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
This is in response to applicant’s amendment/response filed on 03/23/2026, which has been entered and made of record. Claims 1-9 and 20 have been amended. No Claim has been cancelled. No Claim has been added. Claims 1-20 are pending in the application.
The rejections of Claims 1-8 under 35 U.S.C. §101 are withdrawn in view of the amendments to the Claims 1-8.
The objections to drawings are withdrawn in view of the replacement drawings.
The double patenting rejections are maintained.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1, 9, and 20, and the dependent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments directed to amended limitation have been addressed in the detail rejection below with reference by Chemerys et al.
The arguments regarding dependent claims for the virtue of their dependency are moot because the independent claims are not allowable.
Double Patenting
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 9, and 20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over 14, 6, and 1 of co-pending Application No. 18/421,809 in view of Chemerys.
This is a provisional nonstatutory double patenting rejection.
Table: 1
Mapping of contending claims in the co-pending application that contains double patenting issues.
Current Application (18/421,759)
Co-pending Application (18/421,809) in view of Chemerys
1
14
9
6
20
1
Table: 2
Current Application (18/421,759)
Co-pending Application (18/421,809) in view of Chemerys
Claim 1, One or more non-transitory computer-readable media having stored thereon computer-executable instructions for causing a processor system, when programmed thereby, to perform operations to synthesize media using a generative artificial intelligence (“AI”) model, the operations comprising:
receiving input tokens that represent input syntax elements, respectively, of compressed data for input media, the input media having been compressed according to a media compression format to produce the compressed data for the input media, wherein the input tokens are encoded in an input format for the generative AI model;
providing, to the generative AI model, the input tokens; receiving, from the generative AI model, predicted tokens that represent output syntax elements, respectively, of compressed data for output media; and
reconstructing the output media from the predicted tokens.
compressed data in the media compression format for output media;
Claim 14, One or more computer-readable media having stored thereon computer-executable instructions for causing a processor system, when programmed thereby, to perform operations to decompress media using a generative artificial intelligence (“AI”) model, the operations comprising:
receiving input tokens that represent input syntax elements, respectively, of compressed data for a second version of input media, the second version having been compressed according to a media compression format to produce the compressed data for the second version, wherein the second version approximates a first version of the input media, the first version having a first resolution, the second version having a second resolution lower than the first resolution, and wherein the input tokens are encoded in an input format for the generative AI model;
providing, to the generative AI model, the input tokens; receiving, from the generative AI model, predicted tokens that represent output syntax elements, respectively, of compressed data for output media at the first resolution; and
reconstructing the output media from the predicted tokens.
Chemerys, ¶267 reciting “The image display driver 1220 . . . may convert the image data into a signal or data format suitable for delivery to the image display device. For example, the image data may be video data formatted according to compression formats, such as H.264 (MPEG-4 Part 10), HEVC, Theora, Dirac, RealVideo RV40, VP8, VP9, or the like, and still image data may be formatted according to compression formats such as Portable Network Group (PNG), Joint Photographic Experts Group (JPEG), Tagged Image File Format (TIFF) or exchangeable image file format (EXIF) or the like.”
Claim 1 is rejected for obviousness type double patenting over claim 14 of the co-pending application 18/421,809 in view of Chemerys for having similar limitations as described in Table 2.
co-pending application 18/421,809 fails to disclose ”compressed data in the media compression format for output media;” in Claim 1. Chemerys teaches compressing data in the media compression format for output media, and ¶267 recites “The image display driver 1220 . . . may convert the image data into a signal or data format suitable for delivery to the image display device. For example, the image data may be video data formatted according to compression formats, such as H.264 (MPEG-4 Part 10), HEVC, Theora, Dirac, RealVideo RV40, VP8, VP9, or the like, and still image data may be formatted according to compression formats such as Portable Network Group (PNG), Joint Photographic Experts Group (JPEG), Tagged Image File Format (TIFF) or exchangeable image file format (EXIF) or the like.” The suggestions/motivations would have been to apply a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Although the conflicting claims are not identical, they are not patentably distinct from each other because the scope of the inventions is the same. Claim 1 of current application is an obvious variant and anticipated by claim 14 of U.S. Application 18/421,809 in view of Chemerys.
The same logic applies to Claims 9 and 20. They are rejected for obviousness type double patenting under claims 6 and 1 of the co-pending application 18/421,809 in view of Chemerys.
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.
