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 .
DETAILED ACTION
Status of Claims
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission for Application #18/644,263, filed on 03/11/2026, has been entered. The following is a NON-FINAL OFFICE ACTION in response to the request for continued examination.
Claims 1-20 are now pending and have been examined.
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 non-statutory subject matter. The rationale for this finding is explained below.
Per Step 1 of the analysis, the claims are analyzed to determine if they are directed to statutory subject matter. Claim 1 claims a system comprising one or more processors. Therefore, the claim is interpreted as an apparatus. An apparatus is a statutory category for patentability. Claim 12 claims a method, or process. A process is a statutory category for patentability. Claim 20 claims a non-transitory computer-readable storage medium. Therefore, the medium is interpreted as an article of manufacture, which is a statutory category for patentability. Further, the claim is in conformity with the Kappos Memorandum of 2010 regarding medium claims, as it includes the phrase “non-transitory.”
Per Step 2A, Prong 1 of the analysis, the examiner must now determine if the claims are directed to an abstract idea or eligible subject matter. In the instant case, the independent claims are directed towards an abstract idea. Specifically, independent claim 1 is directed to “collating historical work records related to a vehicle model, calculating from the historical work records probabilities of one or more non-conformances that includes a degraded condition during an induction of a vehicle, wherein the induction is an overall record for a heavy maintenance visit, predict the one or more non-conformances that includes the degraded condition and labor time to resolve the one or more non-conformances, and outputting information regarding labor time.” Therefore, the claims are directed to an abstract idea, namely a mental process. A human operator with access to the historical work records can collate the records, calculate probabilities (analyze the data using math), predict labor time (make a judgment or form an opinion), and output manually, verbally, or by other means their judgment or opinion. This could all be done as part of a mental process. The processor simply automates the mental process using a computer. Therefore, the claims are determined to be directed to an abstract idea, namely a mental process.
Per Step 2A, Prong 2 of the analysis, the examiner must now determine if the claims integrate the abstract idea into a practical application. The additional elements of the claims include “one or more processors,” “a user interface display,” and a “storage medium.” However, these additional elements are considered generic recitations of a technical element and are recited at a high level of generality. These additional elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and do not integrate the abstract idea into a practical application. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The claims also include the actual outputting of an electronic signal to a user interface display and showing the information on the display. Absent further detail, this additional element is considered “receiving and/or transmission of data over a network,” listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning- see “receiving or transmittal of data over a network,” citing TLI Communications, OIP Techs v Amazon.com, and buySAFE v Google. Therefore, this additional element is not considered to integrate the abstract idea into a practical application. The limitation added by amendment, automatically control one or more maintenance devices to perform one or more maintenance operations during the labor time based on the one or more non-conformances predicted and the labor time predicted, absent further detail, is considered a generic recitation of a technical element and is recited at a high level of generality, as “automatically control” has no detail as to how this is performed. Are instructions simply sent to a processor of the device? The types of maintenance devices, as added by amendment, do not change the analysis, as the type of maintenance device is considered insignificant extra-solution activity and this additional element does not integrate the abstract idea into a practical application.
Per Step 2B of the analysis, the examiner must now determine if the claims include limitations that are “significantly more” than the abstract idea by demonstrating an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The additional elements of the claims include “one or more processors,” “a user interface display,” and a “storage medium.” However, these additional elements are considered generic recitations of a technical element and are recited at a high level of generality. These additional elements are being used as “tools to automate the abstract idea” (see MPEP 2106.05 (f)), and are not considered significantly more than the abstract idea itself. They are not recitations of a special purpose computer or transformation (see MPEP 2106.05 (b) and (c)). The claims also include the actual outputting of an electronic signal to a user interface display and showing the information on the display. Absent further detail, this additional element is considered “receiving and/or transmission of data over a network,” listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning- see “receiving or transmittal of data over a network,” citing TLI Communications, OIP Techs v Amazon.com, and buySAFE v Google. Therefore, this additional element is not considered significantly more. The limitation added by amendment, automatically control one or more maintenance devices to perform one or more maintenance operations during the labor time based on the one or more non-conformances predicted and the labor time predicted, absent further detail, is considered a generic recitation of a technical element and is recited at a high level of generality, as “automatically control” has no detail as to how this is performed. Are instructions simply sent to a processor of the device? The types of maintenance devices, as added by amendment, do not change the analysis, as the type of maintenance device is considered insignificant extra-solution activity and this additional element does not integrate the abstract idea into a practical application. The claim automates the abstract idea using a computer, and absent further detail is considered a generic recitation of a technical element and is recited at a high level of generality and not significantly more than the abstract idea itself.
