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 under 37 CFR 1.114, and fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 6/2/26 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 7-10 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections – 35 U.S.C. 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.
Claim(s) 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bae et al (2016/0173886) in view of Katsumata et al. (11,902,555) further in view of Kurar (2020/0073969), further in view of Ghafoorian et al. (2022/0277549).
Regarding Claim 7, Bae et al (2016/0173886) discloses a method for operating an automotive lighting arrangement (automotive head unit, paragraph [0046]), comprising:
operating an encoder (“encoding data based on the encoding data”, “encoding data as a pixel value of the image data”, paragraph [0015]), to reduce the data size of the image data (compression methods may reduce a size by 50%”, paragraphs [0136], [0126]; “compression modes, and encoding the image according to the selected mode”, abstract), producing processed image data (“image data”, paragraphs [0136], [0126));
transmitting the processed image data to a decoder (fig. 4);
operating the decoder to process the processed image data (“a compression mode of image data included in encoding data based on the encoding data, and a decoding module that reconstructs the image data from the encoding data according to the identified mode”, paragraph [0018]) to produce a restored image data (“decoding module restores a pixel value”, paragraph [0015]; “reconstructed image data”, Fig. 4); and
operating the lighting module to project a light pattern (“projector displays an image by projecting light”, paragraph [0077]) based on the restored image data (“reconstructing the image”, paragraphs [0013]; “reconstructs the image data from encoding data…decoding module restores a pixel value…”, [0015]) outside the automotive lighting arrangement (“inside or outside the electronic device 201”, paragraph [0077]).
As discussed above, Bae essentially discloses the claimed invention but does not graphically show a lighting module comprising a light source.
However, since Bae teaches that his invention can be used in an automotive head unit (paragraph [0046]), and a projector that projects light (paragraph [0077]), it would have been obvious to one of ordinary skill in the art at the time the invention was made to have included a light source in the automotive head unit of Bae to project light in order to provide light as disclosed in Bae.
As discussed above, Bae essentially discloses the claimed invention but does not literally disclose operating encoder in an automotive control unit and transmitting the processed image to a decoder in a lighting module.
However, Katsumata et al. (11,902,555) discloses operating encoder (32) in a data generation device as an automotive control unit (Fig. 40), transmitting the processed image to a decoder (53) in a data reproduction device as a lighting module (Fig. 41). Katsumata also discloses that his invention is used in an automobile control system (Col. 7, lines 9-21).
It would have been obvious to one of ordinary skill in the art to have located the encoder in a control unit and decoder in a lighting module in Bae to input a secure and compressed image from the encoder to project the light output after being decompressed by the decoder to order to reduce the size of the file and protect the file from being corrupted, crashed or stolen as taught by Katsumata.
Further, it has been held that rearranging the position of claimed element merely involves routine skill in the art. In re Japikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950) (Claims to a hydraulic power press which read on the prior art except with regard to the position of the starting switch were held unpatentable because shifting the position of the starting switch would not have modified the operation of the device.); In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975) (the particular placement of a contact in a conductivity measuring device was held to be an obvious matter of design choice).
As discussed above, Bae essentially discloses the claimed invention but does not explicitly disclose using encoder and decoder of a trained deep autoencoder to process image data.
However, Kurar (2020/0073969) discloses deep learning such as autoencoder to analyzing and recognize objects within video and images (paragraph [0040]) and further discloses deep convolutional encoder-decoder architectures (paragraph [0049]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have provided the encoder-decoder architectures of Kurar in Bae in order to accurately identify subject and provide vehicles with a mechanism to detect visually-based content of interest from visual inventory as taught by Kurar (paragraph [0040]).
As discussed above, Bae in view of Katsumata and Kurar essentially discloses the claimed invention but does not explicitly disclose encoder including a convolution layer, a rectified linear unit layer, and normalization layer, and a decoder including an upsampling convolution layer, a rectified linear unit layer, and a normalization layer.
However, Ghafoorian et al. (2022/0277549) teaches a well-known segmentation network architecture (paragraphs [0106] to [0108]): an encoder-decoder structure with skip connections. The encoder consists of nine down-sampling blocks containing a convolution, batch normalization and ReLu. The decoder blocks are the same, except that the convolutions are replaced by transposed convolutions; and PSP-net, which utilises a pyramid pooling module to capture more contextual information.
