DETAILED ACTION
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on Aug 04th, 2026 has been entered.
Response to Amendment
The Amendment filed on August 4, 2026 has been entered. Claims 1–7 are currently pending. Claims 1, 5, and 6 have been amended, and claim 7 has been added.
Response to Arguments
Applicant’s arguments filed August 4, 2026 have been fully considered and are persuasive to the extent that the prior rejection over Zhou in view of Raman does not address the newly added limitations.
Accordingly, the previous rejection under 35 U.S.C. § 103 as unpatentable over Zhou in view of Raman is withdrawn. However, a new ground of rejection under 35 U.S.C. § 103 is made in this Office Action applying Zhou in view of Raman and further in view of Gulaka to address the newly added limitations of amended claims 1, 5, and 6; as well the newly added claim 7.
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.
Claims 1–7 are rejected under 35 U.S.C. §103 as being unpatentable over Zhou (Zhou et al. US 2019/0205606 A1, 2019), in view of Raman (Raman et al, US 2020/0178839 A1, 2020), further in view of Gulaka (Gulaka et al, US 2016/0026761 A1, 2016).
Regarding claim 1, Zhou discloses an image analysis support apparatus, comprising:
processing circuitry configured to:
acquire a first analysis result obtained through analysis of medical image data performed by a first analysis application, the first analysis result including positional information of an organ region, a lesion region, or an anatomical landmark in the medical image data;
( Zhou, in [0049–0050], [Fig. 1]: discloses a computer system running a master segmentation artificial agent that medical image segmentation detects boundaries of structures such as organs, vessels, different tissues, pathologies, and medical devices in medical images. Zhou further teaches that the master segmentation artificial agent receives a medical image, first analyzes the medical image to determine a current segmentation context / image characteristics, and performs segmentation of a target anatomical structure in the medical image using a selected segmentation algorithm. This “current segmentation context” is a result of analysis performed on the medical image by the first stage of the master agent and constitutes a first analysis result obtained through analysis of medical image data by a first analysis application (the context-determination network of the master agent). The resulting segmentation result / mask identifies the pixels or voxels corresponding to the target anatomical structure and therefore includes positional information of an organ region, lesion / pathology region, or anatomical landmark in the medical image data. )
determine at least one candidate relating to a second analysis application based on the first analysis result, the second analysis application further analyzing the medical image data;
( Zhou, in [0045], [0049–0050], [Fig. 2]: Zhou teaches that the master segmentation artificial agent automatically recognizes a current segmentation context based on the medical image and automatically selects one or more segmentation algorithms from a segmentation algorithm database based on that context. Zhou further teaches selecting not only the type of segmentation algorithm, but also a specific version with parameter settings best suited for the current segmentation task. The selected segmentation algorithm(s) correspond to candidate second analysis applications, and the selected algorithm(s) further analyze the same medical image by segmenting the target anatomical structure. )
Zhou discloses the image-analysis / candidate-selection workflow [0042, 0044-0045, 0047], however Zhou does not clearly teach the amended display feature, where Raman teaches:
display a human body model in which the first analysis result is superimposed as a region at a position corresponding to the positional information.
( Raman, in [0028], [0031–0032], [0038], [Figs. 2, 3, and 5]: Raman teaches warping a 3D generic human body model 30 to generate a 3D human subject model 32 aligned with the patient, mapping estimated body parts onto the adapted 3D human subject model, classifying estimated body parts as regions of interest based on predefined MRI scan protocols, and highlighting the corresponding region of interest on the 3D human subject model. Raman further teaches displaying a simulation of the 3D human subject model on a display. )
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, motivated by the desire to provide an operator with an intuitive body-level visualization of where an image-analysis result is located, to modify Zhou’s image-analysis support apparatus to display Zhou’s segmentation/positional analysis result on a human body model as taught by Raman. Zhou already teaches analyzing medical image data to determine segmentation context, selecting an appropriate downstream segmentation algorithm, and generating a segmentation result identifying the position of a target anatomical structure in the medical image. Raman, in the same medical-imaging context, teaches displaying a 3D human subject model with a corresponding anatomical region of interest highlighted on the model. Applying Raman’s known body-model visualization to Zhou’s positional segmentation result would have predictably improved the operator’s ability to understand the anatomical location of the first analysis result and to select or confirm an appropriate downstream analysis application.
However, Zhou [as modified by Raman] does not expressly disclose conditionally displaying icon-based controls corresponding to the determined candidates in response to the operator selecting the region, or selecting the corresponding second analysis application in response to selection of one of those icons, where Gulakateaches:
displaying a display image including at least one icon respectively corresponding to the determined at least one candidate, when the region is selected by an operator: and
( Gulaka, in [0009–0010], [0051], [0053–0054], [0068], [0070], [0073], [Figs. 3–4]: Gulaka teaches displaying an object-shaped icon having sub-icons corresponding to respective anatomical regions, with each sub-icon associated with one or more medical-image-processing functions. When the operator selects an anatomical sub-icon, the display presents a shortcut menu containing the image-processing functions corresponding to that selected anatomical region. Gulaka further teaches that the functions in the shortcut menu may be presented in picture form or in a combination of text and picture, thereby providing icon-based controls respectively corresponding to the available medical-image-processing functions. Therefore, Gulaka’s region-sensitive interface causes respective icons corresponding to those candidates to be displayed when the corresponding region on human body model is selected. )
selecting the second analysis application corresponding to one icon among the at least one icon, when the one icon is selected by the operator.
