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 .
Status of Claims
This action is in reply to the RCE filed on 4/10/2026.
Claim 1 has been amended and are hereby entered.
Claim 4 has been canceled.
Claims 1-3 and 5-9 are currently pending and have been examined.
Response to Applicant’s Arguments
Objections
The amendment to Claim 1 adding a semicolon between steps (b) and (c) obviates the previous objection thereto; therefore, this objection is withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant first presents a series of Step 2A, Prong One arguments, beginning in relation to step (c). Particularly, Applicant argues that step (c) cannot recite certain methods of organizing human activity nor mental processes due to the CAN-based method of data collection found in step (b) (and Applicant points out that the previous Office Action acknowledged this functionality of step (b) indeed did not recite an abstract idea) which is referenced in step (c). Applicant is mistaken, as the functionality argued by Applicant is that of step (b) rather than step (c). In making this argument, Applicant erroneously conflates the technological manner in which this data is collected with what might be done with such data once it has been collected (e.g., the sending/receiving of such data in the argued step (c)). This would be like saying that because the weight of an object was procured using a digital scale, anything subsequently done with that weight likewise cannot be abstract, e.g., comparing that weight to another weight or a weight threshold, using that weight in a mathematical calculation, etc. Clearly this is a ridiculous notion. The language related to “the real-time CAN data” in step (c) is no more than a reference to the data (and manner in which it is gathered) already claimed in step (b), and does not further recite any new/different CAN-based functionality occurring in step (c). Rather, step (c) is merely the providing/receipt of the data gathered in step (b). In view of the myriad pieces of caselaw and examples in the MPEP and PEG which hold that the transmission/receipt/presentation of such abstract data is itself an abstract step, Applicant’s argument here is unpersuasive.
Also regarding step (c), Applicant additionally argues that this step cannot recite an abstract idea due to “the sheer amount of data being collected, and the rate at which it is collected from disparate devices.” This argument is erroneous in the same way as the above-discussed step (c) argument in that it conflates the gathering of such data with what is done with such data afterwards; however this argument is also unpersuasive for additional reasons. There is nothing in the claim language which requires “the sheer amount of data being collected” to be a particularly large amount of data for individual buses, nor do the claims require such data to be gathered from a large number of buses, with the scope of the “plurality of buses” found in the claims including, for example, two buses. Even were this not the case, the amount of data being gathered and analyzed in no way prevents recitation of a mental process, as this speaks to the scalability of performing this step manually rather than the practical possibility of performing this step mentally (and has no effect whatsoever on the recitation of the also-identified category of certain methods of organizing human activity).
Applicant next presents arguments against the Prong One recitation of abstract ideas in step (e), in which Applicant mischaracterizes the findings of the previous Office Action regarding step (e). Particularly, Applicant leans heavily and exclusively into the claimed “artificial intelligence machine” used to build an “artificial intelligence machine model” here, yet these elements of step (e) were not found to recite abstract ideas but rather were specifically pulled out and identified as additional elements. While Examiner maintains his previous analysis of these claim elements as additional elements, there is nothing in the high-level manner in which the gathered data is used as training data to build a model (e.g., a mathematical, predictive model) which prevents the recitation of the three enumerated categories of abstract idea in this step. The claims contain no technological details of this model building which would differentiate the functionality here from how such an abstract mathematical, predictive model might be trained or built manually or mentally. The original disclosure, containing little to no detail as to how this model would be built of function, does nothing to indicate that this is otherwise, leaving little space to amend step (e) in the claims such that this does not recite an abstract idea. As claimed, there is nothing particularly “complex[]” about the AI machine or the building of the AI model, claimed as it is presently at an extremely high level (essentially as a black box), nor about the type or volume of data used therein (see discussion of step (c) above). Applicant may wish to review Examples 47-49 of the July 2024 PEG Update for further explanation as to how/when AI-based steps may still recite judicial exceptions, many of which contain significantly more detail and closer ties to technology than the high-level AI machine/model of step (e) yet still are found to recite abstract ideas.
Applicant’s conclusory and single-sentence argument against recitation of abstract ideas in step (f) fails for the same reasons discussed above regarding step (c).
