DETAILED ACTION
Response to Amendment
The amendment filed 7/14/26 has been accepted and entered. Claims 1, 3-5, 7-8, 10-12, 14-15, and 17-19 are amended. Claims 2, 9 and 16 are canceled. Accordingly, Claims 1, 3-8, 10-15 and 17-20 are examined herein.
Response to Arguments
Applicant’s arguments with respect to the pending claims have been considered but are moot in view of the newly formulated grounds of rejection necessitated by applicant’s amendment. However, at least one argument remains relevant to the current rejection.
With respect to the 35 U.S.C. § 101 rejection, Applicant asserts the rejection should be withdrawn because “independent claims 1, 8 and 15 have been amended as suggested by the Examiner” (Amend. 7). Applicant further stated that “the Examiner suggested amending the claims to generate a digital twin and apply a difference model to overcome the current rejection under 35 USC § 101” (Amend. 7). However, this is not accurate. This amendment was not suggested by the Examiner, rather this amendment was suggested by Applicant prior to the Interview on June 26, 2026. To the contrary, the Examiner explicitly noted in the Interview Summary that “Examiner suggested generating a digital twin and applying a difference model as amended may fall under mere instructions to apply an exception since the limitations are at a high level and do not appear to indicate how these elements are applied with specificity”. Accordingly, the Examiner suggested to the Applicant that the amendment Proposed by Applicant would likely not overcome the 35 USC § 101 rejection. Accordingly, Applicants arguments as to this point are unpersuasive on this point.
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, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
In sum, claims 1, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and do not include an inventive concept that is something “significantly more” than the judicial exception under the January 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Revised Guidance Step 2A – Prong 1
Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Here, with respect to independent claims 1, 8 and 19, the claims recite the abstract idea of:
obtaining pre-repair sensor data from at least one sensor associated with one or more pre-repair components of a vehicle;
obtaining post-repair sensor data from the at least one sensor associated with one or more post-repair components of the vehicle;
generating a pre-repair digital twin of the vehicle using the pre-repair sensor data and a post-repair digital twin of the vehicle using the post-repair sensor data:
applying a difference model to compare the pre-repair digital twin with the post- repair digital twin: and
verifying performed repairs based on the comparison performed by the difference model
Specifically, a mental process, that can be performed in the human mind since the above limitations could alternatively be performed in the human mind or with the aid of pen and paper. This conclusion follows from CyberSource Corp. v. Retail Decisions, Inc., where our reviewing court held that section 101 did not embrace a process defined simply as using a computer to perform a series of mental steps that people, aware of each step, can and regularly do perform in their heads. 654 F.3d 1366, 1373 (Fed. Cir. 2011); see also In re Grams, 888 F.2d 835, 840–41 (Fed. Cir. 1989); In re Meyer, 688 F.2d 789, 794–95 (CCPA 1982); Elec. Power Group, LLC v. Alstom S.A., 830 F. 3d 1350, 1354–1354 (Fed. Cir. 2016) (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
For example, a human could perform the above limitation entirely mentally since the limitations amount to viewing/ mentally capturing and then comparing data. See, e.g., MPEP 2106.04(a)(2), III, A (“claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include . . . a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC, 659 F.3d 1057, 1067, 100 USPQ2d 1492, 1500 (Fed. Cir. 2011)”)
Furthermore, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource, 654 F.3d at 1375 (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”).
For example, obtaining data could be carried out by viewing data logs, sensor readouts or otherwise mentally recognizing data. Furthermore, generating a pre-repair and post repair “digital twin” could be performed in the human mind using human memory, i.e., a mental picture of the vehicle information before and after the repair, i.e., human memory in place of digital computer memory. The specification indicates a digital twin can be generated “based on the manufacturer sourced data” (¶ 37), which is capable of being held in a human mind. In addition, comparing data using a difference model could also be performed entirely mentally as claimed. For example, the difference model can carry out a detection of differences as simple as noting that a pre-repair sensor has been replaced by an inferior replacement sensor (Spec. ¶ 37 “repair analysis . . . detect differences . . . analysis of represented pairs . . . inferior replacement component”) which can readily be performed in the human mind.
Revised Guidance Step 2A – Prong 2
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the recited features of the abstract idea are being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). This follows conclusion follows from the claim limitations which only recite a generic “computer-readable storage media” (claim 8), “computer processors”, (claim 15) outside of the abstract idea. Claim 1 does not include any components outside of the abstract idea.
