Detailed Office Action
1. This communication is being filed in response to the submission having a mailing date of [06/30/2025] which a (3) month Shortened Statutory Period for Response has been set.
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 .
Acknowledgements
3. Upon new entry, claims (1 -15) appear pending for examination, of which (1, 7, 10-15) are the seven (7) parallel running independent claims on record.
Information Disclosure Statement
4. The Information Disclosure Statement (IDS) that were submitted on the same date (06/30/2025) is/are in compliance with the provisions of 37 CFR 1.97, being considered by the Examiner.
Drawings
5. The submitted Drawings on date [06/30/2025] has been accepted and considered under the 37 CFR 1.121 (d).
Claim Rejections section
35 USC§ 112
6. The following is a quotation of the second paragraph of 35 U.S.C. 112:
(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.
6.1. Independent Claims (1, 7, 10 -15) and their associated dependencies, is/are rejected under 35 U.S.C. l 12(b) 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.
6.2. The cited above claims, recites limitations "encoding/decoding coefficients” and “producing coefficients”, which lacks clear antecedent basis, failing to provide a clear-cut indication of the claim scope, because the functional language is not precise, regarding the origination and nature of such parameter(s).
It is also note, that plurality of (potentially different) instances for term “coefficient(s)” disclosed in the claims, being confusing and unclear. Proper corrections and/or clarification is respectfully requested moving forward.
6.3. See [MPEP 2173.02; …limitations/features in the claims should not been ambiguous, vague, incoherent, opaque, or otherwise unclear in describing and defining the claimed invention. If the claim language, (given its broadest reasonable interpretation) is such that a person of ordinary skill in the relevant art would read it with more than one reasonable interpretation, then a rejection under “35 U.S.C. 112 (b)” is appropriate.
35 USC § 103
7. 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 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.
7.1. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ
459 (1966), that are applied 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.
7.2. Claim (1 -15) is/are rejected under 35 U.S.C. 103 as being unpatentable over the Wen; et al. [“Neural Encoding and Decoding with Deep Learning for dynamic vision”] hereafter “Wen”, in view of Racape; et al. [US 2023/0252273]; hereafter “Racape”).
Claim 1. Wen discloses the principles of the invention substantially as claimed - A method of decoding a plurality of tensors forming a hierarchical representation for a single frame from a bitstream, the method comprising: (e.g. encoding and decoding audio/visual data, employing CNN layers, where deep learning provides models to encode/decode and extract hierarchically organized features (i.e. tensors) from arbitrary natural pictures or videos, and by training a classifier vector (i.e. tensor) to a matched label/layer representation, as illustrated in Fig. 1; [page 2].)
Wen specifically teaches - decoding coefficients from the bitstream for a first tensor, (e.g. see codec architecture of Fig. 1);
producing a second tensor from the first tensor and a set of basis vectors, (e.g. the vector information is combined across 1 -7 layers; during codec, Fig. 1; [page 2]) the second tensor having a same spatial size (e.g. tensors with different dimension and/or pixel resolution (i.e. size is also direct proportional to resolution; [page 22]) disclosed; [pages (5, 10)]) and a higher channel count than the first tensor, (e.g. see similar layer count (i.e. from lower to high) configuration of the vectors (i.e. tensors) in Fig. 2c; [page 10]);
and producing the plurality of decoded tensors from the second tensor using one or more trained convolutional layers; (e.g. producing tensor classifications, from convolutional layer architecture of the same; [page 3]).
Given the teachings of Wen; et al. as a whole, and under the obvious assumption and purpose of his papers, it is noted that some of the functional steps/components as listed (i.e. no encoder/decoder schematic, nor codec flow chart disclosed), are missed or not fully described in the papers.
For the purpose of additional clarification & functional support of the claimed features, Racape discloses (e.g. a codec ecosystem of the same, including (encoder, Fig. 1) and decoder (Fig. 2), memory and processing units (1010, 1020, Fig. 3) respectively, employing tensor production/classification techniques and class reordering, by training convolutional DNN layering architecture, as shown in at least Figs (4-5); [0046]; and spatial size association, showing in Figs (6 -7); [0118]
Therefore, it would have been obvious to one skilled in the art before the effective filing date of the claimed invention, to modify the methodology of Wen with the codec architecture and tensor production algorithm technique of Racape, in order to provide (e.g. improving compression efficiency; [Racape; 0004; 0029].)
