DETAILED ACTION
1. The Office Action is in response to Application 19323243 filed on 09/09/2025. Claims 1-14 are pending.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
3. The information disclosure statements (IDS) submitted on 11/19/2025, 08/06/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Double Patenting
5. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
6. Claim 1-12 are rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claim 1-12 of US Patent US 12432389 indicated below.
For Claim 1-12, although the conflicting claims are not identical, they both are dealing with video compressing method. As clearly indicated in the table below, each claimed limitations of claim 1-12 of the current application are anticipated by the corresponding limitations of claim 1-12 of the reference patent.
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Current Application
US 12432389
Claim 1:
A method of compressing video performed by a data processing apparatus, comprising:
receiving a video sequence of frames;
generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame;
generating from the optical flow, using a first autoencoder neural network:
a predicted optical flow between the first frame and the second frame;
and a confidence mask;
warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame;
generating, using a second autoencoder neural network, a prediction of a residual that is a difference between the second frame and the initial predicted reconstruction of the second frame;
combining the initial predicted reconstruction of the second frame and the prediction of the residual to obtain a predicted second frame;
wherein: each of the first and second autoencoder neural networks respectively comprise an encoder network and a generator network;
and the generator network of the second autoencoder neural network is a component of a generative adversarial neural network (GANN).
claim 2’s limitation:
wherein: the first frame and the second frame are subsequent to a third frame, and wherein the third frame is an initial frame in the video sequence; and further comprising, prior to processing the second and third frames: generating from the third frame, using a third autoencoder neural network, a predicted reconstruction of the third frame; generating, using the flow prediction network, an optical flow between third frame and the first frame; generating from the optical flow, using the first autoencoder neural network: a predicted optical flow between the third frame and the first frame; and a confidence mask; warping the reconstruction of the third frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the first frame; generating, using the second autoencoder neural network, a prediction of a residual that is a difference between the first frame and the initial predicted reconstruction of the first frame; and combining the initial predicted reconstruction of the first frame and the prediction of the residual to obtain a predicted first frame; wherein: the third autoencoder neural network comprises an encoder network and a generator network; the third generator network of the third autoencoder neural network is a component of a generative adversarial neural network (GANN)
Claim 3’s limitation:
encoding, using the second autoencoder neural network, a residual to obtain a residual latent; obtaining, using the third encoder neural network, a free latent by encoding the initial prediction of the second frame; and concatenating the free latent and the residual latent; wherein generating, using the second autoencoder neural network, the prediction of the residual comprises generating the predicted residual by the second autoencoder neural network using the concatenation of the free latent and the residual latent.
claim 4’s limitation:
entropy encoding a quantization of the residual latent, wherein the entropy encoded quantization of the residual latent is included in compressed video data representing the video.
claim 5’s limitation:
processing the residual using the encoder neural network of the second autoencoder neural network to generate the residual latent.
claim 6’s limitation:
processing the initial prediction of the second frame using an encoder neural network to generate the free latent
claim 7’s limitation:
processing the concatenation of the free latent and the residual latent using the generator neural network of the second autoencoder neural network to generate the prediction of the residual.
claim 8’s limitation:
generating the predicted second frame by summing the initial predicted reconstruction of the second frame and the prediction of the residual.
claim 9’s limitation:
processing the optical flow generated by the flow prediction network using the encoder network of the first autoencoder network to generate a flow latent representing the optical flow; and processing a quantization of the flow latent using the generator neural network of the first autoencoder neural network to generate the predicted optical flow.
claim 10’s limitation:
entropy encoding the quantization of the flow latent, wherein the entropy encoded quantization of the flow latent is included in compressed video data representing the video.
claim 11’s limitation:
wherein the first and second autoencoder neural networks have been trained on a set of training videos to optimize an objective function that includes an adversarial loss.
claim 12’s limitation:
wherein for one or more video frames of each training video, the adversarial loss is based on a discriminator score, wherein the discriminator score is generated by operations comprising: generating an input to a discriminator neural network, wherein the input comprises a reconstruction of the video frame that is generated using the first and second autoencoder neural networks; and providing the input to the discriminator neural network, wherein the discriminator neural network is configured to: receive an input comprising an input video frame; and process the input to generate an output discriminator score defining a likelihood that the video frame was generated using the first and second autoencoder neural networks.
