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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/21/2026 has been entered.
Claims 1-20 are currently pending in U.S. Patent Application No. 18/304,651 and an Office Action on the merits follows.
Response to Arguments/Amendments
Applicant’s remarks filed 01/21/2026 have been fully considered but are not persuasive.
The Applicant asserts that none of the prior art referenced in the previous action disclose the amended portion to claims 1, 11, and 17. The Examiner respectfully disagrees. The Examiner notes that the Applicant does not specify how the amended limitations are different from what the prior art discloses. The Applicant notes that none of the prior art specifically disclose “inputting the statistics information into a machine learning model configured to output one or more parameters of an inverse tone-mapping (ITM) curve without iterative optimization or curve-fitting during inference”. The Examiner notes that while Maisa does disclose an iterative optimization process for training a multilinear model (i.e., machine learning model), Maisa does not only train the model but also utilizes the final model (see Equation 4; Maisa) to perform inference. Furthermore, the language in the claim amendment is indefinite due to the usage of “or”, suggesting different potential interpretations of the claim amendment:
“inputting the statistics information into a machine learning model configured to output one or more parameters of an inverse tone-mapping (ITM) curve without iterative optimization [Wingdings font/0xE8] i.e., the inputting of statistics information into the machine learning model (regardless of during inference time or training time) does not involve iterative optimization
“inputting the statistics information into a machine learning model configured to output one or more parameters of an inverse tone-mapping (ITM) curve without iterative optimization [Wingdings font/0xE8] i.e., the inputting of statistics information into the machine learning model during inference does not involve iterative optimization
“inputting the statistics information into a machine learning model configured to output one or more parameters of an inverse tone-mapping (ITM) curve without [Wingdings font/0xE8] i.e., the inputting of statistics information into the machine learning model during inference does not involve curve-fitting
The Examiner notes that under interpretations #1 and #2, Maisa does disclose inputting statistics information into a machine learning model at inference time without utilizing iterative optimization (see 3.1. Image Data, Equation 4; Maisa), as noted in the prior art rejections made below. Additionally, when considering interpretation #3, Maisa’s disclosure does not perform curve-fitting during inference. The Examiner interprets the term “curve-fitting” as the process of tuning the parameters of some function (wherein the function represents a curve) such that the function approximates and fits the distribution of points (i.e., a distribution of target/true points). This process occurs during the training phase of machine learning models, but at inference time there is no “curve-fitting” occurring. Rather, value(s) are being input into a function representing the curve to approximate/estimate the predicted output value associated with the input value(s). As disclosed by Maisa, one a trained model is obtained (see Equation 4), input values of Lh (geometric mean luminance), k1 (image key based on maximum and minimum luminance values), and pov (percentage of overexposed pixels) are input into the model/function to obtain an output γ value. The process described by Maisa to obtain an output γ value are the same steps used to evaluate any equation, and evaluating an equation is fundamentally different from performing “curve-fitting”. Due to a change in scope necessitated by the Applicant’s amendments, a new combination of references is presented below for the 35 U.S.C. 103 rejection.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. As noted in the Examiner’s remarks, claim 1 (and corresponding claims 11 and 17) recite the limitation “inputting the statistics information into a machine learning model configured to output one or more parameters of an inverse tone-mapping (ITM) curve without iterative optimization or curve-fitting during inference;” where it is unclear how “or” should be interpreted (the Examiner notes the potential interpretations #1-#3 listed in the Examiner remarks). Further clarification is required such that the claim is easily interpretable to one skilled in the art.
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.
Claims 1-2, 4, 10-11, and 16-17 are rejected as being unpatentable over Maisa et al. (“Dynamic range expansion based on image statistic”, DOI: https://doi.org/10.1007/s11042-015-3036-0; hereinafter “Maisa”) in view of Su et al. (US 2022/0301124; hereinafter “Su”).
