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
Claims 2-6 are cancelled, and claims 1 and 7-13 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Non-Final Office Action mailed 05/18/2026.
Response to Arguments
Applicant’s arguments filed 08/10/2026 with respect to claim(s) 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 Li is being relied upon to teach dependent claim 8 and 10-11 more-consistently with the instant claim language, as shown below.
Given the amendments to claim 1, reference to Welsh is being relied upon to teach dependent claims 7 and 11 more-consistently with the instant claim language, as shown below.
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.
Claims 1, 7-8, and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20190272634 A1, published September 5, 2019) in view of Welsh et al. (US 20230148147 A1, published May 11, 2023), hereinafter referred to as Li and Welsh, respectively.
Regarding claim 1, and similarly for claims 12 and 13, Li teaches an image processing device comprising:
a processor (see para. 0045 – “The imaging system may be implemented as hardware, firmware, software or a combination thereof which interact with or are otherwise executed by one or more computer processors, which may include the processors of the control system 512.”), wherein the processor is configured to:
acquire an ultrasound image of a cyst and a blood vessel (see para. 0025 – “For example, anatomical image data may be acquired using imaging technology such as…ultrasound…”; see para. 0028 – “With reference to FIGS. 2B, 3A, and 3B, segmentation maps 121, 131, 133 are generated from trained convolutional neural networks. The segmentation map 121 is generated from the anatomic image 120…The segmentation map 133 includes the segmented anatomical features of parenchyma tissue 132, arteries 134, veins 136, masses 138 including abnormal tissue such as tumors, kidney stones, or cysts, and ureter tissue 140.”), and
output a recognition result obtained by executing recognition processing of inputting the ultrasound image to a trained model to cause the trained model to recognize the cyst and the blood vessel to be distinguishable from each other (see para. 0028 – “With reference to FIGS. 2B, 3A, and 3B, segmentation maps 121, 131, 133 are generated from trained convolutional neural networks…The segmentation maps 131, 133 are generated from the anatomic image data used to generate anatomic image 130. The segmentation map 133 includes the segmented anatomical features of parenchyma tissue 132, arteries 134, veins 136, masses 138 including abnormal tissue such as tumors, kidney stones, or cysts, and ureter tissue 140.”),
wherein the trained model is obtained by performing machine learning for recognizing the cyst and the blood vessel to be distinguishable from each other using, as the correct answer data, an annotation capable of specifying the position of the cyst and an annotation capable of specifying the position of the blood vessel (see para. 0027 – “At a process 106, all or some subset of the training patches may be used as inputs to train an artificial neural network…In some embodiments, the artificial neural network may be a U-shaped network that performs down-sampling filter operations to detect features within the anatomical image data and up-sampling filter operations (i.e., deconvolution) to localize the detected features by assigning classification information to the detected features.”),
wherein the recognition result includes position-specifying information capable of specifying the position of the cyst in the ultrasound image (see para. 0028 – “The segmentation maps 131, 133 are generated from the anatomic image data used to generate anatomic image 130. The segmentation map 133 includes the segmented anatomical features of parenchyma tissue 132, arteries 134, veins 136, masses 138 including abnormal tissue such as tumors, kidney stones, or cysts, and ureter tissue 140.” position-specifying information as displayed segmentation maps including cysts), and
wherein the outputting of the recognition result includes outputting the ultrasound image in which the position-specifying information is superimposed on a portion in the ultrasound image where the cyst is shown (see para. 0028 – “In some embodiments, the output segmentation maps [recognition result, position-specifying information] may be displayed with the anatomic image data [ultrasound image]. For example, in some embodiments, all or some of the segmented anatomical features may be superimposed on the anatomic image data…”).
Li teaches inputting information into a machine learning model, but does not explicitly teach where the input information includes a B-mode image and Doppler information.
Whereas, Welsh, in an analogous field of endeavor, teaches acquire an ultrasound image of a target region which is obtained by a B-mode method in an ultrasound diagnosis for a subject and blood flow distribution information obtained by a Doppler method used in combination with the B-mode method in the ultrasound diagnosis; 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 target region (see para. 0071 - “With reference to FIGS. 2 and 3, in some embodiments the system utilizes a fully convolutional neural network (fCNN) [trained model] 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.”).
