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 .
This is a Non-Final rejection on the merits of this application. Claims 1-20 are currently pending, as discussed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Continued Examination Under 37 CFR 1.114
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 filed on 09/29/2025 has been entered.
Response to Amendment and/or Argument
Applicant’s amendments and/or arguments with respect to the Claim Rejection of Claims 1-20 under 35 USC 101 as set forth in the office action of 09 July 2025 have been considered and are NOT persuasive. Specifically, Applicant argues (Pages 8 of Applicant’s Remarks filed on 29 September 2025):
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The Examiner’s Response:
Applicant contends that the newly amended limitation “wherein the machine-learned model is re-trained as the monitoring of the plurality of aircraft of the aircraft entity detects further new signals, the re-training based on the further new signals, and wherein the ordered list has its entries changed based on the refined outputs from the machine-learned model based on the re-training.” is analogous to Example 37 of the 2019 PEG. Examiner has carefully considered Applicant’s arguments and/or amendments and respectfully disagrees.
Applicant argues that monitoring signals across an entire fleet and re-training a model is not practically performed in the human mind like claim 2 of Example 37 and therefore cannot be abstract. However, this argument is not persuasive because claim 2 of Example 37 was found eligible due to the “determining step” requires action by a processor that cannot performed in human mind (i.e. amount of use of each icon that tracks how much memory has been allocated to each app associated with each icon). In contrast, the present claim is directed to collecting information (i.e. receiving signals) and amounts to mere data gathering.
Applicant further asserts that automatically adjusting entries of the ordered list after re-training constitutes a specific manner of automatically displaying updated display elements purportedly improves the user interface like claim 1 of Example 37. Examiner would like to respond that claim 1 of Example 37 was found eligible because the claim recited a particular manner of displaying and updating files in a GUI providing a specific improvement to computer functionality itself. However, the present claim merely requires generating an ordered list of data queries and updating the order based on results of machine-learned model. The claim does not recite any particular GUI structure, layout, display protocol or other technical improvement to how the computer displays information. Updating and reordering a list based on underlying data analysis is considered presentation of information, which is not sufficient to integrate an abstract idea into a practical application. See MPEP 2106.04(a)(2) describing claims including displaying that can be practically performed in the human mind (e.g., including with a pen or pencil and paper):
In contrast, claims do recite a mental process when they contain limitations that can practically be performed in the human mind, including for example, observations, evaluations, judgments, and opinions. Examples of claims that recite mental processes include:
• a claim to "collecting information, analyzing it, and displaying certain results of the collection and analysis," where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
The examiner also notes the recent Federal Circuit case Mobile Acuity, Ltd. v. Blippar Ltd., Case No. 22-2216 (Fed. Cir. Aug. 6, 2024) where the court reiterates that presenting information and displaying data, using nothing more than the conventional operations of generic computer components, IS part of the abstract idea, and that as part of the abstract idea it cannot constitute an improvement to technology, even if it is groundbreaking or brilliant:
As we have by now frequently held, claims reciting generalized steps of collecting, analyzing, and presenting information, using nothing other than the conventional operations of generic computer components, are directed to abstract ideas. See, e.g., AI Visualize, 97 F.4th at 1378 (“We have explained that the steps of obtaining, manipulating, and displaying data, particularly when claimed at a high level of generality, are abstract concepts.”); Elec. Power Grp., 830 F.3d at 1353-54 (finding challenged claim directed to abstract idea of “collecting information, analyzing it, and displaying certain results of the collection and analysis”)
Accordingly, Applicant’s argument is NOT persuasive in this regard and 35 USC 101 rejection is maintained. See 35 U.S.C. 101 rejections below for details.
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.
