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 .
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in the Republic of Korea on 04/30/2024. It is noted, however, that applicant has not filed a certified copy of the KR 10-2024-0057936 application as required by 37 CFR 1.55.
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in the Republic of Korea on 02/24/2025. It is noted, however, that applicant has not filed a certified copy of the KR 10-2025-0023822 application as required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/06/2025 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
FIGS. 1A/1B: Although the specification states “A processor 120 controls the transmission module 113 to from a transmission signal to be applied to each of transducers 117 in consideration of positions and focused points of the plurality of transducers included in the probe 20” [Page 11, Lines 24-26], these figures do not include the label 117. The examiner believes that the “element 115” in FIG. 1B may be a typo that should instead be the label 117.
FIG. 1B: Although the specification states “The probe 20 may include a display 112, the transmission module 113, a battery 114, the transducer 117, a charging module 116, the reception module 115, an input interface 109, a processor 118, and a communication module 119” [Page 14, Line 28-Page 15, Line 1], this figure does not include the labels 117 and 109.
FIG. 1B: Although the specification states “In a case in which the probe 20 includes the image processor 130 capable of generating ultrasonic images using the ultrasonic data, the probe 20 may transmit the ultrasonic data or the ultrasonic images generated by the image processor 130 to the ultrasonic imaging apparatus 40” [Page 17, Lines 17-20], this figure does not include the image processor 130 within the probe 20.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The disclosure is objected to because of the following informalities:
[Page 12, Lines 23-24]: As written it reads “The ultrasonic raw data may also be referred to as RF data”. However, this is the first indication of the term “RF” therefore, the term should be spelled out to provide clarity.
[Page 13, Line 16-17]: As written it reads “a portable device (a smart phone, tablet PC, wearable device”, etc.))”. However, this is the first indication of the term “PC” therefore, the term should be spelled out to provide clarity.
[Page 18, Line 25-27]: As written it reads “For example, the communication module 160 of the ultrasonic imaging apparatus 40 and the communication module 119 of the probe 20 may communicate using any one of wireless LAN […]”. However, this is the first indication of the term “LAN”, therefore, the term should be spelled out to provide clarity.
[Page 23, Lines 12-13]: As written it reads “The ultrasonic imaging apparatus 40a and 40b may include various types of output interfaces such as speakers, LEDs, and vibration devices”. However, this is the first indication of the term LEDs, therefore the term should be spelled out to provide clarity.
[Page 25, Line 16]: As written it reads “non-volatile memory (e.g., ROM and EPROM)”. However, this is the first instance of the terms “ROM” and “EPROM” therefore the terms should be spelled out to provide clarity.
Appropriate correction is required.
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.
Claim(s) 1, 2, 8, 11-12, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 2019/0200962 A1 “Lee” and further in view of Lee et al. US 20230058450 A1 “Lee-2”.
Regarding claims 1 and 11, Lee teaches “An ultrasonic imaging system comprising:” (“FIG. 2 shows the exterior of an ultrasound imaging apparatus, according to an embodiment of the present disclosure” [0057]. Therefore, FIG. 2 shows an ultrasonic imaging system.);
“a probe configured to transmit an ultrasonic signal to an object comprising a liver and receive an ultrasonic echo signal reflected from the object” (“The transducer module 110 may be provided inside an ultrasonic probe P, which may be connected to a main body 101 of the ultrasound imaging apparatus 100 through a cable 106” [0059]; “Referring to FIG. 5, an abdominal ultrasound image IUS generated by the ultrasound imaging apparatus 100 may have a liver area L and a neighboring kidney area K. In a case of a normal liver, echo levels in the liver and the kidney cortex are similar, but in a case of a fatty liver, the echo level increases as fats scatter ultrasound beams. Accordingly, comparison of brightness between the liver area L and the kidney area K in the ultrasound image may be used in detecting a fatty liver. For example, if the liver area L is brighter than the kidney area K, the difference in brightness may be diagnosed as the fatty liver” [0094]. Therefore, the ultrasonic imaging system (i.e. ultrasound imaging apparatus 100, in FIG. 2) includes a probe (i.e. ultrasonic probe P) configured to transmit an ultrasonic signal to an object comprising a liver (see L in FIG. 5) and receive an ultrasonic echo signal reflected from the object.);
“a display configured to display an ultrasonic image” (“The main controller 150 controls the display 160 to display the ultrasound image generated by the image processor 140 and associated diagnostic parameters” [0090]. The display 160 is shown within FIG. 2. Therefore, the ultrasonic imaging system includes a display configured to display an ultrasonic image.);
“an input interface configured to obtain user input” (“The main body 10 may have a control panel 105 at the front. The input device 170 may be formed on the control panel 105 to receive an input from the user. The user may input a command to start a diagnosis, select a portion to be diagnosed, select a diagnosis type, select a mode for the ultrasound image, and/or the like, through the input device 170” [0062]. Therefore, the ultrasonic imaging system includes an input interface (i.e. input device 170) configured to obtain user input.);
“memory configured to store an artificial intelligence model” (“The pulse controller 130, the image processor 140, and the main controller 150 may include at least one memory for storing a program for carrying out operations, which will be described later, and at least one processor for executing the program” [0067]; “Referring to FIG. 6, the main controller 150 may detect the liver area L and the kidney area K in the ultrasound image IUS. For example, the main controller 150 may detect the liver area L and the kidney area K with a feature extraction algorithm or outline extraction algorithm that uses anatomical characteristics of the liver and kidney. It is also possible for the main controller 150 to use machine learning, especially ‘deep learning’, in detecting the liver area L and kidney area K” [0099]. Therefore, the ultrasonic imaging system includes a memory configured to store an artificial intelligence model (i.e. machine learning/deep learning or program for carrying out operations, see [0067], [0099]).); and
