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 Amendment
1. Claims 1, 3, and 5-20 are currently pending.
2. Claims 2 and 4 are canceled.
3. Claim 7 is currently amended.
4. The indicated rejections of Claims 1, 3, and 5-20 the action on filed on 11/26/2025 are withdrawn and rejections based newly cited reference(s) follow. This current rejection replaces the Non-Final rejection of 11/26/2025.
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. 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.
8. Claims 1, 3, and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over Yamanaka (US 20210374440 A1), in view of Parchami (US 20230177842 A1), and in further view of Neitemeier (US 20180084708 A1).
9. Regarding Claim 1, Yamanaka teaches a control system for monitoring operation of a… machine, the control system comprising one or more controllers, and being configured to: receive image data indicative of an input image of a working environment of the… machine (Yamanaka: [0013], [0022], and [0040]);
Encode the image data utilizing an encoder network to map the image data to a lower-dimensional feature space (Yamanaka: [0030] Note that the images being processed with an VAE is equivalent to utilizing an encoder network.);
Decode the encoded data to form a reconstructed image of the working environment (Yamanaka: [0035] Note that the VAE includes an encoded and decoder that reconstructs the image from low dimensional encoding.);
Compare the reconstructed image with the input image to generate an anomaly map by calculating, pixel-wise, a relative perceptual loss between the input image and the reconstructed image... to the input image and to the reconstructed image (Yamanaka: [0036] and [0040]);
And generate and output one or more… signals... in dependence on the comparison (Yamanaka: [0037] and [0038]).
Yamanaka fails to explicitly teach applying a multi-layer artificial neural network to the input image and to the reconstructed image.
However, in the same field of endeavor, Parchami teaches to compare the reconstructed image with the input image to generate an anomaly map by calculating, pixel-wise, a relative perceptual loss between the input image and the reconstructed image by applying a multi-layer artificial neural network to the input image and to the reconstructed image (Parchami: [0017] and [0038]).
Yamanaka and Parchami are considered to be analogous to the claim invention because they are in the same field of image processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamanaka to incorporate the teachings of Parchami to apply a multi-layer artificial neural network to the input image and reconstructed image for comparing the reconstructed image with the input image to generate an anomaly map because it provides the benefit of improving the ability to detect objects while decreasing the time and computing resources to do so. This motivation is explicitly explained in [0013] of Parchami.
Yamanaka and Parchami fail to explicitly teach a control system for monitoring operation of an agricultural machine… and being configured to: generate and output one or more control signals for controlling operation of one or more operable components associated with the machine in dependence on the comparison.
However, in the same field of endeavor, Neitemeier teaches a control system for monitoring operation of an agricultural machine… and being configured to (Neitemeier: [0002]):
Generate and output one or more control signals for controlling operation of one or more operable components associated with the machine in dependence on the comparison (Neitemeier: [0036] and [0067]).
Yamanaka, Parchami, and Neitemeier are considered to be analogous to the claim invention because they are in the same field of image processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamanaka and Parchami to incorporate the teachings of Neitemeier to generate and output control signals for controlling the operation of an agricultural machine based on the comparison because it provides the benefit of determining an anomaly in the environment of the work machine as explained in [0017] of Neitemeier. This provides the additional benefit of collision avoidance to increase the safety of the machine, passengers, and surroundings.
10. Regarding Claim 3, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 1, and further, Neitemeier teaches to generate and output one or more control signals for controlling operation of the one or more operable components associated with the machine in dependence on the generated anomaly map (Neitemeier: [0036] and [0067]).
11. Regarding Claim 5, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 1, and further, Yamanaka teaches to determine a pixel anomaly score for each pixel within the reconstructed image (Yamanaka: [0032], [0037], and [0038] Note that comparing to a preset threshold is equivalent to determining a pixel anomaly score. Also, note that detecting any location/region where the difference is greater than a threshold is equivalent to the pixel anomaly score for each pixel within the reconstructed image.).
12. Regarding Claim 6, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 5, and further, Yamanaka teaches to determine an image anomaly score in dependence on the determined pixel anomaly scores (Yamanaka: [0032], [0037], and [0038] Note that determining if there is a region for which the difference is above a threshold value is equivalent to determining an image anomaly score in dependence of the pixel anomaly score.).
13. Regarding Claim 7, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 6, and further, Yamanaka teaches to compare the image anomaly score with a threshold anomaly score; and determine the presence of an anomaly within working environment in dependence on the comparison (Yamanaka: [0032], [0037], and [0038]).
14. Regarding Claim 8, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 7, and further, Yamanaka teaches to determine the presence of an anomaly within the working environment in dependence on the image anomaly score exceeding the threshold anomaly score (Yamanaka: [0032], [0037], and [0038]).
15. Regarding Claim 9, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 1, and further, Yamanaka teaches to utilize an autoencoder architecture for encoding the received image data and decoding the encoded data (Yamanaka: [0030] Note that the VAE encodes and decodes the data.).
16. Regarding Claim 10, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 9, and further, Yamanaka teaches wherein the autoencoder architecture comprises an encoder network and a decoder network (Yamanaka: [0026] and [0030] Note that the trained VAE that encodes and decodes the data is equivalent to an encoder network and decoder network.).
17. Regarding Claim 11, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 9, and further, Yamanaka teaches wherein the autoencoder architecture is trained utilizing a training dataset (Yamanaka: [0023]).
18. Regarding Claim 12, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 11, and further, Yamanaka teaches wherein the training dataset comprises a normal dataset comprising image data relating to images obtained during normal operation of the agricultural machine within a working environment (Yamanaka: [0023]).
