CTNF 18/818,976 CTNF 82537 Notice of Pre-AIA or AIA Status 07-03-aia AIA 15-10-aia 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 Claims 1-15 are pending in this application and have been examined in response to application filed on 08/29/2024. CONTINUING DATA: This application has PRO 63/535,374 08/30/2023 Claim Objections 07-29-01 AIA Claim 10 objected to because of the following informalities: reference number “(108)” should not be in the claim . Appropriate correction is required. Claim Rejections - 35 USC § 102 07-06 AIA 15-10-15 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. 07-07-aia AIA 07-07 The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action: A person shall be entitled to a patent unless – 07-08-aia AIA (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. 07-15 AIA Claim s 1-6, 9-10 and 12-14 are rejected under 35 U.S.C. 102( a)(1 ) as being unpatentable by Li et al. (US 2020/0134825 A1) . As to INDEPENDENT claim 1, Li discloses a system configured to assist in the medical imaging analysis of a subject, the system comprising: an electronic device comprising: a display device configured to display an image analysis interface (fig.1, “310”; a display unit is described); a user input device configured to receive user input from a user (fig.1, “330”; an operation input unit is described); a computer-readable storage medium having stored thereon machine-readable instructions to be executed by one or more processors ([0190]; a storage medium is described); and one or more processors configured by the machine-readable instructions stored on the computer-readable storage medium to perform the following operations: (i) receive imaging data of the subject; (ii) generate and display on the display device an image analysis interface, wherein the image analysis interface comprises the imaging data and a menu of user-selectable options; (iii) receive user input via the user input device, wherein the user input includes a selection of one or more trained artificial intelligence models; and (iv) analyze the imaging data based on the user input received (fig.1, fig.19, fig.20; a medical image of a subject is captured by an imaging unit and displayed on a UI, wherein the user can select the “Settings” button to select an artificial intelligence model to analyze the displayed medical image). As to claim 2, Li discloses wherein the one or more trained artificial intelligence models includes at least one of a trained object localization model and a trained feature segmentation model (fig.20; [0039]; a model for picking out a region of interest (ROI) of a medical image is selectable). As to claim 3, Li discloses wherein the one or more processors are further configured to receive user input via the user input device, wherein the user input includes an input parameter used to modify the operation of the one or more trained artificial intelligence models (fig.20; [0179]; parameter such as the model size is adjustable). As to claim 4, Li discloses wherein the input parameter includes a region of interest within at least one medical image generated based on the received imaging data of the subject (fig.20, “1922”; auto selecting a region of interest is available to select). As to claim 5, Li discloses wherein image analysis interface generated by the one or more processors and displayed on the display device further comprises an output of the one or more trained artificial intelligence models (fig.19, fig.20; user setting based results are displayed). As to claim 6, Li discloses wherein the output of the one or more trained artificial intelligence models includes at least one of: a bounding box identifying a region within at least one medical image generated based on the received imaging data of the subject, wherein the region contains an anatomical feature of the subject identified by the one or more trained artificial intelligence models; a bounding polygon segmenting an anatomical feature within at least one medical image generated based on the received imaging data of the subject, wherein the segmented anatomical feature was identified by the one or more trained artificial intelligence models; and a classification of an anatomical feature within at least one medical image generated based on the received imaging data of the subject, wherein the anatomical feature was identified by the one or more trained artificial intelligence models (fig.19-fig.21; an anatomical feature is identified with a selected AI model and highlighted with a bounding box). As to claim 9, Li discloses a medical imaging device in communication with the electronic device and configured to obtain imaging data of the subject, wherein the medical imaging device includes at least one of an ultrasound imaging device, a magnetic resonance imaging machine, and a computed tomography machine (fig.1; “100”, “300”; [0005]; an ultrasound imaging device communicates with a user terminal is described). As to INDEPENDENT claim 10, Li discloses a point-of-care imaging system configured to assist in the medical imaging analysis of a subject, the system comprising: a medical imaging device configured to obtain imaging data of the subject (fig.1, “100”); and an electronic device in communication with the medical imaging device, wherein the electronic device comprises: a display device configured to display an image analysis interface; a user input device configured to receive user input from a user (fig.1, “300”); a computer-readable storage medium having stored thereon machine-readable instructions to be executed by one or more processors ([0190]; a storage medium is described); and one or more processors configured by the machine-readable instructions stored on the computer-readable storage medium to perform the following operations: (i) receive imaging data of the subject from the medical imaging device; (ii) generate and display on the display device an image analysis interface, wherein the image analysis interface comprises the imaging data and a menu of user-selectable options; (iii) analyze the received imaging data using at least a first trained artificial intelligence model (fig.1, fig.19, fig.20; a medical image of a subject is captured by an imaging unit and displayed on a UI, wherein the user can select the “Settings” button to select an artificial intelligence model to analyze the displayed medical image), wherein an output of the first trained artificial intelligence model includes a bounding box identifying a region within at least one medical image generated based on the received imaging data, and wherein the region includes an anatomical feature of the subject identified by the first trained artificial intelligence model (fig.19-fig.21; an anatomical feature is identified with a selected AI model and highlighted with a bounding box); (iv) receive user input via the user input device (108), wherein the user input includes an input parameter used to modify the operation of at least a second trained artificial intelligence model (fig.20; different models are selectable under “System Settings”); and (v) analyze the received imaging data using at least the second trained artificial intelligence model based on the input parameter received as user input (fig.19, fig.20; the result is displayed based on user defined settings). As to claim 12, Li discloses wherein the input parameter received as user input includes an adjustment of the output of the first trained