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 Amendment
1. The amendment received on May 18, 2026 has been entered in full.
2. Applicant’s amendment to the claims has been entered and based on the amendments claim interpretation under 35 USC 112(f)/6th paragraph on the respective claims have been withdrawn.
3. The terminal disclaimer filed on 05/18/2026 disclaiming the terminal portion of any patent granted on this application which would extend beyond the expiration date of 18/431,074 has been reviewed and is accepted. The terminal disclaimer has been recorded. In view of the terminal disclaimer filed, double patenting rejection on the respective claims have been withdrawn.
4. Applicant’s amendments to the claims and arguments with respect to amended claims as presented in the amendment filed have been fully considered but are moot in view of new ground(s) of rejection(s).
Claim Rejections - 35 USC § 103
5. 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.
6. 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.
7. Claims 1-4, 13-14, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Glen, U.S. Patent Publication No. 2008/0055228 A1, and further in view of Goodsitt et al., U.S. Patent No. 10,665,204 B1.
Regarding claim 1, Glen discloses A display device comprising a processing circuitry configured to: generate content information corresponding to content that is represented in an image by using input frame data IS for the image (see paras 0021 and 0024, and element #304 of figure 3 - Glen discloses that each time a new image is to be updated/displayed, the types of contents of the image is determined; wherein the "types of content" include "video images", "3D graphics" and "document files");
generate global compensation information IGC by performing a global compensation using the input frame data, the content information, and an illumination signal of the image (see paras 0020, 0023, 0025 and element #306 of figure 3 - Glen discloses whether any of the content being included in an updated display requires an adjusted brightness relative to the current settings of the display. Glen explicitly discloses comparing intensity of the new image content relative to the current display intensity. Glen also explicitly discloses that the comparisons take into account ambient light which is detected via a light sensor. Note, since Glen's updated content intensity comparison are relative to the current entire display intensity, the Examiner interprets such as functionality equivalent to "global" compensation/information type); and
generate local compensation information ILC by performing a local compensation on the input frame data (see para 27 and elements 402 and 4040 of figure 4 - Glen discloses portions of the display image other than the identified content region, is processed to accommodate the adjusted brightness/intensity required by the region. Glen discloses performing such processing using adjustments to those remaining regions via contrast, brightness, color temperature or white point adjustments. Note, it is clear that such processing Glen is functionally equivalent to Applicant's "local compensation information"); and
to generate output frame data OS by blending the global compensation information and the local compensation information (see paras 0026-0028 and elements #310 and #312 of figure 3, and element #404 of figure 4 - Glen discloses performing processing on the other regions of the display not identified as the adjust content region, to accommodate the adjusted brightness required for the identified content region. Glen discloses performing such processing using adjustments to those remaining regions via contrast, brightness, color temperature or white point adjustments. Glen discloses displaying images via the display whether they require adjustments or not. Note, it is clear that in order for the technique of Glen to achieve the desired output and purpose of the invention, the "accommodation" processing of the other image regions in combination with the content adjusted brightness techniques of the image must perform functionally equivalent to produce "blended" output in order to produce a visually appealing output as per the problems solved by the techniques of Glen described in para 0004 of Glen).
The claim 1 as amended adds the limitation “wherein the global compensation information is generated by a second deep neural network (DNN) that receives the input frame data, the content information, and the illumination signal as inputs”.
Claims as originally filed recited first DNN in claim 2, and second DNN in claim 5, where second DNN limitation followed the first DNN limitation. Claim 2 (first DNN) was analyzed and rejected based on combined invention of Glen and Goodsitt, where Goodsitt providing the teachings for using DNNs. Claim 5 depends on claim 2, and since claim 5 had additional limitations in addition to second DNN claim 5 was objected to as all the limitations were not taught by prior art of record. Second DNN was not itself analyzed with respect to the reference Goodsitt.
