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 .
DETAILED ACTION
The instant application having Application No. 17926450 has a total of 7 claims pending in the application.
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 1-2 and 4-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 is a process type claim. Claim 7 is a machine type claim. Therefore, claims 1-2 and 4-7 are directed to either a process, machine, manufacture or composition of matter.
As per claim 1,
2A Prong 1:
“A step of constructing a database in which, for each of one or a plurality of types of samples, a sample identification tag and data belonging to at least two types of categories out of the following categories (1), (2), and (3) are stored in association with each other” A user mentally or with pencil and paper organizes their data with labels.
“category (1): a plurality of types of data relating to a production method of the sample” The user mentally or with pencil and paper organizes their data.
“Category (2): a plurality of types of analysis data acquired by analyzing the sample with one or a plurality of types of analyzers” The user mentally or with pencil and paper organizes their data.
“Category (3): a plurality of types of physical property data that is information representing characteristics of the sample” The user mentally or with pencil and paper organizes their data.
“A selection step of selecting data to be used as training data in supervised learning from the database, the selection step including a step of selecting one or a plurality of types of data as an explanatory variable from one of the categories and a step of selecting one or a plurality types of data as an objective variable from the other category” The user mentally or with pencil and paper selects appropriate inputs to design their model.
“A step of generating training data in which data corresponding to the selected explanatory variable is served as input and data corresponding to the selected objective variable is served as ground truth output” The user mentally or with pencil and paper selects appropriate inputs to design their model.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
Computer implemented (mere instructions to apply the exception using a generic computer component);
“a step of generating the trained model corresponding to the generated training data by performing predetermined machine learning or statistical analysis based on the generated training data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims denote generic machine learning with no additional limitations or details beyond that of generic, off the shelf machine learning.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
Computer implemented (mere instructions to apply the exception using a generic computer component)
“a step of generating the trained model corresponding to the generated training data by performing predetermined machine learning or statistical analysis based on the generated training data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims denote generic machine learning with no additional limitations or details beyond that of generic, off the shelf machine learning.
As per claims 2, and 4-5, these claims contain additional mental steps similar to claim 1, and is rejected for similar reasons.
As per claim 6, this claim contains additional generic machine learning similar to claim 1, and is rejected for similar reasons.
As per claim 7,
2A Prong 1:
“for each of one or a plurality of types of samples, a sample identification tag … and data belonging to at least two types of categories out of the following categories (1), (2), and (3) are stored in association with each other” A user mentally or with pencil and paper organizes their data with labels.
“category (1): a plurality of types of data relating to a production method of the sample” The user mentally or with pencil and paper organizes their data.
“Category (2): a plurality of types of analysis data acquired by analyzing the sample with one or a plurality of types of analyzers” The user mentally or with pencil and paper organizes their data.
“Category (3): a plurality of types of physical property data that is information representing characteristics of the sample” The user mentally or with pencil and paper organizes their data.
“… a selection of data to be used as training data in supervised learning from the database…, select one or a plurality of types of data as an explanatory variable from one of the categories and …select one or a plurality of data as an objective variable from the other category” The user mentally or with pencil and paper selects appropriate inputs to design their model.
“…generate training data in which data corresponding to the selected explanatory variable is served as input and data corresponding to the selected objective variable is served as ground truth output” The user mentally or with pencil and paper selects appropriate inputs to design their model.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A data processing device, An input device, a storage device, configured to store, a processor (mere instructions to apply the exception using a generic computer component);
“generate a trained model corresponding to the generated training data by performing predetermined machine learning or statistical analysis based on the generated training data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims denote generic machine learning with no additional limitations or details beyond that of generic, off the shelf machine learning.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A data processing device, An input device, a storage device, configured to store, a processor (mere instructions to apply the exception using a generic computer component)
“generate a trained model corresponding to the generated training data by performing predetermined machine learning or statistical analysis based on the generated training data” (Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f) – Examiner’s note: Claims denote generic machine learning with no additional limitations or details beyond that of generic, off the shelf machine learning.
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 1-2 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Gueissaz et al (“Study on the discrimination of tires using chemical profiles obtained by Py-GC/MS”) in view of Causin (“Polymers on the Crime Scene”).