When considered as an ordered combination, the claim is still considered to be directed to an abstract idea as the claim steps in the ordered combination simply recite the logical steps for collating the historical records, calculating probabilities, predicting a labor time, and outputting the results. Therefore, the ordered combination does not lead to a determination of significantly more.
When considering the dependent claims, claim 2 is considered part of the abstract idea, as the information in the historical records does not change how the steps of the mental process are performed. Claim 3 includes an “interface display.” This additional element is considered a generic recitation of a technical element and is recited at a high level of generality. This additional element is being used in a conventional manner to do what displays do, namely to transmit and show information, and is therefore not considered significantly more than the abstract idea itself. It is not a recitation of a special purpose interface or transformation (see MPEP 2106.05 (b) and (c)). The claims also include the actual showing of the information on the interface display. Absent further detail, this additional element is considered “receiving and/or transmission of data over a network,” listed in the MPEP 2106.05 (d) (II) (i) as an example of conventional computer functioning- see “receiving or transmittal of data over a network,” citing TLI Communications, OIP Techs v Amazon.com, and buySAFE v Google. Therefore, this additional element is not considered significantly more. Claims 4-8 are considered part of the abstract idea, as determining a probability is still considered part of the analysis of the mental process. Claim 9 is considered part of the abstract idea, as predicting labor time is considered part of the analysis and making a judgment in the mental process. For claim 10, the types of maintenance devices, as added by amendment, do not change the analysis, as the type of maintenance device is considered insignificant extra-solution activity and this additional element does not integrate the abstract idea into a practical application. Claim 11 is considered the equivalent of “apply it,” or using a computer as a tool to automate the abstract idea (see MPEP 2106.05 (f)). The control unit being recited as simply being an AI or ML system does not change the analysis, as absent further detail as recited it is simply a generic recitation of a technical element and is recited at a high level of generality. There is no detail as to how the control unit being an AI or ML system changes the steps of the invention. Is there a trained model? Is there training done? Does the system use any kind of learning/feedback loop? Therefore, as written the system being an AI or ML system does not change the analysis as not being significantly more than the abstract idea itself. The other dependent claims mirror those already discussed above.
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. See Alice Corporation Pty. Ltd. Vs. CLS Bank International et al., 2014 (please reference link to updated publicly available Alice memo at http://www.uspto.gov/patents/announce/alice_pec_25jun2014.pdf as well as the USPTO January 2019 Updated Patent Eligibility Guidance.)
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.
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 1-5, 7, 10-15, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, et al. “Research on Predictive Maintenance of Aircraft based on Long Short-Term Memory Neural Network.” 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, November 2022 (included as a PDF with this Office Action) in view of Yang, et al., Pre-Grant Publication No. 2018/0130030 A1 and in further view of Mojtahedzadeh, et al., Pre-Grant Publication No. 2020/0210968 A1 and in further view of Georgeson, Pre-Grant Publication No. 2020/0363822 A1.