Claim 1 as amended in the amendment filed now recites a second DNN limitation, without citing first DNN anywhere in the claims. However, examiner here cites Goodsitt again for a second DNN. Goodsitt teaches using multiple DNNs for the different parts of the process – first machine learning algorithm (col. 6, lines 16-20) and second machine learning algorithm (col. 6, lines 23-55).
As cited in the rejection of claim 1, Glen teaches generate global compensation information IGC by performing a global compensation using the input frame data, the content information, and an illumination signal of the image, but does not explicitly teach of doing so by using a deep neural network (DNN). However, Goodsitt teaches second machine learning algorithm (DNN) (col. 6, lines 23-55). Goodsitt teaches the concept of using multiple DNNs for the different parts of the process – first machine learning algorithm (col. 6, lines 16-20) and second machine learning algorithm (col. 6, lines 23-55).
Goodsitt teaches in col. 1, lines 34-40 "Screen content may be evaluated utilizing a first machine learning algorithm. The screen content being presented on the mobile device display may be categorized based on an output of the first machine learning algorithm. Based on a category of the screen content, a screen brightness adjustment may be determined to be appropriate. In response to determining that the screen brightness adjustment is appropriate, a degree of the screen brightness adjustment may be determined"; further discloses col. 6, lines 16-20 - "based on an output of the first machine learning algorithm, the screen content being presented on the mobile device display may be categorized. For example, categories of content may be video, photographs, news or book content, social media pages, a QR code, or the like"; and further discloses in col. 15, lines 15- 33 - " Examples of the machine learning algorithms or models may include a neural network classifier, an example of which may be a convolutional neural network, a supervised machine learning algorithm (such as a regression algorithm, linear regression algorithm, or the like), or an unsupervised machine learning algorithm (such as a k-means, Gaussian mixtures, or the like), to determine a category of screen content, thresholds for a screen brightness adjustment, or a degree of screen brightness adjustment. The various machine learning algorithms that may be used in the foregoing examples of FIGS. 1-4 may use one or more different data sets to train the respective machine learning algorithm. In the screen content categorization examples, machine learning algorithms, such as convolutional neural networks, that provide image classification are known and may have already been trained. Others may have rudimentary, general training and require a specific training data set representative of the specific results that the machine learning algorithm is intended to produce" - where convolutional neural network (CNN) is a DNN.
Goodsitt in col. 6, lines 23-55 further teaches “The obtained screen shot may be processed to gather pixel luminance intensity values of, for example, a sample of all of the pixels in the obtained screen shot. The gathered pixel luminance intensity values may be input into a categorical neural network or another machine learning algorithm that has performance similar to the categorical neural network. The output from the categorical neural network may indicate a category of the screen content in the obtained screen shot. Based on the category of the screen content, the screen brightness adjustment application may determine that a screen brightness adjustment is appropriate (240). In response to determining that the screen brightness adjustment is appropriate, a degree of the screen brightness adjustment may be determined (250). The degree of the screen brightness adjustment may be determined in several ways. For example, the degree of the screen brightness adjustment may be determined using a look-up tables, a second machine learning algorithm, a user preference setting related to a category of the screen content, a setting related to a category of the screen content provided by an external server, or the like. In an example, the second machine learning algorithm may include a rules-based algorithm, an unsupervised clustering algorithm, a screen brightness adjustment model, or supervised clustering algorithm. In an example using a second machine learning algorithm, a degree of screen brightness adjustment may be determined based on inputs to the second machine learning algorithm selected from: a user preference setting, an identify of an application presenting screen content on the mobile device display, an identity of an application selected to present content on the mobile device display, a measurement of ambient light intensity, or a user history).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use DNNs as taught by Goodsitt in the invention of Glen. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use DNNs as taught by Goodsitt in the invention of Glen, because generic DNN is simply being substituted in the claim, with no specific details of DNN present and a person of ordinary skill in the art could have made this swap with a reasonable expectation of predictable results (see KSR Rational (B) - MPEP 2143 [R-01.2024] – a simple substitution of one known element for another to obtain predictable results); and further adding, DNN as known enables machine learning, which can train a computer to output results with high accuracy and efficiency. Further adding, Goodsitt as cited teaches using different DNNs for different parts of the process, which enable better accuracy and efficiency.
