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 Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 and 3-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a mental process without significantly more.
Representative claim 1 recites:
“A data processing method comprising:
Analyzing a sample with each of multiple types of analyzers and acquiring analysis data;
Collecting, by a computer processor, analysis file sets from multiple the types of analyzers, respectively, the analysis file sets each storing the analysis data;
Generating, by the computer processor, first and second collection folders in a database;
Storing, by the computer processor, the collected analysis file sets in the first collection folder with the analysis file sets sorted into each material molder; and
Forming, by the computer processor, a subset by searching for target analysis file sets that are identical with each other in (a) a type of a target analyzer, (b) a preprocessing condition of a sample measured by the target analyzer, and (c) a measurement condition of the sample with each other from the first collection folder;
Performing uniformly the same standardization processing of the analysis data on the target analysis file sets constituting the subset;
Calculating, by the computer processor, representative values of the analysis data for the each material folder from the target analysis file sets that have undergone the same standardization processing;
Recording, by the computer processor, the calculated representative values of the analysis data for each material folder in a feature table, as features of each material folder; and
Performing machine learning using analysis file sets obtained from the feature table, each of the analysis file sets consisting of each material and the features of the material,
Wherein the feature table represents a relationship between each material and multiple types of features extracted from the analysis file sets belonging to the material.”
Claims 8-9 recited similar subject matter.
This is a mental process because the independent claims contain data analysis (mental process) steps of “analyzing a sample with each of multiple types of analyzers and acquiring analysis data,” “generating … first and second collection folders in a database,” “forming … a subset by a subset by searching for target analysis file sets that are identical with each other in (a) a type of a target analyzer, (b) a preprocessing condition of a sample measured by the target analyzer, and (c) a measurement condition of the sample with each other from the first collection folder,” “performing uniformly the same standardization processing of the analysis data on the target analysis file sets constituting the subset,” “calculating … representative values of the analysis data for the each material folder from the target analysis file sets that have undergone the same standardization processing.” “performing machine learning using analysis file sets obtained from the feature table, each of the analysis file sets consisting of each material and the features of the material,” and “wherein the feature table represents a relationship between each material and multiple types of features extracted from the analysis file sets belonging to the material.”
A human being equipped with a generic computer or pen and paper is capable of performing all of these data analysis, data observation, and data judgment steps.
The claims include additional elements of collecting analysis files from “analyzers,” “storing … the collected analysis files,” and “recording … the calculated representative values…” and “a computer processor.” Independent claim 8 contains a processor and memory, while independent claim 9 contains a non-transitory computer-readable storage medium.
This judicial exception is not integrated into a practical application because the claims contain no additional elements that appear to improve the processing of a computer, require the use of a particular machine, or provide a technological solution to a technological problem.
The additional element of collecting data from analyzers is insignificant extra-solution activity in the form of data gathering and cannot provide a practical application to a mental process (see MPEP 2106.05(g)(3)). The additional elements of storing the collected analysis files in a specified way and of recording the calculated representative values is similarly extra-solution activity and does not provide a practical application (see MPEP 2106.05(g)(3)). The processor, memory, and computer-readable storage medium of claims 8-9, respectively, appear to be generic machines and do not provide a practical application (see MPEP 2106.04(a)(2)(III)(C)). The ”machine learning” is similarly described at a high level of abstraction appears to be little more than using a generic machine learning algorithm in a particular data context. As set forth in Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC, the addition of a generic machine learning model to a set of claims that processes data in a new data environment, without disclosing improvements to the machine learning model applied, does not integrate a mental process into a practical application.
It is noted that none of the additional elements appear to improve the processing of a computer, require the use of a specific machine, effect a transformation or reduction of a particular article to a different state or thing, or provide a technological solution to a technological problem. As such, none of the additional elements appear to integrate the judicial exception into a practical application.
None of the additional elements are sufficient to amount to significantly more than the judicial exception, in part or in whole.