Applicant next presents arguments against the Prong One recitation of abstract ideas in step (g), which leans into the AI model-based effectuation of the claimed derivation of a bus maintenance priority in similar manner as for step (e) above, which is unpersuasive for substantially the same reasons. That this occurs by way of “step-wise software processing” is in no way dispositive as to whether abstract ideas are recited or not recited, and indeed this contention flies in the face of the seminal Alice and its holding that computer-based performance of abstract steps (necessarily including software-based implementation) neither prevents recitation of abstract ideas nor, in and of itself, renders a claim patent eligible. As with step (e) discussed above, there is nothing in the AI model or functionality related thereto as presently claimed which would differentiate the claimed subject matter from how a non-technological mathematical model could be built and utilized to achieve the claimed derivation of a bus maintenance priority order.
Still regarding step (g), Applicant particularly argues against the recitation of mathematical concepts, arguing that step (g) may involve mathematical concepts, but contains no details of the particular mathematical relationships, formulae, or calculations used. Examiner disagrees. Particularly, the derivation of a bus maintenance priority order is in and of itself representative of a mathematical relationship, with higher-order buses considered in greater need of maintenance/at greater risk of breakdown than lower-order buses. While no mathematical formula or calculation is explicitly described in this step, the derived order nonetheless constitutes a mathematical relationship.
Applicant next makes a conclusory assertion that even if the remaining two un-argued steps recite abstract ideas (in which Applicant erroneously conflates the standards of Step 2A, Prongs One and Two by the language “even if, purely for the sake of argument, the remaining two steps were deemed by the Examiner to be directed to an abstract idea under Step 2A, prong 1” (Examiner’s emphasis)), the claims would nonetheless be integrated into a practical application and/or embody an inventive concept by way of the remaining five steps which, according to Applicant’s arguments above, constitute additional elements (as opposed to “patent eligible steps” as articulated by Applicant – steps constituting additional elements are not automatically deemed “patent eligible”). Even if Applicant’s premise that the steps argued in these Remarks related to the Prong One analysis were non-abstract additional elements (none of which are true, as discussed above), neither Step 2A, Prong Two nor Step 2B is performed in the manner Applicant appears to be asserting here, ie: by way of a numerical comparison of recited abstract ideas against recited additional elements. Indeed, it is particularly telling that Applicant appears unable to articulate either a method by which the claims achieve integration under Step 2A, Prong Two or a purported inventive concept achieved by the invention under Step 2B. Regardless, the vast majority of the claims as presently amended (including those steps argued above in relation to Prong One, with the exception of the claimed computer elements by/via which these steps are performed) constitute abstract ideas. What scant additional elements are present in the claims are insufficient to either integrate the claims into a practical application under Step 2A, Prong Two nor embody an inventive concept under Step 2B.
Regarding the steps presently rolled up into Claim 1 from Claim 4, Applicant returns to the standards of Step 2A, Prong One, arguing that none of these steps recite an abstract idea. Applicant does so in a conclusory manner, and solely in relation to the abstract category of mental processes (ie: ignoring that each of these steps was also previously categorized as reciting certain methods of organizing human activity and mathematical concepts). As with the model built in step (e) and utilized in step (g) discussed above, there is nothing inherently technological nor non-mentally practicable about the generation of state prediction models or steady state models as mathematical, predictive models, and the bare assertion that this is done “through the artificial intelligence model” does nothing to make this otherwise. The claims contain no technological details which would differentiate this from a manual practicing of these steps. Further, the notion that “calculating…a Euclidian distance between the state prediction model and the steady state model” (even were this not presented in a purely conclusory manner absent any reasoning or analysis) does not at least recite a mathematical concept is so obviously untrue that Examiner cannot fathom how Applicant could have reasonably reached this conclusion. Finally, step (g-4) is a narrowing of the functionality asserted in step (g) (with all of steps (g-1) through (g-4) explicitly claimed as sub-steps of step (g) both previously in Claim 4 and presently in Claim 1), by which the derived maintenance priority order is specifically derived in relation to the Euclidian distance calculated in step (g-3). If anything, this makes the mathematical nature of step (g) more apparent and explicit than as argued and discussed above regarding step (g) in isolation. Examiner maintains that each of these steps continue to recite abstract ideas as previously analyzed.
