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 Arguments
Applicant's arguments filed with respect to amended claims on 11/24/2025 have been fully considered but they are not persuasive.
Applicant’s argument regarding amended claim 1 that, “values are calculated such that variances increase. Amemiya does not, however, disclose the use of functions for conversion with necessary limitations in a magnetic resonance simulation, such as the pseudo physical parameters are values that meet a physical limitation, such as, for example, T1 > 0. Claim 1 is patentable over Amemiya” that is incorrect.
In response to applicant’s specific argument, examiner respectfully disagrees with the applicant’s comments. However, see, Amemiya, para [0081], The values of the constants U, V, and W in Expression (13) can also be obtained by calculation in accordance with a biological tissue (hereinafter referred to as a target tissue) desired to be weighted. Various biological tissues have combinations of quantitative values (for example, T1=1.3 seconds and T2=0.1 seconds in gray matter) inherent in each kind of biological tissue. Accordingly, standard quantitative values of a target tissue are obtained from quantitative data or the like of a healthy person and are stored in the storage device 112. Clearly, here , the T1 is greater than 0 as instant application disclose pseudo physical parameters are values that meet a physical limitation, such as, for example, T1 > 0. , which is interpreted as meeting physical limitations.
Therefore, for the above mentioned reasons, examiner has maintained the same ground of rejections.
Claim Rejections - 35 USC § 102
3. 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.
4. Claim(s) 1, 2, 4, 5, 7, 9, 10, 11, 15 and 16 are rejected under 35 U.S.C. 102 (a)(1) as being anticipated by Amemiya et al. (US 2018/0284208) (hereafter Amemiya).
Regarding claims 1, 15 and 16, Amemiya discloses a pseudo data generation apparatus comprising processing circuitry configured to:
collect an image data set including data values of a physical amount of one or more dimensions (see, para [0064], [0065], discloses T1, T2 and PD data (interpreted as physical amounts) for each pixel as quantitative data [0066] (interpreted as data values), see, para [0015], [0016], [0020], [0041], image), the data value correspond to a pixel of image data (see, Figs. 2a-2c, Fig. 8, para [0015], [0065], discloses T1, T2 and PD data (interpreted as physical amounts) for each pixel as quantitative data [0066] (interpreted as data values), [0022], pixel value of the points in accordance with the intermediate information, [0041], pixel value of a subject, [0043], pixel values);
and generate a pseudo physical parameters relating to each of physical amounts by performing conversion of the data values of the physical amount of the one or more dimensions included in the image data set using data processing (see, para [0066] discloses in step S302-1, the function calculation unit 221 of the variable conversion unit 220 calculates the variable conversion functions f1 and f2 by calculation using the quantitative value data T1, T2, and PD received by the quantitative value reception unit 210. Hereinafter, a case in which three kinds of values T1, T2, and PD, are used as quantitative values x, y, and z to be input to the variable conversion unit and two kinds of functions, the variable conversion functions f1 and f2, are calculated will be described. The storage device 112 stores functions expressed with Expressions (5) and (6) below as basic functions of calculating the variable conversion functions f1 and f2 in the storage device 112, f1(T1,T2,PD)=A1.T1+B1.T2+C1. PD+D1 (5) f2(T1,T2,PD)=A2. T1+B2.T2+C1.PD+D2 (6), note that the F1 and F2 are intermediate values is function of the T1, T2, PD etc. as even though its combination of values, it’s a function of this values as broadly interpreted as relating to each of the plurality of physical amounts, unless that relationship is explicitly claimed), the pseudo parameters being used in magnetic resonance simulation (see, Fig. 11, the generate diagnosis image from selected input variables (for example, F1 and F2), S305 and display diagnosis image, S306, [0115]), MRI image, see, Fig. 10, the diagnosis image generation unit , 230 with the pixel value setting function adjusting unit, 701), the conversion of data being performed so that the pseudo physical parameter meet a physical limitation (para [0081], [0081] The values of the constants U, V, and W in Expression (13) can also be obtained by calculation in accordance with a biological tissue (hereinafter referred to as a target tissue) desired to be weighted. Various biological tissues have combinations of quantitative values (for example, T1=1.3 seconds and T2=0.1 seconds in gray matter) inherent in each kind of biological tissue. Accordingly, standard quantitative values of a target tissue are obtained from quantitative data or the like of a healthy person and are stored in the storage device 112. Clearly, here , the T1 is greater than 0 , which is interpreted as meeting physical limitations).
Regarding claim 2, Amemiya further discloses the pseudo data generation apparatus, wherein the conversion is a linear sum of the data values of the one or more dimensions (see, paras [0066], equations 5 and 6).
