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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/4/2026 has been entered.
Response to Amendment
Claims 1, 12, and 20 are amended and claims 7 and 17 are cancelled. Claims 1-6, 8-16, and 18-20 are pending.
Response to Arguments
Applicant's arguments filed 4/4/2026 have been fully considered.
Regarding the rejection of claims 1-6, 8-16, and 18-20 under 112(a), and as noted by Applicant on pages 7-8 of the response, the amendments to independent claims 1, 12, and 20 overcomes the rejections, which are withdrawn.
Regarding the rejections of claims 1-6, 8-16, and 18-20 under 101, Examiner respectfully disagrees with Applicant arguments for the following reasons.
On page 8 of the response, Applicant notes that claim 1 is amended to recite automatically controlling a physical asset during a maintenance period by reconfiguring an operational parameter of the asset, executing a command on the asset, or installing or updating software on the asset, which cannot be performed in the human mind. Applicant contends that this element in combination with the other elements (receiving operational data and generating asset-specific maintenance schedule using machine learning) require coordinated data acquisition, command transmission, remote device control, and execution of software updates none of which can be achieved manually at the level of precision, timing, and interoperability required for industrial assets.
Examiner acknowledges that the “automatically controlling” element is an additional element that falls outside the abstract idea judicial exception. Regarding the combined functions, Examiner submits that while processor implementation of each of the steps including those outside of and those falling within the judicial exception, the potential and even likely utility in terms of speed and precision of such implementation is not dispositive of whether the underlying functions constitute an abstract idea. Applicant does not appear to explain why the function of generating a maintenance schedule based on input data including alert history, maintenance standards, service history, and user-specific data cannot practically be performed in the human mind. Further regarding the combined functions, Examiner submits that the “automatically controlling” function is relatively functionally independent from the other functions (only functional relation being the maintenance actions are performed during designated “maintenance period”) such that there appears to be no reason why the automatic (via processor) implementation of maintenance tasks has any bearing on whether the scheduling determination may be performed via mental processes.
On page 9 of the response, Applicant contends that the system performs the combined steps dynamically based on the maintenance period in the generated schedule and automatically to modify/update the operational state of the asset. Applicant further asserts that this level of rule-based orchestration for remote machine control in an industrial environment is far beyond human capability and necessitates specialized software, hardware, and communication interfaces.
Examiner submits that the specialization conveyed in the claims is confined to software for implementing the steps falling within the abstract idea. The hardware and communications interfaces, individually and in combination, appear to constitute ordinary computer processing and communication components configured in a manner having no particularized functional relation relating to the particular processing steps in the claim.
On page 9 of the response, Applicant further contends that the processor-executed maintenance actions physically alter asset behavior and thereby represent technical results that improve the functioning of computer-implemented maintenance systems by enabling automated, reliable, and schedule-driven maintenance that enables correct operation of the asset.
Examiner acknowledges the potential practical utility of remote (processor implemented) maintenance. However, as noted in the grounds of rejection using a processor to implement remote maintenance was well-known in the art, and furthermore as noted above, the “automatically controlling” function is largely functionally disconnected (except for the facially obvious performance of maintenance during a “maintenance period”) from the step of generating a maintenance schedule such that there is no combined technical effect that appears to embody a technical improvement in the field of determining and implementing maintenance scheduling.
Regarding Step 2A Prong Two, Applicant contends on pages 9-10 that any alleged abstract idea is integrated into a practical application. In support, Applicant points to the “automatically controlling” step providing automatic, processor “control actions” of the asset.
Examiner notes that the “control” of the asset in the claim is provided only as a consequence (prospective operational improvement) of the maintenance action(s), such that the role of the processor is only peripherally and conjecturally related to operational control of the asset. Furthermore, such “control” appears to have little if any functional relation with respect to the steps involved in generating a maintenance schedule, which is clearly the main thrust of the claimed system/method. For example, there appears to be no relation between how the schedule is generated in terms of the types of input data, the manner in which the input data is processed (machine learning), and/or the results embodied in the generated schedule, and the processor implemented maintenance action alternatives (reconfigure an ops parameter, or execute a command on the asset, or install/update asset software) such that there is no unified practical application into which the abstract idea is integrated.
On page 10 of the response, Applicant further contends that the processor-based control of the asset such as via reconfiguring parameters, executing commands, or installing/updating software during a maintenance period constitutes a technical improvement over conventional practices. In support, Applicant asserts that the claimed configuration results in an automated, repeatable, and scalable approach to executing maintenance operations with precise timing and determinism.
