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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 04/16/2026 has been entered.
Response to Amendment
This communication is in response to the action filed on 04/16/2026.
Claims 1, 3, 9, 11, 13, and 19 are currently amended. Claims 2, 4, 12, and 14 are canceled. Claims 21-24 are new. Claims 1, 3, 5-11, 13, and 15-24 are pending.
Response to Arguments
Applicant’s arguments filed on 03/16/2026 on pages 7-10, under REMARKS with respect to 35 U.S.C. 103 claim rejections to claims 1-3, 5-13, and 15-20 have been fully considered and are persuasive. The rejections to the claims have been withdrawn. However, upon further search and consideration, a new ground of rejection is made in view of 35 U.S.C. 103 using US 2023/0154055 A1 to BESENBRUCH et al. (hereinafter “BESENBRUCH”) in view of US 2022/0189070 A1 to REJEB SFAR et al. (hereinafter “REJEB”).
Claim Rejections - 35 USC § 103
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.
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 non-obviousness.
Claims 1, 3, 5-11, 13, and 15-24 are rejected under 35 § U.S.C. 103 as being obvious over US 2023/0154055 A1 to BESENBRUCH et al. (hereinafter “BESENBRUCH”) in view of US 2022/0189070 A1 to REJEB SFAR et al. (hereinafter “REJEB”).
As per claim 1, BESENBRUCH discloses a method for image encoding (a computing system and method for image encoding and decoding based on learned parameters; abstract; figs 1-2, 4; paragraphs [0139], [0196], [0590]), comprising: performing, based on one or more input images (performing the encoding/decoding procedure on input images; abstract; figs 1-2, 4; paragraphs [0505], [0542], [0555]), respective online training based encoder tunings on a plurality of neural image compression (NIC) frameworks (the encoder and decoders are trained using training parameters and training data in order to perform neural network based image compression into a bit stream; abstract; figs 1-2, 4; paragraphs [0506], [0948], [1459]), each of the plurality of NIC frameworks corresponding to an end-to-end NIC model with a respective encoder and a respective decoder (the compression network comprises a structure/frame work which includes both an encoder and a decoder; abstract; figs 1-2, 4; paragraphs [0506], [0948], [1459]), each of the online training based encoder tunings on the respective NIC framework of the plurality of NIC frameworks determining a corresponding updated encoder of the NIC framework with parameters of the corresponding decoder of the NIC framework being kept unchanged (the trained parameters appear as trained hyper parameters for the encoder side of the NIC framework/structure and receives parameter updated based on training and during encoder training and parameter optimization steps the decoder is kept the same/unchanged in its structure/parameter weights; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]); selecting an NIC framework from the plurality of NIC frameworks based on respective performances of the plurality of NIC frameworks with the updated encoders from the online training based encoder tunings (the trained parameters appear as trained hyper parameters for the decoder side of the NIC framework/structure and receives parameter updated based on training and during decoder training and parameter optimization steps the encoder is kept the same/unchanged in its structure/parameter weights; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]); encoding, by the updated encoder of the selected NIC framework, the one or more input images, into a coded bitstream (the encoder after having its hyperparameters and associated parameter weights updated during training performs encoding of an input image into a coded bitstream; abstract; figs 1-2, and 4; paragraphs [00007], [0196], [0293], [1216]); and including a signal indicative of the selected NIC framework in the coded bitstream (during encoding of the neural image compression model the encoded information includes a signal of the frame work type/structure of encoder used; figs 39-40, 43; paragraphs [0617-0624], [0950-0958], [0978]), wherein the encoder of one of the plurality of NIC frameworks corresponds to an encoding network including a main encoder network, a hyper encoder network, and a hyper decoder network (the encoder/decoder frame work corresponds to a encoding/decoding neural network having hyper encoding/decoding parameters and hyper-parameters of hyper encoders/decoders of the model/frame work; figs 18 and 122-124; [0392], [0496-0498], [0644]), the decoder of the one of the plurality of NIC frameworks corresponds to a decoding network including the hyper decoder network and a main decoder network (the encoder/decoder frame work corresponds to a encoding/decoding neural network having hyper encoding/decoding parameters and hyper-parameters of hyper encoders/decoders of the model/frame work; figs 18 and 122-124; [0392], [0496-0498], [0644]). BESENBRUCH fails to disclose, and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks.
