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 Non-Final Office Action Response (“Response”), filed 3 February 2026, have been fully considered and are persuasive. Therefore, the rejections have been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Japanese Publication No. 2010-202314A to Kimura et al. (“Kimura”).
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 1-2, 5 and 7-14 are rejected under 35 U.S.C. 103 as being unpatentable over Japanese Publication No. 2010-202314A to Kimura et al. (“Kimura”)in view of International Publication No. WO 2021/099096 A1 to Grossenbacher et al. (“Gross”).
As per claims 1 and 11-14, the claimed subject matter that is met by Kimura includes:
A process for controlling a conveyor line for general cargo, wherein (Kimura: ¶¶ 0001 and 0006)
the conveyor line comprises a plurality of consecutive conveyor line portions, each of which is driven by a drive, wherein (Kimura: ¶ 0006):
the drives are controlled by a computing unit using an algorithm, the algorithm getting first input data on a basis of current operating information from at least one further conveyor line that it does not control (Kimura: ¶¶ 0022 and 0025),
the algorithm has previously been trained using second input data on a basis of operating information of the at least one further conveyor line, the operating information of the at least one further conveyor line relating to measured values from sensors for detecting general cargo and speeds of conveyor line portions (Kimura: ¶¶ 0025-0026),
wherein the conveyor line and the at least one further conveyor line convey items of general cargo to a common section (Kimura: ¶ 0006 and Fig. 1).
Kimura fails to specifically teach a machine learning model. The Examiner provides Gross to teach and disclose this claimed feature.
The claimed subject matter that is met by Gross includes:
the drives are controlled by a computing unit using a machine learning model, the machine learning model getting first input data on a basis of current operating information from at least one further conveyor line (Gross: ¶¶ 0014-0015, 0020, 0022 and 0028-0029),
the machine learning model has previously been trained using second input data on a basis of operating information of the at least one further conveyor line, the operating information of the at least one further conveyor line relating to measured values from sensors for detecting general cargo and speeds of conveyor line portions (Gross: ¶¶ 0014-0015, 0020, 0022 and 0028-0029),
wherein the conveyor line and the at least one further conveyor line convey items of general cargo to a common section (Gross: Fig. 1).
Kimura teaches a conveyor system and method. Gross teaches a comparable conveyor system and method that was improved in the same way as the claimed invention. Gross offers the embodiment of the drives are controlled by a computing unit using a machine learning model. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of using a machine learning model as disclosed by Gross to the conveyor system and method as taught by Kimura for the predicted result of improved conveyor systems and methods. No additional findings are seen to be necessary.
As per claim 2, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the first and second input data comprise at least one temporal forecast value concerning the arrival of an item of general cargo conveyed on the at least one further conveyor line at a specified position, the at least one temporal forecast value is determined from the operating information (Kimura: ¶¶ 0025-0046 and Gross: ¶¶ 0014, 0019, 0031 and 0042).
The motivation for combining the teachings of Kimura and Gross are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 5, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the at least one temporal forecast value is ascertained by a forecast model that is independent of the machine learning model, the forecast model having been produced using operating information of the at least one further conveyor line (Kimura: ¶¶ 0025-0046 and Gross: ¶ 0014).
The motivation for combining the teachings of Kimura and Gross are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 7, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the machine learning model has been trained using a reinforcement learning algorithm with the stipulation that items of general cargo need to be conveyed on the controlled conveyor line in such a way that the items of general cargo reach the common section at a prescribed distance from items of general cargo on the at least one further conveyor line (Gross: ¶ 0014).
The motivation for combining the teachings of Kimura and Gross are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 8, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the specified position is located on the common section (Gross: ¶ 0014).
The motivation for combining the teachings of Kimura and Gross are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 9, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the machine learning model, for the purpose of control, gets third input data on the basis of current operating information of the conveyor line that it controls, and the machine learning model has been trained using fourth input data on the basis of operating information of the conveyor line that it controls, which operating information is gotten from a simulation (Gross: ¶ 0045).
The motivation for combining the teachings of Kimura and Gross are discussed in the rejection of claim 1, and are incorporated herein.
As per claim 10, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the at least one further conveyor line is controlled using a control algorithm that is unknown to the machine learning model (Kimura: ¶¶ 0019-0022).
The motivation for combining the teachings of Kimura and Gross are discussed in the rejection of claim 1, and are incorporated herein.
Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Kimura in view of Gross as applied in claim 1, and further in view of United States Patent Application Publication No. 2018/0084231 A1 to Learnmonth et al. (“Learnmonth”).
As per claim 3, the claimed subject matter that is met by Kimura and Gross includes:
wherein: the at least one temporal forecast value is ascertained by forming a function that indicates a remaining period of time before an item of general cargo conveyed on the at least one further conveyor line arrives at the specified position (Kimura: ¶¶ 0019-0022 and Gross: ¶ 0014).
Kimura and Gross fail to specifically teach at least one sawtooth function. The Examiner provides Learnmonth to teach and disclose this claimed feature.
The claimed subject matter that is met by Learnmonth includes:
at least one sawtooth function (Learnmonth: ¶ 0020)
Kimura and Gross teach conveyor systems and methods. Learnmonth teaches a comparable conveyor system and method that was improved in the same way as the claimed invention. Learnmonth offers the embodiment of at least one sawtooth function. One of ordinary skill in the art before the effective filing date of the claimed invention would have recognized the adaptation of the sawtooth function as disclosed by Learnmonth to the conveyor systems and methods as taught by Kimura and Gross for the predicted result of improved conveyor systems and methods. No additional findings are seen to be necessary.
As per claim 4, the claimed subject matter that is met by Kimura, Gross and Learnmonth includes:
wherein: the at least one sawtooth function is formed from measured values from a sensor that surveys the specified position in order to detect general cargo (Kimura: ¶¶ 0019-0022, Gross: ¶ 0014 and Learnmonth: ¶ 0020).
The motivation for combining the teachings of Kimura, Gross and Learnmonth are discussed in the rejection of claim 3, and are incorporated herein.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Hunter Wilder whose telephone number is (571)270-7948. The examiner can normally be reached Monday-Friday 8:30AM-5:30PM.
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/A. Hunter Wilder/Primary Examiner, Art Unit 3627