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 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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
In regarding claim 1
Step 1:
Claim 1 is directed towards a process, machine, manufacture or composition of matter which is/are statutory subject matter.
Step 2A:
Claim 1 is directed to a method/system/manufacture executed by one or more processor for a coin identification method. The methods involve in acquiring a coin picture provided by a user; identifying the coin picture to acquire a coin region, and performing segmentation to acquire a coin region picture; identifying the coin region picture to acquire a plurality of feature regions; identifying the plurality of feature regions to acquire a plurality of coin feature information; synthesizing the plurality of coin feature information to acquire classification information of a coin.
Prong 1:The limitations listed below covers performance of the limitation that could be carried out in mental processes.
acquiring a coin picture provided by a user:
Involves in acquiring coin data from the user which is data gathering step that doesn’t add anything beyond the abstract idea of processing information.identifying the coin picture to acquire a coin region, and performing segmentation to acquire a coin region picture:
This limitation could be performed mentally because a person could look at a coin picture and generate a coin region and perform segmentation mentally to acquire a coin region picture. Hence, this operation could be performed mentally, implying that the claim is directed to a mental process.
identifying the coin region picture to acquire a plurality of feature regions:
This limitation could be performed mentally because a person could look at a coin picture and identify the coin region picture to acquire a plurality of feature regions. Hence, this operation could be performed mentally, implying that the claim is directed to a mental process.
identifying the plurality of feature regions to acquire a plurality of coin feature information:
This limitation could be performed mentally because a person could look at a coin picture and identify the plurality of feature regions to acquire a plurality of coin feature information. Hence, this operation could be performed mentally, implying that the claim is directed to a mental process.synthesizing the plurality of coin feature information to acquire classification information of a coin:
This limitation could be performed mentally because a person could look at a coin picture to extract feature information from each feature region and synthesize the plurality of coin feature information to acquire classification information of a coin. Hence, this operation could be performed mentally, implying that the claim is directed to a mental process.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by a mental process, then it falls within the “Mental process” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Prong 2:
This judicial exception is not integrated into a practical application. In particular, the claims only recite additional elements – computer implemented method (claim 1), a non-transitory computer-readable medium (claim 14), an apparatus (claim 13), are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computing component / software application.
Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim(s) is directed to an abstract idea.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception such as improvements to another technology or technical field, or other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment.
For a human, he/she will look at a coin picture and predict classification information of a coin. The recitation of use of memory and processor to execute instruction doesn’t disclose any inventive concept since it merely invokes the use of generic computer to perform tasks that could otherwise be performed mentally.
Thus, the recited generic additional hardware/software (e.g., processor, machine-readable medium) perform no more than their basic computer function. In the court of Alice Corp. v. CLS Bank Intl, the court cites a “data processing system” with a “communications controller” and “data storage unit,” for example, —is purely functional and generic (page 16). In the specification of instant application, processor, machine-readable medium are general computer components ([0023] in publication). Generic computer-implementation of a method is not a meaningful limitation that alone can amount to significantly more than an abstract idea. Moreover, when viewed as a whole with such additional element considered as an ordered combination, claims modified by adding a generic computer are nothing more than a purely conventional computerized implementation of an idea in the general field of computer processing and do not provide significantly more than an abstract idea.
Consequently, the identified additional elements taken into consideration individually or in combination fails to amount of significantly more than the abstract idea above.
Regarding 2-14, these are dependent claims that recite limitations that extend the mental processes of their respective base claim and therefore also fall under mental steps as explained in their base claim.
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 1-4, 10 and 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (Chinese Patent Pub. No.: CN110610575A) hereinafter Zhang, in view of Chen (Chinese Patent Publication Number: CN 113538363 A), hereinafter Chen, further in view of Ma (Chinese Patent Pub. No.: CN107330359A), hereinafter Ma.
Regarding claim 1, Zhang teaches a coin identification method (Coin identification method and device and cash register. Title), characterized in comprising: acquiring a coin picture provided by a user (Step S101: Extract the image block corresponding to each coin from the image to be identified, which includes the coins, to form the input coin set. [0083]); identifying the coin picture to acquire a coin region (The detection module is also used to input the image to be identified into the coin detection model, detect the positions of multiple detection boxes and their confidence scores of belonging to coins; obtain the positions of n detection boxes belonging to coins based on the confidence scores of the multiple detection boxes; and extract n image patches from the image to be identified based on the positions of the n detection boxes. [0056]); identifying the coin region picture to acquire a plurality of feature (The appearance feature acquisition module is used to acquire the appearance features of coins of each denomination in the first coin set; [0047]); identifying the plurality of feature to acquire a plurality of coin feature information (The appearance feature acquisition module is used to acquire the appearance features of coins of each denomination in the first coin set; [0047]); synthesizing the plurality of coin feature information to acquire classification information of a coin (The determination module 503 is used to classify the image blocks of coins whose classification confidence reaches a threshold in the input coin set into the first coin set; and to use the face value indicated by the category of each image block of the first coin set as the final face value of the coin. [0142]).
