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 .
Claims 1-13, 15-21 are pending.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 15 and the claims depending therefrom are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the preamble recites “A computer readable medium….” without any disclosure in Applicant’s Specification limiting the scope of claim 15 to only statutory embodiments. Thus, claim 15 includes within its scope non-statutory embodiments such as signals.
Examiner respectfully recommends amending claim 15 to recite “A non-transitory computer readable medium…” in order to cure this infirmity.
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, 14, 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yang (CN 109272948, Published January 25, 2019 – translation attached).
As to claim 1, Yang discloses a debugging method for a driving waveform of an electronic paper device, comprising:
performing a plurality of iterative debugging processes on a driving waveform of the electronic paper device for an electronic paper device to be debugged (Yang at Figs. 1, 4; Page ## discloses “FIG. 1 is a machine learning-based electronic paper of the invention provide driving debugging method flow diagram of the method can be driven by electronic paper to execute debugging device, wherein the device is operable by software and/or hardware, can be integrated in the computer device, the computer device may be a server, such as computer, electronic paper display terminal. As shown in FIG. 1, the method specifically comprises the following steps: step S110, obtaining the electronic paper to display under the driving of the driving signal of the electronic paper screen image”);
acquiring a target driving waveform corresponding to a latest iterative debugging process when the plurality of iterative debugging processes satisfies a target condition (Yang at Fig. 1; Page ## discloses “It should be noted that the drive signal can be preset in the waveform lookup table of the drive signal, wherein the drive signal set comprises a plurality of preset different drive signal. collection of the waveform look-up table between different gray scale driving signal. In the embodiment of the drive signal corresponding to the size of each waveform and the pulse width may be different to represent the magnitude of the driving signal with different gray scale. For example, from gray scale 1 to gray scale 2 corresponding to the drive signal is the first driving signal, gray scale 1 to gray scale 3 corresponding to the driving signal into a second driving signal, gray scale 2 to gray scale 5 corresponding to the driving signal into a third driving signal and so on. obtaining the electronic paper under different driving signal to drive the electronic paper screen image. In other embodiments, the drive signal can be pre-adjusting the driving signal obtained according to implementation conditions. Optionally, the drive signal can be according to machine learning result each drive signal for combining and frequency conversion, the signal after amplitude conversion”); and
taking the target driving waveform as a driving waveform of the electronic paper device in an actual drive (Yang at Fig. 1, steps S120-S130, Fig. 5 step 132; Pages ## discloses “step S120, the residual image value of the electronic paper screen image input pre-trained afterimage evaluation model, obtaining the incomplete evaluation model output. an electronic paper screen image in this embodiment is driven according to the different driving signal obtained by the electronic paper screen image, can also be understood as a current electronic paper screen image. inputting the characteristic distribution information of the electronic paper screen of pre-trained image sticking evaluation model, obtaining the residual image value of the electronic paper screen image. wherein the characteristic distribution information can be the electronic paper screen image input into the image histogram of the predetermined extraction. Exemplary, afterimage evaluation model by the preset characteristic distribution information of electronic paper screen image and the residual image value, then corresponding to the training to generate the electronic paper screen image residual shadow value evaluation, the afterimage evaluation model will output the residual image value. step S130, determining the optimal driving signal according to the residual image value…. The afterimage evaluation model outputs determines the optimal driving signal, solves the problem that the traditional technology in obtaining driving waveform by manual debugging, and due to lack of model movement of electrophoretic particles in accurately describing the electronic paper cannot be accurately know the distribution state of applying voltage to the electrophoretic particles, only repeated black and white by the driving waveform turning to realize the electronic paper in the distribution state of the electrophoretic particles and the corrected activity level. the electronic paper screen refreshing can accurately reach the corresponding grey scale, due to the high labor cost, a picture refreshing and long time of technical problem, improves the accuracy of the optimal drive signal, the optimal drive signal can realize the direct drive between different gray scales in the electronic paper display process, shortens the electronic paper display screen refresh time, optimize the electronic paper of working performance, meanwhile it improves the debugging efficiency of the drive”);
wherein, during each iterative debugging process, acquiring second driving waveform information corresponding to the iterative debugging process based on first driving waveform information and a debugging network corresponding to the last-time iterative debugging process, debugging the electronic paper device based on the second driving waveform information, and updating the debugging network based on a debugging result; wherein the debugging network is at least used for acquiring adjustment information for adjusting the first driving waveform information based on the input first driving waveform information, and the second driving waveform information is obtained based on the adjustment information and the first driving waveform information (Yang at Fig. 2; page ## discloses “In this embodiment, the preset electronic paper screen image of character distribution information and corresponding to the residual image value as the training sample for training the preset training network, generating the afterimage evaluation model. Optionally, the preset training network is full connection neural network. characteristic distribution information comprises colour distribution situation of the preset electronic paper screen image, the grey scale distribution situation, texture, edge, brightness, whiteness, and saturation information, distributing the preset feature information of the electronic paper screen image and the residual image as the input of the pre-set value corresponding to the training network. the residual image value sequentially input characteristic distribution information to all connecting neural network according to the preset order electronic paper screen image corresponding to the sequence and continuously training the optimizing connection parameters of the neural network to obtain electronic paper residual image evaluation model. Optionally, the preset training network training the electronic paper the residual image evaluation model parameter using stochastic gradient descent method. stochastic gradient descent method is one of the gradient descent method, gradient descent method is that through target function parameters along the opposite gradient direction to continuously update model parameters to achieve minimum point of an objective function method, a stochastic gradient descent method from the training data every time randomly selecting one sample to be learned, continuously updating the model parameter, so each learning speed is very fast, and it can do online updating.” Based on this disclosure, the second driving waveform information is necessarily obtained based on the adjustment information and the first driving waveform information).
