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 .
Response to Amendment
Applicant’s amendment was received on 7/14/25 and has been entered and made of record. Currently, claims 1-16 and 18-20 are pending.
Drawings
The drawings were received on 7/14/25. These drawings are accepted.
Applicant’s amendment to the specification and to Figs. 2, 4A, 15, and 18 ha overcome the objection set forth in the previous Office Action and has therefore been withdrawn.
Specification
The disclosure is objected to because of the following informalities: In paragraph 80, reference numeral 138 should be 38. Appropriate correction is required.
Claim Rejections - 35 USC § 101
Applicant’s amendment to claims 1 and 20, and the arguments presented on pages 8-11 of the remarks, integrates the abstract idea into a practical application and therefore the rejection set forth in the previous Office Action has been withdrawn.
Response to Arguments
Applicant’s arguments, see pages 11-13 of the remarks, filed 7/14/25, with respect to the rejection(s) of claim(s) 1 and 20 under 35 USC 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of newly found prior art necessitated by the current amendment.
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.
Claims 1-16 and 18-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Nozaki et al. (US 2021/0256701) in view of Voegele et al. (US 2008/0255537).
Regarding claim 1, Nozaki discloses a system for recognizing a medical condition in a patient, the system comprising:
an imaging device having a light source and a camera for capturing images of a tissue in the patient (see paras 41, 44, 188, 190, and 192-196, an endoscope is used to capture images of tissue located inside a patient); and
a processor coupled to the imaging device and having a software application with a first set of instructions for recognizing the images captured by the imaging device and a second set of instructions for determining if the tissue contains a medical condition based on the images and the physiological parameter detected by the one or more sensors (see paras 41, 44-46, 63-67, and 190-191, a database of non-image data of a subject and reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images and non-image data, captured images are compared to reference images to determine if a tissue contains a medical condition).
Nozaki does not disclose expressly a coupler device removably coupled to a distal end of the imaging device, the coupler device comprising one or more sensors for detecting a physiological parameter of tissue around the coupler device, wherein the physiological parameter comprises one of a temperature, a type of fluid around the tissue, a presence of pathogens, a dimension of the tissue, a presence of a biological receptor, and a PH of fluid around the tissue.
Voegele discloses a coupler device removably coupled to a distal end of the imaging device, the coupler device comprising one or more sensors for detecting a physiological parameter of tissue around the coupler device, wherein the physiological parameter comprises one of a temperature, a type of fluid around the tissue, a presence of pathogens, a dimension of the tissue, a presence of a biological receptor, and a PH of fluid around the tissue (see Fig. 6 and paras 42 and 46, a sensor can be attached to the distal end of an endoscope that measures temperature).
Regarding claim 20, Nozaki discloses a system for using machine learning to recognize a medical condition in a patient, the system comprising:
an endoscope configure to collect optical images of the patient (see paras 41, 44, 188, 190, and 192-196, an endoscope is used to capture images of tissue located inside a patient);
a processor with a software application having at least one trained machine learning algorithm (see paras 39, 41, and 49, processing system 10 contains a plurality of processors used to implement a trained machine learning algorithm);
a memory in communication with the processor and containing images of representative tissue (see paras 40-41, processing system 10 contains a memory for storing reference images);
wherein the software application has a first set of instructions for recognizing images of tissue in the patient and a second set of instructions for causing the processor to compare the images of the tissue in the patient with the images of representative tissue (see paras 41, 44-46, 63-67, and 190-191, captured images are compared to reference images to determine if a tissue contains a medical condition); and
wherein the trained machine learning algorithm is configured to develop from the images of representative tissue and the physiological parameter at least one set of computer-executable rules useable to recognize the medical condition in the images of the tissue in the patient (see paras 41, 44-46, 63-67, 190-191, 207, 209-213, 217, 220-223, and 232, a database of non-image data of a subject and reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images and non-image data, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Nozaki does not disclose expressly a coupler device removably coupled to a distal end of the endoscope, the coupler device comprising one or more sensors for detecting a physiological parameter of tissue around the coupler device.
Voegele discloses a coupler device removably coupled to a distal end of the endoscope, the coupler device comprising one or more sensors for detecting a physiological parameter of tissue around the coupler device (see Fig. 6 and paras 42 and 46, a sensor can be attached to the distal end of an endoscope that measures temperature).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine the physiological parameter sensor that measures temperature, as described by Voegele, with the system of Nozaki.
The suggestion/motivation for doing so would have been to provide further information of a subject that could provide more information about the presence or absence of an abnormality or disease thereby increasing system efficiency.
Therefore, it would have been obvious to combine Voegele with Nozaki to obtain the invention as specified in claims 1 and 20.
