DETAILED ACTION
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 .
Claim Rejections – 35 U.S.C. § 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 claims, 1-20 are directed to a judicial exception (i.e., law of nature, natural phenomenon, abstract idea) without providing significantly more.
Step 1
Step 1 of the subject matter eligibility analysis per MPEP § 2106.03, required the claims to be a process, machine, manufacture or a composition of matter. Claims 15-20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of statutory subject matter because the broadest reasonable interpretation of the “computer readable storage medium” encompasses signals per se. The specification discloses, “Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing,” [0113]. A claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter. See MPEP 2106.03(II).
It is suggested that claims 15-20 be amended to recite (specifically) a “non-transitory” computer readable medium to overcome this rejection. Accordingly, Claims 15-20 are rejected for failing to recite statutory subject matter under 35 U.S.C. 101.
Step 2A
Claims 1-20 are directed to abstract ideas, as explained below.
Prong one of the Step 2A analysis requires identifying the specific limitation(s) in the claim under examination that the examiner believes recites an abstract idea, and determining whether the identified limitation(s) falls within at least one of the groupings of abstract ideas of mathematical concepts, mental processes, and certain methods of organizing human activity.
Step 2A-Prong 1
The claims recite the following limitations that are directed to abstract ideas, which can be summarized as being directed to a method, the abstract idea, of predicting part consumption using machine learning and flight demographics to reduce downtime and improve system efficiency.
Claim 1 discloses: A method, comprising:
accessing historical aircraft part consumption data indicating prior consumption of aircraft parts; (following rules or instructions, observation, evaluation, judgment, opinion),
determining historical aircraft flight demographics associated with the historical aircraft part consumption data; (following rules or instructions, observation, evaluation, judgment, opinion),
training a model based on the historical aircraft part
consumption data and the historical aircraft flight demographics; (following rules or instructions, observation, evaluation, judgment, opinion), and
deploying the model to predict future aircraft part consumption, (following rules or instructions, observation, evaluation, judgment, opinion).
Additional limitations employ the method defining consumption data as the amount of part types consumed for maintenance for each respective part type during a window of time, (following rules or instructions, observation, evaluation, judgment, opinion – claim 2), where flight demographics includes amounts of each part type in service during a time period, flight times of the part type, numbers of flights for the part type, and destinations of the part types, (following rules or instructions, observation, evaluation, judgment, opinion – claim 3), where training a model comprises generated predicted part consumption by processing the historical demographics and determining a difference between the historical and predicted part consumption and refining the model, (following rules or instructions, observation, evaluation, judgment, opinion – claim-4), forecasting future demographics and predicting future consumption by processing future demographics using the model, (following rules or instructions, observation, evaluation, judgment, opinion – claim 5), where forecasting demographics comprises for each type of aircraft, the number of aircraft that will be in use, and one or more exogenous indicators, (following rules or instructions, observation, evaluation, judgment, opinion – claim 6), where forecasting for numbers of each aircraft type comprises, determining the current number of aircraft of each type, determining the number of expected deliveries of the type, and the number of retirements of aircraft of the type, (following rules or instructions, observation, evaluation, judgment, opinion – claim 7), where the one or more exogenous indicators comprise gross domestic product, crude oil price, and stock price of one or more airlines, (following rules or instructions, observation, evaluation, judgment, opinion – claim 8)
Each of these claimed limitations employ: organizing human activity in the form of following rules or instructions or performing mental processes including, observation, evaluation, judgement, and opinion.
Claims 9-20 recite similar abstract ideas as those identified with respect to claims 1-8.
Thus, the concepts set forth in claims 1-20 recite abstract ideas.
Step 2A-Prong 2
As per MPEP § 2106.04, while the claims 1-20 recite additional limitations which are hardware or software elements such as a machine learning model, a processor, a memory storage device including instructions, a computer-readable storage medium having computer-readable program code embodied, executable by the computer processors, these limitations are not sufficient to qualify as a practical application being recited in the claims along with the abstract ideas since these elements are invoked as tools to apply the instructions of the abstract ideas in a specific technological environment. The mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP § 2106.05 (f) & (h)).
