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
This office action for the 17/480739 application is in response to the communications filed June 24, 2025.
Claims 1, 4, 5, and 21 were amended June 24, 2025.
Claim 3 was cancelled June 24, 2025.
Claims 1, 2, 4-8, 10, 18-21 and 23 are currently pending and considered below.
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.
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, 4-8, 10, 19, 21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Kubota (US 2018/0028056) in view of Zaharchuk et al. (US 2019/0108634; herein referred to as Zaharchuk) in further view of Dirghangi et al. (US 2018/0220889; herein referred to as Dirghangi).
As per claim 1,
An information processing apparatus comprising:
Kubota teaches a storage unit configured to store information individually set for each of a first ophthalmologic imaging device and a second ophthalmologic imaging as transmission settings for a plurality of pieces of imaging data obtained by the first ophthalmologic imaging and the second ophthalmologic imaging, wherein the first ophthalmologic imaging is a type of imaging different from the second ophthalmologic imaging:
(Paragraphs [0007], [0019], [0021], [0043], [0119] and [0130] of Kubota. The teaching describes an ophthalmic image processing apparatus of an embodiment includes a storage unit, a first brightness profile generation unit, a second brightness profile generation unit, and a brightness correction unit. The storage unit is configured to store image data acquired through scanning a subject's eye using optical coherence tomography. Types of the OCT images include an image in an arbitrary cross section mode, a shadowgram obtained by projecting an arbitrary area of three-dimensional data set, a blood vessel enhanced image (i.e., angiogram), and the like. Types of the image in an arbitrary cross section mode include a B mode image, a C mode image, a multi planar reconstruction (MPR) image, and the like. Transmission settings between B mode and C mode are different types of imaging as settings. The ophthalmic imaging apparatus may include a modality other than OCT. Such a modality may be a fundus camera, a scanning laser ophthalmoscope (SLO), a slit lamp microscope, an ophthalmic surgical microscope, or the like. Between the OCT device and these other device which may be included in the apparatus, this constitutes first and second ophthalmologic imaging devices. The three-dimensional data set D formed by the ophthalmic OCT apparatus (or by the computer that processes the data acquired by the OCT apparatus) is transmitted to and stored in an image management server installed in a medical institution, a network, or the like, for example. The B mode image forming unit 415 of the data processor 40 forms a B mode image based on the three-dimensional data set D. The setting of the cross section for the formation of the B mode image is executed by the user or the B mode image forming unit 415. The B mode image display controller 114 displays the formed B mode image H in the B mode image display region 1400 (see FIG. 7). This constitutes transmission settings for the imaging modality. Given that the claim recites the binary alternative of automatic, (i.e. the transmission is either automatic or not) any form of transmission setting would read on this claim language because all information transmission can be separated between automatic and not automatic. As a specific example, when part of the choroid, part of the vitreous body, or the like is designated as a slice area, it is possible to exclude such a slice area from the subject of synthetic front image formation. This means that an automatic setting of transmission (i.e. whether or not an image is automatically a front image) is selected or not selected for a given image. The display screen and the displayed images may be the same as or similar to the aspects shown in FIG. 7. This interface has configurations for image size and location in the GUI.)
Kubota further teaches a transmission unit configured to transmit, after one of the first ophthalmologic imaging and the second ophthalmologic imaging is finished, by switching from an imaging screen of the one of the first ophthalmologic imaging and the second ophthalmologic imaging to another display screen that is not an imaging screen of the other one of the first ophthalmologic imaging and the second ophthalmologic imaging as a trigger to start transmission, imaging data obtained by the one of the first ophthalmologic imaging and the second ophthalmologic imaging information individually set for the one of the first ophthalmologic imaging and the second ophthalmologic imaging:
(Paragraphs [0021] and [0111]-[0124] of Kubota. The teaching describes that controlling the communication device included in the data input and output unit 30, the controller 10 sends the inputted patient ID to the image management server via the network. The image management server receives the patient ID, searches for the image data associated with the patient ID, and transmits the retrieved image data to the ophthalmic image processing apparatus 1. The ophthalmic image processing apparatus as described above, the ophthalmic imaging apparatus of the embodiment includes an optical system, a drive system, a control system, and a data processing system for performing OCT. The ophthalmic imaging apparatus is configured to be capable of performing, for example, Fourier domain OCT (in other words, frequency domain OCT). The Fourier domain OCT includes spectral domain OCT and swept source OCT. The spectral domain OCT is a technique of imaging the subject's eye by acquiring the spectra of interference light in a space-divisional manner using a broadband low coherence light source and a spectroscope and subjecting it to Fourier transform. The swept source OCT is a technique of imaging the subject's eye by acquiring the spectra of interference light by time-divisional manner using a wavelength sweep light source (wavelength tunable light source) and a photodetector (e.g., balanced photodiode etc.) and subjecting it to Fourier transform. The ophthalmic imaging apparatus may include a modality other than OCT. Such a modality may be a fundus camera, a scanning laser ophthalmoscope (SLO), a slit lamp microscope, an ophthalmic surgical microscope, or the like. These images are integrated into a GUI display controller where the user is able to select which images are displayed to the user. This means that there is a scenario where the user selects images relating to one modality to be in the GUI, and then the user can then go back and select other modalities to create a subsequent GUI. These changes function as a trigger to transmit imaging data.)