The additional elements described above, notably collecting data and storing data, have been found to be well understood, routine, and conventional (see MPEP 2016.05(d)(II)). Similarly, the generic computing elements of claims 8 and 9 are also well-understood, routine, and conventional (see MPEP 2016.05(d)(II)). The ”machine learning” is similarly described at a high level of abstraction appears to be little more than using a generic machine learning algorithm in a particular data context. Because the claims contain no additional elements that, in part or in whole, are sufficient to amount to significantly more than the judicial exception, the claims are not patent eligible under 35 USC 101.
Dependent claims 3-7 and 10-14 are additionally directed to mere data analysis and storage steps, and thus do not provide a practical application to the claimed subject matter and do not, in part or in whole, include additional elements that are sufficient to amount to significantly more than the judicial exception.
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.
Claims 1, 3-9, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US Pre-Grant Publication 2018/0357568) in view of Thiebaut-George (US Pre-Grant Publication 2010/0023477), and further in view of Parizy et al. (US Pre-Grant Publication 2020/0116786).
As to claim 1, Ding teaches a data processing method comprising:
analyzing a sample with each of multiple types of analyzers and acquiring analysis data (see Ding paragraphs [0021]-[0023]. Equipment failure data may be analyzed to group the equipment failure data into one or more equipment failure type groups. To identify each failure type, a different type of analysis, or analyzer, is required);
Collecting, by a computer processor, analysis file sets from the multiple types of analyzers, respectively, the analysis file sets each storing the analysis data (see Ding paragraph [0024]-[0025]. Ding receives data from a set of equipment, including “failure data.” The failure data is analysis data from is a result of the analysis);
Generating, by the computer processor, first and second collection folders in a database (see paragraphs [0021]-[0025]. There are multiple equipment failure data groups in the data storage, see Figure 1A. These are functionally equivalent to folders);
Storing, by the computer processor, the collected analysis file sets in the first collection folder with the analysis file sets sorted into each material molder (see Ding paragraphs [0024]-[0025]. The analyzers store data in a database. The data is sorted into collections based on each failure type group, which is specific to materials, such as gas or fluid, paragraph [0023]); and
Forming, by the computer processor, a subset by searching for target analysis file sets that are identical with each other in (a) a type of a target analyzer, (b) a preprocessing condition of a sample measured by the target analyzer, and (c) a measurement condition of the sample with each other from the first collection folder (see Ding paragraph [0025] for creating a subset of data according to a variety of data elements. The subset of data may be grouped based on a type of equipment failure (type of analyzer), expert provided conditions (preprocessing conditions of a sample and measurement conditions), and power data (measurement conditions), see paragraph [0046] for grouping data according to power usage. As noted in paragraph [0025], these factors may all be used to group the data to create a subset of data in view of the selected factors. Because the data is grouped into subsets according to the selected matching factors, it would have been obvious for each data subset to have “identical” factors, or, as described in the specification as filed paragraphs [0063]-[0064]), grouping data according to the same factors).
Ding does not teach:
Performing uniformly the same standardization processing of the analysis data on the target analysis file sets constituting the subset;
Calculating, by the computer processor, representative values of the analysis data for the each material folder from the target analysis file sets that have undergone the same standardization processing;
Recording, by the computer processor, the calculated representative values of the analysis data for each material folder in a feature table, as features of each material folder; and
Performing machine learning using analysis file sets obtained from the feature table, each of the analysis file sets consisting of each material and the features of the material,
Wherein the feature table represents a relationship between each material and multiple types of features extracted from the analysis file sets belonging to the material.”
Thiebaut-George teaches further comprising:
Performing uniformly the same standardization processing of the analysis data on the target analysis file sets constituting the subset (see Thiebaut-George paragraphs [0003] and [0013]-[0016]. As noted in the summary of paragraph [0003], data may exist in a plurality of source tables. This data may be extracted and standardized and normalized. The data may then undergo computations and output to a storage medium. Figure 2 roughly shows this process. As noted in paragraph [0017], data may be extracted for data subsets);
Calculating, by the computer processor, representative values of the analysis data for the each material folder from the target analysis file sets that have undergone the same standardization processing (see Thiebaut-George paragraphs [0016]. Computations on the extracted data may include the calculation of various representative values, such as minimum, maximum, sum, or average. It is noted that this may be done for any requested data, and thus may be done for each “material.” It is noted that Ding teaches classes of materials, see [0023]); and
Recording, by the computer processor, the calculated representative values of the analysis data for each material folder in a feature table, as features of each material folder (see Thiebaut-George paragraph [0016]. Any computations or results may be stored in a “storage medium,” which according to Thiebaut-George includes a table).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ding by the teachings of Thiebaut-George because Thiebaut-George provides the benefit of additional analyses that may be performed on the data of Ding. This will provide more options for a user of Ding to better understand the desired data.
