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 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;
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 at least one identified deployed machine 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; using the generated 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 April 8, 2026 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 the claims cannot be performed in the human mind as discussed in the SME declaration submitted. The declaration states:
The operations "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" cannot be performed in a human mind or using pen and paper because the volume and velocity of heterogeneous data received from multiple machines and diverse data sources exceed human processing capacity.
The operations "with the sequencing module of the first subsystem using the sequential event data, identifying the target event; identifying each occurrence of the target event for each machine" cannot be performed in a human mind or using pen and paper because manually processing and identifying specific events across sequential data for numerous machines would be an impractical and error-prone task.
The operations "with the sequencing module of the first subsystem 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 in 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 in 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" cannot be performed in a human mind or using pen and paper because the generation, identification, and storage of numerous overlapping and distinct event sequences from high-volume sequential data across multiple machines call for computational resources beyond human capabilities.
The operations "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" cannot be performed in a human mind or using pen and paper because the analysis, scoring, and identification of rules across a large sequence database involve computational pattern recognition algorithms not practically performed by a human.
The operations "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" cannot be performed in a human mind or using pen and paper because a human mind lacks a human-computer interface to receive and process real-time event data from an on-board computing subsystem or inherently embody/invoke a trained computer-based machine learning model (for example, a model that includes computer-executable files). A human cannot use the human mind alone to cause receipt of computer-generated data or execution of computer-executable files.
The operations "with the second subsystem disposed at least in part on-board of the particular deployed machine using the generated sequence database, training the machine learning model to implement the one or more rules" cannot be performed in a human mind or using pen and paper because the iterative, computationally intensive process of training a machine learning model on a sequence database to implement complex rules is beyond human manual processing and learning capacity. Additionally, a human cannot use the human mind alone to execution of computer-executable files or change weights and activation function in a computer-based model, such as a neural network.
The operations "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 at least one identified deployed machine to generate a likelihood prediction using the first score, the second score or both" cannot be performed in a human mind or using pen and paper because a human mind lacks the capacity to execute/apply a computer-based machine learning model, such as to cause execution of computer-executable files to process electronic data signals representative of streamed event data. Furthermore, a human lacks a human-computer interface to receive and process streamed event data in the form of computer-generated electronic signals.
The operations "with the target event prediction module having accessible thereto the trained machine learning model indicating, via an on-board user interface, the likelihood prediction that the identified at least one deployed machine will experience the target event" cannot be performed in a human mind or using pen and paper because a human cannot simultaneously process complex predictions and lacks a human-computer interface to cause on-board user interface to display dynamic, real-time event likelihoods for multiple machines.
The Examiner respectfully disagrees viewing that Applicant conflates the additional elements identified by the Examiner and the steps identified as being a part of the abstract idea. The Examiner notes that there is nothing in the claims to suggest that human would be precluded from performing the steps identified because of supposed: volume and velocity of heterogeneous data from multiple machines, impracticality of processing and identifying specific events across sequential data for numerous machines, computational resources for the generation, identification, and storage of numerous overlapping and distinct event sequences from high-volume sequential data across multiple machine call, or the analysis, scoring, and identification of rules across a large sequence database involve computational pattern recognition algorithms. The Examiner respectfully views that these improvements are not reflected in the associated limitations proffered by Applicant. The Examiner further notes MPEP 2106.04 (a)(2) (III)( Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, “[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.” Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015). See also Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318, 120 USPQ2d 1353, 1360 (Fed. Cir. 2016) (‘‘[W]ith the exception of generic computer-implemented steps, there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.’’); Mortgage Grader, Inc. v. First Choice Loan Servs. Inc., 811 F.3d 1314, 1324, 117 USPQ2d 1693, 1699 (Fed. Cir. 2016) (holding that computer-implemented method for "anonymous loan shopping" was an abstract idea because it could be "performed by humans without a computer").
With respect to (1)“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”, (2) “with the second subsystem disposed at least in part on-board of the particular deployed machine … using the generated sequence database, training the machine learning model to implement the one or more rules”, and (3)“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 at least one identified deployed machine to generate a likelihood prediction using the first score, the second score or both:” the Examiner viewed limitation (1) as an additional element and not part of the recited mental process. The Examiner views for limitation (2) that an individual may be trained to recognize and implement the rules based on the analysis of the information in the sequence database where the additional elements are generic computing components to apply the abstract idea. There is nothing to suggest that the training as claimed is a computationally intensive processes or training involved involves changing weights and an activation function or executing computable-executable files. The Examiner views for limitation (3) that a user may generate and indicate a likelihood prediction that the identified at least one deployed machine will experience the target event, where the likelihood prediction is based on the analysis of collected information including using their trained mental model for making a prediction based on the collected streamed data as currently claimed. The abstract concepts as identified are merely applied by generic computing components or involve insignificant extra solution activity in the form of mere data gathering.