Regarding Claim 1, Maisa discloses A method comprising (see 3. Multilinear models for gamma expansion.):
receiving, as input, standard dynamic range (SDR) content (3.1 Input Data, Maisa teaches obtaining images from an SDR image database.);
obtaining statistics information corresponding to the SDR content (3.1. Image Data, Maisa teaches determining a variety of image statistics based on linearized luminance values.);
inputting the statistics information into a machine learning model configured to output one or more parameters of an inverse tone-mapping (ITM) curve without iterative optimization (3.1. Image Data, Equation 4, Maisa discloses obtaining a multilinear regression model (i.e., a machine learning model), which takes input different image statistics information (Lh (geometric mean luminance), k1 (image key based on maximum and minimum luminance values), and pov (percentage of overexposed pixels)). The multilinear regression model outputs a predicted γ value used to perform gamma expansion used for reverse tone-mapping) or curve-fitting during inference (The Examiner notes the Examiner remarks made above regarding how Maisa evaluates the trained regression model to obtain an output value, and how that process is distinctly different from “curve-fitting during inference”.);
determining, the one or more parameters of the ITM curve based on the output of the machine learning model (3.2. Multilinear regression, Maisa teaches utilizing different image statistics to generate a multilinear model (represented by Equation 4) which predicts a γ value used to perform gamma expansion used for reverse tone-mapping.); and
converting the SDR content to high dynamic range (HDR) content using the ITM curve (4. Results, Figs. 5, 8, 9, Maisa teaches utilizing the multilinear model to perform gamma expansion to convert a SDR image to HDR.),
Maisa does not explicitly disclose and converting the SDR content to high dynamic range (HDR) content using the ITM curve (italicized for context), wherein the resulting HDR content is provided to a display device for presentation (The Examiner notes 1. Introduction, 4. Results, Maisa teaches a SDR to HDR image conversion process using a reverse tone mapping operator. The Examiner asserts that the HDR images produced by Maisa (shown in Figs. 8-9), are performed on a computer, and as they are then furthermore visually analyzed, Maisa’s methods involve the claimed “resulting content is provided to a display device”. This is supported by the disclosure in 1. Introduction, wherein Maisa discloses that reverse tone mapping operation taught by Maisa “is simple enough that modern displays can compute it on the fly”. However, for the record the Examiner notes below another reference teaching the claimed limitation.).
Su teaches and converting the SDR content to high dynamic range (HDR) content using the ITM curve (italicized for context), wherein the resulting HDR content is provided to a display device for presentation ([0192], Fig.4B, Su teaches converting an SDR image into an HDR image and presenting the image on a display device.).
Maisa and Su are considered to be analogous to the claimed invention as they are in the same field of SDR to HDR image conversion. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa by incorporating the HDR display capabilities as taught by Su. The motivation for this combination being the ability to display an image to a user at the end of processing.
Claims 11 and 17 are the system and non-transitory processor-readable medium claims corresponding to claim 1, and are similarly rejected (see [0193-1934], Su).
Regarding Claim 2, Maisa in view of Su teaches the method of claim 1, wherein the display device has HDR rendering capabilities ([0020], [0192], Fig. 1, Fig. 4B, Su teaches an HDR display with the ability to display HDR images.).
Regarding Claim 4, Maisa in view of Su teaches the method of claim 1.
The current combination of Maisa in view of Su does not explicitly teach wherein obtaining statistics information corresponding to the SDR comprises: parsing metadata corresponding to the SDR content from SDR signals of the SDR content, wherein the metadata comprises the statistics information.
Su further teaches obtaining statistics information corresponding to the SDR comprises: parsing metadata corresponding to the SDR content from SDR signals of the SDR content, wherein the metadata comprises the statistics information ([0023-0024], Su teaches utilizing SDR image metadata to generate an HDR image. The Examiner notes that Su’s metadata includes composter data to generate backward reshaping mappings, which is analogous to the claimed “statistics information” as both the claimed “statistics information” and composer data are used in the process to generate backward reshaping mappings (i.e., an ITM curve).).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to further modify the invention of Maisa in view of Su such that it includes the incorporation and processing of metadata as taught by Su. The motivation for this combination being the ability to incorporate additional information in the formation of an HDR image from an SDR image.
Regarding Claim 10, Maisa in view of Su teaches the method of claim 1, wherein the machine learning model is implemented in a Digital Signal Processor (DSP) or a central processing unit (CPU) of the display device ([0017-0018], [0036], [0193], Su teaches a display device comprising a processor where machine learning models are generated and stored.).