Evaluated individually, the additional elements do not integrate the identified abstract ideas into a practical application. Evaluating the limitations as an ordered combination reveals an aircraft parts consumption system that employs historical data to determine parts consumption, and trains a machine learning model to predict future consumption of aircraft parts. The model leverages the knowledge from the historical data against planned future operational schedules, predicted flight demographics, aircraft lifecycle availability and exogenous factors to predict future parts consumption based on the operational schedules. The machine learning model determines differences between the historical aircraft part consumption and the predicted aircraft parts consumption to refine the model based on the differences noted. The result is an improvement to the technical field of aircraft maintenance by predicting aircraft parts requirements resulting in improving logistics support efficiency for the operational aircraft fleet, and is determined to be a practical application.
Since the limitations in the claims 1-14 transform the exception into a practical application, claims 1-14 are not rejected under 35 U.S.C. § 101. For claims 15-20, a claim whose BRI covers both statutory and non-statutory embodiments embraces subject matter that is not eligible for patent protection and therefore is directed to non-statutory subject matter, and claims 15-20 are rejected under 35 U.S.C. § 101.
Claim Rejections 35 U.S.C. §102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102(a)(1) that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on
sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-2, 9-10, 15, are rejected under 35 U.S.C. § 102(a)2 as being taught by
Esmalifalak, (US 20220277263 A1), hereafter Esmalifalak, “ System and Method for Predictive Inventory.”
Regarding Claim 1, A method, comprising:
accessing historical aircraft part consumption data indicating prior consumption
of aircraft parts; Esmalifalak teaches, ( systems and methods for forecasting usage of one or more parts. Such systems may receive historical usage data, generate machine learning models, generate predictions using the machine learning models, and perform one or more actions based on the generated predictions, [Abstract],
determining historical aircraft flight demographics associated with the historical
aircraft part consumption data; (Historical work data may include, for example, the amount of work being done by each of one or more users/organizations/entities, what times of year that work is being done, and the type and quantity of parts being used for that work, [0021],
training a machine learning model based on the historical aircraft part
consumption data and the historical aircraft flight demographics; (based on analysis of the historical usage data, a correlation between usage of the part and work orders and to train a machine learning model based on the correlation; a part forecasting module configured to determine a predicted amount of future demand for the part based on the machine learning model, [claim 9], and
deploying the machine learning model to predict future aircraft part consumption, (a machine learning model which may learn, based on historical work data, how much work a user/organization/entity may be doing in the future, and the type and quantity of parts that future work will require, [0022]).
Regarding Claim 2, The method of claim 1, wherein the historical aircraft part consumption data comprises, for each respective part type of a plurality of part types, a respective amount of the respective part type that was consumed for aircraft maintenance during a window of time, Esmalifalak teaches, (training, by the computing device, a machine learning model based on the historical usage data, wherein the training comprises: discovering correlations between usage of the part and work orders based on analysis of the historical usage data, and training the machine learning model based on the correlations, [Claim 1].
Claims 9-10 and 15 are rejected for reasons corresponding to those provided for Claims 1-2. In these claims, the addition of a processor and a memory (claims 9-10), or a computer program product, (claim 15), does not change the rational for the rejections under 35 U.S.C § 102 or the referenced prior art, Esmalifalak teaches each server 102, 106 and client computing device 108 includes a processor114, memory 116, persistent storage 118, network interface 120 and input/output interface 122, [0032], and a non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations described in the invention, [claim 16].
Claim Rejections 35 U.S.C. §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 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 6-8, 13-14, 19-20, are rejected under 35 U.S.C. § 103 as being taught by
Esmalifalak, (US 202202277263 A1), hereafter Esmalifalak, “System and Method for Predictive Inventory,” in view of Jiang, (US 8131582-B2), hereafter Jiang, “Application of Discrete Choice Theory to Forecasting Aircraft Retirements and Fleet Composition.”