“at least one processor, wherein the at least one processor is configured to obtain ultrasonic raw data by processing the ultrasonic echo signal” (“an image processor 140 for using an echo signal output from the beamformer 120 to generate an ultrasound image” [0058]; “The image processor 140 generates an ultrasound image based on the echo signal output from the receive beamformer 122. For example, the image processor 140 may generate at least one of A mode image, B mode image, D mode image, E mode image, and M mode image, based on the echo signal” [0087]. Therefore, the ultrasonic imaging system includes at least one processor (i.e. image processor 140), wherein the at least one processor is configured to obtain ultrasonic raw data by processing the ultrasonic echo signal (i.e. processing the echo signal to generate an ultrasound image.).);
“display a first ultrasonic image generated by processing the ultrasonic raw data on the display” (“a display 160 for displaying the generated ultrasound image and various data required for making a diagnosis” [0058]. Therefore, the at least one processor is configured to display a first ultrasonic image (i.e. the generated ultrasound image) generated by processing the ultrasonic raw data on the display.); […]
“generate a plurality of raw image frames […] and comprising different characteristic information by processing the ultrasonic raw data” (“The combiner 122b combines echo signals output from the respective delaying elements d1 to d5. In this case, the combiner 122b may apply weights to the respective echo signals and combine them” [0086]; “Furthermore, the image processor 140 may generate a 3D ultrasound image based on a plurality of ultrasound images acquired from the echo signals” [0087]. Therefore, since a plurality of ultrasound images are acquired from the echo signals obtained by delaying elements d1 to d5, the at least one processor is configured to generate a plurality of raw image frames […] comprising different characteristic information by processing the ultrasonic raw data (i.e. echo signals).);
“obtain quantitative data about a liver disease from the artificial intelligence model by inputting the plurality of raw image frames into the artificial intelligence model” and “display a second ultrasonic image comprising the quantitative data about the liver disease on the display” (See [0099] above and “Diagnostic parameters of the regions of interest of the liver and kidney are calculated, in 324. In this embodiment, the diagnostic parameters refer to parameters used in diagnosis for a fatty liver, and may be obtained based on brightness values of the ultrasound image. For example, the main controller 150 may calculate a representative gray scale of the region of interest of the liver and a representative gray scale of the region of interest of kidney, at different depths of the ultrasound image. The representative gray scale may assume an average value or a median value” [0148]; “Information about the obtained diagnostic parameters are provided to the user, in 325 […] For example, as described above in connection with FIG. 10, the main controller 150 may control the display 160 to display ratios of representative gray scales of the liver and kidney areas at different depths in the ultrasound image IUS and may also display the reliabilities along with the ratios of representative gray scales. The user may check and use the ratios of representative gray scales and reliabilities displayed on the display 160 in diagnosis for fat liver” [0153]. Therefore, since the main controller 150 may use machine learning, especially “deep learning”, in detecting the liver and diagnostic parameters of the liver are calculated and displayed on the ultrasound image (i.e. see FIG. 10, for example), the at least one processor is configured to obtain quantitative data (i.e. diagnostic parameters, see [0148] about a liver disease from the artificial intelligence model by inputting the plurality of raw image frames (i.e. plurality of ultrasound images, see [0087]) into the artificial intelligence model (i.e. machine learning/deep learning/feature extraction algorithm, see [0099]) and display a second ultrasonic image comprising the quantitative data about the liver disease on the display (see FIG. 10, for example).).
However, Lee does not teach “obtain an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface” or that the plurality of raw image frames are “corresponding to the obtained image frame”.
Lee-2 is within the same field of endeavor as the claimed invention because it involves an ultrasound imaging device which uses diagnostic algorithm to measure a degree of hepatic fibrosis from an ultrasound image of the liver or diagnosing fatty liver (see [0049]).
Lee-2 teaches “obtain an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface” and that the plurality of raw image frames are “corresponding to the obtained image frame” (“In an embodiment, the processor 1400 may automatically freeze an ultrasound image of which the calculated suitability is greater than a preset threshold among the plurality of ultrasound images” [0068]; “In an embodiment, the user inputter 1300 may receive a user input for freezing an ultrasound image among the plurality of ultrasound images based on the numerical value of the calculated suitability, and the processor 1400 may freeze the ultrasound image based on the user input received through the user inputter 1300” [0069]. Therefore, the at least one processor is configured to obtain an image frame of the first ultrasonic image is response to obtaining a freezing command through the input interface (i.e. user inputter 1300), wherein a plurality of raw image frames correspond to the obtained image frame.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system of Lee such that the at least one processor is configured to obtain an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface as disclosed in Lee-2 in order to easily facilitate the collection of ultrasonic images for input into an artificial intelligence model, said images not including motion (i.e. leading to noise). Freezing an ultrasonic image is one of a finite number of techniques which can be used to obtain an image without motion artifacts for insertion into an artificial intelligence model with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system of Lee such that the at least one processor is configured to obtain an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface as disclosed in Lee-2 would yield the predictable result of facilitating the collection of ultrasonic images for input into an artificial intelligence model, said images not containing moving portions/noise.
