Prosecution Insights
Last updated: April 19, 2026
Application No. 19/016,172

Apparatuses and Methods for Encoding or Decoding a Picture of a Video

Non-Final OA §102§103§112
Filed
Jan 10, 2025
Examiner
MESSMORE, JONATHAN R
Art Unit
2482
Tech Center
2400 — Computer Networks
Assignee
Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.
OA Round
1 (Non-Final)
76%
Grant Probability
Favorable
1-2
OA Rounds
2y 11m
To Grant
86%
With Interview

Examiner Intelligence

Grants 76% — above average
76%
Career Allow Rate
375 granted / 491 resolved
+18.4% vs TC avg
Moderate +9% lift
Without
With
+9.3%
Interview Lift
resolved cases with interview
Typical timeline
2y 11m
Avg Prosecution
40 currently pending
Career history
531
Total Applications
across all art units

Statute-Specific Performance

§101
4.0%
-36.0% vs TC avg
§103
46.5%
+6.5% vs TC avg
§102
27.0%
-13.0% vs TC avg
§112
13.4%
-26.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 491 resolved cases

Office Action

§102 §103 §112
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 . Information Disclosure Statement The information disclosure statement(s) (IDS) was/were submitted on 17 March 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner. Election/Restriction This application contains claims directed to the following patentably distinct species I. Fig. 1 and species II. FIG. 14. The species are independent or distinct because the claims to the different species recite mutually exclusive characteristics of such species. In addition, these species are not obvious variants of each other based on the current record. Applicant is required under 35 U.S.C. 121 to elect a single disclosed species, or a single grouping of patentably indistinct species, for prosecution on the merits to which the claims shall be restricted if no generic claim is finally held to be allowable. Currently, there is no generic. There is a serious search and/or examination burden for the patentably distinct species as set forth above because at least the following reason(s) apply: --the species or groupings of patentably indistinct species have acquired a separate status in the art in view of their different classification; --the species or groupings of patentably indistinct species have acquired a separate status in the art due to their recognized divergent subject matter; and/or --the species or groupings of patentably indistinct species require a different field of search (e.g., searching different classes/subclasses or electronic resources, or employing different search strategies or search queries). Applicant is advised that the reply to this requirement to be complete must include (i) an election of a species to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected species or grouping of patentably indistinct species, including any claims subsequently added. An argument that a claim is allowable or that all claims are generic is considered nonresponsive unless accompanied by an election. The election may be made with or without traverse. To preserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the election of species requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable on the elected species or grouping of patentably indistinct species. Should applicant traverse on the ground that the species, or groupings of patentably indistinct species from which election is required, are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing them to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the species unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other species. Upon the allowance of a generic claim, applicant will be entitled to consideration of claims to additional species which depend from or otherwise require all the limitations of an allowable generic claim as provided by 37 CFR 1.141. During a telephone conversation with Daniel McClure on 13 January 2026 a provisional election was made without traverse to prosecute the invention of species II, claims 3-7 and 18. Affirmation of this election must be made by applicant in replying to this Office action. Claims 1-2, 8-17 and 19 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention. Claim Objections Claim 3 is objected to because of the following informalities: Claim 3, an independent claim, starts with “The apparatus…” and should appear to recite --An apparatus--. Appropriate correction is required. Claim 18 is objected to because of the following informalities: Claim 18 starts with “A Method…” which appears to contain a typographical error in that “method” should not be capitalized. Appropriate correction is required. 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 3 and 18 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. Claims 3 and 18 recites the limitation "the features" in line 13. There is insufficient antecedent basis for this limitation in the claim. Examiner suggests amending the claims to recite --the set of features--. Claim Rejections - 35 USC § 102 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 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) 3-7 and 18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Cho et al. (US 2019/0246102 A1). Regarding Claims 3 and 18, Cho discloses an apparatus and method for encoding a picture of a video into encoding a picture of a video into a data stream [Cho: FIG. 1], configured for using a first machine learning predictor to derive a set of features representing a motion estimation for the picture with respect to a previous picture of the video [Cho: ¶ [0010]: The feature vector of the target frame may be generated by a first convolutional neural network. ¶ [0011]: The residual frame may be acquired through motion prediction that uses a motion vector of the target frame. ¶ [0012]: The feature vector of the residual frame may be generated by a second convolutional neural network], encoding the set of features into the data stream [Cho: ¶ [0010]-[0012]], predicting the picture using the set of features to derive a residual picture, by using a second machine learning predictor to determine a set of reconstructed motion vectors based on the features [Cho: ¶ [0013]: The predicted feature vector for the residual frame may be generated by a convolution Long Short Term Memory (LSTM) neural network] , deriving a motion-predicted picture based on the previous picture using the set of reconstructed motion vectors [Cho: ¶ [0014]: The prediction frame may be generated