Prosecution Insights
Last updated: April 19, 2026
Application No. 18/775,935

Intra Predictions Using Linear or Affine Transforms with Neighbouring Sample Reduction

Non-Final OA §103
Filed
Jul 17, 2024
Examiner
PRINCE, JESSICA MARIE
Art Unit
2486
Tech Center
2400 — Computer Networks
Assignee
Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V.
OA Round
2 (Non-Final)
76%
Grant Probability
Favorable
2-3
OA Rounds
3y 1m
To Grant
93%
With Interview

Examiner Intelligence

Grants 76% — above average
76%
Career Allow Rate
535 granted / 700 resolved
+18.4% vs TC avg
Strong +16% interview lift
Without
With
+16.2%
Interview Lift
resolved cases with interview
Typical timeline
3y 1m
Avg Prosecution
37 currently pending
Career history
737
Total Applications
across all art units

Statute-Specific Performance

§101
6.0%
-34.0% vs TC avg
§103
45.8%
+5.8% vs TC avg
§102
14.5%
-25.5% vs TC avg
§112
17.5%
-22.5% vs TC avg
Black line = Tech Center average estimate • Based on career data from 700 resolved cases

Office Action

§103
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 . Allowable Subject Matter The indicated allowability of claims 3, 6 and 9 is withdrawn in view of the newly discovered reference(s) to Lee et al., (U.S. Pub. No. 2017/0006293 A1) Rejections based on the newly cited reference(s) follow. 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. 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) 1-2, 4-8, 10-10-14, 16-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al., (U.S. Pub. No. 2013/0188703 A1) and in view of Lee et al., (U.S. Pub. No. 2017/0006293 A1) and in further view of Han et al., (U.S. Pub. No. 2006/0215762 A1). As per claim 1, Liu teaches a device for predicting at least a portion of a picture, the device comprising: a non-transitory computer-readable medium ([0064]); at least one processor communicatively coupled to non-transitory computer-readable medium, wherein the at least one processor is configured to read instructions form the non-transitory computer-readable medium to perform operations ([0064]) comprising: downsampling set of samples values that neighbor a block of picture (abstract, [0005-0007], [0038-0039], and fig. 1, fig. 4). Lui does not explicitly disclose determining a matrix of weighting values based at least in part on a height and a width of the block of the picture, wherein determining the matrix of weighting values comprises selecting the matrix of weighting values based on in part of whether the height and width of the block of pictures are within a first set of height and width combinations or a second set of height and width combinations that is joint to the first set of height and width combinations, and generating a plurality of predicted values, the generating including applying the matrix of weighted values. However, Lee teaches determining a matrix of weighting values based at least in part on a height and a width of the block of the picture ([0019], “the weighting matrix may be determined based on a size of the current block or a prediction direction for the current block”), wherein determining the matrix of weighting values comprises selecting the matrix of weighting values based on in part of whether the height and width of the block of pictures are within a first set of height and width combinations or a second set of height and width combinations that is joint to the first set of height and width combinations ([0145-0149]; and fig. 12;.. “the encoder may differently determine the weighting matrix according to the size of the current block and/or prediction direction for the current block. In this case, the initial weighting matrix may be determined through training”); and generating a plurality of predicted values, the generating including applying the matrix of weighted values (figs. 1-4, fig. 4, 12). Therefore, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Lee with Liu so image encoding/decoding efficiency may be improved, abstract. Liu (modified by Lee) does not explicitly disclose generating a plurality of predicted values based at least in part on the downsampled set of sample values, the generating including applying the matrix of weighting values. However, Han teaches the known concept of generating a plurality of predicted values based at least in part on the downsampled set of sample values, the generating including applying the matrix of weighting values ([0131-0132] and fig. 15). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Han with Liu (modified by Lee) for the benefit of providing coding efficiency. As per claim 2, Liu (modified by Lee and Han) as whole teaches everything as claimed above, see claim 1. Liu does not explicitly disclose deriving, by upsampling on the plurality of predicted values, further predicted sample values for the block. However, Han teaches deriving, by upsampling on the plurality of predicted values, further predicted sample values for the block (fig. 9, fig. 11, 13, 15). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the teachings of Han with Liu (modified by Lee) for the benefit of providing coding efficiency. As per claim 4, Liu (modified by Lee and Han) as a whole teaches everything as claimed above, see claim 4. In addition, Liu teaches wherein the set of sample values that neighbor the block of the picture extend one-dimensionally along a top portion of the block and one-dimensionally along a left portion of the block (fig. 1 and fig. 4). As per claim 5, Liu (modified by Lee and Han) as a whole teaches everything as claimed above, see claim 1. In addition, Liu teaches wherein the device comprises a decoder (fig. 3), and wherein the device is configured to decode data corresponding to the picture from a data stream (fig. 3). As per claim 6, Liu (modified by Lee and Han) as a whole teaches everything as claimed above, see claim 1. In addition, Liu teaches wherein the device comprises an encoder (fig. 2), and wherein the device is configured to encode data corresponding to the picture into a data stream (fig. 2). As per claim 7, which is the corresponding method for predicting with the limitations of the device for predicting at least a portion of a picture, as recited in claim 1. Thus, the rejection and analysis made for claim 1 also applies here. As per claim 8, which is the corresponding method for predicting with the limitations of the device for predicting at least a portion of a picture, as recited in claim 2. Thus, the rejection and analysis made for claim 2 also applies here. As per claim 10, which is the corresponding method for predicting with the limitations of the device for predicting at least a portion of a picture, as recited in claim 4. Thus, the rejection and analysis made for claim 4 also applies here. As per claim 11, which is the corresponding method for predicting with the limitations of the device for predicting at least a portion of a picture, as recited in claim 5. Thus, the rejection and analysis made for claim 5 also applies here. As per claim 12, which is the corresponding method for predicting with the limitations of the device for predicting at least a portion of a picture, as recited in claim 6. Thus, the rejection and analysis made for claim 6 also applies here. As per claim 13, which is the corresponding non-transitory computer-readable medium including instructions with the limitations of the device for predicting at least a portion of a picture as recited in claim 1. Thus, the rejection and analysis made for claim 1 also applies here. As per claim 14, which is the corresponding non-transitory computer-readable medium including instructions with the limitations of the device for predicting at least a portion of a picture as recited in claim 2. Thus, the rejection and analysis made for claim 2 also applies here. As per claim 16, which is the corresponding non-transitory computer-readable medium including instructions with the limitations of the device for predicting at least a portion of a picture as recited in claim 4. Thus, the rejection and analysis made for claim 4 also applies here. As per claim 17, which is the corresponding non-transitory computer-readable medium including instructions with the limitations of the device for predicting at least a portion of a picture as recited in claim 5. Thus, the rejection and analysis made for claim 5 also applies here. As per claim 18, which is the corresponding non-transitory computer-readable medium including instructions with the limitations of the device for predicting at least a portion of a picture as recited in claim 6. Thus, the rejection and analysis made for claim 6 also applies here. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA PRINCE whose telephone number is (571)270-1821. The examiner can normally be reached M-F 7:30-3:30 P.M.. 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, Jamie Atala can be reached at 571-272-7384. 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. JESSICA PRINCE Examiner Art Unit 2486 /JESSICA M PRINCE/ Primary Examiner, Art Unit 2486
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Prosecution Timeline

Jul 17, 2024
Application Filed
Jul 26, 2025
Non-Final Rejection — §103
Oct 30, 2025
Response Filed
Feb 07, 2026
Non-Final Rejection — §103 (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

2-3
Expected OA Rounds
76%
Grant Probability
93%
With Interview (+16.2%)
3y 1m
Median Time to Grant
Moderate
PTA Risk
Based on 700 resolved cases by this examiner. Grant probability derived from career allow rate.

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