Still regarding the steps presently rolled up into Claim 1 from Claim 4, Applicant next asserts that these steps integrate the claims into a practical application under Step 2A, Prong Two, but as best as Examiner can understand from the arguments presented, does so purely based on some plain meaning definition of “practical application” (e.g., useful purpose) rather than as the term is understood in the 101 subject matter eligibility analysis (e.g., based on the considerations set forth in MPEP 2106.04(d)). Rather, Applicant instead asserts a string of almost entirely abstract considerations and benefits of the present invention, e.g., real-time monitoring, up-to-date and meaningful maintenance priorities, and using predictive and steady state models to derive said maintenance priorities. Even Examiner’s best attempt to steelman this argument into the actual standards of Step 2A, Prong Two (in this case, by treated said list of benefits as assertions of an improvement to a technology), they remain unpersuasive. These do not constitute technological improvements, but rather a sequence of abstract steps (and the results thereof) which are claimed at a high level as being effectuated by various computer elements (including, in some instances, an AI application).
Next, Applicant asserts that “the additional elements of claim 1, both individually and in combination, impose meaningful limits on practicing the alleged exception,” particularly pointing to the content of steps (g-1) through (g-4). Applicant’s use of “meaningful limits” here appears to be a similarly plain meaning use of this language as “practical application” above, and does not comport with how this terminology is understood in the 101 subject matter eligibility analysis. As discussed above, there are far fewer additional elements found in the claims than Applicant argues here (see above), and particularly in relation to steps (g-1) through (g-4), these steps remain entirely abstract other than the computer elements by/upon which they are practiced (ie: the data analysis unit and, in some cases, the artificial intelligence model). As such, the content of steps (g-1) through (g-4) are almost entirely part of the recited judicial exception for which meaningful limits must be established. As abstract ideas may not integrate themselves into a practical application/impose meaningful limits upon themselves, Applicant’s argument here is foundationally flawed.
Regarding Applicant’s cited language purportedly from MPEP 2106.05(a), Examiner corrects the record here to indicate that this language actually comes from MPEP 2106.05(I).
Applicant’s arguments regarding the content of pg. 29 of the previous Office Action as indicating significantly more than the recited judicial exception is entirely unpersuasive. The language referenced here relates to a finding that the content of presently cancelled Claim 4 (the content of which is presently rolled up into Claim 1) is novel and non-obvious. These are standards of 102 and 103, and have nothing to do with subject matter eligibility under 101. Eligibility under the standards of these art-based statutes in no way evidences eligibility under the entirely different standards of 101.
Lastly Applicant appears to return to the standards of Step 2A, Prong Two, asserting a “technological improvement of transportation technology and specific solution to the problem discussed below.” Examiner disputes that any such technological improvement/technological solution to a technological problem is to be found in either the claims as presently drafted or Applicant’s arguments here related thereto. Specifically, the problems articulated by Applicant (e.g., bus maintenance priorities being “nebulous, overlooked, and can lead to down-times for buses, limited or expensive transportation for consumers, and financial losses for companies) are entirely abstract, business-related problems. Predicting maintenance needs (and, by extension, priorities) of commercial assets engaged in providing commercial services (in this case, buses) is an entirely abstract endeavor both as claimed and as described in the original disclosure. For example, the present invention does not set forth some improvement to these buses themselves or particular components thereof which would extend the time needed between maintenance appointments (something which could easily be found to constitute an improvement to a technology). Rather, the present invention predicts the maintenance needs of various buses in a fleet, then prioritizes the scheduling of maintenance appointments based on the varying predicted needs of the respective buses. The resulting “curated, real-time bus monitoring information and maintenance priorities that can help prevent issues that arise from poor bus maintenance and improper maintenance prioritization” is likewise an entirely abstract, business-related benefit rather than a technological one, and does not constitute an improvement to “transportation technology” as vaguely asserted. This does no more than cloak a broader, abstract improvement in technological language. This is not a technological solution nor does it address a technological problem; instead, this is nothing more than an abstract business process, claimed at a high level as being effectuated using/by way of various computer elements (including, for particular discrete steps, AI models).
