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 the Claims
Claims 1-2, 5, 8-16, 18, and 22-28 are currently pending. Claims 1-2, 5, 8-16, and 18 are currently amended. Claims 3, 6-7, 17, and 19-21 are cancelled. Claims 22-28 are newly added. The Objections to claim 15 is withdrawn. The 35 U.S.C. 112(b) rejections of claims 1-2, 5, and 8-16 are withdrawn. This Action is Non-Final.
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 1-2, 5, 8-16, 18, and 22-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to an information-processing method (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for analysing railway related vibration data, the method comprising the steps of:
collecting at least a first dataset from a sensor applied to railway infrastructure,
collecting at least a second dataset from a scheduling component,
curating at least one subset of the first dataset with the second dataset to obtain a first training database,
automatically converting the first data set to at least one time-frequency spectrogram,
unsupervised encoding of the at least one time-frequency spectrogram to at least one feature map, and
predicting at least a likelihood of one train belonging to at least one train-type using the at least one feature map.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “predicting...” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method for analysing railway related vibration data, the method comprising the steps of:
collecting at least a first dataset from a sensor applied to railway infrastructure,
collecting at least a second dataset from a scheduling component,
curating at least one subset of the first dataset with the second dataset to obtain a first training database,
automatically converting the first data set to at least one time-frequency spectrogram,
unsupervised encoding of the at least one time-frequency spectrogram to at least one feature map, and
predicting at least a likelihood of one train belonging to at least one train-type using the at least one feature map.
Regarding the additional limitations of “collecting at least a first dataset…,” “collecting at least a second dataset…,” “curating at least one subset of the first dataset with the second dataset…,” “converting the first data set…,” “encoding…,” the examiner submits that these limitation are insignificant extra-solution activities that merely use a computer to perform the process. In particular, the collecting and curating steps are recited at a high level of generality (i.e. as a general means of gathering railway data, and a general means for pre-processing the gathered data for use in the evaluating step), and amounts to mere data gathering and processing, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of “collecting…,” “curating…,” “converting…,” and “encoding…” the examiner submits that the limitations are insignificant extra-solution activities.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “collecting…,” “curating…,” “converting…,” and “encoding…” is well-understood, routine, and conventional activities because the background recites additional references that utilize data collection and processing. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Furthermore, performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); Hence, the claim is not patent eligible.
Dependent claims 2, 5, 8-16, 18, and 22-28 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2, 5, 8-16, 18, and 22-28 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Claim 15, an apparatus claim (a train classification system), includes limitations analogous to claim 1, a process claim (a method for analysing railway related vibration data), but adds a server, a processing component, and a sensor.
The server and processing component are generically recited computer elements (hardware or software) and do not add significantly more to the abstract idea because they merely amount to implementing the abstract idea on a computer.
The sensor does not add significantly more to the abstract idea because the sensor is a well understood routine and conventional sensor utilized to gather data that the abstract idea is performed with as seen in page 16 of the instant application’s specification which states “the sensor 8 can be an acceleration sensor and/or any other kind of railway specific sensor.”
Accordingly, claim 15 is rejected under 35 U.S.C. 101 because the claim is directed to an abstract idea without significantly more
Therefore, claims 1-2, , 8-16, 18, and 22-28 are ineligible under 35 USC §101.
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 (i.e., changing from AIA to pre-AIA ) 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, 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.
Claim(s) 1, 10-14, 16, 22-23, and 25-27are rejected under 35 U.S.C. 103 as being unpatentable over Berlin (Trainspotting: Combining fast features to enable detection on resource-constrained sensing devices) in view of Iandola (US 20180188733 A1) and Sjogren (US 20210334656 A1).
In regards to claim 1, Berlin teaches a method for analysing railway related vibration data (abstract, lines 12-13), the method comprising the steps of:
collecting at least a first dataset (section V, para. 1, lines 1-2) from a sensor (section V, para. 1, lines 2-3) applied to railway infrastructure (section IV, para. 2, lines 1-2),
collecting at least a second dataset from a scheduling component (section V, para. 2, lines 4-7),
curating at least one subset of the first dataset with the second dataset (section V, para. 7, lines 1-5) to obtain a first training database (section VI, para. 7, lines 2-5),
automatically converting the first data set (section VI, para. 5)
predicting at least a likelihood of one train belonging to at least one train-type (as seen in Fig. 6, the predicted classes A, B, C, and D).
Berlin does not teach the first data set being converted to at least one time-frequency spectrogram.
Iandola teaches data being converted to at least one time-frequency spectrogram (para. [0031]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of converting the first data set of Berlin to include a time-frequency spectrogram as taught by Iandola with a reasonable expectation of success for the purpose of enabling the adjustability of the presented data (see para. [0030], lines 7-9).
Berlin does not teach unsupervised encoding of the at least one time-frequency spectrogram to at least one feature map.
Sjogren teaches unsupervised (para. [0119], lines 3-5) encoding (para. [0257], lines 11-14) of the at least one spectrogram (para. [0107], lines 10-14) to at least one feature map (para. [0141], lines 8-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Berlin to include unsupervised encoding data to feature maps as taught by Sjogren with a reasonable expectation of success for the purpose of ensuring the system is working as intended (see Sjogren, para. [0004], lines 1-7).
