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 .
Response to Arguments
Applicant’s arguments, see page 5, filed 4/30/26, with respect to the rejection(s) of claim(s) 1-20 under 35 U.S.C. 102(a)(2) and 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of U.S. patent application publication 2022/0038653 by Reda et al.
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.
1) Claim(s) 1-6, 8-13 and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0374470 by Kontkanen et al., and further in view of U.S. patent application publication 2022/0038653 by Reda et al.
2) Regarding claim 1, Kontkanen teaches a processor (figure 2, item 235; paragraph 32; a processor) comprising: one or more circuits to execute an application programming interface (API) that defines one or more parameters for frame interpolation (paragraph 25; input frames are “parameters” passed to the frame interpolation module) to enable frame interpolation to use one or more neural networks (paragraph 88; API can be utilized to enable interpolation machine learning module 306 to perform its function [i.e. frame interpolation, paragraph 25], wherein module 306 utilizes neural networks as detailed in paragraph 90), according to the at least one parameter set by the API (paragraph 25; interpolation is performed based on the input images).
Kontkanen does not specifically teach setting, by the API via an API call, at least one value for at least one parameter of the one or more parameters; and enabling frame interpolation according to the at least one parameter set by the API via the API call.
Reda teaches setting, by the API via an API call, at least one value for at least one parameter of the one or more parameters; and enabling frame interpolation according to the at least one parameter set by the API via the API call (paragraph 111; intermediate points for interpolation can be set by an API using a parameter).
Kontkanen and Reda are combinable because they are both from the frame interpolation field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Kontkanen with Reda to add a frame interpolation parameter set by the API. The motivation for doing so would have been so that threads can be organized and executed in parallel (paragraph 250). Therefore it would have been obvious to combine Kontkanen with Reda to obtain the invention of claim 1.
3) Regarding claim 2, Kontkanen teaches the processor of claim 1, wherein the API is to receive one or more input values indicating a feature to perform the frame interpolation (paragraph 25; input values can be the image frames to interpolate between).
4) Regarding claim 3, Kontkanen teaches the processor of claim 1, wherein the API is to receive one or more input values indicating one or more graphics processing units (GPUs) of which the frame interpolation is to be enabled (paragraph 100; interpolation receives the number of GPU cores to utilize in performance of the function).
5) Regarding claim 4, Reda (as combined with Kontkanen in the rejection of claim 4 above) teaches the processor of claim 1, wherein the API is to enable the frame interpolation based, at least in part, on setting a value in a driver of a GPU of which the frame interpolation is to be enabled (paragraph 250; driver allows access to image processing of unit 3100 through an API call, any allowed access being an “enabling” setting).
6) Regarding claim 5, Reda (as combined with Kontkanen in the rejection of claim 4 above) teaches the processor of claim 1, wherein the API is to enable the frame interpolation based, at least in part, on setting a value in a hardware state of a GPU of which the frame interpolation is to be enabled (paragraphs 167, 168 and 171; register for each GPU [table 1] tracks a state of the GPU to enable invocation of the GPU for image processing such as frame interpolation).
7) Regarding claim 6, Kontkanen teaches the processor of claim 1, wherein the API is performed after performing a second API to indicate support to use the one or more neural networks to perform the frame interpolation (paragraph 54; API for filtering module 304 is utilized prior to utilizing the interpolation API for the frame interpolation module, the preparation of feature vectors for interpolation input being an “indication of support”).
8) Claims 8-10 and 13 are taught in the same manner as described in the rejections of claims 1-3 and 6 above, respectively.
9) Claims 11 and 12 are taught in the same manner as described in the rejections of claims 4 and 5 above, respectively.
10) Claims 15-17 and 20 are taught in the same manner as described in the rejections of claims 1-3 and 6 above, respectively, with the exception of: memory storing executable instructions (figure 2, item 237; a memory).
11) Claims 18 and 19 are taught in the same manner as described in the rejections of claims 4 and 5 above, respectively.
12) Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. patent application publication 2022/0374470 by Kontkanen et al., and further in view of U.S. patent application publication 2022/0038653 by Reda et al. as applied to claims 1 and 8 above, and further in view of U.S. patent application publication 2023/0147063 by Kim et al.
14) Regarding claim 7, Kontkanen does not specifically teach the processor of claim 1, wherein the API is performed before performing a second API to disable the frame interpolation.
Kim teaches the processor of claim 1, wherein the API is performed before performing a second API to disable the frame interpolation (paragraph 294 and 337; access to a GPU core can be disabled depending upon the resources required for the image processing API call).
Examiner notes that the API of Kontkanen could enable a successful frame interpolation using a particular GPU and then when another call is later made, the GPU could be disabled as taught by Kim.
Kontkanen and Kim are combinable because they are both from the image processing field of endeavor.
It would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine Kontkanen with Kim to add disabling a GPU. The motivation for doing so would have been to correctly size the utilization of resources (paragraph 294). Therefore it would have been obvious to combine Kontkanen with Kim to obtain the invention of claim 7.
15) Claims 14 is taught in the same manner as described in the rejection of claim 7 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BENJAMIN O DULANEY whose telephone number is (571)272-2874. The examiner can normally be reached Mon-Fri 10-6.
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, Abderrahim Merouan can be reached at (571)270-5254. 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.
BENJAMIN O. DULANEY
Primary Examiner
Art Unit 2676
/BENJAMIN O DULANEY/ Primary Examiner, Art Unit 2683
Prosecution Insights