As to claim 14, Yang discloses a computing processing device, comprising: a memory in which a computer readable code is stored; and one or more processors, wherein when the computer readable code is executed by the one or more processors, the computing processing device perform operations (Yang at Figs. 6-8) comprising:
performing a plurality of iterative debugging processes on a driving waveform of the electronic paper device for an electronic paper device to be debugged (Yang at Figs. 1, 4; Page ## discloses “FIG. 1 is a machine learning-based electronic paper of the invention provide driving debugging method flow diagram of the method can be driven by electronic paper to execute debugging device, wherein the device is operable by software and/or hardware, can be integrated in the computer device, the computer device may be a server, such as computer, electronic paper display terminal. As shown in FIG. 1, the method specifically comprises the following steps: step S110, obtaining the electronic paper to display under the driving of the driving signal of the electronic paper screen image”);
acquiring a target driving waveform corresponding to a latest iterative debugging process when the plurality of iterative debugging processes satisfies a target condition (Yang at Fig. 1; Page ## discloses “It should be noted that the drive signal can be preset in the waveform lookup table of the drive signal, wherein the drive signal set comprises a plurality of preset different drive signal. collection of the waveform look-up table between different gray scale driving signal. In the embodiment of the drive signal corresponding to the size of each waveform and the pulse width may be different to represent the magnitude of the driving signal with different gray scale. For example, from gray scale 1 to gray scale 2 corresponding to the drive signal is the first driving signal, gray scale 1 to gray scale 3 corresponding to the driving signal into a second driving signal, gray scale 2 to gray scale 5 corresponding to the driving signal into a third driving signal and so on. obtaining the electronic paper under different driving signal to drive the electronic paper screen image. In other embodiments, the drive signal can be pre-adjusting the driving signal obtained according to implementation conditions. Optionally, the drive signal can be according to machine learning result each drive signal for combining and frequency conversion, the signal after amplitude conversion”); and
taking the target driving waveform as a driving waveform of the electronic paper device in an actual drive (Yang at Fig. 1, steps S120-S130, Fig. 5 step 132; Pages ## discloses “step S120, the residual image value of the electronic paper screen image input pre-trained afterimage evaluation model, obtaining the incomplete evaluation model output. an electronic paper screen image in this embodiment is driven according to the different driving signal obtained by the electronic paper screen image, can also be understood as a current electronic paper screen image. inputting the characteristic distribution information of the electronic paper screen of pre-trained image sticking evaluation model, obtaining the residual image value of the electronic paper screen image. wherein the characteristic distribution information can be the electronic paper screen image input into the image histogram of the predetermined extraction. Exemplary, afterimage evaluation model by the preset characteristic distribution information of electronic paper screen image and the residual image value, then corresponding to the training to generate the electronic paper screen image residual shadow value evaluation, the afterimage evaluation model will output the residual image value. step S130, determining the optimal driving signal according to the residual image value…. The afterimage evaluation model outputs determines the optimal driving signal, solves the problem that the traditional technology in obtaining driving waveform by manual debugging, and due to lack of model movement of electrophoretic particles in accurately describing the electronic paper cannot be accurately know the distribution state of applying voltage to the electrophoretic particles, only repeated black and white by the driving waveform turning to realize the electronic paper in the distribution state of the electrophoretic particles and the corrected activity level. the electronic paper screen refreshing can accurately reach the corresponding grey scale, due to the high labor cost, a picture refreshing and long time of technical problem, improves the accuracy of the optimal drive signal, the optimal drive signal can realize the direct drive between different gray scales in the electronic paper display process, shortens the electronic paper display screen refresh time, optimize the electronic paper of working performance, meanwhile it improves the debugging efficiency of the drive”);
wherein, during each iterative debugging process, acquiring second driving waveform information corresponding to the iterative debugging process based on first driving waveform information and a debugging network corresponding to the last-time iterative debugging process, debugging the electronic paper device based on the second driving waveform information, and updating the debugging network based on a debugging result; wherein the debugging network is at least used for acquiring adjustment information for adjusting the first driving waveform information based on the input first driving waveform information, and the second driving waveform information is obtained based on the adjustment information and the first driving waveform information (Yang at Fig. 2; page ## discloses “In this embodiment, the preset electronic paper screen image of character distribution information and corresponding to the residual image value as the training sample for training the preset training network, generating the afterimage evaluation model. Optionally, the preset training network is full connection neural network. characteristic distribution information comprises colour distribution situation of the preset electronic paper screen image, the grey scale distribution situation, texture, edge, brightness, whiteness, and saturation information, distributing the preset feature information of the electronic paper screen image and the residual image as the input of the pre-set value corresponding to the training network. the residual image value sequentially input characteristic distribution information to all connecting neural network according to the preset order electronic paper screen image corresponding to the sequence and continuously training the optimizing connection parameters of the neural network to obtain electronic paper residual image evaluation model. Optionally, the preset training network training the electronic paper the residual image evaluation model parameter using stochastic gradient descent method. stochastic gradient descent method is one of the gradient descent method, gradient descent method is that through target function parameters along the opposite gradient direction to continuously update model parameters to achieve minimum point of an objective function method, a stochastic gradient descent method from the training data every time randomly selecting one sample to be learned, continuously updating the model parameter, so each learning speed is very fast, and it can do online updating.” Based on this disclosure, the second driving waveform information is necessarily obtained based on the adjustment information and the first driving waveform information).
