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 .
DETAILED ACTION
2. This action is responsive to the Application filed on 5/30/2024. A filing date 5/30/2024 is acknowledged. Claims 1-20 are pending in this application. Claims 1, 8, 15 are independent claims.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
3. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable overAnthony Griffiths et al (US Publication 20210016786 A1, hereinafter Griffiths), in view of Walid Shalaby et al (US Publication 20220187819 A1, hereinafter Shalaby), and Alexandru Ardel et al (US Publication 20230325292 A1, hereinafter Ardel).
As for independent claim 1, Griffiths discloses: A system for machine-learning (ML)-assisted ([0278], With such machine learning (ML)-based approaches, a model is trained to learn an output function y(x), given a set of training input vectors (x.sub.0, . . . , x.sub.N) together with the intended values of the output function (y.sub.0, . . . , y.sub.N) for those input vectors (the training data) event prediction for industrial machines (Abstract, A computer-implemented method of predicting vehicle failures), the system comprising: a set of condition monitoring sensors for an industrial machine ([0092], The diagnostics data 3 collected by the OBD system 4 comprises raw diagnostics data collected from on-board sensors 6, which are coupled to the OBD system 4 and can arranged to monitor essentially any desired property of the vehicle 1 or its various subsystems and components); at least one processor ([0072], a data processing stage comprising: electronic storage configured to store computer readable instructions; and one or more processors coupled to the electronic storage and configured to execute the computer readable instructions, the computer readable instructions being configured, when executed on the one or more processors, to implement any of the methods or system/device functions); at least one memory; and
one or more non-transitory, computer-readable storage media storing instructions, which, when executed by the at least one processor ([0072], a data processing stage comprising: electronic storage configured to store computer readable instructions; and one or more processors coupled to the electronic storage and configured to execute the computer readable instructions, the computer readable instructions being configured, when executed on the one or more processors, to implement any of the methods or system/device functions), cause the system to:
using the set of condition monitoring sensors, acquire first sensor data for the industrial machine ([0257], The telematics data sets could be sensor readings and/or diagnostic trouble codes); modify the first sensor data by generating a set of [imputed] sensor values ([0107], the datasets 13, 15 are generated by the pre-processing components 12, 14 applying any necessary pre-processing to, respectively, data diagnostics and vehicle repair received at the data processing stage to place them in a form that allows them to be used in the manner described below. This can for example include the removal of duplicate or erroneous records, re-formatting, reformulation of DTC codes etc); using the modified first sensor data, generate a first set of [embeddings] ([0050], determining a significance label for at least one piece of diagnostics data collected that vehicle, the significance label indicating whether or not that vehicle has experienced a fault event within a prediction window); using labeled second sensor data, generate a second set of embeddings, wherein the labeled second sensor data relates to a particular event or condition of industrial machines ([0092], The OBD system 4 collects various diagnostics data, as represented by the set of inputs labelled 3, and applies a diagnostic analysis to the collected data 3 in order to generate summarized diagnostics data; [0093], The OBD system 4 creates a record 10 of the triggering event in response, which comprises the associated DTC (labelled 10A) and a timestamp of the triggering event 10B); execute a trained neural network on the first set of embeddings and the second set of [embeddings] ([0052], The pieces of diagnostics data and their significance labels may be used to train a predictive component, executed at the data processing stage, to learn causal associations between pieces of diagnostics data and vehicle fault events, wherein the vehicle fault event prediction is outputted by the trained predictive component based on the target piece of diagnostics data) to generate a classifier tag for the first set of [embeddings], wherein the trained neural network is trained using training data comprising classifiers that relate to labels in the labeled second sensor data ([0271], each piece of diagnostics data corresponds to an individual DTC event, and the significance label assigned classifies it as repair-associated nor not repair-associated; [0289], a predictive component (model) in the form of a probabilistic classifier 1502 may be trained to make vehicle fault predictions, using the diagnostics data 13 and the linked repair data 15 for the vehicle population 1P—denoted vehicles 0 to N−1—as training data (together with the vehicle records 21 and environmental data if used)); using the classifier tag, generate a binding between the first set of [embeddings] and the classifier ([0289], a predictive component (model) in the form of a probabilistic classifier 1502 may be trained to make vehicle fault predictions, using the diagnostics data 13 and the linked repair data 15 for the vehicle population 1P—denoted vehicles 0 to N−1—as training data (together with the vehicle records 21 and environmental data if used)); and generate a notification that relates to the classifier ([0018], a vehicle alert component to trigger the outputting of an alert for a vehicle in response to the detection of a diagnostic warning event of the target type by an on-board diagnostics system of the vehicle).