In addition, merely “[u]sing a computer to accelerate an ineligible mental process does not make that process patent-eligible.” Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1279 (Fed. Cir. 2012); see also CLS Bank Int’l v. Alice Corp. Pty. Ltd., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) (“simply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.”), aff’d, 573 U.S. 208 (2014). Accordingly, the additional element of a generic “computer-readable storage media” or “computer processors” do not transform the abstract idea into a practical application of the abstract idea.
In addition, the obtaining steps constitute insignificant pre-solution activity that merely gathers data and, therefore, do not integrate the exception into a practical application. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371–72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)).
Revised Guidance Step 2B
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as a generic “computer-readable storage media” or “computer processors” do not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. See, e.g., MPEP §2106.05 I.A; Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Thus, these elements, taken individually or together, do not amount to “significantly more” than the abstract ideas themselves.
The additional elements of the dependent claims merely refine and further limit the abstract idea of the independent claims and do not add any feature that is an “inventive concept” which cures the deficiencies of their respective parent claim under the 2019 PEG analysis. None of the dependent claims considered individually, including their respective limitations, include an “inventive concept” of some additional element or combination of elements sufficient to ensure that the claims in practice amount to something “significantly more” than patent-ineligible subject matter to which the claims are directed.
The elements of the instant process steps when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., “method of sensor enabled vehicle repair validation” claim 1).
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, 3, 5, 8, 10, 12, 15, 17, 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170132578 to Merg et al. (Merg) in view of U.S. 20200359582 to Tran et al. (Tran) and further in view of US 20190266295 to Masuda et al. (Masuda)
With respect to claims 1, 8 and 15, Merg discloses a method of sensor enabled vehicle repair validation, the method comprising:
(i.e., claims 1-2 “determining, by the computing device, that a first repair to the first vehicle is successful . . . or that the first repair to the first vehicle is non-successful . . . outputting, by the computing device, an indication that the first repair is successful or non-successful; Fig. 1 140 does the match with the recorded data indicate successful repair? Yes, No; FIG. 4A, 452 “determine repair successful”, Fig. 4B 452b determine repair unsuccessful, 462b successful, 840, FIG. 8 and corresponding descriptions)
obtaining pre-repair sensor data from at least one sensor associated with one or more pre-repair components of a vehicle;
(110, FIG. 1 “obtain initial condition data (pre-repair) for vehicle V”; 440, FIG. 4A-4B, obtain pre-repair data D1; 520, FIG. 5; ¶¶ 165 obtain initial condition data D4; 30 at block 110, where initial condition data related to a vehicle V, representing a device-under-service, at the onset of a visit to a repair facility can be obtained. The initial condition data can include data from any pre-repair checks, observations, or tests run on the vehicle before any repairs are performed on the vehicle . . . check engine sensor light is on, tire pressure, vehicle light tests, windshield washer functionality, etc.; 79 “Initial-condition data 216 can include any data obtained about initial conditions about the vehicle/device-under-service that are not already part of vehicle data 212 and complaint data 214, such as discussed above in at least in the context of blocks 110 and 122 of FIG. 1”; 32; 143-145)
obtaining post-repair sensor data from the at least one sensor associated with one or more post-repair components of the vehicle;
(FIG. 1, 126 “obtain post repair data PRD”; FIG 4A-B, 444 obtain post repair data D2 and corresponding descriptions; 143-145 “post repair data D2”; claim 15)
generating a stored representation of the vehicle using the pre-repair sensor data and a post-repair stored representation of the vehicle using the post-repair sensor data to apply a difference model for comparison purposes to verify performed repairs
(i.e., stored at server 300, storage module 332, FIG. 3A; representations D1, D2 sent for storage/ comparison at server 300 for verification of performed repairs, FIG. 4A, i.e., repair at 442 “attempt to repair V”; ¶¶ 142-145 At block 444, computing device 422 and/or diagnostic equipment 380 can be used to obtain post-repair data D2, at least in part by running post-repair diagnostics. Post-repair data D2 can be an instance of post-repair data PRD described above with respect to block 126 of FIG. 1 . . . generate post-repair comparison request 450 with some or all of the information from . . . initial condition data D1 and post-repair data D2. Once generated, computing device 422 can send post-repair comparison request 450 to server 300 . . . server 300 can store D1 as initial condition and/or as part of post-repair data D2 . . . server 300 can match post-repair data D2 with recorded data; e.g., data in post-repair database 338, such as discussed above in the context of block 130 of FIG. 1. Server 300 can make a determination whether data values in post-repair data D2 indicate whether the repair performed at block 442 was successful)
Although at least suggested by Merg (¶¶ 143-145), Merg does not disclose that the basis of the comparison of the pre-repair sensor data and the post-repair sensor data is the same at least one sensor data in the pre and post repair data.