Claim 1
A method of compressing video performed by a data processing apparatus, comprising:
receiving a video sequence of frames;
generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame;
generating from the optical flow, using a first autoencoder neural network:
a predicted optical flow between the first frame and the second frame;
and a confidence mask that comprises a plurality of confidence values characterizing uncertainty in the predicted optical flow between the first frame and the second frame;
warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame;
generating, using a second autoencoder neural network, a prediction of a residual that is a difference between the second frame and the initial predicted reconstruction of the second frame;
combining the initial predicted reconstruction of the second frame and the prediction of the residual to obtain a predicted second frame;
wherein: each of the first and second autoencoder neural networks respectively comprise an encoder network and a generator network;
and the generator network of the second autoencoder neural network is a component of a generative adversarial neural network (GANN)
claim 2’s limitation:
wherein: the first frame and the second frame are subsequent to a third frame, and wherein the third frame is an initial frame in the video sequence; and further comprising, prior to processing the second and third frames: generating from the third frame, using a third autoencoder neural network, a predicted reconstruction of the third frame; generating, using the flow prediction network, an optical flow between third frame and the first frame; generating from the optical flow, using the first autoencoder neural network: a predicted optical flow between the third frame and the first frame; and a confidence mask; warping the reconstruction of the third frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the first frame; generating, using the second autoencoder neural network, a prediction of a residual that is a difference between the first frame and the initial predicted reconstruction of the first frame; and combining the initial predicted reconstruction of the first frame and the prediction of the residual to obtain a predicted first frame; wherein: the third autoencoder neural network comprises an encoder network and a generator network; the third generator network of the third autoencoder neural network is a component of a generative adversarial neural network (GANN)
claim 3’s limitation:
encoding, using the second autoencoder neural network, a residual to obtain a residual latent; obtaining, using the third encoder neural network, a free latent by encoding the initial prediction of the second frame; and concatenating the free latent and the residual latent; wherein generating, using the second autoencoder neural network, the prediction of the residual comprises generating the predicted residual by the second autoencoder neural network using the concatenation of the free latent and the residual latent.
claim 4’s limitation:
entropy encoding a quantization of the residual latent, wherein the entropy encoded quantization of the residual latent is included in compressed video data representing the video.
claim 5’s limitation:
processing the residual using the encoder neural network of the second autoencoder neural network to generate the residual latent.
claim 6’s limitation:
processing the initial prediction of the second frame using an encoder neural network to generate the free latent.
claim 7’s limitation:
processing the concatenation of the free latent and the residual latent using the generator neural network of the second autoencoder neural network to generate the prediction of the residual.
claim 8’s limitation:
generating the predicted second frame by summing the initial predicted reconstruction of the second frame and the prediction of the residual.
claim 9’s limitation:
processing the optical flow generated by the flow prediction network using the encoder network of the first autoencoder network to generate a flow latent representing the optical flow; and processing a quantization of the flow latent using the generator neural network of the first autoencoder neural network to generate the predicted optical flow.
claim 10’s limitation:
entropy encoding the quantization of the flow latent, wherein the entropy encoded quantization of the flow latent is included in compressed video data representing the video.
claim 11’s limitation:
wherein the first and second autoencoder neural networks have been trained on a set of training videos to optimize an objective function that includes an adversarial loss.
claim 12’s limitation:
wherein for one or more video frames of each training video, the adversarial loss is based on a discriminator score, wherein the discriminator score is generated by operations comprising: generating an input to a discriminator neural network, wherein the input comprises a reconstruction of the video frame that is generated using the first and second autoencoder neural networks; and providing the input to the discriminator neural network, wherein the discriminator neural network is configured to: receive an input comprising an input video frame; and process the input to generate an output discriminator score defining a likelihood that the video frame was generated using the first and second autoencoder neural networks.
7. Claim 13 is rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claim 13 of US Patent US 12432389 indicated below.
For Claim 13, although the conflicting claims are not identical, they both are dealing with video compressing method/non-transitory computer storage medium encoded with a computer program. As clearly indicated in the table below, each claimed limitations of claim 13 of the current application are anticipated by the corresponding limitations of claim 13 of the reference patent.
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Current Application
US 12432389
Claim 13:
A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations for compressing video, the operations comprising:
receiving a video sequence of frames;
generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame;
generating from the optical flow, using a first autoencoder neural network:
a predicted optical flow between the first frame and the second frame;
and a confidence mask;
warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame;
generating, using a second autoencoder neural network, a prediction of a residual that is a difference between the second frame and the initial predicted reconstruction of the second frame;
combining the initial predicted reconstruction of the second frame and the prediction of the residual to obtain a predicted second frame;
wherein: each of the first and second autoencoder neural networks respectively comprise an encoder network and a generator network;
and the generator network of the second autoencoder neural network is a component of a generative adversarial neural network (GANN).
Claim 13
A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that when executed by data processing apparatus cause the data processing apparatus to perform operations for compressing video, the operations comprising:
receiving a video sequence of frames;
generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame;
generating from the optical flow, using a first autoencoder neural network:
a predicted optical flow between the first frame and the second frame;
and a confidence mask that comprises a plurality of confidence values characterizing uncertainty in the predicted optical flow between the first frame and the second frame;
warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame;
generating, using a second autoencoder neural network, a prediction of a residual that is a difference between the second frame and the initial predicted reconstruction of the second frame;
combining the initial predicted reconstruction of the second frame and the prediction of the residual to obtain a predicted second frame;
wherein: each of the first and second autoencoder neural networks respectively comprise an encoder network and a generator network;
and the generator network of the second autoencoder neural network is a component of a generative adversarial neural network (GANN)
8. Claim 14 is rejected on the ground of non-statutory obviousness-type double patenting as being unpatentable over claim 14 of US Patent US 12432389 indicated below.
