create simulated data, wherein the simulated data represents simulations of actual mechanical operations of the equipment, and wherein the simulator comprises a physical or numerical modeling formulation that guides the simulator when generating the simulated data to ensure the simulation properly represents the actual mechanical operations”.
“using the simulated data to build a model that is operable to detect an event of interest that relates to the equipment wherein the event of interest comprises an anomalous mechanical operation of the equipment in which the equipment is simulated to operate in a manner that deviates away from a non-anomalous mechanical operation, the anomalous mechanical operation being included among the actual mechanical operations that are represented by the simulations of the simulator”.
“using the collected data to refine the model, resulting in a reduction in inaccuracy arising from mismatches between the simulations of the actual mechanical operations, as represented by the simulated data, and the actual equipment behavior, as represented by the sensor data”.
The “additional elements” include: “A non-transitory storage medium” and “one or more hardware processors”.
Moreover, “additional extra-solution elements” include: “collecting data regarding operations of equipment in an operating environment, wherein the data includes sensor data collected from one or more sensors operating on the equipment or in the operating environment”, and “applying the model in a target environment, wherein applying the model comprises deploying the model to a given equipment operating in the target environment”.
Step 2A, prong 2 of the Alice/Mayo analysis is “Does the claim recite additional elements that integrate elements that integrate the judicial exception into a practical application?”
In regards to Step 2A, prong 2 of the Alice/Mayo analysis, this abstract idea is not integrated into a practical application, because:
The claim is directed to an abstract idea with additional generic computer elements. The generically recited computer elements (“A non-transitory storage medium” and “one or more hardware processors”) do not add a meaningful limitation to the abstract idea, because they amount to simply implementing the abstract idea on a computer. The claim amounts to adding the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.
The extra-solution activities (“collecting data regarding operations of equipment in an operating environment, wherein the data includes sensor data collected from one or more sensors operating on the equipment or in the operating environment”, “applying the model in a target environment”, and “wherein applying the model comprises deploying the model to a given equipment operating in the target environment”) do not add a meaningful limitation to the method, as they are insignificant extra-solution activity;
The combination of the abstract idea with the additional elements (generically recited computer elements), and/or with the extra-solution activities, does not integrate the abstract idea into a practical application.
Also, the claim merely generally links the use of the abstract idea to a particular technological environment or field of use (e.g. electric grid data is not claiming the actual electric grid)- see MPEP 2106.05(h) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016)(Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid is not subject matter eligible, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment).
More specifically, the present claims recite the simulation of mechanical operations by a mathematical model, which is not equivalent to claiming the actual mechanical device.
Step 2B of the Alice/Mayo analysis is: “Does the claim recite additional elements that amount to significantly more than the judicial exception?”
In regards to Step 2B of the Alice/Mayo analysis, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea, because:
When considering the elements "alone and in combination" (“A non-transitory storage medium” and “one or more hardware processors”), they do not add significantly more (also known as an "inventive concept") to the exception, because they amount to simply implementing the abstract idea on a computer. Instead, they merely add the words "apply it" (or an equivalent) with the abstract idea, or mere instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform an abstract idea.
In regards to the extra solution activities (“collecting data regarding operations of equipment in an operating environment, wherein the data includes sensor data collected from one or more sensors operating on the equipment or in the operating environment”, and “applying the model in a target environment, wherein applying the model comprises deploying the model to a given equipment operating in the target environment”), these are recognized as such by the court decisions listed in MPEP § 2106.05(d).
More specifically, in regards to the “collecting data regarding operations of equipment in an operating environment, wherein the data includes sensor data collected from one or more sensors operating on the equipment or in the operating environment” step, this is merely data gathering for a process that is claimed at a very high level of abstraction and generality.
See MPEP § 2106.05(g) and the court cases OIP Technologies, Inc. v. Amazon.com, Inc., 788 F.3d at 1363, 115 USPQ2d at 1092-93 (Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responded to the offers; the statistics are then used to calculate an optimized price), and CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)(Obtaining information about transactions using the Internet, and using this information to verify credit card transactions).