It would have been obvious to one of ordinary skill in the art to have provided a well-known segmentation architecture: encoder comprising convolution block as convolution layer, batch normalization block as normalization layer, and a ReLu block as a rectified linear unit layer, and a decoder comprising a transposed convolution block as upsampling convolution layer, a batch normalization block as normalization layer, and a ReLu block as a rectified linear unit layer as taught by Ghafoorian in Bae in view of Katsumata and Kurar in order to improve training stability, speed up convergence and enhance generalization.
Regarding Claim 8, Bae discloses the method according to claim 7, further comprising normalizing the image data (411 or 413) before operating the encoder block (415) to reduce its data size (fig. 4).
Regarding Claim 9, as discussed above, Bae essentially discloses the claimed invention but does not explicitly disclose the method according to claim 8, wherein normalizing the image data includes converting each value of the image data in a converted value between 0 and 1.
However, it would have been obvious and known to a skilled in the art that all processed data are converted between 0 and 1 in Bae since in digital world, all data are binary including Bae in order to compute the data in a computerized system. Bits and bytes are the base language of computer communications.
Regarding Claim 10, Bae discloses the method according to claim 7, further comprising dividing the image data in data subarrays of the same format (“The image data 610 includes a plurality of pixels belonging to different lines in the image”, “the image data 610 in FIG. 6 includes four pixels input, for example, in a 2x2pixel array form or a block form”, paragraph [0122]. Noted pixel as a subarray to form an array. They are same format as image data).
Claim(s) 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bae et al (2016/0173886) in view of Katsumata et al. (11,902,555) further in view of Zadeh et al. (2014/0201126) further in view of Ghafoorian et al. (2022/0277549).
Regarding Claim 7, Bae et al (2016/0173886) discloses a method for operating an automotive lighting arrangement (automotive head unit, paragraph [0046]), comprising:
operating an encoder (encoding data based on the encoding data”, “encoding data as a pixel value of the image data”, paragraph [0015]), to reduce the data size of the image data (compression methods may reduce a size by 50%”, paragraphs [0136], [0126]; “compression modes, and encoding the image according to the selected mode”, abstract), producing processed image data (“image data”, paragraphs [0136], [0126));
transmitting the processed image data to a decoder (fig. 4);
operating the decoder to process the processed image data (“a compression mode of image data included in encoding data based on the encoding data, and a decoding module that reconstructs the image data from the encoding data according to the identified mode”, paragraph [0018]) to produce a restored image data (“decoding module restores a pixel value”, paragraph [0015]; “reconstructed image data”, Fig. 4); and
operating the lighting module to project a light pattern (“projector displays an image by projecting light”, paragraph [0077]) based on the restored image data (“reconstructing the image”, paragraphs [0013]; “reconstructs the image data from encoding data…decoding module restores a pixel value…”, [0015]) outside the automotive lighting arrangement (“inside or outside the electronic device 201”, paragraph [0077]).
As discussed above, Bae essentially discloses the claimed invention but does not graphically show a lighting module comprising a light source.
However, since Bae teaches that his invention can be used in an automotive head unit (paragraph [0046]), and a projector that projects light (paragraph [0077]), it would have been obvious to one of ordinary skill in the art at the time the invention was made to have included a light source in the automotive head unit of Bae to project light in order to provide light as disclosed in Bae.
As discussed above, Bae essentially discloses the claimed invention but does not literally disclose operating encoder in an automotive control unit and transmitting the processed image to a decoder in a lighting module.
However, Katsumata et al. (11,902,555) discloses operating encoder (32) in a data generation device as an automotive control unit (Fig. 40), transmitting the processed image to a decoder (53) in a data reproduction device as a lighting module (Fig. 41). Katsumata also discloses that his invention is used in an automobile control system (Col. 7, lines 9-21).
It would have been obvious to one of ordinary skill in the art to have located the encoder in a control unit and decoder in a lighting module in Bae to input a secure and compressed image from the encoder to project the light output after being decompressed by the decoder to order to reduce the size of the file and protect the file from being corrupted, crashed or stolen as taught by Katsumata.
Further, it has been held that rearranging the position of claimed element merely involves routine skill in the art. In re Japikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950) (Claims to a hydraulic power press which read on the prior art except with regard to the position of the starting switch were held unpatentable because shifting the position of the starting switch would not have modified the operation of the device.); In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975) (the particular placement of a contact in a conductivity measuring device was held to be an obvious matter of design choice).
As discussed above, Bae essentially discloses the claimed invention but does not explicitly disclose using encoder and decoder of a trained deep autoencoder to process image data.