Kubota further teaches wherein the transmission unit is configured to, in a case where information that transmission of a report image is set to on as the transmission setting for the one type of imaging is stored and image quality enhancement processing is on as an initial display setting of a report screen, transmit, as the imaging data, a report image corresponding to a report screen displaying a second medical image obtained by performing the image quality enhancement processing on a first medical image obtained by the one type of imaging:
(Paragraphs [0088]-[0106] of Kubota. The teaching describes an image enhancement process by applying filers, correcting brightness or removing artifacts of medical images and then displaying these images.)
Kubota does not explicitly teach a display control unit configured to control a display unit to display a second medical image on a report screen, the second medical image being higher in image quality than a first medical image obtained through the one of a plurality of different types of imaging, the second medical image being generated from the first medical image by performing image quality enhancement processing using a trained model obtained by training with a medical image of a test subject.
However, Zaharchuk teaches a display control unit configured to control a display unit to display a second medical image on a report screen, the second medical image being higher in image quality than a first medical image obtained through the one of a plurality of different types of imaging, the second medical image being generated from the first medical image by performing image quality enhancement processing using a trained model obtained by training with a medical image of a test subject:
(Paragraphs [0022]-[0032] of Zaharchuk. The teaching describes the use of machine learning models to enhance the image quality of a medical image. For example, a deep learning network is trained using the true 100% full-dose CE-MRI images as the reference ground-truth. The non-contrast (zero-dose) MRI and the 10% low-dose CE-MRI are provided to the network as inputs, and the output of the network is an approximation of the full-dose CE-MRI. During training, this network implicitly learns the guided denoising of the noisy contrast uptake extracted from the difference signal between low-dose and non-contrast (zero-dose) images, which can be scaled to generate the contrast enhancement of a full-dose image)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the image enhancements of the teaching of Kubota, the image enhancement teachings of Zaharchuk. Paragraph [0038] of Zaharchuk teaches that the methods used to enhance images improve the visibility of medical images. This would have improved the images produced by Kubota. One of ordinary skill in the art would have added to the teaching of Kubota, the teaching of Zaharchuk based on this incentive without yielding unexpected results.
The combined teaching of Kubota and Zaharchuk does not explicitly teach wherein the transmission unit is configured to, in a case where information that automatic transmission of report image data is set to on as the transmission setting for the one type of imaging is stored and where the image quality enhancement processing is on as an initial display setting of the report screen, automatically transmit the report image data including the generated second medical image as the trigger to start transmission.