Tran, from the same field of endeavor, also discloses systems and methods of validating repair of autonomous vehicles (¶ 13 “autonomous irrigation vehicles”; 271 Notifications may be generated or received indicating whether the repairs prove to be effective) wherein the vehicle repair validation comparison is between obtained pre-repair sensor data and post-repair sensor data (¶¶ 285-295 “when a current (or “pre-repair”) baseline may be obtained from measurements taken for a targeted irrigation system, prior to the start of the repair. The current baseline and/or one or more other baselines may be created for any attributes that can be measured or calculated for the irrigation system . . . Next, the system determines that a repair is to be performed for a irrigation system . . . Next, the repair is initiated . . . [a]fter the repair is physically completed, the repaired irrigation system may be allowed to stabilize prior to capturing one or more post-repair baselines from measured attributes and performance parameters of the repaired irrigation system . . . post-repair baselines may be used to determine if the repair was effective . . . [o]ne or more algorithms may be applied to compare one or more post-repair baselines with corresponding pre-repair baselines and/or normal baselines to determine if the repair was effective”)
(¶¶ 271 replace a failing irrigation system, and the process moves to a repaired state. In the repaired state 604, the target irrigation system may be tested and/or monitored to determine if the repair was effective. In one example, testing and monitoring may include an analysis of sensor data captured by the sensors . . . if the repair has been deemed or determined to be effective, then in the Effective state 606, the operation and performance of the irrigation system may be monitored for one or more periods of time to determine if the repairs are persistent over time. In one example, monitoring may include an analysis of sensor data provided by one or more Wireless transceivers 218, 226”; 60 FIG. 2 shows the control unit 10 in more details. The system employs a processor with various sensors such as cameras, soil sensors, leaf analytics, GPS/Position sensors, wind sensors, among others; 85 sensors can be used for maintenance prediction . . . irrigation system can monitor brake pad wear and adjusting how hard the brake needs to be applied in light of other vehicles and how fast does the irrigation system need to come to a complete stop . . . estimate the state of a component based on its repair service record. In that regard, the processor may query data or an external database (e.g., a server with which the irrigation system is in wireless communication) for repair records and estimate the wear on a component based on the length of time since the last repair; 265 information obtained from sensors and metadata with performance analysis and benchmarking information and/or information identifying whether repairs result in improved or achievement of expected levels of performance . . . until the repair is determined to have been effective”; 268 Sensor generated information and metadata may be processed at deployed systems in real-time and the results may be stored locally and downloaded for evaluation at a later time, or transmitted periodically through a network for evaluation at a central location. In this manner, results gathered and processed at wireless transceiver deployed devices may be aggregated, analyzed and/or reviewed centrally”;
(¶ 271 In an initial state, repairs are considered to be in a pending state . . . service technician may be provided with information identifying an irrigation system to be repaired . . . replace a failing irrigation system, and the process moves to a repaired state. In the repaired state 604, the target irrigation system may be tested and/or monitored to determine if the repair was effective. In one example, testing and monitoring may include an analysis of sensor data captured by the sensors”; 277 A time-series analysis may be applied to identify features for a single sensor over time, and/or to determine trends or changes, which may indicate the onset of failure. Asset classification may be used to classify or tag assets based on computed values, changes over time, etc. Asset classification may consider all data to determine if an asset should be tagged for repair, for example. Asset tags can be added or removed based on trends; 298; Asset tags may be added, deleted and/or changed in a manner that characterizes the state-of-health of a irrigation system and status of repairs associated with the irrigation system. In one example, asset tagging may include a status indicator that can be incremented, decremented, or otherwise modified to indicate progress, status, state of a repair and/or or changes in performance. The asset tag may be used to control a service provider's next action or response. In this manner, the service provider may remain engaged and accountable until the repair is deemed effective and/or persistent, as determined by desired or targeted levels”)