Regarding claim 11, Lee teaches “A control method of an ultrasonic imaging system, which comprises a probe, an input interface, a display, and at least one processor, comprising a control method executed by the at least one processor, wherein the control method comprises:” (See [0059]; [0094]; [0062]; [0090], [0067], [0099] as discussed with respect to claim 1 above and “FIG. 18 is a flowchart illustrating a control method of an ultrasound imaging apparatus in an occasion when a first object is a liver and a second object is a kidney, according to an embodiment of the present disclosure” [0141].
“controlling the probe to transmit an ultrasonic signal to an object comprising a liver and receive an ultrasonic echo signal reflected from the object; obtaining ultrasonic raw data by processing the ultrasonic echo signal” (See [0058] and [0087] as discussed in claim 1 above, and “The control method of the ultrasound imaging apparatus starts with obtaining an ultrasound image, in 320. As described above, a fatty liver may be diagnosed using an abdominal ultrasound image including a liver and a kidney” [0142]. In order to obtain the ultrasound image data, the probe must be controlled to transmit an ultrasonic signal to an object comprising a liver (i.e. abdominal image) and receive an ultrasonic echo signal reflected from the object. Therefore, the control method involves controlling the probe to transmit an ultrasonic signal to an object comprising a liver and receive an ultrasonic echo signal reflected from the object; and obtaining ultrasonic raw data by processing the ultrasonic echo signal.);
“displaying a first ultrasonic image generated by processing the ultrasonic raw data on the display” (See [0058] as discussed with respect to claim 1 above. Therefore, the control method involves displaying a first ultrasonic image generated by processing the ultrasonic raw data on the display.); […]
“generating a plurality of raw image frames […] and comprising different characteristic information by processing the ultrasonic raw data” (See [0086], [0087] as discussed with respect to claim 1 above. Therefore, the control method performs the step of generating a plurality of raw image frames […] and comprising different characteristic information by processing the ultrasonic raw data;
“obtaining quantitative data about a liver disease from the artificial intelligence model by inputting the plurality of raw image frames into the artificial intelligence model”; and “displaying a second ultrasonic image comprising the quantitative data about the liver disease on the display” (See [0099], [0148] and [0153] as discussed with respect to claim 1 above. Therefore, the control method performs the steps of obtaining quantitative data (i.e. diagnostic parameters) about a liver disease from the artificial intelligence model (i.e. machine learning, deep learning, and/or feature extraction algorithm, see [0099]) by inputting the plurality of raw image frames (i.e. see [0087]) into the artificial intelligence model; and displaying a second ultrasonic image comprising the quantitative data about the liver disease on the display (See FIG. 10, for example).).
Lee does not teach “obtaining an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface”; or that the plurality of raw image frames are “corresponding to the obtained image frame”.
Lee-2 is within the same field of endeavor as the claimed invention because it involves an ultrasound imaging device which uses diagnostic algorithm to measure a degree of hepatic fibrosis from an ultrasound image of the liver or diagnosing fatty liver (see [0049]).
Lee-2 teaches “obtaining an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface”; or that the plurality of raw image frames are “corresponding to the obtained image frame”. (“In an embodiment, the processor 1400 may automatically freeze an ultrasound image of which the calculated suitability is greater than a preset threshold among the plurality of ultrasound images” [0068]; “In an embodiment, the user inputter 1300 may receive a user input for freezing an ultrasound image among the plurality of ultrasound images based on the numerical value of the calculated suitability, and the processor 1400 may freeze the ultrasound image based on the user input received through the user inputter 1300” [0069]. Therefore, the control method performs the step of obtaining an image frame of the first ultrasonic image is response to obtaining a freezing command through the input interface (i.e. user inputter 1300), wherein a plurality of raw image frames correspond to the obtained image frame.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the control method of Lee such that it includes the step of obtaining an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface as disclosed in Lee-2 in order to easily facilitate the collection of ultrasonic images for input into an artificial intelligence model, said images not including motion (i.e. leading to noise). Freezing an ultrasonic image is one of a finite number of techniques which can be used to obtain an image without motion artifacts for insertion into an artificial intelligence model with a reasonable expectation of success. Thus, modifying the control method of Lee such that it performs the step of obtaining an image frame of the first ultrasonic image in response to obtaining a freezing command through the input interface as disclosed in Lee-2 would yield the predictable result of facilitating the collection of ultrasonic images for input into an artificial intelligence model, said images not containing moving portions/noise.
