Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, and 6-7 of U.S. Patent No. 12,333,865. Although the claims at issue are not identical, they are not patentably distinct from each other because the patented claims anticipate claims 1-20 of the instant application, as detailed below:
Instant Application 19/223356
US 12,333,865
1. A device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, configured to:
1. An apparatus for automating prediction of a repair for an aircraft, the apparatus comprising: …a memory device mounted to the aircraft and configured to store computer-readable program code for a knowledge-based system including an inference engine and a knowledge base; and a processing circuit mounted to the aircraft and configured to access the memory device and execute the computer-readable program code to cause the inference engine and thereby the apparatus to:
receive, from one or more sensor devices associated with a vehicle ,measurements of a plurality of operating conditions of the vehicle;
receive, via the communications device from the aircraft sensor device monitoring the aircraft operating system, a time series of measurements of a plurality of real-time operating conditions of the aircraft recorded during a flight operation of the aircraft;
identify a pattern across a plurality of clusters of the measurements, based on: compressing data points in the measurements to form compressed data, wherein the compression is based on: computing a distance between a data point of the data points and a cluster state that represents a mean value of data in a cluster of the plurality of clusters,
cluster the time series of measurements into respective clusters of measurements;
identify a pattern across the clusters of measurements,
including: compressing sensor data points in the time series of measurements to form compressed data,
wherein the compression is based on: computing a distance between a data point of the sensor data points and a cluster state that represents a mean value of data points in a cluster of a plurality of clusters of the clusters of measurements,
and assigning the cluster state to the data point based on determining that the computed distance between the data point and the cluster state is a shortest distance from a set of distances computed between the data point and cluster states of the plurality of clusters,
and assigning the cluster state to the data point based on determining that the computed distance between the data point and the state is the shortest from a set of distances computed between the data point and cluster states of the plurality of clusters,
and sequencing latent states for the compressed data, wherein the sequenced latent states are computed in a sliding window;
and sequencing latent states for the compressed data computed for n seconds in a sliding window;
define a current state of the vehicle that includes the pattern;
define a current state of the aircraft that includes the pattern across the clusters of measurements;
access a database including a set of historical data describing historical problem states including patterns across clusters of measurements of the plurality of operating conditions, wherein the plurality of operating conditions were recorded during previous instances of operation of the vehicle during which failure modes of the vehicle occurred;
access the knowledge base including a set of historical cases describing historical problem states of the aircraft and respective solutions, the historical problem states of the aircraft including patterns across clusters of measurements of the plurality of operating conditions of the aircraft recorded during previous instances of operation of the aircraft during which failure modes of the aircraft operating systems of the aircraft occurred, and the respective solutions including repair actions performed to address respective ones of the failure modes; …
and generate, based on accessing the database, an output indicating an action to address the current state.
…and generate an output display indicating the repair action to address the current state.
2. The device of claim 1, wherein the one or more sensor devices are associated with an avionic system.
[See claim 1 preamble discussing aircraft application, e.g.:] 1. An apparatus for automating prediction of a repair for an aircraft, the apparatus comprising: a communications device mounted to the aircraft and configured to communicate with an aircraft sensor device monitoring an electrical, hydraulic, or propulsion aircraft operating system of the aircraft;…
3. The device of claim 1, wherein the measurements are associated with time series measurements.
[See claim 1:] … receive, via the communications device from the aircraft sensor device monitoring the aircraft operating system, a time series of measurements of a plurality of real-time operating conditions of the aircraft recorded during a flight operation of the aircraft; …
4. The device of claim 1, wherein the one or more processors are further configured to: diagnose the current state based on diagnosing a timing of an event related to the current state.
4. The apparatus of claim 1, wherein the time series of measurements comprises fault data from a vehicle system of the vehicle systems recorded during the flight operation that is or includes a trip of the vehicle, and wherein the processing circuit further causes the apparatus to diagnose the current state that is a failure mode of the vehicle system or another of the vehicle systems from the fault data.
5. The device of claim 1, wherein the database includes information associated with the historical problem states and information associated with respective solutions to the historical problem states.
[see claim 1:] … identify, by searching the knowledge base, a historical case describing a respective solution to a historical problem state of the historical problem states similar to the current state, the respective solution being identified as a candidate solution to the current state, the respective solution to the historical problem state including a repair action; …
6. The device of claim 5, wherein the respective solutions include information associated with one or more repair actions that are weighted based on a success rate related to the one or more repair actions.
6. The apparatus of claim 1, wherein the repair actions in the knowledge base are weighted based on a success rate of the repair actions to address the respective ones of the failure modes, wherein one or more of the historical problem states of one or more of the historical cases match or are within a defined margin of matching the current state, and wherein searching the knowledge base includes selecting the historical case having a highest weighted repair action and thereby a highest success rate of the repair actions of the one or more of the historical cases.