Examiner submits that the potential benefits cited by Applicant’s argument are generalized (apply to almost any process for determining need for maintenance/repair of equipment) and are realized largely if not entirely as the result of computer/processor implementation of the abstract idea rather than as a result of a particularized configuration of data collection and processing steps for addressing a particular problem arising in conventional techniques. Therefore, Examiner submits that the claims do not appear to integrate the abstract idea in a manner representing a technical improvement in the field of maintenance scheduling and implementation.
Regarding Step 2B, Applicant contends on pages 10-11 of the response that amended claim 1 provides an inventive concept and amounts to significantly more than the exception itself. In support, Applicant asserts on page 11 that claim 1 provides an optimized technical solution via a “computer-implemented maintenance-control pipeline” that receives and applies heterogeneous data to a machine learning model to dynamically determine a maintenance period for an asset and “automatically controls operation of the asset during that maintenance period by performing at least one processor-executed maintenance action,” in which “the processor-executed maintenance actions are performed in accordance with the maintenance period output by the model.”
Examiner submits that executing maintenance actions, whether local/onsite and/or remote (via processor), in accordance with maintenance periods established in a maintenance schedule is not itself innovative. Furthermore, this indisputably non-innovative feature is the only apparent functional connection between the “automatically controlling” step and the other steps including generating a maintenance schedule. Given such relative functional independence between the steps, Examiner submits that there is no synergistic or otherwise meaningfully combined functional relation that conveys that the “automatically controlling” step as combined with the other claim elements results in the claim as a whole amounting to significantly more than the abstract idea.
On page 11 of the response, Applicant contends that the system implements “a non-conventional maintenance-control arrangement that is not well understood, routine, or conventional, and further alludes to the benefits of the claimed configuration as including scalability, reduced latency, consistency and reliability as supporting the content that as combined the claim elements result in the claim as a whole amounting to significantly more than the judicial exception.
Examiner submits that the configuration is largely conventional as set forth in the grounds of rejection relating to Step 2B and that the potential benefits recited in Applicant’s arguments are generalized (apply to almost any process for determining need for maintenance/repair of equipment) and are realized largely if not entirely as the result of computer/processor implementation of the abstract idea rather than as a result of a particularized configuration of data collection and processing steps for addressing a particular problem arising in conventional techniques. Therefore, Examiner submits that the claim(s) considered as a whole do not amount to significantly more than the judicial exception.
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 1-6, 8-16, and 18-20 are rejected under 35 U.S.C. 101 because the claimed invention in each of these claims is directed to the abstract idea judicial exception without significantly more.
Independent claim 12, substantially representative also of independent claims 1 and 20, recites:
“[a]n apparatus comprising:
at least one processor; and
at least one memory storing computer-coded instructions that, when executed by the at least one processor, cause the apparatus to:
receive by one or more data sources that include one or more sensors, input data comprising (i) alert history data corresponding to an asset, (ii) maintenance standards data corresponding to the asset, (iii) service history data corresponding to the asset, and (iv) user-specific data corresponding to the asset,
apply the input data to an intelligence machine learning model that generates a maintenance schedule based at least in part on the input data;
output a particular maintenance schedule corresponding to the asset via output from the intelligence machine learning model based at least in part on the input data;
automatically control operation of the asset by performing at least one or more maintenance actions during a maintenance period, wherein the one or more maintenance actions includes reconfiguring an operational parameter of the asset, executing a command on the asset, or installing or updating software on the asset; and
automatically flag at least one performance metric associated with the asset as untrustworthy during a maintenance period associated with the asset, wherein the maintenance period is represented by the particular maintenance schedule.”
The claim limitations considered to fall within in the abstract idea are highlighted in bold font above and the remaining features are “additional elements.”
Step 1 of the subject matter eligibility analysis entails determining whether the claimed subject matter falls within one of the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. Claim 1 recites a method, claim 12 recites an apparatus, and claim 20 recites an article of manufacture, and each therefore falls within a statutory category.
Step 2A, Prong One of the analysis entails determining whether the claim recites a judicial exception such as an abstract idea. Under a broadest reasonable interpretation, the highlighted portions of claim 12 fall within the abstract idea judicial exception. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, the highlighted subject matter falls within the mental processes category (including an observation, evaluation, judgment, opinion). MPEP § 2106.04(a)(2).