Claim 16 is the system claim corresponding to claim 10, and are similarly rejected (see [0193-1934], Su).
Claims 3, 12, and 18 are rejected as being unpatentable over Maisa in view of Su in view of Kuo et al. (“Content-adaptive inverse tone mapping”, DOI: 10.1109/VCIP.2012.6410798; hereinafter “Kuo”).
Regarding Claim 3, Maisa in view of Su teaches the method of claim 1.
Maisa in view of Su does not explicitly teach wherein the statistics information comprises, for each SDR image of the SDR content, at least one of a histogram of the SDR image or linear luminance percentiles sampled from a cumulated distribution function (CDF) of the SDR image based on pre-defined sampling percentage values.
Kuo teaches wherein the statistics information comprises, for each SDR image of the SDR content, at least one of a histogram of the SDR image (Section 3.1.2. Scene Classifier, Kuo teaches extracting image features from training data to input into a support vector machine (SVM), of which one of the features is a histogram.) or linear luminance percentiles sampled from a cumulated distribution function (CDF) of the SDR image based on pre-defined sampling percentage values.
Maisa, Su, and Kuo are considered to be analogous to the claimed invention as they are in the same field of SDR to HDR image conversion. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su such that the percentile statistics calculated by Maisa in view of Su are obtained based on the histogram taught by Kuo. The motivation for this combination being the ability to generate a distribution of luminance values, which can then be used to sample specific percentiles which can be used for further downstream calculations.
Claims 12 and 18 are the system and non-transitory processor-readable medium claims corresponding to claim 3, and are similarly rejected (see [0193-1934], Su).
Claim 5 is rejected as being unpatentable over Maisa in view of Su in view of Wen et al. (US2018/0098094; hereinafter “Wen”) in view of Kim et al. (US2008/0002062; hereinafter “Kim”.).
Regarding Claim 5, Maisa in view of Su teaches the method of claim 1.
Maisa in view of Su does not teach wherein obtaining statistics information corresponding to the SDR content comprises: for each SDR image of the SDR content: calculating a histogram of the SDR image; calculating a cumulated distribution function (CDF) of the SDR image based on the histogram of the SDR image; and sampling linear luminance percentiles from the CDF of the SDR image based on pre-defined sampling percentage values.
Wen teaches obtaining statistics information corresponding to the SDR content comprises: for each SDR image of the SDR content: calculating a histogram of the SDR image ([0066-0067], Wen teaches calculating an SDR histogram.); calculating a cumulated distribution function (CDF) of the SDR image based on the histogram of the SDR image ([0146], Fig. 4A, Wen teaches constructing a CDF of an SDR image based on an SDR histogram.);
Maisa, Su, and Wen are considered to be analogous to the claimed invention as they are in the same field of SDR to HDR image conversion. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su such that the histogram obtained by Maisa in view of Su is used to calculate a CDF of the SDR image as taught by Wen. The motivation for this combination being the ability to use the CDF for further downstream processing and calculations.
Maisa in view of Su in view of Wen does not teach sampling linear luminance percentiles from the CDF of the SDR image based on pre-defined sampling percentage values.
Kim teaches sampling linear luminance percentiles from the CDF of the SDR image based on pre-defined sampling percentage values ([0033], Fig. 2, Kim teaches sampling a high range and low range threshold point from a luminance CDF graph, wherein the threshold points can be defined at 10% and 90%.).
Maisa, Su, Wen, and Kim are considered to be analogous to the claimed invention as they are in the same field improving image quality. Therefore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su in view of Wen such that the CDF was sampled in a similar process as described by Kim. The motivation for this combination being the ability to utilize the CDF and obtain pertinent values relating to the SDR image which are beneficial for further downstream processing.
Claim 6 is rejected as being unpatentable over Maisa in view of Su in view of Wen.
Regarding Claim 6, Maisa in view of Su teaches the method of claim 1.
Maisa in view of Su does not teach wherein the ITM curve is an n-th order polynomial curve.
Wen teaches wherein the ITM curve is an n-th order polynomial curve ([0081], Fig. 4B, Wen teaches using a set of polynomial pieces to approximate a backward reshaping function to convert SDR images to HDR images.).