Parizy teaches:
Performing machine learning using analysis file sets obtained from the feature table, each of the analysis file sets consisting of each material and the features of the material (see Parizy paragraphs [0064] and [0077]. Paragraph [0064] indicates that failure record information includes values for components, or materials, and features of the components. Paragraph [0077] shows how this information is obtained and used in part of a machine learning system. It is noted that Thiebaut-George paragraph [0016]. Shows that information may be stored in a table, as cited above);
Wherein the feature table represents a relationship between each material and multiple types of features extracted from the analysis file sets belonging to the material (see Parizy paragraphs [0064] and [0077]. Each component, or material, may be associated with multiple features in the information store, see paragraphs [0036]-[0037]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date of the invention to have modified Ding by the teachings of Parizy because Parizy provides the benefit of helping to determine a failure rate of a component based on data analysis. This will help a user of Ding to better analyze and make use of the failure information available in Ding to identify failures.
As to claim 3, Ding as modified by Thiebaut-George teaches the data processing method as recited in claim 1, wherein the performing the uniformly the same standardization processing includes transforming the analysis data in the target analysis file sets into a form for comparison or summarization (see Thiebaut-George paragraph [0016]).
As to claim 4, Ding teaches the data processing method as recited in claim 1, further comprising:
Displaying, on a display, the analysis data included in the target analysis file sets constituting the subset to a user (see Ding paragraphs [0050]-[0051]).
As to claim 5, Ding as modified by Thiebaut-George teaches the data processing method as recited in claim 4, wherein the displaying the analysis data to the user includes displaying a subset table to the user, the subset table describing the target analysis data of one analysis file set on one row (see Thiebaut-George paragraph [0016] and Figure 2. A table may be output comprising analysis data of one selected fileset).
As to claim 6, Ding teaches the data processing method as recited in claim 1, further comprising:
Excluding at least one analysis file set from the subset in response to a user instruction (see Ding paragraph [0025] for creating a subset of data. The subsets are exclusionary by definition),
wherein the calculating the representative values includes calculating the representative values of the analysis data for each material folder from the subset from which the at least one analysis file set has been excluded (see Thiebaut-George paragraphs [0013]-[0016]. Only desired data is extracted).
As to claim 7, Ding teaches the data processing method as recited in claim 1, wherein in the forming the subset, the subset is formed over material differences within the first collection folder, and an analysis file set belonging to the second collection folder is not included in the subset (see Ding paragraphs [0024]-[0025]. Subsets may be based on specific types of data that do not include other data types).
As to claim 8, see the rejection of claim 1, including for a processor and memory configured to store programs executed by the processor (see Ding paragraph [0021]).
As to claim 9, see the rejection of claim 1, including for a non-transitory computer readable medium (see Ding paragraph [0121]).
As to claim 14, Ding as modified teaches the data processing method as recited in claim 1,
wherein the measurement conditions of the sample include device parameters of the analyzer to be used, and measurement parameters indicating the measurement conditions (see Ding paragraph [0027]).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US Pre-Grant Publication 2018/0357568) in view of Thiebaut-George (US Pre-Grant Publication 2010/0023477), in view of Parizy et al. (US Pre-Grant Publication 2020/0116786), and further in view of Maekawa et al. (US Patent 11,644,448).
As to claim 10, Ding as modified teaches the data processing method as recited in claim 1.
Ding does not teach wherein the performing uniformly the same standardization processing includes: performing processing to calculate a peak area and peak intensity from a chromatogram.
Maekawa teaches wherein the performing uniformly the same standardization processing includes: performing processing to calculate a peak area and peak intensity from a chromatogram (see 7:19-33 and 11:41-64).
