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 .
Claim Objections
Claims 1-2, 4-5, 7-8, 10-11, 13-15, 17-18, 20 are objected to because of the following informalities: the acronym of RF data is not spelled out initially within the claim itself. Appropriate correction is required.
Claim 6 is objected to because of the following informalities: the "C" in concentration is capitalized. Appropriate correction is required.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 14 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Chen (US 2024/0029410 A1).
Regarding claims 1 and 14, Chen discloses an operating method of an apparatus operated by at least one processor and an imaging apparatus comprising, comprising:
receiving RF data obtained from tissue through an arbitrary ultrasound probe [[0005] ultrasound images … probe position; [0009] captured image data comprising motion between the imaging device and the tissue; [0147] imaging device 102 may include an ultrasound device configured to physically capture ultrasonic waveform data (e.g., RF waveform data)]; and
generating a quantitative ultrasound image from the RF data using a neural network trained to perform a probe domain generalization [[0015] generate new training images; [title] system and method for domain generalization across variations in medical images; [0010] ultrasound images … reconstructed images, or any combination thereof, and the machine-learning-based AI model comprises at least one of the following: a convolutional neural network, a recurrent neural network, a long-short-term-memory neural network, a Bayesian network, a Bayesian neural network; [0142] Non-limiting embodiments or aspects provide for the modification of medical images through a stochastic temporal data augmentation, where the modified images may be used to train an AI model for generalization across various domains of medical images; [0186] computing device 400 may coordinate acquisition of new training image data based on domains outside of the current training domain. Domains outside of the current training domain may include any image feature that the model being trained has not learned].
(claim 14 a memory; and a processor executing instructions loaded to the memory [[0140] “computing device” may refer to one or more electronic devices configured to process data. A computing device may, in some examples, include the necessary components to receive, process, and output data, such as a processor, a display, a memory, an input device, a network interface, and/or the like. A computing device may be a central processing unit (CPU)]).
Allowable Subject Matter
Claims 2-7 and 15-20 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.
The following is a statement of reasons for the indication of allowable subject matter: see below.
Regarding claim 2, the closest prior art Chen does not appear to teach the operating method of claim 1, wherein the generating the quantitative ultrasound image comprises: extracting a generalized quantitative feature from the RF data using a calibration function that meta-learns probe domain generalization; and reconstructing the generalized quantitative feature to generate the quantitative ultrasound image.
Regarding claim 3, the closest prior art Chen does not appear to teach the operating method of claim 2, wherein the calibration function generates a deformation field spatially transforming a probe condition of the arbitrary ultrasound probe to a generalized probe condition.
Regarding claim 4, the closest prior art Chen does not appear to teach the operating method of claim 3, wherein the generating the quantitative ultrasound image comprises applying the deformation field generated by the calibration function to a feature of the RF data to generate a deformed feature to the generalized probe condition.
Regarding claim 5, the closest prior art Chen does not appear to teach the operating method of claim 2, further comprising generating a B-mode image from the RF data, wherein the generating the quantitative ultrasound image comprises generalizing the probe condition inferred from a relationship between the RF data and the B-mode image, using the calibration function; and extracting the generalized quantitative feature from the RF data.
Regarding claim 6, the closest prior art Chen does not appear to teach the operating method of claim 1, wherein the quantitative ultrasound image includes quantitative information for at least one parameter among speed of sound (SoS), attenuation coefficient (AC), effective scatterer Concentration (ESC), and effective scatterer diameter (ESD).
Regarding claim 7, the closest prior art Chen does not appear to teach the operating method of claim 1, wherein the neural network is an artificial intelligence model trained to generalize the probe domain of input RF data using training data augmented with virtual probe conditions.
Regarding claim 15, the closest prior art Chen does not appear to teach the imaging apparatus of claim 14, wherein the processor is configured to: extract a generalized quantitative feature from the RF data using a calibration function that meta-learns probe domain generalization; and reconstruct the generalized quantitative feature to generate the quantitative ultrasound image.
Regarding claim 16, the closest prior art Chen does not appear to teach the imaging apparatus of claim 15, wherein the calibration function generates a deformation field spatially transforming a probe condition of the arbitrary ultrasound probe to a generalized probe condition.
Regarding claim 17, the closest prior art Chen does not appear to teach the imaging apparatus of claim 15, wherein the processor is configured to apply the deformation field generated by the calibration function to a feature of the RF data to generate a deformed feature to the generalized probe condition.
Regarding claim 18, the closest prior art Chen does not appear to teach the imaging apparatus of claim 15, wherein the processor is configured to: generate a B-mode image from the RF data; and generalize a probe condition inferred from a relationship between the RF data and the B-mode image using the calibration function, and then extract the generalized quantitative feature from the RF data.
Regarding claim 19, the closest prior art Chen does not appear to teach the imaging apparatus of claim 14, wherein the quantitative ultrasound image includes quantitative information for at least one parameter among speed of sound (SoS), attenuation coefficient (AC), effective scatterer Concentration (ESC), and effective scatterer diameter (ESD).
Regarding claim 20, the closest prior art Chen does not appear to teach the imaging apparatus of claim 14, wherein the neural network includes: an encoder extracting a quantitative feature generalized to a probe domain from the input RF data using an adaptation module that generalizes the probe condition of the input RF data; and a decoder reconstructing the generalized quantitative feature to generate the quantitative ultrasound image.
Claims 8-13 are allowed. (with the anticipation that objections are addressed as described previously).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Wang (2021, arXiv) explains domain generalization using neural networks in machine learning for classifying images coming from different domains such as sketches, cartoons, art paintings, natural images, or photos. Meng (2020, IEEE) describes using neural networks for classifying unseen categories in different domains with application to fetal ultrasound imaging.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN D ARMSTRONG whose telephone number is (571)270-7339. The examiner can normally be reached M - F 9am-5pm.
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/JONATHAN D ARMSTRONG/ Examiner, Art Unit 3645
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