REJEB discloses, and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks (the training process of training the encoder and decoder for the NIC compression model/frame work includes an offline stage, the offline stage integrating the learning method comprises providing S10-S20 of the dataset of super points according to the learning method, so the offline stage further comprises the learning S20 of the neural network based on the super-points according to the learning method; abstract; figs 1 and 9; paragraphs [0051-0052], [0057]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify BESENBRUCH to have and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks of REJEB reference. The Suggestion/motivation for doing so would have been to provide that the only step that would require internet connection is the online integration stage of the pretrained neural network frame work and would be trained offline for later online integration as suggested by paragraph [0052] of REJEB. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine REJEB with BESENBRUCH to obtain the invention as specified in claim 1.
As per claim 3, BESENBRUCH in view of REJEB discloses the method of claim 1. Modified BESENBRUCH further discloses wherein one of the online training based encoder tunings on the one of the plurality of NIC frameworks comprises at least a value change to a tunable parameter in at least one of the main encoder network and the hyper encoder network (the parameters of the encoder side of the NIC model includes weight adjustable (tune able) parameters (value change) of the encoder side of the model for NIC; paragraph [0995]).
As per claim 5, BESENBRUCH in view of REJEB discloses the method of claim 1. Modified BESENBRUCH further discloses wherein the plurality of NIC frameworks form a set of NIC frameworks, and the signal includes an index indicative of the selected NIC framework in the set of NIC frameworks (the neural image compression models act as a set of models which each have frameworks making them a set of NIC frame works and the encoded signal includes with it an index such as index t to indicate which model to be used for video compression; paragraphs [0609], [1084]).
As per claim 6, BESENBRUCH in view of REJEB discloses the method of claim 1. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks have different neural network structures (the encoding side of the NIC model and the decoding side of the NIC model comprise different frameworks for respective process encoding or decoding the image data/bit stream data and would comprise different neural network structures to accomplish their respective tasks within the NIC model; paragraphs [1207]).
As per claim 7, BESENBRUCH in view of REJEB discloses the method of claim 1. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks have a same network structure, and have different pretrained parameters (multiple encoders of the NIC model have different weighted adjustable parameters causing them to behave/encode differently based on training but comprise the same encoding structure/frame work; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]).
As per claim 8, BESENBRUCH in view of REJEB discloses the method of claim 1. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks are pretrained based on different sets of training data (the NIC framework and corresponding model is able to be pre-trained in order to help with learning and generalization; paragraphs [0852], [1235]).
As per claim 9, BESENBRUCH in view of REJEB discloses the method of claim 1. Modified BESENBRUCH further discloses wherein the selecting the NIC framework comprises: selecting the NIC framework when the NIC framework with the corresponding updated encoder achieves a least loss performance (the loss metrics tracked include rate loss including a rate loss function at paragraph [0982], and further a distortion loss provided by the equation at paragraph [0995] equation 5.1 the loss is calculated to be lossless or “least loss”; fig 40; paragraphs [0656], [0982-0987], [0993-0995], [1193]).
As per claim 10, BESENBRUCH in view of REJEB discloses the method of claim 9. Modified BESENBRUCH further discloses wherein the least loss performance comprises at least one of a least rate loss, a least distortion loss, and a least rate distortion loss (the loss metrics tracked include rate loss including a rate loss function at paragraph [0982], and further a distortion loss provided by the equation at paragraph [0995] equation 5.1; fig 40; paragraphs [0982-0987], [0993-0995]).