It would have been obvious to one of ordinary skill in the art before the earliest filing date to have modified Ding by the teachings of Maekawa because both references are directed towards processing data, and Maekawa provides to Ding provides additional methods of data processing to identify relevant data and standardize the data.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US Pre-Grant Publication 2018/0357568) in view of Thiebaut-George (US Pre-Grant Publication 2010/0023477) in view of Parizy et al. (US Pre-Grant Publication 2020/0116786), and further in view of West et al. (US Pre-Grant Publication 2016/0169915).
As to claim 11, Ding as modified teaches the data processing method as recited in claim 1.
Ding does not teach wherein the performing uniformly the same standardization processing includes: performing alignment processing to correct deviations in retention times of a plurality of total ion chromatograms.
West teaches wherein the performing uniformly the same standardization processing includes: performing alignment processing to correct deviations in retention times of a plurality of total ion chromatograms (see paragraph [0225]).
It would have been obvious to one of ordinary skill in the art before the earliest filing date to have modified Ding by the teachings of West because both references are directed towards processing data, and West provides to Ding provides additional methods of data processing to identify relevant data and standardize the data.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US Pre-Grant Publication 2018/0357568) in view of Thiebaut-George (US Pre-Grant Publication 2010/0023477) in view of Parizy et al. (US Pre-Grant Publication 2020/0116786), and further in view of Taraki et al. (US Patent 6,556,202).
As to claim 12, Ding as modified teaches the data processing method as recited in claim 1.
Ding does not teach wherein the performing uniformly the same standardization processing includes: adjusting scale and offset of waveform data; and
converting the waveform data to a quantitative value.
Taraki teaches wherein the performing uniformly the same standardization processing includes:
adjusting scale and offset of waveform data (see 9:10-30 and Figures 9-10); and
converting the waveform data to a quantitative value (see 9:10-30 and Figures 9-10).
It would have been obvious to one of ordinary skill in the art before the earliest filing date to have modified Ding by the teachings of Taraki because both references are directed towards processing data, and Taraki provides to Ding provides additional methods of data output processing to provide users with additional options to customize how analysis data is presented.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Ding et al. (US Pre-Grant Publication 2018/0357568) in view of Thiebaut-George (US Pre-Grant Publication 2010/0023477), further in view of Parizy et al. (US Pre-Grant Publication 2020/0116786), and further in view of Carbonelli et al. (US Pre-Grant Publication 2022/0011283).
As to claim 13, Ding as modified teaches the data processing method as recited in claim 1.
Ding does not teach wherein the preprocessing of the sample includes processing of the sample to be performed prior to performing an analysis or a measurement of the sample so that the sample become a state suitable for the analysis.
Carbon teaches wherein the preprocessing of the sample includes processing of the sample to be performed prior to performing an analysis or a measurement of the sample so that the sample become a state suitable for the analysis (see Carbon paragraph [0004]. Carbon teaches wherein data is preprocessed before performing an analysis).
It would have been obvious to one of ordinary skill in the art before the earliest filing date to have modified Ding by the teachings of Carbon because both references are directed towards measuring data from sensors, and Carbon provides additional methods of data analysis and ensuring that data is stable and reproducible when submitted for analysis by Ding (see Carbon paragraph [0036]).
Response to Arguments
Applicant's arguments filed 14 April 2026 been fully considered but they are not persuasive.
Response to Arguments in view of the 35 USC 101 Rejection
Applicant asserts that “Applicant’s claims are directed to a patent-eligible technical solution to a technological problem … In the “Background” section of the as-filed Specification, at paragraphs [0005] through [0008], Applicant sets forth the following discussion of the problem … In the “Detailed Description” section of the as-filed Specification, at paragraphs [0058] through [0061] and [0064] through [0065], Applicant sets forth the following further discussion of the problem and the claimed solution.”
It is noted that Applicant merely inserted the cited paragraphs and did not describe how the solution to the problem is linked to any additional elements beyond the mental process in the claims.
