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 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claim(s) 1-25 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wetzstein (US 2021/0203904) in view of either POOL (US 2019/0081637) or KASHYAP (US 2019/0042544).
As to claim 1, Wetzstein discloses at least one memory comprising machine readable instructions to cause at least one processor circuit to at least:
cause at least one tensor core to perform an operation associated with a neural network (para. 0068, e.g. display processing circuitry 130);
provide an input image to the neural network (para. 0091); and
obtain a high dynamic range image based on an output of the neural network, the high dynamic range image to have higher resolution than the input image (para. 0098, 0097, 0132, 0134).
Wetzstein is silent regarding performing a mixed-precision matrix operation associated with a neural network, the mixed-precision matrix operation involving a combination of data precisions including at least half-precision and single precision;
Either POOL or KASHYAP teaches performing a mixed-precision matrix operation associated with a neural network, the mixed-precision matrix operation involving a combination of data precisions including at least half-precision and single precision (POOL, para. 0035; KASHYAP, para. 0055).
It would have been obvious to one of ordinary skill in the art to incorporate either POOL’s teaching or KASHYAP’s teaching into Wetzstein since doing so would merely combine prior art elements according to known methods to yield predictable results, and improve processing capability.
As to claim 2, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 1, wherein the machine readable instructions are to cause one or more of the at least one processor circuit to cause an inverse tone mapping operation to be performed on the output of the neural network (para. 0081, 0098, 0106, e.g., tone mapping of LDR to HDR corresponds to inverse tone mapping).
As to claim 3, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 2, wherein the at least one processor circuit includes a graphics processing unit, and the inverse tone mapping operation is performed by the graphics processing unit (Wetzstein, para. 0068).
As to claim 4, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 1, wherein the neural network is a deep learning neural network (Wetzstein, Fig. 5A, 5B, para. 0091).
As to claim 5, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 1, wherein the neural network is a convolutional neural network (Wetzstein, para. 0091).
As to claim 6, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 1, wherein the input image has a different dynamic range than the high dynamic range image (Wetzstein, para. 0098, 0134).
As to claim 7, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 6, wherein the machine readable instructions are to cause one or more of the at least one processor circuit to perform an operation to cause the high dynamic range image to have a higher dynamic range than the input image (Wetzstein, para. 0098, 0134).
As to claim 8, the combination of Wetzstein and either POOL or KASHYAP discloses the at least one memory of claim 1, wherein the input image is a video frame (Wetzstein, para. 0098, 0117).
As to claims 9-25, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed 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 PHUOC TRAN whose telephone number is (571)272-7399. The examiner can normally be reached 9am-5pm.
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, Vu Le can be reached at 571-272-7332. 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.
/PHUOC TRAN/Primary Examiner, Art Unit 2668
Prosecution Insights