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 Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
"a generation unit" and “a training unit” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. Applicant’s paragraph 0048 describes these elements as “electronic circuit such as a Central Processing Unit (CPU) or Micro Processing Unit (MPU) or an integrated circuit such as an Application Specific Integrated Circuit (ASIC) or a Field Programmable Gate Array (FPGA)”.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claims 1-3 and 7-8 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 7-8 recite “wherein the reference sample is a non-culture sample that is artificially prepare with known concentration of the plurality of components” and “wherein the generation unit further generates a data set of a cultured sample to be the measurement target including spectral data of the cultured sample to be the measurement target and each objective variable determined by the content of each of the components of the cultured sample to be the measurement target, wherein the cultured sample is a cultured sample that is actually cultured in a culture device”. The limitation(s) are not disclosed in Applicants’ Specification.
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.
Claim(s) 1-3 and 7-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kikuta et al. (U.S. 2023/0288353 hereinafter Kikuta) in view of Mortada et al. (U.S. 2023/0152216 hereinafter Mortada) in further view of Guzzonato et al. (U.S. 2025/0035486 hereinafter Guzzonato).
As Claim 1, Kikuta teaches a calibration device, comprising:
a generation unit that generates a data set of a reference sample including spectral data of the reference sample containing a plurality of components (Kikuta (¶0057 line 1-3, ¶0058 line 1-8, ¶0059 line 3-6), substrate is placed on the sample stage. Spectrum for learning is captured. Elements contained in the contamination is known) and each objective variable determined by a content of each of the components of the reference sample (Kikuta (¶0059 last 5 lines), quantitative values of the elements contained in the contamination are known); and
a training unit that trains, by machine learning using the data set of the reference sample (Kikuta (¶0052), learning has been executed fort he estimation unit based on teacher data, including the spectrum for learn and data on the elements contained in the contamination on the surface of the substrate which has been used to acquire the spectrum for learning), a machine learning model that outputs at least one objective variable among the objective variables of each of the components in response to input of the spectral data (Kikuta (¶0054 line 1-6, ¶0055 line 4-7), the estimation unit generates the estimation data in response to the input of the spectrum. The estimation data indicates each element contained in the contamination. The estimation unit also generate the quantitative values of the elements).
an acquisition unit that acquires spectral data measured by a spectral analysis performed on a sample to be a measurement target (Kikuta (¶0059 last 5 lines, ¶0060 line 1-3), quantitative values of the elements (non-culture samples) contained in the contamination are known (non-culture sample is the sample with elements contained in the contamination exist is known)). Spectrum for learning is also recorded. The process is repeated multiple times);
an estimation unit that estimates an objective variable determined by the content of a component contained in the sample to be the measurement target, based on a result acquired by inputting the acquired spectral data into the machine learning model that has been trained (Kikuta (¶0054 line 1-6, ¶0055 line 4-7), the estimation unit generates the estimation data in response to the input of the spectrum. The estimation data indicates each element contained in the contamination. The estimation unit also generate the quantitative values of the elements);
a storage unit that stores therein the spectral data of the reference sample and each of the objective variables (Kikuta (¶0059 last 5 lines, ¶0060 line 1-3), quantitative values of the elements contained in the contamination are known. Spectrum for learning is also recorded. The data is recorded as one item of teacher data. The process is repeated multiple times), wherein
the generation unit: acquires the spectral data of the reference sample and each of the objective variables from the storage unit (Kikuta (¶0052), learning has been executed for the estimation unit based on teacher data, including the spectrum for learn and data on the elements contained in the contamination on the surface of the substrate which has been used to acquire the spectrum for learning); and generates a supervised data set of the reference sample (Kikuta (¶0052), learning has been executed for the estimation unit based on teacher data, including the spectrum for learn and data on the elements contained in the contamination on the surface of the substrate which has been used to acquire the spectrum for learning), and
Kikuta may not explicitly disclose:
the training unit: searches for a first development condition regarding an algorithm or a parameter using cross-validation on the data set of the reference sample; trains the machine learning model based on the first development condition; and stores the first development condition in the storage unit,
wherein the generation unit further generates a data set of a cultured sample to be the measurement target including spectral data of the cultured sample to be the measurement target and each objective variable determined by the content of each of the components of the cultured sample to be the measurement target, wherein the cultured sample is a cultured sample that is actually cultured in a culture device, and
the training unit: further searches for a second development condition regarding the algorithm or the parameter by using the data set of the cultured sample to be the measurement target as a validation set; trains the machine learning model based on the second development condition; and stores the second development condition in the storage unit .