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of effective filing date for the comparison differential of Merg to be between pre-repair sensor data of the particular vehicle and the post-repair sensor data of the particular vehicle, including at least one sensor, as taught by Tran, since Tran teaches pre-repair and normal post-repair baselines could be used interchangeably as a reference for post-repair comparison data (Tran, ¶ 289 “One or more algorithms may be applied to compare one or more post-repair baselines with corresponding pre-repair baselines and/or normal baselines to determine if the repair was effective”). In addition, pre-repair data for a particular vehicle including the same at least one sensor would provide a more accurate basis for repair validation since normal operation data for a particular vehicle is more representative of that vehicles functioning than averaged operation data for similar types of vehicles. Furthermore, the modification allows for efficient repair notifications so a repair may be attempted again (Tran, ¶ 289) and allows for a vehicle specific settling time ensuring accurate evaluation of the persistence of the repair (Tran, ¶ 290-294). In addition, the system of Tran can be automated such that the pre-repair sensor data requires no manual input and facilitates efficient repair and maintenance practices, i.e., including invoices can conditioned on effectiveness of repair (¶ 293 smart sensors; 297-299).
In addition, Merg fails to explicitly disclose the term “digital twin” to describe the stored representation of the vehicle.
Masuda, from the same field of endeavor, discloses generating a digital twin of the vehicle using obtained sensor data (abstract, “generating a digital twin of a vehicle. The method includes receiving digital data recorded by a sensor and describing the vehicle as it exists in a real-world . . . includes updating the digital twin of the vehicle based on the digital data describing the vehicle so that the digital twin is consistent with a condition of the vehicle as it exists in the real-world. The method includes executing a simulation based on the digital twin”; ¶¶ 6, 8, 9 includes updating the digital twin of the vehicle based on the digital data describing the vehicle so that the digital twin is consistent with a condition of the vehicle as it exists in the real-world. The method includes executing a simulation based on the digital twin; 42 generating a simulated version of the vehicle in its new state based on vehicle model for that vehicle (i.e., pre-repair) . . . generating a digital twin based on the modified vehicle model that accurately represents the vehicle in its modified state (i.e., post-repair); 104-106; 110; 125; 127-132, i.e., 131 additional instances of onboard data 156 and/or measured data 172 are received by the scheduler system 199 for this particular vehicle 123. The modeling application 133 modifies the modified digital twin data 177 based on one or more instances of onboard data 156 and measured data 172 that are received from the vehicle 123, and thereby generates a second version of the modified digital twin data 177 (e.g., the Nth modified vehicle model data 161N depicted in FIG. 1B) based on the prior version of the modified digital twin data 177 (e.g., the first modified vehicle model data 161A depicted in FIG. 1B) and the additional instances of one or more of the following: one or more additional instances of onboard data 156; and one or more additional instances of measured data 172)
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of effective filing date to modify the vehicle sensor data of Merg in view of Tran, i.e., the pre and post repair baselines cited above, and use a digital twin framework as taught by Masuda, i.e., by replicating the condition of the vehicle as a whole and individual components as indicated by measured data via simulation (Masuda, ¶¶ 6, 8) such that the difference model is a comparison of pre and post repair digital twins in order to provide a more accurate, persistent and predictive representation of the vehicle and component state before and after repair (i.e., Masuda, ¶ 122 more realistic simulations) rather than relying on isolated sensor snapshots thereby directly improving the repair effectiveness validation already performed in Tran. The modification is a simple substitution of a known data modeling technique (raw baselines) for another known improved technique (digital twin modeling) that yields predictable results: better repair validation, reduced comebacks, and persistent monitoring of whether the mechanics stated repair actually occurred and endured. There is a reasonable expectation of success for at least the reason that all references are in the analogous art of data driven asset maintenance and repair validation wherein Masuda integrate the same type of sensor and repair shop data used by Merg in view of Tran.
With respect to claims 3, 10 and 17, Merg in view of Tran and further in view of Masuda disclose obtaining characteristic data corresponding the one or more pre-repair components and the one or more post-repair components and extracting features from the obtained characteristic data, wherein the extracted features include performance data.
