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 .
Claim Status
This action is in response to the application filed on June 03, 2026. Claims 1, 11, and 20 are amended. Claims 2, 12, and 21 are cancelled. Claim 23 is added. Thus, claims 1, 3-11, 13-20, and 22-23 are pending for examination in this application.
Response to Amendments
Applicant’s arguments regarding the 35 U.S.C. 101 rejections previously set forth in the Non-Final Office Action mailed March 09, 2026, are persuasive. Accordingly, the 35 U.S.C. 101 rejections are withdrawn in response.
Response to Arguments
Applicant’s arguments filed June 03, 2026, regarding the rejection(s) of claim(s) 1, 3-11, 13-20, and 22 have been fully and completely considered but are moot because the arguments do not apply to the new combination of the references, facilitated by Applicant’s newly submitted amendments, including new prior art— BECKMANN et al. (WO 2011129816 A1)—being used in the current rejection.
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.
Claim(s) 1, 3, 5-6, 11, 13, 15-16, 20 and 22-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rajesh et al. (T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network), hereinafter referred to as RAJESH, in view of Pan et al. (CN 113709494 A), hereinafter referred to as PAN, in view of Yang et al. (CN 108802785 B) hereinafter referred to as YANG, in view of BECKMANN et al. (WO 2011129816 A1) hereinafter referred to as BECKMANN, and further in view of ISENMANN et al. (US 20150365683 A1) hereinafter referred to as ISENMANN.
Regarding claim 1, RAJESH discloses
receive an original image from an image sensor ([pg. 3, 2.1 JPEG Compression] Firstly, the RGB channels of the image {where the image is the received image}; [pg. 2, Fig. 1] shows a source image),
and compress the original image, wherein the original image is compressed by causing the apparatus to ([pg. 3, 1. Introduction, The first GAN model is trained directly with JPEG compressed DCT images to generate compressed images from text description]):
divide the original image into subdivisions of the original image ([pg. 4, 2.1 JPEG Compression] Then each channel is divided into 8x8 non-overlapping pixel blocks. [pg. 2, Fig. 1] shows a source image divided into 8x8 blocks), wherein the subdivisions of the original image are a predefined pixel width by a predefined pixel height ([pg. 4, 2.1 JPEG Compression] Then each channel is divided into 8x8 non-overlapping pixel blocks. {where the predefined width and height is 8 pixels});
apply a transformation to the subdivisions of the original image ([pg. 4, 2.1 JPEG Compression] Forward Discrete Cosine Transform (DCT) is applied on each block in each channel to convert the 8x8 pixel block (let's say P(x; y)) from spatial domain to frequency domain.; [pg. 2, Fig. 1] shows forward DCT);
convert the subdivisions of the original image into a frequency domain ([pg. 4, 2.1 JPEG Compression] Forward Discrete Cosine Transform (DCT) is applied on each block in each channel to convert the 8x8 pixel block (let's say P(x; y)) from spatial domain to frequency domain.; [pg. 2, Fig. 1] shows forward DCT);
establish a low-frequency component for the subdivisions ([pg. 4, 2.1 JPEG Compression] Each DCT block, i.e., F(u; v), is quantized to keep only the low frequency coefficients.; [pg. 2, Fig. 1] shows quantization);
store the low-frequency component as a value for the subdivisions of the original image as a compressed image file ([pg. 2 Fig. 1] shows the low-frequency components {which were the outcome of the quantization step above} are stored as “Compressed Image Data”).
RAJESH does not explicitly state
at least one processor and at least one memory including computer program code, the at least one memory and computer program code.
However, PAN teaches the aspects of the apparatus comprising
at least one processor ([pg. 5, last paragraph] the processor calls the executable program code stored in the memory) and
at least one memory including computer program code ([pg. 5, last paragraph] the processor calls the executable program code stored in the memory), the at least one memory and computer program code configured to, with the processor, cause the apparatus to at least:
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH’s store of low-frequency components with the teaching of PAN’s an apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the processor, cause the apparatus to at least. The motivation to combine the teachings of RAJESH and PAN is because both references teach methods of image compression and reconstruction, where PAN’s addition enhances RAJESH’s by providing a means to complete the proposed methodology (PAN [pg. 5]).