Regarding claims 2 and 12, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above, and Lee further teaches "wherein the at least one processor is configured to generate a B-mode image as the first ultrasonic image” (Claim 1); “wherein the first ultrasonic image corresponds to a B-mode image” (Claim 12) (“In another example, one of the displays may display a B mode image and the other display may display a contrast media image” [0064]; “For example, the image processor 140 may generate at least one of A mode image, B mode image, D mode image, E mode image, and M mode image, based on the echo signal” [0087]. Therefore, the at least one processor is configured to generate a B-mode image as the first ultrasonic image. Additionally, the control method involves obtaining the first ultrasonic image which corresponds to a B-mode image.); and
Lee-2 further teaches “generate a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal, as the plurality of raw image frames” (Claim 2); “and the generating of the plurality of raw image frames comprises generating a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal” (Claim 12) (“According to an aspect of the present disclosure, an operation method of an ultrasound imaging device includes obtaining a plurality of ultrasound images by receiving an echo signal reflected from an object using an ultrasound probe and image-processing the received echo signal” [0017]; “In the embodiment shown in FIG. 8, the processor 1400 may determine a tissue attenuation imaging (TAI) algorithm and a tissue scatter distribution imaging (TSI) algorithm as diagnostic algorithms appropriate to diagnose a lesion from the ultrasound image 100. The TAI algorithm is a diagnostic algorithm for diagnosing a lesion of fatty liver by digitizing a degree of attenuation of an ultrasound image of the liver, and the TSI algorithm is a diagnostic algorithm for diagnosing a lesion of fatty liver by digitizing a degree of scattering of an ultrasound image of the liver” [0118].
Therefore, since the processor 1400 may determine a tissue attenuation imaging (TAI) algorithm and a tissue scanner distribution imaging algorithm to diagnose a lesion in an ultrasound image 100 and it can be used on images of the liver and the system obtains a plurality of ultrasound images (see [0017], for example), the at least one processor is configured to generate a first raw image frame comprising attenuation information of the ultrasonic echo signal (i.e. tissue attenuation imagine (TAI) algorithm) and a second raw image frame comprising scattering information of the ultrasonic echo signal (i.e. tissue scatter distribution imaging (TSI) algorithm), as the plurality of raw image frames. Furthermore, the method involves generating a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system and control method of Lee such that the at least one processor is configured to generate a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal, as the plurality of raw image frames and the method further comprises generating a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal as disclosed in Lee-2 in order to effectively distinguish attenuation and scattering within ultrasound images for used in diagnosis of a lesion related to a fatty liver (see [0118]). Attenuation and scattering within liver ultrasound images are two features can be used to indicate the presence of lesions related to a fatty liver with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee such that the at least one processor is configured to generate a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal, as the plurality of raw image frames and the method further comprises generating a first raw image frame comprising attenuation information of the ultrasonic echo signal and a second raw image frame comprising scattering information of the ultrasonic echo signal as disclosed in Lee-2 would yield the predictable result of effectively distinguishing attenuation and scattering within ultrasound images for used in diagnosis of a lesion related to a fatty liver (see [0118]).
Regarding claims 8 and 18, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above, and Lee-2 further teaches “wherein the at least one processor is configured to display the quantitative data about the liver disease in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region” (Claim 8); "wherein the displaying of the second ultrasonic image comprises displaying the quantitative data about the liver disease in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region” (Claim 18) (See [0118] as discussed with respect to claims 2 and 12 above and “Referring to FIG. 9A, the ultrasound imaging device 1000 may display the ultrasound image 100, an algorithm measurement value UI 140a, and a region-of-interest (ROI) UI 150 on a display 1600. […] In the embodiment of FIG. 9A, the ultrasound imaging device 1000 may perform the TAI algorithm and the TSI algorithm” [0123]; “ In the embodiment of FIG. 9A, the algorithm measurement value UI 140a may display 0.71 that is a numerical value of attenuation of the ultrasound image 100 measured by the TAI algorithm, and 0.68 that is a numerical value of scattering of the ultrasound image 100 measured by the TSI algorithm” [0124]. In this case, the TAI algorithm is a tissue attenuation imaging algorithm and the TSI algorithm represents tissue scatter distribution algorithm, which are used to diagnose a lesion of fatty liver. Therefore, since the measurement values of TAI and TSI are displayed on the display 1600 along with the ultrasound image 100, the at least one processor is configured to display the quantitative data about the liver disease (i.e. TAI, TSI, for example) in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region, specifically (i.e. a first region of the display displaying the first ultrasonic image and the second ultrasonic image). Furthermore, the method involves displaying the quantitative data about the liver disease in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region (i.e. a first region of the display displaying the first ultrasonic image and the second ultrasonic image).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system and control method of Lee such that at least one processor is configured to display the quantitative data about the liver disease (i.e. TAI, TSI, for example) in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region and the method includes displaying the quantitative data about the liver disease in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region as disclosed in Lee-2 in order to allow a user to easily view measurements associated with the liver when diagnosing liver function. Displaying quantitative data about liver disease in the same region as a first region displaying the first ultrasonic image and the second ultrasonic image (i.e. not necessarily simultaneously) is one of a finite number of techniques which can be used to allow a user to assess an image with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee such that at least one processor is configured to display the quantitative data about the liver disease (i.e. TAI, TSI, for example) in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region and the method includes displaying the quantitative data about the liver disease in at least one of a first region of the display displaying the first ultrasonic image and the second ultrasonic image and a second region of the display divided from the first region as disclosed in Lee-2 would yield the predictable result of allowing a user to easily view measurements associated with the liver when diagnosing liver function.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 2019/0200962 A1 “Lee” and further in view of Lee et al. US 20230058450 A1 “Lee-2” as applied to claims 1 and 11 above, and further in view of Ye et al. US 2021/0030400 A1 “Ye”.
