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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 October 2, 2025 has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 recite a method (process), Claims 8-14 recite a system (machine), and Claims 15-20 recite one or more non-transitory computer readable medium (manufacture) and therefore fall into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?):
Referring to claims 1-20, the claims are directed to a manner of predicting the likelihood of an event occurring for a machine, which under its broadest reasonable interpretation covers concepts covered under the Mental Processes grouping of abstract ideas.
The abstract idea portion of the claims is as follows:
(Claim 1) A method for predicting a target event associated with a particular deployed machine, [the method performed by a distributed computing system comprising a first subsystem disposed off-board of the particular deployed machine and including a sequencing module and a rule mining module, and a second subsystem disposed at least in part on-board of the particular deployed machine and including a target event prediction module,] the method comprising:
(Claim 8) [A system for predicting a target event associated with a particular deployed machine, the system comprising: one or more processors; and one or more memory devices having stored thereon instructions that when executed by the one or more processors cause the one or more processors to:]
(Claim 15) [One or more non-transitory computer-readable media storing computer- executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:]
[with the sequencing module of the first subsystem,]
receiving sequential event data for each of a plurality of machines, wherein the sequential event data comprises (i) telematics event data received [from a first data source comprising sensors on each of the plurality of machines] and [a different second data source comprising] at least one of (ii) simulation event data or transactional data;
[and generating a sequence database by]: using the sequential event data, generating a set of event sequences for each machine, the set of event sequences comprising a first event sequence and a second event sequence that overlaps at least in part with the first event sequence and is distinct from the first event sequence, including: identifying a first occurrence of the target event in the sequential event data and generating the first event sequence that corresponds to the target event; and storing the set of event sequences [in the sequence database]; [with the rule mining module of the first subsystem,] analyzing the event sequences [in the sequence database] to identify one or more rules corresponding to the target event, comprising generating a first score that corresponds to the first event sequence and a second score that corresponds to the second event sequence, wherein the first score, the second score, or both relate to a respective first event sequence or second event sequence occurring in conjunction with the target event;
[with the second subsystem disposed at least in part on-board of the particular deployed machine, streaming event data to the target event prediction module, wherein the target event prediction module comprises] a [machine learning] model trained, [using the generated sequence database], to implement the one or more rules and [with the target event prediction module having accessible thereto the trained machine learning model,]
applying the [trained machine learning] model to the streamed event data to generate a likelihood prediction using the first score, the second score or both; and indicating, [via an on-board user interface], the likelihood prediction that the identified at least one deployed machine will experience the target event.
Where the portions not bracketed recite the abstract idea.
Here the claims are directed to concepts capable of being performed in the human mind or via pen and paper (including an observation, judgement, evaluation, opinion) but for the recitation of generic computer components. In the present application concepts directed to a manner of predicting the likelihood of an event occurring for a machine (See paragraphs 1-2).
If a claim limitation, under its broadest reasonable interpretation, covers concepts capable of being performed in the human mind or via pen and paper, it falls under the Mental Processes grouping of abstract ideas. See MPEP 2106.04.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?):
The examiner views the following as the additional elements:
A system. (See paragraph 23)
One or more processors. (See paragraphs 36-37)
One or more memory devices. (See paragraph 40)
Instructions. (See paragraph 36)
Sequence database. (See paragraphs 23 and 49-50)
Sensors. (See paragraph 23)
Plurality of machines/deployed machines. (See paragraph 23)
One or more non-transitory computer-readable media. (See paragraphs 8 and 40)
Computer-executable instructions. (See paragraphs 8 and 36)
Distributed computing system. (See paragraph 47)
First subsystem disposed off-board. (See paragraph 27)
Sequencing module. (See paragraphs 24 and 27)
Rule mining module. (See paragraphs 25 and 27)
Second subsystem disposed at in part on-board. (See paragraphs 27 and 34-35)
Target event prediction module. (See paragraph 27)
First data source. (See paragraphs 23-24)
Second data source. (See paragraphs 23-24)
Machine learning/trained machine learning. (See paragraph 35)
On-board user interface. (See paragraph 34)
These additional elements are recited at a high-level of generality such that they act to merely “apply” the abstract idea using generic computing components and do not integrate the abstract idea into a practical application. (See MPEP 2106.05 (f))
Regarding generating a sequence database; the target event prediction module having accessible thereto the trained machine learning model the examiner views these limitations as results-oriented steps given that there is no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result are currently present such that this limitation is viewed as equivalent to “apply it” for merely implementing the abstract idea. (See MPEP 2106.05 (f) and paragraphs 24 and 27 of the Specification)
The combination of these additional elements and/or results oriented steps are no more than mere instructions to apply the exception using generic computing components. (See MPEP 2106.05 (f)).