However, Zadeh et al. (2014/0201126) discloses a deep autoencoder comprising decoder and encoder to produce data based on the features learned/captured at the coding layer (paragraph [1734]) and the autoencoder is determine by receiving training samples (paragraph [01743]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have provided a deep autoencoder comprising decoder and encoder to process and analyze image data in Bae in order to produce reliable information benefiting from deep learning algorithm and training samples as taught by Zadeh (paragraph [0094]).
As discussed above, Bae in view of Katsumata and Zadeh essentially discloses the claimed invention but does not explicitly disclose encoder including a convolution layer, a rectified linear unit layer, and normalization layer, and a decoder including an upsampling convolution layer, a rectified linear unit layer, and a normalization layer.
However, Ghafoorian et al. (2022/0277549) teaches a well-known segmentation network architecture (paragraphs [0106] to [0108]): an encoder-decoder structure with skip connections. The encoder consists of nine down-sampling blocks containing a convolution, batch normalization and ReLu. The decoder blocks are the same, except that the convolutions are replaced by transposed convolutions; and PSP-net, which utilises a pyramid pooling module to capture more contextual information.
It would have been obvious to one of ordinary skill in the art to have provided a well-known segmentation architecture: encoder comprising convolution block as convolution layer, batch normalization block as normalization layer, and a ReLu block as a rectified linear unit layer, and a decoder comprising a transposed convolution block as upsampling convolution layer, a batch normalization block as normalization layer, and a ReLu block as a rectified linear unit layer as taught by Ghafoorian in Bae in view of Katsumata and Zadeh in order to improve training stability, speed up convergence and enhance generalization.
Regarding Claim 8, Bae discloses the method according to claim 7, further comprising normalizing the image data (411 or 413) before operating the encoder block (415) to reduce its data size (fig. 4).
Regarding Claim 9, as discussed above, Bae essentially discloses the claimed invention but does not explicitly disclose the method according to claim 8, wherein normalizing the image data includes converting each value of the image data in a converted value between 0 and 1.
However, it would have been obvious and known to a skilled in the art that all processed data are converted between 0 and 1 in Bae since in digital world, all data are binary including Bae in order to compute the data in a computerized system. Bits and bytes are the base language of computer communications.
Regarding Claim 10, Bae discloses the method according to claim 7, further comprising dividing the image data in data subarrays of the same format (“The image data 610 includes a plurality of pixels belonging to different lines in the image”, “the image data 610 in FIG. 6 includes four pixels input, for example, in a 2x2pixel array form or a block form”, paragraph [0122]. Noted pixel as a subarray to form an array. They are same format as image data).
Claim(s) 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bae et al (2016/0173886) in view of Katsumata et al. (11,902,555) further in view of Munawar (2017/0076224), further in view of Ghafoorian et al. (2022/0277549).
Regarding Claim 7, Bae et al (2016/0173886) discloses a method for operating an automotive lighting arrangement (automotive head unit, paragraph [0046]), comprising:
operating an encoder (“encoding data based on the encoding data”, “encoding data as a pixel value of the image data”, paragraph [0015]), to reduce the data size of the image data (compression methods may reduce a size by 50%”, paragraphs [0136], [0126]; “compression modes, and encoding the image according to the selected mode”, abstract), producing processed image data (“image data”, paragraphs [0136], [0126));
transmitting the processed image data to a decoder (fig. 4);
operating the decoder to process the processed image data (“a compression mode of image data included in encoding data based on the encoding data, and a decoding module that reconstructs the image data from the encoding data according to the identified mode”, paragraph [0018]) to produce a restored image data (“decoding module restores a pixel value”, paragraph [0015]; “reconstructed image data”, Fig. 4); and
operating the lighting module to project a light pattern (“projector displays an image by projecting light”, paragraph [0077]) based on the restored image data (“reconstructing the image”, paragraphs [0013]; “reconstructs the image data from encoding data…decoding module restores a pixel value…”, [0015]) outside the automotive lighting arrangement (“inside or outside the electronic device 201”, paragraph [0077]).
As discussed above, Bae essentially discloses the claimed invention but does not graphically show a lighting module comprising a light source.
However, since Bae teaches that his invention can be used in an automotive head unit (paragraph [0046]), and a projector that projects light (paragraph [0077]), it would have been obvious to one of ordinary skill in the art at the time the invention was made to have included a light source in the automotive head unit of Bae to project light in order to provide light as disclosed in Bae.
As discussed above, Bae essentially discloses the claimed invention but does not literally disclose operating encoder in an automotive control unit and transmitting the processed image to a decoder in a lighting module.