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 inputting information into a machine learning model, as disclosed in Li, by having the input information include a B-mode image and Doppler information, as disclosed in Welsh. One of ordinary skill in the art would have been motivated to make this modification in order to improve segmentation accuracy, as taught in Welsh (see para. 0071).
Furthermore, regarding claim 7, Welsh further teaches wherein the blood flow distribution information is a map capable of specifying an intensity of a blood flow (see para. 0020 – “The neural network may be trained on a combination of power Doppler and greyscale imaging.”; see para. 0081 – “Attenuation is a non-linear effect, impacting on the power Doppler intensity values meaning that for an organ, 2-3 cm in depth attenuation will have varying impact on intensity values despite the same underlying bloodflow.” Where a map capable of specifying an intensity of a blood flow is inherently power Doppler imaging, which is known in the art).
Furthermore, regarding claim 8, Li further teaches wherein the outputting of the recognition result includes displaying the recognition result on a first screen (see para. 0028 – “In some embodiments, the output segmentation maps [recognition result, position-specifying information] may be displayed with the anatomic image data [ultrasound image]. For example, in some embodiments, all or some of the segmented anatomical features may be superimposed on the anatomic image data…”).
Furthermore, regarding claim 10, Li 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 (see para. 0028 – “In some embodiments, the output segmentation maps [recognition result, position-specifying information] may be displayed with the anatomic image data [ultrasound image]. For example, in some embodiments, all or some of the segmented anatomical features may be superimposed on the anatomic image data…” displaying output segmentation maps as recognition processing executed).
Furthermore, regarding claim 11, Li further teaches an endoscope system comprising: an ultrasound probe that emits ultrasound and detects a reflected wave of the ultrasound in a state in which the ultrasound probe is inserted into a body of the subject (see para. 0025 – “For example, anatomical image data may be acquired using imaging technology such as…ultrasound…”; see para. 0045 – “In some embodiments, the imaging system includes endoscopic imaging instrument components that may be integrally or removably coupled to medical instrument 504.”), and
Welsh further teaches wherein the ultrasound image and the blood flow distribution information are generated based on the reflected wave detected by the ultrasound probe (see para. 0053 – “With reference to FIG. 1 , a typical ultrasound (US) system comprises, for example, a transmitter, an ultrasound probe…”; see para. 0060 – “Acquired ultrasound scan data may be processed in real-time during a scanning session as the color flow and B-mode echo signals are received.”).
The motivation for claims 7 and 11 was shown previously in claim 1.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Li in view of Welsh, as applied to claim 1 above, and in further view of Wu (CN 102078202A, published June 1, 2011), hereinafter referred to as Wu.
Regarding claim 9, Li in view of Welsh teaches all of the elements disclosed in claim 1 above.
Li in view of Welsh 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 Li in view of Welsh, 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).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Yoshikawa (US 20210022702 A1, published January 28, 2021) discloses in the case of a non-cyst, the vector distribution in the central region is a distribution having a certain direction. In a case of a cyst with a high viscosity, a vector distribution includes a disorder of the direction. In a case of a cyst with a low viscosity, the vector distribution is disordered in the direction, and the magnitude of the vector is reduced. On the other hand, in the case of a non-viscous fluid (liquid), the vector distribution is 0, that is, disappears.
Hunter et al. (US 20180000453 A1, published January 4, 2018) discloses an anatomical feature of interest may include in its label whether the lesion is a cyst, mass, duct or blood vessel.
Roux et al. (US 20040158286 A1, published August 12, 2004) discloses vascular structures (arteries and veins) may be differentiated from nonvascular structures such as cysts or a dilated calyx using color Doppler sonography.
Schwartz (US 6117080 A, published September 12, 2000) discloses the resulting diagnostic image will thus highlight or opaquely render the stationary fluid pool of a fluid filled cyst while suppressing the display of blood vessel lumens as highlighted or opaque structures through recognition of their Doppler characteristics.
Reicher et al. (US 20190188849 A1, published June 20, 2019) discloses learn via stored data or dynamically-learned experience that an anechoic structure on an ultrasound with sharp borders and posterior acoustic enhancement is a simple cyst, and learn that a tubular linear or curvilinear structure with such characteristics most likely represents a blood vessel.
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 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pascal Bui-Pho can be reached on 571-272-2714. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/N.C./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798