To this same end, Applicant attempts to analogize the present invention to that of McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 120 USPQ2d 1091 (Fed. Cir. 2016). Examiner finds this unpersuasive, as there is no reasonable analogy to be made between the improvement to the unquestionably technological endeavor of automation of computer facial animation (in that case, allowing for said automation to achieve realistic facial animation results, whereas such realistic results were only previously achievable through manual computer facial animation, e.g., by an animator) in McRO and the improvement to the abstract, business concept of prioritizing maintenance of a set of commercial assets (in this case, buses) to prevent downtime, breakdown, and consequent downstream price increasing.
Claim Rejections – 35 USC § 102/103
Examiner agrees that the present amendment to Claim 1 which incorporates the additional limitations previously drafted in Claim 4 (identified as novel and non-obvious subject matter in the Final Rejection of 12/10/2025) renders Claim 1 as novel and non-obvious subject matter. Claims 2-3 and 5-9 likewise now constitute novel and non-obvious subject matter due to their dependency upon Claim 1.
Claim Interpretation
While the claims contain several terms which constitute generic placeholders (e.g., “a data processing unit,” “a monitoring unit,” and “a data analysis unit” of Claim 1; “an analysis unit” of Claim 8) which are modified by functional language, these terms are not given interpretation under 112(f) as they are all modified by sufficient structure (e.g., “an operational server”) to perform the respectively recited functions, either directly or indirectly (e.g., “an analysis unit” of Claim 8 is not directly tied to the operational server, but it is tied to the data processing unit, which is in turn modified by the operational server in language of Claim 1). See MPEP 2181 for more information on this standard.
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 1-3 and 5-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, the limitations of (a) grouping and classifying a plurality of buses to be monitored according to a preset classification criterion; (c) receiving bus monitoring information including the real-time CAN data and a plurality of additional types of data collected; (d) performing refinement on the bus monitoring information; (e) inputting the bus monitoring information refined in the operation (d) as training data to build a model for determining bus maintenance priorities; (f) re-receiving the bus monitoring information collected for each group classified according to the operation (a); (g) deriving a maintenance priority of the plurality of buses through the model based on the bus monitoring information re-received in the operation (f); wherein the operation (g) comprises: (g-1) generating a state prediction model of the bus to be monitored through the model; (g-2) generating a steady state model of the bus to be monitored through the model; (g-3) calculating a Euclidean distance between the state prediction model and the steady state model; and g-4) deriving maintenance priorities in the decreasing order of the Euclidean distance calculated in the operation (g-3), as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of (a) grouping and classifying a plurality of buses to be monitored according to a preset classification criterion; (c) receiving bus monitoring information including the real-time CAN data and a plurality of additional types of data collected; (d) performing refinement on the bus monitoring information; (e) inputting the bus monitoring information refined in the operation (d) as training data to build a model for determining bus maintenance priorities; (f) re-receiving the bus monitoring information collected for each group classified according to the operation (a); (g) deriving a maintenance priority of the plurality of buses through the model based on the bus monitoring information re-received in the operation (f); wherein the operation (g) comprises: (g-1) generating a state prediction model of the bus to be monitored through the model; (g-2) generating a steady state model of the bus to be monitored through the model; (g-3) calculating a Euclidean distance between the state prediction model and the steady state model; and g-4) deriving maintenance priorities in the decreasing order of the Euclidean distance calculated in the operation (g-3), as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of (e) inputting the bus monitoring information refined in the operation (d) as training data to build a model for determining bus maintenance priorities; (g) deriving a maintenance priority of the plurality of buses through the model based on the bus monitoring information re-received in the operation (f); wherein the operation (g) comprises: (g-1) generating a state prediction model of the bus to be monitored through the model; (g-2) generating a steady state model of the bus to be monitored through the model; (g-3) calculating a Euclidean distance between the state prediction model and the steady state model; and g-4) deriving maintenance priorities in the decreasing order of the Euclidean distance calculated in the operation (g-3), as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a data processing unit, an operational server, a plurality of buses, (b) collecting real-time Controller-Area-Network (CAN) data with vehicle information collection devices installed on the buses for each group classified according to the operation (a), a monitoring unit, vehicle information collection devices installed on the buses, an artificial intelligence machine, an artificial intelligence model, and a data analysis unit. A data processing unit, an operational server, a monitoring unit, vehicle information collection devices installed on the buses, an artificial intelligence machine, an artificial intelligence model, and a data analysis unit amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). (b) collecting real-time Controller-Area-Network (CAN) data with vehicle information collection devices installed on the buses for each group classified according to the operation (a) amounts to no more than insignificant extra-solution activity (see MPEP 2106.05(g)). A plurality of buses amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, insignificant extra-solution activity, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional activity as "receiving or transmitting data over a network" in MPEP 2106.05(d). These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 2-3 and 5-9, describing various additional limitations to the method of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 discloses wherein the preset classification criterion in the operation (a) has at least one criterion among a manufacturer, fuel, a model, year, and a system type (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 3 discloses (d-1) filtering, by the data processing unit, a data error in the bus monitoring information (an abstract idea in the form of a certain method of organizing human activity and a mental process); (d-2) performing, by the data processing unit, preprocessing on data of the bus monitoring information (an abstract idea in the form of a certain method of organizing human activity and a mental process); and (d-3) backing up and loading, by the data processing unit, the data of the bus monitoring information into a database of the operational server (insignificant extra-solution activity), which do not integrate the claim into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional as storing and retrieving information in memory (see MPEP 2106.05(d)).
