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 .
DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
1. This action is responsive to communications: Application filed on October 19, 2023, and Drawings filed on October 19, 2023.
2. Claims 1–24 are pending in this case. Claim 1, 23, 24 are independent claims.
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 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.
Allowable Subject Matter
Claims 10-22 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.
With regard to claim 10, the prior arts do not disclose the document creation support apparatus according to claim 2, wherein the processor is configured to: perform control to display the medical image after generating the plurality of comments on findings; receive designation of a region of interest on the medical image; and perform control to display the plurality of comments on findings for two or more regions of interest in the same group as the designated region of interest.
With regard to claim 18, the prior arts do not disclose the document creation support apparatus according to claim 1, wherein the processor is configured to: classify two or more regions of interest into one group based on an input from a user; and generate a plurality of comments on findings only for the two or more regions of interest included in the one group among the plurality of regions of interest.
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 3, 4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regard to claim 3, applicant claims the limitation of the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest into the same group based on a degree of similarity of an image of a region-of-interest portion included in the medical image.
It is unclear what constitutes “based on a degree of similarity of an image of a region-of-interest portion included in the medical image.” It is unclear how “an image of a region of interest portion” relates to the medical image. It is unclear how a region of interest relates to the “regions of interest portion” of claim 1. It is unclear whether the two or more regions of interest are similar to each other to similar to a third region of interest portion in the image. For the purpose of a compact prosecution, the image is interpreted as different from the medical image, the region of interest is one of the regions of in interest from claim 1.
With regard to claim 4, applicant claims the limitation of “the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest into the same group based on a degree of similarity of a feature amount extracted from an image of a region-of-interest portion included in the medical image.”
It is unclear what constitutes “based on a degree of similarity of a feature amount extracted from an image of a region-of-interest portion.” It is unclear how “an image of a region of interest portion” relates to the medical image. It is unclear how a region of interest relates to the regions of interest portion of claim 1. It is unclear whether the two or more regions of interest are similar to each other to similar to a feature amount extracted from a third region of interest portion in the image. For the purpose of a compact prosecution, the image is interpreted as different from the medical image, the region of interest is one of the regions of in interest from claim 1.
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.
Claim(s) 1, 23, 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gurson et al., Pub. No.: 20200160982 A1, in view of MacKay, et al., Pub. No.: 20200110914 A1.
With regard to claim 1:
Gurson discloses a document creation support apparatus comprising at least one processor, wherein the processor is configured to: acquire a medical image and information indicating a plurality of regions of interest included in the medical image (wherein the medical image CT, x-ray or MRI image, paragraph 26: “The method includes receiving a set of inputs S110 associated with a patient, which functions to prompt the subsequent processes of the method 100. The inputs can include any or all of: a set of one or more images, a series, and/or a study from an imaging modality (e.g., radiography/x-ray, computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET)/CT, other forms of nuclear medicine, mammography, digital breast tomosynthesis, PET/MRI, etc.); images from one or more procedures (e.g., procedures involving fluoroscopy, molecular imaging, mammography, etc.); video data (e.g., kinetics action data, video data of blood vessels, etc.), patient information (e.g., patient metadata, demographic information, etc.); patient condition information (e.g., predicted medical condition, previous medical condition, patient history, etc.); and/or any other suitable information.”); generate a plurality of comments on findings for two or more regions of interest among the plurality of regions of interest (see fig. 3B and 7B for multiple comments , paragraphs 79 and 80: “The way in which annotations are displayed (e.g., type of trigger, displayed automatically versus displayed in response to a trigger, duration of display, whether or not to display corresponding annotations among subsequent images, type of annotation, etc.) preferably depends on one or more labels, such as a label specifying the type of finding (e.g., abnormal finding versus normal finding). This can be integrated within the overlay, transmitted to a viewer (e.g., PACS viewer) and/or to another system (e.g., RIS) as a set of control commands, integrated within the viewer itself, or otherwise configured to enable the annotations to be displayed properly, if at all. In preferred variations, the way in which annotations are displayed is configured to bring radiologist