DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application is being examined under the pre-AIA first to invent provisions.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on applications filed in Korea on 12/28/2023 and 02/28/2024. It is noted, however, that applicant has not filed a certified copy of each application as required by 37 CFR 1.55.
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2 and 4-5 are rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (KR 102534088 B1) [See Google Translation] in view of Rohan (US Pub. No. 2010/0268732 A1).
Regarding claim 1, Park discloses, a method of displaying a retrieved medical image, comprising: extracting metadata of a target medical image; (See Kim p.7 2nd para, “The meta-information of the lesion may be information acquired using a pretrained neural network model. The pre-learned neural network model can output meta-information about lesions by taking medical data as an input.”
obtaining at least one category for the extracted metadata and a category value corresponding to each category; (See Kim p. 6. 2nd to last para, “For example, meta-information about medical data that is a CT image of a lung may include nodule type, calcification, diameter, average HU, volume, nodule needle presence or not, and the like.”
Further see Kim p. 10, 9th para, “The processor 120 may receive search meta information. That is, the user can set conditions for medical data to be found. The processor 120 may extract medical data corresponding to a condition set by the user from the database and return it as a search result. A condition for medical data may be search meta information. The search meta-information may include at least one of setting an item value range for one or more items included in the meta-information or setting whether to correspond to an item condition. Meta information may include one or more items. Meta information may include different items for each type of medical data. As described above, for example, the meta information may include items such as the size, location, and diameter of the lesion.”)
and displaying medical images data associated with a category value determined according to a variable relevance criterion level. (See Kim p. 10, 2nd to last para, “Search meta information may include two or more items. For example, two or more items may include age, sex, nodule type, size, calcification, fat content, and the like. For example, a search may be performed by setting a range or conditions for each of two or more items. For example, the nodule type item 410 may include a solid nodule, a part-solid nodule, or a ground glass nodule (GGN) condition. The user can select the type of nodule to be searched for. The processor 120 may extract, as a search result, medical data having a nodule of a type selected and input by the user.”
Further see Kim p. 11, 2nd para, “The search result may include medical data or additional information matched to the medical data. A search result may be the same as a search result using the aforementioned search medical data. The processor 120 may display the search results on the user interface. The processor 120 may display information on one or more pieces of medical data included in the search results on the user interface. Hereinafter, search results will be described with reference to FIG. 5. 5 is a diagram illustrating a user interface including search results by way of example. The processor 120 may display at least one of one or more medical data included in the search result, meta-information about the medical data, or case information on the user interface.”)
Kim discloses the above limitations, whereby medical image data such as name of images are displayed but he fails to disclose actually displaying the image itself.
However, Rohan discloses, and displaying medical images, (See Rohan ¶100, “The search result window W3 is a window for displaying the target image detected in the search execution processing (i.e. detected image Di). That is, as shown in FIG. 11, in the search result window W3, a detected image display area Ar62 is included in addition to a query image display area Ar61. In the detected image display area Ar62, the detected images Di are displayed in line in the order of their rank (i.e. in the order of their score) indicating the overall similarities with the query image.”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the displaying a list of images that are ordered by their rank as suggested by Rohan to Kim’s displaying of a list of images by their name. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is to display medical images to a doctor, thereby improving diagnostic accuracy and preventing medical errors by allowing for visual confirmation immediately without the need to open each image file.
Regarding claim 2, Kim and Rohan disclose, the method of claim 1, wherein when the metadata is numerical metadata, the variable relevance criterion level is a numerical range, (See Kim p. 10, 9th para, “A condition for medical data may be search meta information. The search meta-information may include at least one of setting an item value range for one or more items included in the meta-information or setting whether to correspond to an item condition. Meta information may include one or more items. Meta information may include different items for each type of medical data. As described above, for example, the meta information may include items such as the size, location, and diameter of the lesion.”)
and the displaying includes: identifying a numerical range corresponding to the variable relevance criterion level; (See Kim p. 10, 9th para, “For example, the search meta-information may include a condition included in the range of 6 mm to 8 mm for the diameter of the lesion.”)
and selecting a medical image corresponding to metadata included in the numerical range as the associated medical image. (See Kim p. 10, 2nd to last para, “For example, with respect to the nodule diameter item 420, a user setting input for a diameter range may be received. When the diameter range input by the user is 6 mm or more, the processor 120 may extract medical data having a lesion of 6 mm or more as a search result.”)
Regarding claim 4, Kim and Rohan disclose, the method of claim 1, wherein the metadata is divided according to a plurality of features, and a distinct user interface element that allows the relevance criterion level to be adjusted for each divided metadata is implemented, (See Kim p. 10, 9th para, “A condition for medical data may be search meta information. The search meta-information may include at least one of setting an item value range for one or more items included in the meta-information or setting whether to correspond to an item condition. Meta information may include one or more items. Meta information may include different items for each type of medical data. As described above, for example, the meta information may include items such as the size, location, and diameter of the lesion.”
and the displaying includes: setting a placement area of the associated medical image for each classified metadata; and displaying the associated medical image on each set placement area. (See Kim p. 11 2nd para, “The processor 120 may display information on one or more pieces of medical data included in the search results on the user interface. Hereinafter, search results will be described with reference to FIG. 5. 5 is a diagram illustrating a user interface including search results by way of example.”