In regards to claim 10, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 wherein the at least one feature map is a lower-dimensional feature map (Sjogren, para. [0321]), wherein the method comprises automatically calculating at least one nearest sample neighbour (section VI, para. 8, lines 1-3) in the lower-dimensional feature map (Sjogren, para. [0321])
In regards to claim 11, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 further comprising using the at least one feature map (Sjogren, para. [0321]) and the second dataset (section V, para. 2, lines 4-7) to label (section VI, para. 7, lines 5-8) the at least one subset (section V, para. 7, lines 1-5) of the first dataset (section V, para. 1, lines 1-2).
In regards to claim 12, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 further comprising automatically calculating the at least one nearest sample neighbour (section VI, para. 8, lines 1-3) in the feature map (Sjogren, para. [0321]) and iteratively (section VI, para. 8, lines 3-8) extending a label (section VI, para. 7, lines 5-8) from the at least one subset (section V, para. 7, lines 1-5) of the first dataset (section V, para. 1, lines 1-2) to the at least one nearest sample neighbour (section VI, para. 8, lines 1-3).
In regards to claim 13, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 wherein the at least one feature map is a lower-dimensional feature map (Sjogren, para. [0321]), the method further comprising predicting a likelihood of a train being of a certain type (as seen in Fig. 6, the predicted classes A, B, C, and D) using the lower dimensional feature map (Sjogren, para. [0321], see rejection of claim 9 above).
In regards to claim 14, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 further comprising predicting a likelihood of a train being of a certain type (as seen in Fig. 6, the predicted classes A, B, C, and D) using the first training database (section VI, para. 7, lines 2-5).
In regards to claim 16, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 comprising further associating at least one weight (section VI, para. 13, lines 3-5) with at least one distinctive feature of the train.
In regards to claim 22, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 comprising automatically collecting the first dataset (Berlin, section V, para. 1, lines 1-2), wherein the first dataset comprises a vibration signal associated with a motion of a rail vehicle (Berlin, abstract, lines 12-13).
In regards to claim 23, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 22, wherein the vibration signal (Berlin, abstract, lines 12-13) comprises at least one of:
at least frequency data;
at least displacement data;
at least velocity data; or
at least acceleration data (Berlin, section IV, para. 3, lines 8-10).
In regards to claim 25, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 further comprising extracting at least one feature map (Sjogren, para. [0141], lines 8-10) from the first dataset (Berlin, section V, para. 1, lines 1-2) and further comprising extracting a feature map (Sjogren, para. [0141], lines 8-10) related to an observed environment of the sensor (Berlin, section V, para. 1, lines 2-3).
In regards to claim 26, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1 further comprising automatically generating at least one acceleration trace (Berlin, Fig. 5(a)-(f), acceleration data is pictured with respect to time) associated with the first dataset (Berlin, section V, para. 1, lines 1-2) and automatically converting the at least one acceleration trace (Berlin, Fig. 5(a)-(f), acceleration data is pictured with respect to time) to at least one time-frequency spectrogram (Iandola, para. [0031]).
In regards to claim 27, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 26 further comprising scaling (Iandola, para. [0031], “size of the sensor data can be adjusted”) at least one spectrogram value (Berlin, section VI, para. 7, lines 9-12) within a pre-determined region.
Claim(s) 2, 5, 8-9, 15, 18, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Berlin (Trainspotting: Combining fast features to enable detection on resource-constrained sensing devices) in view of Iandola (US 20180188733 A1), Sjogren (US 20210334656 A1), and Jovenall (US 20190197923 A1).
In regards to claim 2, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 1. Berlin does not teach connecting the at least one sensor to at least one server, wherein the server comprises at least one processing component.
Jovenall teaches connecting (para. [0047]) the at least one sensor (606) (Fig. 6) to at least one server (616), wherein the server comprises at least one processing component (618).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the sensor of Berlin to include connecting the sensor to a server as taught by Jovenall with a reasonable expectation of success for the purpose of enabling data to be collected remotely rather than manually in-person (see Jovenall, para. [0047]).
In regards to claim 5, the combination of Berlin as modified by Iandola, Sjogren, and Jovenall above teaches the method according to claim 2 comprising pre-processing the first dataset (section VI, para. 3), in the at least one processing component (618) (Jovenall, Fig. 6, see the rejection of claim 2 above).
In regards to claim 8, the combination of Berlin as modified by Iandola, Sjogren, and Jovenall above teaches the method according to claim 2 comprising facilitating the processing component (618) (Jovenall, Fig. 6, see the rejection of claim 2 above). Berlin does not teach a neural network (NN) component, wherein the NN component is configured to automatically learn at least one lower-dimensional feature map.
Sjogren teaches a neural network (NN) (100) (Fig. 1) component, wherein the NN component (100) is configured to automatically learn (para. [0212]) at least one lower-dimensional feature map (para. [0321]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Berlin to include a neural network learning from a feature map as taught by Sjogren with a reasonable expectation of success for the purpose of increasing the resiliency and adaptability of the system (see Sjogren, para. [0212]), lines 4-7).