7. The device of claim 1, wherein the current state is indicated by a failure mode, wherein the failure mode is reported by the vehicle during operation of the vehicle.
7. The apparatus of claim 1, wherein the current state is indicated by a failure mode reported by the aircraft during the flight operation that is or includes a flight of the aircraft.
The remaining claims 8-20 in the instant application have similar limitations to claims 1-7 above, and are likewise rejected based on a similar rationale. Specifically:
Claims 8 and 15 are similar to claim 1,
Claims 9 and 16 are similar to claim 2,
Claims 10 and 17 are similar to claim 3,
Claims 11 and 18 are similar to claim 4,
Claims 12 and 19 are similar to claim 5,
Claims 13 and 20 are similar to claim 6, and
Claim 14 is similar to claim 7.
Thus, the remaining claims are likewise rejected on the ground of nonstatutory double patenting, similar to claims 1-7 above.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
In January, 2019 (updated October 2019), the USPTO released new examination guidelines setting forth a two-step inquiry for determining whether a claim is directed to non-statutory subject matter. According to the guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear that claim 1 is directed toward non-statutory subject matter, as shown below:
STEP 1: Does claim 1 fall within one of the statutory categories? Yes. The claim is directed toward a process, which falls within one of the statutory categories.
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea? Yes, the claim is directed to an abstract idea.
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claim 1 recites:
A device, comprising:
one or more memories; and one or more processors, coupled to the one or more memories, configured to:
receive, from one or more sensor devices associated with a vehicle, measurements of a plurality of operating conditions of the vehicle;
identify a pattern across a plurality of clusters of the measurements, based on:
compressing data points in the measurements to form compressed data, wherein the compression is based on:
computing a distance between a data point of the data points and a cluster state that represents a mean value of data in a cluster of the plurality of clusters, and
assigning the cluster state to the data point based on determining that the computed distance between the data point and the cluster state is a shortest distance from a set of distances computed between the data point and cluster states of the plurality of clusters, and
sequencing latent states for the compressed data, wherein the sequenced latent states are computed in a sliding window;
define a current state of the vehicle that includes the pattern;
access a database including a set of historical data describing historical problem states including patterns across clusters of measurements of the plurality of operating conditions, wherein the plurality of operating conditions were recorded during previous instances of operation of the vehicle during which failure modes of the vehicle occurred;
and generate, based on accessing the database, an output indicating an action to address the current state.
The highlighted portion of claim 1 above recites mathematical calculations and is therefore an abstract idea. It merely consists of identifying a pattern across a plurality of clusters of measurements based on compressed data points, based on computing a distance between a data point and a cluster state, assigning the cluster state to the data point based on a shortest distance from the cluster out of a set of clusters, sequencing latent states for the compressed data in a sliding window, defining a current state that includes the determined pattern, and accessing a database to find a matching historical problem state pattern. Thus, the claim merely describes mathematical calculations at a high level.
The mathematical concepts grouping is defined as mathematical relationships, mathematical formulas or equations, and mathematical calculations. The Supreme Court’s rationale for identifying these "mathematical concepts" as judicial exceptions is that a ‘‘mathematical formula as such is not accorded the protection of our patent laws,’’ Diamond v. Diehr, 450 U.S. 175, 191, 209 USPQ 1, 15 (1981) (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ2d 673 (1972)), and thus ‘‘the discovery of [a mathematical formula] cannot support a patent unless there is some other inventive concept in its application.’’ Parker v. Flook, 437 U.S. 584, 594, 198 USPQ2d 193, 199 (1978).1
A claim that recites a mathematical calculation, when the claim is given its broadest reasonable interpretation in light of the specification, will be considered as falling within the "mathematical concepts" grouping. There is no particular word or set of words that indicates a claim recites a mathematical calculation. That is, a claim does not have to recite the word "calculating" in order to be considered a mathematical calculation. For example, a step of "determining" a variable or number using mathematical methods or "performing" a mathematical operation may also be considered mathematical calculations when the broadest reasonable interpretation of the claim in light of the specification encompasses a mathematical calculation.2
As such, a identifying a pattern based on mathematical operations including compressing (or clustering) data and sequencing the latent state data in order to find a matching historical pattern represents mere mathematical calculation, i.e. an abstract idea. The mere nominal recitation that the calculations are being performed by a computer does not take the limitation out of the mathematical calculation grouping. Notably, the claim does not positively recite any limitations regarding actual use of the identified pattern and historical pattern data in controlling the vehicle in a specific manner or actually mitigating the issue. Thus, the claim recites a mathematical calculation.