RAJESH in view of PAN does not explicitly state
the image corresponding to a geographical location.
based at least in part on image detection, wherein the image information of relatively higher importance than a majority of the original image comprises at least one sign within the image information, wherein the at least one sign is identified through image detection;
However, YANG teaches
the image corresponding to a geographical location ([pg. 4, step 4] the monocular vision module transmits the collected road original information to the image processing module).
based at least in part on image detection, wherein the image information of relatively higher importance than a majority of the original image comprises at least one sign within the image information, wherein the at least one sign is identified through image detection (see Yang pg. 5, (4.1) by the machine learning method, each pixel of the image is classified. In one example, through the PSPnet network, the city typical data set for training the network, the network calculates the probability of each pixel belonging to a certain semantic type, and outputting the maximum probability of semantics. As shown in FIG. 4, semantic classification with lane line, traffic sign board, traffic light, traffic lamp, tree, street lamp and so on. The result of the pixel-level semantic classification is shown in FIG. 5, wherein 1, 2 is a street lamp post, 3 is a traffic lamp post, 4, 5, 6 is lane line, 7, 9 is traffic light, 8 traffic sign board.);
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN’s apparatus to store the low-frequency components with the teaching of YANG’s the image corresponding to a geographical location and calculating the probability of pixel belonging to a certain semantic type. The motivation to combine the teachings of RAJESH in view of PAN and YANG is because all references teach image analysis where YANG ties in the image analysis for (semi-) automated driving using geographical information. YANG enhances RAJESH in view of PAN’s apparatus by satisfying the intelligent vehicle high precision positioning requirement, reducing the cost of the positioning system, and improving the robustness of the vehicle positioning in the urban dynamic change scene (YANG [pg. 2 Contents of the Invention]).
RAJESH in view of PAN and further in view of YANG does not explicitly state wherein the apparatus is further caused to:
identify automatically, within the original image, image information of relatively higher importance than a majority of the original image using a machine learning model,
However, BECKMANN teaches
identify automatically, within the original image, image information of relatively higher importance than a majority of the original image based at least in part on image detection using a machine learning model ([Paragraph 0017] In some embodiments, a human expert can initially specify important ("high utility") regions in data and features that make the regions important, and then pattern matching algorithms can identify the regions automatically in data received from sensors. In some embodiments, a human expert can specify the high utility regions, and statistical and machine learning techniques can identify relative features that make the regions important, and then pattern matching algorithms can identify the regions automatically in data received from sensors.),
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN and further in view of YANG’s apparatus with the teaching of BECKMANN’s wherein the apparatus is further caused to: identify automatically, within the original image, image information of relatively higher importance than a majority of the original image based at least in part on image detection using a machine learning model. The motivation to combine the teachings of RAJESH in view of PAN and further in view of YANG and BECKMANN is because the references teach image compression and storage. BECKMANN enhances the apparatus because the image is compressed by regions that are identified as important (“high utility”) regions in data and therefore, identifying an optimal compression technique for each region (BECKMANN [Paragraph [0017 and 0018]).
RAJESH in view of PAN in view of YANG and in further view of BECKMANN does not explicitly state wherein the apparatus is further caused to:
and store the image information of relatively higher importance than the majority of the original image with the compressed image file;
However, ISENMANN teaches wherein the apparatus is further caused to:
and store the image information of relatively higher importance than the majority of the original image with the compressed image file ([Paragraph 0009] Different resolutions are therefore used in order to store important and less important image regions. As a result, the important regions of the image can remain very detailed in a targeted manner.);
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN in view of YANG and further in view of BECKMANN’s method with the teaching of ISENMANN’s wherein the apparatus is further caused to: and store the image information of relatively higher importance than the majority of the original image with the compressed image file. The motivation to combine the teachings of RAJESH in view of PAN in view of YANG and further in view of BECKMANN and ISENMANN is because the references teach image compression and storage. ISENMANN enhances the apparatus because the image is compressed by regions of increased user interest are stored at a high resolution and other "less important regions" are being stored at a lower resolution and therefore saving storage space (ISENMANN [Paragraphs 0010 and 0011]).