However, Dirghangi teaches wherein the transmission unit is configured to, in a case where information that automatic transmission of report image data is set to on as the transmission setting for the one type of imaging is stored and where the image quality enhancement processing is on as an initial display setting of the report screen, automatically transmit the report image data including the generated second medical image as the trigger to start transmission:
(Paragraphs [0002] and [0010] of Dirghangi. The teaching describes that the present invention is directed to a medical imaging device attachment with onboard sensor array and computational processing unit, which can be adapted to and reversibly attached to multiple models of binocular indirect ophthalmoscopes (“BIOs”), enabling enhanced diagnostic capabilities to ophthalmologists and optometrists beyond the traditional manual ophthalmic examination, such as wireless automatic capture and transmission of high-fidelity images directly from the perspective of a user performing an eye examination; while allowing the unimpaired, full use of the examination instrument via a custom form-fitted mechanical and optical design; and enabling simultaneous or time-delayed viewing and collaborative review of photographs or videos from said eye examination. The invention also includes an integrated system for onboard detection and enhancement of clinical imagery with ambient examination-related feedback to the user via visual and non-visual interactive notifications to aid in the diagnostic examination, as well as the coordinated collection, transmission, management, and maintenance of imaging and related metadata from ophthalmic examinations, and additionally allows for multi-user collaboration generated by one or more device(s) or networks of devices and multiple users. The design allows for transmission of data, an image trigger that automatically captures when certain features are present in the ophthalmoscope's viewfinder, manual focus, closed-loop and open-loop autofocus, and other optical and sensor-assisted algorithmic techniques such as focus stacking, software-selectable focus planes, expanded depth of field imaging, and region of interest (ROI)-based focus and exposure controls to ensure crisp focus and exposure without or with minimal user intervention in routine clinical examination settings, image enhancement, automatic montaging to see more complete pictures of, for example, the retina, and annotation of findings. Focus stacking is similar to expanded depth of field imaging; also known as focal plane merging and z-stacking or focus blending; it is a digital image processing technique which combines multiple images taken at different focus distances to give a resulting image with a greater depth of field (DOF) than any of the individual source images.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the combined teaching of Kubota and Zaharchuk the automated report generation and transmission teachings of Dirghangi. Paragraph [0008] of Dirghangi describes that the usage of the disclosed invention enables improved ability for the user to simultaneously capture ophthalmic features manually; automatic device capture of images integrating onboard integrated sensors, computational processing capabilities, and tightly-integrated on-device and off-device algorithmic processing of imagery; allowing for feature recognition of the eye and ocular features of interest; and/or automatically montaging multiple overlapping images to more broadly map and redisplay the ocular features via networked software programs; all included in a very small, integrated form factor such that the claimed device can be self-contained and reversibly mounted by the user on a pre-existing ophthalmoscope without specialized training or tools—and without procuring a new ophthalmoscope, and without damaging or directly modifying the preexisting instrument. In other words, the presently taught device allows an existing ophthalmoscope to be retrofitted with a mobile embedded imaging system to make it an imaging device with wireless capture and transmission capabilities, in preferred embodiments, and it can be implemented by the user without having to obtain a new ophthalmoscope. The device taught herein improves upon prior optical design by simplifying the optical system by requiring only one centrally-positioned triangular reflecting mirror block (as is typically found inside the optical system of most BIOS) and an onboard linear plate beamsplitter, not requiring a prism or laterally-located mirror blocks; as opposed to two or more laterally-positioned mirror blocks in coordination with a prism (such as a pentaprism) or centrally-positioned or laterally-positioned beamsplitter, which ordinarily may totally or partially occlude the optical pathway to the instrument eyepieces. Such prior configurations introduce significant device complexity to the mechanical design and opportunities for distortion of imagery, and cannot ensure consistent correspondence between camera and examiner views in many examination scenarios. The optical system here described allows for a greater fidelity of correspondence between onboard camera and examiner views over a much-greater breadth of examination scenarios. In one embodiment, a mechanical adjustment lever allows for customization of the optical system by tilting, in a coplanar relationship, the beamsplitter mirror and embedded camera assembly, to permit a greater range of examiner wearing patterns of the instrument, such as physicians who wear the instrument at a significant downward tilt to examine patients significantly below their eye level, or physicians who wear spectacles. In another embodiment, the use of a wider field of view camera lens, along with a high-resolution camera sensor, may be used to customize the viewing angle of the captured imagery to the preferred instrument viewing position of individual users, by setting the desired viewing region by cropping out extraneous imagery via a software interface. In one instance, this software-enabled field of view image crop control may be used in combination with an initial user calibration procedure using, but not requiring, the use of a standardized target to allow for automatic configuration of the camera view—without requiring interaction with the onboard mechanical device controls such as adjustment levers or dials.. One of ordinary skill in the art would have added to the combined teaching of Kubota and Zaharchuk, the teaching of Dirghangi based on this incentive without yielding unexpected results.