However, Katsumata et al. (11,902,555) discloses operating encoder (32) in a data generation device as an automotive control unit (Fig. 40), transmitting the processed image to a decoder (53) in a data reproduction device as a lighting module (Fig. 41). Katsumata also discloses that his invention is used in an automobile control system (Col. 7, lines 9-21).
It would have been obvious to one of ordinary skill in the art to have located the encoder in a control unit and decoder in a lighting module in Bae to input a secure and compressed image from the encoder to project the light output after being decompressed by the decoder to order to reduce the size of the file and protect the file from being corrupted, crashed or stolen as taught by Katsumata.
Further, it has been held that rearranging the position of claimed element merely involves routine skill in the art. In re Japikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950) (Claims to a hydraulic power press which read on the prior art except with regard to the position of the starting switch were held unpatentable because shifting the position of the starting switch would not have modified the operation of the device.); In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975) (the particular placement of a contact in a conductivity measuring device was held to be an obvious matter of design choice).
As discussed above, Bae essentially discloses the claimed invention but does not explicitly disclose using encoder and decoder of a trained deep autoencoder to process image data.
However, Munawar (2017/0076224) teaches autoencoders are artificial neural networks that have been widely used for learning representations of input signals in many applications. If a hidden layer is narrower than an input layer, compression can be achieved by the autoencoder. In deep learning architecture, input signals can be transformed into feature space. Reconstructions or features obtained from the autoencoder can be used for classification tasks (paragraphs [0004], [0007]). The method comprises training the classification model based on the positive class data to adjust one or more parameters of the classification model (paragraph [0009]).
It would have been obvious to one of ordinary skill in the art at the time the invention was filed to have provided the widely used autoencoder to encode the image via encoder and reconstruct the image via decoder (fig. 1 of Munawar) in Bae in order to improve restored image output benefiting from deep learning and training as taught by Munawar.
As discussed above, Bae in view of Katsumata and Munawar essentially discloses the claimed invention but does not explicitly disclose encoder including a convolution layer, a rectified linear unit layer, and normalization layer, and a decoder including an upsampling convolution layer, a rectified linear unit layer, and a normalization layer.
However, Ghafoorian et al. (2022/0277549) teaches a well-known segmentation network architecture (paragraphs [0106] to [0108]): an encoder-decoder structure with skip connections. The encoder consists of nine down-sampling blocks containing a convolution, batch normalization and ReLu. The decoder blocks are the same, except that the convolutions are replaced by transposed convolutions; and PSP-net, which utilises a pyramid pooling module to capture more contextual information.
It would have been obvious to one of ordinary skill in the art to have provided a well-known segmentation architecture: encoder comprising convolution block as convolution layer, batch normalization block as normalization layer, and a ReLu block as a rectified linear unit layer, and a decoder comprising a transposed convolution block as upsampling convolution layer, a batch normalization block as normalization layer, and a ReLu block as a rectified linear unit layer as taught by Ghafoorian in Bae in view of Katsumata and Munawar in order to improve training stability, speed up convergence and enhance generalization.
Regarding Claim 8, Bae discloses the method according to claim 7, further comprising normalizing the image data (411 or 413) before operating the encoder block (415) to reduce its data size (fig. 4).
Regarding Claim 9, as discussed above, Bae essentially discloses the claimed invention but does not explicitly disclose the method according to claim 8, wherein normalizing the image data includes converting each value of the image data in a converted value between 0 and 1.
However, it would have been obvious and known to a skilled in the art that all processed data are converted between 0 and 1 in Bae since in digital world, all data are binary including Bae in order to compute data in a computerized system. Bits and bytes are the base language of computer communications.
Regarding Claim 10, Bae discloses the method according to claim 7, further comprising dividing the image data in data subarrays of the same format (“The image data 610 includes a plurality of pixels belonging to different lines in the image”, “the image data 610 in FIG. 6 includes four pixels input, for example, in a 2x2pixel array form or a block form”, paragraph [0122]. Noted pixel as a subarray to form an array. They are same format as image data).
Claim(s) 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (2020/0034948) in view of Katsumata et al. (11,902,555).
