DETAILED ACTION
Claims 1, 2, 5-8, 10, 13-17, 19-24 filed October 29th 2025 are pending in the current 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 Arguments
Applicant’s arguments with respect to claim(s) 1, 2, 5-8, 10, and 14-17 have been considered but are moot as they do not fully apply to the current combination. Ok et al. (US2020/0058249) which was previously applied to claim 12, teaches that the image data of Hack may be substituted with voltage and current data to determine aging of the circuit. Sun et al. (US12,205,534) provides a more direct application of machine learning to pixel aging data. While one of ordinary skill in the art could infer that the measured past data and the predicted future data meet at the present, the Examiner decided to keep the citation of Feyneh et al. (US2022/0260630) Fig. 11C to graphically represent what happens when the measured aging values are updated with a predicted aging value as outlined in the flowcharts of Hack Fig. 4 and Sun Fig. 7.
With regards towards applicant’s argument that “Fayneh relies on additional reading form the workload sensor to establish bounds in an attempt to narrow the unknown degradation,” the Examiner must respectfully disagree. As interpreted, the claimed first period exists in the past and the claimed second period exists in the future. Similarly, the claimed first period will grow as more measurements are taken and the resulting prediction in the claimed second period will become more accurate as time goes on. Thus, the description of Fayneh does not contradict the claimed operation.
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, 2, 5, 6, 9-11, and 14-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hack et al. (US2022/0059003) in view of Ok et al. (US2020/0058249) in view of Sun et al. (US12,205,534) in view of Feyneh et al. (US2022/0260630)
Consider claim 1, where Hack teaches a method of predicting a lifetime of a display device, the method comprising: creating a model based on prior degradation rate data according to a degradation time for each of pixels; (See Hack ¶72-77 where a predictive algorithm, a prediction scheme, or a predictive simulator may calculated the degradation for each sub-pixel over time based on the luminance and temperature history) measuring a first degradation rate data for each of the pixels by inputting a voltage to each of the pixels; (See Hack ¶72 where These schemes are typically based on either measuring (e.g., using external circuitry) the current flow through an OLED device at a given data voltage) predicting a second degradation rate data for each of the pixels using the model; (See Hack ¶72 where by using predictive algorithms which require accurate information on pixel behavior over time, based on luminance, temperature, and other profiles. Typical schemes can also include measurement-based algorithms, which require additional hardware to be added to the display to measure and characterize the display pixels over time.) and estimating a degradation rate for each of the pixels according to a degradation time based on the first degradation rate data and the second degradation rate data. (See Hack ¶86 where at operation 450, the controller may adjust an algorithm for degradation for degradation of the type B pixels to match the measured data (e.g., the measured luminance of the type B pixels). At operation 460, the controller may adjust the algorithm for degradation of type A pixels at operation 420)
Hack teaches measuring a luminance decay using a camera, however Hack does not explicitly teach the model predicting degradation amount information of a driving element for each of the pixels and degradation amount information of a light emitting device (LED) of each pixel to produce the second degradation rate; However, in an analogous field of endeavor Ok teaches the model predicting degradation amount information of a driving element for each of the pixels and degradation amount information of a light emitting device (LED) of each pixel to produce the second degradation rate; (See Ok ¶36-37 where the degradation compensation device according to exemplary embodiments may not accumulate the degradation amount based on image data (digital gradation), but rather measures the voltage for actual pixel output according to the characteristics of each panel in the display driver stage. The degradation of the pixel will be affected by the cumulative through-current since the voltage for the actual pixel output on the display driver stage is directly related to the through-current. The degradation rate estimator 120 may utilize a stretched exponential decay model in which the cumulative degradation amount information is defined by a degradation rate function over time, to estimate degradation rates for a plurality of respective pixels.) Therefore, it would have been obvious for one of ordinary skill in the art to substitute the luminance data from the captured image of Hack with the measured for voltage/ cumulative through current as taught by Ok. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using other known metrics in the art to measure degradation data and yield similar results.
Hack teaches creating a predictive algorithm (See Hack ¶80 and equation 1); however, Hack does not explicitly teach a machine learning model. However, in an analogous field of endeavor Sun teaches a machine learning model. (See Sun col 19 line 60- col 28 where For example, a machine learning model(s) may be trained by calculating weight parameters according to a neural network architecture using software and/or computing resources described above where Sun Col 7 line 41- col 8 line 22 teaches collecting long term aging (LTA) data in order to determine a decay rate).Therefore, it would have been obvious for one of ordinary skill in the art that the predicted degradation modeled by equation 1 of Hack could be the result of machine learning analysis performed on long term aging (LTA) data as taught by Sun. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using other known applications of machine learning for predicting pixel degradation.