attention to the most important outputs (e.g., abnormal finding measurements) as quickly and as easily as possible. This can involve any or all of: removing or minimizing a number of actions (e.g., clicks, hovers, hotkeys, etc.) required to display an abnormal finding annotation; requiring more actions to display a normal finding annotation as compared to an abnormal finding annotation; configuring (e.g., programming, hard coding, etc.) an abnormal finding annotation to have a longer duration than a normal finding annotation; configuring an abnormal finding annotation to be more noticeable (e.g., bigger in size, more brightly colored, animated, etc.) than a normal finding annotation; and/or otherwise differentiating the way in which abnormal finding annotations and normal finding annotations are displayed. Additionally or alternatively, the way in which annotations are displayed can depend on a type of label (e.g., measurement versus name of finding). In some variations, for instance, annotations with findings can be automatically displayed whereas annotations with measurements require an action (e.g., click, click+hotkey) to be displayed. In preferred variations (e.g., as shown in FIGS. 3A-3B), annotations corresponding to abnormal findings (e.g., arrow and text box containing a measurement of an abnormal finding) are displayed automatically upon the presentation of inputs (e.g., images in a study) to a radiologist (e.g., at a radiologist workstation, on a PACS viewer), whereas annotations corresponding to normal findings (e.g., hover boxes indicating an anatomical feature) are only displayed in response to a trigger (e.g., radiologist hovering over corresponding area with mouse cursor).”); perform control to display the plurality of comments on findings (see fig. 3B and 7B for multiple comments , paragraphs 79 and 80: “The way in which annotations are displayed (e.g., type of trigger, displayed automatically versus displayed in response to a trigger, duration of display, whether or not to display corresponding annotations among subsequent images, type of annotation, etc.) preferably depends on one or more labels, such as a label specifying the type of finding (e.g., abnormal finding versus normal finding). This can be integrated within the overlay, transmitted to a viewer (e.g., PACS viewer) and/or to another system (e.g., RIS) as a set of control commands, integrated within the viewer itself, or otherwise configured to enable the annotations to be displayed properly, if at all. In preferred variations, the way in which annotations are displayed is configured to bring radiologist attention to the most important outputs (e.g., abnormal finding measurements) as quickly and as easily as possible. This can involve any or all of: removing or minimizing a number of actions (e.g., clicks, hovers, hotkeys, etc.) required to display an abnormal finding annotation; requiring more actions to display a normal finding annotation as compared to an abnormal finding annotation; configuring (e.g., programming, hard coding, etc.) an abnormal finding annotation to have a longer duration than a normal finding annotation; configuring an abnormal finding annotation to be more noticeable (e.g., bigger in size, more brightly colored, animated, etc.) than a normal finding annotation; and/or otherwise differentiating the way in which abnormal finding annotations and normal finding annotations are displayed. Additionally or alternatively, the way in which annotations are displayed can depend on a type of label (e.g., measurement versus name of finding). In some variations, for instance, annotations with findings can be automatically displayed whereas annotations with measurements require an action (e.g., click, click+hotkey) to be displayed. In preferred variations (e.g., as shown in FIGS. 3A-3B), annotations corresponding to abnormal findings (e.g., arrow and text box containing a measurement of an abnormal finding) are displayed automatically upon the presentation of inputs (e.g., images in a study) to a radiologist (e.g., at a radiologist workstation, on a PACS viewer), whereas annotations corresponding to normal findings (e.g., hover boxes indicating an anatomical feature) are only displayed in response to a trigger (e.g., radiologist hovering over corresponding area with mouse cursor).”); receive one or more comment on findings from among the plurality of comments on findings; and generate a medical document including the one or more comment on findings (The report document is generated based on one or more comments/annotations, paragraphs 93 to 94: “3.7 Method—Transmitting a Set of Outputs to a Radiologist Report S170 The method can additionally or alternatively include transmitting any or all of the set of outputs to a radiologist report S170, which can function to: minimize or eliminate interactions between a radiologist and a voice recognition platform (through which radiologists conventionally report findings for a radiologist report), increase the accuracy and/or uniformity of radiologist reports, and/or perform any other suitable function. S170 preferably involves the automatic insertion of radiologist-appropriate finding and/or comparison text into a report within the voice recognition platform, which can be initiated, for instance, with a hotkey and/or click. Alternatively, S170 can be performed in absence of a voice recognition platform. In some variations, abnormal findings and any corresponding measurements are integrated into a radiologist report upon prompting (e.g., hotkey and click) by a radiologist, wherein the abnormal findings and measurements, in conjunction with any additional information received at a voice recognition platform, are dictated into a radiologist report.”).
