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 (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.
Claims 1, 3-9, 11-15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Ha et al. (US 2019/0180695) in view of Greenebaum et al. (US 2020/0105179) and Kim et al. (US 2022/0319382).
Regarding claim 1, Ha discloses a display device comprising: a display panel including a sub-pixel (fig. 1, abstract, ¶ 51-53);
a driving controller (fig. 1, ¶ 55-59, ¶ 65, controller 120);
a memory device configured to store a first gamma lookup table and a second gamma lookup table (figs. 9-12, ¶ 92-100, e.g., gamma lookup tables 321-323; see also fig. 18, ¶ 123-132, ¶ 149);
a data driver configured to generate a data voltage based on a gamma voltage and provide the data voltage to the sub-pixel (fig. 1, data driver 150, ¶ 56, ¶ 60-65);
and a gamma voltage generator configured to generate the gamma voltage based at least in part on the second gamma lookup table, and provide the gamma voltage to the data driver (fig. 1, controller 120, voltage generator 130, and data driver 150, ¶ 60-61, ¶ 65; see also figs. 9-12, ¶ 92-100, e.g., gamma lookup tables 321-323; see also fig. 18, ¶ 123-132),
the second gamma lookup table comprising gamma voltages for a target driving frequency and the first gamma lookup table comprises gamma voltages (figs. 9-12, ¶ 92-100, gamma correction circuit selects one of lookup tables 321-323 corresponding to variable frequency signal; see also fig. 18, ¶ 65, ¶ 123-132, lookup table 610 stores a plurality of grayscale selection signals and receives grayscale compensation signal GCC; see also fig. 14, ¶ 103-110, luminance varies with operating frequency).
Ha fails to disclose receiving the first gamma lookup table and the second gamma lookup table from the memory device, wherein an artificial intelligence model is trained by the driving controller to generate the second gamma lookup table using the first gamma lookup table, and the first gamma lookup table comprises gamma voltages used as training data for the artificial intelligence model, wherein the gamma voltages used as the training data include measured gamma voltages of a sample display device different from the display device and driven at different respective sample driving frequencies that each differ from the target driving frequency.
Greenebaum teaches receiving the first gamma lookup table and the second gamma lookup table from the memory device (figs. 1-2, ¶ 30-32, ¶ 41-42, see also figs. 5 and 8, ¶ 51-54, engine 515 implemented using any combination of chipset, firmware, and/or software associated with a display pipeline or residing in the display device; see also ¶ 63-76, artificial intelligence model dynamically synthesizes a LUT based on stored correction tables, smooth transition from current to target values disclosed),
wherein an artificial intelligence model is trained by the driving controller to generate the second gamma lookup table using the first gamma lookup table, and the first gamma lookup table comprises gamma voltages used as training data for the artificial intelligence model (figs. 5 and 8, ¶ 24, ¶ 63-76, artificial intelligence model dynamically synthesizes a gamma LUT based on stored correction tables),
wherein the gamma voltages used as the training data include measured gamma voltages of a sample display device different from the display device (figs. 5 and 8, ¶ 24, ¶ 63-76, artificial intelligence model dynamically synthesizes a gamma LUT based on stored correction tables; see ¶ 75, model may be created for multiple display devices based on extensive measurements of one or a small set of display devices).
Ha and Greenebaum are both directed to gamma correction for display devices. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the device of Ha with the artificial intelligence model of Greenebaum since such a modification provides updated correction based on dynamically changing display characteristics (Greenebaum, ¶ 1) and provides a math model that may be trained to account for constant changes in as many characteristics as desired (Greenebaum, ¶ 74-75).
Kim teaches wherein the gamma voltages used as the training data include measured gamma voltages of a sample display device driven at different respective sample driving frequencies that each differ from the target driving frequency (figs. 1-2, ¶ 9-12, display tested at factory for different refresh rates and display brightnesses; see also ¶ 23-38, gamma values remapped based on base lookup table and delta lookup tables).
Ha in view of Greenebaum and Kim are both directed to gamma correction for display devices. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the device of Ha in view of Greenebaum with the gamma tables of Kim since such a modification determines how a display responds to a driving current while at the refresh rate and the display brightness that corresponds to the gamma lookup table (Kim, ¶ 12) and reduces an amount of memory used for gamma correction (Kim, ¶ 11).
Regarding claim 3, Greenebaum further teaches wherein the memory device is configured to store a parameter of the artificial intelligence model (figs. 5 and 8, ¶ 24, ¶ 63-76, artificial intelligence model dynamically synthesizes a gamma LUT based on stored correction tables; additional primary, secondary, or tertiary factors may be modeled as well, math model customized for individual display devices).