Griffiths does not expressly disclose generating a set of embeddings, in an analogous art of predicting failure event using machine learning, Shalaby discloses: generate a first set of embeddings (Shalaby: [0043], events are translated to some integer values and use embedding mechanism similar to the one found in language models to convert the events to feature vectors);
Griffiths and Shalaby are analogous arts because they are in the same field of endeavor, predicting failure event using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Griffiths using the teachings of Shalaby to include generating a set of embeddings data. It would provide Griffiths’s system with enhanced capabilities of performing event-base prediction more accurately.
Further, Griffiths discloses pre-processing sensor data including modifying sensor data, but does not clearly disclose generating imputed data, in another analogous art of predicting failure event using machine learning, Ardel discloses: modify the first sensor data by generating a set of imputed sensor values ([0060], add to the sensor data 102, such as imputation to fill in estimated values for missing data samples or to equalize sampling rates of two or more sensors; [0089], the preprocessor 104 may resample the sensor data 102, may filter the sensor data 102, may impute data, may use the sensor data (and possibly other data) to generate new feature data values, may perform other preprocessing operations);
Griffiths and Ardel are analogous arts because they are in the same field of endeavor, predicting failure event using machine learning. Therefore, it would have been obvious to one with ordinary skill in the art before the effective filing date of the claimed invention, to modify the invention of Griffiths using the teachings of Ardel to include imputing sensor data. It would provide Griffiths’s system with enhanced capabilities of performing event-base prediction more accurately.
As for claim 2, Griffiths-Shalaby-Ardel discloses: determine, using the first sensor data or the modified first sensor data, an additional classifier that relates to a combination of (i) at least one of a machine type and a machine serial number and (ii) the particular event or condition (Griffiths: Abstract, the diagnostic warning events and vehicle fault events are associated in their respective datasets with cooperating vehicle identifiers; [0012], a predictive algorithm executed at the data processing stage determines whether or not each diagnostic warning event of a target type is time-associated with a vehicle fault event in that its associated timing is within a predetermined time window relative to that of any vehicle fault event associated with a matching vehicle identifier); generate additional training data using the first sensor data, the additional training data comprising the additional classifier; and
further train the neural network using the additional training data (Griffiths: [0119], By matching the VINs to VINs of the vehicle records 34, the data linking component can additionally augment the records of the linked dataset 18 with vehicle data derived therefrom; Ardel: [0125], the classifier 802, or both are further trained to determine whether to generate an alert 230 based on additional data received via the received outputs, such as residuals data, anomaly scores, other data received in one or more of the outputs from the multiple anomaly detection models 224, or combinations thereof).
As for claim 3, Griffiths-Shalaby-Ardel discloses: wherein the additional classifier is determined using sensor signaling channel information associated with the first sensor data (Ardel: [0125], the classifier 802, or both are further trained to determine whether to generate an alert 230 based on additional data received via the received outputs, such as residuals data, anomaly scores, other data received in one or more of the outputs from the multiple anomaly detection models 224, or combinations thereof).
As for claim 4, Griffiths-Shalaby-Ardel discloses: wherein at least one of the classifier tag or the additional classifier relates to a particular failure mode of the industrial machine (Ardel: [0067], the behavior model is trained using data representing normal operation of a monitored system (or operation associated with a particular operational mode); [0125], the classifier 802, or both are further trained to determine whether to generate an alert 230 based on additional data received via the received outputs, such as residuals data, anomaly scores, other data received in one or more of the outputs from the multiple anomaly detection models 224, or combinations thereof).