Regarding claims 1, 12, and 20, Lee teaches:
A system comprising: one or more processors configured to:
collate historical work records related to a vehicle model (see page 152 at Section III in which the predictive maintenance database is established by obtaining, organizing, and correlating in the database aircraft type data, flight data, navigation-related parameters, historical maintenance data of aircraft components, and other data; the examiner notes that “collate” is interpreted by broadest reasonable interpretation to mean its ordinary meaning in the information arts to be such as to collect and combine data and information into some kind of order or organization)
calculate, from the historical work records, probabilities of one or more non- conformances during an induction of a vehicle, wherein the induction is an overall record for a heavy maintenance depot visit (see page 150-151, Section 1, and page 152 Section 3 and Figure 2 in which probabilities for needed maintenance during an airplane depot visit are calculated based on historical maintenance records)
predict labor time to resolve the one or more non-conformances (see page 153, top of Column 1, in which “this method uses digital and artificial intelligence skills to calculate and predict the maintenance and replacement time of aircraft parts”)
Lee, however, does not appear to specify:
output an electronic signal including information regarding the labor time, as predicted by the one or more processors, to a user interface display
showing…the labor time, as predicted by the one or more processors, on the user interface display (claim 12 only)
Yang teaches:
output an electronic signal including information regarding the labor time, as predicted by the one or more control units, to a user interface display and showing…the labor time, as predicted by the one or more control units, on the user interface display (claim 12 only) (see at least Figure 4 #404, [0050], [0149], and claim 17 in which the results of the control unit are displayed on an interface, the results pertain to maintenance prediction and scheduling for airplanes)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Yang with Lee because Lee already teaches predictive maintenance times and other information or those making maintenance decisions and outputting them on an interface would allow for readily available visual data, allowing those making the decisions to have easy access to the data and make quick and informed decisions.
Lee and Yang, however, does not appear to specify:
one or more non-conformances that includes a degraded condition
Mojtahedzadeh teaches:
one or more non-conformances that includes a degraded condition (see [0031]-[0033] in which the maintenance work tasks that are predicted as being probable include a level of corrosion, wear, or other such degraded condition)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Mojtahedzadeh with Lee and Yang because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance such as part replacement, and determining a probability for corrosion, wear, or other degraded condition applies the teachings to an aspect of airplanes that is always present to some capacity but might normally be more difficult to discern when the best time is to address the issue.
Lee, Yang, and Mojtahedzadeh, however, does not appear to specify:
wherein the one or more control units are further configured to automatically control one or more maintenance devices to perform one or more maintenance operations during the labor time
Georgeson teaches:
automatically control one or more maintenance devices to perform one or more maintenance operations during the labor time based on the labor time predicted, (see Abstract, Figures 4 and 8, [0035], and [0040] in which drones are controlled to do repairs on airplanes when a necessary repair is detected)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Georgeson with Lee, Yang, and Mojtahedzadeh because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for types of maintenance on airplanes, and using drones and other such devices to complete the repairs would allow for quick and efficient repairs on airplanes, which are large and more difficult for humans to position themselves for some repairs.
Lee, Yang, Mojtahedzadeh, and Georgeson, however, does not appear to specify:
automatically control one or more maintenance devices based on the one or more non-conformances predicted)
Lee however teaches identification of non-conformances in page 153, top of Column 1, in which “this method uses digital and artificial intelligence skills to calculate and predict the maintenance and replacement time of aircraft parts.” Mojtahedzadeh teaches the non-conformances including a degraded condition. And Georgeson teaches automatically control one or more maintenance devices to perform one or more maintenance operations during the labor time based on the labor time predicted, in such as Abstract, Figures 4 and 8, [0035], and [0040] in which drones are controlled to do repairs on airplanes when a necessary repair is detected.
Therefore, it would be obvious to one of ordinary skill in the art at the time of filing of the application to combine automatically control one or more maintenance devices based on the one or more non-conformances predicted with Lee, Yang, Mojtahedzadeh, and Georgeson because Lee however teaches identification of non-conformances in page 153, top of Column 1, in which “this method uses digital and artificial intelligence skills to calculate and predict the maintenance and replacement time of aircraft parts.” Mojtahedzadeh teaches the non-conformances including a degraded condition. And Georgeson teaches automatically control one or more maintenance devices to perform one or more maintenance operations during the labor time based on the labor time predicted, in such as Abstract, Figures 4 and 8, [0035], and [0040] in which drones are controlled to do repairs on airplanes when a necessary repair is detected and basing the control decisions for the maintenance devices on also the non-conformance prediction would allow for a better and earlier decision on the possibility of non-conformance, which would allow for the potential prevention of the degraded condition, leading to longer overall vehicle life.
