Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 24 December 2025 has been entered.
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 3-12 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 extracting from the first collection folder at least one target analysis file set in which a type of a target analyzer, preprocessing conditions of a sample measured by the target analyzer, and measurement conditions of the sample all match;
Performing standardization processing of the analysis data on the at least one target analysis file set constituting the subset;
Calculating, by the computer processor, representative values of the analysis data for the each material folder from the at least one target analysis file set that has undergone the 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 merely receive data, store the data in a particular way, then perform analyses on the data to store the data. It is noted that, upon being stored, the data is merely standardized, subject to additional analyses, with the results of the analyses being stored in specific locations. Standardization and calculations are both mental processes. A human being equipped with a generic computer is capable of performing all of these steps.
The claims include additional elements of collecting analysis files from analyzers, storing the collected analysis files, and recording calculated representative values. 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.
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 10-12 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 and 3-9 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 extracting from the first collection folder at least one target analysis file set in which a type of a target analyzer, preprocessing conditions of a sample measured by the target analyzer, and measurement conditions of the sample all match (see Ding paragraph [0025] for creating a subset of data. The subset of data is based on the type of equipment failure (type of analyzer), expert provided conditions (preprocessing conditions of a sample), and power data (measurement conditions), see paragraph [0046]. These data elements all “match” because they are used together to produce a subset).
Ding does not teach:
Performing standardization processing of the analysis data on the at least one target analysis file set constituting the subset;
Calculating, by the computer processor, representative values of the analysis data for the each material folder from the at least one target analysis file set that has undergone the 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 standardization processing of the analysis data on the at least one target analysis file set 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 at least one target analysis file set that has undergone the 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 standardization processing includes transforming the analysis data in the at least one target analysis file set 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 at least one target analysis file set 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]).
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 standardization processing includes: performing processing to calculate a peak area and peak intensity from a chromatogram.
Maekawa teaches wherein the performing 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 standardization processing includes: performing alignment processing to correct deviations in retention times of a plurality of total ion chromatograms.
West teaches wherein the performing 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 standardization processing includes: adjusting scale and offset of waveform data; and
converting the waveform data to a quantitative value.
Taraki teaches wherein the performing 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.
Response to Arguments
Applicant's arguments filed 24 December 2025 have 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 encompasses mental process steps. 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, the claimed data processing method, apparatus, and non-transitory storage medium enable efficient summarization of analysis file sets required to undergo a common standardization process, even when the underlying data originate from different analyzer types, sample preprocessing conditions, and measurement conditions. As a result, the claims eliminate the need for a user to manually search for analysis file sets that satisfy multiple matching conditions, thereby improving the efficiency and reliability of the data processing system itself.”
As noted above, the improvement appears to lie in automation of manual data analysis steps. Automating a manual data analysis process, in view of MPEP 2106.05(a)(I), as cited above, is not patent eligible.
Applicant argues that “Moreover, by applying a uniform standardization process to all analysis data within a defined subset, the claims enable the generation of a feature table that accurately and efficiently records feature values for each material. This structured and automated preprocessing directly improves the preparation of data for subsequent machine learning operations, yielding a concrete technological benefit beyond any abstract idea.”
As noted above, the standardization process and generation of a feature table appears to be an automation of manual processes and is thus patent ineligible in view of MPEP 2106.05(a)(I).
Applicant argues that “Moreover, assuming arguendo the claims recite a judicial exception, which Applicant does not concede, the recitations of Applicant's claims are patent-eligible under Step 2B because they recite more than any alleged abstract idea. Under MPEP 2106.05, claims provide "significantly more" when they include meaningful limitations that are not well-understood, routine, or conventional.”
Applicant adds “Applicant's amended independent claim 1 recites, inter alia, "performing machine learning using analysis data sets obtained from the feature table, each of the analysis data 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 plurality of analysis file sets belonging to the material." Amended independent claims 8 and 9 include similar recitations.”
Applicant concludes “These claim features are not merely generic data manipulation or result-oriented steps. Rather, they define a particularized data structure (the feature table) and a specific application of machine learning that operates on that structure to process analysis data across multiple materials and feature types. When considered individually and, especially, in combination, these limitations impose concrete technical constraints that meaningfully limit the scope of the claims and reflect more than the alleged abstract idea itself.”
It is additionally noted that the machine learning process is claimed at a high level of abstraction without any details regarding any learning process. In view of Recentive Analytics Inc v. Fox Corp (2023-2437), this appears to be the mere application of generic machine learning technology to a different context and does not provide a practical application nor represent significantly more than the abstract idea.
Response to Arguments in view of the 35 USC 103 Rejection
Applicant argues that “Ding and Thiebaut-George, however, fail to disclose “the subset” and “the feature table” as recited in Applicant’s amended independent claims 1, 8, and 9.
Applicant elaborates, arguing that “The claimed embodiment is configured to generate the subset from the analysis file sets belonging to one collection folder as a preliminary step to generate the feature table to be used for machine learning. The significant feature of the claimed embodiment is to extract the at least one target analysis file set to be subjected to the same standardization processing to compare or summarize the analysis data, from the collection folder and to form one subset. Specifically, the subset is constituted by the at least one target analysis file set in which the sample preprocessing conditions, the analyzer type, and the measurement conditions all match. Since the subset contains the analysis data regardless of material dissimilarity, the same standardization processing can be uniformly performed on the analysis data in the target analysis file sets constituting the subset. Then, by obtaining the representative value for each material from the subset with standardization processing, it is possible to efficiently generate the feature table that records the feature for each material.”
Applicant argues that “In contrast, Ding merely groups the equipment failure data in accordance with the type of equipment failure or the failure criterion. Thus, Ding does not disclose at all the technical concept of grouping together the analysis data to perform the same standardization processing into a single subset. Further, Ding does not teach or suggest performing the same standardization processing on the analysis data constituting the subset and obtaining the representative value for each material.”
In response to this argument, it is noted that the claims do not require a determination that verifies that “the sample preprocessing conditions, the analyzer type, and the measurement conditions all match.” Rather, the claims only require the identification and extraction of a subset in which all of those conditions happen to match. This is shown in Ding, see Ding paragraph [0025] for creating a subset of data. The subset of data is based on the type of equipment failure (type of analyzer), expert provided conditions (preprocessing conditions of a sample), and power data (measurement conditions), see paragraph [0046]. These data elements all “match” because they are used together to produce a subset.
It is additionally noted that Applicant provides no definition for the extent of the phrase “match.” Applicant is reminded that unclaimed features from the specification have no patentable weight until claimed.
It is noted that Thiebaut-George is relied upon to teach the step of “performing standardization processing on the analysis data” and “obtaining the representative value for each material.” 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 argues that “Thiebaut-George does not disclose grouping together analysis data to perform the same standardization process into a single subset. Further, Thiebaut-George also does not teach or suggest performing the same standardization processing on the analysis data constituting the subset and obtaining the representative value for each material.”
In response to this argument, it is noted that Ding is relied upon to teach grouping together analysis data (see Ding paragraph [0025] for creating a subset of data. The subset of data is based on the type of equipment failure (type of analyzer), expert provided conditions (preprocessing conditions of a sample), and power data (measurement conditions), see paragraph [0046]).
Thiebaut-George teaches performing the same standardization processing on the analysis data 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). Thiebaut-George also teaches “obtaining the representative value for each material” (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]).
Applicant argues that “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 independent claim 1.”
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
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/CHARLES D ADAMS/Primary Examiner, Art Unit 2152