Claim(s) 1-5, 7-10, 12-17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (WO 2021196087 A1), in view of Chemerys et al. (US 20240395028 A1).
Regarding Claim 1, Chen discloses One or more non-transitory computer-readable media having stored thereon computer-executable instructions for causing a processor system, when programmed thereby, to perform operations to synthesize media using a generative artificial intelligence (“AI”) model, (¶82 reciting “the device may store the software instructions in a suitable non-transitory computer-readable storage medium, and may use one or more processors to execute the instructions in hardware to perform the technology of this disclosure.” ¶176 reciting “when the first enhancement model is used for super-resolution, the AI enhancement model built on CNN may include: . . . super-resolution generative adversarial network (SRGAN), etc.”) the operations comprising:
receiving input tokens that represent input syntax elements, respectively, of compressed data for input media, the input media having been compressed according to a media compression format to produce the compressed data for the input media (Fig. 5, step S610. ¶169 disclosing S610: Decode the to-be-processed video to obtain syntax elements of multiple frames, and the to-be-processed video may be a compressed video (stream) (compressed video). In addition, ¶83 disclosing video protocols H.263, H.264, HEVV, MPEG-2, MPEG-4, VP8, VP9, or next-generation video standard protocols (such as H.266). Further, ¶178 disclosing S640: Determine a first part of the image blocks in the multiple image blocks in the non-I frame based on the syntax elements of the non-I frames in the multiple frames);
providing, to the generative AI model, the input tokens; (¶189 disclosing S650: Perform block enhancement on the first partial image block to obtain an enhanced first partial image block. Specifically, performing block enhancement on the first partial image block may include: inputting the first partial image block into the second enhancement model to perform block enhancement on the first partial image block. . . , the second enhanced model may be an AI enhanced model.)
receiving, from the generative AI model, predicted tokens that represent output syntax elements, respectively, of compressed data for output media; (¶203 disclosing S660: an enhanced non-I-frame is obtained based on the enhanced first part of the image block.) and
reconstructing the output media from the predicted tokens. (¶75 disclosing Video decoding is performed on the destination side and typically involves inverse processing relative to the encoder to reconstruct video images.)
However, Chen does not explicitly disclose wherein the input tokens are encoded in an input format for the generative AI model; compressed data in the media compression format for output media.
Chemerys teaches “a system for improving machine learning models. ” (ABST). More specifically, ¶334 recites “Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data. The latent space can be manipulated to generate new variations of the output data.”
Chemerys teaches compressing data in the media compression format for output media, and ¶267 recites “The image display driver 1220 . . . may convert the image data into a signal or data format suitable for delivery to the image display device. For example, the image data may be video data formatted according to compression formats, such as H.264 (MPEG-4 Part 10), HEVC, Theora, Dirac, RealVideo RV40, VP8, VP9, or the like, and still image data may be formatted according to compression formats such as Portable Network Group (PNG), Joint Photographic Experts Group (JPEG), Tagged Image File Format (TIFF) or exchangeable image file format (EXIF) or the like.”
It would have been obvious to one with ordinary skill, before the effective filing date of the claimed invention, to encode the input data and compress the output data in the media compression format for output media (taught by Chemerys) for the generative AI (taught by Chen). The suggestions/motivations would have been to apply a known technique to a known device (method, or product) ready for improvement to yield predictable results.
Regarding Claim 2, Chen in view of Chemerys discloses The one or more non-transitory computer-readable media of claim 1, wherein the operations further comprise:
receiving the compressed data for the input media; (Chen, Fig. 5, step S610. ¶169 disclosing S610: Decode the to-be-processed video to obtain syntax elements of multiple frames, and the to-be-processed video may be a compressed video (stream) (compressed video). )
partially decompressing the compressed data for the input media, thereby producing the input syntax elements according to the media compression format; (¶179 disclosing in the video encoding and decoding process, the frame can be divided into multiple macro blocks, and then encoding and decoding are performed in units of macro blocks. ) and
converting the input syntax elements into the input tokens. (Chemerys, ¶334 reciting “Variational autoencoders (VAEs): These are models that encode input data into a latent space (a compressed representation) and then decode it back into output data. The suggestions/motivations would have been the same as that of Claim 1 rejections.)
