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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 2/27/2026 has been entered.
Response to Arguments
Applicant’s response to the last Office Action, filed 2/27/2026, has been entered and made of record.
Applicant has amended claims 1, 4, 5, and 10. Claims 1-5 and 7-10 are currently pending.
Applicant’s arguments, filed 2/27/2026, with respect to the rejection of claim 1-5 and 7-10 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Li (U.S. Patent Pub. No. 2020/0008707).
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 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-5 and 7-10 are rejected under 35 U.S.C. 103 as being unpatentable over Takeshima (U.S. Patent Pub. No. 2020/0003858) in view of Li (U.S. Patent Pub. No. 2020/0008707) in view of Goshen (U.S. Patent Pub. No. 2019/0282192).
Regarding Claim 1, Takeshima teaches a medical information processing apparatus comprising processing circuitry configured to:
acquire a first medical image having a first data resolution and a second medical image having a second data resolution different than the first data resolution (Fig. 16, #1 & #2 Medical Image; ¶133 a medical image #1 having noise, a medical image #2 having noise, numerical data #3 obtained by digitizing acquisition conditions relating to medical image #1 and numerical data #4 obtained by digitizing the acquisition condition relating to the medical image #2; ¶133 The medical image 2 may be another medical image acquired under the same acquisition condition as that for the medical image #1, or may be a medical image acquired under another acquisition condition; ¶23 The medical image diagnostic apparatus according to the present embodiment may be … a SPECT/CT apparatus)
acquire an acquisition condition for medical data relating to at least one of the first medical image and the second medical image; and (Fig. 16, #3 & #4 Numerical Data; ¶133 numerical data #3 obtained by digitizing acquisition conditions relating to medical image #1 and numerical data #4 obtained by digitizing the acquisition condition relating to the medical image #2.)
output a result (¶133 machine learning model 521-5 is a DNN in which parameters has been trained to output a medical image having no noise) integrated at least a piece of information that is based on the first medical image and the second medical image by inputting the first medical image, the second medical image and the acquisition condition to a trained model (¶133 when inputting a combination of a medical image #1 having noise, a medical image #2 having noise, numerical data #3 obtained by digitizing acquisition conditions relating to medical image #1 and numerical data #4 obtained by digitizing the acquisition condition relating to the medical image #2,) the trained model being trained by input, as input data, a plurality of medical images having different data resolutions and an acquisition condition for at least one of the plurality of medical images, and input, as a correct data, a result obtained by integrating at least a piece of information that is based on the plurality of medical images (¶145 The machine learning model shown above can be generated by the model learning apparatus 6 using supervised machine learning. The training sample is prepared by acquiring medical data under various acquisition conditions. Specifically, the input data of the training sample includes input medical data acquired under a certain acquisition condition, and numerical data obtained by digitizing the acquisition condition. The output data of the training sample includes output medical data corresponding to the medical data and according to the purpose of the machine learning model. The output data is, for example, medical data in which noise is reduced as compared to input medical data if the purpose of the machine learning model is de-noising.)
Takeshima teaches a spectral CT image device which hints at spectrum information being included with the CT image, but does not explicitly disclose acquire a first medical image having a first data resolution and a second medical image having a second data resolution lower than the first data resolution and having spectrum information relating to a chemical shift or a spectral CT image.
Li is in the same field of art of image analysis. Further, Li teaches acquire a first medical image having a first data resolution and a second medical image having a second data resolution lower than the first data resolution (¶46 the set of reconstructed images may include a first set of images having a low temporal rate (“LTR”), or frame rate, and a high spatial resolution (“HSR”), and a second set of images having a high temporal rate (“HTR”) and a low spatial resolution (“LSR”)) and having spectrum information (¶45 The image data may include one-dimensional (“1D”), two-dimensional (“2D”), three-dimensional (“3D”) image data, and combinations thereof. The image data may include magnetic resonance imaging (“MM”) data, as well as X-ray data, computed tomography (“CT”) data, positron emission tomography (“PET”) data, ultrasound (“US”) data, or optical image data.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Takeshima by using a second image which is lower resolution and contains spectrum information (PET/ultrasound data) that is taught by Li; thus, one of ordinary skilled in the art would be motivated to combine the references to decompose or combine received or acquired image data, or images reconstructed therefrom (Li ¶47).