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim 1 recites:
A device, comprising:
one or more memories; and one or more processors, coupled to the one or more memories, configured to:
receive, from one or more sensor devices associated with a vehicle, measurements of a plurality of operating conditions of the vehicle;
identify a pattern across a plurality of clusters of the measurements, based on:
compressing data points in the measurements to form compressed data, wherein the compression is based on:
computing a distance between a data point of the data points and a cluster state that represents a mean value of data in a cluster of the plurality of clusters, and
assigning the cluster state to the data point based on determining that the computed distance between the data point and the cluster state is a shortest distance from a set of distances computed between the data point and cluster states of the plurality of clusters, and
sequencing latent states for the compressed data, wherein the sequenced latent states are computed in a sliding window;
define a current state of the vehicle that includes the pattern;
access a database including a set of historical data describing historical problem states including patterns across clusters of measurements of the plurality of operating conditions, wherein the plurality of operating conditions were recorded during previous instances of operation of the vehicle during which failure modes of the vehicle occurred;
and generate, based on accessing the database, an output indicating an action to address the current state.
The highlighted portion of claim 1 above does not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. The control system in the body of the claim does not constitute a particular machine or manufacture that is integral to the claim, either. It merely receives data, performs the mathematical calculation, and does not perform any further meaningful functions.
Also, as noted above, merely including instructions to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea is indicative that the judicial exception has not been integrated into a practical application. In the instant case, the calculations are performed on a computer, and thus it is clear that the abstract idea is merely implemented on a computer, which is indicative of the abstract idea having not been integrated into a practical application.
The receiving measurements steps recited in the claim are recited at a high level of generality (i.e., as a general means of gathering an electronic representation of an area or navigational data or planned path data), and amount to mere data gathering, which is a form of insignificant extra-solution activity.
The output generating steps are also recited at a high level of generality (i.e. as a general action or change being taken based on the results of the generating steps) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity.
The one or more data networks, one or more processors, one or more memories storing computer readable instructions, and the computer readable storage medium comprising computer-readable instructions merely describes how to generally “apply” the otherwise mental judgments in a generic or general-purpose computing environment. The one or more data networks, one or more processors, one or more memories storing computer readable instructions, and the computer readable storage medium comprising computer-readable instructions are recited at a high level of generality and merely automate the generating steps.
The additional limitation of a sensor is claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more is more than a drafting effort designed to monopolize the exception.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? No, the claim does not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically, that examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
Claim 1 does not recite any specific limitation or combination of limitations that are not well-understood, routine, conventional (WURC) activity in the field. Receiving and calculating data are fundamental, i.e. WURC, activities performed by computers, such as the computer in claim 1. Further, applicant’s specification does not provide any indication that the process steps are performed using anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere performance of an action is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function.
CONCLUSION
Thus, since claim 1 is: (a) directed toward an abstract idea, (b) does not recite additional elements that integrate the judicial exception into a practical application, and (c) does not recite additional elements that amount to significantly more than the judicial exception, it is clear that claim 1 is directed towards non-statutory subject matter.
Independent claims 8 and 15 have similar limitations to claim 1 above, and are therefore rejected based on a similar rationale.
Dependent claims 2-7, 9-14, and 16-20 are likewise rejected. The claims either add to the mental process (claims 4-7, 11-14, and 18-20) or add mere data gathering (claims 2-3, 9-10, and 16-17). Thus, the dependent claims are likewise rejected as ineligible.
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over US20200310400 by Jha et al. (hereinafter “Jha”), further in view of US20160071334 by Johnson et al. (hereinafter “Johnson”).
Regarding claim 1, Jha teaches A device, comprising: one or more memories; and one or more processors, coupled to the one or more memories, see for example Figs. 1A and 1B, as well as paragraphs [0037]-[0044], describing system architecture.
configured to: receive, from one or more sensor devices associated with a see for example paragraphs [0038] and [0045]-[0046] describing acquisition of time series sensor measurements.
identify a pattern across a plurality of clusters of the measurements, see for example paragraph [0047] describing capturing target and current machine states based on classification.
based on: compressing data points in the measurements to form compressed data, wherein the compression is based on: computing a distance between a data point of the data points and a cluster state that represents a mean value of data in a cluster of the plurality of clusters, see for example paragraph [0067], describing clustering data and classifying the sensor measurements to a cluster based on the cluster centroids.
and assigning the cluster state to the data point based on determining that the computed distance between the data point and the cluster state is a shortest distance from a set of distances computed between the data point and cluster states of the plurality of clusters, see again for example paragraph [0067], describing clustering data and classifying the sensor measurements to a cluster based on the cluster centroids.
and sequencing latent states for the compressed data, wherein the sequenced latent states are computed in a sliding window; see for example paragraphs [0064]-[0067] and [0050]-[0053], where the system embeds the time series measurement data in order to predict future faults based on measurement time windows.
define a current state of the vehicle that includes the pattern; see for example paragraphs [0050]-[0051] and [0008], where the system learns behaviors based on historical operation and time series measurements in order to predict future faults of the machine.
access a database including a set of historical data describing historical problem states including patterns across clusters of measurements of the plurality of operating conditions, wherein the plurality of operating conditions were recorded during previous instances of operation of the see for example paragraphs [0050]-[0051], [0061]-[0064], and [0008], where the system learns behaviors based on historical operation and time series measurements in order to predict future faults of the machine.
and generate, based on accessing the database, an output indicating an action to address the current state.