Regarding claim 4, Amemiya further discloses the pseudo data generation apparatus, wherein: the data values of the one or more dimensions include coordinate information and a pixel value in each pixel in the image data (see, para [0065] and [0066]).
Regarding claim 5, Amemiya further discloses the pseudo data generation apparatus, wherein: one of the physical amounts is a value proportional to a number of protons ([0003]); and the processing circuitry generates the pseudo physical parameter relating to the value proportional to the number of protons from the pixel value in the each pixel (see, para [0066], equations 5 and 6 disclose generating pseudo parameters).
Regarding claim 7, Amemiya further discloses the pseudo data generation apparatus, wherein the processing circuitry generates the pseudo physical parameter by allocating a predetermined value or a random value to some of the physical amounts (para [0066], equations 5 and 6, applying the coefficients values A1…D2 to the physical amounts as shown in equation).
Regarding claim 9, Amemiya further discloses the pseudo data generation apparatus, wherein the physical amounts include a value proportional to a number of protons, a longitudinal relaxation time or a longitudinal relaxation rate, and a transverse relaxation time or a transverse relaxation rate (see, para [0004], MR examination, when a user selects and performs a pulse sequence, it is possible to acquire a weighted image in which a relative difference such as physical properties (for example, T1: longitudinal relaxation time, T2: transverse relaxation time, PD: proton density, D: diffusion coefficient, χ: magnetic susceptibility, v: flow rate, and Cs: chemical shift) of a biological tissue is weighted. [0008], [0009], [0010] The longitudinal relaxation rate (R1) and the transverse relaxation rate (R2), which are reciprocals of the longitudinal relaxation time T1 and the transverse relaxation time T2, are also used as physical properties for quantitative diagnosis, [0041], the physical property of a tissue of the subject obtained using a nuclear magnetic resonance phenomenon include a longitudinal relaxation time T1, a longitudinal relaxation rate, a transverse relaxation time T2, a transverse relaxation rate, a chemical shift of a precession frequency in nuclear spin of the subject, a proton density PD in the subject, magnetic susceptibility, a diffusion coefficient of a molecule in the subject, and a flow rate of a liquid such as blood).
Regarding claim 10, Amemiya further discloses the pseudo data generation apparatus the pseudo data generation apparatus according to wherein the physical amounts are physical amounts for use in simulation relating to chemical shift measurement of molecules including metabolites in a living body (see, para [0004], [0013], measuring physical properties in a living body, such as a method of measuring a flow rate in accordance with a phase contrast method, a method of calculating a magnetization ratio in a quantitative susceptibility mapping, and a method of calculating a chemical shift in magnetic resonance (MR) spectroscopy, have been proposed, [0041]).
Regarding claim 11, Amemiya further discloses the pseudo data generation apparatus, wherein the processing circuitry sets, for each of the molecules, a chemical shift amounts specific to a molecule, and generates the pseudo physical parameters for each of the molecules relating to a value proportional to a number of protons, a longitudinal relaxation time or a longitudinal relaxation rate, and a transverse relaxation time or a transverse relaxation rate ([0041], a physical property of a tissue of the subject, a pixel value of a subject image imaged so that a specific physical property is weighted, a characteristic value in the subject obtained by an agent such as a contrast agent administered to the subject, or a physical amount or a physical property obtained by radiating and measuring acoustic or electromagnetic waves to the subject can be used as the quantitative value. Examples of the physical property of a tissue of the subject obtained using a nuclear magnetic resonance phenomenon include a longitudinal relaxation time T1, a longitudinal relaxation rate, a transverse relaxation time T2, a transverse relaxation rate, a chemical shift of a precession frequency in nuclear spin of the subject, a proton density PD in the subject, magnetic susceptibility, a diffusion coefficient of a molecule in the subject, and a flow rate of a liquid such as blood, para [0066], the function calculation unit 221 of the variable conversion unit 220 calculates the variable conversion functions f1 and f2 by calculation using the quantitative value data T1, T2, and PD received by the quantitative value reception unit 210. Hereinafter, a case in which three kinds of values T1, T2, and PD, are used as quantitative values x, y, and z to be input to the variable conversion unit and two kinds of functions, the variable conversion functions f1 and f2).
Claim Rejections - 35 USC § 103
5. 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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 non-obviousness.
6. Claim(s) 3, 8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Amemiya in view of Qing Lyu, “Quantitative MRI- Absolute T1, T2 and proton density parameters from deep learning” (hereafter Lyu) (See IDS).
