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 claims of the present application being a 371 of PCT International Patent Application No. PCT/CN2022/099318, filed June 17, 2022, and claim 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 1-13 are pending.
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.
Claim 1 recites the limitations “wherein each sample set comprises image features of a predefined number of original images” and “selecting the image features of a predefined number of original images…”. There is insufficient antecedent basis for this limitation in the claim. It is unclear whether the predefined number is the same, or different in these two limitations. For examination purposes, Examiner interprets both predefined numbers to be the same number. It is suggested that Applicant amend the claim by replacing “a” in “selecting the image features of a predefined number of original images…” with the word “the”. Claims 2-11 are rejected due to their respective dependencies from claim 1.
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.
Claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter as follows.
Claim 13 is directed to “A computer-readable storage medium”. The specification is silent with respect to the definition of a “computer-readable storage medium”. The broadest reasonable interpretation of a claim drawn to a “computer-readable storage medium” typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer-readable media, particularly when the specification is silent. See Subject Matter Eligibility of Computer Readable Media, 1351 OG 212 (26 Jan 2010). See MPEP 2111.01. Signals are nothing but the physical characteristics of a form of energy, and as such is nonstatutory natural phenomena. See, e.g., In re Nuitjen, 500 F. 3d 1346, 1357 (Fed. Cir. 2007) (slip. op. at 18) (“A transitory, propagating signal like Nuitjen’s is not a process, machine, manufacture, or composition of matter.’ … Thus, such a signal cannot be patentable subject matter.”). Accordingly, claim 13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. It is suggested that Applicant amend the claim by inserting the term “non-transitory” before “computer-readable” in the preamble of the claim.
Allowable Subject Matter
The device claim 12 is allowed. Claims 1-11 and 13 are allowable over prior art and will be allowed once the above-described rejections of these claims under 35 U.S.C. 112(b) and 35 U.S.C. 101 are overcome. Consider the independent claims 1 and 13, 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, 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, Introduction and Methods sections. Examiner interprets a predefined number to encompass every image input into a model.), “performing disease recognition (Owais, Figure 2 shows a CNN and LSTM framework, but does not include the 2nd neural network performing the disease recognition on the test sample sets.), “and superimposing the disease recognition results for the plurality of disease categories to obtain a case diagnosis result;” (Owais, Discussion discloses; “In this research, we used the strength of recent ANNs in endoscopy and proposed a high-performance CAD framework to diagnose multiple GI diseases simultaneously in a given endoscopic video.” Examiner interprets “diagnose multiple GI diseases simultaneously” to be equivalent to superimposing to obtain diagnosis result.), “(Park, Abstract). 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 second neural network model performs a weighted combination of a plurality of image features within the test sample sets to obtain the disease recognition results, 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. In addition, claim 13 is not rejected over prior art, because it is mapped in the same manner to independent claim 1. Therefore, claim 13 has the same reasons for allowability as 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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