As per claim 2,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Kubota further teaches wherein the transmission unit is configured to, in a case where information that automatic transmission is set to on in the transmission setting for the one type of imaging is stored, transmit the imaging data obtained by the one type of imaging based on the stored information with an examiner's instruction to switch an imaging screen for performing the one type of imaging to another display screen as a trigger to start transmission:
(Paragraphs [0007] and [0019], [0111], [0130] and [0142] and Figures 5 and 7 of Kubota. The teaching describes an ophthalmic image processing apparatus of an embodiment includes a storage unit, a first brightness profile generation unit, a second brightness profile generation unit, and a brightness correction unit. The storage unit is configured to store image data acquired through scanning a subject's eye using optical coherence tomography. Types of the OCT images include an image in an arbitrary cross section mode, a shadowgram obtained by projecting an arbitrary area of three-dimensional data set, a blood vessel enhanced image (i.e., angiogram), and the like. Types of the image in an arbitrary cross section mode include a B mode image, a C mode image, a multi planar reconstruction (MPR) image, and the like. Transmission settings between B mode and C mode are different types of imaging as settings. The teaching further describes that controlling the communication device included in the data input and output unit 30, the controller 10 sends the inputted patient ID to the image management server via the network. The image management server receives the patient ID, searches for the image data associated with the patient ID, and transmits the retrieved image data to the ophthalmic image processing apparatus 1. This means that for any given image, its content, type and destination are known by the controller. It is also possible to configure to automatically select a front image. For example, it is possible to select a front image based on the slice area having been set. As a specific example, when part of the choroid, part of the vitreous body, or the like is designated as a slice area, it is possible to exclude such a slice area from the subject of synthetic front image formation. This means that an automatic setting of transmission (i.e. whether or not an image is automatically a front image) is selected or not selected for a given image. The display screen and the displayed images may be the same as or similar to the aspects shown in FIG. 7. This interface has configurations for image size and location in the GUI. This display of data begins with a trigger from the user to display a plurality of images like that in Figure 7 from a display with only a single image like that in Figure 5.
As per claim 4,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Zaharchuk further teaches wherein the image quality enhancement processing is processing for generating the second medical image from the first medical image by using a trained model obtained by training with a medical image of a test subject:
(Paragraphs [0022]-[0032] of Zaharchuk. The teaching describes the use of machine learning models to enhance the image quality of a medical image. For example, a deep learning network is trained using the true 100% full-dose CE-MRI images as the reference ground-truth. The non-contrast (zero-dose) MRI and the 10% low-dose CE-MRI are provided to the network as inputs, and the output of the network is an approximation of the full-dose CE-MRI. During training, this network implicitly learns the guided denoising of the noisy contrast uptake extracted from the difference signal between low-dose and non-contrast (zero-dose) images, which can be scaled to generate the contrast enhancement of a full-dose image)
As per claim 5,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Kubota further teaches wherein the transmission settings are configured such that a transmission setting for data including the first medical image and the second medical image as a set is includable:
(Paragraphs [0088]-[0106] of Kubota. The teaching describes an image enhancement process by applying filers, correcting brightness or removing artifacts of medical images and then displaying these images.)
As per claim 6,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 5.
Zaharchuk further teaches wherein the data including the set is training data for additional training:
(Paragraphs [0022]-[0032] of Zaharchuk. The teaching describes the use of machine learning models to enhance the image quality of a medical image. For example, a deep learning network is trained using the true 100% full-dose CE-MRI images as the reference ground-truth. The non-contrast (zero-dose) MRI and the 10% low-dose CE-MRI are provided to the network as inputs, and the output of the network is an approximation of the full-dose CE-MRI. During training, this network implicitly learns the guided denoising of the noisy contrast uptake extracted from the difference signal between low-dose and non-contrast (zero-dose) images, which can be scaled to generate the contrast enhancement of a full-dose image. The resulting images are used in a deep learning training stage 304 for training a deep learning network to synthesize a full-dose image 312.)
As per claim 7,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Kubota further teaches wherein a plurality of ophthalmologic imaging devices configured to perform the plurality of different types of imaging on an eye to be examined of the test subject includes a fundus camera and an optical coherence tomography (OCT) device:
(Paragraphs [0007], [0019] and [0021] of Kubota. The teaching describes an ophthalmic image processing apparatus of an embodiment includes a storage unit, a first brightness profile generation unit, a second brightness profile generation unit, and a brightness correction unit. The storage unit is configured to store image data acquired through scanning a subject's eye using optical coherence tomography. Types of the OCT images include an image in an arbitrary cross section mode, a shadowgram obtained by projecting an arbitrary area of three-dimensional data set, a blood vessel enhanced image (i.e., angiogram), and the like. Types of the image in an arbitrary cross section mode include a B mode image, a C mode image, a multi planar reconstruction (MPR) image, and the like. Transmission settings between B mode and C mode are different types of imaging as settings. The ophthalmic imaging apparatus may include a modality other than OCT. Such a modality may be a fundus camera, a scanning laser ophthalmoscope (SLO), a slit lamp microscope, an ophthalmic surgical microscope, or the like.)