Regarding claims 3 and 13, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 2 and 12 above and Lee further teaches “a plurality of raw image frames” (See [0086] and [0087] as discussed with respect to claims 1 and 11 above.).
However, the combination does not teach "wherein the at least one processor is configured to further generate at least one of a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data and a fourth raw image frame comprising frequency spectrum information of the ultrasonic raw data, as the plurality of raw image frames” (Claim 3); “wherein the generating of the plurality of raw image frames further comprises generating at least one of a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data and a fourth raw image frame comprising frequency spectrum information of the ultrasonic raw data, as the plurality of raw image frames” (Claim 13).
Ye is within a related field of endeavor to the claimed invention because it involves an apparatus and method for processing an ultrasound image and using a neural network to output in-phase data and quadrature phase data (see [Abstract]).
Ye teaches "wherein the at least one processor is configured to further generate at least one of a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data and a fourth raw image frame comprising frequency spectrum information of the ultrasonic raw data, as the plurality of raw image frames” (Claim 3); “wherein the generating of the plurality of raw image frames further comprises generating at least one of a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data and a fourth raw image frame comprising frequency spectrum information of the ultrasonic raw data, as the plurality of raw image frames” (Claim 13) (“When the IQ data, that is, the inphase data and the quadrature phase data are output over the neural network in S130, in S140, an envelope of the inphase data and the quadrature phase data may be detected and an ultrasound image for the data cube may be reconstructed using log compression” [0050].
Thus, since the inphase data and quadrature data are output over the neural network such that an envelope can be detected and an ultrasound image can be reconstructed, the reconstructed ultrasound image includes in-phase component information and quadrature component information. Therefore, the at least one processor is configured to further generate a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data. Furthermore, the control method involves generating a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the step of generating a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data as disclosed in Ye in order to allow a user to better understand the components included within the ultrasonic images. Obtaining and presenting in-phase component information and quadrature component information is one of a finite number of techniques which can be used to present a user with information for use in assessing an image with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the step of generating a third raw image frame comprising in-phase component information and quadrature component information of the ultrasonic raw data as disclosed in Ye would yield the predictable result of allowing a user to better understand the components included within the ultrasonic images when performing an assessment thereof.
Claim(s) 6 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 2019/0200962 A1 “Lee” and further in view of Lee et al. US 20230058450 A1 “Lee-2” as applied to claims 1 and 11 above, and further in view of Yao et al. US 2011/0172533 A1 “Yao”.
Regarding claims 6 and 16, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above. However, the combination does not teach “wherein the at least one processor is configured to obtain at least one of a fat fraction of the liver and severity of liver steatosis as the quantitative data about the liver disease” (Claim 6); "wherein the quantitative data about the liver disease comprises at least one of a fat fraction of the liver and severity of liver steatosis” (Claim 16).
Yao is within the same field of endeavor as the claimed invention because it involves an ultrasonic diagnosis apparatus which can be used to image the liver and obtaining information about the liver therefrom (see [0061]).
Yao teaches “wherein the at least one processor is configured to obtain at least one of a fat fraction of the liver and severity of liver steatosis as the quantitative data about the liver disease” (Claim 6); "wherein the quantitative data about the liver disease comprises at least one of a fat fraction of the liver and severity of liver steatosis” (Claim 16) (“This ultrasonic diagnosis apparatus sets a region of interest including the boundary line of the liver in an ultrasonic image of the liver, and calculates an irregularity degree index indicating the irregularity degree of the boundary line of the liver and an irregularity feature index indicating the feature of the boundary line of the liver in consideration of both the boundary line of the liver acquired by contour extraction processing including smoothing and the boundary line of the liver acquired by contour extraction processing including no smoothing. […] Observing the displayed irregularity degree index and irregularity feature index, therefore, the doctor or the like can determine the type of liver disease, the stage (severity) of the disease, and the like, thus accurately and quickly making comprehensive evaluation. This can improve the accuracy of liver function diagnosis and contribute to a decrease in diagnosis time, thereby supporting liver function diagnosis” [0061]. Thus, the irregularity degree index and the irregularity feature index, calculated by the control processor 28 (See [0050], [0052]), are used to determine the type or stage (i.e. severity) of liver disease. Therefore, the at least one processor is configured to obtain at least one of a fat fraction of the liver and severity of liver steatosis (i.e. stage of the disease) as the quantitative data about the liver disease. Furthermore, the quantitative data about the liver disease comprises at least one of a fat fraction of the liver and severity of liver steatosis (i.e. stage of the disease).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to obvious to modify the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to obtain at least one of a fat fraction of the liver and severity of liver steatosis as the quantitative data about the liver disease and the quantitative data about the liver disease comprises at least one of a fat fraction of the liver and severity of liver steatosis as disclosed in Yao in order to accurately and quickly make a comprehensive evaluation of liver function and thereby support liver function diagnosis (See Yao: [0061]). Obtaining a severity of the liver disease, through calculating irregularity degree index and irregularity feature index, is one of a finite number of techniques which can be used to diagnose liver function with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to obtain at least one of a fat fraction of the liver and severity of liver steatosis as the quantitative data about the liver disease and the quantitative data about the liver disease comprises at least one of a fat fraction of the liver and severity of liver steatosis as disclosed in Yao would yield the predictable result of accurately and quickly making a comprehensive evaluation of liver function and thereby support liver function diagnosis (See Yao: [0061]).
