Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 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 recite concepts covers 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;
using the sequential event data, identifying the target event;
identifying each occurrence of the target event for each machine;
[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 identified each occurrence of the target event sequential event data and generating the first event sequence that corresponds to the target event;
identifying a second occurrence of the target event in the identified each occurrence of the target event sequential event data and generating the second 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 [trained machine learning] model
[using the generated sequence database], training the [machine learning] model to implement the one or more rules
[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 for the particular machine to generate a likelihood prediction of machine downtime or at least one component failure within a time threshold using the first score, the second score or both;
and indicating, [via an on-board user interface], the likelihood prediction of the machine downtime or the at least one component failure, and
[triggering, via a control system, at least one action of the particular deployed machine to avoid the machine downtime or the at least one component failure within the time threshold].
Where the portions not bracketed recite the abstract idea.
Here the claims recite 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;”, “using the generated sequence database”, “the target event prediction module having accessible thereto the trained machine learning model”, and “triggering, via a control system, at least one action of the particular deployed machine to avoid the machine downtime or the at least one component failure within the time threshold” 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, 27, 34-35 and 52 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 July 21, 2026 have been fully considered.
Applicant’s amendments and arguments, on pages 11-19 of the Remark, regarding the 101 rejection the Examiner finds unpersuasive.
Applicant argues under Step 2A Prong 2 that the improvement achieved by the claimed process is an improvement in a technical field because the claims recite a technical process for coordinating between a distributed computing system comprising a first subsystem disposed off-board of a particular deployed machine (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 (including a target event prediction module with a trained machine learning model) to obtain real-time data (e.g., receiving streaming event data from sensors on deployed machines) and transforming the data into a likelihood prediction of machine downtime or at least one component failure within a time threshold. According to Applicant, these features are for a technical process that produces a real-time control action of triggering, via a control system, at least one action of the particular deployed machine to avoid the machine downtime or the at least one component failure within the time threshold, where the data is analyzed using rules and a machine learning model does not make the coordination between the off-board and on-board computing subsystems and the triggering of the control action to avoid machine downtime or component failure not a technical field. Applicant contends the improvements achieved by the claimed process are improvements to a technical field, namely, machine monitoring and predictive maintenance systems that prevent equipment failures and reduce downtime. Applicant compares the claims to Example 42 where the claims recited an abstract idea but was integrated into a practical application because "the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user." (Id.) According to Applicant, the instant claims recites a combination of additional elements that present a specific, discrete implementation triggering an action to avoid machine downtime or a component failure of a machine are specific steps that are not inherent in the purported abstract idea of "Mental Processes," (Office Action, pg. 2,) and these claim features provide meaningful limitations that prevent the claims covering the purported abstract idea as a whole: (See pages 14-15 of the Remarks)
The Examiner respectfully disagrees viewing the additional elements such as the subsystem, modules, and sensors identified by Applicant amount to mere instructions to perform the abstract idea using generic computing components for example the manner of identifying a target event, the steps that make up the generation a sequence database, and performing analysis on gathered data to make and indicate a likelihood prediction as claimed. The Examiner views that the streaming event data as claimed amounts to mere data gathering as discussed in the Step 2A Prong 2 Analysis. The usage of machine learning as claimed by Applicant amounts to mere instructions to apply machine learning to performing steps of the abstract idea i.e. “generate a likelihood prediction of machine downtime or at least one component failure within a time threshold using the first score, the second score or both” and does not amount to a practical application. The Examiner the asserted triggering step amounts to results-solution oriented language equivalent to mere instructions to apply the abstract idea using generic computing components. MPEP 2106.05 (f). MPEP 2106.05 (f) states:
Whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words “apply it”. See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356 (Fed. Cir. 2016); Intellectual Ventures I v. Symantec, 838 F.3d 1307, 1327 (Fed. Cir. 2016); Internet Patents Corp. v. Active Network, Inc., 790 F.3d 1343, 1348 (Fed. Cir. 2015). In contrast, claiming a particular solution to a problem or a particular way to achieve a desired outcome may integrate the judicial exception into a practical application or provide significantly more. See Electric Power, 830 F.3d at 1356.