As per claims 1 and 7, Gueissaz discloses, “a method of generating a trained model, comprising” (Pg.711, particularly “Classification according to brand” section; EN: this denotes training a model).
“a step of constructing a database” (pg.706-711, particularly section 3.1 and 3.2; EN: this denotes a database about the materials of the tyres including chemical aspects, brand, manufacturing time, etc). “in which, for each of one of a plurality of types of samples, a sample identification tag” (Pg.709, Table 5; EN: this denotes brand, model type, and other identification tags for the particular tire). “and data belonging to at least two types of categories out of the following categories, (1), (2), and (3) are stored in association with each other” (pg.709, section 3.2.2; EN: this denotes compounds associated with the tires).
“Category (2): a plurality of types of analysis data acquired by analyzing the sample” (Pg.706, particularly section 2.2.3; EN: this denotes working on chemical profiles). “with one of a plurality of types of analyzers” (pg.704, particularly C2, introduction section; EN: this denotes PY-GC/FID and PY-GC/MS as potential ways to examine the specimen, but chooses to use Py-GC/MS).
“A selection step of selecting data to be used as training data” (Pg.711, particularly the Classification according to Brand” section; EN: this denotes creating the training data set). “in supervised learning from the database” (g.706, particularly section 2.2.3; EN: this denotes QDA being a supervised method). “the selection step including a step of selecting one or a plurality of types of data as an explanatory variable from one of the categories” (Pg.706, particularly section 3.1.1; EN: this denotes the inputs to the simulation, which is category 2, the types of analysis data gained by analyzing the sample). “and a step of selecting one or a plurality types of data of an objective variable from the other category” Pg.711, particularly the “Classification according to Brand” section; EN: this denotes using the evaluation of the different brands of tires as the outputs of the supervised learning, with the classification based on the physical properties of the tire (i.e. category 3).
“A step of generating training data in which data corresponding to the selected explanatory variable is served as input” (Pg.706, particularly Quadratic Discriminant analysis section and section 3.2; EN: this denotes creating the training data set, with the input being the pyrolysis of the sample with PY-GC/MS, which was the use of a mass spectrometer). “and data corresponding to the selected objective variable is served as ground truth output” (Pg.711-712, particularly the Classification according to Brand section; EN: this denotes using the different brands of tires as the outputs).
“A step of generating the trained model corresponding to the generated training data by performing predetermined machine learning or statistical analysis based on the generated training data” (Pg.711-712, particularly the Classification according to Brand section; EN: this denotes training the model and the results).
However, Gueissaz fails to explicitly disclose, “Category (1): a plurality of types of data relating to a production method of the sample.”
Causin discloses, “Category (1): a plurality of types of data relating to a production method of the sample” (pg.132-134, particularly section 4.5.1; EN: this denotes how the manufacturing (i.e. production method) of tires can relate to classifying tires).
Gueissaz and Causin are analogous art because both involve tire classification.
Before the effective filing date it would have been obvious to one skilled in the art of tire classification to combine the work of Gueissaz and Causin in order to include manufacturing processes for classification of tires.
The motivation for doing so would be because “the most interesting parts of a tyre for a forensic scientist are the tread and sidewall. These components are manufactured by extrusion” (Causin, Pg.134, second paragraph) or in the case of Gueissaz, allow the system to include the manufacturing process of the tires in order to consider features that might be helpful in classifying the tire.
Therefore before the effective filing date it would have been obvious to one skilled in the art of tire classification to combine the work of Gueissaz and Causin in order to include manufacturing processes for classification of tires.
As per claim 2, Gueissaz discloses, “wherein the category (2) includes one or a plurality of types of feature data extracted from the analysis data” (Pg.706, particularly section 3.1.1; EN: this denotes the inputs to the simulation, which is category 2, the types of analysis data gained by analyzing the sample).
Claim Rejections - 35 USC § 103
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Gueissaz et al (“Study on the discrimination of tires using chemical profiles obtained by Py-GC/MS”) in view of Causin (“Polymers on the Crime Scene”) and further in view of Breckenridge et al (US 20120191630 A1).