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 2019/0200962 A1 “Lee” and further in view of Lee et al. US 20230058450 A1 “Lee-2” as applied to claims 1 and 11 above, and further in view of Zhang et al. CN 116110543 A1 “Zhang”.
Regarding claims 7 and 17, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above. However, the combination does not teach "wherein the at least one processor is configured to obtain additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface, and input the plurality of raw image frames and the additional information about the object into the artificial intelligence model” (Claim 7); “further comprising: obtaining additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface; and inputting the plurality of raw image frames and the additional information about the object into the artificial intelligence model” (Claim 17).
Zhang is within the same field of endeavor as the claimed invention because it involves a method and device for obtaining high quality liver ultrasonic images based on a neural network (see [Abstract]).
Zhang teaches "wherein the at least one processor is configured to obtain additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface, and input the plurality of raw image frames and the additional information about the object into the artificial intelligence model” (Claim 7); “further comprising: obtaining additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface; and inputting the plurality of raw image frames and the additional information about the object into the artificial intelligence model” (Claim 17) (“As a preferred solution, the basic information of the patient corresponding to the screened ultrasonic image and ultrasonic scanning parameter input neural network the model for training, obtaining the neural network specifically comprises the following contents: A: performing data pre-processing operation to the basic information and ultrasonic scanning parameter of the patient corresponding to the screened ultrasonic image, wherein the patient basic information is used as data, the ultrasonic scanning parameter is used as the label; B: dividing the data set of the pre-processed data, specifically dividing into a training set, a verification set and a test set; C: loading the training set to the neural network model to train; D: storing the trained and optimal model weight; E: loading model weight and performing actual test on the test set to obtain the final result.” [Page 3, Line 23-Page 4, Line 5]; “The obtaining device of high quality liver ultrasonic image based on neural network, wherein it comprises: patient basic information collecting device, the patient basic information collecting device for collecting basic information of a plurality of patients, basic information comprises one or more of the following information: height, weight, vascular position depth, BMI, gender, age, hepatitis and hepatitis type and degree, whether there is liver cirrhosis and liver cirrhosis degree, whether there is fatty liver and fatty liver degree, whether there is drinking history, whether there is smoking history, whether long-term taking medicine; ultrasonic image collecting device, the ultrasonic image collecting device is used for under different ultrasonic scanning parameters, collecting the liver ultrasonic image and the mould ultrasonic image of multiple patients; screening device, the screening device is used for screening the ultrasonic image collected by the ultrasonic image collecting device; model training device, the model training device is used for inputting the basic information and ultrasonic scanning parameter of the patient corresponding to the screened ultrasonic image into the neural network model for training, obtaining the trained neural network model” [Claim 7].
In this case, the obtaining device includes the patient basic information device to collect patient basic information including one or more of BMI, gender, and hepatitis/cirrhosis/fatty liver degree (i.e. representing underlying diseases). Additionally, since the basic information and ultrasonic scanning parameter information corresponding to the screened ultrasonic image is input to the neural network model when performing training (i.e. training being performed with multiple ultrasound images obtained from multiple patients), the at least one processor is configured to perform the steps of 1) obtaining additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface, and 2) inputting the plurality of raw image frames and the additional information about the object into the artificial intelligence model (i.e. neural network model).).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the steps of obtaining additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface, and 2) inputting the plurality of raw image frames and the additional information about the object into the artificial intelligence model (i.e. neural network model) as disclosed in Zhang in order to effectively characterize the liver of the patient. Inputting additional information (i.e. BMI, gender, age, underlying disease (i.e. hepatitis, cirrhosis, fatty liver degree/severity) and ultrasonic images to an artificial intelligence model (i.e. neural network model) is one of a finite number of techniques which can be used to train the neural network model such that it can accurately identify characteristics of the liver within ultrasound images (i.e. input during a subsequent testing phase) with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the steps of obtaining additional information about the object comprising at least one of a subcutaneous fat thickness, a body mass index (BMI), gender, age, and an underlying disease through the input interface, and 2) inputting the plurality of raw image frames and the additional information about the object into the artificial intelligence model (i.e. neural network model) as disclosed in Zhang would yield the predictable result of effectively characterizing the liver of the patient.
Claim(s) 9-10 and 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. US 2019/0200962 A1 “Lee” and further in view of Lee et al. US 20230058450 A1 “Lee-2” as applied to claims 1 and 11 above, and further in view of Woo et al. KR 2019/0060606 A “Woo”.
Regarding claims 9 and 19, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above. However, the combination does not teach "wherein the at least one processor is configured to further display a heat map visualizing a distribution of the quantitative data about the liver disease on the display” (Claim 9); “further comprising displaying a heat map visualizing a distribution of the quantitative data about the liver disease on the display” (Claim 19).
Woo is within the same field of endeavor as the claimed invention because it involves a metical image diagnostic apparatus that can acquire the lesion degree within the body according to the determination result of a learning network model, the degree of internal body lesion including a degree of liver lesion of a degree of liver fibrosis (see [Page 7, Line 43-Page 8, Line 3]).
