Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed 03/18/2026 has been entered. Claims 1-13 remain pending in the application. Applicant’s amendments to the Specification and Claims have overcome each and every objection and 112(b) rejections previously set forth in the Non-Final Office Action mailed 01/09/2026.
Response to Arguments
Applicant’s arguments filed 03/18/2026 with respect to claim 1 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Given the amendments to claim 1, reference to Firouzi is being relied upon to teach dependent claims 2, 4-8, and 10 more-consistently with the instant claim language, as shown below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 11, 12, and 13 recites a judicial exception (abstract idea). The “acquire an ultrasound image…” and “output a recognition result…” steps do not specify how to acquire an ultrasound image and output a recognition result. The physician can print and view an ultrasound image, then output a result based on the printed image using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
General system elements (i.e., processor, image processing device, ultrasound probe, computer) related to the insignificant extra solution activity steps that do not integrate the abstract idea into a practical application as it does not impose any meaningful limits on practicing the abstract idea.
Claims 2 and 5 merely specifies the target region and the result. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 3 merely specifies the lesion is a cyst. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claims 4 and 6 merely specifies the training of the trained model. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 7 merely specifies the blood flow distribution information is a map of blood flow intensity. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 8 merely specifies the result is displayed on a screen. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim 9 recites a judicial exception (abstract idea). The “ultrasound image is displayed on a second screen via foreground processing” and “recognition processing is executed via background processing” steps do not specify how to perform foreground processing and background processing. The physician can print and view an ultrasound image (foreground processing) and draw regions (recognition processing) in the printed image (background processing) using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
General system elements (i.e., screen) related to the insignificant extra solution activity steps that do not integrate the abstract idea into a practical application as it does not impose any meaningful limits on practicing the abstract idea.
Claim 10 recites a judicial exception (abstract idea). The “output information indicating that the recognition processing is being executed” step does not specify how to output information. The physician can draw regions (recognition processing) and view the drawn regions (output information) in the printed image using their mind and a pen (see MPEP 2106, section III, step 2A of subject matter eligibility test flowchart). This judicial exception is not integrated into a practical application because the step in the claim can be considered as processes that can be performed in the human mind (see MPEP 2106.04(a)(2)(III)).
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because no details are given surrounding the step (see MPEP 2106, section III, step 2B of subject matter eligibility test flowchart). Therefore, the claim is not eligible subject matter under 35 US.C. 101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 4-8, 10, and 12-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Firouzi et al. (US 20220110604 A1, published April 14, 2022), hereinafter referred to as Firouzi.
Regarding claim 1, and similarly for claims 12 and 13, Firouzi teaches an image processing device (Fig. 6, wearable device 600 as image processing device) comprising:
a processor (Fig. 6, processor 604), wherein the processor is configured to:
acquire an ultrasound image which is obtained by a B-mode method in an ultrasound diagnosis for a subject and in which an observation target region of the subject is shown, and blood flow distribution information obtained by a Doppler method used in combination with the B-mode method in the ultrasound diagnosis (Fig. 10, B-mode and CFI (color flow imaging) data 1062 obtained; see para. 0141 – “In some embodiments, the data may then undergo scan conversion 1016 for generating B-mode images. Finally, any suitable techniques 1018 may be used for post-processing the scan converted images.”; see para. 0142 – “For CFI, the data may undergo phase estimation 1024, which may be used to inform velocity estimation 1026. In some embodiments, after velocity estimation 1024, the data may undergo scan conversion 1016 to generate CF images. Any suitable techniques 1018 may be used for post-processing the scan converted CF images.”), and
output a recognition result obtained by executing recognition processing of inputting the ultrasound image and the blood flow distribution information to a trained model to cause the trained model to recognize the observation target region (Fig. 10, inputting B-mode and CFI data 1062 to ML 1064 (trained model) to perform auto-locking/auto-detection 1068 (1066) (recognize observation target region); see para. 0144 – “In some embodiments, any suitable data (e.g., data acquired from any point in pipeline 1020) may be used as input to machine learning techniques 1044, 1064 …”; see para. 0145 – “In some embodiments, the machine learning techniques 1044, 1064 may include one or more machine learning techniques that inform the beam-steering strategy 1046, 1066. For to example, the machine learning techniques may include techniques for detecting a region of interest, localizing a region of interest, segmenting one or more anatomical structures, locking on a region of interest…”),
the recognition result including information capable of specifying a position of the observation target in the ultrasound image (see para. 0145 – “For to example, the machine learning techniques may include techniques for detecting a region of interest, localizing a region of interest, segmenting one or more anatomical structures [specifying a position of the observation target ]…”).