As per claim 11, BESENBRUCH discloses an apparatus for image encoding (a computing system and method for image encoding and decoding based on learned parameters; abstract; figs 1-2, 4; paragraphs [0139], [0196], [0590]), comprising processing circuitry configured to: perform, based on one or more input images (performing the encoding/decoding procedure on input images; abstract; figs 1-2, 4; paragraphs [0505], [0542], [0555]), respective online training based encoder tunings on a plurality of neural image compression (NIC) frameworks (the encoder and decoders are trained using training parameters and training data in order to perform neural network based image compression into a bit stream; abstract; figs 1-2, 4; paragraphs [0506], [0948], [1459]), each of the plurality of NIC frameworks corresponding to an end-to-end NIC model with a respective encoder and a respective decoder (the compression network comprises a structure/frame work which includes both an encoder and a decoder; abstract; figs 1-2, 4; paragraphs [0506], [0948], [1459]), each of the online training based encoder tunings on the respective NIC framework of the plurality of NIC frameworks determining a corresponding updated encoder of the NIC framework with parameters of the corresponding decoder of the NIC framework being kept unchanged (the trained parameters appear as trained hyper parameters for the encoder side of the NIC framework/structure and receives parameter updated based on training and during encoder training and parameter optimization steps the decoder is kept the same/unchanged in its structure/parameter weights; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]); select an NIC framework from the plurality of NIC frameworks based on respective performances of the plurality of NIC frameworks with the updated encoders from the online training based encoder tunings (the trained parameters appear as trained hyper parameters for the decoder side of the NIC framework/structure and receives parameter updated based on training and during decoder training and parameter optimization steps the encoder is kept the same/unchanged in its structure/parameter weights; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]); encode, by the updated encoder of the selected NIC framework, the one or more input images, into a coded bitstream (the encoder after having its hyperparameters and associated parameter weights updated during training performs encoding of an input image into a coded bitstream; abstract; figs 1-2, and 4; paragraphs [00007], [0196], [0293], [1216]); and include a signal indicative of the selected NIC framework in the coded bitstream (during encoding of the neural image compression model the encoded information includes a signal of the frame work type/structure of encoder used; figs 39-40, 43; paragraphs [0617-0624], [0950-0958], [0978]), wherein the encoder of one of the plurality of NIC frameworks corresponds to an encoding network including a main encoder network, a hyper encoder network, and a hyper decoder network (the encoder/decoder frame work corresponds to a encoding/decoding neural network having hyper encoding/decoding parameters and hyper-parameters of hyper encoders/decoders of the model/frame work; figs 18 and 122-124; [0392], [0496-0498], [0644]), the decoder of the one of the plurality of NIC frameworks corresponds to a decoding network including the hyper decoder network and a main decoder network (the encoder/decoder frame work corresponds to a encoding/decoding neural network having hyper encoding/decoding parameters and hyper-parameters of hyper encoders/decoders of the model/frame work; figs 18 and 122-124; [0392], [0496-0498], [0644]). BESENBRUCH fails to disclose and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks.
REJEB discloses and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks (the training process of training the encoder and decoder for the NIC compression model/frame work includes an offline stage, the offline stage integrating the learning method comprises providing S10-S20 of the dataset of super points according to the learning method, so the offline stage further comprises the learning S20 of the neural network based on the super-points according to the learning method; abstract; figs 1 and 9; paragraphs [0051-0052], [0057]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify BESENBRUCH to have and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks of REJEB reference. The Suggestion/motivation for doing so would have been to provide that the only step that would require internet connection is the online integration stage of the pretrained neural network frame work and would be trained offline for later online integration as suggested by paragraph [0052] of REJEB. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine REJEB with BESENBRUCH to obtain the invention as specified in claim 11.
As per claim 13, BESENBRUCH in view of REJEB discloses the apparatus of claim 11. Modified BESENBRUCH further discloses wherein one of the online training based encoder tunings on the one of the plurality of NIC frameworks comprises at least a value change to a tunable parameter in at least one of the main encoder network and the hyper encoder network (the parameters of the encoder side of the NIC model includes weight adjustable (tune able) parameters (value change) of the encoder side of the model for NIC; paragraph [0995]).