Regarding Claim 6, The method of claim 5, wherein forecasting the future aircraft flight demographics comprises:
forecasting, for each respective aircraft type of a plurality of aircraft types, a
respective number of aircraft of the respective type that will be used; Esmalifalak does not teach, Jiang teaches, (an apparatus and method for forecasting aircraft fleet composition, aircraft retirements and fleet spares requirements, includes obtaining aircraft fleet data, obtaining data on independent variables, determining a cycle indicator, determining the operation type of the aircraft, estimating the model using certain estimation steps using the aircraft fleet data, examining and updating the model based on changes in the independent variables, and forecasting on a year-to-year basis by age and operation cohort, [Abstract]), and
forecasting one or more exogenous indicators, Jiang teaches, (the use of indexes drawn from other econometric variables, such as, Consumer Price Index, or GDP, [ 5:67-6: 1]).
Esmalifalak and Jiang are both considered to be analogous to the claimed invention because they are both in the field of predicting parts requirements. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the parts forecasting techniques of Esmalifalak with the external indicators and aircraft fleet composition forecasting of Jiang so that operators are able to predict the annual fleet size and retirements by the delivery year and/or operation type of the aircraft, Jiang, [1:32-33], and accommodate for the spare parts for the air vehicles they do have in service, [1: 44-46].
Regarding Claim 7, The method of claim 6, wherein forecasting, for each respective aircraft type of a plurality of aircraft types, the respective number of aircraft of the respective type that will be used comprises, for each respective aircraft type of the plurality of aircraft types:
determining a respective current number of active aircraft associated with the
respective aircraft type; Esmalifalak does not teach, (Jiang teaches, referring to FIG. 6, the forecast can be performed in the following manner. The forecasting process includes the steps of collecting and analyzing data, estimating and evaluating fleet models, collecting forecast of independent variables and forecasting fleet composition and aircraft retirements, [7: 60-65]);
determining a respective number of expected deliveries of aircraft associated
with the respective aircraft type; (provide a fleet and retirement forecast by aircraft's delivery
year to capture the collective effects of fleet size and the aircraft age and the disclosure accommodates the need, [3:45-48]), and
determining a respective number of retirements of aircraft associated with the
respective aircraft type, (the disclosure relates to forecasting of aircraft retirements and fleet composition, [1:9-10]).
Esmalifalak and Jiang are both considered to be analogous to the claimed invention because they are both in the field of predicting parts requirements. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the parts forecasting techniques of Esmalifalak with the external indicators and aircraft fleet composition forecasting of Jiang so that operators are able to predict the annual fleet size and retirements by the delivery year and/or operation type of the aircraft, Jiang, [1:32-33], and accommodate for the spare parts for the air vehicles they do have in service, [1: 44-46].
Regarding Claim 8, The method of claim 6, wherein the one or more exogenous indicators comprise:
gross domestic product (GDP) growth;
crude oil price; and
stock price of one or more airlines, Esmalifalak does not teach, (Jiang teaches, Jiang teaches, (the use of indexes drawn from other econometric variables, such as, Consumer Price Index, or GDP, [ 5:67-6: 1]).
Esmalifalak and Jiang are both considered to be analogous to the claimed invention because they are both in the field of predicting parts requirements. It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the parts forecasting techniques of Esmalifalak with the external indicators and aircraft fleet composition forecasting of Jiang so that operators are able to predict the annual fleet size and retirements by the delivery year and/or operation type of the aircraft, Jiang, [1:32-33], and accommodate for the spare parts for the air vehicles they do have in service, [1: 44-46].
Claims 13-14, 19-20, are rejected for reasons corresponding to those provided for Claims 6-8. In these claims, the addition of a processor and a memory (claims 13-14), or a computer program product, (claims 19-20), does not change the rational for the rejections under 35 U.S.C § 103 or the referenced prior art, Esmalifalak teaches each server 102, 106 and client computing device 108 includes a processor114, memory 116, persistent storage 118, network interface 120 and input/output interface 122, [0032], and a non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations described in the invention, [claim 16].
Conclusion
Claims 3-5, 11-12, and 16-18, are not rejected by prior art under 35 U.S.C. § 103.
The closest prior art to the invention includes Esmalifalak, (US 202202277263 A1), “ System and Method for Predictive Inventory,” Butters, (US 20210119771 A1), “Systems and Methods for Providing Provenance and Anti-counterfeiting of a Part Using Blockchain Technology,” and Avery, (US 20070112486 A1), Centralized Management of Maintenance and Materials for Commercial Aircraft Fleets with Information Feedback to Customer.”