As for claim 5, Griffiths-Shalaby-Ardel discloses: wherein the instructions further cause the system to generate a remaining useful life estimate for the industrial machine using the modified first sensor data and an additional set of predicted sensor data generated using the modified first sensor data (Shalaby: Abstract, predicting failures and remaining useful life (RUL) for equipment, which can involve, for data received from the equipment comprising fault events, conducting feature extraction on the data to generate sequences of event features based on the fault events).
As for claim 6, Griffiths-Shalaby-Ardel discloses: wherein the instructions further cause the system to generate a predicted usage profile for the industrial machine using the modified first sensor data (Griffiths: [0287], the prediction window could correspond to a pre-determined change in mileage, such that its duration depends on the level of usage experienced by a vehicle; Shalaby: [0043], Other sequence features inferred from equipment usage (distance since the fault code first appeared, distance the fault code has been, distance from the previous fault code) are numerical).
As for claim 7, Griffiths-Shalaby-Ardel discloses: cause a computing device to display the notification, wherein the computing device comprises at least one of an on-board computing system, an on-board navigation system, or a mobile computing device communicatively coupled to the industrial machine (Griffiths: [0021], controlling a display device to display, to a user of the display device, an indication of the target diagnostic warning event and the significance value assigned to it; [0155], These results can be outputted to a user on a display device, via a user interface (UI) rendered on the display device; [0266], displaying the analysis and predictive results, a front-end which displays the results may be provided; Ardel: [0084], Responsive to the display output 290, the GUI 266 is displayed at the display device 208 to provide the operator 260 with the alert indication 268).
As per claim 8, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As per claim 9, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As per claim 10, it recites features that are substantially same as those features claimed by claim 3, thus the rationales for rejecting claim 3 are incorporated herein.
As per claim 11, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As per claim 12, it recites features that are substantially same as those features claimed by claim 5, thus the rationales for rejecting claim 5 are incorporated herein.
As per claim 13, it recites features that are substantially same as those features claimed by claim 6, thus the rationales for rejecting claim 6 are incorporated herein.
As per claim 14, it recites features that are substantially same as those features claimed by claim 7, thus the rationales for rejecting claim 7 are incorporated herein.
As per claim 15, it recites features that are substantially same as those features claimed by claim 1, thus the rationales for rejecting claim 1 are incorporated herein.
As per claim 16, it recites features that are substantially same as those features claimed by claim 2, thus the rationales for rejecting claim 2 are incorporated herein.
As per claim 17, it recites features that are substantially same as those features claimed by claim 3, thus the rationales for rejecting claim 3 are incorporated herein.
As per claim 18, it recites features that are substantially same as those features claimed by claim 4, thus the rationales for rejecting claim 4 are incorporated herein.
As per claim 19, it recites features that are substantially same as those features claimed by claim 5, thus the rationales for rejecting claim 5 are incorporated herein.
As per claim 20, it recites features that are substantially same as those features claimed by claim 6, thus the rationales for rejecting claim 6 are incorporated herein.
Examiner’s Note
Examiner has cited particular columns/paragraph and line numbers in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner.
In the case of amending the Claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure relied on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention. This will assist in expediting compact prosecution. MPEP 714.02 recites: “Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” Amendments not pointing to specific support in the disclosure may be deemed as not complying with provisions of 37 C.F.R. 1.131(b), (c), (d), and (h) and therefore held not fully responsive. Generic statements such as “Applicants believe no new matter has been introduced” may be deemed insufficient.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. Applicants are required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Trinh (US Publication 20200379454) MACHINE LEARNING BASED PREDICTIVE MAINTENANCE OF EQUIPMENT
Singhal (US Publication 20240028026) METHOD AND SYSTEM FOR CAUSAL INFERENCE AND ROOT CAUSE IDENTIFICATION IN INDUSTRIAL PROCESSES
Gullikson (US Publication 20230110056) ANOMALY DETECTION BASED ON NORMAL BEHAVIOR MODELING
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hua Lu whose telephone number is 571-270-1410 and fax number is 571-270-2410. The examiner can normally be reached on Mon-Fri 9:00 am to 6:00 pm EST. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Scott Baderman can be reached on 571-272-3644. The fax phone number for the organization where this application or proceeding is assigned is 703-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Hua Lu/
Primary Examiner, Art Unit 2118
Prosecution Insights