(Merg, 110, FIG. 1 “obtain initial condition data (pre-repair) for vehicle V”; 440, FIG. 4A-4B, obtain pre-repair data D1; 520, FIG. 5; ¶¶ 165 obtain initial condition data D4; 30 at block 110, where initial condition data related to a vehicle V, representing a device-under-service, at the onset of a visit to a repair facility can be obtained. The initial condition data can include data from any pre-repair checks, observations, or tests run on the vehicle before any repairs are performed on the vehicle)
(Tran, ¶¶ 285-295 “when a current (or “pre-repair”) baseline may be obtained from measurements taken for a targeted irrigation system, prior to the start of the repair. The current baseline and/or one or more other baselines may be created for any attributes that can be measured or calculated for the irrigation system . . . Next, the system determines that a repair is to be performed for a irrigation system . . . Next, the repair is initiated . . . [a]fter the repair is physically completed, the repaired irrigation system may be allowed to stabilize prior to capturing one or more post-repair baselines from measured attributes and performance parameters of the repaired irrigation system . . . post-repair baselines may be used to determine if the repair was effective . . . [o]ne or more algorithms may be applied to compare one or more post-repair baselines with corresponding pre-repair baselines and/or normal baselines to determine if the repair was effective”)
(Masuda, 42 generating a simulated version of the vehicle in its new state based on vehicle model for that vehicle (i.e., pre-repair) . . . generating a digital twin based on the modified vehicle model that accurately represents the vehicle in its modified state (i.e., post-repair); 104-106; 110; 125; 127-132, i.e., 131 additional instances of onboard data 156 and/or measured data 172 are received by the scheduler system 199 for this particular vehicle 123. The modeling application 133 modifies the modified digital twin data 177 based on one or more instances of onboard data 156 and measured data 172 that are received from the vehicle 123, and thereby generates a second version of the modified digital twin data 177 (e.g., the Nth modified vehicle model data 161N depicted in FIG. 1B) based on the prior version of the modified digital twin data 177 (e.g., the first modified vehicle model data 161A depicted in FIG. 1B) and the additional instances of one or more of the following: one or more additional instances of onboard data 156; and one or more additional instances of measured data 172)
With respect to claims 5, 12 and 19, Merg in view of Tran and further in view of Masuda disclose the pre-repair sensor data and the post-repair sensor data is obtained by IoT sensors.
(Tran, ¶¶ 7 sensors such as IoT (internet of things) sensors can share data; 259 With the sensors, the system's predictive maintenance leverages the Internet of Things (IoT) by continuously analyzing real-time equipment sensor data via machine monitoring to understand when maintenance will be required)
Claims 4, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170132578 to Merg et al. (Merg) in view of U.S. 20200359582 to Tran et al. (Tran) and further in view of US 20120035803 to Singh et al. (Singh)
With respect to claims 4, 11 and 18, Merg in view of Tran and further in view of Masuda disclose applying the difference model further comprises applying the difference model to the pre-repair digital twin and the post-repair digital twin
(i.e., Tran ¶¶ 285-295 “post-repair baselines may be used to determine if the repair was effective . . . [o]ne or more algorithms may be applied to compare one or more post-repair baselines with corresponding pre-repair baselines and/or normal baselines to determine if the repair was effective” as modified by Masuda ¶¶ 42 generating a simulated version of the vehicle in its new state based on vehicle model for that vehicle (i.e., pre-repair) . . . generating a digital twin based on the modified vehicle model that accurately represents the vehicle in its modified state (i.e., post-repair); 104-106; 110; 125; 127-132, i.e., 131 additional instances of onboard data 156 and/or measured data 172 are received by the scheduler system 199 for this particular vehicle 123. The modeling application 133 modifies the modified digital twin data 177 based on one or more instances of onboard data 156 and measured data 172 that are received from the vehicle 123, and thereby generates a second version of the modified digital twin data 177 (e.g., the Nth modified vehicle model data 161N depicted in FIG. 1B) based on the prior version of the modified digital twin data 177 (e.g., the first modified vehicle model data 161A depicted in FIG. 1B) and the additional instances of one or more of the following: one or more additional instances of onboard data 156; and one or more additional instances of measured data 172)
However, Merg in view of Tran and further in view of Masuda fail to disclose applying the difference model comprises determining whether a threshold KL divergence exists