Woo teaches "wherein the at least one processor is configured to further display a heat map visualizing a distribution of the quantitative data about the liver disease on the display” (Claim 9); “further comprising displaying a heat map visualizing a distribution of the quantitative data about the liver disease on the display” (Claim 19) (“FIG. 3B shows information indicating the degree of lesion, in which a plurality of areas are displayed in different colors. At this time, each of the different colors represents different class values as lesion degree. In this case, the image may be displayed in the form of a heat map based on a formula that inputs a plurality of class values in a manner similar to RMI (Reliable Measurement Index)” [Page 8, Lines 31-34]; “In addition, the medical imaging diagnostic apparatus 1000 can obtain the lesion reliability corresponding to each of a plurality of regions of the input image. Lesion reliability can be shown, for example, as the Liver Fibrosis Index (LFI). Specifically, the medical image diagnostic apparatus 1000 may apply the input image to the learning network model to obtain the lesion reliability corresponding to the lesion severity along with the lesion severity corresponding to each of the plurality of areas” [Page 8, Lines 37-41]; “The medical imaging diagnostic apparatus 1000 may display information indicating the lesion reliability (e.g., LFI value) corresponding to each of the plurality of areas on the screen” [Page 9, Lines 1-2] and “The right image at the lower end of FIG. 3B is information indicating the lesion reliability, and indicates that a plurality of regions are displayed in different colors. At this time, different colors indicate different LFI values as lesion reliability. [In FIG.] 3B, the LFI 50 indicates that the reliability of the region indicated by the LFI 50 is 50%, and similarly, the reliability of LIF 90 is 90% and the reliability of LFI 70 is 70% . In this case, the image can be displayed in the form of a heat map based on a formula for inputting a plurality of LFI values in a manner similar to RMI” [Page 9, Lines 3-7].
In this case, the medical image diagnostic apparatus 1000 may be an ultrasound diagnostic apparatus (see [Page 3, Lines 41-42]). Therefore, since the right image at the lower end of FIG. 3B is an image which displays different colors to indicate different LFI values (i.e. liver fibrosis index values) in the form of a heat map (i.e. displayed on the screen of the medical imaging diagnostic apparatus 1000), the ultrasonic imaging system includes at least one processor configured to further display a heat map visualizing a distribution of the quantitative data (i.e. liver fibrosis index values, for example) about the liver disease on the display. Furthermore, the method carried out by the ultrasonic imaging system involves displaying a heat map visualizing a distribution of the quantitative data about the liver disease on the display.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the step of displaying a heat map visualizing a distribution of the quantitative data about the liver disease (i.e. liver fibrosis) on the display as disclosed in Woo in order to allow a user to easily visualize the severity of liver disease (i.e. fibrosis) in multiple areas of the liver when performing diagnosis thereof. Displaying a heat map is one of a finite number of techniques which can be used to indicate to a user the severity of a condition (i.e. liver fibrosis, for example) in different areas such that a treatment plan can be developed with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the step of displaying a heat map visualizing a distribution of the quantitative data about the liver disease (i.e. liver fibrosis) on the display as disclosed in Woo would yield the predictable result of allowing a user to easily visualize the severity of liver disease (i.e. fibrosis) in multiple areas of the liver when performing diagnosis thereof.
Regarding claims 10 and 20, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above. However, the combination does not teach “wherein the at least one processor is configured to display the second ultrasonic image on the display based on obtaining a command for activating an artificial intelligence-based function through the input interface” (Claim 10); "wherein the displaying of the second ultrasonic image is performed based on obtaining a command for activating an artificial intelligence-based function through the input interface” (Claim 20).
Woo teaches “wherein the at least one processor is configured to display the second ultrasonic image on the display based on obtaining a command for activating an artificial intelligence-based function through the input interface” (Claim 10); "wherein the displaying of the second ultrasonic image is performed based on obtaining a command for activating an artificial intelligence-based function through the input interface” (Claim 20) (“In addition, the medical image diagnostic apparatus 1000 can display information indicating the degree of lesion (for example, liver fibrosis value) on the screen based on the inputted medical image. In addition, the medical imaging diagnostic apparatus 1000 can calculate the lesion reliability (e.g., LFI value) based on the evaluated degree of lesion (for example, liver fibrosis value) and display the information indicating the lesion reliability on the screen” [Page 9, Lines 40-44]; “The medical image diagnostic apparatus 1000 may receive a user input for selecting at least one of the medical images displayed on the screen. In response to the user input, the medical imaging device 1000 may determine the medical images, selected by the user as an input value to be applied to the learning network model” [Page 11, Lines 28-30]; “The image input unit can receive the image selected by the user as input. The view recognizing unit can determine the appropriateness of the input image based on the anatomical structure and image quality of the input image, and set the area used for diagnosis. The signal processing unit can determine the liver fibrosis and calculate the LFI using the learned discrimination model (for example, learning network model) by calling the RF signal of the area selected by the view recognition unit. The storage and display unit may display and store the results of the image processing unit” [Page 13, Lines 28-32].