In regards to claim 9, the combination of Berlin as modified by Iandola, Sjogren, and Jovenall above teaches the method according to claim 8 further comprising teaching (Sjogren, para. [0212]) the NN component (100) (Sjogren, Fig. 1) the at least one lower-dimensional feature map (Sjogren, para. [0321]).
In regards to claim 15, Berlin teaches a train classification system, the system comprising:
a sensor (section V, para. 1, lines 2-3) configured to provide at least a first dataset (section V, para. 1, lines 1-2) and configured to be applied to railway infrastructure (section IV, para. 2, lines 1-2),
a scheduling component configured to provide at least a second dataset (section V, para. 2, lines 4-7),
curate at least one subset of the first dataset with the second dataset (section V, para. 7, lines 1-5) to obtain a first training database (section VI, para. 7, lines 2-5),
classify at least one train type (as seen in Fig. 6, the predicted classes A, B, C, and D),
wherein, the system is configured to execute a method according to method claim 1 (see rejection of claim 1 above in view of Berlin as modified by Iandola and Sjogren).
Berlin does not teach a server and a processing component, wherein the system is configured to execute the method according to any of the method claims.
Jovenall teaches a server (616) (Fig. 6) and a processing component (618), wherein the system is configured to execute a method (para. [0038], lines 3-6).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Berlin to include a server and a processor as taught by Jovenall with a reasonable expectation of success for the purpose of enabling data to be collected remotely rather than manually in-person (see Jovenall, para. [0047]).
In regards to claim 18, the combination of Berlin as modified by Iandola, Sjogren, and Jovenall above teaches the method according to claim 5, further comprising automatically generating at least one acceleration trace (Berlin, Fig. 5(a)-(f), acceleration data is pictured with respect to time) associated with the first data set, wherein the pre-processing further comprises at least one of:
flagging at least one noisy component of the first dataset,
removing at least one exponential wakeup,
cutting off the edge of the at least one acceleration trace,
stretching the at least one first dataset to a pre-determined size,
representing the at least one first dataset (section VI, para. 5) as a time-frequency spectrogram (Iandola, para. [0031]).
In regards to claim 24, the combination of Berlin as modified by Iandola, Sjogren, and Jovenall above teaches the method according to claim 18 comprising using the pre-processed first dataset for predicting the likelihood of the train being of a certain type (Berlin, as seen in Fig. 6, the predicted classes A, B, C, and D).
Claim(s) 28 is rejected under 35 U.S.C. 103 as being unpatentable over Berlin (Trainspotting: Combining fast features to enable detection on resource-constrained sensing devices) in view of Iandola (US 20180188733 A1), Sjogren (US 20210334656 A1), and Jeon (US 20210142162 A1).
In regards to claim 28, the combination of Berlin as modified by Iandola and Sjogren above teaches the method according to claim 27 comprising generating at least one spectrogram value
Berlin does not teach utilizing hyperparameter optimization.
Jeon teaches utilizing hyperparameter optimization (para. [0014]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Berlin to include hyperparameter optimization as taught by Jeon with a reasonable expectation of success for the purpose of improving the efficiency of the system (see Jeon, para. [0018]).
Response to Arguments
Applicant’s arguments, see Applicant’s Remarks, filed 10 November 2025, with respect to the rejection(s) of claim(s) 1 under Berlin in regards to the at least one time-frequency spectrogram have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Berlin as modified by Iandola and Sjogren as seen above.
In response to Applicant’s argument that the Examiner’s conclusion of obviousness is based upon improper hindsight reasoning in relation to the utilization of Sjogren to modify Berlin, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. In re McLaughlin, 443 F.2d 1392; 170 USPQ 209 (CCPA 1971). See MPEP § 2145(X)(A).
Applicant argues that Sjogren does not teach unsupervised encoding. The examiner responds that Sjogren, as indicated in the rejections above, para. [0119], lines 1-5 indicate the usage of an “autoencoder” in an “unsupervised manner.”
Applicant's arguments in regards to the 35 U.S.C. 101 rejections have been fully considered but they are not persuasive. The amended limitations of claim 1 are not directed to significantly more than an abstract idea. As discussed in the 35 U.S.C. 101 rejections above, the limitations “collecting …,” “curating…,” “converting…,” and “encoding…,” are insignificant extra-solution activities that merely use a computer to perform the process.
In regards to the sensor, it is a well understood routine and conventional sensor utilized to gather data that the abstract idea is performed with as seen in page 16 of the instant application’s specification which states “the sensor 8 can be an acceleration sensor and/or any other kind of railway specific sensor.” The sensor is able to be any other kind of railway specific sensor including acceleration sensors, vibration sensors, etc. These sensors are well understood and conventional in the art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES WILLIAM JONES whose telephone number is (571)270-7063. The examiner can normally be reached M-F: 11am-7pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Samuel Morano can be reached at (571) 272-6684. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES WILLIAM JONES/ Examiner, Art Unit 3615
/S. Joseph Morano/ Supervisory Patent Examiner, Art Unit 3615