As per claim 8,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Kubota further teaches wherein the transmission settings are configured such that a plurality of patterns is registrable, and wherein the transmission unit is configured to transmit the test subject's imaging data obtained by the one type of imaging based on the stored information in order of the plurality of registered patterns:
(Paragraphs [0057] and [0061] of Kubota. The teaching describes front image forming unit 412 can form a front image by projecting the data included in the set slice area in the depth direction (i.e., Z directional projection). Such projection front image is referred to as a shadowgram. The projection front image that spans the entire area in the Z direction of the three-dimensional data set D is referred to as a projection image, and is used for the registration between a fundus photograph and the three-dimensional data set D, or the like. The registration between the plurality of front images is unnecessary since the plurality of front images to be synthesized are constructed from the same three-dimensional data set D. Alternatively, it is possible to apply natural registration to the plurality of front images based on the locations of the plurality of front images (i.e., the locations of the plurality of slice areas) in the three-dimensional data set D.)
As per claim 10,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Kubota further teaches wherein the transmission settings include a transmission content, a transmission type, and a transmission destination as common settings, and an image size and a presence or absence of automatic transmission as individual settings:
(Paragraphs [0007] and [0019], [0111], [0130] and [0142] and Figure 7 of Kubota. The teaching describes an ophthalmic image processing apparatus of an embodiment includes a storage unit, a first brightness profile generation unit, a second brightness profile generation unit, and a brightness correction unit. The storage unit is configured to store image data acquired through scanning a subject's eye using optical coherence tomography. Types of the OCT images include an image in an arbitrary cross section mode, a shadowgram obtained by projecting an arbitrary area of three-dimensional data set, a blood vessel enhanced image (i.e., angiogram), and the like. Types of the image in an arbitrary cross section mode include a B mode image, a C mode image, a multi planar reconstruction (MPR) image, and the like. Transmission settings between B mode and C mode are different types of imaging as settings. The teaching further describes that controlling the communication device included in the data input and output unit 30, the controller 10 sends the inputted patient ID to the image management server via the network. The image management server receives the patient ID, searches for the image data associated with the patient ID, and transmits the retrieved image data to the ophthalmic image processing apparatus 1. This means that for any given image, its content, type and destination are known by the controller. It is also possible to configure to automatically select a front image. For example, it is possible to select a front image based on the slice area having been set. As a specific example, when part of the choroid, part of the vitreous body, or the like is designated as a slice area, it is possible to exclude such a slice area from the subject of synthetic front image formation. This means that an automatic setting of transmission (i.e. whether or not an image is automatically a front image) is selected or not selected for a given image. The display screen and the displayed images may be the same as or similar to the aspects shown in FIG. 7. This interface has configurations for image size and location in the GUI.)
As per claim 19,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
Kubota further teaches wherein the display control unit is configured to display an image, information, or a result obtained by inputting a plurality of medical images obtained by the plurality of different types of imaging into a trained model on the display unit:
(Paragraph [0100] of Kubota. The teaching describes that based on the image data on which the brightness correction has been performed in the step S5, the display controller 11 displays, at step 630 (also referred to herein as “S6”), an image on the display device 2. Thereby, for example, an en-face image in which the artifacts have been removed or reduced as shown in FIG. 5B is displayed on the display device 2. This terminates the present operation of the ophthalmic image processing apparatus 1 (END))
As per claim 21,
Claim 21 is substantially similar to claim 1. Accordingly, claim 21 is rejected for the same reasons as claim 1.
As per claim 23,
Claim 23 is substantially similar to claim 1. Accordingly, claim 23 is rejected for the same reasons as claim 1.
Claim 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kubota in view of An et al. (US 2018/0368679; herein referred to as An).
As per claim 18,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
The combined teaching of Kubota, Zaharchuk and Dirghangi does not explicitly teach wherein the display control unit is configured to display at least one of (a) an analysis result related to a medical image obtained by the one type of imaging on the display unit, the analysis result being generated using a trained model for analysis result generation obtained by training with a medical image of a test subject, (b) a diagnostic result related to a medical image obtained by the one type of imaging on the display unit, the diagnostic result being generated using a trained model for diagnostic result generation obtained by training with a medical image of a test subject, (c) as information about an abnormal region, information about a difference between (i) a medical image obtained by the one type of imaging and (ii) an image obtained by input of the medical image to a generative adversarial network or an auto-encoder on the display unit, (d) a similar case image related to a medical image obtained by the one type of imaging on the display unit, the similar case image being searched for by using a trained model for similar case image search obtained by training with a medical image of a test subject, and (e) an object recognition result or a segmentation result related to a medical image obtained by the one type of imaging on the display unit, the object recognition result or the segmentation result being generated using a trained model for object recognition or a trained model for segmentation obtained by training with a medical image of a test subject.