It is noted that many features from paragraphs [0058] through [0061] and [0064] through [0065] remain unclaimed. Those features include the solutions directed toward “the standardization process includ[ing] … alignment processing to correct the retention time discrepancy so that the total ion chromatograms (TIC) acquired from a GC-MS can be easily compared with each other,” “processing to calculate a peak area and a peak intensity from chromatograms acquired from a GC-MS and processing to calculate a particle area and a particle diameter from electron microscope images acquired from an SEM,” “processing to calculate a peak area and a peak intensity from chromatograms acquired from a GC-MS and processing to calculate a particle area and a particle diameter from electron microscope images acquired from an SEM,” “electron microscope images acquired by an SEM or a TEM, chromatograms acquired by a GC-MS or an LC-MS, analysis data such as spectra acquired by an FT-IR or an NMR, and” a plurality of other data variables.
Applicant is reminded that unclaimed features from the specification receive no patentable weight until claimed.
From paragraphs [0064]-[0065], the “technological problem” appears described in the statement that “However, as the types and the number of analysis data stored in the database increase, there is a concern that these works require much time and effort.” The solution to this problem appears to be “performin[ing] batch normalization processing in a subset table unit and [generating] a feature table using the data generated by the normalization processing.”
However, the claimed steps that appear to encompass these solutions, notably, the “forming … by extracting,” “performing standardization processing,” and “calculating … representative values,” are all mental process steps that a human being equipped with pen and paper or a generic computer is capable of performing. Even if there is an improved process that provides a solution to a technological problem, the claimed solution appears to also be improved mental process steps of data analysis and data preparation. An improved mental process remains a mental process.
While the claims now recite the performance of “machine learning,” it is noted that this is claimed at a high level of abstraction in which no specific learning process is claimed. As noted in Recentive Analytics Inc v. Fox Corp (2023-2437), the mere inclusion of generic machine learning without more is little more than applying a generic computer process to a specific problem and is neither a practical application nor significantly more than the abstract idea.
Applicant argues that “Here, the amended claims are directed to a specific technological solution that improves the processing of analysis data generated by multiple types of analyzers. Applicant's amended claims provide a data processing method, apparatus, and storage medium storing computer- executable programs that make it is possible to easily summarize analysis file sets that are required to perform the same standardization process from a wide variety of analysis file sets obtained from the multiple types of analyzers, without need for the user to search analysis file sets in which the sample preprocessing conditions, the analyzer type, and the measurement conditions all match. In addition, by applying the same standardization process to all the analysis data that make up the subset, it is possible to create the feature table that records the feature values for each material appropriately and efficiently. Consequently, it is possible to efficiently perform preprocessing for machine learning.”
In response to this argument, it is noted that merely automating a manual process may not be sufficient to show an improvement in computer functionality, as explained in MPEP 2106.05(a)(I):
Examples that the courts have indicated may not be sufficient to show an improvement in computer-functionality:
…
iii. Mere automation of manual processes, such as using a generic computer to process an application for financing a purchase, Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017) or speeding up a loan-application process by enabling borrowers to avoid physically going to or calling each lender and filling out a loan application, LendingTree, LLC v. Zillow, Inc., 656 Fed. App'x 991, 996-97 (Fed. Cir. 2016) (non-precedential);
Any improvements or efficiency gains in the discussed pre-processing analysis appear to be nothing more than automating manual steps that a user with a generic computer is capable of performing. Or, to rephrase, an improved mental process is still a mental process, and thus patent ineligible.
Applicant continues, arguing that “That is, amended independent claim 1 recites specific technical steps including” the claimed searching step and the claimed performing step.
In response to this argument, it is noted that these steps are data analysis steps. They are merely ways to analyze data, notably, by searching for data that matches multiple conditions and then standardizing the data. A human being equipped with a generic computer or pen and paper is capable of searching data that fulfills conditions sought and standardizing the identified data.
Applicant argues that “The Office Action alleges that these are mental processes that can be performed by a human with pen and paper. However, automatically forming the subset by searching for target analysis file sets that are identical with each other in specific conditions (e.g., type of analyzer, sample preprocessing conditions, and measurement conditions) from a massive amount of diverse analysis data, and uniformly applying the same standardization processing to the subset, is overly complex, excessively time-consuming, and practically impossible for a human to perform manually. Rather than merely automating a manual task, these steps provide a direct improvement to the data processing functionality of the computer itself..”