Furthermore, regarding claim 2, Firouzi further teaches wherein the observation target region is a lesion, and the recognition result is information capable of distinguishing the lesion from a portion other than the lesion (see para. 0005 – “The beam-steering techniques may be implemented in an acoustic device and used to sense, detect, diagnose, and monitor brain functions and conditions including but not limited to detection of…mass lesions…”).
Furthermore, regarding claim 4, Firouzi further teaches wherein the trained model is obtained by performing machine learning for recognizing the lesion or machine learning for recognizing the lesion and a blood vessel shown in the ultrasound image (see para. 0005 – “The beam-steering techniques may be implemented in an acoustic device and used to sense, detect, diagnose, and monitor brain functions and conditions including but not limited to detection of…mass lesions…”).
Furthermore, regarding claim 5, Firouzi further teaches wherein the observation target region is a blood vessel, and the recognition result is information capable of distinguishing the blood vessel from a portion other than the blood vessel (Fig. 8D; see para. 0115 – “At 834, data from the detected signal is provided to a machine learning model to obtain an output indicating the location of the blood vessels. In some embodiments, the date comprises image data, such as brightness mode (B-mode) image data and/or color flow image (CFI) image data.”).
Furthermore, regarding claim 6, Firouzi further teaches wherein the trained model is obtained by performing machine learning for recognizing the blood vessel or machine learning for recognizing a lesion shown in the ultrasound image and the blood vessel shown in the ultrasound image (Fig. 8D; see para. 0115 – “At 834, data from the detected signal is provided to a machine learning model to obtain an output indicating the location of the blood vessels. In some embodiments, the date comprises image data, such as brightness mode (B-mode) image data and/or color flow image (CFI) image data.”).
Furthermore, regarding claim 7, Firouzi further teaches wherein the blood flow distribution information is a map capable of specifying an intensity of a blood flow (see para. 0142 – “For CFI [color flow imaging], the data may undergo phase estimation 1024, which may be used to inform velocity estimation 1026. In some embodiments, after velocity estimation 1024, the data may undergo scan conversion 1016 to generate CF images. Any suitable techniques 1018 may be used for post-processing the scan converted CF images.” Where it is inherent and known in the art that blood flow velocity corresponds to intensity in color flow map).
Furthermore, regarding claim 8, Firouzi further teaches wherein the outputting of the recognition result includes displaying the recognition result on a first screen (Fig. 15C and 17C, displaying localized/segmented ROI as recognition result, where it is inherent and known in the art to display an image on a screen; see para. 0145 – “For to example, the machine learning techniques may include techniques for detecting a region of interest, localizing a region of interest, segmenting one or more anatomical structures [recognition processing]…”).
Furthermore, regarding claim 10, Firouzi further teaches wherein, in a case where the recognition processing is being executed, the processor is configured to output information indicating that the recognition processing is being executed (Fig. 15C and 17C, displaying localized/segmented ROI as recognition processing executed; see para. 0145 – “For to example, the machine learning techniques may include techniques for detecting a region of interest, localizing a region of interest, segmenting one or more anatomical structures [recognition processing]…”).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Firouzi in view of Errico et al. (US 20210113190A1, published April22, 2021), hereinafter referred to as Errico.
Regarding claim 3, Firouzi teaches all of the elements disclosed in claim 2 above.
Firouzi teaches detecting a lesion, but does not explicitly teach detecting the lesion as a cyst.
Whereas, Errico, in an analogous field of endeavor, teaches wherein the lesion is a cyst (Fig. 5, output 538 of deep learning network 510 includes benign lesion class 550 and malignant lesion class 540 of images 504; see para. 0045 - "Malignant FLLs can be classified into hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and metastasis. In some instances, malignant FLL classifications can be based on B-mode and Doppler flow images for their infiltrating vessels or hypoechoic halos.”; Fig. 8; see para. 0056 – “…the benign lesion subclasses 552 may include haemangioma, FNH, and Hepatocellular adenoma." Where cysts are benign lesions).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified detecting a lesion, as disclosed in Firouzi, by also detecting the lesion as a cyst, as disclosed in Errico. One of ordinary skill in the art would have been motivated to make this modification in order to provide more consistent lesion assessment results, eliminating variations that can arise from different interpretations across different clinicians, and allowing for diagnosis by non-expert users, as taught in Errico (see para. 0083).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Firouzi in view of Wu (CN 102078202A, published June 1, 2011), hereinafter referred to as Wu.