Regarding Claim 3. Chen in view of Chemerys discloses The one or more non-transitory computer-readable media of claim 1, wherein:
for a given input syntax element among the input syntax elements, a given input token among the input tokens indicates a syntax structure that includes the given input syntax element, a type of the given input syntax element, and a value of the given input syntax element; and
for a given output syntax element among the output syntax elements, a given predicted token among the predicted tokens indicates a syntax structure that includes the given output syntax element, a type of the given output syntax element, and a value of the given output syntax element.
(Chen, ¶214 teaches S710 video decoder to decode the compressed video to obtain the bitstream. Further, Chen teaches the syntax elements including macroblock size, macroblock matching, macroblock source position (x’, y’), macroblock target position (x, y), and differential information, etc.)
Regarding Claim 4. Chen in view of Chemerys discloses The one or more non-transitory computer-readable media of claim 1, wherein the operations further comprise:
partitioning the input tokens into blocks that correspond to frames of the input media, wherein the input tokens are provided to the generative AI model on a block-by-block basis for the frames, respectively, of the input media.
(Chen, ¶179 disclosing in the video encoding and decoding process, frames can be divided into multiple macroblocks, and then encoding and decoding can be performed on a macroblock basis.)
Regarding Claim 5. Chen in view of Chemerys discloses The one or more non-transitory computer-readable media of claim 1, wherein the operations further comprise, with the generative AI model, processing the input tokens to determine the predicted tokens, including:
converting the input tokens into input embedding vectors; (Chemerys, ¶116 reciting “To convert the text prompt to features in the latent space, the text encoder utilizes one or more techniques, such as tokenizing the text prompt into subword units or individual tokens and then mapping them to embedding vectors.”)
determining, based on the input embedding vectors, output embedding vectors using multiple layers of a decoder of the generative AI model; and
converting the output embedding vectors into the predicted tokens.
(Chemerys, Fig. 4. ¶116-¶135 teaching a latent diffusion mode)
Regarding Claim 7. Chen in view of Chemerys discloses The one or more non-transitory computer-readable media of claim 1, wherein the reconstructing the output media from the predicted tokens includes:
converting the predicted tokens to the output syntax elements, respectively, in the media compression format; and
decompressing the output syntax elements using a media decoder for the media compression format.
(Chen, ¶75 disclosing Video decoding is performed on the destination side and typically involves inverse processing relative to the encoder to reconstruct video images.)
Regarding Claim 8. Chen in view of Chemerys discloses The one or more non-transitory computer-readable media of claim 1, wherein the compressed data represents pictures of a video sequence, audio of an audio sequence, or an image. (Chen, ¶169 disclosing S610: the to-be-processed video may be a compressed video (stream) (compressed video) )
Regarding Claim 9. Chen in view of Chemerys discloses In a computer system that implements a generative artificial intelligence (“AI”) model, a method of training the generative AI model to synthesize media, the method comprising:
identifying a set of training data; (Chen, ¶130) and
training the generative AI model in multiple training iterations using the set of training data, including, in a given training iteration of the multiple training iterations (Chen, ¶127):
receiving input tokens that represent input syntax elements, respectively, of compressed data for input media, the input media having been compressed according to a media compression format to produce the compressed data for the input media, wherein the input tokens are encoded in an input format for the generative AI model; providing, to the generative AI model, the input tokens; receiving, from the generative AI model, predicted tokens that represent output syntax elements, respectively, of compressed data in the media compression format for output media; (See Claim 1 rejections for detailed analysis.)
determining a measure of loss based at least in part on the predicted tokens; and
updating one or more parameters of the generative AI model based at least in part on the measure of loss.
(Chen, ¶127)
Regarding Claim 10. Chen in view of Chemerys discloses The method of claim 9, wherein the set of training data includes, for each of multiple examples of input media, input tokens that represent input syntax elements, respectively, of compressed data for that example of input media, and wherein, for each of the multiple examples of input media, that example of input media has been compressed according to the media compression format.