Claim 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
101 Analysis – Step 1 – YES
Claim 1 is directed to non-transitory computer-readable medium, Claim 11 is directed to a method. Therefore, claims 1 and 11 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claim 11 is rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
A non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions, when executed, causing one or more processors to perform operations, the instructions comprising instructions to:
receive a data query from a source, the data query associated with a hardware component;
determine a likelihood that an aircraft of an aircraft entity associated with the data query will require the hardware component by:
retrieving a plurality of signals received from the aircraft entity that are associated with the hardware component;
applying the plurality of signals to a machine-learned model; and
receiving, as output from the machine-learned model, the likelihood that the aircraft of the aircraft entity will require the hardware component,
wherein the machine-learned model is at least partially trained by:
monitoring a plurality of aircraft of the aircraft entity for a new signal;
detecting, at a given aircraft of the aircraft entity, the new signal
determining whether, within a threshold amount of time of receiving the new signal, the hardware component was replaced, the threshold amount of time representing a typical amount of time for the hardware component to be replaced; and
updating a strength of association between the new signal and the likelihood that the hardware component will be replaced within the threshold amount of time based on whether the hardware component was replaced;
determine a score corresponding to the data query based on the likelihood that the aircraft of the aircraft entity will require the hardware component; and
generate for display an ordered list of a plurality of data queries including the data query, as ordered based on respective scores of the plurality of data queries, wherein the machine-learned model is re-trained as the monitoring of the plurality of aircraft of the aircraft entity detects further new signals, the re-training based on the further new signals, and wherein the ordered list has its entries changed based on refined outputs from the machine-learned model based on the re-training.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” and/or “mathematical concepts” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind and/or covers mathematical calculations . For example, “receive a data query…”, “determine a likelihood…”, “applying the plurality…”, “receiving… the likelihood…”, “determine whether…” and “…machine-learned model is re-trained…” steps in the context of the claim encompasses a person reviewing data to make a decision/judgement/evaluation on whether an aircraft requires a (e.g. replacement) hardware component soon either mentally or using a pen and pencil. Further, “updating a strength…”, “determine a score…” and “…re-training…” steps in the context of claims encompass a person (e.g. maintenance schedule/planner) reviewing newly received data to determine whether a component is replaced to predict a future event (e.g. predicting hardware needs) and whether the previously determined component replacement time is still valid either mentally or using a pen and paper and/or a mathematical concepts.
Examiner would also note MPEP 2106.04(a)(2)(III): The courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same). Accordingly, the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A non-transitory computer-readable medium comprising memory with instructions encoded thereon, the instructions, when executed, causing one or more processors to perform operations, the instructions comprising instructions to:
receive a data query from a source, the data query associated with a hardware component;
determine a likelihood that an aircraft of an aircraft entity associated with the data query will require the hardware component by:
retrieving a plurality of signals received from the aircraft entity that are associated with the hardware component;
applying the plurality of signals to a machine-learned model; and
receiving, as output from the machine-learned model, the likelihood that the aircraft of the aircraft entity will require the hardware component,
wherein the machine-learned model is at least partially trained by:
monitoring a plurality of aircraft of the aircraft entity for a new signal;
detecting, at a given aircraft of the aircraft entity, the new signal;
determining whether, within a threshold amount of time of receiving the new signal, the hardware component was replaced, the threshold amount of time representing a typical amount of time for the hardware component to be replaced; and
updating a strength of association between the new signal and the likelihood that the hardware component will be replaced within the threshold amount of time based on whether the hardware component was replaced;
determine a score corresponding to the data query based on the likelihood that the aircraft of the aircraft entity will require the hardware component; and
generate for display an ordered list of a plurality of data queries including the data query, as ordered based on respective scores of the plurality of data queries, wherein the machine-learned model is re-trained as the monitoring of the plurality of aircraft of the aircraft entity detects further new signals, the re-training based on the further new signals, and wherein the ordered list has its entries changed based on refined outputs from the machine-learned model based on the re-training.
For the following reason(s), the examiner submits that the above identified limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitation of “retrieving a plurality of signals…” “machine-learned model…”, “monitoring…”, “detecting…the new signal” and “generate for display…”, the examiner submits that these “retrieving…”, “monitoring…”, “detecting…” and “generating…” limitations are insignificant extra-solution activities that merely uses a computer (processor) to perform the process. In particular, the “retrieving…”, “monitoring…” and “detecting…” steps are recited at a high level of generality (i.e. as a general means of acquiring data) and amounts to mere data gathering which is a form of insignificant extra-solution activity. The “generating…” step amounts to mere post salutation activities. Lastly, “machine-learned model” merely describes how to generally “apply” the otherwise abstract ideas and/or additional limitations in a generic or general-purpose computer environment, where “machine-learned model” is recited as generic processor performing a generic computer functions of gathering and processing data. This generic “machine-learned model” limitation is no more than mere instructions to apply the exception using a generic computer component and merely automates the steps.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impost any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, representative independent claim 1 do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “machine-learned model”, the examiner submits that the machine-learned model is recited at a high-level of generality (i.e. as a generic computer component performing generic calculation) such that it amounts no more than mere instruction to apply the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations of “retrieving a plurality of signals…” “receiving a new signal…” and “generate for display…”, discussed above are insignificant extra-solutions activities.
As explained, the additional elements are recited at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. See, e.g., MPEP §2106.05; Alice Corp. v. CLS Bank, 573 U.S., 208,223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention”). Electric Power Group, LLC v, Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016) (Selecting information for collection, analysis and display constitute insignificant extra-solution activity). Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1243-44, 120 USPQ2d 1844, 1855-57 (Fed. Cir. 2016)( Generating a second menu from a first menu and sending the second menu to another location as performed by generic computer components). Hence, the claims are not patent eligible.