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su such the inverse tone mapping operator taught by Maisa in view of Su is the set of polynomial pieces used to approximate a backward reshaping function as taught by Wen. The motivation for this combination being the ability to define an explicit function and its individual components which can be used for SDR to HDR conversion.
Claims 7, 13, and 19 are rejected as being unpatentable over Maisa in view of Su in view of Wen in view of Yeung et al. (US2020/0043125; hereinafter “Yeung”).
Regarding Claim 7, Maisa in view of Su in view of Wen teaches the method of claim 6.
Maisa in view of Su in view of Wen does not teach wherein the n-th order polynomial curve is one of a Bernstein polynomial curve or a Bézier curve.
Yeung teaches wherein the n-th order polynomial curve is one of a Bernstein polynomial curve or a Bézier curve ([0046-0047], Yeung teaches using a Bézier curve for tone-mapping.).
Maisa, Su, Wen, and Yeung are considered to be analogous to the claimed invention as they are in the same field improving image quality. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su in view of Wen such that the polynomial curve taught by Maisa in view of Su in view of Wen is the Bézier curve taught by Yeung. The motivation for this combination being the ability to use a smooth defined curve which can be mathematically defined.
Regarding Claim 13, Maisa in view of Su teaches the system of claim 11.
Maisa in view of Su does not teach wherein the ITM curve is an n-th order polynomial curve, and the n-th order polynomial curve is one of a Bernstein polynomial curve or a Bézier curve.
Wen teaches wherein the ITM curve is an n-th order polynomial curve ([0081], Fig. 4B, Wen teaches using a set of polynomial pieces to approximate a backward reshaping function to convert SDR images to HDR images.),
It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su such the inverse tone mapping operator taught by Maisa in view of Su is the set of polynomial pieces used to approximate a backward reshaping function as taught by Wen. The motivation for this combination being the ability to define an explicit function and its individual components which can be used for SDR to HDR conversion.
Maisa in view of Su in view of Wen does not teach and the n-th order polynomial curve is one of a Bernstein polynomial curve or a Bézier curve.
Yeung teaches and the n-th order polynomial curve is one of a Bernstein polynomial curve or a Bézier curve ([0046-0047], Yeung teaches using a Bézier curve for tone-mapping.).
Maisa, Su, Wen, and Yeung are considered to be analogous to the claimed invention as they are in the same field improving image quality. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su in view of Wen such that the polynomial curve taught by Maisa in view of Su in view of Wen is the Bézier curve taught by Yeung. The motivation for this combination being the ability to use a smooth defined curve which can be mathematically defined.
Claim 19 is the non-transitory processor-readable medium claim corresponding to claim 13, and is similarly rejected (see [0193-1934], Su).
Claims 8 and 14 are rejected as being unpatentable over Maisa in view of Su in view of Kadu et al. (US2021/0350512; hereinafter “Kadu”).
Regarding Claim 8, Maisa in view of Su teaches the method of claim 1.
Maisa in view of Su does not teach wherein the machine learning model is trained offline.
Kadu teaches wherein the machine learning model is trained offline ([0059], Kadu teaches training a machine learning model offline.).
Maisa, Su, and Kadu are considered to be analogous to the claimed invention as they are in the same field processing SDR content. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the invention of Maisa in view of Su in view of Wen such that the machine learning model used to produce parameters of an ITM curve was trained offline as taught by Kadu. The motivation for this combination being the ability to reduce the workload on the system such that portions of the computation can be performed offline instead of in real-time.
Claim 14 is the system corresponding to claim 8, and is similarly rejected (see [0193-1934], Su).
Citation of Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wanat et al. (US 2025/0095125)
Su and Song (US 2021/0150812)
Dangi et al. (US 2021/0360179)
Conclusion
No claims are allowed. Claims 9, 15, and 20 are free of the prior art. Final allowability is contingent on overcoming all presented rejections.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PROMOTTO TAJRIAN ISLAM whose telephone number is (703)756-5584. The examiner can normally be reached Monday - Friday 8:30 am - 5:00 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PROMOTTO TAJRIAN ISLAM/Examiner, Art Unit 2669
/CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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