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 . In the event the determination of the status of the application as subject to 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.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1 July 2026 has been entered.
Status of Claims
Claims 1-20 are currently pending and are being hereby examined herein. Claims 1, 11, and 19 are amended.
Response to Amendment / Remarks
Any reference to the prior office action refers to the Final Rejection dated 1 April 2026.
Applicant’s arguments, with respect to the prior art of record from the prior office action, have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2 July 2026 was reviewed by the Examiner. At this time, the U.S. Patent reference was considered; however, the other documents were lined through / not considered, as the Examiner did not find the corresponding documents to consider in the file wrapper of the instant application.
Claim Rejections - 35 USC § 103
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.
The factual inquiries 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.
Claims 1-9, 11-17, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over the English Translation of CN 116704713 A (Dai et al., hereinafter, Dai) in view of U.S. Patent No. 3,946,973 (Budway and McGlumphy, hereinafter, Budway).
Note: all page number references to Dai refer to the non-patent literature attachment to this Office Action.
Regarding Claim 1, Dai discloses A method of automatically tuning control parameters (see at least page 3: decides to optimize the prediction model), comprising:
generating a set of production predictions associated with one or more car events at a first point of a route (see at least page 3: regression equation predicts maximum speed of the roller coaster based on boost speed, total number of cycles of operation, number of passengers, and boost current);
obtaining actual measurements associated with the one or more car events at the first point of the route (see at least pages 2 and 7: data of actual normal operation periods is obtained);
estimating a candidate set of control parameters associated with the first point of the route based on the actual measurements associated with the one or more car events at the first point of the route, wherein the one or more car events have already occurred at the first point of the route at a time the candidate set of control parameters is estimated (see at least pages 3 and 7: parameter optimization is performed using data from actual operation);
generating a set of backoffice predictions associated with the one or more car events at the first point of the route using the candidate set of control parameters associated with the first point of the route (see at least pages 3 and 7: one of ordinary skill in the art understands this is part of optimizing the regression parameters);
comparing the set of production predictions associated with the one or more car events at the first point of the route and the set of backoffice predictions associated with the one or more car events at the first point of the route to determine which of the production set of control parameters or the candidate set of control parameters for the first point of the route yields more accurate predictions for the one or more car events at the first point of the route, wherein the set of backoffice predictions is generated for a same one or more car events after the same one or more car events have already occurred using the candidate set of control parameters that was estimated from the actual measurements associated with the same one or more car events, and wherein comparing the set of production predictions and the set of backoffice predictions includes evaluating a predictive accuracy of the set of production predictions based on the actual measurements associated with the same one or more car events, prior to replacing the production set of control parameters (see at least pages 3 and 7: one of ordinary skill in the art understands this is part of the regression parameter optimization performed using data from actual operation);
determining to replace the production set of control parameters for the first point of the route with the candidate set of control parameters in response to a determination that the candidate set of control parameters yields more accurate predictions for the one or more car events at the first point of the route than the production set of control parameters (see at least pages 3 and 7: one of ordinary skill in the art understands this is part of the regression parameter optimization performed using data from actual operation to optimize the early warning; as would be understood to one of ordinary skill in the art, the best fitting known parameters are selected for safety); and
controlling operations (see at least pages 3 and 6: if the predicted highest speed is too high, the roller coaster stops).
As shown by the claim language with strikethroughs above, Dai does not explicitly disclose / is not directed to a classification yard and railcar cuts. Dai is instead directed to the similar device of roller coaster in an amusement facility (see at least page 2).
Budway, in the same field of railway controls, and therefore analogous art, teaches control for a classification yard and railcar cuts (see at least column 1 lines 7-9: “Our invention pertains to a retarder speed control system and particularly to such control systems to regulate car speeds in automatic railroad classification yards”).