Zhang does not teach the following limitations as further recited, but Chen further teaches performing segmentation (Lung medical image segmentation method and device based on improved U-Net. Title) to acquire a region picture (compared with the prior art, the invention has the beneficial effects that: the lung image segmentation optimization algorithm provided by the invention can effectively improve the image detail quality, the boundary between two lungs, a tiny cavity and peripheral blood vessels of all levels of bronchus in the lung are more accurate. Page 4 4th paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang to incorporate the teachings of Chen to perform segmentation to acquire a coin region picture in order to improve the image detail quality.
The combination of Zhang and Chen does not teach the following limitations as further recited, but Ma further teaches identifying the coin region picture to acquire a plurality of feature regions (Step S110: Obtain a face image and divide the face image into a first sub-region, a second sub-region, and a third sub-region. [0066]); identifying the plurality of feature regions to acquire a plurality of coin feature information (Step S120: Extract the global face features of the face image, the first sub-feature of the first sub-region, the second sub-feature of the second sub-region, and the third sub-feature of the third sub-region. [0072]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teachings of Ma to identify the coin region picture to acquire a plurality of feature regions and identify the plurality of feature regions to acquire a plurality of coin feature information in order to quickly and accurately locate similar objects in the coin image.
Regarding claim 2, Zhang in the combination teaches the coin identification method according to claim 1, wherein identifying the coin picture to acquire the coin region comprises: processing the coin picture (Step S101: Extract the image block corresponding to each coin from the image to be identified, which includes the coins, to form the input coin set. [0083]) to acquire the coin region (The detection module is also used to input the image to be identified into the coin detection model, detect the positions of multiple detection boxes and their confidence scores of belonging to coins; obtain the positions of n detection boxes belonging to coins based on the confidence scores of the multiple detection boxes; and extract n image patches from the image to be identified based on the positions of the n detection boxes. [0056]).
Chen in the combination further teaches processing the picture by using a UNet-based image segmentation (Lung medical image segmentation method and device based on improved U-Net. Title) to acquire the region (The purpose of the invention is as follows: the invention provides a lung medical image segmentation method and device based on improved U-Net, aiming at the problems of fuzzy boundary, loss of non-connected regions and the like of the conventional lung CT image segmentation algorithm. Page 3 11th paragraph).
Regarding claim 3, Zhang in the combination teaches the coin identification method according to claim 1, wherein identifying the coin picture to acquire the coin region, and performing segmentation to acquire the coin region picture comprises: identifying the coin region according to a region detection model (The detection module is also used to input the image to be identified into the coin detection model, detect the positions of multiple detection boxes and their confidence scores of belonging to coins; obtain the positions of n detection boxes belonging to coins based on the confidence scores of the multiple detection boxes; and extract n image patches from the image to be identified based on the positions of the n detection boxes. [0056]).
Chen in the combination further teaches to acquire a mask region (in the convolution process, the size of the feature graph is reduced step by step, the upper path and the lower path are not absolutely symmetrical, and the top feature graph is cut by adopting the Crop operation (which reads on “acquire a coin mask region”) during the superposition operation. Page 7 10th paragraph), and performing segmentation (Lung medical image segmentation method and device based on improved U-Net. Title) to acquire the region picture (compared with the prior art, the invention has the beneficial effects that: the lung image segmentation optimization algorithm provided by the invention can effectively improve the image detail quality, the boundary between two lungs, a tiny cavity and peripheral blood vessels of all levels of bronchus in the lung are more accurate. Page 4 4th paragraph).
Regarding claim 4, Zhang in the combination teaches the coin identification method according to claim 1, wherein the method further comprises: performing ellipse detection (performing elliptical fitting on the detection frame of each coin to obtain an elliptical detection frame. [0034]) and circular correction on the coin region picture (calculating the diameter of the elliptical detection frame as the diameter of the coin. [0034]).
Regarding claim 10, Ma in the combination teaches the coin identification method according to claim 1, wherein identifying the coin region picture to acquire the plurality of feature regions comprises: identifying the coin region picture according to a pre-trained (The method for collecting surveillance data generally involves using existing face detection algorithms to extract multiple high-quality facial photos of a person from the surveillance video. These facial photos need to be manually labeled with IDs and cleaned to ensure that each person has one and only one unique ID, which is then used as training data. [0069]) feature region identification model (The first extraction module is used to extract the global face features of the face image, the first sub-feature of the first sub-region, the second sub-feature of the second sub-region, and the third sub-feature of the third sub-region. [0032]) to acquire the plurality of feature regions (Step S110: Obtain a face image and divide the face image into a first sub-region, a second sub-region, and a third sub-region. [0066]. Furthermore, the first sub-region, the second sub-region, and the third sub-region are one or more combinations of the eye region, nose region, mouth region, ear region, cheek region, or forehead region, and the first sub-region, the second sub-region, and the third sub-region are different from each other. [0028]).