Moreover, in regards to the “applying the model in a target environment, wherein applying the model comprises deploying the model to a given equipment operating in the target environment” step, this is merely outputting and deploying an updated version of the model (analogous to outputting a new version of a software program).
Moreover, in regards to “apply it”, according to MPEP § 2106.05(f)(2):
Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Similarly, "claiming the improved speed or efficiency inherent with applying the abstract idea on a computer" does not integrate a judicial exception into a practical application or provide an inventive concept. Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015).
In contrast, a claim that purports to improve computer capabilities or to improve an existing technology may integrate a judicial exception into a practical application or provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). See MPEP §§ 2106.04(d)(1) and 2106.05(a) for a discussion of improvements to the functioning of a computer or to another technology or technical field.
The Examiner holds that the independent claims “use a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)” or “simply add a general purpose computer or computer components after the fact to an abstract idea”.
Independent claim 1 is rejected on the same grounds as independent claim 11. Independent claim 11 is also rejected on the grounds that it recites a computer-readable medium, which is merely another generic computer component.
All dependent claims are also rejected, because they merely further define the abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 7, 8, 17, and 18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The application’s US PG-PUB US-2023/0359926-A1 does not define the claimed term “edge devices”, and instead provides the following example embodiments in para. [0016]:
[0016] In general, example embodiments of the invention are concerned with the domain of automation and management support at on-premise edge environments, one non-limiting example of which is logistics warehouses where materials are stored. Some example embodiments may thus comprise forklifts as mobile, far-edge, or simply ‘edge,’ devices, and may also comprise a near-edge, on-premise, central node operable to communicate with the edge nodes.
Claims 7, 8, 17, and 18 are rejected under 35 U.S.C. 112(b), as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
More specifically, because the claimed term “edge devices” is not defined in the specification, and only example embodiments are described in the specification, the metes and bounds of the term “edge devices” are indefinite when the term is recited in the claims.
Claim Rejections - 35 USC § 102
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, 3-8, 10-11, 13-18, and 20 are rejected under 35 U.S.C. §§102(a)(1) and (a)(2) as being anticipated by US-2018/0017467-A1 to Hiruta et al. (“Hiruta”, Filed on July 13, 2016. Published on Jan. 18, 2018).
In regards to claim 1, Hiruta discloses:
1. A method, comprising:
collecting data regarding operations of equipment in an operating environment, wherein the data includes sensor data collected from one or more sensors operating on the equipment or in the operating environment, and wherein the sensor data reflects an actual mechanical operation of the equipment;
(See Hiruta, para. [0020]: “The physical model may be created using experimental data and domain knowledge regarding the target equipment. The physical model may be used by a simulator to generate simulated data regarding normal operation modes, possible failure modes, and possible degrees of failure. Next, a statistical model generator may generate the statistical model using both historical data and the simulated data. During use of the statistical model for performing analysis, the use of the statistical model may include a normalization process for the received sensor data being analyzed and a prediction process. A predictor may use the statistical model to predict a failure mode and a degree of failure based on the received sensor data. In some examples, the sensor data may be received, such as through a direct connection or over a network, from one or more sensors associated with the target equipment and analyzed in near real time. The predictor may provide an analysis result to an operator, which may perform at least one operation based on the analysis results, such as controlling the target equipment and/or sending a notification to equipment personnel, or the like.”)
causing a simulator to use the collected data to create simulated data, wherein the simulated data represents simulations of actual mechanical operations of the equipment, and wherein the simulator comprises a physical or numerical modeling formulation that guides the simulator when generating the simulated data to ensure the simulation properly represents the actual mechanical operations;
(See Hiruta, para. [0021]: “Some implementations herein may integrate historical data and simulated data into the statistical model. For example, the simulator may generate simulated data corresponding to historical data, such as for a normal operating mode and abnormal operating modes, which may include all possible failure modes and all possible degrees of failure. Moreover, the simulated data generated from the physical model may be measured, quantified, and/or normalized according to the same quantities as the sensor data measured by the sensors associated with the target equipment.”)