As per claim 4, Gueissaz fails to explicitly disclose, “a step of outputting the training data in a CSV (Comma-separated-values) format.”
Breckenridge discloses, “a step of outputting the training data in a CSV (Comma-separated-values) format” (pg.3, particularly paragraph 0033; EN: this denotes storing training data in CSV format).
Gueissaz and Breckenridge are analogous art because both involve training data.
Before the effective filing date it would have been obvious to one skilled in the art of training data to combine the work of Gueissaz and Breckenridge in order to use CSV for training data.
The motivation for doing so would be to have the training data “in any convenient form that is understood by the modeling system 206 to define a set of records, where each record includes an input and a corresponding desired output” (Breckenridge, Pg.3, paragraph 0033) or in the case of Gueissaz, allow the system to store the training data in well-known formats such as CSV for use by the system.
Therefore before the effective filing date it would have been obvious to one skilled in the art of training data to combine the work of Gueissaz and Breckenridge in order to use CSV for training data.
As per claim 6, Gueissaz fails to explicitly disclose, “wherein an algorithm of the machine learning is a support vector machine (SVM).”
Breckenridge discloses, “wherein an algorithm of the machine learning is a support vector machine (SVM)” (pg.4, particularly paragraph 0037; EN: this denotes using the training data on an SVM).
Gueissaz and Breckenridge are analogous art because both involve training data.
Before the effective filing date it would have been obvious to one skilled in the art of training data to combine the work of Gueissaz and Breckenridge in order to use an SVM.
The motivation for doing so would be to use “some examples of training functions that can be used to train a static predictive model include (without limitation) …. Support vector machine (SVM)” (Breckenridge, Pg.4, paragraph 0037) or in the case of Gueissaz, allow the system to use whatever well-known machine learning algorithm they wish to perform the classifications of the Gueissaz reference.
Therefore before the effective filing date it would have been obvious to one skilled in the art of training data to combine the work of Gueissaz and Breckenridge in order to use an SVM.
Claim Rejections - 35 USC § 103
Claims 5 is rejected under 35 U.S.C. 103 as being unpatentable over Gueissaz et al (“Study on the discrimination of tires using chemical profiles obtained by Py-GC/MS”) in view of Causin (“Polymers on the Crime Scene”) and further in view of Yabe et al (US 20090066223 A1).
Gueissaz discloses, “Wherein the feature data includes at least … types of data selected from a group consisting of a peak of a chromatograph, a peak area of a spectrum, a Young’s modules, tensile strength, a deformation amount, a strain amount, and a fracture time” (Pg.707, particularly the Repeatability study sections; EN: this denotes looking at the peaks of the mass spectrometer data).
However, Gueissaz fails to explicitly disclose, “Wherein the analyzers include at least two types of analyzers selected from a group consisting of a gas chromatograph mass spectrometer, a liquid chromatograph mass spectrometer, a Fourier transform infrared spectrophotometer, and a tensile testing machine”, and “at least two types of data selected from a group consisting of a peak area of a chromatogram, a peak area of a spectrum, a young’s modulus, a tensile strength, a deformation amount, a strain amount, and a fracture time.”
Yabe discloses, “Wherein the analyzers include at least two types of analyzers selected from a group consisting of a gas chromatograph mass spectrometer, a liquid chromatograph mass spectrometer, a Fourier transform infrared spectrophotometer, and a tensile testing machine”, and “at least two types of data selected from a group consisting of a peak area of a chromatogram, a peak area of a spectrum, a young’s modulus, a tensile strength, a deformation amount, a strain amount, and a fracture time” (Pg.37, particularly paragraph 0245; EN: this denotes combining various types of mass spectrometers, infrared spectrophometers, and other devices to identify compounds in material).
Gueissaz and Yabe are analogous art because both involve mass spectrometry.
Before the effective filing date it would have been obvious to one skilled in the art of mass spectrometry to combine the work of Gueissaz and Yabe in order to use multiple analysis tools for analysis.