The recited function “apply the input data to” “generate[s] a maintenance schedule based at least in part on the input data,” may be performed as mental processes (e.g., evaluation of observed data and judgment in determining a maintenance schedule). The function “automatically flag at least one performance metric associated with the asset as untrustworthy during a maintenance period associated with the asset, wherein the maintenance period is represented by the particular maintenance schedule” in a broadest reasonable interpretation falls within the judicial exception because it can be performed via mental processes (e.g., evaluation and mental noting (flagging) of performance metrics and associations with maintenance activity that may render a performance metric as untrustworthy).
Step 2A, Prong Two of the analysis entails determining whether the claim includes additional elements that integrate the recited judicial exception into a practical application. “A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception” (MPEP § 2106.04(d)).
MPEP § 2106.04(d) sets forth considerations to be applied in Step 2A, Prong Two for determining whether or not a claim integrates a judicial exception into a practical application. Based on the individual and collective limitations of claim 12 and applying a broadest reasonable interpretation, the most applicable of such considerations appear to include: improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)); applying the judicial exception with, or by use of, a particular machine (MPEP 2106.05(b)); and effecting a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)).
Regarding improvements to the functioning of a computer or other technology, none of the “additional elements” including
“at least one processor,” “at least one memory storing computer-coded instructions,” “receive by one or more data sources that include one or more sensors, input data comprising (i) alert history data corresponding to an asset, (ii) maintenance standards data corresponding to the asset, (iii) service history data corresponding to the asset, and (iv) user-specific data corresponding to the asset,” “output a particular maintenance schedule corresponding to the asset via output from the intelligence machine learning model based at least in part on the input data,” and “automatically control operation of the asset by performing at least one or more maintenance actions during a maintenance period, wherein the one or more maintenance actions includes reconfiguring an operational parameter of the asset, executing a command on the asset, or installing or updating software on the asset,” in any combination appear to integrate the abstract idea in a manner that technologically improves any aspect of a device or system that may be used to implement the highlighted step or a device for implementing the highlighted step such as a signal processing device or a generic computer.
The processor and memory represent ordinary, non-particularized computer processing functionality having no particularized functional relation to the underlying processing steps except for implementing the steps, such that these features constitute insignificant extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
The recited “data sources” including “sensors” are not positively recited as being part of the recited “apparatus” and furthermore represent high level and ordinary data collection means having no particularized functional relation to the underlying processing steps, instead only providing a means for providing the input data to the processor, and therefore, individually and in combination with the processor/memory and other elements of the claims also constitute insignificant extra solution activity.
The function of outputting a particular maintenance schedule corresponding to the asset via output from the intelligence machine learning model based at least in part on the input data, represents computer processing functionality (outputting results based on processing) having no significant functional interrelation with the processing steps in terms such that a clear technological improvement is evident, and therefore, individually and in combination also constitutes extra solution activity. Similarly, the function of automatically controlling asset operation by performing at least one or more maintenance actions including any one of reconfiguring an asset operational parameter, executing a command on the asset, or installing/updating asset software, merely delineates possible maintenance-related operations in one of three ordinary and mutually distinct manners that do not convey any technological improvement significance as combined with the processing steps such that there appears to be no meaningful integration of the judicial exception into a practical application.
Regarding application of the judicial exception with, or by use of, a particular machine, the additional elements are configured and implemented in a conventional manner (processor and memory configured to enable execution of instructions, using sensors to collect equipment information, and a generalized maintenance scheduling outputting function including performing maintenance related actions not tied to the steps for determining the schedule or the schedule itself in any particularized manner) rather than a particularized manner of implementing maintenance scheduling of assets.
Regarding a transformation or reduction of a particular article to a different state or thing, claim 12 does not include any such transformation or reduction. Instead, claim 12 as a whole entails using conventional computer processing components (processor and memory) to receiving input information such as may include sensor-derived information ( asset information including alert history data, maintenance standards data, service history data, and user-specific data) and applying standard processing function of the processing components to implement the features constituting an abstract idea type judicial exception (i.e., generating a maintenance schedule based on the input data, implementing maintenance related actions have no particularized relation to the schedule or the manner for determining the schedule) with the additional elements failing to provide a meaningful integration of the abstract idea (generating/determining a maintenance schedule and implementing maintenance actions based on asset-specific input information) in an application that transforms an article to a different state.