Regarding claims 2 and 13, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1…
Lee further teaches:
wherein the historical work records specify one or more work tasks, non-conformances discovered during the one or more work tasks, any replacement parts required for the one or more work tasks, and one or more past labor times required to resolve the one or more non-conformances (see page 152 Section III and page 154 Section IV in which the historical records include aircraft type data, historical maintenance data of aircraft components, maintenance and service data of parts)
Regarding claim 3, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1
Lee further teaches:
labor time, as predicted by the one or more control units (see page 153, top of Column 1, in which “this method uses digital and artificial intelligence skills to calculate and predict the maintenance and replacement time of aircraft parts”)
Yang further teaches:
a user interface display, wherein the one or more control units are further configured to show the labor time, as predicted by the one or more control units, on the user interface display (see at least Figure 4 #404, [0050], [0149], and claim 17 in which the results of the control unit are displayed on an interface, the results pertain to maintenance prediction and scheduling for airplanes)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Yang with Lee because Lee already teaches predictive maintenance times and other information or those making maintenance decisions and outputting them on an interface would allow for readily available visual data, allowing those making the decisions to have easy access to the data and make quick and informed decisions.
Regarding claims 4 and 14, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1…
Mojtahedzadeh further teaches:
wherein the one or more control units are further configured to determine a probability of the induction having a given work task, and a probability of the induction having one or more corrosion non-conformances (see Abstract, [0028]-[0030], and [0045]-[0047] which teaches prediction of a maintenance work task and [0031]-[0033] in which the maintenance work tasks that are predicted include a level of corrosion)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Mojtahedzadeh with Lee and Yang because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance such as part replacement, and determining a probability for corrosion applies the teachings to an aspect of airplanes that is always present to some capacity but might normally be more difficult to discern when the best time is to address the issue.
Regarding claims 5 and 15, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 4…
Lee further teaches:
wherein the one or more control units are configured to determine a probability of having the one or more non-conformances for the given task work from the probability of the induction having the given work task (see page 150-151, Section 1, and page 152 Section 3 and Figure 2 in which probabilities for needed maintenance during an airplane depot visit are calculated based on historical maintenance records; see also page 153 Column 2 which gives an example of a probability of a given work task to replace a part)
Mojtahedzadeh further teaches:
the probability of the induction having the one or more corrosion non-conformances (see Abstract, [0028]-[0030], and [0045]-[0047 which teaches prediction of a maintenance work task and [0031]-[0033] in which the maintenance work tasks that are predicted include a level of corrosion)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Mojtahedzadeh with Lee and Yang because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance such as part replacement, and determining a probability for corrosion applies the teachings to an aspect of airplanes that is always present to some capacity but might normally be more difficult to discern when the best time is to address the issue.
Regarding claims 7 and 17, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1…
Lee further teaches:
wherein the one or more control units are further configured to determine a probability having a given work task with the one or more non- conformances requiring a part (see page 150-151, Section 1, and page 152 Section 3 and Figure 2 in which probabilities for needed maintenance during an airplane depot visit are calculated based on historical maintenance records; see also page 153 Column 2 which gives an example of a probability of a given work task to replace a part)
Regarding claim 10, the combination of Lee, Yang, and Mojtahedzadeh teaches:
the system of claim 1
Lee, Yang, and Mojtahedzadeh, however, does not appear to specify:
the maintenance devices selected from a group consisting of ultrasonic inspection devices, x-ray imaging systems, vision systems, and crawlers
Georgeson teaches:
the maintenance devices selected from a group consisting of ultrasonic inspection devices, x-ray imaging systems, vision systems, and crawlers(see Abstract, Figures 4 and 8, [0035], and [0040] in which drones are controlled to do repairs on airplanes when a necessary repair is detected; see also [0060] in which the UAV’s/drones have ultrasonic and/or x-ray sensors to use for maintenance inspections, which would therefore make them ultrasonic inspection devices or x-ray imaging systems as claimed)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Georgeson with Lee, Yang, and Mojtahedzadeh because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for types of maintenance on airplanes, and using drones and other such devices to complete the repairs would allow for quick and efficient repairs on airplanes, which are large and more difficult for humans to position themselves for some repairs.