Applicant argues the claims provide a technical improvement in the field for example Oliner because Oliner requires both a search window and a separate control window, which increases the number of data sampling operations and does not enable prediction of comparative likelihoods of target events occurring in response to different sequences of preceding events. (Oliner Abstract and Figure 19) According to Applicant, the present claims provide an improved technical solution suitable to processing of high-volume sensor data signals in industrial machinery where separate windows of search and control samples are not required.
The Examiner respectfully disagrees viewing the concepts reflected in the claims do not address issues with processing high-volume sensor data and that the concepts that purportedly provide for the ability to not require separate windows of search and control samples are provided by steps of the abstract idea rather than the additional elements alone or in combination providing such a technical improvements.
Applicant argues the claims recite “with the rule mining module of the first subsystem, [operations comprising] 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”. According to Applicant, the improvement is generating separate likelihood scores for distinct event sequences that can led to a target event. Applicant contends the rule mining module operates on where the claims provide for the detailed operations in generating the sequence data as reflected in the claims.
The Examiner respectfully disagrees viewing the steps pertaining how the sequences are obtained for storage in the sequence database are a part of the abstract idea and not additional elements. The Examiner views that the additional elements identified are merely applied for performing steps of the abstract idea for example analyzing the event sequences to identify one or more rules corresponding to the target event. The Examiner views the purported technical improvement in generating separate likelihood scores is an improvement to the abstract idea rather a technical improvement.
Applicant argues the claims recite using the generated sequences to train models (with the second subsystem disposed at least in part on-board of the particular deployed machine using the generated sequence database, training the machine learning model to implement the one or more rules). According to Applicant, it would be difficult due to the volume of data to train model on raw sensor data instead of training models on the generated event sequence data database, where a technical improvement includes reducing the size of the training data set while preserving predictive value, so that the predictive value of the data set is preserved in the sequence database because the generated sequences, while including a smaller data set, still enables capture of prior values. Applicant contends predictive value of the data set is enhanced because distinct sequences, which may include overlapping events, offer a nuanced picture of paths to machine failure, where training models on such distinct sequences enables the models to more accurately predict likelihood of failure for specific sequences of events. According to Applicant, the claims reflect a technical solution to the technical problem of accurately predicting events associated with equipment.
The Examiner respectfully disagrees viewing the training is recited at a high level and under its broadest reasonable interpretation encompasses a user gaining experience to implement the rules over time. The Examiner does not view there is any recitation pertaining to high-volume data to suggest a user could not be trained to implement the rules as contested by Applicant. The Examiner views the steps pertaining to the generation of the sequence database are steps of the abstract idea as claimed that pertain to reducing the size of the training data set and does not constitute an improvement to technology as presently claimed. The Examiner views the improvements that result in more accurately predicting events associated with equipment the Examiner views is provided by the steps of identified abstract idea and that the improvement in accuracy as claimed is an improvement to the abstract idea rather than to technology.
Applicant argues the amended claims explain how the data structure to train such a model is generated to enable event predictions increased accuracy. SME Declaration 10-15 and 26-28. According to Applicant, the data structure elements used in training machine learning model can also contribute to, patentability, in particular, an example of a patent eligible improvement is a method of training machine learning models using data structure elements reciting adjustments in values to plurality of performance parameters while preserving prior values. Applicant contends generating sequence of values for training machine learning models serves a similar purpose preserving prior data points in sensor collected sequence of values while adding mode data points from additional data sets. See SME. Declaration at 27. Applicant contends that claims enable preservation of prior data points via the generated sequences while eliminating memory-inefficient multiple sampling processes (search window and control window) and therefore improve on inefficient approaches in the prior art for example Oliner.
The Examiner respectfully disagrees viewing the steps pertaining to generating a sequence database the examiner views can be performed in the human mind and/or via pen and a paper by making an observation (identification of the first and second occurrence of the target event determination). See also paragraphs 24 and 32. The Examiner views training the model as claimed as a user who acquires experience as explained prior. The Examiner does not view these particular steps or the other steps of the recited abstract idea to provide for such technical improvements as asserted by Applicant such as the elimination of memory-inefficient multiple sampling process used in Oliner but rather provide for an improvement to the abstract idea of predicting the likelihood of an event occurring for a machine based on the analysis of collected information.
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.
Shalaby et al. (US 20220187819) – directed to event-based failure prediction and remaining useful life estimation.
Sevakula et al. (US 20210027205) -directed to machine learning for failure event identification and prediction.
Kloepper et al. (US 20190294998) -directed to monitoring the status of a technical system.
Helwani et al. (US 20220101191) -directed to prognostics and health management service.
Fridley et al. (US 20220260988) – directed to predicting manufacturing process risks.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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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/M.J.M./Examiner, Art Unit 3629
/SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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