using the simulated data to build a model that is operable to detect an event of interest that relates to the equipment wherein the event of interest comprises an anomalous mechanical operation of the equipment in which the equipment is simulated to operate in a manner that deviates away from a non-anomalous mechanical operation, the anomalous mechanical operation being included among the actual mechanical operations that are represented by the simulations of the simulator;
(See Hiruta, para. [0022]: “For discussion purposes, some example implementations are described in the environment of a computing device that trains a statistical model based on simulated data, receives sensor data from target equipment, and uses the statistical model to determine a condition of the equipment, such as for sending a notification and/or a control signal for controlling a brake or other equipment. However, implementations herein are not limited to the particular examples provided, and may be extended to other types of equipment, other types of environments, other system architectures, other types of computational models, and so forth, as will be apparent to those of skill in the art in light of the disclosure herein.”)
using the collected data to refine the model, resulting in a reduction in inaccuracy arising from mismatches between the simulations of the actual mechanical operations, as represented by the simulated data, and the actual equipment behavior, as represented by the sensor data; and
(See Hiruta, para. [0059]: “At 412, when the difference D between the simulated data and the experimental data is more than the threshold deviation, the computing device may update or otherwise change one or more parameters of the candidate physical model, and the process may return to 406 to perform the simulation again with the changed parameter(s). The process from 406-410 may be repeated until the difference D is smaller than the threshold deviation.”)
(See Hiruta, para. [0060]: “At 414, when the difference D between the simulated data and the experimental data is less than the threshold deviation, the physical model may be considered to be sufficiently accurate, and the computing device may use the physical model and the simulator to generate simulated data for use in generating the statistical model, as discussed below with respect to FIG. 5.”)
In regards to claim 2, it has been cancelled.
In regards to claim 3, Hiruta discloses:
3. The method as recited in claim 1, wherein the model is operable to predict occurrence of the event of interest.
(See Hiruta, para. [0078]: “FIG. 8 is a flow diagram illustrating an example process 800 by which the predictor may predict a failure mode and degree of failure according to some implementations. In some examples, the process 800 may be executed by the computing device 102 or other suitable computing device.”)
In regards to claim 4, Hiruta discloses:
4. The method as recited in claim 1, wherein the operating environment and the target environment are the same environment.
(See Hiruta, para. [0022]: “For discussion purposes, some example implementations are described in the environment of a computing device that trains a statistical model based on simulated data, receives sensor data from target equipment, and uses the statistical model to determine a condition of the equipment, such as for sending a notification and/or a control signal for controlling a brake or other equipment. However, implementations herein are not limited to the particular examples provided, and may be extended to other types of equipment, other types of environments, other system architectures, other types of computational models, and so forth, as will be apparent to those of skill in the art in light of the disclosure herein.”)
In regards to claim 5, Hiruta discloses:
5. The method as recited in claim 1, wherein the operating environment and the target environment are different respective environments.
(See Hiruta, para. [0022]: “For discussion purposes, some example implementations are described in the environment of a computing device that trains a statistical model based on simulated data, receives sensor data from target equipment, and uses the statistical model to determine a condition of the equipment, such as for sending a notification and/or a control signal for controlling a brake or other equipment. However, implementations herein are not limited to the particular examples provided, and may be extended to other types of equipment, other types of environments, other system architectures, other types of computational models, and so forth, as will be apparent to those of skill in the art in light of the disclosure herein.”)
The Examiner interprets that this claim is directed to intended use. It is obvious that the trained model of an automobile brake system which is trained in one intended use (e.g. one environment, such as asphalt roads) can then be used in another intended use (another environment, such as gravel roads).
In regards to claim 6, Hiruta discloses:
6. The method as recited in claim 1, wherein the data comprises any one or more of:
video data;
positioning data regarding one or more locations of the equipment; or
data regarding the actual mechanical operation of the equipment.