Dependent Claims
Dependent claims 2-10, and 12-20 do not recite any further limitations that causes the claims to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial except and/or additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10 and 12-20 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
As such, claims 1-20 are rejected under 35 USC § 101 as being drawn to an abstract idea without significant more, and thus are ineligible.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten or amended to overcome the rejection(s) under 35 U.S.C. 101 as set forth in this office action.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior art Floyd et al. (US 2019/0177008A1) disclose a system for supporting maintenance of an aircraft, comprising: a plurality of database importers configured to import a plurality of datasets from a respective plurality of data sources to a database with a composite dataset including data of the plurality of datasets, wherein the composite dataset includes at least aircraft identifier information that describes the aircraft, aircraft utilization information that indicates an in-service state of the aircraft, and data from task cards that describe maintenance tasks performed on the aircraft, an aircraft illustrated parts catalog (AIPC) that describes aircraft parts of the aircraft, and a maintenance planning document (MPD) that describes maintenance intervals for the aircraft; a database-management system (DBMS) configured to manage the database with the composite dataset; and a client application coupled to the DBMS and configured to receive a user request for a demand for replacement aircraft parts for maintenance during an in-service lifecycle of the aircraft, the client application being configured to interpret the user request to produce a query executable by the DBMS to retrieve data of the composite dataset from the database including at least the aircraft identifier information, the aircraft utilization information, and data from the task cards, the AIPC and the MPD, the client application also being configured to predict and thereby produce a prediction of the demand based on the data retrieved from the composite dataset, the prediction including a schedule of maintenance events over the in-service lifecycle of the aircraft in which numbers of aircraft parts of the aircraft are replaced with corresponding numbers of replacement aircraft parts, wherein the client application is configured to produce a graphical user interface through which the client application is configured to receive the user request, and through which the client application is configured to display a report including at least the prediction.
Further, prior art Katsuri et al. (US Patent No. 10,157,347B1) discloses A system, or platform, for processing enterprise data is configured to adapt to different domains and analyze data from various data sources and provide enriched results. The platform includes a data extraction and consumption module to translate domain specific data into defined abstractions, breaking it down for consumption by a feature extraction engine. A core engine, which includes a number of machine learning modules, such as a feature extraction engine, analyzes the data stream and produces data fed back to the clients via various interfaces. A learning engine incrementally and dynamically updates the training data for the machine learning by consuming and processing validation or feedback data. The platform includes a data viewer and a services layer that exposes the enriched data results. Integrated domain modeling allows the system to adapt and scale to different domains to support a wide range of enterprises.
Still further, prior art Gentle et al. (US 2021/0065479 A1) discloses a system and method for determining a distance of movement of a component of a machine; determining a load factor on the component over the distance of movement; estimating a remaining useful life of at least one of the component, or a wear component associated with the component, based on the distance of movement and the load factor; and performing one or more actions based on the estimated remaining useful life.
In regards to Claim 1 (Similarly claim 11), prior arts Floyd, Katsuri and Gentle either taken individually or in combination with each other or other prior art of record fails to teach or render obvious:
receive a data query from a source, the data query associated with a hardware component;
determine a likelihood that an aircraft of an aircraft entity associated with the data query will require the hardware component by:
retrieving a plurality of signals received from the aircraft entity that are associated with the hardware component;
applying the plurality of signals to a machine-learned model; and
receiving, as output from the machine-learned model, the likelihood that the aircraft of the aircraft entity will require the hardware component,
wherein the machine-learned model is at least partially trained by:
monitoring a plurality of aircraft of the aircraft entity for a new signal;
detecting, at a given aircraft of the aircraft entity, the new signal
determining whether, within a threshold amount of time of receiving the new signal, the hardware component was replaced, the threshold amount of time representing a typical amount of time for the hardware component to be replaced; and
updating a strength of association between the new signal and the likelihood that the hardware component will be replaced within the threshold amount of time based on whether the hardware component was replaced;
determine a score corresponding to the data query based on the likelihood that the aircraft of the aircraft entity will require the hardware component; and
generate for display an ordered list of a plurality of data queries including the data query, as ordered based on respective scores of the plurality of data queries, wherein the machine-learned model is re-trained as the monitoring of the plurality of aircraft of the aircraft entity detects further new signals, the re-training based on the further new signals, and wherein the ordered list has its entries changed based on refined outputs from the machine-learned model based on the re-training.
Based on the above, the combination of feature is considered to be allowable.
Claims 2-10 and 12-20 would be allowable because it is dependent on claims 1 and 11.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANA F ARTIMEZ whose telephone number is (571)272-3410. The examiner can normally be reached M-F: 9:00 am-3:30 pm EST.
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/DANA F ARTIMEZ/ Examiner, Art Unit 3667
/FARIS S ALMATRAHI/ Supervisory Patent Examiner, Art Unit 3667