In this case, once the user has provided an input for selecting at least one of the medical images, the learning network model (i.e. artificial intelligence model) is activated to determine the liver fibrosis and calculate the LFI (i.e. liver fibrosis index) and display the results. Therefore, the user providing an input image activates an artificial intelligence-based function within the learning network model to perform LFI and thus the determination of liver fibrosis. Thus, the at least one processor is further configured to display the second ultrasonic image (i.e. ultrasonic image with the quantitative data (i.e. LFI value) on the display based on obtaining a command for activating an artificial intelligence-based function through the input interface. Additionally, the control method performs the step of displaying the second ultrasonic image based on obtaining a command for activating an artificial intelligence-based function through the input interface.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the step of displaying the second ultrasonic images based on obtaining a command for activating an artificial intelligence-based function through the input interface as disclosed in Woo in order to easily facilitate the calculation of quantification data about the liver. Predicting the progress of fibrosis of the liver by visual inspection is not easy (see Woo: [Page 13, Lines 10-11]). Therefore, activating an artificial intelligence-based function when a user provides an input (i.e. such as a selection of a desired image) is one of a finite number of techniques which can be used to accurately perform calculation of liver fibrosis progression with a reasonable expectation of success. Thus, modifying the ultrasonic imaging system and control method of Lee in view of Lee-2 such that the at least one processor is configured to perform the step of displaying the second ultrasonic images based on obtaining a command for activating an artificial intelligence-based function through the input interface as disclosed in Woo would yield the predictable result of easily facilitating the calculation of quantification data (i.e. liver fibrosis index LFI, for example) about the liver without the need for a user to perform visual inspection to perform said calculation.
Allowable Subject Matter
Claims 4-5 and 14-15 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 4 and 14, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 1 and 11 above. However, the examiner acknowledges that the combination does not teach “wherein the at least one processor is configured to identify a first region of interest in the image frame of the first ultrasonic image, determine a second region of interest corresponding to the first region of interest in each of the plurality of raw image frames, extract a partial image frame corresponding to the second region of interest in each of the plurality of raw image frames, and obtain the quantitative data about the liver disease in the first region of interest by inputting a plurality of the partial image frames corresponding to each of a plurality of the second regions of interest into the artificial intelligence model” (Claim 4); "wherein the obtaining of the quantitative data about the liver disease comprises: identifying a first region of interest in the image frame of the first ultrasonic image; determining a second region of interest corresponding to the first region of interest in each of the plurality of raw image frames; extracting a partial image frame corresponding to the second region of interest in each of the plurality of raw image frames; and obtaining the quantitative data about the liver disease in the first region of interest by inputting a plurality of the partial image frames corresponding to each of a plurality of the second regions of interest into the artificial intelligence model” (Claim 14).
The examiner acknowledges that the prior art references of Yao, Lundberg, Woo, Ye and Zhang do not cure the deficiencies of Lee and Lee-2 with respect to the limitations noted above.
During the examiner’s search the following prior art reference(s) was/were found: Canfield et al. WO 2022/069208 A1 “Canfield”.
Canfield discloses “In some aspects, the processor is configured to: identify the first region of interest in a plurality of ultrasound image frames; and output the plurality of ultrasound image frames to the display” [0006]. Therefore, Canfield teaches “wherein the at least one processor is configured to: identify a first region of interest in the image frame of the first ultrasonic image, determine a second region of interest corresponding to the first region of interest in each of the plurality of raw image frames” (Claim 4) and “[…] identifying a first region of interest in the image frame of the first ultrasonic image; determining a second region of interest corresponding to the first region of interest in each of the plurality of raw image frames” (Claim 14). However, Canfield does not teach that the at least one processor is configured to “extract a partial image frame corresponding to the second region of interest in each of the plurality of raw image frames, and obtain the quantitative data about the liver disease in the first region of interest by inputting a plurality of the partial image frames corresponding to each of the plurality of the second regions of interest into the artificial intelligence model” (Claim 4); or “extracting a partial image frame corresponding to the second region of interest in each of the plurality of raw image frames; and obtaining the quantitative data about the liver disease in the first region of interest by inputting a plurality of the partial image frames corresponding to each of a plurality of the second regions of interest into the artificial intelligence model” (Claim 14).
Therefore, not prior art references have been found to teach the above claim limitations both alone or in combination with the other limitations of claims 4 and 14.
Thus, claims 4 and 14 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claims 5 and 15, Lee in view of Lee-2 discloses all features of the claimed invention as discussed with respect to claims 4 and 14 above. However, the combination does not teach "wherein the at least one processor is configured to obtain first coordinate information of the first region of interest in the image frame of the first ultrasonic image, convert the first coordinate information into second coordinate information in each of the plurality of raw image frames, and determine the second region of interest based on the second coordinate information” (Claim 5); “wherein the determining of the second region of interest comprises: obtaining first coordinate information of the first region of interest in the image frame of the first ultrasonic image; converting the first coordinate information into second coordinate information in each of the plurality of raw image frames; and determining the second region of interest based on the second coordinate information” (Claim 15).
The examiner acknowledges that the prior art references of Yao, Lundberg, Woo, Ye and Zhang do not cure the deficiencies of Lee and Lee-2 with respect to the limitations noted above.
Therefore, not prior art references have been found to teach the above claim limitations both alone or in combination with the other limitations of claims 5 and 15.
Thus, claims 5 and 15 would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lundberg et al. US 2019/0269384 A1 “Lundberg” is pertinent to the applicant’s disclosure because it discloses “In the embodiment described above, images are sent to the neural network when the user hits a “freeze” button or similar feature” [0034].
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/KAITLYN E SEBASTIAN/Examiner, Art Unit 3797
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