As per claim 15, BESENBRUCH in view of REJEB discloses the apparatus of claim 11. Modified BESENBRUCH further discloses wherein the plurality of NIC frameworks form a set of NIC frameworks, and the signal includes an index indicative of the selected NIC framework in the set of NIC frameworks (the neural image compression models act as a set of models which each have frameworks making them a set of NIC frame works and the encoded signal includes with it an index such as index t to indicate which model to be used for video compression; paragraphs [0609], [1084]).
As per claim 16, BESENBRUCH in view of REJEB discloses the apparatus of claim 11. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks have different neural network structures (the encoding side of the NIC model and the decoding side of the NIC model comprise different frameworks for respective process encoding or decoding the image data/bit stream data; paragraphs [1207]).
As per claim 17, BESENBRUCH in view of REJEB discloses the apparatus of claim 11. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks have a same network structure, and have different pretrained parameters (multiple encoders of the NIC model have different weighted adjustable parameters causing them to behave/encode differently based on training but comprise the same encoding structure/frame work; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]).
As per claim 18, BESENBRUCH in view of REJEB discloses the apparatus of claim 11. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks are pretrained based on different sets of training data (the NIC framework and corresponding model is able to be pre-trained in order to help with learning and generalization; paragraphs [0852], [1235]).
As per claim 19, BESENBRUCH in view of REJEB discloses the apparatus of claim 11. Modified BESENBRUCH further discloses wherein the processing circuitry is configured to: select the NIC framework when the NIC framework with the corresponding updated encoder achieves a least loss performance (the loss metrics tracked include rate loss including a rate loss function at paragraph [0982], and further a distortion loss provided by the equation at paragraph [0995] equation 5.1 the loss is calculated to be lossless or “least loss”; fig 40; paragraphs [0656], [0982-0987], [0993-0995], [1193]).
As per claim 20, BESENBRUCH in view of REJEB discloses the apparatus of claim 19. Modified BESENBRUCH further discloses wherein the least loss performance comprises at least one of a least rate loss, a least distortion loss, and a least rate distortion loss (the loss metrics tracked include rate loss including a rate loss function at paragraph [0982], and further a distortion loss provided by the equation at paragraph [0995] equation 5.1; fig 40; paragraphs [0982-0987], [0993-0995]).
As per claim 21, BESENBRUCH discloses a non-transitory computer-readable storage medium storing instructions which, when executed by at least one processor, cause the at least one processor to perform a method of image encoding, the method comprising (a computing system and method for image encoding and decoding based on learned parameters; abstract; figs 1-2, 4; paragraphs [0139], [0196], [0590]): performing, based on one or more input images (performing the encoding/decoding procedure on input images; abstract; figs 1-2, 4; paragraphs [0505], [0542], [0555]), respective online training based encoder tunings on a plurality of neural image compression (NIC) frameworks (the encoder and decoders are trained using training parameters and training data in order to perform neural network based image compression into a bit stream; abstract; figs 1-2, 4; paragraphs [0506], [0948], [1459]), each of the plurality of NIC frameworks corresponding to an end-to-end NIC model with a respective encoder and a respective decoder (the compression network comprises a structure/frame work which includes both an encoder and a decoder; abstract; figs 1-2, 4; paragraphs [0506], [0948], [1459]), each of the online training based encoder tunings on the respective NIC framework of the plurality of NIC frameworks determining a corresponding updated encoder of the NIC framework with parameters of the corresponding decoder of the NIC framework being kept unchanged (the trained parameters appear as trained hyper parameters for the encoder side of the NIC framework/structure and receives parameter updated based on training and during encoder training and parameter optimization steps the decoder is kept the same/unchanged in its structure/parameter weights; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]); selecting an NIC framework from the plurality of NIC frameworks based on respective performances of the plurality of NIC frameworks with the updated encoders from the online training based encoder tunings (the trained parameters appear as trained hyper parameters for the decoder side of the NIC framework/structure and receives