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 03/27/2025 has been made record of and considered by the examiner. The submission is in compliance with the provisions of 37 CFR 1.97.
Note Regarding 35 USC § 101
Claim 18-20 disclose “[a] computer-readable recording medium …”. According to the U.S. Court of Appeals for the Federal Circuit (CAFC) precedential ruling in Sequoia Technology, LLC v. Dell, Inc. (available at: https://www.cafc.uscourts.gov/opinions-orders/21-2263.OPINION.4-12-2023_2109603.pdf) a “recording” is a “record” which is defined by Marriam-Websters’ online dictionary as follows: 1c. “to register permanently by mechanical means”; 3. “to cause (sound, visual images, data, etc.) to be registered in something (such as a disc or magnetic tape) in reproducible form”. Therefore, the permanent, reproducible nature of a “record” as indicated by the plain meaning weighs against interpretation as a transitory signal which is fleeting and does not persist over time. Therefore, since a “recording medium” is directed to statutory subject matter, a 35 USC § 101 rejection is not applied to claims 18-20.
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, 7, 10 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Morita et al. (WO 2022264757 A1) in view of Sugimura et al. (JP 2008541889 A).
Regarding Claim 1, Morita et al. teaches an image analysis device comprising: a memory; and a processor configured to execute instructions stored in the memory, wherein the processor is configured to: detect an indeterminate region or an abnormal region from an input medical image using an artificial intelligence (AI) model trained(¶49-56: teaches an image analysis device comprising a memory and a processor configured to execute instructions for analyzing an input medical image using trained artificial intelligence models.) to detect a suspicious region and a lesion region in medical images; (¶53-56: teaches lesion detection AI models (AI18B-AI18F) configured to detect lesion regions within the input medical image. The lesion detection models are trained using labeled medical images identifying disease regions and output probabilities indicating the presence of lesion regions, wherein pixels exceeding predetermined thresholds are identified as lesion regions.) and determine the input medical image as a normal case when the indeterminate region or the abnormal region is not detected in the input medical image. (¶49-52: teaches a normality determination AI (AI 16D), implemented as a trained convolutional neural network, that receives a CT image as input and determines whether the CT image is normal or abnormal. The normality determination AI is trained using labeled normal and abnormal CT images and outputs a degree of normality that is compared to a predetermined threshold to determine whether the CT image is normal.)
Morita et al. is silent on the remaining limitations of Claim1. However, Sugimura et al. teaches detecting a suspicious region using an AI model. (¶9: teaches a computer-aided diagnosis system including a lesion locator configured to analyze medical images and identify suspicious lesions; ¶19, ¶30, ¶43 and ¶45: teaches computer-aided detection and evaluation of suspicious lesion regions, extraction of features from suspicious lesion regions, and AI-based diagnostic assessment using artificial intelligence models, neutral networks, and other machine learning techniques trained using accumulated diagnostic image data and biopsy results.)
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Morita’s AI image analysis system to incorporate Sugimura’s AI-based suspicious lesion detection so that the system detects suspicious regions in addition to lesion regions before determining whether the input medical image is a normal case, thereby improving automated diagnostic assessment while using known AI image analysis techniques.
Regarding Claim 7, Morita teaches wherein the processor is configured to provide analysis results and normal case information for the input medical image obtained by the AI model to a designated device. (¶62: teaches providing analysis results indication that an analyzed CT image has been determined to be normal by displaying explanatory information stating, “It has been determined as normal by CAD,” thereby providing normal case information generated by the AI analysis.)
Morita is silent on the remaining limitations of Claim 7. Sugimura et al. teaches the input medical image obtained by the AI model to a designated device. (¶24, ¶25, and ¶59: teaches providing processed medical image analysis results to designated output devices. Specifically teaching transmitting processed images, annotations, lesion boundaries, BI-RADS assessments, selected features, and associated reports to DICOM-compliant devices for storage and sharing with physicians and radiologists.)
It would have been obvious to one in ordinary skill in the art to provide Morita’s AI generated normal case determination together with Sugimura’s transmitted analysis results. Combining the both would have predictably enabled clinicians to receive both AI analysis results and the normal case determination through the same diagnostic reporting workflow.
