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 .
Election
Applicants’ election of claims 7-12, without traverse, is acknowledged and made final. Claims 1-6, 13-15, are withdrawn from further consideration.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/07/2025 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 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.
Claim(s) 7-12, is/are rejected under 35 U.S.C. 103 as being unpatentable over Yazdi (US 20180192864) in view of Hunter (US 20220375114) and further in view of Inglis (US 20210259536).
Regarding claim 7, Yazdi discloses a video laryngoscope (FIG. 1; abstract) comprising:
a handle portion (handle portion 108);
a display screen (monitor 106; FIG.1) coupled to the handle portion;
a blade portion (blade portion 110), coupled to the handle portion and coupled to a blade (blade 102; FIG. 1; blade identification; para [0062]), configured to be inserted into a mouth of a patient;
a camera (camera module 114; FIG. 1), positioned at a distal end of the blade portion, that acquires a video feed while the video laryngoscope is powered on (Para [0020]).
Yazdi does not expressly disclose a memory storing a trained machine-learning (ML) model; and a processor that operates to: receive an image of the video feed from the camera, the image including a portion of the blade; provide the image as input to the trained ML model; receive an output from the trained ML model in response to the input image, wherein the output includes an identification of the blade; and transmit the blade identification to a remote device.
Hunter is directed to methods for performing three-dimensional measurements of a surgical space using two-dimensional endoscopic images (abstract) and teaches a memory storing a trained machine-learning (ML) model (Machine learning models to identify a tool in an image; Para [0064], [0270]); and
a processor that operates to: receive an image of the video feed from the camera (video data taken from an endoscopic imaging device; Para [0007), the image including a portion of the blade (The type of the tool in the pictured surgical space can be identified using a machine learning model; Para [0222], [0223], [0259]; The identified tool can be a shaver; Para [0259]);
provide the image as input to the trained ML model (images are used for machine learning model; Para [0222]);
receive an output from the trained ML model in response to the input image, wherein the output includes an identification of the blade (Tool identification; The machine learning models can include an instrument type classifier that is configured to identify the type of tool pictured in a surgical space; Para [0222]; Tool can be a shaver; Para [0259]; The instrument identification machine learning model is trained using one or more training images annotated with a type of instrument pictured in the training image. Para [0031]).
Inglis is directed to laryngoscope system (abstract) and teaches transmitting a blade identification to a remote device (laryngoscope 12 may be configured to provide a laryngoscope ID (e.g., numerical or descriptive identifier) to the monitor 50; para [0033]; Display the laryngoscope ID on the display screen 54 to enable the medical professional to confirm that the monitor 50 is receiving data from the appropriate laryngoscope 12. Para [0028], [0030]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yazdi to have/include machine learning model in accordance with the teaching of Hunter so that the tool used in the surgery could be identified and tracked during surgery using a machine learning model.
Further, it would have been obvious to further modify Yazdi to so that the laryngoscope identification information could be sent to a remote device (e.g., a display) in accordance with the teaching of Inglis because sending identification data to a display can be used to confirm that the monitor is receiving data from an appropriate laryngoscope (para [0033] of Inglis).
Regarding claim 8, Yazdi as modified, teaches wherein the remote device is a hospital computer for maintaining at least one of inventory or patient records (Inglis: Remote device can be a computing system, a display, and/or a hospital data storage system. Para [0030]).
Regarding claim 10, Yazdi as modified, discloses wherein the blade identification is displayed on the display screen (Inglis: laryngoscope 12 may be configured to provide a laryngoscope ID (e.g., numerical or descriptive identifier) to the monitor 50; para [0033]; Display the laryngoscope ID on the display screen 54 to enable the medical professional to confirm that the monitor 50 is receiving data from the appropriate laryngoscope 12. Para [0028], [0030).
Regarding claim 9, Yazdi disclose wherein the video laryngoscope further includes lighting on the blade portion (camera module 114; lighting elements; FIG. 1; Para [0018]), and wherein the processor further operates to adjust at least one of a lighting setting of the lighting or a camera setting of the camera, based on the blade identification ( The data cable have a most up-to-date set of image capture settings based on the identified laryngoscope blade; and transmit the most up-to-date set of image capture settings to the laryngoscope blade for use in capturing intra-airway images; abstract; Para [0030]).
Regarding claim 11, Yazdi as modified, teaches wherein the blade identification is determined automatically (Hunter: Since the learning algorithm is used, blade identification is determined automatic.).
Regarding claim 12, Yazdi as modified, teaches wherein the determination of the blade identification is based only on the image. (Hunter: The image of the tool is processed by the machine learning algorithm for determining the tool; para [0270]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO – 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHANKAR R GHIMIRE whose telephone number is (571)272-0515. The examiner can normally be reached 8 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anhtuan Nguyen can be reached on 571-272-4963. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHANKAR RAJ GHIMIRE/Examiner, Art Unit 3795
/ANH TUAN T NGUYEN/Supervisory Patent Examiner, Art Unit 3795 4/5/26