parameter updated based on training and during decoder training and parameter optimization steps the encoder is kept the same/unchanged in its structure/parameter weights; abstract; figs 1-2, 4, 122-124; paragraphs [0496-0498], [0506], [0510], [0948], [1049], [1459]); encoding, by the updated encoder of the selected NIC framework, the one or more input images, into a coded bitstream (the encoder after having its hyperparameters and associated parameter weights updated during training performs encoding of an input image into a coded bitstream; abstract; figs 1-2, and 4; paragraphs [00007], [0196], [0293], [1216]); and including a signal indicative of the selected NIC framework in the coded bitstream (during encoding of the neural image compression model the encoded information includes a signal of the frame work type/structure of encoder used; figs 39-40, 43; paragraphs [0617-0624], [0950-0958], [0978]), wherein the encoder of one of the plurality of NIC frameworks corresponds to an encoding network including a main encoder network, a hyper encoder network, and a hyper decoder network (the encoder/decoder frame work corresponds to a encoding/decoding neural network having hyper encoding/decoding parameters and hyper-parameters of hyper encoders/decoders of the model/frame work; figs 18 and 122-124; [0392], [0496-0498], [0644]), the decoder of the one of the plurality of NIC frameworks corresponds to a decoding network including the hyper decoder network and a main decoder network (the encoder/decoder frame work corresponds to a encoding/decoding neural network having hyper encoding/decoding parameters and hyper-parameters of hyper encoders/decoders of the model/frame work; figs 18 and 122-124; [0392], [0496-0498], [0644]). BESENBRUCH fails to disclose and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks.
REJEB discloses and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks (the training process of training the encoder and decoder for the NIC compression model/frame work includes an offline stage, the offline stage integrating the learning method comprises providing S10-S20 of the dataset of super points according to the learning method, so the offline stage further comprises the learning S20 of the neural network based on the super-points according to the learning method; abstract; figs 1 and 9; paragraphs [0051-0052], [0057]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify BESENBRUCH to have and parameters of the main decoder network and the hyper decoder network are kept at pretrained values from an offline training of the one of the plurality of NIC frameworks of REJEB reference. The Suggestion/motivation for doing so would have been to provide that the only step that would require internet connection is the online integration stage of the pretrained neural network frame work and would be trained offline for later online integration as suggested by paragraph [0052] of REJEB. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine REJEB with BESENBRUCH to obtain the invention as specified in claim 21.
As per claim 22, BESENBRUCH in view of REJEB discloses the non-transitory computer-readable storage medium of claim 21. Modified BESENBRUCH further discloses wherein one of the online training based encoder tunings on the one of the plurality of NIC frameworks comprises at least a value change to a tunable parameter in at least one of the main encoder network and the hyper encoder network (the parameters of the encoder side of the NIC model includes weight adjustable (tune able) parameters (value change) of the encoder side of the model for NIC; paragraph [0995]).
As per claim 23, BESENBRUCH in view of REJEB discloses the non-transitory computer-readable storage medium of claim 21. Modified BESENBRUCH further discloses wherein the plurality of NIC frameworks form a set of NIC frameworks, and the signal includes an index indicative of the selected NIC framework in the set of NIC frameworks (the neural image compression models act as a set of models which each have frameworks making them a set of NIC frame works and the encoded signal includes with it an index such as index t to indicate which model to be used for video compression; paragraphs [0609], [1084]).
As per claim 24, BESENBRUCH in view of REJEB discloses the non-transitory computer-readable storage medium of claim 21. Modified BESENBRUCH further discloses wherein at least two NIC frameworks in the plurality of NIC frameworks have different neural network structures (the encoding side of the NIC model and the decoding side of the NIC model comprise different frameworks for respective process encoding or decoding the image data/bit stream data and would comprise different neural network structures to accomplish their respective tasks within the NIC model; paragraphs [1207]).
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/D J DHOOGE/Examiner, Art Unit 2677
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