19. Regarding Claim 13, Yamanaka, Er Parchami ol, and Neitemeier remains as applied above in Claim 1, and further, Neitemeier teaches wherein the one or more operable components comprise a user interface associated with the machine (Neitemeier: [0067]).
20. Regarding Claim 14, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 13, and further, Neitemeier teaches operable to present, on the user interface, a representation of an anomaly map to the operator to inform the operator of a detected anomaly within the working environment of the machine (Neitemeier: [0038] and [0068]).
21. Regarding Claim 15, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 14, and further, Neitemeier teaches wherein the representation comprises an overlay on an image presented to the operator indicating the relative position of a detected anomaly with respect to the machine (Neitemeier: [0038] and [0068] Note that the colored anomalies are equivalent to an overlay on an image presented to the operator.).
22. Regarding Claim 16, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 1, and further, Neitemeier teaches wherein the one or more operable components comprises a steering system and/or propulsion system of the machine for automating motion of the machine in dependence on the comparison of the reconstructed image with the input image (Neitemeier: [0037], [0038], and [0067]).
23. Regarding Claim 17, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 16, and further, Neitemeier teaches to control operation of the propulsion system of the machine to reduce an operating speed of the machine in dependence on the identification of an anomaly within the working environment of the machine (Neitemeier: [0067]).
24. Regarding Claim 18, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 16, and further, Neitemeier teaches to control operation of the steering system of the machine to guide the machine along an operational path which avoids a location of the detected anomaly within the working environment of the machine (Neitemeier: [0037], [0038], and [0067]).
25. Regarding Claim 19, Yamanaka, Parchami, and Neitemeier remains as applied above in Claim 1, and further, Yamanaka teaches an operator assistance system for an agricultural machine, comprising: one or more image sensors (Yamanaka: [0022]);
And the control system of claim 1 (Yamanaka: [0013]).
26. Regarding Claim 20, Yamanaka teaches a method of monitoring operation of a… machine, comprising: receiving image data indicative of an input image of a working environment of the… machine (Yamanaka: [0013], [0022], and [0040]);
Encoding the image data utilizing an encoder network to map the image data to a lower-dimensional feature space (Yamanaka: [0030] Note that the images being processed with an VAE is equivalent to utilizing an encoder network.);
Decoding the encoded data to form a reconstructed image of the working environment (Yamanaka: [0035] Note that the VAE includes an encoded and decoder that reconstructs the image from low dimensional encoding.);
Comparing the reconstructed image with the input image to generate an anomaly map by calculating, pixel-wise, a relative perceptual loss between the input image and the reconstructed image… to the input image and to the reconstructed image (Yamanaka: [0036] and [0040]);
And controlling operation… in dependence on the comparison (Yamanaka: [0037] and [0038]).
Yamanaka fails to explicitly teach applying a multi-layer artificial neural network to the input image and to the reconstructed image.
However, in the same field of endeavor, Parchami teaches to comparing the reconstructed image with the input image to generate an anomaly map by calculating, pixel-wise, a relative perceptual loss between the input image and the reconstructed image by applying a multi-layer artificial neural network to the input image and to the reconstructed image (Parchami: [0017] and [0038]).
Yamanaka and Parchami are considered to be analogous to the claim invention because they are in the same field of image processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamanaka to incorporate the teachings of Parchami to apply a multi-layer artificial neural network to the input image and reconstructed image for comparing the reconstructed image with the input image to generate an anomaly map because it provides the benefit of improving the ability to detect objects while decreasing the time and computing resources to do so. This motivation is explicitly explained in [0013] of Parchami.
Yamanaka and Parchami fail to explicitly teach a method of monitoring operation of an agricultural machine, comprising: controlling operation of one or more operable components associated with the machine in dependence on the comparison.
However, in the same field of endeavor, Neitemeier teaches a method of monitoring operation of an agricultural machine, comprising (Neitemeier: [0002]):
Controlling operation of one or more operable components associated with the machine in dependence on the comparison (Neitemeier: [0036] and [0067]).
Yamanaka, Parchami, and Neitemeier are considered to be analogous to the claim invention because they are in the same field of vehicle environment image processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to modify Yamanaka and Parchami to incorporate the teachings of Neitemeier to generate and output control signals for controlling the operation of an agricultural machine based on the comparison because it provides the benefit of determining an anomaly in the environment of the work machine as explained in [0017] of Neitemeier. This provides the additional benefit of collision avoidance to increase the safety of the machine, passengers, and surroundings.
Response to Arguments
27. Applicant’s arguments, see Page 5, filed 2/11/2026, with respect to the rejections of Claims 1, 3, and 5-20 under U.S.C. 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 Parchami (US 20230177842 A1). Parchami has been applied to teach the subject matter of applying a multi-layer artificial neural network to the input image and to the reconstructed image in the rejection above.
28. Yamanaka (US 20210374440 A1), in view of Parchami (US 20230177842 A1), and in further view of Neitemeier (US 20180084708 A1) teaches all aspects of the invention. The rejection is modified according to the newly amended language but still maintained with the current prior art of record.
29. Claims 1, 3, and 5-20 remain rejected under their respective grounds and rational as cited above, and as stated in the prior office action which is incorporated herein. Also, although not specifically argued, all remaining claims remain rejected under their respective grounds, rationales, and applicable prior art for these reasons cited above, and those mentioned in the prior office action which is incorporated herein.
Conclusion
30. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL T SILVA whose telephone number is (571)272-6506. The examiner can normally be reached Mon-Tues: 7AM - 4:30PM ET; Wed-Thurs: 7AM-6PM ET; Fri: OFF.
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/MICHAEL T SILVA/Examiner, Art Unit 3663