Further see Kim p. 11, 4th para, “The processor 120 may display medical data or additional information matched to the medical data on the user interface in the order of highest degree of correspondence. For example, if there is one piece of medical data corresponding to all conditions set by the user, the processor 120 may display the corresponding medical data at the top of the search result list. The processor 120 may display medical data with a higher degree of correspondence to a condition set by the user at a higher position in the search result list.”)
Regarding claim 5, Kim and Rohan disclose, the method of claim 4, further comprising: displaying an associated medical image satisfying a first relevance criterion level corresponding to a first feature related to the metadata on a first placement area; and displaying an associated medical image satisfying a second relevance criterion level corresponding to a second feature on a second placement area. (See Kim p. 11, 4th para, “The processor 120 may display medical data or additional information matched to the medical data on the user interface in the order of highest degree of correspondence. For example, if there is one piece of medical data corresponding to all conditions set by the user, the processor 120 may display the corresponding medical data at the top of the search result list. The processor 120 may display medical data with a higher degree of correspondence to a condition set by the user at a higher position in the search result list.”)
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Park et al. (KR 102534088 B1) [See Google Translation] in view of Rohan (US Pub. No. 2010/0268732 A1) and in further view of Kim (KR 20160084147 A) [See Google Translation].
Regarding claim 3, Park and Rohan disclose, the method of claim 1, but they fail to disclose, wherein when the metadata is text metadata, the text metadata is assigned to any one of categories hierarchized into a plurality of layers, and the displaying includes: selecting any one of the plurality of layers according to the relevance criterion level; and selecting a medical image of metadata included in the category of the selected layer as the associated medical image.
However, Kim discloses, wherein when the metadata is text metadata, the text metadata is assigned to any one of categories hierarchized into a plurality of layers, and the displaying includes: The data structure generation module 520 may generate a data structure for storing information (e.g., metadata) related to the image, for example. According to one embodiment, the data structure generation module 520 may generate a hierarchical data structure (e.g., a tree structure) including a plurality of layers including at least a first layer and a second layer, for example.
selecting any one of the plurality of layers according to the relevance criterion level; and selecting a medical image of metadata included in the category of the selected layer as the associated medical image. (See Kim, p. 18, 4th para, “According to one embodiment, the plurality of electronic devices 1121 through 1125 may send an image provision request 1136 to the server 1110, including, for example, level information relating to image provisioning. The server 1110 can determine the level of the image to be provided based on the level information included in the image providing request 1136. According to one embodiment, the server 1110 may determine the level of the image to be provided (see 1135) at the same level as the level information contained in the image provision request 1136. Alternatively, the server 1110 may provide, based on the level information contained in the image provision request and other information (e.g., the availability of an electronic device (e.g., the fifth electronic device 1125) You can determine the level of the image to be made.”
Further see Kim p. 18, last para, “According to one embodiment, in operation 1225, the search server 1202 may search for a block included in the image 1210 using the metadata of the image 1210 based on the search condition 1220. The search server 1202 may retrieve a block (or sub-block) 1230 corresponding to at least a portion of the search condition 1220 (e.g., a shot location 1222) from metadata forming a multi-level hierarchical structure, for example. In addition, the search server 1202 may determine the level of the block based on another part of the search condition 1220 (e.g., resolution 1224, depth 1225). For example, when the retrieved block corresponds to a lower-level sub-block in the hierarchical structure, the search server 1202 searches for a lower-level sub-block corresponding to the other part (for example, resolution 1224 and depth 1225) Blocks can be searched.”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the hierarchical level searching of images as suggested by Kim to Park and Rohan’s displaying of images that are retrieved based on relevant criteria levels. This can be done using known engineering techniques, with a reasonable expectation of success. The motivation for doing so is granularity control by the user, whereby you can choose the exact level of detail you need, such as searching broadly or specifically.
Allowable Subject Matter
Claims 6-7 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.
Regarding claim 6, the method of claim 5, wherein the setting of the placement area includes: setting an overlapping area in which the first placement area and the second placement area overlaps partially; and displaying an associated medical image satisfying both the first relevance criterion level and the second relevance criterion level on the overlapping area. (The disclosed prior art of record fails to disclose the limitations of this claim.)
Regarding claim 7, this claim is objected to since it depends from objected to claim 6.
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
Irani (US Pat. No. 12,586,179 B1) A computer-implemented method of evaluating a user image of a patient to enable identification of a historical twin of the patient. The method includes organizing a plurality of medical images in an archive and receiving from a medical professional each of: (i) a region of interest; (ii) a textual description; (iii) selections for binary criteria; and (iv) weights of weighable criteria. The method comprises using a natural language search to create a relevant set of medical images and creating an optimal set from the relevant set of medical images by discarding medical images from the relevant set based at least on the selections for binary criteria.
Bates (US Pub. No. 2020/0159716 A1) Systems, methods, and apparatus providing hierarchical data filtering are disclosed and described. An example system includes a hierarchical data filter including a plurality of levels including at least a parent level and a child level, wherein a parent filter is to apply to the parent level and the child level and a child filter is to apply to the child level. An example processor is to execute the instructions to at least: analyze data to match a type of the data to a level in the plurality of levels of the hierarchical data filter; apply one or more filters from the hierarchical data filter to the data to filter the data according to the corresponding level of the data from the hierarchical data filter; and output filtered data from the applying of the one or more filters to the data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID PERLMAN whose telephone number is (571) 270-1417.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chineyere Wills-Burns can be reached at (571) 272-9752. The fax phone number for the organization where this application or proceeding is assigned is
(571) 273-8300.
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/DAVID PERLMAN/Primary Examiner, Art Unit 2673
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