Hack teaches a predictive degradation based on luminance and temperature history; however Hack does not expressly teach measuring, during a first period; predicting, during a second period, wherein an end time of the first period and a start time of the second period are the same. However, in an analogous field of endeavor Feyneh teaches measuring, during a first period; predicting, during a second period, wherein an end time of the first period and a start time of the second period are the same. (See Feyneh Figs 11A-C, and ¶169 where the measured data from days 0-250 are used to predict the degradation curves (linearly 1250 or exponential decay 1254) from days 250-1000) Therefore, it would be obvious to those of ordinary skill in the art that the predictive degradation over a second period would start right after the measured period as exemplified by Feyneh. In short, those of ordinary skill in the art would recognize that the measurement history of Hack and the predicted future degradation of Hack meet at the present (as graphed in Feyneh Fig. 11C). Thus, Feyneh provides the teaching of an inherent property of the actions being performed by Hack.
Consider claim 2, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 1, wherein the measuring of the first degradation rate data is performed in a first period having a first-time length, and the predicting of the second degradation rate data is performed in a second period having a second time length. (See Feyneh Figs 11A-C, and ¶169 where the measured data from days 0-250 (first period with a length of 250 days) are used to predict the degradation curves (linearly 1250 or exponential decay 1254) from days 250-1000 (second period with a length of 750 days)) Therefore, it would be obvious to those of ordinary skill in the art that the predictive degradation over a second period would start right after the measured period as exemplified by Feyneh. In short, those of ordinary skill in the art would recognize that the measurement history of Hack and the predicted future degradation of Hack meet at the present (as graphed in Feyneh Fig. 11C). Thus, Feyneh provides the teaching of an inherent property of the actions being performed by Hack.
Consider claim 5, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 2, wherein the first-time length of the first period and the second time length of the second period are same. (See Feyneh Figs 11A-C, and ¶169 where the measured data from days 0-250 (first period with a length of 250 days) are used to predict the degradation curves (linearly 1250 or exponential decay 1254) from days 250-1000 (second period with a length of 750 days). While Feyneh provides the explicit example of measuring over 250 days during a 1000 day simulation, it would be obvious to one of ordinary skill in the art that the measurement could be performed over 500 days during a 1000 day simulation, thus resulting in the first period and second period being the same length ) Therefore, it would be obvious to those of ordinary skill in the art that the predictive degradation over a second period would start right after the measured period as exemplified by Feyneh. In short, those of ordinary skill in the art would recognize that the measurement history of Hack and the predicted future degradation of Hack meet at the present (as graphed in Feyneh Fig. 11C). Thus, Feyneh provides the teaching of an inherent property of the actions being performed by Hack.
Consider claim 6, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 2, wherein the first-time length of the first period is shorter than the second time length of the second period. (See Feyneh Figs 11A-C, and ¶169 where the measured data from days 0-250 (first period with a length of 250 days) are used to predict the degradation curves (linearly 1250 or exponential decay 1254) from days 250-1000 (second period with a length of 750 days)) Therefore, it would be obvious to those of ordinary skill in the art that the predictive degradation over a second period would start right after the measured period as exemplified by Feyneh. In short, those of ordinary skill in the art would recognize that the measurement history of Hack and the predicted future degradation of Hack meet at the present (as graphed in Feyneh Fig. 11C). Thus, Feyneh provides the teaching of an inherent property of the actions being performed by Hack.
Consider claim 10, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 1, wherein in the estimating of the degradation rate for each of the pixels, the degradation rate is estimated by modeling degradation amount information of the light emitting device with a degradation model defined as a degradation rate function over time. (See Hack ¶78-80 and equation 1 where OLED degradation has been characterized as a stretched exponential function with respect to time, with an exponential temperature dependence. One may determine the pixel luminance L, for a fixed drive current, in terms of its luminance (L.sub.0) at t=0, such that
L
L
0
=
e
-
(
T
T
0
)
e
-
(
t
τ
(
I
)
)
β
where T is a temperature and T.sub.0 is a characteristic temperature, and τ is as characteristic time constant which is dependent on drive current (I), and β a factor that relates to the stretched exponential behavior.)
Consider claim 14, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 1, wherein the light emitting device includes an organic material. (See Hack ¶76 where the emissive elements such as OLEDS which stands for organic light emitting diode)
Consider claim 15, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 1, where there is a stretched exponential model. (See Hack ¶78 where OLED degradation has been characterized as a stretched exponential function) however, they do not explicitly teach wherein the measuring the first degradation rate data comprises measuring each pixel multiple times. However, in an analogous field of endeavor Ok teaches wherein the measuring the first degradation rate data comprises measuring each pixel multiple times. (See Ok Fig. 3-5 and ¶34-42 where a degradation amount acquisitor collects data from a plurality of pixels over an elapsed time to generate a stretched exponential function to model the degradation) Therefore, it would have been obvious for one of ordinary skill in the art to recognize that the stretched exponential function disclosed in Hack would naturally collect data over a period of time. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of collecting the requisite data to correctly model the stretched exponential function.