Regarding claim 11, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1
Lee further teaches:
wherein the one or more control units is an artificial intelligence or machine learning system (see at least Section III which teaches using digital intelligence or AI models to predict the maintenance needs)
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, et al. “Research on Predictive Maintenance of Aircraft based on Long Short-Term Memory Neural Network.” 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, November 2022 (included as a PDF with this Office Action) in view of Yang, et al., Pre-Grant Publication No. 2018/0130030 A1 and in further view of Mojtahedzadeh, et al., Pre-Grant Publication No. 2020/0210968 A1 and in further view of Georgeson, Pre-Grant Publication No. 2020/0363822 A1 and in further view of Official Notice.
Regarding claims 6 and 16, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1…
Lee, Yang, Mojtahedzadeh, and Georgeson, however, does not appear to specify:
wherein the one or more control units are further configured to determine a probability that a disposition code is for a given work task
The examiner takes Official Notice that it is old and well known to use disposition or task/work codes when doing various types of maintenance, repair, and operations on such as machinery, fleets of vehicles, and other such equipment. Companies such as heavy machinery companies, builders, and vehicle manufacturing companies have done so for over a decade prior to the effective filing date of the application.
Therefore, it would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine wherein the one or more control units are further configured to determine a probability that a disposition code is for a given work task with Lee, Yang, Mojtahedzadeh, and Georgeson because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for such as airplanes, and using disposition codes would allow for specific tasks to be easily understood and communicated when making decisions based on predictions for maintenance.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, et al. “Research on Predictive Maintenance of Aircraft based on Long Short-Term Memory Neural Network.” 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, November 2022 (included as a PDF with this Office Action) in view of Yang, et al., Pre-Grant Publication No. 2018/0130030 A1 and in further view of Mojtahedzadeh, et al., Pre-Grant Publication No. 2020/0210968 A1 and in further view of Georgeson, Pre-Grant Publication No. 2020/0363822 A1 and in further view of Adegan, Patent No. 10,360,601 B1.
Regarding claims 8 and 18, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1…
Mojtahedzadeh further teaches:
a given work task with corrosion (see Abstract, [0028]-[0030], and [0045]-[0047 which teaches prediction of a maintenance work task and [0031]-[0033] in which the maintenance work tasks that are predicted include a level of corrosion)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Mojtahedzadeh with Lee and Yang because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance such as part replacement, and determining a probability for corrosion applies the teachings to an aspect of airplanes that is always present to some capacity but might normally be more difficult to discern when the best time is to address the issue.
Lee, Yang, Mojtahedzadeh, and Georgeson, however, does not appear to specify:
wherein the control unit is configured to predict the labor time, at least in part, by determining an average labor time as a sum of man hours for a given work task divided by a historical count of given work tasks
Adegan teaches:
wherein the control unit is configured to predict the labor time, at least in part, by determining an average labor time as a sum of man hours for a given work task divided by a historical count of given work tasks (see Column 10, lines 59-65, Column 19, lines 5-31, Column 31, line 59-Column 32, line 5 which uses a regression to estimate based on past similar repairs, and Column 32, lines 23-26 in which the historical data is used to identify past similar repairs of vehicles in order to determine the predicted labor hours for the current repair; the Examiner notes that while the reference does not specifically say it uses a division in its calculation, the use of an algorithm as described would inherently take the average of all past repairs with some kind of weighted vector for the number of times in order to calculate based on all the other repairs what the current labor time for the current repair would be, and therefore this is considered an obvious variant; the Examiner notes that while this reference does not teach the repair to be to “corrosion,” that aspect has already been taught above by another reference)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Adegan with Lee, Yang, Mojtahedzadeh, and Georgeson because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance, and using past similar repairs and averaging them would allow for an informed calculation based on already completed similar repairs for the same vehicle type.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee, et al. “Research on Predictive Maintenance of Aircraft based on Long Short-Term Memory Neural Network.” 2022 Asia Conference on Advanced Robotics, Automation, and Control Engineering, November 2022 (included as a PDF with this Office Action) in view of Yang, et al., Pre-Grant Publication No. 2018/0130030 A1 and in further view of Mojtahedzadeh, et al., Pre-Grant Publication No. 2020/0210968 A1 and in further view of Georgeson, Pre-Grant Publication No. 2020/0363822 A1 and in further view of Adegan, Patent No. 10,360,601 B1 and in further view of Ashikawa, et al., Pre-Grant Publication No. 2014/0046735 A1.
