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 .
Status of Claims
In the preliminary amendment filed June 2, 2025, Applicant amended claims 1-13, and claim 15 was canceled. Claims 1-14 are pending in the current application.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) received on June 6, 2025 has been considered by examiner.
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 1-14 are rejected under 35 U.S.C. 103 as being unpatentable over Paik et al. (US 2021/0264212 A1) in view of Garnavi et al. (US 10,002,311 B1).
Regarding claims 1 and 13, Paik discloses a method for controlling an interface displaying medical images to a user on a display, the method comprising:
receiving a medical image of a patient (Paragraph [0066]: display said medical image on said display);
displaying the medical image at the display for the user to perform an image reading (Paragraph [0066]: display said medical image on said display);
identifying a region of interest of the user in the medical image using an eye tracker configured to provide eye tracking data tracked from the user (Paragraph [0066]: detect said position or said movement of said eye of said user with said eye-tracking component);
analyzing the region of interest using a first artificial intelligence (Al) module (Paragraph [0066]: analyze said medical image and identify a plurality of features within said medical image);
determining an anatomical structure from the medical image by correlating the identified region of interest with data from the first Al module (Paragraph [0066]: determine a feature of said plurality of features upon which said user has directed their vision, based at least in part on said position or said movement of said eye of said user );
controlling the interface and the displayed medical image depending on the eye tracking data (Paragraph [0066]: receive an input from said user; and associate said feature with said input from said user).
Paik discloses the limitations above. Paik does not explicitly disclose:
evaluating a fatigue status of the user using a second artificial intelligence (Al) module indicating an ability of the user to perform the image reading, wherein the second Al module uses the eve tracking data from the eve tracker.
Garnavi teaches:
evaluating a fatigue status of the user using a second artificial intelligence (Al) module indicating an ability of the user to perform the image reading, wherein the second Al module uses the eye tracking data from the eye tracker (Column 8, lines 6-10): The eye tracking patterns can also be used to identify onset of fatigue in the expert. For example, when the expert starts to get tired his eye fixation is not steady and his eye gaze movement is beyond the normal acceptable range for normal gazing).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Paik to disclose evaluating a fatigue status of the user using a second artificial intelligence (Al) module indicating an ability of the user to perform the image reading, wherein the second Al module uses the eye tracking data from the eye tracker as taught by Garnavi. Using the annotated image system of Garnavi can determine whether recorded data may not be accurate, so the period of time during which an expert's gaze is associated with fatigue, may be discounted or not used as part of eye-gaze data in generating the knowledgebase or learning model to generate a more accurate model (Garnavi Column 8, lines 10-16).
Regarding claim 2, Paik discloses the method according to claim 1, further comprising simultaneously displaying on the display the current medical image and the region of interest, wherein the medical image and the region of interest are displayed next to each other on the display (Fig. 5; Paragraph [0128]).
Regarding claim 3, Paik discloses the method according to claim 1, further comprising simultaneously displaying the current medical image of the patient and/or displaying a historical medical image of the patient on the display, wherein the first Al module analyses and indicates a region of interest of the historical medical image similar to the region of interest of the current medical image (Fig. 5; Paragraph [0128]).
Regarding claim 4, Paik discloses the method according to claim 1, further comprising:
displaying the region of interest by the first Al module in the medical image displayed on the display (Paragraph [0080]);
generating an enhanced view of the indicated region of interest by the first Al module (Paragraph [0080]);
removing the enhanced view of the indicated region of interest by the first Al module (Paragraph [0080]).
Regarding claim 5, Paik discloses the method according to claim 4, wherein, when the eye tracker determines that the user is staring at the region of interest for a period of time, indicating the region of interest with a boundary using the first Al module, wherein the boundary is a rectangle, wherein the generating of the enhanced view comprises improving the quality of the enhanced view of the medical image, wherein, when the eye tracker determines that the user removes his view from the region of interest, removing the enhanced view of the region of interest (Paragraphs [0127] and [0129]).
Regarding claim 6, Paik discloses the method according to claim 1, further comprising:
wherein analyzing the region of interest using the first Al module comprises at least one of an object detection Al, a segmentation Al, and an instance segmentation Al (Paragraph [0082]),
wherein the object detection Al uses a deep convolutional neural network configured for object detection (Paragraph [0082]),
wherein the segmentation Al uses a deep convolutional neural network configured for segmentation (Paragraph [0082]).
Regarding claim 7, Paik discloses the method according to claim 1, further comprising
correlating the image reading of the medical image by the user with reading guidelines using the first Al module (Paragraph [0174]),
indicating at the medical image whether the image reading was performed in line with the reading guidelines or not (Paragraph [0174]).
Regarding claim 8, Paik discloses the method according to claim 1, wherein controlling the interface comprises at least one of controlling the size of the medical image, controlling a part of the medical image, controlling the size of the part of the medical image, controlling a scrolling through a plurality of medical images, controlling medical image data to be displayed, controlling a highlighting of the medical image and/or part of the medical image, and controlling patient data displayable at the display with reference to the medical image (Paragraph [0082]).
Regarding claim 9, Paik discloses the method according to claim 1,wherein the eye tracking is used to monitor eye tracking data which is at least one of an eye movement of the user, a viewing direction of the eyes of the user, an average fixation time of the eyes, a pupil area, a period of time at which the user has looked at a point on the display, an eye lid movement, an eye color, and a head movement of the user (Paragraph [0122]).
Regarding claim 10, Paik discloses the method according to wherein the first Al module is trained with data from medical training images comprising information similar to the medical image to be displayed, wherein the medical training images used for training the first Al module are annotated by medical specialist annotating anatomical structures in each of the medical training images, and/or wherein the first Al module is a pre-trained Al module trained on non-medical data (Paragraph [0207]).
Regarding claim 11, Paik discloses the method according to claim 1, wherein the eye tracker, the first Al module and the second Al module work simultaneously during the displaying of the medical image to the user on the display (Paragraph [0066]).
Regarding claim 12, Paik discloses the method according to claim 1,wherein the second Al module evaluates at least one of an eye movement, eye lid movement, an eye color, and a pupil dilation for evaluating the ability of the user to perform the image reading (Paragraph [0122]).
Regarding claim 14, Paik discloses the system according to claim 12, further comprising a camera configured to communicate with the processor and configured for capturing at least one of an eye movement of the user, an eye lid movement, a viewing direction of the eyes of the user, an average fixation time of the eyes, a pupil area, a period of time at which the user has looked at a point on the display, and a head movement of the user (Paragraph [0122]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINYERE MPAMUGO whose telephone number is (571)272-8853. The examiner can normally be reached Monday-Friday, 9am-5pm.
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, Kambiz Abdi can be reached at (571) 272-6702. 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.
/CHINYERE MPAMUGO/Primary Examiner, Art Unit 3685
Prosecution Insights