As to claim 15, Yang discloses a computer-readable medium having stored thereon computer programs/instructions, wherein the computer programs/instructions when executed by a processor implement the debugging method for the driving waveform of the electronic paper device (Yang at Figs. 6-8) according to claim 1 (See rejection of claim 1 above).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Yang (CN 109272948, Published January 25, 2019 – translation attached) in view of Zhi (CN 114373430, Published April 19, 2022 – translation attached).
As to claim 10, Yang discloses debugging method for the driving waveform of the electronic paper device according to claim 1.
Yang does not disclose wherein before acquiring first driving waveform information, the method further comprises: displaying a parameter setting interface of the debugging network; performing an update coefficient setting on the debugging network in response to an update coefficient input by a user via the parameter setting interface, wherein the update coefficient is used for adjusting a parameter update process of the debugging network.
However, Zhi does disclose wherein before acquiring first driving waveform information, the method further comprises: displaying a parameter setting interface of the debugging network; performing an update coefficient setting on the debugging network in response to an update coefficient input by a user via the parameter setting interface, wherein the update coefficient is used for adjusting a parameter update process of the debugging network (Zhi at Figs. 1, 3. Page 6 discloses “FIG. 3 is a schematic diagram of a visualization interface in an embodiment of the present disclosure. It can be understood that the electrophoretic particle driving data can be stored in the corresponding document, such as electrophoretic particle driving data stored in the WF.C document, using the display method in the present disclosure, the electrophoretic particle driving data is performed prior processing. The WF.C document can be opened by selecting the storage path of the WF.C document as shown in FIG. 3, so as to perform prophase processing on the WF.C document…. after opening the WF.C document, pre-processing the WF.C document. The electrophoretic particle driving data may include a plurality of driving waveform data and a plurality of driving time data, adding driving waveform identifier for each driving waveform data, adding temperature segment identifier for each driving time data.”).
Yang discloses a base display device upon which the claimed invention is an improvement. Zhi discloses a comparable display device which has been improved in the same way as the claimed invention. Hence, it would have been obvious to a person having ordinary skill in the art before the effective filing date to modify or add to Yang the teachings of Zhi for the predictable result of improving the convenience of driving data debugging and improve efficiency and accuracy (Zhi at Page 4)
Allowable Subject Matter
Claims 2-9, 11, 12, 16-21 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 objected to claim and all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Liu (CN 109448615 A, Published March 8, 2019 – translation attached) is made of record for its relevance to claims 1 and 14 by its disclosure of An Electronic Paper Drive Waveform Automatic Debugging Method, where claim 1 recites:
“1. An electronic paper drive waveform automatic debugging method, wherein providing an upper computer and a main control board, connected through the communication between the upper computer and the main control board, the debugging method comprises the following steps: S1. the upper computer setting area setting each parameter and waveform storing address, address selecting waveform in the waveform library, S2 the main control panel receives the waveform and the target parameter, judging whether the test of the electronic paper film is black and white module or three-colour module, S3. The black-and-white mode or three-colour module, using the waveform driving the electronic paper module to contrast test, the afterimage test and corrosion test, and recording the corresponding main control board uploading test data, S4, to the optical value, upper computer receiving optical value data to be stored, and displayed in a list, S5, judging whether finishing the test waveform, if so, ending, if not, it returns step S1.”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Sanjiv D Patel whose telephone number is (571)270-5731. The examiner can normally be reached Monday - Friday, 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, William Boddie can be reached at 571-272-0666. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Sanjiv D. Patel/Primary Examiner, Art Unit 2625
06/30/2026
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