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
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/24/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
(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.
Claim(s) 2-6, 10-11 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Arita et. al. (United States Patent Application Publication US 2024/0074696 A1).
Regarding claim 2, Arita et. al. discloses an information processing method comprising: acquiring first observation data, which is observation data of a target event; generating a first feature amount from the first observation data using deep metric learning (Arita et. al. [0017]-[0018]: observation of a target event, “edema”); matching the first feature amount to second feature amounts generated using deep metric learning from the second observation data, which is observation data of a reference event generated from second component amounts, which are known component amounts of the reference event, and has a nonlinear relationship with the second component amounts (Arita et. al. [0037]: the first estimation function estimates an in-vivo component volume from a pixel value, that is, a spectral reflectance R, of the pixel to be learned using a machine learning model MDL1 defined by the first model information in the memory. The in-vivo component volume is an example of a “feature quantity”. [0040]: The machine learning model MDL1 may be implemented, for example, using a neural network which has been trained such that an output result of the numerical simulation model MDL2 (the spectral reflectance R^ of each pixel of the edema image of a certain patient) becomes close to the spectral reflectance R of each pixel of the edema image of the patient.); and estimating the second component amounts corresponding to the second feature amount matched to the first feature amount as first component amounts, which are component amounts of the target event (Arita et. al. [0045]-[0047]: first and second estimation functions).
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Regarding claim 3, Arita et. al. discloses the information processing method according to claim 2, wherein the first feature amount is matched to the second feature amount using optimal transport (Arita et. al. [0050]-[0053]).
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Regarding claim 4, Arita et. al. discloses the information processing method according to claim 3, wherein the optimal transport transports the first feature amount so that a distance between the first feature amount is the same before and after transporting the first feature amount from a first feature space in which the first feature amount is distributed to a second feature space in which the second feature amount is distributed, and the first feature amount is matched with the second feature amount in the second feature space (Arita et. al. [0050]-[0053]).
Regarding claim 5, Arita et. al. discloses the information processing method according to claim 2, wherein the first feature amount is matched to the second feature amount using a gradient method (Arita et. al. [0058]: when the machine learning model MDL1 is implemented using a neural network, the training function adjusts parameters (weighting factors or bias components) of the neural network such that the loss decreases using a stochastic gradient descent method or a steepest descent method.).
Regarding claim 6, Arita et. al. further discloses the information processing method according to claim 2, wherein the first observation data and the second observation data include a spectral reflectance, and the first component amounts and the second component amounts include any one of a hemoglobin concentration, a water concentration, and a melanin concentration (Arita et. al. [0037]-[0038]).
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Regarding claim 10, which is an information processing apparatus comprising, the rejection analysis of claim 2 is incorporated herein.
Regarding claim 11, which is a computer-readable non-transitory storage medium which has stored a program causing a computer to execute, the rejection analysis of claim 2 is incorporated herein.
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, 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Eguizabal et. al. (United States Patent Application Publication US 2023/0326100 A1) in view of Arita et. al. (United States Patent Application Publication US 2024/0074696 A1).
Regarding claim 1, Eguizabal et. al. discloses an X-ray computed tomography apparatus comprising processing circuitry configured to: acquire a number of photons by performing a CT scan on a target event (Eguizabal et. al. [0008]: an x-ray computed tomography (CT) system includes an x-ray source and an x-ray detector arranged in such a way that projection images of the subject or object can be acquired in different view angles covering at least 180 degrees. [0053]: by way of example, the x-ray detector may be a photon-counting x-ray detector. [0058]-[0061]: A photon-counting detector counts the number of photons that have interacted in the detector during a measurement time.).
However, Eguizabal et. al. fails to disclose generate a first feature amount from the number of photons; match the first feature amount to second feature amounts generated using a machine learning model from the number of photons of a reference event, the number of photons of the reference event being a number of photons generated from known component amounts of the reference event and having a nonlinear relationship with the known component amounts; and estimate the known component amounts corresponding to the second feature amount matched to the first feature amount as component amounts of the target event.
Arita et. al. teaches generate a first feature amount from the number of photons (Arita et. al. [0037]: the first estimation function estimates an in-vivo component volume from a pixel value, that is, a spectral reflectance R, of the pixel to be learned using a machine learning model MDL1 defined by the first model information in the memory. The in-vivo component volume is an example of a “feature quantity”.); match the first feature amount to second feature amounts generated using a machine learning model from the number of photons of a reference event, the number of photons of the reference event being a number of photons generated from known component amounts of the reference event and having a nonlinear relationship with the known component amounts (Arita et. al. [0040]: The machine learning model MDL1 may be implemented, for example, using a neural network which has been trained such that an output result of the numerical simulation model MDL2 (the spectral reflectance R^ of each pixel of the edema image of a certain patient) becomes close to the spectral reflectance R of each pixel of the edema image of the patient.); and estimate the known component amounts corresponding to the second feature amount matched to the first feature amount as component amounts of the target event (Arita et. al. [0045]-[0047]: first and second estimation functions).