(See Hiruta, para. [0033]: “In some examples, the computing device 102 may receive the sensor data 144 from the one or more sensors 104 that are associated with the equipment 108 for measuring one or more characteristics or other metrics of the equipment 108. For instance, the management application 118 may use the received sensor data 144 about the equipment 108 for analyzing the condition of the equipment 108. Based on the determined condition of the equipment 108, the management application 118 may send a control signal 150 for controlling the equipment 108, and/or may send a notification 152 to the user computing device 110 to notify a user 154, such as equipment personnel, regarding a condition of the equipment 108. Thus, in some examples, at least one user computing device 110 may be part of a monitoring system 148 for monitoring the equipment 108.
(See Hiruta, para. [0037]: “FIG. 2 illustrates an example of operations of the system 100 according to some implementations. In this example, suppose that the equipment 108 includes a brake 202 that is a component of a machine 204. As one example, the machine 204 may be an elevator that uses the brake 202 for controlling movement of the elevator, such as during stopping of the elevator, holding the elevator in position while passengers load or unload, and so forth. In other examples, the machine 204 may be a vehicle, such as a train, truck, bus, automobile, airplane, or the like, and the brake 202 may be used for slowing or stopping the vehicle. In still other examples, the machine 204 may be any other type of machine that uses a brake during its operation.”)
In regards to claim 7, Hiruta discloses:
7. The method as recited in claim 1, wherein the equipment comprises one of a group of edge devices that operate in the operating environment.
(See Hiruta, para. [0037]: “FIG. 2 illustrates an example of operations of the system 100 according to some implementations. In this example, suppose that the equipment 108 includes a brake 202 that is a component of a machine 204. As one example, the machine 204 may be an elevator that uses the brake 202 for controlling movement of the elevator, such as during stopping of the elevator, holding the elevator in position while passengers load or unload, and so forth. In other examples, the machine 204 may be a vehicle, such as a train, truck, bus, automobile, airplane, or the like, and the brake 202 may be used for slowing or stopping the vehicle. In still other examples, the machine 204 may be any other type of machine that uses a brake during its operation.”)
The Examiner interprets that “a brake 202 that is a component of a machine 204” is an example of an “edge device”, given that Applicant’s definition of the term is indefinite.
In regards to claim 8, Hiruta discloses:
8. The method as recited in claim 1, wherein the model is created and refined at a near-edge node with which an edge node associated with the equipment is operable to communicate.
(See Hiruta, para. [0037]: “FIG. 2 illustrates an example of operations of the system 100 according to some implementations. In this example, suppose that the equipment 108 includes a brake 202 that is a component of a machine 204. As one example, the machine 204 may be an elevator that uses the brake 202 for controlling movement of the elevator, such as during stopping of the elevator, holding the elevator in position while passengers load or unload, and so forth. In other examples, the machine 204 may be a vehicle, such as a train, truck, bus, automobile, airplane, or the like, and the brake 202 may be used for slowing or stopping the vehicle. In still other examples, the machine 204 may be any other type of machine that uses a brake during its operation.”)
The Examiner interprets that “a brake 202 that is a component of a machine 204” is an example of an “near-edge node”, given that Applicant’s definition of the term is indefinite.
In regards to claim 10, Hiruta discloses:
10. The method as recited in claim 1, wherein the simulated data comprises simulated operations of the equipment.
(See Hiruta, para. [0018]: “In some examples, the computing device may enable condition-based control and or maintenance by receiving data from the equipment in the field, such as through a network connection or a direct connection. The computing device may store and analyze the received field data using a statistical model. When an analysis result indicates a threshold condition of the equipment, such as the occurrence of a particular failure mode of the equipment along with the degree of failure, the computing device may perform at least one action based on the indicated condition. The system can predict the failure mode and degree of failure through a comparison between the received sensor data and simulated data. The system may create the simulated data in advance of receiving the sensor data. Accordingly, the system is able to predict a failure mode and a degree of failure for equipment that has limited sensors and/or a low sampling rate.”)