Regarding streaming event data to the target event prediction module, the examiner views these limitations to be insignificant extrasolution activity in the form of mere data gathering for facilitating the performance of the abstract idea. MPEP 2106.05 (g). Here the streaming event step is necessary for making a likelihood prediction and does not add a meaningful limitation to the process of making a likelihood prediction.
Accordingly, even in combination these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Therefore, the claim is directed to an abstract idea.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?):
As noted above, the claims as a whole merely describes a method that generally “apply” the concepts discussed in prong 1 above. (See MPEP 2106.05 f (II)) In particular applicant has recited the computing components at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. As the court stated in TLI Communications v. LLC v. AV Automotive LLC, 823 F.3d 607, 613 (Fed. Cir. 2016) merely invoking generic computing components or machinery that perform their functions in their ordinary capacity to facilitate the abstract idea are mere instructions to implement the abstract idea within a computing environment and does not add significantly more to the abstract idea. Additionally, the step of streaming event data is generally well understood, routine and conventional activity in view of MPEP 2106.05 (g) and as taught by the Specification. See paragraph 27 of the Specification. Accordingly, these additional computer components do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore, even when viewed as a whole, nothing in the claim adds significantly more (i.e. an inventive concept) to the abstract idea and as a result the claim is not patent eligible.
Dependent claims 2-7, 9-13, and 16-20 further define the abstract idea as identified. Therefore claims 2-7, 9-13, and 16-20 are considered to be patent ineligible.
Dependent claim 14 further defines the abstract idea as identified. Additionally, the claim recites the generic instructions (See paragraph 36) for merely implementing the abstract idea using generic computing components which does not integrate the abstract idea into a practical application or adds significantly more. Therefore claim 14 is considered to be patent ineligible.
In conclusion the claims do not provide an inventive concept, because the claims do not recite additional elements or a combination of elements that amount to significantly more than the judicial exception of the claims. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and the collective functions merely provide conventional computer implementation. Therefore, whether taken individually or as an order combination, the claims are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's arguments filed October 2, 2025 have been fully considered.
Applicant’s amendments and arguments, on pages 10-14 of the Remark, regarding the 101 rejection the examiner finds unpersuasive.
Applicant argues that the claims are not directed to a mental process as the submitted amendments cannot be applied in the human mind. According to Applicant generating a sequence database “can involve creating a structured database of event sequences from vast amounts of sequential event data, needing computational resources and data processing capabilities” is beyond the human mind. Applicant further contends that “[s]treaming event data to the target event prediction module by a second subsystem disposed on-board of the particular deployed machine is a task that can include electronic signaling as specific hardware and software configurations” and “[s]treaming even data to the target event prediction module, which comprises a machine learning model trained using the generated sequence database, can entail computer execution of complex algorithms and computational power” that are beyond the human mind. Applicant also contends that humans cannot mentally process electronically streamed data, or via a trained machine learning model as claimed.
The Examiner respectfully disagrees noting the Step 2 A Prong 1 Analysis that the Examiner determined to constitute additional elements. The Examiner determined that the additional elements constituted were mere to apply the abstract idea using generic computing components such as the type of computational model (machine learning/trained machine learning model) used to make a likelihood prediction or the results oriented step of generating a sequence database viewed as merely equivalent to merely apply it or insignificant activity such as the streaming event data that the Examiner viewed as mere data gathering for facilitating the abstract idea. (See MPEP 2106.05 (f) and MPEP 2106.05 (g)). The Examiner further notes that the steps used for generating the database the Examiner views as steps of the abstract idea and not outlining a specific manner of creating a sequence databased and further is not limited to a vast amount of sequential data.