Gurson does not disclose receive selection of one comment on findings from among the plurality of comments on findings; and generate a medical document including the one comment on findings.
However MacKay discloses the aspect of receive selection of one comment on findings from among the plurality of comments on findings; and generate a document including the one comment on findings (paragraph 15: “FIG. 15 shows an example of a user interface for retrieving annotations from an indexed inventory and recycling an annotation or set of annotations into another document, according to one embodiment. In the example, user 205 is viewing the main analytics panel for “Introduction to Ancient Greek.” Apparently, user 205 has created the environment, because user 205 is able to see all users 1506 for that environment (see Log Protocol 1000). By selecting an avatar for an authorized user or avatars for a set of authorized users, user 205 can filter out data from other authorized users in the central data visualization. Moreover, user 205 can analyze any set annotations for similarities by applying the clustering algorithm behind the Local Index 1532. Once the new set of data is scored, clusters of annotations will be listed in descending order of the largest and least idiosyncratic cluster. For example, the colorized annotation 1512 apparently comes from a cluster with more similar members than the less colorized annotation below. When user 205 wants to generate a document from an annotation or set of annotations then, user 205 need only select an annotation or as many as annotations as desired from the left-panel (similar to the annotation protocol 800, if more than one annotation has been selected, subsequent annotations will fill out the textbox 1510 and corresponding fields for log tags 1524 and log files 1526). If an annotation has an attachment, that attachment will be included, but another attachment 1522 may always be added to a prior annotation that has been recycled.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply MacKay to Gurson so the user can easily select and choose which comment to add to the document and which comment is not added to the document for greater user power to control input.
Claim 23 is rejected for the same reason as claim 1.
Claim 24 is rejected for the same reason as claim 1.
Claim 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gurson et al., Pub. No.: 20200160982 A1, in view of MacKay, and further in view of Pham, WO 2020264029 A1
With regard to claim 2:
Gurson and MacKay do not disclose the document creation support apparatus according to claim 1, wherein the processor is configured to: classify the plurality of regions of interest into at least one group through an analysis process on the medical image; and generate a plurality of comments on findings for two or more regions of interest included in the same group.
However Pham disclose the document creation support apparatus according to claim 1, wherein the processor is configured to: classify the plurality of regions of interest into at least one group through an analysis process on the medical image (“As described herein, in contrast to conventional approaches of intersection contention area detection using high-definition (HD) maps, the current systems and methods provide techniques to detect and classify intersection areas using outputs from sensors (e.g., cameras, RADAR sensors, LIDAR sensors, etc.) of a vehicle in real-time or near real-time. As such, for each intersection, live perception of the vehicle may be used to detect locations and/or attributes or classifications of the intersection areas corresponding to the intersection. Computer vision and/or machine learning model(s) (e.g., deep neural networks (DNNs), such as convolutional neural networks (CNNs)) may be trained to compute outputs that - after decoding, in embodiments - result in detected intersection areas, and/or classifications or attributes thereof, and the outputs may be used by the vehicle in conjunction with object and/or lane detections to effectively and accurately navigate the intersection(s) while conforming to associated traffic priority rules. An output of the computer vision and/or machine learning model(s) may include, in some embodiments, signed distance functions that represent pixel- based locations of the specific regions in the image where the various classified intersection areas are detected. In addition, in some embodiments, post-processing may be performed on the signed distance functions to generate instance segmentation masks corresponding to each detected intersection area and/or type.” ); and generate a plurality of comments on findings for two or more regions of interest included in the same group (claim 14: “A method comprising: receiving image data representative of an image depicting an intersection; generating annotations representative of bounding shapes corresponding to areas of the intersection and corresponding semantic class labels corresponding to semantic classes for each of the areas; computing signed distance functions for each semantic class type of the semantic class types corresponding to the intersection, the signed distance functions including first signed values for first pixels of the image interior to the areas, second signed values for second pixels of the image exterior to the areas, and third signed values for third pixels of the image along boundaries of the areas; and training a deep neural network (DNN) using the signed distance functions as ground truth data.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Pham to Gurson and MacKay so the system can better organize different type of regions of interest and generate comments based on classification for greater clarity and allow comments to be generated based on class needs of the user.