Substituting the railcar cuts in a classification yard of Budway for the roller coaster in an amusement facility of Dai, would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of predictably and quickly optimizing calculations related to railcar cuts in a classification yard as more / new real data is available to improve calculations related to coupling in a railyard with the motivation of ensuring coupling of railcar cuts (see at least Budway column 1 lines 59-64) as changes to the track occur overtime (see Dai page 7). In view of this substitution, one of ordinary skill in the art would add / modify terms in the regression equation of Dai based on teachings of Budway and would make calculations in at least areas that Budway teaches impact speed of the railyard / where speed should be known.
Regarding Claim 2, Dai and Budway combination teaches Claim 1. Furthermore, Dai further discloses wherein the first point of the route includes one or more of a route segment and a device of the classification yard (see at least page 7: the sliding stage is the route segment considered). Furthermore, Budway further teaches (with the same motivation to Combine as Claim 1 / as part of the same combination as Claim 1) wherein the first point of the route includes one or more of a route segment and a device of the classification yard (see at least column 2, column 5, column 7, Fig. 1, and Fig. 2: location D is a wheel detector at the end of retarder 2).
Regarding Claim 3, Dai and Budway combination teaches Claim 2. Furthermore, Budway further teaches (with the same motivation to combine as Claim 1 / as part of the same combination as Claim 1) wherein the device of the classification yard includes one or more of: a switch; a retarder; and a wheel detector (see at least column 2, columns 5-7, Fig. 1, and Fig. 2: “This route passes in succession through four retarders, a master, an intermediate, a group, and a tangent point retarder, each shown by a conventional block. It is to be noted that the size of the block representing a particular retarder is not representative of the size or length of that retarder with relation to the others along the route. No diverging track routes are shown in this simplified sketch but track switches for diverting cars over such other routes to other bowl tracks exist between each pair of adjacent retarders. In other words, one or two switches, would be located between the master and intermediate retarders to divert cars through other intermediate retarders directed towards other selected storage tracks. Similar track switches would exist between the intermediate and group, and group and tangent point retarders. It may also be noted that, although the first three retarders are common to more than one route, each tangent retarder is used to control the car speed in a particular single bowl track such as the track BT shown immediately to the right of the tangent retarder illustrated”; “Wheel detectors for detecting the passage of each wheel-axle set of a cut of cars are shown by conventional symbols located at selected points along the route from the hump to bowl track BT”; location D is a wheel detector at the end of retarder 2).
Regarding Claim 4, Dai and Budway combination teaches Claim 1. Furthermore, the Dai and Budway combination (with the same motivation to combine as Claim 1 / as part of the same combination as Claim 1) further teaches wherein the one or more car events include one or more of: a railroad cut traveling through the first point of the route at a first speed; the railroad cut arriving at the first point of the route at a first time; the railroad cut entering at an entry point of the first point of the route at an entry speed; and the railroad cut exiting at an exit point from the first point of the route at an exit speed (see at least Dai page 3: calculates maximum speed, in view of Budway this would occur for a railroad cut).
Furthermore, Budway further teaches (with the same motivation to combine as Claim 1 / as part of the same combination as Claim 1) wherein the one or more car events include one or more of: a railroad cut traveling through the first point of the route at a first speed; the railroad cut arriving at the first point of the route at a first time; the railroad cut entering at an entry point of the first point of the route at an entry speed; and the railroad cut exiting at an exit point from the first point of the route at an exit speed (see at least column 7 lines 50-65: “The variables and constants of equation (1) are defined on the basis that it is being applied for control of retarder 2”).
Regarding Claim 5, Dai and Budway combination teaches Claim 1. Furthermore, Dai further discloses wherein the production set of control parameters for the first point of the route includes one or more of: rolling resistance coefficients; temperature coefficients; regression coefficients; switch coefficients; retarder coefficients; detector coefficients; and angle coefficients (see at least page 3: a11, a12, a13, a14, and b11 are regression parameters). Furthermore, Budway further teaches (with the same motivation to combine as Claim 1 / as part of the same combination as Claim 1) wherein the production set of control parameters for the first point of the route includes one or more of: rolling resistance coefficients; temperature coefficients; regression coefficients; switch coefficients; retarder coefficients; detector coefficients; and angle coefficients (see at least columns 8-9: “A3 and B3 - linear regression coefficients (associated with the path between D and E) that relate R3 to R1, stored constants for each path between intermediate and group retarders, and for each path between group and tangent retarders”; “R1 - measured average cut rolling resistance between locations A and B”; “G'3 - the average effective grade between locations D' and E. A stored constant for each different path between adjacent retarders based on the statistics of actual car behavior”).
