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 .
Response to Arguments
Applicant’s arguments, see Remarks, filed 05/12/2026, with respect to the rejection(s) of claims 1-20 under 35 USC § 102 and 35 USC § 103 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 Therefore, the rejection has been withdrawn.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 Mallampati et al US 2025/0145196.
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.
Claims 1, 3, 5-10, 13, 15, 17, and 20-23 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor et al US 2023/0206762 in view of Mallampati et al US 2025/0145196.
Regarding claim 1, Taylor et al discloses a system for determining locomotive assignments, the system comprising: a processor (see paragraph [0021]); and a non-transitory computer readable medium that stores instructions that when executed by the processor causes the processor to perform operations comprising (see paragraph [0021]): receiving railroad information related to a railroad system including locomotives within the railroad system (see paragraphs [0042]-[0045]); determining railroad data from the railroad information by processing the railroad information, the railroad data associated with schedules, conditions, demand of locomotives, and availability of locomotives (see paragraph [0094]); determining a set of rules for assigning locomotives to trains, locations, and maintenance, the set of rules determined by a machine learning model trained using historical railroad data tagged with locomotive assignment information (see paragraphs [0094] and [0095]); determining assignments for locomotives within the railroad system using the set of rules in response to the railroad data; and outputting the assignments and executing the assignments of the locomotives. See FIG. 1-4 and paragraphs [0042]-[0049].
Taylor et al fails to explicitly disclose, but Mallampati et al discloses the assignments including consecutive assignments to one or more locomotives. See paragraphs [0115]-[0117].
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include the teachings of Mallampati et al in the system of Taylor et al ensuring that the sequences allow for adequate preparation and completion of all operational tasks without overlap or conflict.
Regarding claim 3, Taylor et al discloses wherein receiving the railroad information comprises accessing one or more messages conveyed by computing devices of the railroad system and determining the railroad data further comprises processing the messages using a large language model. See paragraphs [0084] and [0094].
Regarding claim 5, Taylor et al discloses wherein the railroad information comprises: information related to a position and status of the locomotives (see paragraphs [0058]-[0062]); information related to a condition of the railroad system; and information related to a demand for locomotives within the railroad system. See FIG. 1-4 and paragraphs [0042]-[0049].
Regarding claims 6 and 13, Taylor et al discloses wherein the set of rules describe conditions for locomotive assignments and determining the assignments comprises applying the set of rules to the railroad data. See paragraphs [0044]-[0054].
Regarding claim 7, Taylor et al discloses wherein the railroad information further comprises locomotive maintenance schedules for locomotives in the railroad system. See paragraphs [0033]-[0036] and [0060].
Regarding claim 8, Taylor et al discloses a computer-implemented method for determining locomotive assignments, the method comprising: receiving railroad information related to a railroad system including locomotives within the railroad system (see paragraphs [0042]-[0045]); determining railroad data from the railroad information by processing the railroad information, the railroad data associated with schedules, conditions, demand of locomotives, and availability of locomotives (see paragraph [0094]); determining a set of rules for assigning locomotives to trains, locations, and maintenance, the set of rules determined by a machine learning model trained using historical railroad data tagged with locomotive assignment information (see paragraphs [0094] and [0095]); determining assignments for locomotives within the railroad system using the set of rules in response to the railroad data; and outputting the assignments and executing the assignments of the locomotives. See FIG. 1-4 and paragraphs [0042]-[0049].
Regarding claims 9 and 17, Taylor et al discloses wherein determining the assignments comprises using a second machine learning model configured to produce the assignments in response to the railroad data and the set of rules, wherein the second machine learning model is trained using historical locomotive assignment data, historical rule data, and one or more evaluation parameters. See paragraphs [0061], [0062], [0094], and [0095].
Regarding claim 10, Taylor et al discloses receiving one or more additional conditions for assigning the locomotives; and determining, using the machine learning model, a second set of rules based on inputs of the set of rules and the one or more additional conditions. See paragraphs [0061], [0062], [0094], and [0095].
Regarding claim 20, Taylor et al discloses accessing a set of conditions, the set of conditions describing natural language inputs associated with locomotive assignments, wherein the set of rules are further provided to the machine learning model as inputs. See paragraphs [0094] and [0095].
Regarding claim 15, Taylor et al discloses a method for determining locomotive assignments, the method comprising: receiving, at a computing device of a fleet management system, railroad information related to a railroad system including locomotives within the railroad system (see paragraphs [0042]-[0045]); determining, by the computing device, railroad data from the railroad information by processing the railroad information, the railroad data associated with schedules, conditions, demand of locomotives, and availability of locomotives (see paragraph [0094]); determining, by the computing device, a set of rules for assigning locomotives to trains, locations, and maintenance, the set of rules determined by a machine learning model trained using historical railroad data tagged with locomotive assignment information (see paragraphs [0094] and [0095]); determining, by the computing device, assignments for locomotives within the railroad system using the set of rules in response to the railroad data; and outputting the assignments and executing the assignments of the locomotives. See FIG. 1-4 and paragraphs [0042]-[0049].
Regarding claim 21, Taylor et al fails to explicitly disclose, but Mallampati et al discloses wherein determining the one or mor sets of consecutive assignments to the one or more locomotives is based at least in part on: a travel direction of the one or more locomotives, and a cargo load of the one or more locomotives associated with the travel direction. See FIG. 4-6 and paragraphs [0087]-[0091] and [0116].
Regarding claim 22, Taylor et al fails to explicitly disclose, but Mallampati et al discloses wherein the consecutive assignments to the one or more locomotives is determined based at least in part on regular travel patterns of locomotives. See FIG. 3 and paragraphs [0096]-[0099].
Regarding claim 23, Taylor et al fails to explicitly disclose, but Mallampati et al discloses wherein outputting the assignments and executing the assignments of the locomotives includes: assessing existing assignments, and amending or canceling the existing assignments. See paragraphs [0057], [0095], [0100], and [0130].
Claims 2, 4, 11, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Taylor et al and Mallampati et al, as applied above, in view of Malde et al US 2024/0308555.
Regarding claims 2 and 11, Taylor et al discloses wherein outputting the assignments and executing the assignments comprises: preparing at least one command for at least one locomotive, wherein the command is based on the assignments. See FIG. 1-4 and paragraphs [0042]-[0049].
Taylor et al fails to explicitly disclose, but Malde et al discloses transmitting the at least one command to a railyard associated with the at least one locomotive. See paragraphs [0006], [0014], [0033], and [0041].
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to include the railyard command as disclosed by Malde et al in the system of Taylor et al as the efficiency of railroad switching operations may be increased and availability/efficiency of the railroad track may be increased.
Regarding claims 4, 12, and 19, Taylor et al fails to explicitly disclose, but Malde et al discloses wherein the operations further include: displaying the assignments on an output device (see paragraph [0089]); receiving an indication inputted into an input device by a user, the indication associated with an adjustment to one or more of the assignments (see paragraphs [0089]); determining one or more additional rules using the machine learning model; and determining a second set of assignments using the set of rules and the one or more additional rules. See paragraphs [0036]-[0050].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JOSEPH J DALLO whose telephone number is (313)446-4844. The examiner can normally be reached 7am-7pm ET M-Th.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Logan Kraft can be reached at 571-270-5065. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOSEPH J DALLO/ Primary Examiner, Art Unit 3747
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