However, An teaches wherein the display control unit is configured to display at least one of (a) an analysis result related to a medical image obtained by the one type of imaging on the display unit, the analysis result being generated using a trained model for analysis result generation obtained by training with a medical image of a test subject, (b) a diagnostic result related to a medical image obtained by the one type of imaging on the display unit, the diagnostic result being generated using a trained model for diagnostic result generation obtained by training with a medical image of a test subject, (c) as information about an abnormal region, information about a difference between (i) a medical image obtained by the one type of imaging and (ii) an image obtained by input of the medical image to a generative adversarial network or an auto-encoder on the display unit, (d) a similar case image related to a medical image obtained by the one type of imaging on the display unit, the similar case image being searched for by using a trained model for similar case image search obtained by training with a medical image of a test subject, and (e) an object recognition result or a segmentation result related to a medical image obtained by the one type of imaging on the display unit, the object recognition result or the segmentation result being generated using a trained model for object recognition or a trained model for segmentation obtained by training with a medical image of a test subject:
(Paragraphs [0055]-[0070] and [0080] of An. The teaching describes a classifier for use with an OCT device to determine the morphology of images collected in ophthalmology. To classify these morphological states, a machine learning model is used and training data in the form of patient image data in a training set is used to train the model to identify morphological patterns. This classification is construed as a diagnostic result.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the combined teaching of Kubota, Zaharchuk and Dirghangi, the classifier of An. Paragraph [0070] of An teaches that high accuracy rates in the machine learning model were resultant from the training data to determine a morphological condition. One of ordinary skill in the art in possession of the combined teaching of Kubota, Zaharchuk and Dirghangi would have looked to An’s classifier to achieve an accurate assisting process in the diagnostic process of Kubota. One of ordinary skill in the art would have added to the combined teaching of Kubota, Zaharchuk and Dirghangi, the teaching of An based on this incentive without yielding unexpected results.
As per claim 20,
The combined teaching of Kubota, Zaharchuk and Dirghangi teaches the limitations of claim 1.
The combined teaching of Kubota, Zaharchuk and Dirghangi does not explicitly teach wherein an examiner's instruction about a trigger for the transmission unit to start transmission is information obtained by using at least one trained model among a trained model for character recognition, a trained model for voice recognition, and a trained model for gesture recognition.
However An teaches wherein an examiner's instruction about a trigger for the transmission unit to start transmission is information obtained by using at least one trained model among a trained model for character recognition, a trained model for voice recognition, and a trained model for gesture recognition:
(Paragraphs [0055]-[0070] and [0080] of An. The teaching describes a classifier for use with an OCT device to determine the morphology of images collected in ophthalmology. To classify these morphological states, a machine learning model is used and training data in the form of patient image data in a training set is used to train the model to identify morphological patterns. This morphology is construed as a character recognition which triggers a condition to transmit an image to a user.)
It would have been obvious to one of ordinary skill in the art before the time of filing to add to the combined teaching of Kubota, Zaharchuk and Dirghangi, the classifier of An. Paragraph [0070] of An teaches that high accuracy rates in the machine learning model were resultant from the training data to determine a morphological condition. One of ordinary skill in the art in possession of the combined teaching of Kubota, Zaharchuk and Dirghangi would have looked to An’s classifier to achieve an accurate assisting process in the diagnostic process of Kubota. One of ordinary skill in the art would have added to the combined teaching of Kubota, Zaharchuk and Dirghangi, the teaching of An based on this incentive without yielding unexpected results.
Response to Arguments
Applicant's arguments filed June 24, 2025 have been fully considered.
Applicant arguments pertaining to rejections made under 35 U.S.C. 102/103 are rendered moot in light of the new combination of references used in the current rejection. The rejections made under 35 U.S.C. 102 are withdrawn and new rejections made under 35 U.S.C. 103 apply.
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 CHAD A NEWTON whose telephone number is (313)446-6604. The examiner can normally be reached M-F 8:00AM-4:00PM (EST).
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/CHAD A NEWTON/Primary Examiner, Art Unit 3681