In the instant claims the asserted limitation provides for a proffered solution i.e. triggering via a control system at least one action of the particular deployed machine to avoid the machine downtime or the at least one component failure within the time threshold. However, there is no restriction on how this result is accomplished or mechanism for accomplishing the result instead of claiming a particular solution to a problem or particular way to achieve a desired outcome.
The Examiner does not view the claims to be directed to coordinated communications between devices but rather the focus is analyzing collected information for making a likelihood prediction of an event occurring for a machine that is merely applied using generic computing components or insignificant extra solution activity that alone or in combination do not integrate the abstract idea into a practical application. The Examiner views the claimed improvements are unlike those in Example 42 based on making an improvement to likelihood predictions amounts to improving an administrative process as claimed rather than improving technology or other consideration enumerated under MPEP 2106.04 (d).
Applicant argues the claims are similar to SME Example 45 claim 2 as the claims provide for a system that receives sequential event data and triggers, via a control system, at least one action of the particular deployed machine to avoid the machine downtime or the at least one component failure within the time threshold. Applicant contends this limitation mirrors Example 45's claim 2 limitation (d) in that it "does not merely link the judicial exceptions to a technical field, but instead adds a meaningful limitation in that it employs the information provided by the judicial exceptions... to control the operation of the injection molding apparatus." (October 2019 Subject Matter Eligibility Examples at p. 24.) According to Applicant as claim 2 in Example 45 was found eligible because "the claim as a whole thus improves upon previous controllers used in this technical field of injection molding" and "using the information obtained via the judicial exception to take corrective action and control the injection molding apparatus in a particular way is an 'other meaningful limitation' that integrates the judicial exception into the overall control scheme," (Id.) claim 1 of the present application likewise uses the prediction generated by the machine learning model to take the corrective action of triggering at least one action via a control system to avoid predicted machine downtime or component failure, thereby integrating the judicial exception into a practical application that improves machine operation and maintenance.
The Examiner does not view the asserted limitation to be similar to limitation d in Example 45, where in the Example the “claimed controller opens the mold and ejects the molded polyurethane at the time when the target percentage of cure is reached, the claims controllers avoids the technical problems associated with undercure and overcure, which would otherwise negatively affect the cured polyurethane’s strength and wear performance” and also “use the judicial exception to take corrective action and control the injection molding apparatus in a particular way”. In the instant claims there is no purported technical improvement associated with the asserted step by Applicant as reflected in the Specification (See paragraphs 34-35). Further, the asserted step does not amount to taking corrective action and control the machine in a particular way as presently claimed. Therefore, the Examiner maintains that the asserted step does not integrate the abstract idea into a practical application.
Applicant argues under Step 2B the claims recite significantly more than a mental process and the combination of claim elements add specific limitations and combinations of limitations that are able to be performed in the human mind via pen or and paper. According to Applicant the claims recite a combination of additional elements that present a specific, discrete implementation triggering an action to avoid machine downtime or a component failure of a machine. (See pages 17-18 of the Remarks). Applicant contends these steps cannot reasonably be said to be performed in the human mind or with pen and paper and thus these additional features provide an inventive concept that render the claims patent eligible under step 2B when considering the additional elements in combination add significantly more.
The Examiner respectfully disagrees maintaining that the steps identified by Applicant recite steps of the Abstract idea that is merely applied via generic computing components or performing mere data gathering for facilitating the performance of the abstract idea and do not alone or in combination amount to significantly more than the abstract idea. The Examiner reiterates they view the asserted triggering step amounts to results-solution oriented language equivalent to mere instructions to apply the abstract idea using generic computing components and does not significantly more to the abstract idea. MPEP 2106.05 (f).
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.
Bharadwaj et al. (US Patent No. 11,269,752) – directed to unsupervised anomaly prediction.
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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/Michael J. Monaghan/Examiner, Art Unit 3629
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