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 .
Priority
Acknowledgement is made of Applicant’s claim of the present application being a 371 od PCT International Patent Application No. PCT/CN2022/099318, filed June 17, 2022, and claiming priority and benefit under 35 U.S.C. 119(a) to Chinese Patent Application No. CN202110695472.3, filed June 23, 2021.
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 12/23/2023 has been reviewed and the listed references were noted.
Drawings
The 5-page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-11 are allowed. Claims 12 and 13 are pending. Claims 1 and 13 are amended.
Response to Arguments
Applicant argues that, with the amendment of claim 1, the rejection of the claim under 35 U.S.C. 112(b) should be withdrawn, Examiner agrees. The limitation of “selecting the image features of the predefined number of original images with the highest classification probabilities” now takes its antecedent basis from the earlies “establishing” limitation. Examiner finds this argument persuasive and withdraws the rejection of claim 1 under 35 U.S.C. 112(b).
Applicant argues that, with the amendment of claim 13, the rejection of the claim under 35 U.S.C. 101 should be withdrawn, Examiner agrees. The claim now reads, “A non-transitory computer-readable medium…”. With the addition of the term “non-transitory”, the claim is now limited to tangible non-transitory media and cannot be read to cover a transitory propagating signal. Examiner finds this argument persuasive and withdraws the rejection of claim 13 under 35 U.S.C. 101.
Additional search and consideration have revealed that the following prior art can be used to in relations to claims 12 and 13, which warrants new grounds of rejection. These new grounds of rejection were not necessitated by any claim amendment. For this reason, the following prior art requires additional consideration by Applicant. As a result, this Office Action has been placed in the form of a Non-Final Office Action.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Owais et al. (“Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning-Based Classification and Retrieval Framework with a Large Endoscopic Database: Model Development and Validation”), in view of Fujimoto et al. (JP 2002205953 A).
Regarding claim 12, Owais teaches, “An electronic device, comprising a memory and a processor” (Owais, Pg 15, “Experimental Setup” discloses; “All the experiments were conducted using a standard desktop computer with a 3.50 GHz Intel Core i7-3770K central processing unit [50] and 16 GB RAM, an NVIDIA GeForce GTX 1070 graphics card [51], and the Windows 10 operating system.”) “wherein the memory stores computer programs that run on the processor, and the processor executes the computer programs to implement the steps in the endoscopic image recognition method, wherein the method comprises: performing disease prediction for a plurality of disease categories for a plurality of original images respectively using a first neural network model;” (Owais, Pg. 2, “Methods” discloses; “Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database.”) “establishing test sample sets for the plurality of disease categories based on the disease prediction results for the plurality of original images, wherein each test sample set comprises image features of a predefined number of original images;” (Owais, Pg. 12, Para. 2 discloses; “It predicts the class label for the given testing sample by calculating the distance to the different neighbor samples and selecting the neighbor with the minimum distance. In our case, there were a total of 37 different categories related to the human GI tract, including both normal and abnormal cases. Therefore, the KNN algorithm finds the best class prediction for the given input testing data sample by identifying the nearest neighbor (based on Euclidean distance) of the 37 different neighbors.” Examiner interprets the sets of Owais to contain a “predefined number of original images”.) “(Owais, Pgs. 11-12, discloses; “Because the last hidden state hn of the network (with a feature dimension of 1 × 600 pixels) includes the complete spatiotemporal information for all the input feature vectors (f1, f2, f3 …, fn), it was therefore selected as the final output feature vector for classification.” Owais Figure 2 also shows these hidden states being combined corresponding to the weights “W”, “R”, and “b”.) Owais does not explicitly teach, “performing disease recognition for the test sample sets of the plurality of disease categories respectively using a second neural network model; and superimposing the disease recognition results for the plurality of disease categories to obtain a case diagnosis result”. Since Owais does not explicitly disclose these limitations, Examiner relies on the teachings of Fujimoto in an analogous field of endeavor. Specifically, Fujimoto teaches, “performing disease recognition for the test sample sets of the plurality of disease categories respectively using a second neural network model;” (Fujimoto, Pg. 10, Para. [0082] discloses; “As the multi-class discriminator 21, for example, a discriminator such as a multi-class classification by multi-stage execution of linear discrimination or an artificial neural network may be used.” And Fujimoto, Pg. 10, Para. [0083] discloses; “As the identification target of the differential diagnosis, a disease such as a benign tumor, a primary lung cancer, or a lung metastatic cancer is identified and output as the differential diagnosis result 22.” It would have been obvious to combine the disease recognition of Fujimoto with the sample sets of Owais.) “and superimposing the disease recognition results for the plurality of disease categories to obtain a case diagnosis result;” (Fujimoto, Pg. 15, Para. [0123] discloses; “A nodule-shadow editing unit that executes shading editing processing is connected, and the nodule-shadow candidate and the differential diagnosis result are superimposed on the temporal difference image or the digital chest image and displayed on the image display device.”)
