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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 08/29/2024 has been made record of and is being considered by examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Specification
The disclosure is objected to because it contains an embedded hyperlink and/or other form of browser-executable code. Applicant is required to delete the embedded hyperlink and/or other form of browser-executable code; references to websites should be limited to the top-level domain name without any prefix such as http:// or other browser-executable code. See MPEP § 608.01.
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.
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.
Claim(s) 1-6, 8, 10-11, 13-18, and 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over Saikou (WO 2024084838 A1, international priority date of Aug. 31, 2023) in view of both Golden et al. (US 20200380675 A1) and Duffy et al. (US 20230245314 A1).
Regarding Claim 1, Saikou teaches a medical support device comprising a processor (¶0035-0036: discloses a medical support device comprising a processor configured to perform lesion detection on an endoscopic image using a lesion region inference model), wherein the processor is configured to: display, on a display device, processing result information which is information related to a processing result of an object recognition process performed on a first medical image generated by being captured by a camera; (¶0043, ¶0053-0054: discloses displaying processing result information including lesion detection results, lesion reliability maps, text messages, and lesion region information on a display device through a display control unit) (see also Saikou ¶0050-0052: teaches comparing generated lesion reliability maps with representative images used for learning and calculating similarity values between the images.) (¶0041: teaches the calculated similarity is used to determine weighting coefficients applied to lesion reliability maps when generating an integrated lesion detection result)
Saikou is silent on the remaining limitations of Claim 1. However, Golden et al. teaches making a display aspect of the processing result information different. (¶0484: teaches presenting medical model outputs in a graphical user interface together with associated confidence information) (¶0036-0038: teaches visually differentiating displayed medical image information by adjusting display characteristics, including opacity, wherein certain displayed anatomical structure are displayed with lower opacity than other structures). Golden therefor teaches modifying a display aspect of displayed medical information through different visual presentation techniques.
Saikou and Golden do not explicitly teach that the different display aspect is according to the similarity or difference between the first medical image and the stored image. However, Duffy et al. teaches determining image reliability by evaluating image quality and determining whether medical images are outside a training distribution using similarity-related distance measures (¶0005-0006). Duffy further teaches presenting image quality information, registration quality information, and out-of-distribution detection information to a user through a graphical user interface (¶0067).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the display of lesion detection results of Saikou with the display aspect of the processing result information according to reliability information derived from similarity-related image evaluation, as taught by Duffy et al., using visual presentation techniques such as differing opacity or confidence-based display as taught by Golden et al. to improve the interpretability and reliability assessment of lesion detection results and assist clinician in evaluating the trustworthiness of the displayed result.
Regarding Claim 2, Saikou teaches wherein in a case where the similarity exceeds a first threshold value or the difference is equal to or less than a second threshold value, the processing result information is displayed in a first display aspect capable of specifying that the similarity exceeds the first threshold value or the difference is equal to or less than the second threshold value, and in a case where the similarity is equal to or less than the first threshold value or the difference exceeds the second threshold value, the processing result information is displayed in a second display aspect capable of specifying that the similarity is equal to or less than the first threshold value or the difference exceeds the second threshold value. (¶52: calculates similarity values)
Golden et al. also teaches (¶0484: different confidence presentations, for example, low, medium, or high confidence). It would have been obvious to establish threshold levels for similarity and associate different display modes with different similarity ranges because threshold-based categorization is a well-known technique for simplifying interpretation of continuous metrics.
Regarding Claim 3, Golden teaches wherein the second display aspect has higher visibility than the first display aspect. (¶0036-0037: teaches varying opacity). Changing opacity is known for method for increasing or decreasing visibility of displayed information. It would have been obvious to make lower-confidence or less-reliable information more visually prominent depending on the desired user workflow.
Regarding Claim 4, Golden teaches wherein the processing result information is not displayed in the first display aspect, and the processing result information is displayed in the second display aspect. (¶0036-0038: certain structures de-emphasized or made less visible) Suppressing one mode is a predictable variation. It would have been obvious to recognize that unreliable or low-priority information may be hidden to reduce clutter or cognitive load on the clinician.