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These features generated from the number of photons counted are important to the claimed invention because X-ray detectors with photon counting have an advantage because in principal the energy for each x-ray can be measured which yields additional information about the composition of the person. This information can be used to increase the image quality and/or to decrease the radiation dose. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Eguizabal et. al. and Arita et. al. so that photons can be counted on the x-ray CT apparatus.
Regarding claim 7, Arita et. al. discloses the information processing method according to claim 2. However, Arita et. al. fails to disclose wherein the first observation data and the second observation data include the number of photons detected by a photon counting CT device, and the first component amounts and the second component amounts include any one of soft tissue, calcium, and iodine.
Eguizabal et. al. teaches wherein the first observation data and the second observation data include the number of photons detected by a photon counting CT device, and the first component amounts and the second component amounts include any one of soft tissue, calcium, and iodine (Eguizabal et. al. [0008]: an x-ray computed tomography (CT) system includes an x-ray source and an x-ray detector arranged in such a way that projection images of the subject or object can be acquired in different view angles covering at least 180 degrees. [0053]: by way of example, the x-ray detector may be a photon-counting x-ray detector. [0058]-[0061]: A photon-counting detector counts the number of photons that have interacted in the detector during a measurement time. [0148]: the basis functions may represent the attenuation of water and calcium; or calcium and iodine, [0208]).
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This is important to the claimed invention because X-ray detectors with photon counting have an advantage because in principal the energy for each x-ray can be measured which yields additional information about the composition of the person. This information can be used to increase the image quality and/or to decrease the radiation dose. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Eguizabal et. al. and Arita et. al. so that photons can be counted on the x-ray CT apparatus.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Arita et. al. (United States Patent Application Publication US 2024/0074696 A1) in view of Paik et. al. (United States Patent Application Publication US 2024/0112776 A1).
Regarding claim 8, Arita et. al. discloses the information processing method according to claim 2. However, Arita et. al. fails to disclose wherein the first observation data and the second observation data include an echo signal measured by an ultrasound diagnostic device, and the first component amounts and the second component amounts include an elasticity of biological tissue.
Paik et. al. teaches wherein the first observation data and the second observation data include an echo signal measured by an ultrasound diagnostic device, and the first component amounts and the second component amounts include an elasticity of biological tissue (Paik et. al. [0011]: in some embodiments, said medical image is a radiographic image, a magnetic resonance imaging (MRI) image, an ultrasound image, an endoscopy image, an elastography image, a thermogram image, a positron emission tomography (PET) image, a single photon emission computed tomography (SPECT) image, an optical coherence tomography (OCT) image, a computed tomography (CT) image, a microscopy image, or a medical photography image.).
This is important to the claimed invention because the information processed can be from many different types of imaging modalities. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Arita et. al. and Paik et. al. so that data processing is possible from multiple imaging modalities.
Regarding claim 9, Arita et. al. discloses the information processing method according to claim 2, and the first component amounts and the second component amounts include electrical characteristic parameters of biological tissue (Arita et. al. [0047]: calculating or simulating the spectral reflectance). However, Arita et. al. fails to disclose wherein the first observation data and the second observation data include a magnetic field observed by a magnetic resonance imaging device.
Paik et. al. teaches wherein the first observation data and the second observation data include a magnetic field observed by a magnetic resonance imaging device (Paik et. al. [0011]: in some embodiments, said medical image is a radiographic image, a magnetic resonance imaging (MRI) image, an ultrasound image, an endoscopy image, an elastography image, a thermogram image, a positron emission tomography (PET) image, a single photon emission computed tomography (SPECT) image, an optical coherence tomography (OCT) image, a computed tomography (CT) image, a microscopy image, or a medical photography image.).
This is important to the claimed invention because the information processed can be from many different types of imaging modalities. Thus, it would have been obvious to one skilled in the art prior to the effective filing date of the claimed invention to have combined the teachings of Arita et. al. and Paik et. al. so that data processing is possible from multiple imaging modalities.
Conclusion
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/JESSICA YIFANG LIN/Examiner, Art Unit 2668 September 23, 2026
/ANAND P BHATNAGAR/
Primary Examiner, Art Unit 2668
September 26, 2026
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