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 .
Claim Objections
Claims 8-14 are objected to because of the following informalities:
Regarding claim 8, the limitation “index GOF” should be changed to “index goodness-of-fit (GOF)” in order to properly define what GOF is.
Regarding claim 9, the limitation “index GOF” should be changed to “index goodness-of-fit (GOF)” in order to properly define what GOF is. Claims 10-14 are objected to by virtue of their dependency.
Appropriate correction is required.
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.
Claims 1-2 and 8-10 are rejected under 35 U.S.C. 103 as being unpatentable over Mahvash (U.S. 2024/0060914) in view of Hui (CN 107271468; notations directed to translated Hui).
Regarding claim 1:
Mahvash discloses an analysis apparatus for calculating an index indicating an optimization degree of a model including information of an electron density distribution with respect to measurement data, a probability distribution function followed by the measurement data being known, comprising:
processing circuitry (Fig. 1, 131) configured to
acquire the measurement data (Fig. 12, 301),
acquire calculation data (Fig. 12, 303, data), and
calculate an index goodness-of-fit including a ratio of a residual ([0105]-[0106], residual goodness of fit), which is defined by a predetermined mathematical formula including the measurement data and the calculation data ([0105]-[0106], residual goodness of fit), and an expected value of the residual, which is defined based on the probability distribution function and the predetermined mathematical formula ([0105]-[0106], residual goodness of fit from model).
However, Mahvash fails to disclose acquire calculation data calculated from the model.
Hui teaches acquire calculation data calculated from the model (Translated Hui; [0055]-[0067], data obtained from model).
It would have been obvious to one of an ordinary skill in the art before the effective filing data to combine the system of Mahvash with the calculation taught by Hui in order to improve data quality by increasing calculation accuracy (Translated Hui; [0153]). KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
Regarding claim 2:
The combination of Mahvash and Hui discloses the analysis apparatus according to claim 1, wherein the processing circuitry is further configured to evaluate convergence of the GOF (Mahvash; [106], evaluation of goodness of fit and determination if it is within or outside the acceptable range).
Regarding claim 7:
The combination of Mahvash and Hui discloses a system comprising,
an X-ray analysis apparatus comprising an X-ray generating section (Mahvash; Fig. 1, 110) for generating X-rays, a sample table (Fig. 1, 140) for placing a sample, and a detector ( Mahvash; Fig. 1, 116) for detecting X-rays, and
an analysis apparatus according to claim 1 (as rejected above).
Regarding claim 8:
Mahvash discloses a method for calculating an index indicating an optimization degree of a model including information of an electron density distribution with respect to measurement data, a probability distribution function followed by the measurement data being known, comprising:
acquiring the measurement data (Fig. 12, 301),
acquiring calculation data (Fig. 12, 303, data), and
calculating an index goodness-of-fit including a ratio of a residual ([0105]-[0106], residual goodness of fit), which is defined by a predetermined mathematical formula including the measurement data and the calculation data ([0105]-[0106], residual goodness of fit), and an expected value of the residual, which is defined based on the probability distribution function and the predetermined mathematical formula ([0105]-[0106], residual goodness of fit from model).
However, Mahvash fails to disclose acquire calculation data calculated from the model.
Hui teaches acquiring calculation data calculated from the model ([0055]-[0067], data obtained from model).
It would have been obvious to one of an ordinary skill in the art before the effective filing data to combine the method of Mahvash with the calculation taught by Hui in order to improve data quality by increasing calculation accuracy (Translated Hui; [0153]). KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
Regarding claim 9:
Mahvash discloses a non-transitory computer-readable storage medium storing computer-readable instructions thereon for calculating an index indicating an optimization degree of a model including information of an electron density distribution with respect to measurement data, a probability distribution function followed by the measurement data being known which, when executed by a computer, causes the computer to perform a method, the method comprising:
acquiring the measurement data (Fig. 12, 301),
acquiring calculation data (Fig. 12, 303, data), and
calculating an index goodness-of-fit including a ratio of a residual ([0105]-[0106], residual goodness of fit), which is defined by a predetermined mathematical formula including the measurement data and the calculation data ([0105]-[0106], residual goodness of fit), and an expected value of the residual, which is defined based on the probability distribution function and the predetermined mathematical formula ([0105]-[0106], residual goodness of fit from model).
However, Mahvash fails to disclose acquire calculation data calculated from the model.
Hui teaches acquiring calculation data calculated from the model (Translated Hui; [0055]-[0067], data obtained from model).