Claim 10 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. The rationale and motivation to combine the Morita et al. and the Sugimura et al. references presented in Claim 1, apply to this claim.
The proposed combination as well as the motivation for combining the Morita et al. and Sugimura et al. references presented in the rejection of Claim 7, apply to Claim 15.
Claim(s) 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Morita et al. (WO 2022264757 A1) in view of Seungwon et al (WO 2021067624 A1) and in further view of Sugimura et al. (JP 2008541889 A).
Regarding Claim 18, Morita et al teaches a computer program stored in a computer-readable recording medium, the computer program comprising instructions causing a processor to: (¶48-56: teaches a computer program executed by a processor for analyzing medical images using artificial intelligence. Specifically teaching a normality determination AI (16D) configured to receive a CT image and output a degree of normality indicating whether the image is normal. Also teaches that a first determination unit inputs the CT image into the normality determination AI, obtains a first determination result, and provides the result to a processor, which determines whether the CT image is normal based on the AI analysis result.) display a worklist including a study case list for image reading task by interworking with an image storage device storing analysis results for medical images; and when a specific medical image is determined to be a normal case based on normal case information included in the analysis results for the medical images, (¶62; teaches determining a medical image to be a normal case based on normal case information included in AI generated analysis results. Also teaches, displaying an indication that the CT image has been determined as normal by CAD, thereby providing a graphical indicator identifying the medical image as a normal case) display an indicator indicating that the specific medical image is a normal case in the image list or in a preview image of the specific medical image, or display an indicator indicating that there is a pre-generated report for the specific medical image.
Morita does not expressly teach displaying a worklist including a study case list for an image reading task. However, Seungwon (¶0009, ¶0010, ¶0016) teaches a unified radiology workflow platform including a patient worklist, worklist management module, and worklist management interface that allows radiologists to select, reserve, and review lists of medical cases and images for interpretation.
Morita and Seungwon are both silent on the limitation of displaying the normal case indicator in a preview image. However, Sugimura (¶0082-0083) teaches displaying analyzed medical images as thumbnail images for user review, wherein the thumbnail images correspond to analyzed medical images and may display automatically detected lesion candidates generated by the CAD system.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Morita’s AI based medical image analysis system to incorporate Seungwon’s worklist management interface in order to integrate AI generated analysis results into a radiologist’s worklist workflow, thereby improving workflow efficiency and facilitating prioritization of medical image review. It further would have been obvious to utilize Sugimura’s thumbnail preview display within the worklist because preview images provide efficient navigation and review of analyzed medical images while presenting AI generated analysis result.
Regarding Claim 20, Morita teaches further comprising instructions causing the processor to: display the pre-generated report for the specific medical image in response to a user input; and store a report approved by the user. (Morita teaches the underlying AI assisted medical image analysis system as stated above)
Morita is silent on the remaining limitations of Claim 20. Seungwon teaches (¶ 0009-0010) an integrated radiology workflow platform including a graphical user interface configured to generate a textual description of a finding using artificial intelligence, present the generated finding to a user for review and acceptance, and automatically incorporate the accepted finding into a medical report. Further teaches presenting computer-generated findings to a user for acceptance and optional editing before automatically populating the medical report with the accepted findings.
Seungwon and Morita do not expressly teach storing the finalized report after user approval. However, Sugimura teaches (¶0059) generating diagnostic reports and storing those reports, including saving reports in PDF format or DICOM structured report format and storing or transmitting the reports together with associated image information using DICOM-complaint storage devices.
It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate Sugimura’s report storage techniques into Seungwon’s AI assisted reporting workflow because both references are directed to improving radiology reporting by integrating AI generated findings into diagnostic reports while preserving finalized reports for subsequent retrieval, sharing, and clinical use. Furthermore, because Seungwon teaches generating AI derived report content to a user for review and acceptance before incorporation into the report, it would have been obvious to present the AI generated report content (ex. a draft or pre-finalized report) in response to user interaction prior to storing the finalized report, as such presentation is a predictable step in allowing the user to review and approve AI generated report content before finalization.
Allowable Subject Matter
Claim(s) 2-6, 8-9, 11-14, and 16-17, 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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