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 .
The IDS filed 02 July 2024 has been received and considered.
Claims 1 – 6 are pending.
Claims 1 – 6, all of the claims pending in this application, have been rejected.
Specification
The title of the invention has been objected to for the reasons that follow:
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed. Loss in title brevity is “more than offset by the gain in its informative value in indexing, classifying, searching, etc.”, MPEP 606.01.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 – 6 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the claim language “based on first feature quantity data and first control data”. It is unclear as to what the first feature quantity data is supposed to be or what it is supposed to represent. The specification fails to further define this language. Therefore, claim 1 has been rejected. Claims 2 – 6 are also rejected by virtue of their dependency on claim 1.
Claim 1 recites the claim language “second feature quantity data”. It is unclear as to what the second feature quantity data is supposed to be or what it is supposed to represent. The specification fails to further define this language.
Claim 2 recites the claim language “based on third feature quantity data comprising the captured image data”. It is unclear as to where this data (third) was derived from or what it is actually supposed to comprise or represent. Furthermore, it is unclear as to how you can generate a feature quantity data (fourth) which comprises data that corresponds to a feature quantity data (third) that has yet to be generated. Also, since it is not clearly defined as to what the third feature quantity data is, it is unclear as to what the fourth feature quantity data is supposed to be or represent.
Claim 2 recites the claim language “the pieces of feature quantity data comprise feature quantity data corresponding to the fourth feature quantity data.”. It is unclear as to what the pieces of feature quantity data are referring to or what that data is supposed to represent and where it came from/corresponds to. Furthermore, it is unclear how the pieces of feature quantity data (which the Examiner is interpreting as possibly being the fourth quantity data that was just generated) comprises feature quantity data corresponding to the fourth feature quantity data. Simply put, it is unclear as to how data is generated that comprises data that corresponds to the data from which was just generated.
Claim 4 recites the claim language “generate second control data”. It is unclear as to what the second control data is or what it is supposed to represent. The specification fails to further define the second control data as well.
Claim 4 recites the claim language “performing a rule-based process”. It is unclear as to what the rule-based process is or what it is supposed to represent. Furthermore, it is unclear as to what is happening in this rule-based process (which the Examiner is interpreting as a function or algorithm) that causes the second control data to be generated.
Claims 5 and 6 have similar claim language as claim 4 and are therefore rejected for the same reasons as applied to claim 4.
Claim 6 recites the claim language “performing a fourth convolution process on the first control data, based on the first control data.”. Similar situation to that of claim 2, it is unclear as to how data is generated by performing a process on the first control data…based on first control data.
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 1 – 6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 fails to produce a real-world application and has been rejected, accordingly. Claims 2 – 6 fail to cure this deficiency and are hereby rejected along with also being rejected as being dependent on independent claim 1.
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 1 is rejected (AS BEST UNDERSTOOD) under 35 U.S.C. 103 as being unpatentable over US Publication No. 2024/0282085 A1 to KONDO in view of US Publication No. 2025/0014198 A1 to Park et al. (hereinafter Park).
Claim 1
Regarding claim 1, KONDO teaches an image processing apparatus comprising: an encoder comprising a first convolution processor (Figures. 3A and 3B, "EC"), the first convolution processor being configured to generate, based on first feature quantity data and first control data, second feature quantity data by performing a first convolution process on the first feature quantity data ("The machine learning model DN is an image recognition model that generates output data indicating the results of image recognition. The machine learning model DN includes an encoder EC, and a classifier fc. The encoder EC performs dimensionality reduction on the input image data ID to extract features of the image represented by the inputted image data ID. The encoder EC is a Convolutional Neural Network (CNN) that contains N convolutional layers conv1-convN (where N is an integer greater than or equal to two). Each convolutional layer performs convolution using a filter of a predetermined size to generate feature maps. The output values of each convolution are values transformed by inputting the input image data ID to which biases are added into a prescribed activation function. The feature maps outputted from each convolutional layer are inputted into the next layer (another convolutional layer or a fully-connected layer of the classifier fc).", Paragraph [0038]), wherein the output from the first convolutional layer would be the second feature quantity data.
PNG
media_image1.png
351
526
media_image1.png
Greyscale
Although KONDO teaches the first feature quantity data comprising captured image data (KONDO, Paragraphs [0038]; [0075 – 0079]), KONDO does not explicitly teach the remaining language of that limitation being “and depth image data”.
Furthermore, KONDO does not teach the depth image data comprising map data on a depth value of a subject corresponding to the captured image data;
the first control data comprising map data on validity of the depth value corresponding to the depth image data.
However, Park teaches the first feature quantity data comprising captured image data and depth image data ("Particularly, the processor 200 according to an example of the present disclosure may train the deep learning model such that the results of learning an original image obtained by the camera 110 and partial images obtained by masking a portion of the original image are similar to each other, thus more accurately obtaining a depth value. To this end, the processor 200 may obtain a first depth value based on learning the image using the deep learning model. Furthermore, the processor 200 may obtain a partial image by masking a partial region of the image and may obtain a second depth value based on learning the partial image. Furthermore, the processor 200 may train the deep learning model to reduce a deviation between the first depth value and the second depth value.", Paragraph [0049]; "According to an example, the processor may learn training data expanded based on dimension conversion of the image or gradation conversion of the image to obtain the first depth value.", Paragraph [0152]), where the dimension or gradation conversion of the image is the creation of a depth image (or depth image data) for obtaining a depth value.
the depth image data comprising map data on a depth value of a subject corresponding to the captured image data (Rejected as applied directly above).
the first control data comprising map data on validity of the depth value corresponding to the depth image data (Also rejected as applied directly above)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of KONDO to incorporate feature quantity data further including depth data (depth image and values), as disclosed by Park. The suggestion/motivation for doing so would have been to not only to apply filters to the image to detect various objects/subjects, but to also be able to calculate a distance to said object and using the depth data to determine if the distance calculated is accurate.