Regarding claim 4, Greenebaum further teaches wherein the gamma voltages used as training data are measured gamma voltages of a plurality of sample display devices that are each driven at the different respective sample driving frequencies (figs. 5 and 8, ¶ 24, ¶ 63-76, model created based on extensive measurements of one or a small set of display devices).
Regarding claim 5, Ha discloses at least one of a top voltage, a bottom voltage less than the top voltage, a data swing range, and a gamma voltage of a lowest grayscale among grayscales supported by the display panel (fig. 18, ¶ 65, ¶ 123-132, resistor strings 622, 632 disclosed).
Greenebaum further teaches wherein the artificial intelligence model is trained additionally using at least one of a top voltage, a bottom voltage less than the top voltage, a data swing range, and a gamma voltage of a lowest grayscale among grayscales supported by the display panel (figs. 5 and 8, ¶ 24, ¶ 63-76, artificial intelligence model dynamically synthesizes a gamma LUT based on stored correction tables; additional primary, secondary, or tertiary factors may be modeled as well, math model customized for individual display devices; see also figs. 3-4).
Regarding claim 6, Ha discloses wherein the gamma voltage is determined as a voltage between the top voltage and the bottom voltage (fig. 18, ¶ 65, ¶ 123-132, resistor strings 622, 632 disclosed).
Regarding claim 7, Ha discloses wherein a difference between the gamma voltage of the lowest grayscale and the gamma voltage of a highest grayscale among the grayscales increases as the data swing range increases (fig. 18, ¶ 65, ¶ 123-132, gamma reference voltages determined).
Regarding claim 8, Ha discloses wherein the first gamma lookup table includes the gamma voltages according to the sample driving frequencies and a dimming level, and the second gamma lookup table includes the gamma voltages according to the target driving frequency and the dimming level (figs. 9-12, ¶ 92-100, gamma correction circuit selects one of lookup tables 321-323 corresponding to variable frequency signal; see also fig. 18, ¶ 65, ¶ 123-132, lookup table 610 stores a plurality of grayscale selection signals and receives grayscale compensation signal GCC; see also fig. 14, ¶ 103-110, luminance varies with operating frequency).
Regarding claim 9, this claim is rejected under the same rationale as claim 1.
Regarding claim 11, this claim is rejected under the same rationale as claim 3.
Regarding claim 12, this claim is rejected under the same rationale as claim 5.
Regarding claim 13, this claim is rejected under the same rationale as claim 8.
Regarding claim 14, this claim is rejected under the same rationale as claim 1.
Regarding claim 15, this claim is rejected under the same rationale as claim 1.
Regarding claim 18, this claim is rejected under the same rationale as claims 5 and 6.
Regarding claim 19, this claim is rejected under the same rationale as claim 5.
Regarding claim 20, this claim is rejected under the same rationale as claim 7.
Response to Arguments
Applicant's arguments filed 2/6/26 have been fully considered but they are not persuasive. Regarding claims 1, 9, and 15, Applicant argues Kim does not teach the newly added limitations (Remarks, pp. 6-7). However, whether or not this is true, Examiner has relied upon the disclosure of Greenebaum to teach the newly added limitations. As cited above, ¶ 75 of Greenebaum explicitly states the artificial intelligence model may be created for multiple display devices based on extensive measurements of one or a small set of display devices.
Applicant further argues that Examiner’s combination of references “would have changed the principle of operation of the prior art being modified” because Greenebaum relates to the use of an artificial intelligence model to generate training data for use in enhancing gamma correction and reducing memory usage whereas Kim involves a solution based on gamma lookup compression” (Remarks, pp. 7-8). Examiner disagrees. First, Kim is relied upon to merely teach that gamma lookup tables may be provided for different display conditions, e.g., different refresh rates and display brightnesses (see Kim, ¶ 9-12, display tested at factory for different refresh rates and display brightnesses; see also ¶ 23-38), and thus Applicant’s argument regarding gamma lookup compression is not germane. Second, Examiner finds no evidence that using an AI model to generate training data and using gamma lookup compression are incompatible in any way.
The rejection of the claims is maintained.
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 KEITH L CRAWLEY whose telephone number is (571)270-7616. The examiner can normally be reached Monday - Friday 10-6 ET.
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/KEITH L CRAWLEY/Primary Examiner, Art Unit 2626