Regarding claims 9 and 19, the combination of Lee, Yang, Mojtahedzadeh, and Georgeson teaches:
the system of claim 1…
Lee further teaches:
a probability of a work task having a non-conformance (see page 150-151, Section 1, and page 152 Section 3 and Figure 2 in which probabilities for needed maintenance during an airplane depot visit are calculated based on historical maintenance records)
Mojtahedzadeh further teaches:
a given work task with corrosion (see Abstract, [0028]-[0030], and [0045]-[0047 which teaches prediction of a maintenance work task and [0031]-[0033] in which the maintenance work tasks that are predicted include a level of corrosion)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Mojtahedzadeh with Lee and Yang because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance such as part replacement, and determining a probability for corrosion applies the teachings to an aspect of airplanes that is always present to some capacity but might normally be more difficult to discern when the best time is to address the issue.
Lee, Yang, Mojtahedzadeh, and Georgeson, however, does not appear to specify:
wherein the control unit is configured to determine an average labor time required for each work task
Adegan teaches:
wherein the control unit is configured to determine an average labor time (see Column 10, lines 59-65, Column 19, lines 5-31, Column 31, line 59-Column 32, line 5 which uses a regression to estimate based on past similar repairs, and Column 32, lines 23-26 in which the historical data is used to identify past similar repairs of vehicles in order to determine the predicted labor hours for the current repair; the Examiner notes that while the reference does not specifically say it uses a division in its calculation, the use of an algorithm as described would inherently take the average of all past repairs with some kind of weighted vector for the number of times in order to calculate based on all the other repairs what the current labor time for the current repair would be, and therefore this is considered an obvious variant; the Examiner notes that while this reference does not teach the repair to be to “corrosion,” that aspect has already been taught above by another reference)
It would be obvious to one of ordinary skill in the art before the effective date of filing of the application to combine Adegan with Lee, Yang, Mojtahedzadeh, and Georgeson because Lee already teaches predictive maintenance times and other information or those making maintenance decisions for other types of maintenance, and using past similar repairs and averaging them would allow for an informed calculation based on already completed similar repairs for the same vehicle type.
Lee, Yang, Mojtahedzadeh, Georgeson, and Adegan, however, does not appear to specify:
wherein the control unit is configured to predict the labor time, at least in part, by determining an overall value for all work tasks by multiplying…
Ashikawa teaches:
wherein the control unit is configured to predict the labor time, at least in part, by determining an overall value for all work tasks by multiplying… (see [0056] in which the average work time is multiplied by the to be performed tasks in order to arrive at an overall value)
It would be obvious to one of ordinary skill in the art at the time of filing of the application to combine Ashikawa… with Lee, Yang, Mojtahedzadeh, Georgeson, and Adegan because Lee and Adegan already teach the values being multiplied, and determining an overall value by using multiplication as taught by Ashikawa to arrive at one of dozens various values that could be arrived at by one of ordinary skill in the art using simple known mathematics from the values already shown to be determined by the cited references, and therefore the “multiplying” is considered an obvious combination of the references that would allow for decisions regarding repair to be based on both probability and labor time, which is an important measure of cost, while being able to make comparisons using a single value.