Claim 5 discloses between the operations (d) and (e), operation (ex1) of setting, by the data processing unit of the operational server, a threshold standard for determining normal/abnormal data based on the bus monitoring information refined in the operation (d) (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 6 discloses wherein the operation (ex1) includes one or more operations selected from the group consisting of: (ex1-1) receiving, by the data processing unit, an expert threshold value calculated by an automobile-related expert and setting the received expert threshold value as a first threshold standard (an abstract idea in the form of a certain method of organizing human activity and a mental process); (ex1-2) deriving, by the data processing unit, a numerical threshold value calculated through a statistical technique and setting the derived numerical threshold value as a second threshold standard (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and (ex1-3) deriving, by the data processing unit, a displacement difference threshold value calculated through an offset method and setting the derived displacement difference threshold value as a third threshold standard (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 7 discloses after the operation (ex1), operation (ex2) of processing, by the data processing unit, the bus monitoring information to build the training data to be input to the artificial intelligence machine in the operation (e) (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 8 discloses (ex2-1) standardizing, by the data processing unit, the bus monitoring information (an abstract idea in the form of a certain method of organizing human activity and a mental process); (ex2-2) performing, by the data processing unit, preprocessing on the bus monitoring information standardized in the operation (ex2-1) (an abstract idea in the form of a certain method of organizing human activity and a mental process); (ex2-3) setting, by the data processing unit, an analysis unit (mere instructions to apply a judicial exception) for the bus monitoring information preprocessed in the operation (ex2-2) (an abstract idea in the form of a certain method of organizing human activity and a mental process); and (ex2-4) classifying, by the data processing unit, each piece of data of the bus monitoring information as normal data or abnormal data according to the threshold standard set in the operation (ex1) (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 9 discloses (h) displaying the maintenance priority of the plurality of buses (an abstract idea in the form of a certain method of organizing human activity and a mental process) on a graphical user interface of a video output device connected to the operational server (mere instructions to apply a judicial exception); and (i) updating the maintenance priority displayed on the graphical user interface in real-time as new bus monitoring information is received by the monitoring unit (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Discussion of Prior Art Cited but Not Applied
For additional information on the state of the art regarding the claims of the present application, please see the following documents not applied in this Office Action (all of which are prior art to the present application):
PGPub 20210005027 – “System and Method for Battery Maintenance Management,” Handiaz et al, disclosing a system for monitoring battery conditions for a fleet of vehicles, predicting necessary timings for battery maintenance via machine learning model, using feedback to update said model over time, determining a priority and schedule for battery charging operations of the fleet
Zhang et al, Research on Ship Maintenance Task Arranging Model Based on CSP, IEEE 2010 Int’l Conf. on Electrical and Computer Engineering (2010)
Massaro et al, Predictive Maintenance of Bus Fleet by Intelligent Smart Electronic Board Implementing Artificial Intelligence, IoT, Vol. 1, Issue 2, published by MDPI (2020)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK C CLARE whose telephone number is (571)272-8748. The examiner can normally be reached Monday-Friday 6:30am-2:30pm EST.
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/MARK C CLARE/Examiner, Art Unit 3628
/MICHAEL P HARRINGTON/Primary Examiner, Art Unit 3628
Prosecution Insights