Claim 3, 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gurson et al., Pub. No.: 20200160982 A1, in view of MacKay and Pham, and further in view of Boughman, Pub. NO.: CN 114730488 A.
With regard to claim 3:
Gurson and Mackay and Pham do not disclose the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest into the same group based on a degree of similarity of an image of a region-of-interest portion included in the medical image.
However Boughman discloses the aspect of the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest into the same group based on a degree of similarity of an image of a region-of-interest portion included in the medical image (“ As an example, FIG. 2 shows an image area (H. Samet, "The Quadtree and Related Hierarchical Data Structures", 1984), which is generated by the application of the application of the quad tree. Subsequently, the image area is grouped in step 120. In this way, the group is assigned to at least one image area. The image areas that are not assigned to the group may also be retained. Such a packet is also referred to as a classification, classification or clustering. No image area can be allocated to more than one group, so that each image area is assigned to only one group or no group. For example, the image area is grouped based on one or more of the attributes thereof, such that the same or similar image area is assigned to the same group. The measure of similarity can be changed to obtain more or less set. Such standard classification techniques are known to those of skill in the art and are therefore not further explained herein. One or more attribute criteria of the image area can be used for grouping the image area. Examples of such attribute criteria are the intensity of the image area, the orientation of the intensity gradient in the image area, the color or color value of the image area, the structure in the image area, the edge orientation, and under the condition of the image area of different sizes, the size of the image area can be used for grouping. It is also possible to think of other characteristics”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Boughman to Gurson and MacKay and Pham so the system can group regions of interest based on similarity wherein regions that share similar visual property tend to share other property as well so the user can better organize them based on their distinct visual features.
With regard to claim 4:
Gurson and Mackay and Pham and Boughman disclose the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest into the same group based on a degree of similarity of a feature amount extracted from an image of a region-of-interest portion included in the medical image (Boughman: “As an example, FIG. 2 shows an image area (H. Samet, "The Quadtree and Related Hierarchical Data Structures", 1984), which is generated by the application of the application of the quad tree. Subsequently, the image area is grouped in step 120. In this way, the group is assigned to at least one image area. The image areas that are not assigned to the group may also be retained. Such a packet is also referred to as a classification, classification or clustering. No image area can be allocated to more than one group, so that each image area is assigned to only one group or no group. For example, the image area is grouped based on one or more of the attributes thereof, such that the same or similar image area is assigned to the same group. The measure of similarity can be changed to obtain more or less set. Such standard classification techniques are known to those of skill in the art and are therefore not further explained herein. One or more attribute criteria of the image area can be used for grouping the image area. Examples of such attribute criteria are the intensity of the image area, the orientation of the intensity gradient in the image area, the color or color value of the image area, the structure in the image area, the edge orientation, and under the condition of the image area of different sizes, the size of the image area can be used for grouping. It is also possible to think of other characteristics). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Boughman to Gurson and MacKay and Pham so the system can group regions of interest based on similarity wherein regions that share similar visual property tend to share other property as well so the regions can better organize them based on their distinct visual features.
Claim 5, 6, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gurson et al., Pub. No.: 20200160982 A1, in view of MacKay and Pham, and further in view of Che, Pub. No.: CN 110322436 B.
With regard to claim 5:
Gurson and MacKay and Pham do not disclose the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest having the same disease name derived from the region of interest into the same group
However Che discloses the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest having the same disease name derived from the region of interest into the same group(“S210, obtaining the preset with similar relation of at least two annotation information; Specifically, it is to obtain the marking information of at least two areas corresponding to the class with similar pathology class; S212, in the digital medical image, the area corresponding to at least two annotation information with similar relation is classified into the same type; Specifically, in the digital medical image, the area corresponding to at least two annotation information with similar relationship is classified into the same pathology. S214, the adjacent region corresponding to the same type is connected to obtain the connected region, each connected region corresponding to the same category. Specifically, the adjacent areas with the same pathological type are connected to obtain a connected area. the connection area and the other area around the difference is large, through the connection area can accurately diagnose the severity of pathology, so as to improve the accuracy of pathological diagnosis.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Che to Gurson and MacKay and Pham so the system can group regions of interest based on their shared disease so the regions can better organize them based on their distinct disease features.