Regarding Claim 7, Park et al. (2020/0034948) discloses a method for operating lighting arrangement (display), comprising:
operating an encoder, to reduce the data size of image data, producing processed image data (“the input MR image is encoded sequentially as a feature map of reduced spatial dimension…through the encoder layers”, paragraph [0035], fig. 17) , with the encoder being part of a trained deep autoencoder (auto-encoder, Claim 1, train De-noising Auto-encoder, Figs. 7B, 9), with the encoder including a convolution layer (Convolution, paragraph [0149]), a rectified linear unit layer (ReLU, paragraph [0149]), and a normalization layer (BatchNorm, paragraph [0149]);
transmitting the processed image data to a decoder, in a lighting module (display) with the lighting module including at least one light source (pixel), with the decoder being part of the trained deep autoencoder (figs. 7B, 9), with the decoder includes an upsampling convolution layer (de-convolution, claim 2; Upsample, fig, 14), a rectified linear unit layer (ReLU, Claim 11), and a normalization layer (BatchNorm, Claim 11);
operating the decoder to process the processed image data to produce a restored image data (“the decoder layers recover spatial information and reconstruct the output sCT image”, paragraph [0035], fig. 17); and
operating the lighting module to project a light pattern (project image as the light pattern in Figs 1, 2) based on the restored image data (“the decoder layers recover spatial information and reconstruct the output sCT image”, paragraph [0035], fig. 17).
As discussed above, Park essentially discloses the claimed invention but does not explicitly disclose the module is outside the automotive lighting arrangement.
However, Park teaches that the method of his invention can be mounted on vehicle (paragraph [0177]) and implemented on autonomous vehicle decision-making models (paragraph [0178]).
It would have been obvious to one of ordinary skill in the art to have mounted Park’s invention with automotive or automobile in order to utilize its image processing on the vehicle as taught by Park.
As discussed above, Bae essentially discloses the claimed invention but does not literally disclose operating encoder in an automotive control unit and transmitting the processed image to a decoder in a lighting module, operating the lighting module outside the automotive lighting arrangement.
However, Katsumata et al. (11,902,555) discloses operating encoder (32) in a data generation device as an automotive control unit (Fig. 40), transmitting the processed image to a decoder (53) in a data reproduction device as a lighting module (Fig. 41). Katsumata also discloses that his invention is used in an automobile control system (Col. 7, lines 9-21).
It would have been obvious to one of ordinary skill in the art to have located the encoder in a control unit and decoder in a lighting module in Park to input a secure and compressed image from the encoder to project the light output after being decompressed by the decoder to order to reduce the size of the file and protect the file from being corrupted, crashed or stolen as taught by Katsumata.
Further, it has been held that rearranging the position of claimed element merely involves routine skill in the art. In re Japikse, 181 F.2d 1019, 86 USPQ 70 (CCPA 1950) (Claims to a hydraulic power press which read on the prior art except with regard to the position of the starting switch were held unpatentable because shifting the position of the starting switch would not have modified the operation of the device.); In re Kuhle, 526 F.2d 553, 188 USPQ 7 (CCPA 1975) (the particular placement of a contact in a conductivity measuring device was held to be an obvious matter of design choice).
Regarding Claim 8, Park discloses the method according to claim 7, further comprising normalizing the image data (image is normalized at the starting point, fig. 1) before operating the encoder block to reduce its data size (it is reduced to 2x2x512) (fig. 1).
Regarding Claim 9, as discussed above, Park essentially discloses the claimed invention but does not explicitly disclose the method according to claim 8, wherein normalizing the image data includes converting each value of the image data in a converted value between 0 and 1.
However, it would have been obvious and known to a skilled in the art that all processed data are converted between 0 and 1 in Park since, in digital world, all data are binary including Park in order to compute data in a computerized system. Bits and bytes are the base language of computer communications.
Regarding Claim 10, as discussed above, Park essentially discloses the claimed invention but does not literally disclose the method according to claim 7, further comprising dividing the image data in data subarrays of the same format.
However, Park discloses that the image is formed by a large plurality of pixels (Figs. 2 and 19). It would have been obvious one of ordinary skill in the art at the time the invention was filed to have recognized or considered each square divided in the image formed by the pixels (Figs, 2, 19) in Park as a subarray. Since all these subarrays formed by pixels are displayable in the same image, they are in the same format.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Wilson Lee whose telephone number is (571) 272-1824. Proposed amendment and interview agenda can be submitted to Examiner’s direct fax at (571) 273-1824.
If attempts to reach the examiner by telephone are unsuccessful, examiner’s supervisor, Alexander Taningco can be reached at (571) 272-8048. Papers related to the application may be submitted by facsimile transmission. Any transmission not to be considered an official response must be clearly marked "DRAFT". The official fax number is (571) 273-8300.
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/WILSON LEE/ Primary Examiner, Art Unit 2845
Prosecution Insights