Regarding claim 12, Ma in the combination teaches the coin identification method according to claim 1, wherein synthesizing the plurality of coin feature information to acquire the classification information of the coin comprises: adopting a similarity comparison method to acquire a comprehensive similarity of the coin (The first comprehensive similarity is obtained by weighted summing of the global similarity, the first similarity, the second similarity, and the third similarity. [0019]), and sorting the classification information of the coin according to the comprehensive similarity (The output module is used to filter out output images based on the first comprehensive similarity and the second comprehensive similarity, or to classify the comparison images in the comparison library based on the first comprehensive similarity and the second comprehensive similarity. [0040]).
Regarding claim 13, Zhang in the combination teaches a coin identification system, characterized in comprising a processor and a memory, wherein the memory stores a program, and when the program is executed by the processor, the coin identification method according to claim 1 is implemented (At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the coin recognition method described above. [0062]).
Regarding claim 14, Zhang in the combination teaches a non-transitory computer readable storage medium, which stores a program, wherein when the program is executed, the coin identification method according to claim 1 is implemented (Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing computer instructions, characterized in that the computer instructions are used to cause a computer to execute the above-described coin recognition method. [0063]).
Claims 5-6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (Chinese Patent Pub. No.: CN110610575A) hereinafter Zhang, in view of Chen (Chinese Patent Publication Number: CN 113538363 A), hereinafter Chen, further in view of Ma (Chinese Patent Pub. No.: CN107330359A), hereinafter Ma, further in view of Dumaliang (Coin Identification and Conversion System using Image Processing, 2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), 2021), hereinafter Dumaliang.
Regarding claim 5, Zhang, Chen and Ma teach all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Dumaliang teaches wherein the method further comprises: performing image preprocessing on the coin region picture acquired by segmentation to acquire a coin region picture with background (Once Grayscale is done, the input image will undergo binarization, where any gray image is converted into black-white. This process is done to differentiate the object from its background pixels. Page 3 right column 1st paragraph) and noise removed (Detection comprises some mathematical methods for determining the points in a digital image where the image brightness suddenly changes or, more formally, where there are discontinuities. Page 3 right column 1st paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Chen and Ma to incorporate the teachings of Dumaliang to perform image preprocessing to acquire a coin region picture with background and noise removed in order to improve coin recognition accuracy.
Ma in the combination further teaches wherein identifying the coin region picture to acquire the plurality of feature regions comprises: identifying a preprocessed coin region picture to acquire the plurality of feature regions (Step S110: Obtain a face image and divide the face image into a first sub-region, a second sub-region, and a third sub-region. [0066]. Furthermore, the first sub-region, the second sub-region, and the third sub-region are one or more combinations of the eye region, nose region, mouth region, ear region, cheek region, or forehead region, and the first sub-region, the second sub-region, and the third sub-region are different from each other. [0028]).
Regarding claim 6, Dumaliang in the combination teaches the coin identification method according to claim 1, wherein the method further comprises: performing image preprocessing on the coin picture provided by the user to acquire a coin picture with background (Once Grayscale is done, the input image will undergo binarization, where any gray image is converted into black-white. This process is done to differentiate the object from its background pixels. Page 3 right column 1st paragraph) and noise removed (Detection comprises some mathematical methods for determining the points in a digital image where the image brightness suddenly changes or, more formally, where there are discontinuities. Page 3 right column 1st paragraph).
Zhang in the combination further teaches wherein identifying the coin picture to acquire the coin region, and performing segmentation to acquire the coin region picture comprises: identifying a preprocessed coin picture to acquire the coin region (The detection module is also used to input the image to be identified into the coin detection model, detect the positions of multiple detection boxes and their confidence scores of belonging to coins; obtain the positions of n detection boxes belonging to coins based on the confidence scores of the multiple detection boxes; and extract n image patches from the image to be identified based on the positions of the n detection boxes. [0056]).
Chen in the combination further teaches performing segmentation (Lung medical image segmentation method and device based on improved U-Net. Title) to acquire the coin region picture (compared with the prior art, the invention has the beneficial effects that: the lung image segmentation optimization algorithm provided by the invention can effectively improve the image detail quality, the boundary between two lungs, a tiny cavity and peripheral blood vessels of all levels of bronchus in the lung are more accurate. Page 4 4th paragraph).