Goshen is in the same field of art of image analysis. Further, Goshen explicitly teaches that a spectral CT image contains spectrum information (¶4 Spectral CT is for example used in medical applications for non-invasively inspecting the internal structure of the body of a subject. Furthermore, spectral CT may be particularly suitable for quantitative imaging applications, since the additional spectral information improves the quantitative information that can be measured about the scanned object and its material composition.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Takeshima by using the spectral information that is given during a spectral CT scan which is taught by Goshen; thus, one of ordinary skilled in the art would be motivated to combine the references for improved quantitative information (Goshen ¶4).
Regarding claim 2, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein the first medical image and the second medical image have different unit data sizes (Takeshima, ¶23 The medical image diagnostic apparatus according to the present embodiment may be … and also may be a combined modality apparatus such as a PET/CT apparatus, a SPECT/CT apparatus, a PET/MRI apparatus, and a SPECT/MRI apparatus; ¶133 The medical image 2 may be another medical image acquired under the same acquisition condition as that for the medical image #1, or may be a medical image acquired under another acquisition condition.)
Regarding claim 3, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein at least one of the first medical image and the second medical image is time-series data, and the first medical image and the second medical image are acquired in different durations in a timeline (Takeshima, ¶133 The medical image 2 may be another medical image acquired under the same acquisition condition as that for the medical image #1, or may be a medical image acquired under another acquisition condition; Embodiment in Fig. 1 teaches: ¶38 Examples of applicable imaging parameters according to the present embodiment include various imaging parameters set directly or indirectly for performing MR imaging such as imaging time)
Regarding claim 4, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein the first medical image is a magnetic resonance (MR) image, the second medical image is the chemical shift image, and the acquisition condition is an imaging condition relating to at least one of the MR image and the chemical shift image (Takeshima, ¶23 The medical image diagnostic apparatus according to the present embodiment may be … and also may be a combined modality apparatus such as a PET/CT apparatus, a SPECT/CT apparatus, a PET/MRI apparatus, and a SPECT/MRI apparatus; ¶133 The medical image 2 may be another medical image acquired under the same acquisition condition as that for the medical image #1, or may be a medical image acquired under another acquisition condition.)
Regarding claim 5, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein the first medical image is a computed tomography (CT) image, the second medical image is the spectral CT image, and the acquisition condition is an imaging condition relating to at least one of the CT image and the spectral CT image (Takeshima, ¶23 The medical image diagnostic apparatus according to the present embodiment may be … and also may be a combined modality apparatus such as a PET/CT apparatus, a SPECT/CT apparatus, a PET/MRI apparatus, and a SPECT/MRI apparatus; ¶133 The medical image 2 may be another medical image acquired under the same acquisition condition as that for the medical image #1, or may be a medical image acquired under another acquisition condition.)
Regarding claim 7, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein the acquisition condition is a condition relating to at least one of an imaging parameter, a pulse sequence, and a type of image reconstruction process (Takeshima, Claim 6: the acquisition condition includes a type of pulse sequence, a frame number, a type of k-space filling trajectory, a number of times of repetition of repetition operation and/or an acceleration factor of parallel imaging.)
Regarding claim 8, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein the acquisition condition includes a ratio of physical values relating to imaging conditions between the first medical data and the second medical data (Takeshima, Fig. 16, Numerical Data; ¶121 The acquisition conditions to be digitized are not limited to the above, and may be, for example, data processing conditions for raw data or image processing conditions for medical images; One with ordinary skill in the art could create a ratio of the numerical data taught and use that as the acquisition condition for the neural network.)
Regarding claim 9, Takeshima in view of Li in view of Goshen teaches the medical information processing apparatus according to claim 1, wherein the processing circuitry is further configured to generate a result in which data having a relatively low data resolution among one of the first medical image and the second medical image is integrated as data having a higher data resolution (Takeshima, ¶143 The machine learning model may output super resolution data of the medical data from the medical data and the numerical data. Super resolution data is medical data having a higher spatial resolution than input medical data. It is possible to generate the machine learning model by making DNN learned based on input data including medical data and numerical data and super resolution data that is teacher data.)
Regarding claim 10, claim 10 has been analyzed with regard to claim 1 and is rejected for the same reasons of obviousness as used above as well as in accordance with Takeshima further teaching on: a medical information processing method (¶2 Embodiments described herein relate generally to a medical information processing apparatus and a medical information processing method)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUSTIN BILODEAU whose telephone number is (571)272-1032. The examiner can normally be reached 9am-5pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Mehmood can be reached at (571) 272-2976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DUSTIN BILODEAU/Examiner, Art Unit 2664
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
Prosecution Insights