Claim 2. Wen/ Racape discloses - The method according to claim 1 wherein the second tensor is a projection of the coefficients and the basis vectors produced using a dot product operation. (Examiner’s note is taken regarding the well-known technique at the time the invention was made. See also reference (4) in section 8 for additional details.)
Claim 3. Wen/ Racape discloses - The method according to claim 1 wherein the basis vectors are decoded from the bitstream. (The same rational and motivation apply as given to Claim1 above.)
Claim 4. Wen/ Racape discloses - The method according to claim 1 wherein the trained convolutional layers implement spatial resizing to recover the hierarchical representation of the frame. (The same rational and motivation apply as given to Claim1 above.)
Claim 5. Wen/ Racape discloses - The method according to claim 2 wherein a channel count of the projection is equal to a channel count of each of the tensors of the plurality of tensors. (The same rational and motivation apply as given to Claim1 above.)
Claim 6. Wen/ Racape discloses - The method according to claim 1 wherein a convolution with fewer output channels than input channels is applied to the second tensor prior to producing the plurality of tensors. (The same rational and motivation apply as given to Claim1 above.)
Claim 7. Wen/ Racape discloses - A method of encoding a plurality of tensors forming a hierarchical representation for a single frame into a bitstream, the method comprising:
producing a first tensor from the plurality of tensors using one or more downsampling filters;
producing a second tensor from the first tensor by scaling the first tensor according to a scaling tensor;
producing coefficients for a third tensor using the second tensor and a set of basis vectors, the third tensor having the same spatial size and fewer channels than the second tensor; and
encoding the coefficients of the third tensor into the bitstream for the single frame. (Current lists all the same elements as recite in Claim 1 above, but in “method of encoding” form instead, and is/are therefore on the same premise.)
Claim 8. Wen/ Racape discloses - The method according to claim 7, wherein the second tensor is produced using an output of an addition of tensors including the result of an activation layer. (The same rational and motivation apply as given to Claim1 above. In addition, see flow chart (Figs. 4-5) [Racape] for more details.)
Claim 9. Wen/ Racape discloses - The method according to claim 7, wherein generating the second tensor further comprises applying a convolutional layer to the scaled first tensor to reduce the channel count. (The same rational and motivation apply as given to Claim1 above. In addition, see flow chart (Figs. 6 -7) [Racape] for more details.)
Claim 10. Wen/ Racape discloses - A decoder for decoding a plurality of tensors forming a hierarchical representation for a single frame from a bitstream, the decoder configured to:
decode coefficients from the bitstream for a first tensor,
produce a second tensor from the first tensor and a set of basis vectors, the second tensor having a same spatial size and a higher channel count than the first tensor, and
produce the plurality of decoded tensors from the second tensor using one or more trained convolutional layers. (Current lists all the same elements as recite in Claim 1 above, but in “decoder for decoding” form instead, and is/are therefore on the same premise.)
Claim 11. Wen/ Racape discloses - A non-transitory computer-readable storage medium which stores a program for executing a method of decoding a plurality of tensors forming a hierarchical representation for a single frame from a bitstream, the method comprising:
decoding coefficients from the bitstream for a first tensor,
producing a second tensor from the first tensor and a set of basis vectors, the second tensor having a same spatial size and a higher channel count than the first tensor, and
producing the plurality of decoded tensors from the second tensor using one or more trained convolutional layers. (Current lists all the same elements as recite in Claim 1 above, but in “CRM” form instead, and is/are therefore on the same premise.)
Claim 12. Wen/ Racape discloses - A system comprising:
a memory; and a processor, wherein the processor is configured to execute code stored on the memory for implementing a method of decoding a plurality of tensors forming a hierarchical representation for a single frame from a bitstream, the method comprising:
decoding coefficients from the bitstream for a first tensor,
producing a second tensor from the first tensor and a set of basis vectors, the second tensor having a same spatial size and a higher channel count than the first tensor, and
producing the plurality of decoded tensors from the second tensor using one or more trained convolutional layers. (Current lists all the same elements as recite in Claim 1 above, but in “system” form instead, and is/are therefore on the same premise.)