Regarding claim 9, Firouzi teaches all of the elements disclosed in claim 1 aobve.
Firouzi teaches displaying an ultrasound image and a result on a screen, but does not explicitly teach displaying an ultrasound image via foreground processing while recognition processing is executed via background processing.
Whereas, Wu, in an analogous field of endeavor, teaches wherein the ultrasound image is displayed on a second screen via foreground processing, and the recognition processing is executed via background processing (see para. 0021 "...the foreground of the ultrasound imaging device maintaining B-mode or B module/colourful blood imaging mode on the display module display and a background executing PWD [pulse wave doppler] mode of ultrasonic imaging device;").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified displaying an ultrasound image and a recognition result on a screen, as disclosed in Firouzi, by displaying an ultrasound image via foreground processing while recognition processing is executed via background processing, as disclosed in Wu. One of ordinary skill in the art would have been motivated to make this modification in order to improve the working efficiency of the doctor, as taught in Wu (see para. 0004).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Firouzi in view of Hibi (US 20080242983 A1, published October2, 2008), hereinafter referred to as Hibi.
Regarding claim 11, Firouzi teaches all of the elements disclosed in claim 1 above, and
Firouzi further teaches an ultrasound probe that emits ultrasound and detects a reflected wave of the ultrasound (see para. 0055 – “Accordingly, in some aspects, the inventors have developed techniques for detecting a signal from a region of interest of a brain of a person. The techniques include using a transducer to detect the signal from the region of interest by forming a beam in a direction relative to the brain of the person…For example, the transducer can be an acoustic/ultrasound transducer…The detected signal can be the result of a signal applied to the brain. For example, the transducer may detect a signal that has been applied to brain and reflected, scattered, and/or modulated in an acoustic frequency range, after interacting with the brain.”),
wherein the ultrasound image and the blood flow distribution information are generated based on the reflected wave detected by the ultrasound probe (see para. 0080 – “In some aspects, the AEG device includes probes that are acoustic transducers, such as piezoelectric transducers…”; see para. 0070 – “In some embodiments, the AEG device includes core modes of measurements and functionalities, including ability to take the pulse of the brain, ability to measure pulse wave velocity (PWV) by probing multiple regions of interest at one time, and ability to measure other ultrasound modes in the brain, including B-mode (brightness-mode)…color flow imaging (CFI)…”).
Firouzi teaches an ultrasound imaging system including an image processing device (see claim 1 above), but does not explicitly teach the ultrasound imaging system includes an endoscope system where the ultrasound probe is inserted into a body of the subject.
Whereas, Hibi, in an analogous field of endeavor, teaches an ultrasound imaging system includes an endoscope system where the ultrasound probe is inserted into a body of the subject (Fig. 1; see para. 0020 – “The ultrasonic endoscope 22 is provided with an electronic scanning type ultrasonic transducer at a distal end portion of an insertion section thereof which can be inserted into the living body.”; see para. 0053 "Further, for example, upon detection that the ultrasonic endoscope 22 is connected to the connector 23b, the CPU 23c determines that the B-mode and the color Doppler mode are both usable.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified an ultrasound imaging system, as disclosed in Firouzi, by including an endoscope system to the ultrasound imaging system, as disclosed in Hibi. One of ordinary skill in the art would have been motivated to make this modification in order to obtain a tomographic image of an internal part of a living body as a subject to be examined, as taught in Hibi (see para. 0019).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sutton et al. (US 20220225963 A1, published July 21, 2022) discloses the locations of the vessels (as shown by the Doppler images) may be shown in relation to the structural features (as shown by the B-mode data), and by training the convolutional neural network using these images, the network may infer the presence of a vessel of interest based on B-mode data alone.
Ayinde et al. (US 20230285005 A1, published September 14, 2023) discloses the facility trains and applies a convolutional neural network whose independent variables are color Doppler and B-mode images, and whose dependent variable is a probability map signifying the spatial location of aliasing in the image and the direction of the flow.
Welsh et al. (US 20230148147 A1, published May 11, 2023) discloses a fully convolutional neural network (fCNN) for example using a dual-modality input fusion block that incorporates both 3D B-Mode and power Doppler (PD) volumes, in order to provide increased complementary features and improve segmentation accuracy.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nyrobi Celestine whose telephone number is 571-272-0129. The examiner can normally be reached on Monday - Thursday, 7:00AM - 5:00PM EST.
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/N.C./Examiner, Art Unit 3798
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