Regarding claim 2, Nozaki further discloses a memory in communication with the processor, wherein the memory contains images of representative tissue and wherein the second set of instructions causes the processor to compare the images of the tissue captured by the imaging device with the images of representative tissue (see paras 41, 44-46, 63-67, and 190-191, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 3, Nozaki further discloses wherein the software application is configured to develop from the images of representative tissue at least one set of computer- executable rules useable to recognize a medical condition in the tissue images captured by the optical imaging device (see paras 41, 44-46, 63-67, 190-191, 207, 217, and 220-223, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 4, Nozaki further discloses an artificial neural network coupled to the processor comprising at least one trained machine learning algorithm configured to recognize the medical condition based on the images of representative tissue (see paras 41, 44-46, 63-67, 190-191, 207, 209-213, 217, 220-223, and 232, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 5, Nozaki further discloses wherein the images of representative tissue include images from patients with known medical conditions, disorders or diseases (see paras 45, 57, 60, and 217, reference images can be from patients with known gastric medical issues, such as cancer).
Regarding claim 6, Nozaki further discloses wherein the memory contains images of tissue from previous surgeries on the patient (see para 44, a reference image can be an earlier image of the patient).
Regarding claim 7, Nozaki further discloses wherein the second set of instructions causes the processor to identify objects in the tissue images and wherein the memory includes a set of representative objects associated with the medical condition (see paras 190-191, 199, 204, 207, 217, and 221-223, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 8, Nozaki further discloses wherein the software application includes a third set of instructions for comparing the objects in the tissue images with the representative objects to determine if the tissue contains a medical condition (see paras 190-191, 199, 204, 207, 217, and 221-223, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 9, Nozaki further discloses wherein the second set of instructions causes the processor recognize abnormalities in the tissue based on characteristics of the representative tissue (see paras 190-191, 199, 204, 207, 217, and 221-223, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 10, Nozaki further discloses wherein the abnormalities are selected from the group consisting essentially of tissue color, tissue texture, tissue shape, tissue size and tissue topography (see paras 188, 190-191, 199, and 204, abnormalities include tissue color, shape, size, and topography).
Regarding claim 11, Nozaki further discloses wherein the second set of instructions causes the processor to determine if the tissue deviates from a threshold value (see paras 46-46, 60, 63-67, 190-191, 104, and 207, reference images can be categorized by severity or stage of the cancer, thresholds are used to determine the different severities or stages).
Regarding claim 12, Nozaki further discloses wherein the processor includes a third set of instructions for determining a differentiation value of the tissue images, wherein the differentiation value provides a quantitative measure for a grade or level of development of the medical condition, disorder or disease (see paras 46-46, 60, 63-67, 190-191, 104, and 207, reference images can be categorized by severity or stage of the cancer, thresholds are used to determine the different severities or stages).
Regarding claim 13, Nozaki further discloses wherein the medical condition is a cancerous tissue, a tumor, a polyp, an ulcer, a lesion, an inflammation or a diseased tissue (see paras 61, 190-191, 199, and 204, the medical condition can be cancer, a tumor, lesion, diseased tissue).
Regarding claim 14, Nozaki further discloses wherein the images of representative tissue comprises a topographic representation of tissue within a target area of the patient (see Fig. 24 and paras 204-205, reference images contain topographic representations).
Regarding claim 15, Nozaki further discloses wherein the imaging device is an optical imaging device (see paras 41, 44, 188, and 190, an endoscope is an optical imaging device).
Regarding claim 16, Voegele further discloses wherein the coupler device comprises a main body having a visualization section configured to allow viewing of the surgical site, and an attachment section having a proximal end configured for removable attachment to a distal end portion of the optical imaging device (see Fig. 6 and paras 42 and 46, a sensor can be attached to the distal end of an endoscope that measures temperature, the endoscope contains a camera for acquiring images of a surgical site).
Regarding claim 18, Nozaki further discloses wherein the memory contains data regarding physiological parameters of known medical conditions and wherein the software application comprises a third set of instructions for recognizing the medical condition in the patient based on the physiological parameter detected by the sensor and the physiological parameters of known medical conditions (see paras 41, 44-46, 63-67, 190-191, 207, 217, and 220-223, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Regarding claim 19, Nozaki further discloses an artificial neural network coupled to the processor comprising at least one trained machine learning algorithm configured to develop from the physiological parameters at least one set of computer-executable rules useable to recognize a medical condition in the physiological parameters detected by the one or more sensors (see paras 41, 44-46, 63-67, 190-191, 207, 209-213, 217, 220-223, and 232, a database of reference images of abnormal images, images with gastric cancer, lesions, tumors, etc. are stored, a deep learning model can also be trained with these reference images, captured images are compared to reference images to determine if a tissue contains a medical condition, the process can be performed automatically by a processor or by a deep learning model, AI).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MARK R MILIA whose telephone number is (571)272-7408. The examiner can normally be reached Monday-Friday, 8am-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, Akwasi Sarpong can be reached at 571-270-3438. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MARK R MILIA/Primary Examiner, Art Unit 2681
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