It would have been obvious to one of an ordinary skill in the art before the effective filing data to combine the method of Mahvash with the calculation taught by Hui in order to improve data quality by increasing calculation accuracy (Translated Hui; [0153]). KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 415-421, 82 USPQ2d 1385, 1395-97 (2007).
Regarding claim 10:
The combination of Mahvash and Hui discloses the non-transitory computer-readable storage medium of claim 9, further comprising evaluating convergence of the GOF (Mahvash; [106], evaluation of goodness of fit and determination if it is within or outside the acceptable range).
Allowable Subject Matter
Claims 3-7 and 11-14 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.
The following is a statement of reasons for the indication of allowable subject matter:
The closest prior arts are Mahvash (U.S. 2024/0060914) and Hui (CN 107271468; notations directed to translated Hui).
Regarding claim 3:
The combination of Mahvash and Hui discloses the analysis apparatus according to claim 1.
However, the combination of Mahvash and Hui fails to disclose wherein the predetermined mathematical formula includes a sum of squared residuals of a logarithm of the measurement data and a logarithm of the calculation data.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 4:
The combination of Mahvash and Hui discloses the analysis apparatus according to claim 1.
However, the combination of Mahvash and Hui fails to disclose wherein the predetermined mathematical formula includes a weight parameter that multiplies the measurement data by a constant value when the measurement data has an intensity equal to or less than a predetermined value and that takes a logarithm of the measurement data when the measurement data has an intensity more than the predetermined value.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 5:
The combination of Mahvash and Hui discloses the analysis apparatus according to claim 1.
However, the combination of Mahvash and Hui fails to disclose wherein the probability distribution function is a Poisson distribution, and the processing circuitry is further configured to calculate an expected value of the residual by approximating the expected value with a Gaussian distribution.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 6:
The combination of Mahvash and Hui discloses the analysis apparatus according to claim 2, wherein the processing circuitry is further configured to generate the model (Mahvash; Fig. 12, 301), and calculate the calculation data based on the generated model (Translated Hui; [0055]-[0067], data obtained from model), acquire the calculation data (Mahvash; Fig. 12, 303, data).
However, the combination of Mahvash and Hui fail to disclose output the model in a case that the GOF has converged, update a model parameter of the model, generate the model and calculate the calculation data in a case that the GOF has not converged, and calculate the GOF based on the calculated calculation data.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 11:
The combination of Mahvash and Hui discloses the non-transitory computer-readable storage medium of claim 9.
However, the combination of Mahvash and Hui fails to disclose wherein the predetermined mathematical formula includes a sum of squared residuals of a logarithm of the measurement data and a logarithm of the calculation data.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 12:
The combination of Mahvash and Hui discloses the non-transitory computer-readable storage medium of claim 9.
However, the combination of Mahvash and Hui fails to disclose wherein the predetermined mathematical formula includes a weight parameter that multiplies the measurement data by a constant value when the measurement data has an intensity equal to or less than a predetermined value and that takes a logarithm of the measurement data when the measurement data has an intensity more than the predetermined value.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 13:
The combination of Mahvash and Hui discloses the non-transitory computer-readable storage medium of claim 9.
However, the combination of Mahvash and Hui fails to disclose wherein the probability distribution function is a Poisson distribution, further comprising: calculating an expected value of the residual by approximating the expected value with a Gaussian distribution.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Regarding claim 14:
The combination of Mahvash and Hui discloses the non-transitory computer-readable storage medium of claim 10, further comprising generating the model (Mahvash; Fig. 12, 301), and calculating the calculation data based on the generated model (Translated Hui; [0055]-[0067], data obtained from model), acquiring the calculation data (Mahvash; Fig. 12, 303, data).
However, the combination of Mahvash and Hui fail to disclose outputting the model in a case that the GOF has converged, updating a model parameter of the model, generating the model and calculate the calculation data in a case that the GOF has not converged, and calculating the GOF based on the calculated calculation data.
Since the prior art of record fails to teach the details above, nor is there any reason to modify or combine prior art elements absent of applicant’s disclosure, the claim is deemed patentable over the prior art of record, if rewritten in independent form to include all of the limitations of the base claim and any intervening claim.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOORENA KEFAYATI whose telephone number is (469)295-9078. The examiner can normally be reached M to F, 7:30 am to 4:30 pm.
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/S.K./Examiner, Art Unit 2884
/DAVID J MAKIYA/Supervisory Patent Examiner, Art Unit 2884