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 .
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 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 of this title, 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.
Claims 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Gao et al. (US 2020/0382793) in view of Wang et al. (US 2017/0347061).
Regarding claim 1, Gao discloses an electronic apparatus (see 2300 in fig. 23) comprising: a communication interface comprising communication circuitry (see 2303 in fig. 23); and a processor (see 2301 in fig. 23) that is configured to: receive image data (see S1902 in fig. 19) and a filter index (see S1904 in fig. 19; e.g. see down sampling in ¶ [0074]) from an external server via the communication interface (see 120 in fig. 1), decode the received image data (see 302 in fig. 3), in response to the filter index being not null (see S1904 in fig. 19; e.g. see ¶ [0130]), obtain an artificial intelligence (AI) upscaling model (see S1905 in fig. 19; e.g. see ¶ [0060]) corresponding to the filter index from among a plurality of AI upscaling models (see “proportion” and “resolution information” in S1905 in fig. 19), upscale the decoded image data using the AI upscaling model and provide the upscaled image data (see S1906-S1908 in fig. 19), and in response to the filter index being null (see S1904 in fig. 19; e.g. see ¶ [0060]), provide the decoded image data without performing an upscaling process (see S1910 in fig. 19).
Although Gao discloses the plurality of Al upscaling models (e.g. see ¶ [0148]), it is noted that Gao does not provide the particular wherein the models comprise two or more models selected from film, sports, music video, documentary, or news.
However, Wang discloses a machine learning for visual processing wherein the models comprise two or more models selected from film, sports, music video, documentary, or news (see 930 in fig. 9; e.g. see ¶ [0584]).
Given the teachings as a whole, it would have been obvious to one of ordinary skill in the art before the effective filing date to incorporate Wang teachings of AI models into Gao AI encoding/decoding for the benefit of improving image quality from artifacts and prevent over smoothing of visual data.
Regarding claims 2 and 9, Gao further discloses comprising: a memory (see 2302 in fig. 23), wherein the processor is further configured to: obtain the AI upscaling model in which one of a plurality of trained filter sets stored in the memory is applied based on the filter index (see S504-S504C for ¶ [0060], although not clearly shown, it is obvious that applying CNN algorithm for determining parameter downscaling proportion would involves storing of information), and upscale the decoded image data by inputting the decoded image data into the obtained Al upscaling model (see S1910 in fig. 19).
Regarding claims 3 and 10, Gao further discloses wherein the image data is obtained by encoding downscaled image data acquired by inputting original image data (see S1602 in fig. 16) corresponding to the image data into an AI downscaling model for downscaling the original image data (see S806 in fig. 16).
Regarding claims 4 and 11, Gao further discloses wherein a number of filters of the AI upscaling model is smaller than a number of filters of the AI downscaling model (see S504-S504C for ¶ [0060], although not clearly shown, it is obvious that applying CNN algorithm for determining parameter downscaling proportion would involve training more filters than selection for upscaling in decoding).
Regarding claims 5 and 12, Gao further discloses wherein the filter index is identified by the external server to reduce a difference between the upscaled image data obtained by the AI upscaling model and the original image data (see S504C in fig. 7).
Regarding claims 6 and 13, Gao further discloses wherein the AI upscaling model is a Convolutional Neural Network (CNN) (e.g. see ¶ [0060]).
Regarding claims 7 and 14, Gao further discloses comprising: a display (see 110 in fig. 1), wherein the processor is configured to provide the upscaled image data or the decoded image by controlling the display to display the upscaled image data or the decoded image (see 110 in fig. 1).
Regarding claim 8, the claim(s) recite a method with analogous limitations to claim 1, and is/are therefore rejected on the same premise.
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
1. Hutchet et al. (US 2009/0034622), discloses scaling images including weighted valued to Al filter learning.
2. Joshi et al. (US 2020/0228820), discloses scaling image and signaling of filter sets.
3. Mukherjee et al. (US 10,009,622), discloses scaling images with Al filter learning.
Response to Arguments
Applicant's arguments with respect to claims 1-14 have been considered but are moot in view of the new ground(s) of rejection.
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 RICHARD T TORRENTE whose telephone number is (571)270-3702. The examiner can normally be reached M-F: 6:45-3:15 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jay Patel can be reached at (571) 272-2988. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RICHARD T TORRENTE/Primary Examiner, Art Unit 2485
