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 .
Election/Restrictions
Applicant's election with traverse of I (Claims 1-11 and 20) in the reply filed on 05/18/2026 is acknowledged. The traversal is on the ground(s) that “encoding and decoding are integrally linked” ; “Inventions are directed to different facets of the same overall end-to-end trainable system”; and “they do not constitute "independent or distinct" inventions within the meaning of 35 U.S.C. § 121 because they are not separately patentable and are, in fact, obvious variants of each other.”: This is not found persuasive. The Examiner agrees that I and II are directed to two different facets of the same overall system, hence the sub combination usuable restriction. The Examiner appreciated Applicant’s admission that they are obvious variants of each other. The Examiner respectfully disagrees, But in the case it is proven to be true, the restriction will be withdrawn. Group I discloses decoding a bitstream to obtain feature information and inputting the feature into a task network. Group II discloses acquiring an image, encoding the image using an encoding network in order to output a bitstream and end-to-end training the encoding and decoding network during model training and outputting feature information of a task analysis network. These claims are clearly doing two different things.
The requirement is still deemed proper and is therefore made FINAL.
Claim 12-19 are withdrawn from further consideration pursuant to 37 CFR 1.142(b), as being drawn to a nonelected invention, there being no allowable generic or linking claim. Applicant timely traversed the restriction (election) requirement in the reply filed on 05/18/2026.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “decoding network”, task analysis network” and “feature adapter” in claims 1 and 2.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claims 5-10 are being interpreted under Ex parte Schulhauser.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-11 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to mathematical concepts without significantly more. The claim(s) 1 recite(s)
“inputting a feature bitstream of a current picture into a decoding network and obtaining first feature information outputted by an i-th middle layer of the decoding network, wherein i is a positive integer;” , which can be interpreted as a person taking an intermediate calculation.
“and inputting the first feature information into a j-th middle layer of a task analysis network and obtaining a task analysis result outputted by the task analysis network, wherein j is a positive integer.” , which can be interpreted as using the intermediate calculation to generate another value.
This judicial exception is not integrated into a practical application. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements “decoding network” and “task analysis network” do not amount to more than “apply it”. See MPEP 21065.05(f)
Claim 20 is rejected under similar grounds as claim 1.
Claims 2-11 are rejected as dependent on a rejected claim and not adding significantly more.
Claim Rejections - 35 USC § 112
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.
Claims 1-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “i-th” and “j-th” middle layers and ONLY recites that I and j are positive numbers. The boundaries of this claim cannot be reasonably determined.
There is no claimed relationship between I and J. The scope of the claim includes taking the second layer of the decoding unit and inputting it into the 1,234 the layer of the task network, taking the 5,345 layer of the decoding unit and inputting it into the 3rd layer of the task network, as well as an infinite number of combinations. Furthermore the claim does not disclose how to select I and J.
The middle layer is undefined. The claim assumes that there is an identifiable set of “middle layers”. The claim does not specify what “middle layer” means. Assuming the networks are neural networks, A basic neural network has an input layer, hidden layers and output layer. Does that mean the middle layer is any hidden layer? There are also other layers,such as convolutional layers, pooling layers, normalization layers etc.. The scope of “middle layers” is not clear.
Claim 2 recites “consistent with”. This term is a relative term that is not defined by the claims and the specification does not provide a standard for ascertaining the requisite degree of “consistent with”.
Claim 2 recites “inputting the first feature information into the j-th middle layer of the task analysis network” and “inputting the second feature information into the j-th middle layer and obtaining the task analysis result outputted by the task analysis network”. It is not clear if both the first and second feature information are being input into the task analysis network or just the second. Additionally it is not clear if there are two outputs or one output.
Claim 2 recites “a preset input of the jth middle layer”. It is not clear if this is the same as the “the first feature information”, since that is the information which is input into the the jth middle layer.
Claim 3, “a magnitude” should be “the magnitude”. Additionally, it is not clear if “the feature information” refers to the first or second feature information.
Claim 6 recites “when the feature adapter is the non-neural network-based feature adapter, inputting the first feature information into the feature adapter, so that the feature adapter selects channels with the input channel number of the j-th middle layer from channels of the first feature information by using a Principal Component Analysis (PCA) mode or a random selection mode; when the feature adapter is the neural network-based feature adapter, inputting the first feature information into the feature adapter, and reducing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by at least one convolution layer in the feature adapter.” This claim limitation does not make sense. Claim 4 states that the feature adaptber comprises a neural network based and non-neural network based feature adapters. The feature adapter contains both feature adapters, so when is statement will always either be false, since it is always both according to claim 4.
Claim 7 recites “when the when the feature adapter is the non-neural network-based feature adapter” But claim 4 indicates that the feature adapter is both. Therefore this limitation is always false.
Claim 7 line 2: recites “the when the channel” It is not clear what Applicant is attempting to claim. Should the be and?
Claim 8 recites “when the when the feature adapter is the neural network-based feature adapter” But claim 4 indicates that the feature adapter is both. Therefore this limitation is always false.
Claims 2-11 are rejected as dependent upon a rejected claim.
Claim 20 is rejected under similar grounds as claim 1 above.
Claim Rejections - 35 USC § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-11 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Choi2021 Choi2021 (“Latent-Space Scalability for Multi-Task Collaborative Intelligence”).