Regarding claim 8, Dumaliang in the combination teaches the coin identification method according to claim 1, wherein acquiring the coin picture provided by the user comprises: providing at least two windows for user selection on an interactive interface (It is common knowledge that two windows can be provided, one for the face-up side and the other for the reverse side, for user selection.), allowing the user to provide pictures of a current coin to be identified (Fig. 7 demonstrates the GUI interface of the system. At the top is the system title. The image on the left is the camera’s view, on the right is the scanned coin where the bounding box will appear, below is the coin value, and a drop-down box where the user can choose which currency he/she would like to convert the denomination into. Page 4 1st paragraph.
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Zhang in the combination further teaches to select and provide pictures of different sides of a current coin (In one implementation, the category also includes "upside down," which refers to the front or back of a coin. In step S103, image blocks of coins in the input coin set that have a first face up (e.g., heads) and a classification confidence level of a threshold (e.g., 0.8) can be classified into the first coin set. [0112]).
Regarding claim 9, Zhang in the combination teaches the coin identification method according to claim 8, wherein it is determined whether the coin picture provided by the user belongs to an obverse side (Image patches from the input coin set that have the first face up and whose classification confidence reaches the threshold are assigned to the first coin set. [0019]) or a reverse side (In one implementation, the category also includes "upside down," which refers to the front or back of a coin. In step S103, image blocks of coins in the input coin set that have a first face up (e.g., heads) and a classification confidence level of a threshold (e.g., 0.8) can be classified into the first coin set. [0112]) according to a pre-trained obverse-reverse identification model (In one implementation, the category also includes "upside down," which refers to the front or back of a coin. In step S103, image blocks of coins in the input coin set that have a first face up (e.g., heads) and a classification confidence level of a threshold (e.g., 0.8) can be classified into the first coin set. [0112]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang (Chinese Patent Pub. No.: CN110610575A) hereinafter Zhang, in view of Chen (Chinese Patent Publication Number: CN 113538363 A), hereinafter Chen, further in view of Ma (Chinese Patent Pub. No.: CN107330359A), hereinafter Ma, further in view of OPENCV-PYTHON (Image Sharpening, Noise Reduction, Blur | Gaussian, Median, Bilateral FILTERING, https://www.youtube.com/watch?v=SEj1imXHjqM, Aug 1, 2021), hereinafter OPENCV-PYTHON.
Regarding claim 7, Zhang, Chen and Ma teach all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, OPENCV-PYTHON teaches wherein after performing segmentation to acquire the coin region picture, the method further comprises: performing post-processing on the coin region picture using a CV algorithm to remove redundant noise points (Noise Reduction using medianBlur() and Bilateral Filtering. Page 1 last paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Chen and Ma to incorporate the teachings of OPENCV-PYTHON to perform post-processing on the coin region picture using a CV algorithm to remove redundant noise points in order to improve coin recognition accuracy.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Zhang (Chinese Patent Pub. No.: CN110610575A) hereinafter Zhang, in view of Chen (Chinese Patent Publication Number: CN 113538363 A), hereinafter Chen, further in view of Ma (Chinese Patent Pub. No.: CN107330359A), hereinafter Ma, further in view of Paul (Self-supervised contrastive learning with SimSiam, The Wayback Machine - https://web.archive.org/web/20220319214256/https://keras.io/examples/vision/simsiam/, Last modified: 2021/03/20), hereinafter Paul.
Regarding claim 11, Zhang, Chen and Ma teach all of the elements of the claimed invention as stated in claim 1 except for the following limitations as further recited. However, Paul teaches wherein a self-supervised pre-trained backbone model based on contrastive learning (Self-supervised contrastive learning with SimSiam. Title. One of the main reasons behind training networks with these kinds of approaches is to utilize the learned representations for downstream tasks like classification. This is why this particular training phase is also referred to as pre-training. Page 6 1st paragraph. The most popularly used method to evaluate a SSL method in computer vision (or any other pre-training method as such) is to learn a linear classifier on the frozen features of the trained backbone model (in this case it is ResNet20) and evaluate the classifier on unseen images. Page 8 last paragraph) is adopted to identify the plurality of feature regions respectively to acquire the plurality of coin feature information (The most popularly used method to evaluate a SSL method in computer vision (or any other pre-training method as such) is to learn a linear classifier on the frozen features of the trained backbone model (in this case it is ResNet20) and evaluate the classifier on unseen images. Page 8 last paragraph)).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Zhang, Chen and Ma to incorporate the teachings of Paul to adopt a self-supervised pre-trained backbone model based on contrastive learning to identify feature regions to acquire the coin feature information in order to improve the identification accuracy.
Conclusion
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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