Singh, from the same field of endeavor, also discloses detecting outliers in difference models in the context of vehicle sensor and repair data (abstract) including identifying faults based on probability distribution differentials (¶¶ 1-6 “identifying detection of faults and anomalies in the service repair data . . . parameter identification-based fault isolation technique . . . to detect faults in a circuit of a current serviced vehicle) determining whether a threshold KL divergence exists (¶¶ 35 divergence of the PIDs is used for determining fault classification; 37-39 Fault classification can be performed by determining the Kulback-Leibler divergence (KL divergence) between the joint probability of the independent components in the training set PIDs and that of the PIDs in the testing set. The KL divergence quantifies the proximity between two probability distribution functions; 41-48; Fig. 8-9)
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of effective filing date to Merg in view of Tran and further in view of Masuda to determine whether a threshold KL divergence exists between the pre-repair data and the post-repair in order to accurately isolate particular vehicle circuit faults in a quicker and more efficient manner (Singh, ¶¶ 1-6, 35-39 and 41-48)
Claims 6-7, 13-14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US 20170132578 to Merg et al. (Merg) in view of U.S. 20200359582 to Tran et al. (Tran) and further in view of US 20190266295 to Masuda et al. (Masuda) and further in view of US 20190251489 to Berti et al. (Berti)
With respect to claims 6, 13 and 20, Merg in view of Tran and further in view of Masuda disclose fail to explicitly disclose the IoT sensors include RFID sensors
Berti, from the same field of endeavor, also discloses tracking and updating digital twin data (¶ 98) in IoT environments (¶ 168) for vehicle repair including use of sensor operating data (¶ 111) further including use of RFID sensors (¶¶ 93-96).
Accordingly, it would have been obvious to one of ordinary skill in the art at the time of effective filing date to implement RFID tags for various component parts in the vehicle in order to streamline and increase ease of tracking, logging and identifying components for pre and post repair scenarios (Berti, ¶¶ 93-96 “tracking of total log entries of the asset to identify if any updates or changes have occurred on the asset. Another element of the present invention, applicable to some assets or their parts, is serialization of an asset such that each unit or collection of units of the asset is tagged with a unique serial number. Such serialization allows for tracking of an individual asset throughout the performance and maintenance life cycle, in addition to the encryption key . . . asset tag may be any number of technologies such as: a radio frequency identification (RFID) tag, a Near-field Communication (NFC) tag, a Bluetooth® tag, a Quick Response (QR) code, a bar code, a uniform resource identifier (URL), a polyester tag, a bar-code polyester tag, a metal tag, or any tag known in the art . . . the electronic asset, part, and maintenance log provides a means for recording each change made to the asset, part, and maintenance of the asset, change history, including recording of specifications and identity (e.g., serial number(s)) of each asset in the system . . . initial custody record includes a description of the asset, and any applicable identifying information, including production year, model, configuration, bill of material, diagrams, images, specifications and parts associated with the equipment).
With respect to claims 7 and 14, Merg in view of Tran in view of Masuda and further in view of Berti disclose
obtaining data related to represented vehicle repairs, extracting features from the obtained data related to the represented vehicle repairs and comparing the extracted features from the obtained data related to the represented vehicle repairs with the extracted features corresponding to the one or more pre-repair components and the one or more post-repair components.
(Tran, ¶ 13 “autonomous irrigation vehicles”; 271 Notifications may be generated or received indicating whether the repairs prove to be effective) wherein the vehicle repair validation comparison is between obtained pre-repair sensor data and post-repair sensor data; ¶¶ 285-295 “when a current (or “pre-repair”) baseline may be obtained from measurements taken for a targeted irrigation system, prior to the start of the repair. The current baseline and/or one or more other baselines may be created for any attributes that can be measured or calculated for the irrigation system . . . Next, the system determines that a repair is to be performed for a irrigation system . . . Next, the repair is initiated . . . [a]fter the repair is physically completed, the repaired irrigation system may be allowed to stabilize prior to capturing one or more post-repair baselines from measured attributes and performance parameters of the repaired irrigation system . . . post-repair baselines may be used to determine if the repair was effective . . . [o]ne or more algorithms may be applied to compare one or more post-repair baselines with corresponding pre-repair baselines and/or normal baselines to determine if the repair was effective”)
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 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.
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/KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667
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