(Chen, Fig. 5, step S610. ¶169 disclosing S610: Decode the to-be-processed video to obtain syntax elements of multiple frames, and the to-be-processed video may be a compressed video (stream) (compressed video). In addition, ¶83 disclosing video protocols H.263, H.264, HEVV, MPEG-2, MPEG-4, VP8, VP9, or next-generation video standard protocols (such as H.266). Further, ¶178 disclosing S640: Determine a first part of the image blocks in the multiple image blocks in the non-I frame based on the syntax elements of the non-I frames in the multiple frames)
Regarding Claim 12. Chen in view of Chemerys discloses The method of claim 9, further comprising: receiving the compressed data for the input media; partially decompressing the compressed data for the input media, thereby producing the input syntax elements according to the media compression format; and converting the input syntax elements into the input tokens. (See Claim 2 rejections for detailed analysis)
Regarding Claim 13. Chen in view of Chemerys discloses The method of claim 9, further comprising, with the generative AI model, processing the input tokens to determine the predicted tokens, including: converting the input tokens into input embedding vectors; determining, based on the input embedding vectors, output embedding vectors using multiple layers of a decoder of the generative AI model; and converting the output embedding vectors into the predicted tokens. (See Claim 5 rejections for detailed analysis)
Regarding Claim 14. Chen in view of Chemerys discloses The method of claim 9, wherein the determining the measure of loss includes:
determining a measure of conformity of the predicted tokens to syntax of the media compression format, wherein the measure of conformity of the predicted tokens to syntax of the media compression format quantifies loss in terms of deviations from the syntax of the media compression format.
(Chen, ¶127)
Regarding Claim 15. Chen in view of Chemerys discloses The method of claim 14, wherein the determining the measure of conformity of the predicted tokens to syntax of the media compression format includes: converting the predicted tokens to the output syntax elements, respectively, in the media compression format; and measuring syntax errors in the output syntax elements. (Chen, ¶75 disclosing Video decoding is performed on the destination side and typically involves inverse processing relative to the encoder to reconstruct video images.)
Regarding Claim 16. Chen in view of Chemerys discloses The method of claim 9, wherein the training further includes, in the given training iteration, reconstructing the output media from the predicted tokens, and wherein the determining the measure of loss includes: determining, based on feedback from a reviewer, a rating of the output media, wherein the rating of the output media quantifies loss in terms of compression artifacts and/or consistency with the input media. (Chemerys, ¶304. ¶315 reciting “Validation, refinement or retraining 1512: This may include updating a model based on feedback generated from the prediction phase, such as new data or user feedback.” The suggestions/motivations would have been the same as that of Claim 1 rejections.)
Regarding Claim 17. Chen in view of Chemerys discloses The method of claim 16, wherein the rating of the output media is a reward signal for reinforcement learning, and wherein the updating the one or more parameters of the generative AI model adjusts a policy of the reinforcement learning. (Chemerys , ¶304 reciting “Reinforcement learning involves training a model to make decisions in a dynamic environment by receiving feedback in the form of rewards or penalties. Examples of reinforcement learning algorithms include Q-learning and policy gradient methods.” The suggestions/motivations would have been the same as that of Claim 1 rejections.)
20. A computer system comprising a processor system and memory, wherein the computer system is configured to perform operations to synthesize media using a generative artificial intelligence (“AI”) model, the operations comprising: receiving input tokens that represent input syntax elements, respectively, of compressed data for input media, the input media having been compressed according to a media compression format to produce the compressed data for the input media, wherein the input tokens are encoded in an input format for the generative AI model; providing, to the generative AI model, the input tokens; receiving, from the generative AI model, predicted tokens that represent output syntax elements, respectively, of compressed data in the media compression format for output media; and reconstructing the output media from the predicted tokens.
(See Claim 1 rejections for detailed analysis. )
Allowable Subject Matter
Claim 6 would be allowable if rewritten to include all of the limitations of the base claim and any intervening claims.
Claim 6 is distinguished from the closest known prior art alone or in reasonable combination, in consideration of the claim as a whole, particularly the limitations similar to “wherein each of the multiple layers of the decoder of the generative AI model includes: a masked multi-head attention sub-layer that is configured to accept, as input to a masked multi-head attention function, keys, queries, and values based on linear projections of the input embedding vectors, and that is configured to produce, as output, normalized results from the masked multi-head attention function; a multi-head attention sub-layer that is configured to accept, as input to a multi-head attention function, keys, queries, and values based on linear projections of the output of the masked multi-head attention sub-layer, and that is configured to produce, as output, normalized results from the multi-head attention function; and a feed-forward neural network sub-layer that is configured to accept, as input, the output of the multi-head attention sub-layer, and that is configured to produce, as output, the output embedding vectors.” in combination with the remaining aspects of the claim, any intervening claims and the base claim.
Claims 11, 18, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YI WANG whose telephone number is (571)272-6022. The examiner can normally be reached 9am - 5pm.
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/YI WANG/Primary Examiner, Art Unit 2619
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