Claim 13. Wen/ Racape discloses - An encoder for encoding a plurality of tensors forming a hierarchical representation for a single frame into a bitstream, the encoder configured to:
produce a first tensor from the plurality of tensors using one or more downsampling filters;
produce a second tensor from the first tensor by scaling the first tensor according to a scaling tensor;
produce coefficients for a third tensor using the second tensor and a set of basis vectors, the third tensor having the same spatial size and fewer channels than the second tensor; and encode the coefficients of the third tensor into the bitstream for the single frame. (Current lists all the same elements as recite in Claim 1 above, but in “encoder for encoding” form instead, and is/are therefore on the same premise.)
Claim 14. Wen/ Racape discloses - A non-transitory computer-readable storage medium which stores a program for executing a method of encoding a plurality of tensors forming a hierarchical representation for a single frame into a bitstream, the method comprising:
producing a first tensor from the plurality of tensors using one or more downsampling filters;
producing a second tensor from the first tensor by scaling the first tensor according to a scaling tensor;
producing coefficients for a third tensor using the second tensor and a set of basis vectors, the third tensor having the same spatial size and fewer channels than the second tensor; and encoding the coefficients of the third tensor into the bitstream for the single frame. (Current lists all the same elements as recite in Claim 1 above, but in “CRM” form instead, and is/are therefore on the same premise.)
Claim 15. Wen/ Racape discloses - A system comprising:
a memory; and a processor, wherein the processor is configured to execute code stored on the memory for implementing a method of encoding a plurality of tensors forming a hierarchical representation for a single frame into a bitstream, the method comprising:
producing a first tensor from the plurality of tensors using one or more downsampling filters;
producing a second tensor from the first tensor by scaling the first tensor according to a scaling tensor;
producing coefficients for a third tensor using the second tensor and a set of basis vectors, the third tensor having the same spatial size and fewer channels than the second tensor; and encoding the coefficients of the third tensor into the bitstream for the single frame. (Current lists all the same elements as recite in Claim 1 above, but in “system” form instead, and is/are therefore on the same premise.)
Prior Art Citations
8. The following List of prior art, made of record and not relied upon, is/are considered pertinent to applicant's disclosure:
8.1. Patent documentation
US 20150256828 A1 H04N19/70; H04N19/61; H04N19/80; Dong; Jie et al.
US 20230252273 A1 G06N3/045; G06N3/0464; H04N19/59; Racape; et al.
US 20230353766 A1 G06N3/045; G06N3/0464; H04N19/59; Alshina; et al.
US 20230353764 A1 G06N3/0464; G06N3/0475; G06N3/088; Ikonin; et al
US 20250142066 A1 H04N19/119; H04N19/85; H04N19/137; Sauer; et al.
US 20250254339 A1 H04N19/174; H04N19/184; H04N19/50 Rosewarne; et al.
US 12,556,703 B2 H04N19/124; G06N3/0464; G06N20/10; Rosewarne; et al.
8.2. Non-Patent Literature:
1_ WD-4 of Compression of neural networks for multimedia content description; 04/2020.
2_ AI in the Multimedia compression standard JTP 1_SC 29 - Sullivan; Dec-2023.
3_ Neural encoding and decoding with deep learning for dynamic natural vision; Wen – 2018.
4_ Dot Product Matrix Compression for Machine Learning; Dec-2019.
CONCLUSIONS
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to LUIS PEREZ-FUENTES (luis.perez-fuentes@uspto.gov) whose telephone number is (571) 270 -1168. The examiner can normally be reached on Monday-Friday 8am-5pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, WILLIAM VAUGHN can be reached on (571) 272-3922. The fax phone number for the organization where this application or proceeding is assigned is (571) 272 -3922. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated system, please call (800) 786 -9199 (USA OR CANADA) or (571) 272 -1000.
/LUIS PEREZ-FUENTES/
Primary Examiner, Art Unit 2481.
Prosecution Insights