Regarding Claim 6, Dai and Budway combination teaches Claim 1. Furthermore, Dai further discloses wherein estimating the candidate set of control parameters associated with the first point of the route based on the actual measurements associated with the one or more car events at the first point of the route includes applying a regression algorithm to the actual measurements associated with the one or more car events at the first point of the route to obtain the candidate set of control parameters associated with the first point of the route (see at least page 7: prediction model is a regression model that is updated).
Regarding Claim 7, Dai and Budway combination teaches Claim 1. Furthermore, the Dai and Budway combination (with the same motivation to combine as Claim 1 / as part of the same combination as Claim 1) further teaches wherein one or more of the set of production predictions and the set of backoffice predictions include predictions of one or more of: energy of a railroad cut at the first point of the route; speed of the railroad cut at the first point of the route; and arrival time of the railroad cut at the first point of the route (see at least Dai page 7: predicted maximum speed is the result of the equation, in view of Budway this would occur for a railroad cut).
Furthermore, Budway further teaches (with the same motivation to Combine as Claim 1 / as part of the same combination as Claim 1) wherein one or more of the set of production predictions and the set of backoffice predictions include predictions of one or more of: energy of a railroad cut at the first point of the route; speed of the railroad cut at the first point of the route; and arrival time of the railroad cut at the first point of the route (see at least column 8 lines 5-10: “TE - The "target" travel time between locations B' and E' for the center of a cut”).
Regarding Claim 8, Dai and Budway combination teaches Claim 1. Furthermore, Dai further discloses wherein comparing the set of production predictions associated with the one or more car events at the first point of the route and the set of backoffice predictions associated with the one or more car events at the first point of the route includes applying a statistical comparison between the set of production predictions associated with the one or more car events at the first point of the route and the set of backoffice predictions associated with the one or more car events at the first point of the route (see at least page 7: one of ordinary skill in the art would consider that this occurs during optimization of the regression equation).
Regarding Claim 9, Dai and Budway combination teaches Claim 1. Furthermore, Dai further discloses wherein comparing the set of production predictions associated with the one or more car events at the first point of the route and the set of backoffice predictions associated with the one or more car events at the first point of the route includes: calculating a production absolute value average difference between the set of production predictions associated with the one or more car events at the first point of the route and the actual measurements associated with the one or more car events at the first point of the route; calculating a backoffice absolute value average difference between the set of backoffice predictions associated with the one or more car events at the first point of the route and the actual measurements associated with the one or more car events at the first point of the route; comparing the production absolute value average difference and the backoffice absolute value average difference to determine which one of the production absolute value average difference and the backoffice absolute value average difference is smaller; determining that the production set of control parameters yields more accurate predictions for the one or more car events at the first point of the route than the candidate set of control parameters in response to a determination that the production absolute value average difference is smaller than the backoffice absolute value average difference for the first point of the route; and determining that the candidate set of control parameters yields more accurate predictions for car events at the first point of the route than the production set of control parameters in response to a determination that the production absolute value average difference is not smaller than the backoffice absolute value average difference for the first point of the route (see at least page 7: deviations between the actual and predicted values are determined, the regression parameters are optimized which one of ordinary skill in the art would know means that the best known coefficients are chosen based on which provide the most accurate results / have the least difference between predicted maximum speed and the speed that actually occurred).