In response to this argument, it is noted that “a massive amount of diverse analysis data” is not claimed. Applicant is reminded that unclaimed features from the specification receive no weight until claimed.
Examiner additionally notes that Examiner also specified that these steps are data analysis steps that may be performed by a human being with pen and paper or a human being with a generic machine.
As noted above, merely automating a manual activity is not sufficient to show a practical application or providing a technological solution to a technical problem. The problems cited by Applicant in Applicant’s specification are all directed towards the scale of the data analysis (see paragraph [0064], “there is a concern that these works require a great deal of time and effort”). The solution appears to be a data analysis oriented solution, notably, organizing and arranging data in a particular manner for processing, or pre-processing the data (see paragraph [0065]). The organization and analysis of data is mental process step that a human being equipped with a generic computer is capable of manually performing.
Nothing in the cited paragraphs of the specification that is reflected in the claimed subject matter supports the argument that the claimed pre-processing elements are “overly complex, excessively time-consuming, and practically impossible for a human to perform manually” while using a generic computer. Applicant is reminded that unclaimed features from the specification, particularly those recited above in paragraphs [0058]-[0065], receive patentable weight until claimed.
Applicant argues that “Furthermore, the generated "feature table" is not a mere record of calculation results. It is defined as a "particularized data structure" that integrates information from multiple analysis sources in a format specifically optimized for machine learning. This structured preprocessing yields a "concrete technological benefit" by directly improving the accuracy and reliability of the subsequent machine learning operations. Therefore, the claims are directed to a patent-eligible practical application and recite significantly more than any abstract idea.”
In response to this argument, it is noted that the “machine learning analysis” is claimed at a high level of abstraction. No claimed elements are directed towards the internal learning process that is improved by any “particularized” data in the data structure of the feature table. As noted above, the generic application of machine learning to a claim, without providing any evidence of an improvement to a machine learning process itself, is not sufficient to overcome a rejection under 35 USC 101 as described in Recentive Analytics, Inc. v. Fox Corp., Fox Broadcasting Company, LLC, Fox Sports Productions, LLC.
Applicant is reminded that unclaimed features from the specification receive no patentable weight until claimed.
Response to Arguments in view of the 35 USC 103 Rejection
Applicant summarizes Ding and Thiebaut-George, then argues that the combined references fail to teach the amended subject matter.
Applicant adds “even assuming this mapping, Ding entirely fails to teach or suggest grouping data for the purpose of extracting data to collectively perform the same standardization processing.”
In response to this argument, it is noted that Ding and Thiebaut-George do teach the claimed subject matter for the reasons provided in the rejection above.
It is also noted that Thiebaut-George is relied upon to teach “collectively perform[ing] the same standardization processing” (see Thiebaut-George paragraphs [0003] and [0013]-[0017] and the rationale provided in the rejection of claim 1.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant then describes the benefits of the inventive subject matter, how “this configuration dramatically enhances the comparability of data in analytical chemistry – a technological achievement that is neither taught nor suggested by the cited references, individually or in combination.”
In response to this argument, it is noted that none of the claimed limitations are directed towards “analytical chemistry,” nor do they have any limitations that are specific to “analytical chemistry.” Applicant is reminded that unclaimed features from the specification have no patentable weight until claimed.
Applicant summarizes the additional cited art, then argues that “Parizy, Maekawa, West, and Taraki fail to overcome the deficiencies of Ding and Thiebaut-George, including the failure of Ding and Thiebaut-George to teach or suggest the above-quoted recitations of amended claim 1, and the similar recitations of independent claims 8 and 9.”
In response to this argument, it is noted that Maekawa, West, and Taraki are not relied upon to teach the subject matter of claim 1.
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 CHARLES D ADAMS whose telephone number is (571)272-3938. The examiner can normally be reached M-F, 9-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached at 5712701760. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES D ADAMS/Primary Examiner, Art Unit 2165
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