Regarding claim 3, Amemiya does not discloses the pseudo data generation apparatus, wherein the conversion is performed by applying a trained model to the data values of the one or more dimensions, the trained model being trained so as to receive one or more pieces of data as an input and output one or more pseudo physical parameters. However, in same field of endeavor, Lyu teaches II. Methodology, teaches Our deep learning approach targets a data-driven quantitative mapping from sampled MRI data to an intrinsic tissue parameter matrix. The overall idea is illustrated in Figure 1. This workflow includes two main parts: (1) the MRI signal data generation part for generating T1, T2 and p-weighted signals and (2) the neural network part for mapping MRI signals to intrinsic tissue parameters. A. data generation discloses The Bloch equation governs this data generation process. The detailed description of this process can be seen in [9]. Gaussian noise was added into the simulated MRI signals. For generating Ti-weighted MRI signals, the repetition time (TR) was set to 500 ms and the echo time (TE) to 15 ms. For T2-weighted MRI signals, the TR and TE were set to 10,000 ms and 300 ms respectively. For p-weighted MRI signals, TR and TE were 10,000 ms and 15 ms respectively. Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Lyu with the Amemiya, as a whole, so as to use the neural network training to generate the pseudo data, the motivation is to provide absolute quantification help segment MRI tissue images with better accuracy.
Regarding claim 8, Amemiya does not explicitly disclose the pseudo data generation apparatus, wherein the physical amounts are physical amounts for use in magnetic resonance simulation using a Bloch equation. However, in same field of endeavor, Lyu teaches in II. Methodology, MRI signals were produced with the popular spin-echo pulse sequence. The Bloch equation governs this data generation process. The detailed description of this process can be seen in [9]. Gaussian noise was added into the simulated MRI signals. For generating T1-weighted MRI signals, the repetition time (TR) was set to 500 ms and the echo time (TE) to 15 ms. For T2-weighted MRI signals, the TR and TE were set to 10,000 ms and 300 ms respectively. For p-weighted MRI signals, TR and TE were 10,000 ms and 15 ms respectively. Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Lyu with the Amemiya, as a whole, so as to use the bloch equation to generate the pseudo data, the motivation is to predict signal behavior under different conditions.
Regarding claim 12, the combined teachings further disclose the pseudo data generation apparatus, wherein the processing circuitry is further configured to execute magnetic resonance simulation using the pseudo physical parameters relating to each of the one or more physical amounts, and generate pseudo collection data simulating a magnetic resonance signal (Lyu, A. Data generation , second paragraph, MRI signals were produced with the popular spin-echo pulse sequence. The Bloch equation governs this data generation process. The detailed description of this process can be seen in [9]. Gaussian noise was added into the simulated MRI signals. For generating Ti-weighted MRI signals, the repetition time (TR) was set to 500 ms and the echo time (TE) to 15 ms. For T2-weighted MRI signals, the TR and TE were set to 10,000 ms and 300 ms respectively).
7. Claim(s) 17 is rejected under 35 U.S.C. 103 as being unpatentable over Amemiya in view of Wismuller (US 2022/0058211) (hereafter Wismuller).
Regarding claim 17, Amemiya discloses a pseudo data generation apparatus comprising processing circuitry configured to: physical amounts), the pseudo parameters being used in magnetic resonance simulation (see, Fig. 11, the generate diagnosis image from selected input variables (for example, F1 and F2), S305 and display diagnosis image, S306, [0115]), MRI image, see, Fig. 10, the diagnosis image generation unit , 230 with the pixel value setting function adjusting unit, 701), the conversion of data being performed so that the pseudo physical parameter meet a physical limitation (para [0081], The values of the constants U, V, and W in Expression (13) can also be obtained by calculation in accordance with a biological tissue (hereinafter referred to as a target tissue) desired to be weighted. Various biological tissues have combinations of quantitative values (for example, T1=1.3 seconds and T2=0.1 seconds in gray matter) inherent in each kind of biological tissue. Accordingly, standard quantitative values of a target tissue are obtained from quantitative data or the like of a healthy person and are stored in the storage device 112. Clearly, here , the T1 is greater than 0 , which is interpreted as meeting physical limitations).
But does not explicitly disclose crossed-out limitations.
However, in same field of endeavor, Wismuller teaches [0118] An exemplar whole brain fMRI multivariate time-series dataset is composed of 100,000 voxels (volume pixels) within the brain that are sampled every 0.5 seconds for four minutes. Therefore, it would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to combine the teachings of Wismuller with the Amemiya, as a whole, so as to use the time series dataset using sampling to generate the pseudo parameters used in magnetic resonance simulation, the motivation is to generate casualty analysis of time series or feature datasets generated by MRI scans.
Allowable Subject Matter
8. Claims 13 and 14 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.
Conclusion
THIS ACTION IS MADE FINAL. 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 DHAVAL V PATEL whose telephone number is (571)270-1818. The examiner can normally be reached Monday to Friday (8:00am-4:30pm).
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, Hannah Wang can be reached at 571-272-9018. 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.
/DHAVAL V PATEL/Primary Examiner, Art Unit 2631