Regarding claim 2, claim 2 recites "The display device of claim 1, wherein the content information is generated by a first deep neural network (DNN) that has been modeled to identify the content and that receives the input frame data as an input". As cited in the rejection of claim 1, Glen teaches identifying the content and configured to generate/determine the content information corresponding to the content of the input frame data, but does not explicitly teach of doing so by using a deep neural network (DNN). However, Goodsitt teaches in col. 1, lines 34-40 "Screen content may be evaluated utilizing a first machine learning algorithm. The screen content being presented on the mobile device display may be categorized based on an output of the first machine learning algorithm. Based on a category of the screen content, a screen brightness adjustment may be determined to be appropriate. In response to determining that the screen brightness adjustment is appropriate, a degree of the screen brightness adjustment may be determined"; further discloses col. 6, lines 16-20 - "based on an output of the first machine learning algorithm, the screen content being presented on the mobile device display may be categorized. For example, categories of content may be video, photographs, news or book content, social media pages, a QR code, or the like"; and further discloses in col. 15, lines 15-33 - " Examples of the machine learning algorithms or models may include a neural network classifier, an example of which may be a convolutional neural network, a supervised machine learning algorithm (such as a regression algorithm, linear regression algorithm, or the like), or an unsupervised machine learning algorithm (such as a k-means, Gaussian mixtures, or the like), to determine a category of screen content, thresholds for a screen brightness adjustment, or a degree of screen brightness adjustment. The various machine learning algorithms that may be used in the foregoing examples of FIGS. 1-4 may use one or more different data sets to train the respective machine learning algorithm. In the screen content categorization examples, machine learning algorithms, such as convolutional neural networks, that provide image classification are known and may have already been trained. Others may have rudimentary, general training and require a specific training data set representative of the specific results that the machine learning algorithm is intended to produce" - where convolutional neural network (CNN) is a DNN. Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to use DNN to determine content type as taught by Goodsitt in the invention of Glen. A person having ordinary skill in the art would have been motivated before the effective filing date of the claimed invention to use DNN to determine content type as taught by Goodsitt in the invention of Glen, as DNN enables machine learning, which can train a computer to identify contents with high accuracy and efficiency. Further adding, the same reasons of using DNNs are applied here as cited in the rejection of claim 1.
Regarding claim 3, the combined invention of Glen and Goodsitt discloses "The display device of claim 2, wherein the processing circuitry is configured to generate the content information comprising class information CL corresponding to the content of the input frame data and image brightness level information BL of the input frame data" (see Glen as cited in the rejection of claim 1; further see Goodsitt - col. 6, lines 16-20 - "based on an output of the first machine learning algorithm, the screen content being presented on the mobile device display may be categorized. For example, categories of content may be video, photographs, news or book content, social media pages, a QR code, or the like"; col. 6, lines 32-45 - "Based on the category of the screen content, the screen brightness adjustment application may determine that a screen brightness adjustment is appropriate (240). In response to determining that the screen brightness adjustment is appropriate, a degree of the screen brightness adjustment may be determined (250). The degree of the screen brightness adjustment may be determined in several ways. For example, the degree of the screen brightness adjustment may be determined using a look-up tables, a second machine learning algorithm, a user preference setting related to a category of the screen content, a setting related to a category of the screen content provided by an external server, or the like"; col. 6, lines 9-15 - "Based on the type of scene correlated to the video and ambient light having a value of X, the screen brightness adjustment application may determine that the degree of screen adjustment is a value YY, which may be in an approximate range of Z (the degree of screen brightness adjustment made by the user when video is presented on the mobile device screen)").