With regard to claim 6:
Gurson and MacKay and Pham and Che disclose the document creation support apparatus according to claim 2, wherein the processor is configured to: generate a comment on findings for each of the plurality of regions of interest; and classify two or more regions of interest into the same group based on a degree of similarity of the generated comment on findings (Che: “S210, obtaining the preset with similar relation of at least two annotation information; Specifically, it is to obtain the marking information of at least two areas corresponding to the class with similar pathology class; S212, in the digital medical image, the area corresponding to at least two annotation information with similar relation is classified into the same type; Specifically, in the digital medical image, the area corresponding to at least two annotation information with similar relationship is classified into the same pathology. S214, the adjacent region corresponding to the same type is connected to obtain the connected region, each connected region corresponding to the same category. Specifically, the adjacent areas with the same pathological type are connected to obtain a connected area. the connection area and the other area around the difference is large, through the connection area can accurately diagnose the severity of pathology, so as to improve the accuracy of pathological diagnosis.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Che to Gurson and MacKay and Pham so the system can group regions of interest based on their shared comment contents so the regions can better organize them based on their distinct contents.
With regard to claim 9:
Gurson and MacKay and Pham and Che disclose The document creation support apparatus according to claim 2, wherein the processor is configured to classify the plurality of regions of interest into at least one group based on relevance of disease features of the regions of interest (Che: “S210, obtaining the preset with similar relation of at least two annotation information; Specifically, it is to obtain the marking information of at least two areas corresponding to the class with similar pathology class; S212, in the digital medical image, the area corresponding to at least two annotation information with similar relation is classified into the same type; Specifically, in the digital medical image, the area corresponding to at least two annotation information with similar relationship is classified into the same pathology. S214, the adjacent region corresponding to the same type is connected to obtain the connected region, each connected region corresponding to the same category. Specifically, the adjacent areas with the same pathological type are connected to obtain a connected area. the connection area and the other area around the difference is large, through the connection area can accurately diagnose the severity of pathology, so as to improve the accuracy of pathological diagnosis.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Che to Gurson and MacKay and Pham so the system can group regions of interest based on their shared disease so the regions can better organize them based on their distinct disease features.
Claim 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gurson et al., Pub. No.: 20200160982 A1, in view of MacKay and Pham, and further in view of Gutmann et al., Pub. No.: US 20210312625 A1.
With regard to claim 7:
Gurson and MacKay and Pham do not disclose the document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest of which a distance between the regions of interest is less than a threshold value into the same group.
However Gutmann discloses the aspect wherein The document creation support apparatus according to claim 2, wherein the processor is configured to classify two or more regions of interest of which a distance between the regions of interest is less than a threshold value into the same group (paragraph 129: “At operation 1424, adjusted second image hinge locations associated with the plurality of hinges in the second image are calculated. The adjusted second image hinge locations may be calculated based, at least in part, on the projected second image hinge locations (determined at operation 1418) and the candidate second image hinge locations (determined at operation 1422). For example, FIG. 14B shows an example of a group of sub-operations 1424A-F that may be included in operation 1424 in order to calculate the adjusted second image hinge locations. In particular, at sub-operation 1424A, the hinge candidates are grouped into a set of hinge candidate groups. In some examples, the hinge candidates may be grouped based at least in part on a similarity of size characteristics (e.g., radius lengths) and/or locations. For example, in some cases, two or more hinge candidates may be grouped together if their respective size characteristics (e.g., radius lengths) are within a selected allowed threshold size/length of one another. Also, in some cases, two or more hinge candidates may be grouped together if their respective locations are within a selected allowed threshold distance of one another. For example, two or more hinge candidates may be grouped together if their respective X coordinate location (e.g., center point) values are within a selected allowed threshold distance of one another and their respective Y coordinate location (e.g., center point) values are within a selected allowed threshold distance of one another. For example, as shown in FIG. 19, four of the rows of the hinge candidate list 1900 are underlined in order to indicate an example of four hinge candidates that may be grouped into a hinge candidate group. Specifically, it can be seen that the four underlined rows include X coordinate values (X:134, X:134, X:136 and X:138) in close proximity to one another, for example such that they are within a selected allowed X coordinate threshold distance. It can also be seen that the four underlined rows include Y coordinate values (Y:376, Y:378, Y:378 and Y:378) in close proximity to one another, for example such that they are within a selected allowed Y coordinate threshold distance. In some examples, the radius lengths of the four underlined rows (11, 12, 12, and 14) are close to one another, for example such that they are within a selected allowed radius length threshold. Thus, four hinge candidates corresponding to the four underlined rows in FIG. 19 may be grouped into a respective hinge candidate group. As should be appreciated, although not shown in FIG. 19, other hinge candidates corresponding to other rows in FIG. 19 may also be grouped into other hinge candidate groups.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Gutmann to Gurson and MacKay and Pham so the system can group region that close together into a single group since region that are within certain threshold distance might share some other properties as well so grouping can help the user understand the difference between each group.