Consider claim 16, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 2, where there is a stretched exponential model. (See Hack ¶78 where OLED degradation has been characterized as a stretched exponential function) however, they do not explicitly teach wherein the measuring the first degradation rate data comprises measuring each pixel multiple times throughout the first period. However, in an analogous field of endeavor Ok teaches wherein the measuring the first degradation rate data comprises measuring each pixel multiple times throughout the first period (See Ok Fig. 3-5 and ¶34-42 where a degradation amount acquisitor collects data from a plurality of pixels over an elapsed time to generate a stretched exponential function to model the degradation) Therefore, it would have been obvious for one of ordinary skill in the art to recognize that the stretched exponential function disclosed in Hack would naturally collect data over a period of time. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of collecting the requisite data to correctly model the stretched exponential function.
Consider claim 17, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 2, where there is a stretched exponential model. (See Hack ¶78 where OLED degradation has been characterized as a stretched exponential function) However, they do not explicitly teach wherein a sum of the first-time length and the second time length correspond to the lifetime of the display device. However, in an analogous field of endeavor Ok teaches wherein a sum of the first-time length and the second time length correspond to the lifetime of the display device. (See Ok Fig. 3-5 and ¶34-42 where a degradation amount acquisitor collects data from a plurality of pixels over an elapsed time to generate a stretched exponential function to model the degradation over a lifetime (τ), where the predictive model spans beyond the data collection period to the end of the lifetime) Therefore, it would have been obvious for one of ordinary skill in the art to recognize that the stretched exponential function disclosed in Hack would naturally collect data over a period of time. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of collecting the requisite data to correctly model the stretched exponential function.
Claim(s) 7, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hack in view of Ok in view of Sun of Feyneh as applied to claim 1 above, in further view of Reddy et al. (US2020/0226718)
Consider claim 7, where Hack in view of Feyneh teaches the method of claim 1 however they do not explicitly teach, wherein the machine learning model is created based on Linear Regression, Polynomial Regression, Principal Components Regression, Partial Least Squares Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Ridge Regression, and/or Lasso Regression. However, in an adjacent field of endeavor Reddy teaches wherein the machine learning model is created based on Linear Regression, Polynomial Regression, Principal Components Regression, Partial Least Squares Regression, Random Forest, Gradient Boosting, Extreme Gradient Boosting, Ridge Regression, and/or Lasso Regression. (See Reddy ¶24 where different machine learning techniques—such as linear support vector machine (linear SVM), boosting for other processes (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps—may be used in different embodiments.) Therefore, it would have been obvious for one of ordinary skill in the art to apply known types of machine learning techniques as taught by Reddy to implement the machine learning model used for analysis in Feyneh. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known techniques applicable to a machine learning model to yield predictable results.
Consider claim 8, where Hack in view of Ok in view of Sun of Feyneh teaches the method of claim 1 however they do not explicitly teach, wherein the machine learning model is created based on Multilayer Perceptron, Bayesian Neural Networks, Radial Basis Functions, Generalized Regression Neural Networks, K-Nearest Neighbor Regression, Classification And Regression Tree, Support Vector Regression, and/or Gaussian Processes. However, in an adjacent field of endeavor Reddy teaches wherein the machine learning model is created based on Multilayer Perceptron, Bayesian Neural Networks, Radial Basis Functions, Generalized Regression Neural Networks, K-Nearest Neighbor Regression, Classification And Regression Tree, Support Vector Regression, and/or Gaussian Processes. (See Reddy ¶24 where different machine learning techniques—such as linear support vector machine (linear SVM), boosting for other processes (e.g., AdaBoost), neural networks, logistic regression, naive Bayes, memory-based learning, random forests, bagged trees, decision trees, boosted trees, or boosted stumps—may be used in different embodiments.) Therefore, it would have been obvious for one of ordinary skill in the art to apply known types of machine learning techniques as taught by Reddy to implement the machine learning model used for analysis in Feyneh. One of ordinary skill in the art would have been motivated to perform the modification for the advantage of/ benefit of using known techniques applicable to a machine learning model to yield predictable results.
Allowable Subject Matter
Claims 13, 19-24 are allowed.
The following is an examiner’s statement of reasons for allowance: In prior office actions, claim 13 was previously objected to as allowable. In the present amendment claim 13 has been re-written as an independent claim. Thus, claim 13 is now allowed. Claims 19-24 are allowed based upon their dependence from claim 13.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
THIS ACTION IS MADE FINAL. 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 WILLIAM LU whose telephone number is (571)270-1809. The examiner can normally be reached 10am-6:30pm.
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WILLIAM LU
Primary Examiner
Art Unit 2624
/WILLIAM LU/Primary Examiner, Art Unit 2624