In sum, the additional elements represent extra-solution activity that does not integrate the judicial exception into a practical application. In view of the various considerations encompassed by the Step 2A, Prong Two analysis, claim 12 does not include additional elements that integrate the recited abstract idea into a practical application.
Therefore, claim 12 is directed to a judicial exception and requires further analysis under Step 2B.
Regarding Step 2B, and as explained in the Step 2A Prong Two analysis, the additional elements in claim 12 constitute extra solution activity and therefore do not result in the claim as a whole amounting to significantly more than the judicial exception. Furthermore, the additional elements appear to be generic and well understood as evidenced by the disclosures of Saneyoshi (US 2020/0058081 A1) and Mishra (US 11,017,321) each of which teach virtually the same data collection, processing, and outputting structures for implementing maintenance scheduling.
Saneyoshi teaches “at least one processor (FIG. 22 computer system 120 includes processor 121; [0013]),” “at least one memory (FIG. 22 computer system 120 includes storage apparatus 122; [0013]) storing computer-coded instructions (FIG. 22 processor 121 configured to receive instructions/data from storage apparatus 122; [0013], [0270]),” and “one or more sensors” for collecting equipment data ([0066]; FIG. 7 sensor(s) 108, [0144]-[0145]), and “output a particular maintenance schedule corresponding to the asset via output from the intelligence machine learning model based at least in part on the input data ([0052] and [0148] repair determination section 101 determines repair method and timing (schedule) “for the facility” based on the input data. Examiner notes that the generation of information by computer processing (e.g., machine learning model) inherently entails an outputting of the result (repair/maintenance schedule information including repair method and time) from the machine learning model (i.e., repair determination section 101); in a further aspect of “outputting” FIG. 1 depicts repair determination section 101 configured to provide output to recovery plan creation section 102, which itself provides a form of a maintenance schedule “output” based on data from section 101; FIG. 13 step S14 providing replanning/repair timing options as output; FIG. 14 output in the form of proposals that constitute a maintenance schedule in terms of indicting timing (e.g., immediate) of maintenance/repair activities),” as does Mishra (FIG. 1 monitoring device 102 including processor 104 and memory 106, FIG. 1 sensor(s) 152, FIG. 2 block 202 (sensors for data collection), FIG. 1 output 116 via interface from monitoring device 102 to display device 140; FIG. 5 blocks 502, 504, 506, 508, and 510). Furthermore, regarding “automatically control operation of the asset by performing at least one or more maintenance actions during a maintenance period, wherein the one or more maintenance actions includes reconfiguring an operational parameter of the asset, executing a command on the asset, or installing or updating software on the asset,” Mishra teaches that remote maintenance action(s) by a processor were known (col. 40 lines 23-31) as does Yang (US 2017/0205791 A1) (FIG. 13 and [0258]).
Therefore, the additional elements are insufficient to amount to significantly more than the judicial exception.
Independent claim 12 is therefore not patent eligible under 101.
Independent claims 1 and 20 includes substantially the same elements falling within the judicial exception as claim 12 and include no additional elements that either integrate the judicial exception into a practical application or result in the claim as a whole amounting to significantly more than the judicial exception. Claims 1 and 12 are therefore also not patent eligible under 101.
Claims 2-6 and 8-11 depending from claim 1, and claims 13-16 and 18-19 depending from claim 12 provide additional features/steps which are part of an expanded algorithm that includes the abstract idea of claims 1 and 12 (Step 2A, Prong One). None of dependent claims 2-6, 8-11, 13-16, and 18-19 recite additional elements that integrate the abstract idea into practical application (Step 2A, Prong Two), and all fail the “significantly more” test under the step 2B for substantially similar reasons as discussed with regards to the independent claims.
For example, claim 2, representative also of claim 13, characterizes the nature of the data generated by the “applying” element in claim 1 and therefore is an extension of and also falls within the judicial exception recited in claim 1.
Claim 3, representative also of claim 14, further recites the additional element that the machine learning model “outputs at least one narrative associated with the maintenance schedule” and further describes the nature of the data in the narrative, which entails conventional, routine data processing functions (outputting data generated by processing means) and therefore constitutes extra-solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole as amounting to significantly more than the judicial exception.