Owais and Fujimoto are considered to be analogous to the claimed invention because they are in the same field of using neural networks to diagnose diseases from images. Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Owais to incorporate the teachings of Fujimoto in order to use a second neural network to obtain a disease recognition result and superimpose the results. One of ordinary skill in the art would have been motivated to combine the previously described device of Owais with the teachings of Fujimoto to identify and display the results for easy use of the device by a user. Thus, the device recited in claim 12 is met by Owais and Fujimoto.
Claim 13 recites a computer-readable storage medium storing a program with instructions corresponding to the steps recited in Claim 12. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding steps in its corresponding device claim. Additionally, the rationale and motivation to combine the Owais and Fujimoto references, presented in rejection of Claim 12, apply to this claim. Finally, the combination of Owais and Fujimoto references discloses a computer readable storage medium (Owais, Pg. 15, “Experimental Setup” discloses; “All the experiments were conducted using a standard desktop computer with a 3.50 GHz Intel Core i7-3770K central processing unit [50] and 16 GB RAM, an NVIDIA GeForce GTX 1070 graphics card [51], and the Windows 10 operating system.”)
Allowable Subject Matter
The method of claim 1 is allowed. Claims 1-11 are allowable over prior art. Consider independent claim 1, the closest prior art reference, Owais et al. (“Automated Diagnosis of Various Gastrointestinal Lesions Using Deep Learning-Based Classification and Retrieval Framework with a Large Endoscopic Database: Model Development and Validation”) discloses, “An endoscopic image recognition method, comprising: performing disease prediction for a plurality of disease categories for a plurality of original images respectively using a first neural network model;” (Owais, Pg. 2, “Methods” discloses; “Our proposed framework comprises a deep learning–based classification network followed by a retrieval method. In the first step, the classification network predicts the disease type for the current medical condition. Then, the retrieval part of the framework shows the relevant cases (endoscopic images) from the previous database.”) “establishing test sample sets for the plurality of disease categories based on the disease prediction results for the plurality of original images, wherein each test sample set comprises image features of a predefined number of original images;” (Owais, Pg. 12, Para. 2 discloses; “It predicts the class label for the given testing sample by calculating the distance to the different neighbor samples and selecting the neighbor with the minimum distance. In our case, there were a total of 37 different categories related to the human GI tract, including both normal and abnormal cases. Therefore, the KNN algorithm finds the best class prediction for the given input testing data sample by identifying the nearest neighbor (based on Euclidean distance) of the 37 different neighbors.” Examiner interprets the sets of Owais to contain a “predefined number of original images”.) “(Owais, Pgs. 11-12, discloses; “Because the last hidden state hn of the network (with a feature dimension of 1 × 600 pixels) includes the complete spatiotemporal information for all the input feature vectors (f1, f2, f3 …, fn), it was therefore selected as the final output feature vector for classification.” Owais Figure 2 also shows these hidden states being combined corresponding to the weights “W”, “R”, and “b”.) “(Fujimoto, Pg. 10, Para. [0082] discloses; “As the multi-class discriminator 21, for example, a discriminator such as a multi-class classification by multi-stage execution of linear discrimination or an artificial neural network may be used.” And Fujimoto, Pg. 10, Para. [0083] discloses; “As the identification target of the differential diagnosis, a disease such as a benign tumor, a primary lung cancer, or a lung metastatic cancer is identified and output as the differential diagnosis result 22.” It would have been obvious to combine the disease recognition of Fujimoto with the sample sets of Owais.) “and superimposing the disease recognition results for the plurality of disease categories to obtain a case diagnosis result;” (Fujimoto, Pg. 15, Para. [0123] discloses; “A nodule-shadow editing unit that executes shading editing processing is connected, and the nodule-shadow candidate and the differential diagnosis result are superimposed on the temporal difference image or the digital chest image and displayed on the image display device.”). Further, in an analogous field of endeavor, Tada et al. (US 12,048,413 B2) discloses a convolutional neural network with the ability to diagnose a disease based on endoscopic images and selecting the diagnosis with the highest probability score (Tada, Column 22, Lines 55-57 discloses; “A value with the maximum value among these three probability scores was selected as the seemingly most reliable “diagnosis made by the CNN”.”) However, none of the cited prior art references, alone or in combination, provides a motivation to teach the ordered combination of the above-described limitations with “wherein the step of ‘establishing test sample sets for the plurality of disease categories’ comprises: for different disease categories within the plurality of disease categories, selecting the image features of a predefined number of original images with the highest classification probabilities from the plurality of original images to create the test sample sets.” Dependent claims 2-11, inherently includes the above-described allowable subject matter due to their dependencies from claim 1.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN M. OAKES whose telephone number is (571)272-9379. The examiner can normally be reached 7:30am-5pm.
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/JUSTIN M OAKES/
Examiner, Art Unit 2662
/Siamak Harandi/Primary Examiner, Art Unit 2662
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