DETAILED ACTION
1. This is a first office action in response to application no. 19/138,417 filed on June 12, 2025 in which claims 1-4, 6-16 and 19-20 are presented for examination. Claims 5, 17 and 18 were canceled by a preliminary amendment filed on June 12, 2025.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
2. 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.
3. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
4. Claims 1, 3-4, 6-7, 9-13-15, 16, 19 and 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Rezazadegan Tavakoli et al. (US Patent Application Publication no. 2022/0256227) “Tavakoli”.
Regarding claim 1, Tavakoli discloses a decoding method for decoding a bitstream comprising (See Tavakoli’s Abstract), at a decoding device obtaining a neural network from at least one group of neural networks comprising at least one neural network encoded in reference to the at least one reference neural network (See Tavakoli [0004], and [0006]), the neural networks of the at least one group being available to the decoding device (See Tavakoli [0007], [0025]), and decoding the bitstream with the obtained neural network (See Tavakoli [0038]-[0039]).
As per claim 14, Tavakoli discloses a decoding device comprising at least one processor, and at least one memory having stored thereon program instructions (See Tavakoli [0053]), which when executed by the at least one processor configure the decoding device to decode a bitstream by obtaining a neural network from at least one group of neural networks comprising at least one neural network encoded in reference to at least one reference neural network (See Tavakoli [0004], and [0006]), the neural network of the at least one group being available to the decoding device (See Tavakoli [0007], [0025]), and decoding the bitstream with the obtained neural network (See Tavakoli [0038]-[0039]).
As per claim 15, Tavakoli further discloses a non-transitory computer readable medium comprising a computer program stored thereon, which when executed by the decoding device causes the decoding device to execute the method for decoding a bitstream (See Tavakoli [0064]-[0065]).
As per claim 16, Tavakoli discloses a method implemented by an encoding device and comprising generating a bitstream comprising at least one of data signaling whether the bitstream should be decoded by a decoding device adapted to obtain a decoded neural network from at least one group pf compressed neural networks (See Tavakoli [0006], [0300]), data signaling predetermined characteristics of a group of compressed neural networks adapted to decode the bitstream (See Tavakoli [0301]), data for identifying at least one group of compressed networks (See Tavakoli [0003], or data identifying a compressed neural network to use for decoding the current video sequence (See Tavakoli [0007]), among the compressed neural networks of the at least one group (See Tavakoli [0039] and [0237]), and transmitting the bitstream to another device or storing the bitstream in a non-transitory computer readable medium for future reading and decoding (See Tavakoli [0220], [0247]).
As per claim 3, Tavakoli further discloses wherein obtaining a neural network comprises decoding a neural network of the at least one group, wherein the neural networkof the at least one group is decodable in reference to the at least one reference neural network (See Tavakoli [0004] and [0022]).
As per claim 4, Tavakoli further discloses wherein the at least one reference neural network belongs to the at least one group (See Tavakoli [0255]).
As per claim 6, Tavakoli further discloses obtaining the network decoder compliant with neural network compression and representation standard (See Tavakoli [0153]-[0157]).
As per claim 7, Tavakoli further discloses decoding adapted to obtain a neural network form at least one group neural networks (See Takavoli [0319]).
As per claim 9, Takavoli further discloses identifying at least one group of neural networks (See Takavoli [0255]).
As per claims 10-11, Takavoli further discloses identifying a neural network to use for decoding video sequences (See Takavoli [0225], [0255],
As per claim 12, Takavoli further discloses wherein the bitstream comprises data signaling that the bitstream further comprises refinement data that can be used for obtaining the neural network (See Takavoli [0007], [0016]-[0017]).
As per claim 13, Takavoli further discloses determining wherein the neural network adapted to decode the current sequence of the bitstream can be obtained or not by the decoding device, based on the data identifying a neural network to use for decoding the current video sequence (See Takavoli’s Abstract, [0003] and [0006]).
As per claim 15, Takavoli further discloses a non-transitory computer readable medium comprising a computer program thereon, which on execution by the decoding device causes the decoding device to execute the method for decoding a bitstream (See Takavoli [0064]-[0066]).
As per claim 19, Takavoli further discloses wherein the neural network is decodable in reference to at least one reference neural network using decoded differential data (See Takavoli [0226], [0452]-[0454]).
As per claim 20, most of the limitations of this claim have been noted in the above rejection of claim 3. In addition, Takavoli further discloses applying a neural network decoder an the at least one reference neural network to obtain a decoded reference neural network (See Takavoli [0452]), and applying incremental neural network decoder taking the decoded reference neural network and decoding differential data associated to the neural network (See Takavoli [0255], [0271]).
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. Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Rezazadegan Tavakoli et al. (US Patent Application Publication no. 2022/0256227) in view of Sirat et al. (US Patent 5,134,396).
Regarding claim 2, it is noted that Tavakoli is silent about wherein neural networks of at least one group are adapted to decode media data bitstreams with the same type of content.
However, Sirat teaches wherein neural networks of at least one group are adapted to decode media data bitstreams with the same type of content (See Sirat col. 2, lines 19-37; col. 6, lines 45-52).
Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying Tavakoli to incorporate Sirat wherein neural networks of at least one group are adapted to decode media data bitstreams with the same type of content. The motivation for performing such a modification in Tavakoli is to increase the compression rate without increasing the processing load imposed on a neural network.
8. Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Rezazadegan Tavakoli et al. (US Patent Application Publication no. 2022/0256227) in view of Zhang et al. (US Patent Application Publication no. 2023/0325639).
Regarding claim 8, it is noted that Tavakoli is silent about wherein the bitstream comprises data signaling characteristics of a group of neural networks adapted to decode the bitstream.
However, Zhang teaches wherein the bitstream comprises data signaling characteristics of a group of neural networks adapted to decode the bitstream (See Zhang [0388]-[0390]).
Therefore, it is considered obvious that one skilled in the art, before the effective filing date of the claimed invention, would recognize the advantage of modifying Takavoli to incorporate Zhang’s teachings wherein the bitstream comprises data signaling characteristics of a group of neural networks adapted to decode the bitstream. The motivation for performing such a modification in Takavoli is to be able to group or cluster samples from a dataset into two or more groups or clusters.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See the Notice of Reference Cited (PTO-892).
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GIMS S PHILIPPE whose telephone number is (571)272-7336. The examiner can normally be reached Maxi Flex.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benjamin Bruckart can be reached at 571-272-3982. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GIMS S PHILIPPE/Primary Examiner, Art Unit 2424
Prosecution Insights