artificial intelligence model (fig.20; [0179]; parameter such as the model size is adjustable). As to INDEPENDENT claim 13, Li discloses a computer-implemented method for personalized, semi-automatic feature analysis using a medical imaging system including a medical imaging device and an electronic device in communication with the medical imaging device, the method comprising: obtaining, via the medical imaging device, imaging data of a subject (fig.1; “100”, “300”; [0005]; an ultrasound imaging device communicates with a user terminal is described); displaying, on a display device of the electronic device, an image analysis interface comprising one or more images generated based on the obtained imaging data, and a menu of user-selectable options; receiving, via a user input device of the electronic device, user input including a selection of at least a first trained artificial intelligence model, wherein at least the first trained artificial intelligence model is selected from the menu of user-selectable options; in response to receiving the selection of at least the first trained artificial intelligence model, analyzing the obtained imaging data using at least the first trained artificial intelligence model (fig.1, fig.19, fig.20; a medical image of a subject is captured by an imaging unit and displayed on a UI, wherein the user can select the “Settings” button to select an artificial intelligence model to analyze the displayed medical image); updating the image analysis interface displayed via the display device of the electronic device to include an output of at least the first trained artificial intelligence model selected from the menu of user-selectable options (fig.19, fig.20; the result based on user settings is outputted); receiving, via the user input device of the electronic device, user input including an input parameter used to modify the operation of one or more trained artificial intelligence models (fig.20, “System Settings”; input parameters are modifiable); receiving, via the user input device of the electronic device, user input including a selection of at least a second trained artificial intelligence model selected from the menu of user-selectable options; in response to receiving the selection of at least the second trained artificial intelligence model, analyzing the obtained imaging data using at least the second trained artificial intelligence model and the input parameter received as user input; and updating the image analysis interface displayed via the display device of the electronic device to include an output of at least the second trained artificial intelligence model (fig.1, fig.19, fig.20; a medical image of a subject is captured by an imaging unit and displayed on a UI, wherein the user can select the “Settings” button to select an artificial intelligence model to analyze the displayed medical image). As to claim 14, Li discloses wherein the input parameter received as user input includes an adjustment of the output of the first trained artificial intelligence model (fig.20; [0179]; parameter such as the model size is adjustable) . Claim Rejections - 35 USC § 103 07-06 AIA 15-10-15 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. 07-20-aia AIA 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. 07-21-aia AIA Claim s 7-8, 11 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Gao et al. (US 2025/0308663 A1) . As to claim 7, Li discloses wherein the menu of user-selectable options of the image analysis interface comprises: a first option to select a first trained artificial intelligence model from the one or more trained artificial intelligence models (fig.20; “1930”; different ML models are selectable); a second option to select a second trained artificial intelligence model from the one or more trained artificial intelligence models, wherein the second trained artificial intelligence model is a trained feature segmentation model (fig. 20; [0154]; features are analyzed and segmented); a third option to modify an output of at least one of the first and second trained artificial intelligence models (fig.20; different display settings such as “ROI” and “UV” are selectable); and a fourth option to provide an input parameter used to modify the operation of at least one of the first and second trained artificial intelligence models (fig.19, “”1901”; parameters of a selected model are adjustable). Li does not expressly disclose wherein the first trained artificial intelligence model is a trained object localization model. In the same field of endeavor, Gao discloses a trained object localization model ([0095]; different ML models including an object localization model are selectable). It would have been obvious to one of ordinary skill in the art, having the teaching of Li and Gao before him prior to the effective filling date, to modify the medical imaging analysis user interface taught by Li to include different image analysis models taught by Gao with the motivation being to enhance usability by allowing the user to select different image analysis models. As to claim 8, Li does not expressly wherein the display device is a touch-enabled display, the user input device includes the touch-enabled display, and the user input comprises touch data received via the touch-enabled display. In the same field of endeavor, Gao discloses wherein the display device is a touch-enabled display, the user input device includes the touch-enabled display, and the user input comprises touch data received via the touch-enabled display ([0138], [0139]; a touchscreen input display is described). It would have been obvious to one of ordinary skill in the art, having the teaching of Li and Gao before him prior to the effective filling date, to modify the medical imaging analysis user interface taught by Li to include a touchscreen taught by Gao with the motivation being to enhance usability by allowing touch inputs. As to claim 11, Li discloses wherein the second trained artificial intelligence model is a trained feature segmentation mode (fig. 20; [0154]; features are analyzed and segmented). Li does not expressly disclose the first trained artificial intelligence model is a trained object localization model. In the same field of endeavor, Gao discloses the first trained artificial intelligence model is a trained object localization model ([0095]; different ML models including an object localization model are selectable). It would have been obvious to one of ordinary skill in the art, having the teaching of Li and Gao before him prior to the effective filling date, to modify the medical imaging analysis user interface taught by Li to include different image analysis models taught by Gao with the motivation being to enhance usability by allowing the user to select different image analysis models. As to claim 15 is rejected under the same rationale addressed in the rejection of claim 11 above. Conclusion Any inquiry concerning this communication or earlier communications from the examiner should be directed to HAOSHIAN SHIH whose telephone number is (571)270-1257. The examiner can normally be reached M-F 8:00-5:00. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, FRED EHICHIOYA can be reached at (571) 272-4034. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /HAOSHIAN SHIH/Primary Examiner, Art Unit 2179 Application/Control Number: 18/818,976 Page 2 Art Unit: 2179
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