Applicant argues that the recite features are not extra solution activity as the generating a sequence database is done using particularly-recited operations and operations and using particularly-recited data inputs, where the sequence database is accessible to an on-board second subsystem that applies one or more generated rules via an embedded pretrained ML module to the generated sequence database and streaming event data to perform predictive operations and configure the on-board interface to output the likelihood prediction where this output cannot be done but for the preceding recited operations.
The Examiner respectfully disagrees viewing the steps for generating the sequence database constitute steps of the abstract idea as identified in Step 2A Prong 1 Analysis. The Examiner further views the claims only recite applying the model to the streamed event data and not the database but rather the model is trained to using the sequence database. The Examiner views the additional elements of the type of computational model used (machine learning), on-board interface, or sequence database to be mere instructions to apply the abstract idea using generic computing components and does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. The step of streaming event data amounts to mere data gathering that the Examiner views as insignificant activity that does not integrate the abstract idea into a practical application or adds significantly more to the abstract idea. (See MPEP 2106.05 (g)).
Applicant argues that the claims are integrated into a practical application by enabling new on-board insights not ordinarily available by processing sensor data without more. According to Applicant streaming event data is not sufficient by itself for an onboard prediction but rather needs to use data not on-board including part replacement/engineering data. Applicant contends these operations enhance on-board functionality of on-board messages are captured. Applicant argues the claim languages regarding the data sources clarifies IOT/sensor data from the first data source (on-board sensors) is supplemented by additional data, such as simulation or transactional data to generate new insights. A particular sequence of sensor data values can be uninformative by itself but supplementing sensor data with transaction data can enable predicting that the particular sequence of sensor values is likely to result in part replacement.
The Examiner respectfully disagrees viewing the information collected does not provide for a technical improvement to making a likelihood prediction as claimed. There is no specific technical manner in terms of how the information is collected or how it utilized in the claimed. The Examiner views that the improvements proffered by Applicant are improvement to the abstract idea and/or administrative field making predicting maintenance which the Examiner does not consider as an improvement enumerated under MPEP 2106.04 (d).
Applicant argues the claims are eligible because of the specific application of training a machine learning model using the generated sequence database. According to Applicant the claimed training process improves the machine learning model’s ability to predict target events by leveraging the sequence database. Applicant continues this training enables to model to learn complex patterns and relationships in the data allowing it to generate accurate likelihood predictions when applied to streamed event data. According to Applicant the recite training process is a practical application of machine learning that provides a technical improvement in predictive analytics applied to real-time operations and on-board alerts pertaining thereto.
The Examiner respectfully disagrees viewing the claimed machine learning model and generated sequence database are mere instructions to apply the abstract idea. The claims do not train the machine learning model in any particular way utilizing the sequence database or how the machine learning model specifically learns complex patterns and relations to that would merit consideration as an improvement to machine learning or to another technical field. The Examiner does not view the training of the machine learning model is a technical improvement but rather mere instructions to apply the abstract idea using generic computing components and does not provide for integration into a practical application or adds significantly more to the abstract idea.
Therefore, the Examiner has maintained the 101 rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ristovski et al. (US 20190235484) – directed to using a deep learning architecture for maintenance predictions with multiple modes.
Jadhav et al. (US 20230090297) – directed to industrial work order management.
Sertbas (US 20220207493) – directed to the estimation of malfunctions in long periods by processing the old service records of the customer equipment and the physical data obtained instantly by machine learning methods.
Zheng et al. (US 20210279597) – directed to predictive maintenance using discriminant generative adversarial networks.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J MONAGHAN whose telephone number is (571)270-5523. The examiner can normally be reached on Monday- Friday 8:30 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached on (571) 270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael J. Monaghan/Examiner, Art Unit 3629