Choi2021 Choi2021 discloses 1.A method of video decoding, applied to a decoder, the method comprising:
inputting a feature bitstream of a current picture into a decoding network and obtaining first feature information outputted by an i-th middle layer of the decoding network, wherein i is a positive integer; and (Choi2021 , Fig. 2, Side Bitstream is input into decoder (RIGHT big green box) Ybase is the first feature information
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inputting the first feature information into a j-th middle layer of a task analysis network and obtaining a task analysis result outputted by the task analysis network, wherein j is a positive integer(Choi2021 1, Fig. 2, Y1 is inputted into the LST (feature adapter) and then input into the (l) layer of the back-end(task analysis network), Additionally see section 3.4
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, which give the example of l =13 of the YOLOv3 back-end).
Choi2021 discloses 2. The method of claim 1, wherein inputting the first feature information into the j-th middle layer of the task analysis network and obtaining the task analysis result outputted by the task analysis network comprises: inputting the first feature information into a feature adapter for feature adaptation and obtaining second feature information, wherein a magnitude of the second feature information is consistent with a magnitude of a preset input of the j-th middle layer; and inputting the second feature information into the j-th middle layer and obtaining the task analysis result outputted by the task analysis network. (Choi2021, Fig. 2, Latent Space Decoder, see rejection of claim 1 above.)
Choi2021 discloses 3. The method of claim 2, wherein a magnitude of feature information comprises at least one of a size of the feature information or a channel number of the feature information. (Choi2021 , Fig. 6, Size
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Choi2021 discloses 4. The method of claim 3, wherein the feature adapter comprises a neural network-based feature adapter and a non-neural network-based feature adapter. (Choi2021 , Fig. 4)
Choi2021 discloses 5. The method of claim 4, wherein when the magnitude of the feature information comprises the channel number of the feature information, inputting the first feature information into the feature adapter for feature adaptation comprises: when a channel number of the first feature information is larger than an input channel number of the j-th middle layer, reducing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by the feature adapter; when the channel number of the first feature information is less than the input channel number of the j-th middle layer, increasing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by the feature adapter.(addressed the alternative embodiment)
Choi2021 discloses 6. The method of claim 5, wherein reducing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by the feature adapter comprises: when the feature adapter is the non-neural network-based feature adapter, inputting the first feature information into the feature adapter, so that the feature adapter selects channels with the input channel number of the j-th middle layer from channels of the first feature information by using a Principal Component Analysis (PCA) mode or a random selection mode; when the feature adapter is the neural network-based feature adapter, inputting the first feature information into the feature adapter, and reducing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by at least one convolution layer in the feature adapter. (addressed the alternative embodiment)
Choi2021 discloses 7. The method of claim 5, wherein when the feature adapter is the non-neural network-based feature adapter, the when the channel number of the first feature information is less than the input channel number of the j-th middle layer, increasing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by the feature adapter comprises: when the input channel number of the j-th middle layer is an integer multiple of the channel number of the first feature information, copying channels of the first feature information by the integer multiple so that a number of copied channels the first feature information is the same as the input channel number of the j-th middle layer; or when the input channel number of the j-th middle layer is not an integer multiple of the channel number of the first feature information, copying the channels of the first feature information by N time(s), selecting M channel(s) from the channels of the first feature information, copying the M channel(s), and merging copied M channel(s) with channels of the first feature information that is copied N time(s) so that a number of merged channels of the first feature information is the same as the input channel number of the j-th middle layer, wherein N is a quotient of the input channel number of the j-th middle layer divided by the channel number of the first feature information, M is a remainder of the input channel number of the j-th middle layer divided by the channel number of the first feature information, and both N and M are positive integers; or selecting P main feature channel(s) from the channels of the first feature information, copying the P main feature channel(s) and merging copied P main feature channel(s) with the channels of the first feature information, so that a number of merged channels of the first feature information is the same as the input channel number of the j-th middle layer, wherein P is a difference between the input channel number of the j-th middle layer and the channel number of the first feature information, and P is a positive integer. (addressed the alternative embodiment)
Choi2021 discloses 8. The method of claim 5, wherein when the feature adapter is the neural network-based feature adapter, the when the channel number of the first feature information is less than the input channel number of the j-th middle layer, increasing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by the feature adapter comprises: inputting the first feature information into the feature adapter, and increasing the channel number of the first feature information to be the same as the input channel number of the j-th middle layer by at least one convolution layer in the feature adapter. (addressed the alternative embodiment)
Choi2021 discloses 9. The method of claim 4, wherein when the magnitude of the feature information comprises the size of the feature information, inputting the first feature information into the feature adapter for feature adaptation comprises: when a size of the first feature information is larger than an input size of the j-th middle layer, down-sampling the first feature information to have a size that is the same as the input size of the j-th middle layer by the feature adapter; when the size of the first feature information is smaller than the input size of the j-th middle layer, up-sampling the first feature information to have a size that is the same as the input size of the j-th middle layer by the feature adapter.(Choi2021 , Fig 4 see above upsampling)
Choi2021 discloses 10. The method of claim 9, wherein when the size of the first feature information is larger than the input size of the j-th middle layer, down-sampling the first feature information to have the size that is the same as the input size of the j-th middle layer by the feature adapter comprises: when the feature adapter is the non-neural network-based feature adapter, down-sampling the first feature information by the feature adapter so that a size of down-sampled first feature information is the same as the input size of the j-th middle layer; when the feature adapter is the neural network-based feature adapter, down-sampling the first feature information by at least one pooling layer in the feature adapter so that a size of down-sampled first feature information is the same as the input size of the j-th middle layer. (addressed the alternative embodiment. See Claim interpretation)
Choi2021 discloses 11. The method of claim 10, wherein the pooling layer is any one of a maximum pooling layer, an average pooling layer, or an overlapping pooling layer. (addressed the alternative embodiment. See Claim interpretation)
Claim 20 is rejected under similar grounds as claim 1 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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/GANDHI THIRUGNANAM/ Primary Examiner, Art Unit 2672
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