Mortada teaches:
the training unit: searches for a first development condition regarding an algorithm or a parameter using cross-validation on the data set of the reference sample (Mortada (¶0052 line 1-10), machine learning uses temperature reading to correct the measured spectrum); trains the machine learning model based on the first development condition (Mortada (¶0052 line 1-10, ¶0169 line 9-11), drift physical equations can be fed to a machine learning algorithm along with spectrometry raw data to create an adaptive corrective model for correction matrix. System is trained by feeding spectrometry linked to temperature values); and stores the first development condition in the storage unit (Mortada (¶0052 line 1-10), machine learning uses temperature reading to correct the measured spectrum),
wherein the generation unit further generates a data set of a cultured sample to be the measurement target including spectral data of the cultured sample to be the measurement target and each objective variable determined by the content of each of the components of the cultured sample to be the measurement target (Mortada (¶0004 any sample is construed as biology or cultured sample, ¶0169 line 9-17), “. The ML algorithm can be trained by feeding an enough amount of spectrometry data (spectral data of the cultured sample) linked to temperature values (e.g., PDS, T data) 3908 (objective variable). The data 3908 can be collected from the production line or from a plurality of spectrometer units 3902a, 3902b, ... ,3902N by connecting the units 3902a, 3902b, ... , 3902N to the cloud 3906 and sending their temperature states and the measured power spectral density in the presence of a reference sample,”), wherein the cultured sample is a cultured sample that is actually cultured in a culture device (Mortada (¶0004 any sample is construed as biology or cultured sample, ¶0169 line 9-17), “. The ML algorithm can be trained by feeding an enough amount of spectrometry data linked to temperature values (e.g., PDS, T data) 3908. The data 3908 can be collected from the production line or from a plurality of spectrometer units 3902a, 3902b, ... ,3902N by connecting the units 3902a, 3902b, ... , 3902N to the cloud 3906 and sending their temperature states and the measured power spectral density in the presence of a reference sample (cultured sample),”), and
the training unit: further searches for a second development condition regarding the algorithm or the parameter by using the data set of the cultured sample to be the measurement target as a validation set (Mortada (¶0052 line 1-10, ¶0050 line 1-5), machine learning uses temperature reading to correct the measured spectrum. Data could be temperature drift or humidity drift); trains the machine learning model based on the second development condition (Mortada (¶0052 line 1-10, ¶0050 line 1-5, ¶0169 line 9-11), drift physical equations can be fed to a machine learning algorithm along with spectrometry raw data to create an adaptive corrective model for correction matrix. Data could be temperature drift or humidity drift. System is trained by feeding spectrometry linked to temperature values.); and stores the second development condition in the storage unit (Mortada (¶0052 line 1-10, ¶0050 line 1-5), machine learning uses temperature reading to correct the measured spectrum. Data could be temperature drift or humidity drift).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify machine learning system of Kikuta instead be a machine learning system taught by Mortada, with a reasonable expectation of success. The motivation would be to “enable online-compensation for instrumental response change causes including temperature drift, humidity drift, light source inefficiency, optical misalignment and other suitable causes” (Mortada (¶0084 line 1-4)).
Kikuta in view of Mortada may not explicitly disclose:
wherein the reference sample is a non-culture sample that is artificially prepare with known concentration of the plurality of components.
Guzzonato teaches:
wherein the reference sample is a non-culture sample that is artificially prepare with known concentration of the plurality of components (Guzzonato (¶0054), “array of spectrometer output intensities of the calibration sample may be generated for each of a plurality of calibration samples of an analyte (non-culture sample) at different known concentrations”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify samples of Kikuta in view of Mortada instead be a samples taught by Mortada, with a reasonable expectation of success. The motivation would be to allow “a machine-learning computational model may be trained, using the plurality of known concentrations of the analyte in the calibration samples and the associated plurality of arrays of spectrometer output intensities, to output a concentration of the analyte in a target sample based on an input array of spectrometer output intensities of the target sample.” (Guzzonato (¶0055 line 1-8)).
As Claim 2, besides Claim 1, Kikuta in view of Mortada in further view of Guzzonato teaches wherein the generation unit:
executes a spectral analysis on a plurality of non- cultured samples having different objective variables determined by the contents of the components (Kikuta (¶0059 last 5 lines, ¶0060 line 1-3), quantitative values of the elements contained in the contamination are known. Spectrum for learning is also recorded. The process is repeated multiple times); and
generates a data set of the reference sample including the spectral data of the reference sample acquired by the spectral analysis and each of the objective variables (Kikuta (¶0059 last 5 lines, ¶0060 line 1-3), quantitative values of the elements contained in the contamination are known. Spectrum for learning is also recorded. The data is recorded as one item of teacher data. The process is repeated multiple times).
As Claim 3, besides Claim 2, Kikuta in view of Mortada in further view of Guzzonato teaches wherein the generation unit:
executes a spectral analysis on the non-cultured samples created by using components contained in each of a plurality of cultured samples to be a measurement target (Kikuta (¶0059 last 5 lines, ¶0060 line 1-3), quantitative values of the elements contained in the contamination are known. Spectrum for learning is also recorded. The process is repeated multiple times); and
generates a data set of the reference sample including the spectral data of the reference sample acquired by the spectral analysis and each of the objective variables (Kikuta (¶0059 last 5 lines, ¶0060 line 1-3), quantitative values of the elements contained in the contamination are known. Spectrum for learning is also recorded. The data is recorded as one item of teacher data. The process is repeated multiple times).
As Claim 7 and 8, the Claims are rejected for the same reasons as Claim 1.
Response to Arguments
Claim Rejections under 35 U.S.C. §103:
As Kikuta, Applicants argue that Kikuta does not disclose new amended limitation(s) (first paragraph of page 8 in the remarks).
PNG
media_image1.png
179
652
media_image1.png
Greyscale
Applicant’s arguments are not persuasive because new reference Guzzonato disclose non-culture sample. Mortada (¶0004) disclose any sample which is construed as biology or cultured sample
As Kikuta, Applicants argue that Kikuta is not analogous art with the Applicant’s field (third paragraph of page 9 in the remarks).
PNG
media_image2.png
87
653
media_image2.png
Greyscale
Applicant’s arguments are not persuasive because Kikuta is related to spectrometer field. Therefore, it would be obvious for one of ordinary skill in the art to look into Kituta for improving Applicant’s spectrometer device.
Claim Rejections under 35 U.S.C. §101:
Applicants’ arguments are persuasive; therefore, 35 U.S.C. §101 rejection(s) are respectfully withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 NHAT HUY T NGUYEN whose telephone number is (571)270-7333. The examiner can normally be reached M-F: 12:00-8:00 EST.
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, Viker Lamardo can be reached at 571-270-5871. 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.
/NHAT HUY T NGUYEN/ Primary Examiner, Art Unit 2147
Prosecution Insights