Response to Arguments
Regarding the rejections based on 35 USC 101:
Regarding the applicant's argument on page 7 of the response that a human being would be incapable of efficiently analyzing the amounts of data in such a short time: The examiner responds that the speed of a computer versus a human does not change the analysis of a mental process. Almost every computer-based technology is faster than a human. Yet, there are a vast number of Supreme Court decisions (buySAFE v Google, OIP Techs v Amazon.com, even Alice Corp. itself) in which multiple servers, processors, networks, and other components are recited that clearly perform steps faster than a human, yet the claims were still found to be patent ineligible.
Regarding the applicant's argument on page 8 of the response based on the Enfish decision: In the Enfish decision, the Court pointed out that the claimed invention created a new type of database that was an improvement on databases that are found in or used by generic computers, and the specification laid out the improvements. So, each and every computer that uses this database literally functions faster and needs less memory and processing time when storing, accessing, and using the data. In the claimed invention, the components are each doing what they do. The processor is being used to perform the calculations and predictions. There is no improvement to the processor. An electronic signal used to display the output is not an improved signal or display.
Regarding the applicant' s arguments on page 8 of the response based on the McRo decision: In the McRo decision, the software rules do not simply automate known human animation processes, but they go beyond what could be done manually and the rules include a new and improved technical process for allowing an otherwise generic computer to match the audio to the animation during the animation process of the lip-sync game. In the present invention, the invention is "rooted in computer technology" only insofar in that a processor and electronic signal are used to automate the mental steps and the outputting step. The amendment only adds "automatically control" with no detail as to how this is done, much less a specific set of technical steps. Further, the claimed invention does not include elements that the examiner can point to that, as stated by the Court in the McRo decision, include an "improvement to computer-related technology" by use of a set of "rules." In the McRo decision, the computer does not simply automate a known set of processes, but improves the known techniques for animation in such a manner that does not simply automate what a human operator could do.
Regarding the applicant' s argument on pages 9-10 of the response that the claims integrate the judicial exception into a practical application: The applicant quotes the MPEP 2106 as stating that claims integrate a judicial exception into a practical application when they include "an improvement in the functioning of a computer, or an improvement to other technology or technical field." The examiner agrees no such improvement in the claimed invention. The claims only USE a processor and a display (a vehicle is not actually recited) to automate the abstract idea and display the results. These are not improvements to the computer, the technology, or the technical field. Further, the applicant points out Example 20 of the UPSTO's 101 examples. In claim 20, the entire claim set is directed to a robotic arm assembly. The assembly is an improved robotic arm assembly that performs specific functions. The current claimed invention uses a generic processor to calculate and predict and then send an output to an interface for display. This is not an improved processor, but a processor automating mental steps.
Regarding the applicant' s argument on pages 10-12 of the response based on the Kim Memo that the examiner is oversimplifying the claims and that the applicant improves a technical field: The examiner first responds that the claims were considered as a whole. There is even a separate section in the 101 analysis in which the claims are considered as a whole. Further, the 101 rejection in this case is not a close call. The applicant states that the "claims clearly present an improvement to a computer," but there is no improvement whatsoever to the computer. The computer is being used to perform calculating and predicting steps using math and an algorithm. This is in sharp contrast to Enfish, in which the computer processes data faster and more efficiently and is therefore improved. Regarding the applicant' s argument on claim 11 that the claims pose no risk of preemption and are more than a drafting effort meant to monopolize the judicial exception, and therefore are patent eligible, the examiner responds as follows: Page 8 of the USPTO July 2015 Update for Subject Matter Eligibility, when discussing the role of preemption, states that “courts do not use preemption as a stand-alone test for eligibility” (see as reference buySAFE, 765 F.3d at 1355, Ultramercial, 772 F.3d at 716). Further, it states that “the absence of complete preemption does not guarantee that a claim is eligible” (Alice Corp., 134 S. Ct. at 2358).
Therefore, the arguments are not persuasive and the rejection is sustained.