The motivation for doing so would be “Examples of procedures for identifying primary amine-and secondary amine-containing compounds includes processes using …. Fourier transform infrared spectrophotometers, as well as mass spectrometry (MS, LC/MS, GC/MS, and MS/MS. Where necessary, other apparatuses can be used in combination. Examples of such apparatus include gas chromatographs (GC), high performance chromatographs (HPLC)…” (Yabe, PG.37, paragraph 0245) or in the case of Gueissaz, allow the system to use whatever devices are needed to get appropriate data to examine the tires as needed.
Therefore before the effective filing date it would have been obvious to one skilled in the art of mass spectrometry to combine the work of Gueissaz and Yabe in order to use multiple analysis tools for analysis.
Response to Arguments
In pg.10-11, the Applicant argues in regards to the rejection under U.S.C. 101,
Here, Applicant's amended claims avoid unnecessary principal component analysis (PCA) calculations, which are known to be computationally intensive, in order to produce a tangible technological benefit in the operation of the computer system. This distinction over Gueissaz underscores that the claims do not merely automate a known analysis, but change how the computer performs the task.
In response, the Examiner maintains the rejection as shown above. Applicant appears to be arguing that the current claims “avoid unnecessary principal component analysis (PCA) calculations” and thereby shows an improvement to the operation of the computer system. Choosing to not do something does not improve the underlying hardware. A processor is not improved by deciding not to process data with it, the processor remains the same. Simply choosing not to perform PCA is not an improvement, and therefore the rejection under U.S.C. 101 is maintained as shown above and in the previous office action.
In pg.11-12, the Applicant argues in regards to the rejection under U.S.C. 101,
Here, the Office Action has not established that selectively using particular types of data as explanatory variables for training-data generation, while intentionally excluding PCA-based acquisition of explanatory variable, was well-understood, routine, or conventional at the time of filing. To the contrary, Applicant's amended claims impose specific constraints on the computer system that materially affect how the system functions. This is precisely the type of inventive concept recognized in MPEP § 2106.05(a) and § 2106.05(f).
In response, the Examiner maintains the rejection as shown above and in the previous office action. Once again Applicant states their choice to not use PCA-based acquisition is somehow an improvement over the computer system. The specification and claims at no time discuss PCA in any form, let alone disclose avoiding it in some effort to improve over the prior art. Regardless, the process of selecting training data is a mental process as described in the 101 rejection above. The only additional elements in the rejection was the use of generic computer equipment and generic machine learning models, which is not enough to be significantly more than the abstract idea. Therefore the rejection is maintained as shown above.
In pg.13, the Applicant argues in regards to the rejection under U.S.C. 103 of claim 1,
However, both Gueissaz and Causin are silent about accepting an operation to select one or more of types of data as explanatory variables. In particular, since Gueissaz focuses on evaluating the ability to identify the tire brand and the tire model, the explanatory variables in Gueissaz are to be fixed to the tire brand and the tire model. Accordingly, a person skilled in the art would not be motivated to change Gueissaz to accept an operation to select one or more of types of data as explanatory variables. Thus, Gueissaz and Causin each fail to teach or suggest the above-quoted recitations of amended independent claims 1 and 7.
In response, the Examiner maintains the rejection as shown above and in the previous office action. The Gueissaz reference does not need to be motivated to modify Gueissaz to accept an operation to select one or more types of data as an explanatory variable, Gueissaz discloses the explanatory variable as the data gained by analyzing the specimen (See rejection above and Gueissaz Pg.706, section 3.1.1 and Pg.711, particularly the classification according to Brand section). The Gueissaz reference denotes choosing training data for the system based on Brand, but that includes what an analyzer would find when looking at those brands of tires. The Causin reference was brought in to show that it would be obvious to one of ordinary skill in the art of tire classification to include aspects of the manufacturing process when it comes to classifying tires. Since the combined references meet the claimed limitation, the rejection is maintained as shown above.
Applicant's remaining arguments with respect to claims 1-2 and 4-7 have been considered but are either conclusory statements or repetitions of the above arguments, and therefore rejected for similar reasons given above.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEN M RIFKIN whose telephone number is (571)272-9768. The examiner can normally be reached Monday-Friday 9 am - 5 pm.
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/BEN M RIFKIN/ Primary Examiner, Art Unit 2123