( Gulaka, in [0011], [0054–0055], [0068–0069], [0077], [Figs. 3–4]: Gulaka teaches that the operator selects one image-processing function from among the functions displayed in the shortcut menu and that, in response to the selection, the controller performs medical-image processing using the selected function. The displayed functions include computer-executed medical-image-analysis operations such as segmentation, propagation, selection, and contour processing, thereby selecting one displayed icon selects the corresponding candidate second analysis application. )
It would have been prima facie obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, motivated by the desire to reduce display clutter and enable efficient selection of only those medical-image-analysis functions relevant to a selected anatomical region, to incorporate Gulaka’s region-sensitive icon interface into the Zhou [as modified by Raman]'s system. Gulaka expressly recognizes that displaying too many processing icons makes selection cumbersome and teaches displaying region-relevant functions after selection of an anatomical region. Applying that known interface to Zhou [as modified by Raman]’s determined candidate applications and selectable body-model region would have predictably allowed the operator to display and select the appropriate second analysis application with reduced search burden and a reasonable expectation of success.
Regarding claim 2, Zhou [as modified by Raman and Gulaka] discloses the image analysis support apparatus according to claim 1, wherein the first analysis result includes at least one of a name and a type of the first analysis application.
( Zhou, in [0041]: Zhou discloses a master segmentation artificial agent 102 that automatically selects, for a given segmentation task, a name and a type [0040, version of segmentation algorithm] and a specific version of that algorithm from a segmentation algorithm database 108 storing multiple versions of each segmentation algorithm for different target anatomical structures and imaging modalities. The segmentation result therefore automatically recognizes an algorithm version based on medical images of a patient and automatically selects one or more of the algorithms in segmentation algorithm database 108. )
Regarding claim 3. Zhou [as modified by Raman and Gulaka] discloses the image analysis support apparatus according to claim 1, wherein the processing circuitry is further configured to select, among the at least one candidate determined, one candidate according to a selection operation performed by an operator,
( Zhou, in [0047]: Zhou teaches that although the master segmentation artificial agent acts autonomously to select one or more segmentation algorithms, a user or clinical site may be provided with a manual override option on a user interface displayed on a display device, allowing the user to override the master segmentation artificial agent and manually choose a specific segmentation algorithm. )
and convert a data format of the first analysis result such that the data format of the first analysis result matches a data format that can be understood by the second analysis application corresponding to the selected candidate.
( Zhou, in [0045], [0049]: Zhou teaches that the master segmentation artificial agent selects not only the type of segmentation algorithm but also a specific version of the algorithm with parameter settings optimized for the current segmentation task, and further teaches automatically switching between different segmentation algorithms for different target anatomical structures and imaging conditions. In doing so, the agent necessarily converts or adapts the intermediate analysis information (segmentation context / features) and the medical image data into the representation required by the selected algorithm version and its parameters, so that the selected algorithm can correctly perform the further segmentation. Thus, Zhou teaches converting the data format of the first analysis result such that it matches a format understood by the selected second analysis application. )
Regarding claim 4, Zhou [as modified by Raman and Gulaka] discloses the image analysis support apparatus according to claim 3, wherein the processing circuitry is further configured to transmit the first analysis result after the conversion to the second analysis application corresponding to the selected candidate.
( Zhou, in [0050]: Zhou teaches that, after the master segmentation artificial agent selects one or more segmentation algorithms, the computer program instructions corresponding to the selected segmentation algorithm(s) are loaded into memory and executed by one or more processors to perform segmentation of the target anatomical structure in the medical image. )
Regarding claim 5, the rationale in the rejection of claim 1 is provided herein. In addition, the image analysis support system recited in claim 5 corresponds to the functions performed by the image analysis support apparatus of claim 1. In addition, Zhou [as modified by Raman] further teaches a computer / display that presents the 3D human subject model and simulation to the user [Zhou: 0051; Raman: 0025 ].
Regarding claim 6, the rationale in the rejection of claim 1 is provided herein. In addition, the method steps recited in claim 6 corresponds to the functions performed by the image analysis support apparatus of claim 1.
Regarding claim 7, Zhou [as modified by Raman and Gulaka] discloses the image analysis support apparatus according to claim 1, wherein each of the at least one icon includes a unique mark.
( Gulaka, in [0098–0103], [0123], [Figs. 5–8 and 12]: Gulaka teaches that image-processing functions may be represented by icons in a picture form or a combination of text and picture so that the function associated with each icon is intuitively recognizable by the user. Gulaka further teaches visually distinguishing icons/ sub-icons using different states, colors, lines, and markings, including a dotted line in Fig. 6, a changed color in Fig. 7, and “X” marks on the icon in Fig. 8, with the respective visual indication associated with a corresponding image-processing function. Fig. 12 further shows a plurality of individual icons having different pictorial symbols, each icon corresponding to a respective image-processing function. )
Conclusion
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KEN KUDO
Examiner
Art Unit 2671
/KEN KUDO/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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