Regarding the rejections based on 35 USC 103:
Regarding the applicant's argument on page 13 of the response that there is no prima facie case of obviousness to support the combination with Yang: The examiner disagrees, as Lee's goal is to manage fleet maintenance by getting ahead of potential maintenance issues, and anyone managing the fleet would customarily view some kind of data regarding the management of the maintenance. Further, In many situations there is neither a motivation or evident lack of motivation to make a modification articulated in cited references. Numerous scenarios typically find the prior art reference disclosing aspects of claimed subject matter, but fail to specifically point the way toward the combination to arrive at Applicant' s invention. A judgment must be made whether a person of ordinary skill in the art would have had sufficient motivation to combine individual elements forming the claimed invention, as in this particular situation. In re Clinton, 527 F.2d 1226, 1228, 188 USPQ 365, 367 (CCPA 1976). A suggestion, teaching, or motivation to combine the relevant prior art teachings does not have to be found explicitly in the prior art, as the teaching, motivation, or suggestion may be implicit from the prior art as a whole, rather than expressly stated in the references. . . . The test for an implicit showing is what the combined teachings, knowledge of one of ordinary skill in the art, and the nature of the problem to be solved as a whole would have suggested to those of ordinary skill in the art. In re Leonard R. Kahn (CAFC, 04-1616, 3/22/2006). KSR forecloses the argument that a specific teaching is required for a finding of obviousness (citing KSR, 127 S.Ct. at 1741, 82 USPQ2d at 1396). See Board decision Ex parte Smith, --USPQ2d--, slip op. at 20, (Bd. Pat. App. & Interf. June 25, 2007). "It must be presumed that the artisan knows something about the art apart from what the references disclose. In re Jacoby, 309 F.2d 513, 135 USPQ 317 (CCPA 1962). The problem cannot be approached on the basis that artisans would only know what they read in references; such artisans must be presumed to know something about the art apart from what the references disclose. In re Jacoby. Also, the conclusion of obviousness may be made from common knowledge and common sense of a person of ordinary skill in the art without any specific hint of suggestion a particular reference. In re Bozek, 416 F.2d 1385, USPQ 545 (CCPA 1969). And, every reference relies to some extent on knowledge or persons skilled in the art to complement that which is disclosed therein. In re Bode, 550 F.2d 656, USPQ 12 (CCPA 1977)."
Regarding the applicant’s argument on pages 15-16 of the response that the Official Notice in the rejection of claims 6 and 16 does not include any evidence:
First of all, the examiner disagrees that any reference must teach all the claim limitations of claim 6 or that the Official Notice must be for the entire claim. There is no basis for that, and the other references are shown to teach other aspects of the claim. As far as evidence, the examiner puts forth the following: Dickman, Pre-Grant Publication No. 2007/0106784 A1 at [0039]-[0040], Beaman, Patent No. 6,845,148 B1 at Abstract, Column 4, line 63-Column 5, line 8 and Column 20, line 66-Column 21, line 65. Azzouz, et al., Pre-Grant Publication No. 2008/0071410 A1 at [0042]-[0044]. Beamon, Pre-Grant Publication No. 2005/0100137 A1. In the prior Office Action, Parsons, et al., Pre-Grant Publication No. 2020/0082639 A1 and Fulton, et al., Pre-Grant Publication No. 2005/0086098 A1 (see at least [0045]) were also cited.
Regarding the applicant's argument on page 16 of the response that Mojtahedzadeh and Georgeson does not teach the portion of claim 10 added by amendment: Georgeson clearly teaches in at least [0060] that the drones, which are a type of device, have x-ray or or ultrasonic sensors to use for maintenance inspections, which would therefore make them ultrasonic inspection devices or x-ray imaging systems as claimed.
All other applicant’s arguments have been considered in light of the applicant’s amendments to the claims but are MOOT in light of the new grounds of rejection necessitated by the applicant’s amendments.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Luis A. Brown whose telephone number is 571.270.1394. The Examiner can normally be reached on M-F 8:30am-4:30pm EST. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, JESSICA LEMIEUX can be reached at 571.270.3445.
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/LUIS A BROWN/
Primary Examiner, Art Unit 3626