DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/13/2026 has been entered.
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 .
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-4 and 6-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer (US 7,793,772) in view of Tokic (WO 2021099096 A1) [all citations to Tokic are directed toward U.S. equivalent US 2022/0397887] .
Regarding the independent claims 1, 9, and 10, Schafer discloses a process/system/computer program product/computer-readable medium for controlling a conveyor line for general cargo, wherein the conveyor line comprises a plurality of consecutive conveyor line portions (N+2, N+1, N, N-1, N-2), each of which is driven by a drive (12, 12’, 12”), and one or more sensors (10, 10’, 1”) for detecting general cargo are located on at least some of the consecutive conveyor line portions, the process comprising: controlling the drives by means of a computing unit repeatedly receiving input data comprising a vector of a fixed length (see at least col. 2, lines 29-57; col. 6 lines 4-18 and Fig. 1), each vector element being associated with a section of a conveyor line and indicating a current proportional occupancy of the respective section by an item of general cargo (see col. 3, lines 5-15), the conveyor line being split into a plurality of the sections of identical size (see col. 7, lines 20-25). Schafer discloses all the limitations of the claims, but it does not explicitly disclose that the computing unit uses a machine learning model. However, Tokic discloses a similar process which includes a computing unit which utilizes a machine learning model for the purpose of eliminating necessary sophistication for manual adjustment of control function (see U.S. equivalent US 2022/0397887, para 0014). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the applicant’s invention, to utilize a machine learning model, as disclosed by Tokic, for the purpose of eliminating necessary sophistication for manual adjustment of control function.
Regarding dependent claim 2, Schafer discloses that the computing unit receives updated input data with a temporal clocking (see col. 7, lines 60-65), and with the same clocking outputs information about a speed to be set for each conveyor line portion (see col. 7, line 65 – col. 8, line 3). Schafer discloses all the limitations of the claims, but, as stated above, it does not explicitly disclose that the computing unit uses a machine learning model. However, Tokic discloses a similar process which includes a computing unit which utilizes a machine learning model for the purpose of eliminating necessary sophistication for manual adjustment of control function (see U.S. equivalent US 2022/0397887, para 0014). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the applicant’s invention, to utilize a machine learning model, as disclosed by Tokic, for the purpose of eliminating necessary sophistication for manual adjustment of control function.
Regarding dependent claims 3, 4, 6, and 13, Schafer discloses the current proportional occupancy is ascertained from measurement results from the one or more sensors and speeds of conveyor line portions (see col. 2, lines 29-45). The current proportional occupancy for a specified item of general cargo is ascertained over time by virtue of a sensor detecting the item of general cargo and assigning a measurement result to the respective section(s)containing an applicable position, computational ascertainment of the applicable position of the item of general cargo taking place up to a next sensor by using the speed of the respective conveyor line portion, and a computation result being assigned to the respective section(s) containing the applicable position, the next sensor detecting the item of general cargo and assigning the measurement result to the respective section(s) containing the applicable position (see col. 7, line 54 – col. 8, line 3). The input value comprises current measurement results from the one or more sensors (see col. 2, lines 45-55). The vector contains the current proportional occupancy for each of the plurality of sections, providing a complete snapshot of the conveyor line.
Regarding dependent claims 7 and 8, Schafer discloses employing a target function comprising the similarity of speeds of adjacent conveyor line portions (see col. 2, lines 29-45). Schafer discloses all the limitations of the claims, but it does not disclose a machine learning model trained before the conveyor line is controlled or trained as reinforcement learning in which a reward or penalty is ascertained. However, Tokic discloses a similar process in which a machine learning model trained before the conveyor line is controlled (see U.S. equivalent US 2022/0397887, para 0040) and trained as reinforcement learning in which a reward or penalty is ascertained (see U.S. equivalent US 2022/0397887, paras 0041-0042) for the purpose of eliminating necessary sophistication for manual adjustment of control function (see U.S. equivalent US 2022/0397887, para 0014). It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the applicant’s invention, to utilize a a machine learning model trained before the conveyor line is controlled and trained as reinforcement learning in which a reward or penalty is ascertained, as disclosed by Tokic, for the purpose of eliminating necessary sophistication for manual adjustment of control function.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schafer (US 7,793,772) in view of Tokic (WO 2021099096 A1), as applied to claim 1 above, and further in view of Schroader et al. (WO 2021011833 A1). The combination of Schafer and Tokic discloses all the limitations of the claim, but it does not disclose that each vector element indicates the current proportional occupancy, using a non-binary value, using a numerical value between 0 and 1. However, Schroader discloses a similar process which includes using vector elements which indicate a current proportional occupancy of a conveyor line using a non-binary value, using a numerical value between 0 and 1 (see p. 18, lines 15-25) for the purpose of determining a percentage of occupancy. It would have been obvious for a person of ordinary skill in the art, before the effective filing date of the applicant’s invention, to have each vector element indicate the current proportional occupancy using a non-binary value, using a numerical value between 0 and 1, as disclosed by Schroader, for the purpose of determining a percentage of occupancy.
Response to Arguments
Applicant's arguments filed 5/13/2026 and entered via RCE on 6/15/2026 have been fully considered but they are not persuasive.
The applicant states that claim 1 is distinct from the combination of Schafer and Tokic because the combination does not render obvious, “the machine learning model repeatedly receiving input data comprising a vector of a fixed length, each vector element being associated with a section of a conveyor line and indicating a current proportional occupancy of the respective section by an item of general cargo” as recited in the claim. The examiner disagrees with the applicant. In at least col. col. 2, lines 29-57; col. 6 lines 4-18 and Fig. 1 Schafer discloses repeatedly receiving input data comprising a vector of a fixed length, each vector element being associated with a section of a conveyor line”. A vector of fixed length is a transmission of data. Schafer discloses a plurality of sensors transmitting data to a PLC. The same data is transmitted to the PLC over a series of time intervals. This reads on this language of claim 1. In at least col. 3, lines 5-15, Schafer discloses indicating a current proportional occupancy of the respective section by an item of general cargo. Finally, Tokic discloses analyzing the data utilizing a machine learning model. Therefore, the combination of Schafer and Tokic reads on the independent claim 1.
The applicant states that the occupancy determination by Schafer is for a single segment and not for all segments. The examiner disagrees with the applicant. Schafer discloses determining the occur each segment, therefore, it discloses determining the occupancy for all segments.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PATRICK HEWEY MACKEY whose telephone number is (571)272-6916. The examiner can normally be reached M - F 9-5.
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/PATRICK H MACKEY/Primary Examiner, Art Unit 3653
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