Regarding claim 4, the combined invention of Glen and Goodsitt discloses "The display device of claim 3, wherein the image characteristic information extractor 100 comprises: a class analysis unit 112 comprising the first DNN and configured to generate the class information corresponding to the content of the input frame data by the first DNN" (see the citations made in the rejection of claims 2 and 3). Regarding "an average brightness operation unit 114 configured to operate average brightness of the input frame data and to generate the image brightness level information as results of the operation". Although Glen does disclose that a plurality of intensity settings based on a corresponding plurality of content types are obtained, Glen does not explicitly disclose utilizing an average calculation to produce the brightness/intensity. At the time the invention was filed, it would have been obvious to one of ordinary skill in the art to perform a multitude of different mathematical equations to produce image brightness values including performing an average or mean computation. Applicant has not disclosed that explicitly utilizing such an average/mean computation provides an advantage, is used for a particular purpose, or solves a stated problem. One of ordinary skill in the art, furthermore, would have expected Applicant's invention to perform equally well with the brightness/intensity computations of Glen because the exact calculation chosen to derive the image brightness values in this context is a matter of engineering design choice as preferred by the inventor and/or to which best suits the application at hand. Further, the Examiner sees no immediate to the criticality of utilizing specifically an average vs. other type of mathematical computation to derive image brightness in this context in SO much that the techniques of Glen would provide equivalent output. Therefore, it would have been obvious to one of ordinary skill in this art to modify the combined invention of Glen and Goodsitt to obtain the invention as specified in claim 4.
Regarding claim 13, Glen discloses "A method of improving a visibility of an image for a display device, the method comprising: generating content information comprising class information (see paras 0021 and 0024, and element #304 of figure 3 - Glen discloses that each time a new image is to be updated/displayed, the types of contents of the image is determined; wherein the "types of content" include "video images", "3D graphics" and "document files" - where the different types of contents are considered here different classes), and image brightness level information corresponding to content that is represented in an image by using input frame data for the image (see paragraph 0022 and element #306 of Figure 3 - Glen discloses that a plurality of intensity settings based on corresponding plurality of content types are obtained. Glen further discloses that a table of intensity settings indexed by content type may be pre-stored in memory associated with the host or co- processors; para 0014 - determining a region of the displayed image corresponding to the content requiring the adjusted brightness, where to adjust image brightness information is required); and generating global compensation information by performing a global compensation using the input frame data, the class information, the image brightness level information, and an illumination signal of the image" (see paras 0020, 0023, 0025 and element #306 of figure 3 - Glen discloses whether any of the content being included in an updated display requires an adjusted brightness relative to the current settings of the display. Glen explicitly discloses comparing intensity of the new image content relative to the current display intensity. Glen also explicitly discloses that the comparisons take into account ambient light which is detected via a light sensor. Note, since Glen's updated content intensity comparison are relative to the current entire display intensity, the Examiner interprets such as functionality equivalent to "global" compensation/information type)). Other claim 13 limitations of using DNN have been analyzed and rejected as per arguments made in the rejection of claim 1.
Regarding claim 14, claim 14 has been similarly analyzed and rejected as per citations made in the rejection of claims 2-4.
Regarding claim 18, claim 18 has been similarly analyzed and rejected as per citations made in the rejection of claim 13.
Regarding claim 19, claim 19 has been similarly analyzed and rejected as per citations made in the rejection of claims 2-4.
None of the closest prior art(s) of the record teach subject matter as recited in claims 5-11, 15-17 and 20. Claims 5-11, 15-17 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Manav Seth whose telephone number is (571) 272-7456. The examiner can normally be reached on Monday to Friday from 8:30 am to 5:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Sumati Lefkowitz, can be reached on (571) 272-3638. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Manav Seth/
Primary Examiner, Art Unit 2672
July 28, 2026
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