by a deconvolutional neural network. ¶ [0015]: In accordance with another aspect, there is provided a prediction method, including generating a virtual frame; and performing inter prediction that uses the virtual frame, wherein the virtual frame is generated using a neural network to which a previously decoded frame is input], and deriving the residual picture based on the motion-predicted picture and the picture [Cho: ¶ [0016]: The virtual frame may be generated based on the previously decoded frame, a residual frame, and a reconstructed prediction residual frame], and encoding the residual picture into the data stream, wherein the apparatus is configured for optimizing the features with respect to a rate-distortion measure for the features, the rate-distortion measure being determined based on a distortion between the picture and the motion- predicted picture [Cho: ¶ [0140]: Rate-distortion optimization: An encoding apparatus may use rate-distortion optimization so as to provide high coding efficiency by utilizing combinations of the size of a coding unit (CU), a prediction mode, the size of a prediction unit (PU), motion information, and the size of a transform unit (TU)]. Regarding Claim 4, Cho discloses all the limitations of Claim 3, and is analyzed as previously discussed with respect to that claim. Furthermore, Cho discloses configured for quantizing the features to acquire quantized features [Cho: ¶ [0174]: Quantization Parameter (QP): A quantization parameter may be a value used to generate a transform coefficient level for a transform coefficient in quantization. Alternatively, a quantization parameter may also be a value used to generate a transform coefficient by scaling the transform coefficient level in dequantization. Alternatively, a quantization parameter may be a value mapped to a quantization step size], and determining the set of reconstructed motion vectors using the second machine learning predictor based on the quantized features [Cho: ¶ [0178]: A quantized level or a quantized transform coefficient level generated by applying quantization to a transform coefficient or a residual signal may also be included in the meaning of the term “transform coefficient”]. Regarding Claim 5, Cho discloses all the limitations of Claim 3, and is analyzed as previously discussed with respect to that claim. Furthermore, Cho discloses configured for optimizing the features using a gradient descent algorithm with respect to the rate-distortion measure [Cho: ¶ ¶ [0739]: In the learning process by the RNN, a vanishing gradient problem, in which previously input data (i.e. past data) vanishes with the lapse of time, may occur. The LSTM may be used to solve the vanishing gradient problem. The structure of the LSTM allows the gradient of errors to propagate backwards in time in the neural network. In other words, the structure of the LSTM may be configured such that data previously input to the neural network influences the current output of the neural network, either more continuously or more strongly]. Regarding Claim 7, Cho discloses all the limitations of Claim 3, and is analyzed as previously discussed with respect to that claim. Furthermore, Cho discloses wherein the second machine learning predictor comprises a convolutional neural network comprising a plurality of linear convolutional layers using rectifying linear units as activation functions [Cho: ¶ [0058]: FIG. 25 illustrates an operation in a Rectified Linear Unit (ReLu) layer according to an example; and ¶ [0697]: The first CNN of the generation encoder may include a convolution layer, a pooling layer, and a Rectified Linear Unit (ReLu) layer. The convolution layer, the pooling layer, and the ReLu layer may each include multiple layers] and/or wherein the second machine learning predictor comprises a linear transfer function. Claim Rejections - 35 USC § 103 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. The factual inquiries 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 nonobviousness. Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cho as applied to claim 3 above, and further in view of Bendapudi et al. (US 2023/0108645 A1). Regarding Claim 6, Cho discloses all the limitations of Claim 3, and is analyzed as previously discussed with respect to that claim. Cho may not explicitly disclose configured for determining a rate measure for the rate-distortion measure based on the residual picture using a spatial-to-spectral transformation, and/or determining the distortion between the picture and the motion-predicted picture based on the residual picture using a spatial-to-spectral transformation. However, Bendapudi discloses configured for determining a rate measure for the rate-distortion measure based on the residual picture using a spatial-to-spectral transformation, and/or determining the distortion between the picture and the motion-predicted picture based on the residual picture using a spatial-to-spectral transformation [Bendapudi: ¶ [0095] As part of residual coding, in the transformer/scaler/quantizer (530), when a frequency transform is not skipped, a frequency transformer converts spatial-domain video information into frequency-domain (i.e., spectral, transform) data]. It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the transformation of Bendapudi with the processing of Cho in order to provide processing on more types of data, improving usability. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN R MESSMORE whose telephone number is (571)272-2773. The examiner can normally be reached Monday-Friday 9-5 EST/EDT. 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, Chris Kelley can be reached at 571-272-7331. 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. /JONATHAN R MESSMORE/Primary Examiner, Art Unit 2482
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Prosecution Timeline

Jan 10, 2025
Application Filed
Jan 27, 2026
Non-Final Rejection — §102, §103, §112 (current)

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Study what changed to get past this examiner. Based on 5 most recent grants.

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Prosecution Projections

1-2
Expected OA Rounds
76%
Grant Probability
86%
With Interview (+9.3%)
2y 11m
Median Time to Grant
Low
PTA Risk
Based on 491 resolved cases by this examiner. Grant probability derived from career allow rate.

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