Claim 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gurson et al., Pub. No.: 20200160982 A1, in view of MacKay and Pham, and further in view of Shivpure, WO 2021084352 A1.
With regard to claim 8:
Gurson and MacKay and Pham do not disclose the document creation support apparatus according to claim 2, wherein the processor is configured to classify the plurality of regions of interest into at least one group based on anatomical relevance of the regions of interest
However Shivpure discloses the document creation support apparatus according to claim 2, wherein the processor is configured to classify the plurality of regions of interest into at least one group based on anatomical relevance of the regions of interest (paragraph 70; “Region-based methods are based on continuity. These techniques divide the entire image into sub-regions depending on some rules like all the pixels in one region must have the same grey level. Region-based techniques rely on common patterns in intensity values within a cluster of neighboring pixels. The cluster is referred to as the region in addition to group the regions according to their anatomical or functional roles are the goal of the image segmentation. A threshold is the simplest way of segmentation. Using thresholding technique regions can be classified on the basis of range values, which is applied to the intensity values of the image pixels. Thresholding is the transformation of an input image to an output that is a segmented binary image — segmentation methods based on finding the regions for abrupt changes in the intensity value. [0071] When images are processed for enhancement, and while performing some operations like thresholding, more is the chance for distortion of the image due to noise. As a result, imperfections exist in the structure of the image. The primary goal of the morphological operation is to remove this imperfection that mainly affects the shape and texture of images. It is evident that morphological operations can be instrumental in image segmentation as the process directly deals with 'shape extraction' in an image. Morphology in the context of image processing means the description of the shape and structure of the object in an image. Morphological operations work on the basis of set theory and rely more on the relative ordering of the pixel instead of the numerical value. This characteristic makes them more useful for image processing. Those skilled in the art would appreciate the significance of these techniques in the image segmentation.”). It would have been obvious to one of ordinary skill in the art, at the time the filing was made to apply Shivpure to Gurson and MacKay and Pham so the system can group regions of interest based on their related anatomy so the regions can better organize them based on their distinct anatomical features.
Pertinent Arts
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fuchigami, Patent Number: 11238976 B2, A support program is a program for supporting creation of an interpretation report including a medical image obtained by examining a patient and a region of interest to focus on in the medical image. The support program causes a computer to function as: a reading unit that reads a plurality of interpretation reports created by a plurality of examinations on the same part of the same patient; a difference detection unit that sets one of the plurality of interpretation reports as a detection target interpretation report and sets the others as comparison target interpretation reports, compares regions of interest of the detection target interpretation report and the comparison target interpretation reports, and detects a region of interest, which is present in the comparison target interpretation reports but is not present in the detection target interpretation report, as a difference region; and a notification screen distribution unit that, in a case where the difference region is detected, notifies that the difference region has been detected.
REMISZEWSKI, Pub. No.: 20140286561 A1, The method may include assigning region of interest pixels to a new class 320. In an aspect, the system may create an annotation region for the region of interest pixels, and assign the annotation region a new class based upon the difference analysis. For example, the system may determine that the magenta regions of the prediction image in FIG. 6B are different from the same regions in the true image in FIG. 6A, and may create annotation regions around the magenta regions of the prediction image to assign a new class. The method may proceed to annotation (308) where a medical professional may provide an annotation to the image, for example, indicating whether the biological sample contains the new class.
Conclusion
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/DI XIAO/Primary Examiner, Art Unit 2178