For Claim 14, although the conflicting claims are not identical, they both are dealing with video compressing method/system. As clearly indicated in the table below, each claimed limitations of claim 14 of the current application are anticipated by the corresponding limitations of claim 14 of the reference patent.
.
Current Application
US 12432389
Claim 14:
A system, comprising: a data processing apparatus; and a computer storage medium encoded with a computer program, the program comprising instructions that when executed by the data processing apparatus cause the data processing apparatus to perform operations for compressing video, the operations comprising:
receiving a video sequence of frames;
generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame;
generating from the optical flow, using a first autoencoder neural network:
a predicted optical flow between the first frame and the second frame;
and a confidence mask;
warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame;
generating, using a second autoencoder neural network, a prediction of a residual that is a difference between the second frame and the initial predicted reconstruction of the second frame;
combining the initial predicted reconstruction of the second frame and the prediction of the residual to obtain a predicted second frame;
wherein: each of the first and second autoencoder neural networks respectively comprise an encoder network and a generator network;
and the generator network of the second autoencoder neural network is a component of a generative adversarial neural network (GANN).
Claim 14
A system, comprising: a data processing apparatus; and a computer storage medium encoded with a computer program, the program comprising instructions that when executed by the data processing apparatus cause the data processing apparatus to perform operations for compressing video, the operations comprising:
receiving a video sequence of frames;
generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame;
generating from the optical flow, using a first autoencoder neural network:
a predicted optical flow between the first frame and the second frame;
and a confidence mask that comprises a plurality of confidence values characterizing uncertainty in the predicted optical flow between the first frame and the second frame;
warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame;
generating, using a second autoencoder neural network, a prediction of a residual that is a difference between the second frame and the initial predicted reconstruction of the second frame;
combining the initial predicted reconstruction of the second frame and the prediction of the residual to obtain a predicted second frame;
wherein: each of the first and second autoencoder neural networks respectively comprise an encoder network and a generator network;
and the generator network of the second autoencoder neural network is a component of a generative adversarial neural network (GANN)
Claim Objection
9. Claim 1 and its dependent claims 2-12 are objected to because of the following informalities:
Claim 1 and its dependent claims 2-12 recites limitation of: “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame”. Please changed it to: “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according to the confidence mask to obtain an initial predicted reconstruction of the second frame”. Appropriated action is required.
10. Claim 13 is objected to because of the following informalities:
Claim 13 recites limitation of: “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame”. Please changed it to: “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according to the confidence mask to obtain an initial predicted reconstruction of the second frame”. Appropriated action is required.
11 Claim 14 is objected to because of the following informalities:
Claim 14 recites limitation of: “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame”. Please changed it to: “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according to the confidence mask to obtain an initial predicted reconstruction of the second frame”. Appropriated action is required.
Claim Rejections - 35 USC § 112
12. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
13. Claim 1 and its dependent claims 2-12 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
For claim 1, it recites limitations of “applying a blurring operation” in “warping a reconstruction of the first frame according to the predicted optical flow and subsequently applying a blurring operation according the confidence mask to obtain an initial predicted reconstruction of the second frame”; However, it is not clear the blurring operation is applied to which frame: the first frame or the second frame or a reconstruction of the first frame or any frame? In addition to it, it is not clear how the blurring operation is operated according the confidence mask to obtain an initial predicted reconstruction of the second frame.
it recites limitations of “generating, using a flow prediction network, an optical flow between two sequential frames, wherein the two sequential frames comprise a first frame and a second frame that is subsequent the first frame” first, then it recites “generating from the optical flow, using a first autoencoder neural network: a predicted optical flow between the first frame and the second frame”; However, it is not clear explain what is difference between the optical flow (which is between a first frame and a second frame) and the predicted optical flow (which is also between the first frame and the second frame)? If the optical flow is already between a first frame and a second frame, the predicted optical flow is also between the first frame and the second frame, then what is the difference?.
Thus the scope of the claim and its dependent claim 2-12 are unclear.
14. Claim 13 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention for the similar reason as for claim 1 and its dependent claim 2-12.
15. Claim 14 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention for the similar reason as for claim 1 and its dependent claim 2-12.
15. Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See form 892.
16. Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAIHAN JIANG whose telephone number is (571)272-1399. The examiner can normally be reached on flexible.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sath Perungavoor can be reached on (571)272-7455. The fax phone number for the organization where this application or proceeding is assigned is 571-270-0655.
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/ZAIHAN JIANG/Primary Examiner, Art Unit 2488
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