None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Regarding claim 3, The method of claim 2, wherein the historical aircraft flight demographics comprise:
amounts of each respective part type, of the plurality of part types, that were in
aircraft service during the window of time;
flight times associated with each respective part type, of the plurality of part
types, during the window of time;
numbers of flights associated with each respective part type, of the plurality of
part types, during the window of time; and
destination locales of flights associated with each respective part type, of the
plurality of part types, during the window of time. Esmalifalak does not teach flight demographics, but teaches historical usage data for said one or more parts; Historical work
data may include, for example, the amount of work being done by each of one or more users/organizations/entities, what times of year that work is being done, and the type and quantity of parts being used for that work.
Butters teaches tracking individual parts, providing provenance and anti-counterfeiting of a part using blockchain technology, for the lifecycle of the part, but not tracking numbers of parts in service, flight times per part type, or destinations for each part type, during a time period.
Avery teaches aircraft maintenance for number of flight hours an aircraft was in service, and aircraft maintenance by destination, but not by part type.
No evidence of calculating flight demographics, to include the tracking of aircraft parts by type in service, flight times, numbers of flights or destinations for the part types for any specified time period was found.
These individually or in combination did not teach the complete scope of the claim.
Regarding Claim 4, The method of claim 1, wherein training the machine learning model comprises:
generating predicted aircraft part consumption by processing the historical
aircraft flight demographics using the machine learning model; Predicting parts consumption based on processing flight demographics was not found in prior art. The closest prior art includes Esmalifalak, teaching, (systems may receive historical usage data, generate machine learning models, generate predictions using the machine learning models, and perform one or more actions based on the generated predictions, [Abstract]),
determining a difference between the historical aircraft part consumption data
and the predicted aircraft part consumption; Prior art teaches a specific approach to differences between model predictions, Esmalifalak teaches, (Models may be evaluated by, for example, calculating the corresponding root-mean-square (RMS) error. RMS error is a metric which measures the difference between observed values and values estimated by the models, [0048]), and
refining the machine learning model based on the difference. (model generation is an iterative process in which a plurality of iterations are performed. [ ] Within each iterative loop, a model may be trained and the model's hyperparameters (e.g. number and size of layer and inputs) may be tuned using an optimization process. In some embodiments, models may be updated or refreshed as new clean data 415 is received in model generation module 420, Esmalifalak, [0045]),
Regarding claim 5, The method of claim 1, further comprising:
forecasting future aircraft flight demographics; and
generating predicted future aircraft part consumption by processing the future
aircraft flight demographics using the machine learning model, Generating predicted flight demographics was not taught in the prior art. The closest prior art to the invention includes Esmalifalak, but teaches generic historical usage data applied to a machine learning model to generate predictions for usage of one or more parts.
Butters teaches parts tracking for anti-counterfeiting, but not tracking parts in service for consumption purposes; Avery tracks flight hours per flight, but for performing maintenance operations. Prior art does not specifically teach the combination of amounts of parts in service, flight times by type of part, numbers of flights by type of part or destination locales by type of part during a window of time.
Avery teaches aircraft maintenance for number of flight hours an aircraft was in service, and aircraft maintenance by destination, but not by part type.
None of the prior art alone or in combination teach the claimed invention as recited in this claim wherein the novelty is in the combination of all the limitations and not in a single limitation.
Claims 11-12, 16-18, are not rejected for reasons corresponding to those provided for Claims 3-5. In these claims, the addition of a processor and a memory (claims 11-12), or a computer program product, (claims 16-18), does not change the rational for the non-rejections under 35 U.S.C § 103 or the referenced prior art, Esmalifalak teaches each server 102, 106 and client computing device 108 includes a processor114, memory 116, persistent storage 118, network interface 120 and input/output interface 122, [0032], and a non-transitory computer-readable medium having stored thereon instructions that, in response to execution, cause a system comprising a processor to perform operations described in the invention, [claim 16].
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure or directed to the state of the art is listed on the enclosed PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL BOROWSKI whose telephone number is (703)756-1822. The examiner can normally be reached M-F 7:30 - 4:00.
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, Jerry O'Connor can be reached on (571)272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MB/
Patent Examiner, Art Unit 3624
/MEHMET YESILDAG/Primary Examiner, Art Unit 3624