Jha does not explicitly teach applying the system to a vehicle, though paragraph [0045] strongly suggests such. Neither does Jha explicitly teach that the system should generate, based on accessing the database, an output indicating an action to address the current state.
However, Johnson suggests associating vehicle faults in a database. See for example paragraph [0025] where the system in question can be a vehicle, such as an aircraft.
Johnson also suggests that a database system should generate, based on accessing the database, an output indicating an action to address the current state. See for example paragraphs [0089], where the system retrieves a potential fix from a database.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fault prediction system of Jha with the repair association database of Johnson with a reasonable expectation of success. Doing so allows the system to determine not only a present or future fault, but also an associated repair action for that fault, greatly increasing the utility of the system.
Claims 8 and 15 have similar limitations to claim 1 above, and are therefore rejected based on a similar rationale.
Regarding claim 2, Jha does not explicitly teach, but Johnson teaches wherein the one or more sensor devices are associated with an avionic system. See for example paragraph [0025] where the system in question can be a vehicle, such as an aircraft.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fault prediction system of Jha with the repair association database of Johnson with a reasonable expectation of success. Doing so allows the system to determine not only a present or future fault, but also an associated repair action for that fault, greatly increasing the utility of the system.
Claims 9 and 16 have similar limitations to claim 2 above, and are therefore rejected based on a similar rationale.
Regarding claim 3, Jha teaches wherein the measurements are associated with time series measurements. See for example paragraphs [0038] and [0045]-[0046] describing acquisition of time series sensor measurements.
Claims 10 and 17 have similar limitations to claim 3 above, and are therefore rejected based on a similar rationale.
Regarding claim 4, Jha teaches wherein the one or more processors are further configured to: diagnose the current state based on diagnosing a timing of an event related to the current state. See for example paragraphs [0009], [0039]-[0040], [0047], and [0055], where the system accepts current time-series data representing the current state of the machine in order to predict a future state.
Claims 11 and 18 have similar limitations to claim 4 above, and are therefore rejected based on a similar rationale.
Regarding claim 5, Jha does not explicitly teach, but Johnson teaches wherein the database includes information associated with the historical problem states and information associated with respective solutions to the historical problem states. See for example paragraphs [0089]-[0090], where the system retrieves repair actions based on historical results for the given malfunction.
Claims 12 and 19 have similar limitations to claim 5 above, and are therefore rejected based on a similar rationale.
Regarding claim 6, Jha does not explicitly teach, but Johnson teaches wherein the respective solutions include information associated with one or more repair actions that are weighted based on a success rate related to the one or more repair actions. See for example paragraph [0090], where the repair information includes a successful fix percentage associated with the repair information.
Claims 13 and 20 have similar limitations to claim 6 above, and are therefore rejected based on a similar rationale.
Regarding claim 7, Jha teaches wherein the current state is indicated by a failure mode, wherein the failure mode is reported by the . See for example paragraphs [0037], [0040], and [0047], where the system captures the current state of the system.
Jha does not explicitly teach a vehicle.
However, Johnson teaches wherein the current state is indicated by a failure mode, wherein the failure mode is reported by the vehicle during operation of the vehicleSee for example paragraphs [0026]-[0027] and [0056], where vehicle faults are identified during operation. See for example paragraph [0025] where the system in question can be a vehicle, such as an aircraft.
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the fault prediction system of Jha with the repair association database of Johnson with a reasonable expectation of success. Doing so allows the system to determine not only a present or future fault, but also an associated repair action for that fault, greatly increasing the utility of the system.
Claim 14 has similar limitations to claim 7 above, and is therefore rejected based on a similar rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US20190096145 by Sundareswara et al. teaching machine learning aircraft fault classification and prediction, including latent state sequencing of time-series measurement data.
JP2019095836A by Shinohara et al. teaching classification of vehicle states to detect defect patterns.
CN110110803A by Pan et al. teaching fault clustering for failure diagnosis.
CN109871862A by Fang et al. teaching K-means fault clustering of time sequence data using machine learning.
US20230058585 by Honda et al. teaching time-series sensor data clustering and latent state sequencing to predict failure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JORDAN THOMAS SMITH whose telephone number is (571)272-0522. The examiner can normally be reached Monday - Friday, 9am - 5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached at (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JORDAN T SMITH/ Examiner, Art Unit 3666
1 See MPEP 2106.04(a)(2).
2 See id.
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