Regarding Claim 11, Dai discloses A system for automatically tuning control parameters…, comprising: at least one processor; and a memory operably coupled to the at least one processor and storing processor-readable code that, when executed by the at least one processor, is configured to perform operations (see at least page 4). All other limitations are substantially similar to Claim 1, and therefore Claim 11 is rejected for the same reasons as Claim 1.
Regarding Claim 12, Claim 12 is substantially similar to Claim 2, and therefore rejected for the same reasons as Claim 2.
Regarding Claim 13, Claim 13 is substantially similar to Claim 4, and therefore rejected for the same reasons as Claim 4.
Regarding Claim 14, Claim 14 is substantially similar to Claim 5, and therefore rejected for the same reasons as Claim 5.
Regarding Claim 15, Claim 15 is substantially similar to Claim 6, and therefore rejected for the same reasons as Claim 6.
Regarding Claim 16, Claim 16 is substantially similar to Claim 7, and therefore rejected for the same reasons as Claim 7.
Regarding Claim 17, Claim 17 is substantially similar to Claim 9, and therefore rejected for the same reasons as Claim 9.
Regarding Claim 19, Dai discloses A computer-based tool for automatically tuning control parameters for operations of a classification yard, the computer-based tool including non-transitory computer readable media having stored thereon computer code which, when executed by a processor, causes a computing device to perform operations (see at least page 4). All other limitations are substantially similar to Claim 1, and therefore Claim 19 is rejected for the same reasons as Claim 1.
Regarding Claim 20, Claim 20 is substantially similar to Claim 9, and therefore rejected for the same reasons as Claim 9.
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Dai in view of Budway in further view of the English Translation of JP 2010098849 A (Yamamoto et al., hereinafter, Yamamoto).
Note: reference to paragraph numbers of Yamamoto refer to the previously-provided Non Patent Literature (dated 27 October 2025).
Regarding Claim 10, Dai and Budway combination teaches Claim 1. Furthermore, Budway further teaches (with the same motivation to combine as Claim 1 / as part of the same combination as Claim 1) wherein the one or more car events at the first point of the route are classified into a bucket classification (see at least column 5 lines 40-65: “classify each passing car into one of a number of predetermined weight classes”). The Dai and Budway combination does not explicitly teach wherein the one or more car events at the first point of the route are classified into a bucket classification, the bucket classification including one or more of: a wet classification to classify the one or more car events occurring during wet weather conditions; a dry classification to classify the one or more car events occurring during dry weather conditions; a cold classification to classify the one or more car events occurring during cold weather conditions; a warm classification to classify the one or more car events occurring during warm weather conditions; a hot classification to classify the one or more car events occurring during hot weather conditions; and a resilience bearing type classification to classify the one or more car events associated with a railroad cut including one or more train cars having a resilience type bearing.
Yamamoto, in the same field of train controls, and therefore analogous art, teaches wherein the one or more car events at the first point of the route are classified into a bucket classification, the bucket classification including one or more of: a wet classification to classify the one or more car events occurring during wet weather conditions; a dry classification to classify the one or more car events occurring during dry weather conditions; a cold classification to classify the one or more car events occurring during cold weather conditions; a warm classification to classify the one or more car events occurring during warm weather conditions; a hot classification to classify the one or more car events occurring during hot weather conditions; and a resilience bearing type classification to classify the one or more car events associated with a railroad cut including one or more train cars having a resilience type bearing (see at least [0024]-[0025]: separate models based on rainy / sunny / fine weather).
Combining the classifications based on weather condition of Yamamoto with the Dai and Budway combination would have been obvious, before the effective filing date of the invention, with a reasonable expectation of success, to one having ordinary skill in the art, with the motivation of acquiring data relating to various weather conditions which are known to impact the rollability of trains (see at least Yamamoto [0004]-[0005]).
Regarding Claim 18, Claim 18 is substantially similar to Claim 10, and therefore rejected for the same reasons as Claim 10.
Conclusion
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/A.R.M./Examiner, Art Unit 3658
/THOMAS E WORDEN/Supervisory Patent Examiner, Art Unit 3658
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