Regarding claim 3, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN teaches the apparatus of claim 1,
wherein causing the apparatus to identify, within the original image, the image information of relatively higher importance than the majority of the original image comprises causing the apparatus to:
identify one or more bounding boxes within the original image, the image information of relatively higher importance than the majority of the original image being contained within the one or more bounding boxes (ISENMANN [Paragraph 0010] A key aspect of the invention is that the digital image is compressed by regions of increased user interest (“important regions”) being stored at a high resolution and other regions (“less important regions”) being stored at a lower resolution. {where the important regions or regions of interest are interpreted as being bounded regions}).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN in view of YANG and in further view of BECKMANN’s method with the teaching of ISENMANN’s identify one or more bounding boxes within the original image, the image information of relatively higher importance than the majority of the original image being contained within the one or more bounding boxes. The motivation to combine the teachings of RAJESH in view of PAN in view of YANG and in further view of BECKMANN and ISENMANN is because the references teach image compression and storage. ISENMANN enhances the apparatus because the image is compressed by regions of increased user interest are stored at a high resolution and other "less important regions" are being stored at a lower resolution and therefore saving storage space (ISENMANN [Paragraphs 0010 and 0011]).
Regarding claim 5, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN further teaches the apparatus of claim 1, wherein the predefined pixel width is eight pixels and the predefined pixel height is eight pixels, wherein the subdivisions of the original image are eight- by-eight pixel blocks (RAJESH [pg. 4, 2.1 JPEG Compression] Then each channel is divided into 8x8 non-overlapping pixel blocks. {where the predefined width and height is 8 pixels}).
Regarding claim 6, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN further teaches the apparatus of claim 5, wherein causing the apparatus to apply the transformation to the subdivisions of the original image comprises causing the apparatus to apply a Discrete Cosine Transformation to the subdivisions of the original image to convert values of each subdivision to a frequency domain (RAJESH [pg. 4, 2.1 JPEG Compression] Forward Discrete Cosine Transform (DCT) is applied on each block in each channel to convert the 8x8 pixel block (let's say P(x; y)) from spatial domain to frequency domain.; [pg. 2, Fig. 1] shows forward DCT).
As per claim 11, Claim 11 claims a method comprising the same limitations as Claim 1. Therefore, the rejection and rationale are analogous to that made in Claim 1.
As per claim 13, Claim 13 claims the same limitations as Claim 3 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 3.
As per claim 15, Claim 15 claims the same limitations as Claim 5 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 5.
As per claim 16, Claim 16 claims the same limitations as Claim 6 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 6.
As per claim 20, Claim 20 claims a computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program storage code instructions stored therein, the computer-executable program code instructions comprising program code instructions to perform the same limitations as Claim 1. Therefore, the rejection and rationale are analogous to that made in Claim 1.
PAN further teaches, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein (PAN [pg. 14, Embodiment 6] The embodiment of the invention claims a computer program product, the computer program product comprises a non-transitory computer-readable storage medium storing a computer program), the computer-executable program code instructions comprising program code instructions to:
ISENMANN further teaches, generate a compressed image file (see ISENMANN Paragraph [0049], “The image can now be stored in a certain file format in compressed form. A distinction is made in the process between lossless and lossy compression methods.”)