Claim 4, substantially representative also of claim 15, further recites “wherein the intelligence machine learning model comprises at least one natural language processing model that generates the at least one narrative in a natural language format.” Generating at least one narrative in natural language format falls within the judicial exception (entailed within the “applying” element in claim 1) because it may be performed via mental processes (e.g., evaluation of input data and judgement/opinion as to an articulation of the manner of maintenance scheduling). Using a machine learning model that comprises (e.g., an extension of the processing) a natural language processing model to implement this function entails well-known, routine processing techniques (natural language output from a processor that is user-readable) for formatting the output data, which constitutes extra solution activity that neither integrates the judicial exception (i.e., analysis of input data to determine maintenance scheduling) into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 5 characterizes the nature of the data generated by the “applying” element in claim 1 and therefore is an extension of and also falls within the judicial exception recited in claim 1.
Claim 6, representative also of claim 16, further recites “generating an advance notification corresponding to an upcoming maintenance event represented in the particular maintenance schedule,” which represents conventional, routing data processing output activity that bears no significant functional relation to the judicial exception recited in claim 1 and therefore constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 8, substantially representative also of claim 18, further recites the additional element “automatically toggling activation of at least one alert generation rule based at least in part on the particular maintenance schedule,” which entails conventional, routine data processing function (activating or deactivating an alert rule via processing that constitutes extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception) for implementing what otherwise falls within the judicial exception (determination of whether to toggle based on information in the maintenance schedule, which may be performed via mental processes such as evaluation and judgement).
Claim 9, substantially representative also of claim 19, further recites the additional element “automatically initiating at least one maintenance action associated with the asset,” which in a broadest reasonable interpretation in view of Applicant’s specification may entail outputting scheduling information (e.g., via display) that constitutes conventional, routine data processing activity (outputting processing results obtained via the elements constituting the judicial exception) and is therefore extra solution activity that neither integrates the judicial exception into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 10 further recites “wherein the intelligence machine learning model comprises at least one natural language processing model that determines at least a portion of the user-specific data corresponding to the asset based at least in part on text data stored by a user associated with the asset.” Determining a portion of user-specific data corresponding to the asset based at least in part on text data stored by a user associated with the asset falls within the judicial exception (entailed within the “applying” element in claim 1) because it may be performed via mental processes (e.g., evaluation of input data and judgement/opinion in determining its relevance to maintenance). Using a machine learning model that comprises (e.g., an extension of the processing) a natural language processing model to implement this function entails well-known, routine processing techniques (translation of natural language input to a processor), which constitutes extra solution activity that neither integrates the judicial exception (i.e., analysis of input data to determine maintenance scheduling) into a practical application nor results in the claim as a whole amounting to significantly more than the judicial exception.
Claim 11 characterizes the nature of the data input for processing and therefore is an extension of and also falls within the judicial exception recited in claim 1.
Subject Matter Patentably Distinguishable Over the Prior Arts
Claims 1-6, 8-16, and 18-20 have been found to be patentably distinct over the prior arts.
The most pertinent prior arts are represented by Saneyoshi (US 2020/0058081 A1).
Regarding claim 1, and as set forth in the grounds for rejecting claim 1 under 102 in the Non-Final Office Action dated 8/13/2025, Saneyoshi teaches:
“[a] computer-implemented method for generating a dynamic maintenance schedule for at least one asset comprising:
receiving input data comprising (i) alert history data corresponding to an asset, (ii) maintenance standards data corresponding to the asset, (iii) service history data corresponding to the asset, and (iv) user-specific data corresponding to the asset,
applying the input data to an intelligence machine learning model that generates a maintenance schedule based at least in part on the input data; and
outputting a particular maintenance schedule corresponding to the asset via output from the intelligence machine learning model based at least in part on the input data.”
Saneyoshi further teaches that the input data is received via “one or more sensors ([0066]; FIG. 7 sensor(s) 108, [0144]-[0145])” and that the application of the input data to the machine learning model and the outputting of a particular maintenance schedule is “by one or more processors (FIG. 22 computer system 120 includes processor 121; [0013]).”
Neither Saneyoshi nor the other prior arts of record appear to fairly teach or suggest “automatically flagging at least one performance metric associated with the asset as untrustworthy during a maintenance period associated with the asset, wherein the maintenance period is represented by the particular maintenance schedule,” taken in combination with the other limitations of claim 1.
Claims 2-6 and 8-11 depend from claim 1 and are likewise allowable over the prior arts for the same reasons.
Independent claims 12 and 20 include substantially the same combination of features that distinguish claim 1 from the prior arts and are allowable over the prior arts for the same reasons.
Claims 13-16 and 18-19 depend from claim 12 and are likewise allowable over the prior arts for the same reasons.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857
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