KONDO, in view of Park, further teaches the encoder being configured to generate pieces of feature quantity data comprising feature quantity data corresponding to the second feature quantity data (Rejected as applied above, specifically KONDO, Paragraph [0038]), wherein the output of a convolutional layer is input to a next convolutional layer and there are N layers; and
a decoder configured to generate, based on the pieces of feature quantity data, an inference result of an environment around an imager that has generated the captured image data ("As in the first embodiment, the encoder ECb is a CNN that includes a plurality of convolutional layers, for example. The feature maps fm outputted from the encoder ECb, i.e., the feature maps fm produced by the last convolutional layer are inputted into the decoder DC. The decoder DC performs dimensional restoration on the feature maps fm to generate output image data ODb (see FIG. 11). The decoder DC includes a plurality of up-convolutional layers not illustrated in the drawing. Each up-convolutional layer performs up-convolution using a filter having a prescribed size. The calculated values of each up-convolution are values transformed by inputting the input image data ID to which biases are added into a prescribed activation function. A well-known function such as ReLU described above is used as the activation function in the present embodiment. The output image data ODb is RGB image data having the same size as the input image data ID, for example.", Paragraph [0099]).
Examiner notes that Park also teaches the image data being that of monitored environments outside of a vehicle (sensors), Paragraphs [0003 -0005] which corresponds to Figure 2.
Claims 3 – 6 are rejected (AS BEST UNDERSTOOD) under 35 U.S.C. 103 as being unpatentable over US Publication No. 2024/0282085 A1 to KONDO in view of US Publication No. 2025/0014198 A1 to Park et al. (hereinafter Park) in further view of Non-Patent Literature “Convolutional Feature Masking for Joint Object and Stuff Segmentation” to Dai et al. (hereinafter Dai).
Claim 3
Regarding claim 3, dependent on claim 1, KONDO, in view of Park, teaches the invention as claimed in claim 1.
Neither KONDO, or Park, or the combination explicitly teach wherein the first convolution processor is configured to: generate multiplication data by multiplying a value in the first feature quantity data and a value in the first control data at coordinate positions corresponding to each other; and
generate the second feature quantity data by performing the first convolution process, based on the multiplication data.
However, Dai teaches wherein the first convolution processor is configured to: generate multiplication data by multiplying a value in the first feature quantity data and a value in the first control data at coordinate positions corresponding to each other ("On the feature map, each position will collect multiple pixels projected from a binary mask. These binary values are then averaged and thresholded (by 0.5). This gives us a mask on the feature maps (Figure 2). This mask is then applied on the convolutional feature maps. Actually, we only need to multiply this bi nary mask on each channel of the feature maps. We call the resulting features as segment features in our method.", Section 2.1. Convolutional Feature Masking Layer), wherein there would already be an assigned coordinate system which would enable the feature layers to align with other data sets (mask, depth, ect), especially if acquired by multiple sensors; and
generate the second feature quantity data by performing the first convolution process, based on the multiplication data (Figure 2), wherein a CFM (Convolutional Feature Masking) layer is generated.
PNG
media_image2.png
329
287
media_image2.png
Greyscale
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the teachings of KONDO, in view of Park, to incorporate multiplying values of the image data and depth data and using the newly calculated values to generate a second feature quantity data, as disclosed by Dai. The suggestion/motivation for doing so would have been to produce multiple segments for more accurate feature extraction of objects present in the image.
Claim 4
Regarding claim 4, dependent on claim 3, KONDO, in view of Park and Dai, teaches the invention as claimed in claim 3.
Neither KONDO, or Park, or the combination teach wherein the first convolution processor is configured to: generate second control data by performing a rule-based process, based on the first control data; and
output the second feature quantity data and the second control data.
However, Dai further teaches wherein the first convolution processor is configured to: generate second control data by performing a rule-based process, based on the first control data (As best understood, Rejected as applied to claim 3), wherein the Examiner is interpreting a rule-based process as simply being parameters or mathematical functions used in the convolution process; and
output the second feature quantity data and the second control data (As best understood, Rejected as applied to claim 3).
Claim 5
Regarding claim 5, dependent on claim 3, KONDO, in view of Park and Dai, teaches the invention as claimed in claim 3.
Neither KONDO, or Park, or the combination teach wherein the first convolution processor is configured to: generate second control data by performing a third convolution process, based on the multiplication data; and
output the second feature quantity data and the second control data.
However, Dai further teaches wherein the first convolution processor is configured to: generate second control data by performing a third convolution process, based on the multiplication data (As best understood, Rejected as applied to claim 3); and
output the second feature quantity data and the second control data (As best understood, Rejected as applied to claim 3).
Claim 6
Regarding claim 6, dependent on claim 3, KONDO, in view of Park and Dai, teaches the invention as claimed in claim 3.
Neither KONDO, or Park, or the combination teach wherein the first convolution processor is configured to: generate second control data by performing a fourth convolution process on the first control data, based on the first control data; and
output the second feature quantity data and the second control data.
However, Dai further teaches wherein the first convolution processor is configured to: generate second control data by performing a fourth convolution process on the first control data, based on the first control data (As best understood, Rejected as applied to claim 3); and
output the second feature quantity data and the second control data (As best understood, Rejected as applied to claim 3).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ronde Miller whose telephone number is (703) 756-5686 The examiner can normally be reached Monday-Friday 8:00-4:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Gregory Morse can be reached on (571) 272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RONDE LEE MILLER/Examiner, Art Unit 2663
/GREGORY A MORSE/ Supervisory Patent Examiner, Art Unit 2698
Prosecution Insights