Regarding Claim 22, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN further teaches the aspects of the apparatus of claim 1, wherein causing the apparatus to identify image information of relatively high importance than a majority of the original image based at least in part on image detection comprises causing the apparatus to:
YANG further teaches identify, using a trained machine learning model, image information of relatively higher importance than a majority of the original image including a sign ((Section 4.1) by the machine learning method, each pixel of the image is classified. In one example, through the PSPnet network, the city typical data set for training the network, the network calculates the probability of each pixel belonging to a certain semantic type, and outputting the maximum probability of semantics. As shown in FIG. 4, semantic classification with lane line, traffic sign board, traffic light, traffic lamp, tree, street lamp and so on. The result of the pixel-level semantic classification is shown in FIG. 5, wherein 1, 2 is a street lamp post, 3 is a traffic lamp post, 4, 5, 6 is lane line, 7, 9 is traffic light, 8 traffic sign board.);
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to further add the limitations taught by YANG’s wherein the original image from the image sensor is captured along a road of a first functional class at the geographical location, wherein the machine learning model is trained using image data from a geographic region within a predetermined degree of similarity to the geographical location and captured along road segments of the first functional class. The motivation to further include the teachings of YANG is because the network calculates the probability of each pixel belonging to a certain semantic type, and outputting the maximum probability of semantics, where semantic classification includes lane line, traffic sign board, traffic light, traffic lamp, tree, street lamp and so on (YANG [pg. 5, Step 4.1]).
ISENMANN further teaches and specify the image information of relatively higher importance using a bounding box ([Paragraph 0010] A key aspect of the invention is that the digital image is compressed by regions of increased user interest (“important regions”) being stored at a high resolution and other regions (“less important regions”) being stored at a lower resolution. {where the important regions or regions of interest are interpreted as being bounded regions}).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN in view of YANG in view of BECKMANN’s method with the teaching of ISENMANN’s identifying one or more bounding boxes within the original image, the image information of relatively higher importance than the majority of the original image being contained within the one or more bounding boxes. The motivation to combine the teachings of RAJESH in view of PAN in view of YANG in view of BECKMANN and ISENMANN is because the references teach image compression and storage. ISENMANN enhances the apparatus because the image is compressed by regions of increased user interest are stored at a high resolution and other "less important regions" are being stored at a lower resolution and therefore saving storage space (ISENMANN [Paragraphs 0010 and 0011]).
Regarding Claim 23, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN further teaches the apparatus of claim 1, further comprising: causing the compressed image file to be reconstructed together with the image information of relatively higher importance than the majority of the original image (RAJESH [pg. 2, 1. Introduction, Fig. 1, JPEG Compression and Decompression architecture and extraction of JPEG
Compressed DCT image which is used in the proposed approach. {RAJESH teaches a reconstructed image as shown in Fig. 1, therefore, it is obvious image reconstruction is included in decompression of an image and the combination of references teaches storing image information of relatively higher importance which is used in the image reconstruction}]).
Claims 4 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Rajesh et al. (T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network), hereinafter referred to as RAJESH, in view of Pan et al. (CN 113709494 A), hereinafter referred to as PAN, in view of Yang et al. (CN 108802785 B), hereinafter referred to as YANG, in view of BECKMANN et al. (WO 2011129816 A1) hereinafter referred to as BECKMANN, in view of ISENMANN et al. (US 20150365683 A1), hereinafter referred to as ISENMANN, and further in view of Jamali et al. (Robust Watermarking in Non-ROI of Medical Images Based on DCT-DWT), hereinafter referred to as JAMALI.
Regarding claims 4, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN further teaches the apparatus of claim 2, wherein causing the apparatus to apply the transformation to the subdivisions of the original image comprises causing the apparatus to selectively apply a Discrete Cosine Transformation to each subdivision of the original image to convert values of each subdivision to a frequency domain ([pg. 4, 2.1 JPEG Compression] Forward Discrete Cosine Transform (DCT) is applied on each block in each channel to convert the 8x8 pixel block (let's say P(x; y)) from spatial domain to frequency domain.; [pg. 2, Fig. 1] shows forward DCT)
RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN does not explicitly state
not including the image information of relatively higher importance than a majority of the original image.