In regards to claim 11, it is rejected on the same grounds as claim 1.
In regards to claim 12, it has been cancelled (as has claim 2).
In regards to claim 13, it is rejected on the same grounds as claim 3.
In regards to claim 14, it is rejected on the same grounds as claim 4.
In regards to claim 15, it is rejected on the same grounds as claim 5.
In regards to claim 16, it is rejected on the same grounds as claim 6.
In regards to claim 17, it is rejected on the same grounds as claim 7.
In regards to claim 18, it is rejected on the same grounds as claim 8.
In regards to claim 20, it is rejected on the same grounds as claim 10.
Claim Rejections - 35 USC § 103
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US-2018/0017467-A1 to Hiruta et al. (“Hiruta”, Filed on July 13, 2016. Published on Jan. 18, 2018) in view of Baimukashev et al. (“Baimukashev”) "End-to-End Deep Fault Tolerant Control", eprint pub date: 30 Nov 2021.
In regards to claim 9, Hiruta discloses the following (emphasis added) in para. [0001]:
“A statistical model may be used to make determinations and other types of predictions based on received data. For example, a statistical model is type of computational model that includes a set of assumptions concerning data based in part on information about similar data determined from a larger population of data. As one example, the set of assumptions included in a statistical model may describe a plurality of probability distributions. For instance, a statistical model may be configured with one or more mathematical equations that relate one or more random variables and, in some cases, non-random variables. Typically, prior to being used to make predictions, a statistical model may be trained using a quantity of historical data. However, in cases in which a sufficient quantity of training data is not available, it may be difficult to generate an accurate statistical model.”
However, under a conservative interpretation of Hiruta, it could be argued that Hiruta does not explicitly teach the features of claim 9, which are taught by Baimukashev (including “a single recurrent neural network (RNN)” and “its training is based on supervised learning”):
9. The method as recited in claim 1, wherein the model comprises a supervised machine learning model.
(See Baimukashev , page 2, col. 1, para. 2: “In this work, we focus on end-to-end learning in FTC, and in particular on the case of abrupt sensor faults. The stages of FDI and control are replaced with a single recurrent neural network (RNN) with sensor measurements as input and control variables as output, in order to obtain a faster design process compared to classical methods. In contrast to [18], our deep FTC (DFTC) method has no explicit representation of the observed system states, and its training is based on supervised learning, rather than reinforcement learning. DFTC only requires (i) the availability of a (non-fault-tolerant) full state feedback control law, which is used as an ideal reference during the training phase, and (ii) the observability of the state vector using only the available non-faulty sensors, for all considered sensor faults. The model equations are not needed for designing our DFTC scheme, although it is assumed that a simulator for the system (e.g., a physics engine) is available in order to be able to train the control law; alternatively, one could directly use the actual system in real-life experiments, but this would cause issues related to safety and to the amount of time needed to obtain a sufficient amount of data for training.”)
It would have been obvious to a person having ordinary skill in the art (PHOSITA), before the effective filing date of the claimed invention, to include in the systems and methods to test an autonomous vehicle, as taught by Hiruta, with a supervised machine learning model, as taught by Baimukashev, because “one could directly use the actual system in real-life experiments, but this would cause issues related to safety and to the amount of time needed to obtain a sufficient amount of data for training” (as taught by Baimukashev , page 2, col. 1, para. 2).
In regards to claim 19, it is rejected on the same grounds as claim 9.
Conclusion
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications should be directed to Examiner Ayal Sharon, whose telephone number is (571) 272-5614, and fax number is (571) 273-1794. The Examiner can normally be reached from Monday to Friday between 9 AM and 6 PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, SPE Christine Behncke can be reached at (571) 272-8103 or at christine.behncke@uspto.gov. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
Sincerely,
/Ayal I. Sharon/
Examiner, Art Unit 3695
May 26, 2026
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