Regarding Claim 5, Duffy et al. teaches wherein the second medical image is an exceptional image determined as a medical image that is inappropriate for use in the object recognition process. (¶0006: teaches determining whether images are outside the training distribution and therefor inappropriate for reliable model inference). It would have been obvious to have recognized that images falling outside the training distribution are unsuitable for reliable recognition and therefore represent exceptional images that should be handled differently to prevent inaccurate diagnostic outputs.
Regarding Claim 6, Duffy et al. teaches wherein the exceptional image is an image in which an imaging target that is inappropriate for use in the object recognition process is shown. (¶0005: teaches determining image quality and identifying images that are unsuitable for reliable processing due to artifacts, blur, noise, motion effects, missing information, or other image degradation conditions). Images containing artifacts or interfering structures degrade model performance. It would have been obvious to have considered such images inappropriate for reliable object recognition and would have classified them as exceptional images.
Regarding Claim 8, Duffy et al. teaches wherein the exceptional image is an image having a composition that is inappropriate for use in the object recognition process. (¶0005: as stated above, teaches image quality defects, shading, missing information, and artifacts of medical imaging). Image composition directly impacts recognition performance. It would have been obvious to have understood that images having poor framing, missing content, or other compositionally defects may reduce recognition accuracy and should be treated as unsuitable images.
Regarding Claim 10, Duffy et al. teaches wherein the exceptional image is an image having an image quality that is inappropriate for use in the object recognition process. (¶0005-0006: teaches that blur and motion artifacts degrade image quality)
Regarding Claim 11, Duffy et al. teaches wherein the image quality includes out-of-focus and/or motion blur. (¶0005: teaches blur and motion artifacts)
Regarding Claim 13, Saikou teaches wherein the processing result information includes feature region information for specifying a feature region recognized by the object recognition process, characteristic information indicating characteristics of the feature region, and/or site specifying information for specifying a site where the feature region is present. (¶ 0043 & 0054: teaches displaying lesion regions, legion masks, and textual indications of lesion presence)
Regarding Claim 14, Saikou teaches wherein the display device displays similarity-related information which is information related to the similarity, or difference-related information which is information related to the difference. (¶0050-0052: Similarity calculation)
Saikou is silent on the remaining limitations of Claim 14. However, Duffy teaches displaying quality information. (¶0067).
Regarding Claim 15, Saikou teaches wherein the similarity-related information includes the similarity, information based on the similarity, the second medical image used to obtain the similarity, and/or an image based on the second medical image used to obtain the similarity, and the difference-related information includes the difference, information based on the difference, the second medical image used to obtain the difference, and/or an image based on the second medical image used to obtain the difference. (¶0050-0052: Similarity index)
Regarding Claim 16, Saikou teaches wherein the camera is mounted on an endoscope and is inserted into a body to image an inside of the body. (¶0035: teaches endoscopic images)
Regarding Claim 17, Saikou teaches wherein the object recognition process is a process of recognizing a lesion. (¶0035-0036: teaches lesion detection)
Regarding Claim 18, Saikou teaches wherein the processing result is generated by a trained model by inputting the first medical image to the trained model. (¶0050: teaches the lesion reliability map generated by the lesion region inference model from the model input image)
Regarding Claim 20, Saikou teaches the medical support device according to claim 1; and the camera. (¶0035: teaches endoscopic images)
Claim 21 recites a method with steps corresponding to the elements of the apparatus recited in Claim 1. Therefore, the recited steps of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Saikou, Golden et al. and Duffy et al. references, presented in rejection of Claim 1, apply to this claim.
Claim 22 recites a computer-readable storage medium storing a program with instructions corresponding to the apparatus recited in Claim 1. Therefore, the recited programming instructions of this claim are mapped to the proposed combination in the same manner as the corresponding elements in its corresponding apparatus claim. Additionally, the rationale and motivation to combine the Saikou, Golden et al. and Duffy et al. references, presented in rejection of Claim 1, apply to this claim.
Allowable Subject Matter
Claim(s) 7, 9, 12, and 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RAVEN S. JONES whose telephone number is (571)272-7759. The examiner can normally be reached M-Th 7:00a.m. - 5:00p.m..
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/RAVEN SIMONE JONES/Examiner, Art Unit 2665
/Stephen R Koziol/Supervisory Patent Examiner, Art Unit 2665
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