However, JAMALI teaches
not including the image information of relatively higher importance than a majority of the original image ([pg. 1201, A. ROI Region Extraction Using Saliency Detection] This phase can be considered as a preprocessing stage where we employ a saliency detection method to extract important part of an image. [B. Embedding Scheme] Embedding watermark into whole image can effect on quality of image more than hiding it into some blocks of the image. Also important parts of an image remain intact based on the above mentioned block selection method. In the embedding phase, after transforming the selected blocks into the wavelet domain, blocks of horizontal, vertical and diagonal coefficients are DCT transformed.).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN in view of YANG in view of BECKMANN and in view of ISENMANN’s apparatus with the teaching of JAMALI’s not including the image information of relatively higher importance than a majority of the original image. The motivation to combine the teachings of RAJESH in view of PAN in view of YANG in view of BECKMANN and in further view of ISENMANN and JAMALI is because the references use DCT, where JAMALI adds region of interest extraction and image analysis is performed according to how important information is, such as medical information in medical imaging. JAMALI enhances the apparatus because ROIs are very important in medical images and special attention must be paid to these parts to keep the content intact (JAMALI [pg. 1201, II. Proposed Method]).
As per claim 14, Claim 14 claims the same limitations as Claim 4 and is dependent on a similarly rejected independent claim. Therefore, the rejection and rationale are analogous to that made in Claim 4.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Rajesh et al. (T2CI-GAN: Text to Compressed Image generation using Generative Adversarial Network), hereinafter referred to as RAJESH in view of Pan et al. (CN 113709494 A), hereinafter referred to as PAN in view of Yang et al. (CN 108802785 B) hereinafter referred to as YANG, in view of BECKMANN et al. (WO 2011129816 A1) hereinafter referred to as BECKMANN, in view of ISENMANN et al. (US 20150365683 A1), hereinafter referred to as ISENMANN, and further in view of KURNIAWAN et al. (Implementation of Image Compression Using Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT)), hereinafter referred to as KURNIAWAN.
Regarding claim 7, RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN teaches the apparatus of claim 1,
RAJESH further teaches wherein a value for each pixel of the Portable Graphics Format image file comprises a value corresponding to the low-frequency component for a corresponding subdivision of the original image ([pg. 4, 2.1 JPEG Compression] Then each channel is divided into 8x8 non-overlapping pixel blocks. Forward Discrete Cosine Transform (DCT) is applied on each block in each channel to convert the 8 x 8 pixel block (let's say P(x; y)) from spatial domain to frequency domain. Each DCT block, i.e., F(u; v), is quantized to keep only the low frequency coefficients.).
RAJESH in view of PAN in view of YANG in view of BECKMANN and further in view of ISENMANN does not explicitly state
wherein the compressed image file comprises a Portable Graphics Format image file.
However, KURNIAWAN teaches
wherein the compressed image file comprises a Portable Graphics Format image file ([pg. 13952, Lossless and Lossy Compression] The images in file formats like .png and .gif must be in lossless compression formats.).
Therefore, it would have been obvious to persons of ordinary skill in the art, before the effective filing date of the claimed invention, to combine the teaching of RAJESH in view of PAN in view of YANG in view of BECKMANN and in further view of ISENMANN’s apparatus with the teaching of KURNIAWAN’s wherein the compressed image file comprises a Portable Graphics Format image file. The motivation to combine the teachings of RAJESH in view of PAN in view of YANG in view of BECKMANN and in further view of ISENMANN and KURNIAWAN is because the references teach image compression using DCT where KURNIAWAN enhances the apparatus by using a Portable Graphics Format, which uses lossless compression and thereby overcomes the downfall of lossy compression where images cannot be reconstructed due to degradation in the data (KURNIAWAN [pg. 13952, Lossless and Lossy Compression]).
Allowable Subject Matter
Claims 8-10 and 17-19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any 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 DOMINIQUE JAMES whose telephone number is (703)756-1655. The examiner can normally be reached 9:00 am - 6:00 pm EST.
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/DOMINIQUE JAMES/Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666
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