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 action is in response to claims dated 3/26/2024
Claims pending in the case: 1-10
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.
Claim(s) X-X is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step1: determine whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If YES, proceed to Step 2A, broken into two prongs.
Step 2A, Prong 1: determine whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If YES, the analysis proceeds to the second prong
Step 2A, Prong 2: determine whether or not the claims integrate the judicial exception into a practical application. If NOT, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B).
Step 2B: If any element or combination of elements in the claim is sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Step 1 Analysis
According to the first part of the analysis, the instant case all claims are directed to one of the statutory categories of invention.
Step 2A Prong 1, Step 2A Prong 2, and Step 2B Analysis
Independent Claim 1 includes the following recitation of an abstract idea:
classifying the training dataset into N clusters, where N is an integer greater than 1 (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.);
calculating a distance between centroids of the clusters (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.)
calculating a weight between the clusters, using both the distance between the centroids of the clusters and a parameter representing a feature of the training dataset (This is practical to perform in the human mind under its broadest reasonable interpretation. This is a recitation of a mental process.)
Claim 1 recites the following additional elements, which, considered individually and as an ordered combination do not integrate the abstract idea into a practical application:
acquiring a training dataset (This is insignificant extra-solution activity, which does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(g). Moreover, sending, receiving, storing and retrieving information is well-understood, routine, conventional as evidenced by the court cases cited at MPEP 2106.05(d), example i. Receiving or transmitting data and iv. Storing and retrieving information and MPEP 2106.05(g), example iv. Obtaining information about transactions using the Internet to verify credit card transactions)
generating a trained clustering model, using the training dataset and a clustering model,
and generating, for the N clusters, respective trained prediction models (This high-level recitation of training of the model is a mere instruction to apply the judicial exception. It only appears to amount to the use of a generically recited, off the shelf component, as a tool to implement the process and is not an inventive concept. Since the model is used merely as a tool to implement an existing process, this does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).)
These claimed limitations therefore do not integrate the abstract idea into a practical application.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. In this case, after considering all claim elements individually and as an ordered combination, it is determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception for the reasons given above with respect to integration of the abstract idea into a practical application.
Therefore the claim is not patent eligible.
Independent Claims 7 and 9, are similar in scope as claim XX and therefore rejected under the same rationale. The additional elements of “a processor; and a memory storing program instructions” in claim 7 and “non-transitory computer-readable recording medium” in claim 9 also do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea (This is a high level recitation of generic computer components for applying a result of the abstract idea. The computer is used merely as a tool to implement an existing process. This does not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. See MPEP 2106.05(f).).
The dependent claims recite at least the abstract idea identified above in the claim upon which it depends and recites the following additional elements which, considered individually and as an ordered combination with the additional elements from the claim upon which it depends, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Dependent claim 2, 8, 10 pertain to using a model to get output
Dependent claim 3-6 pertain to standard algorithms and functions to be used (This is series of mathematical calculations based on information that falls into the mathematical concepts group of abstract ideas.)
The dependent claims therefore, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea
Hence these claims are rejected as being abstract.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 3-4, 6 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin (Clustering Complex Data with Group-Dependent Feature Selection).
Please refer to the attached documents for claim mapping.
Regarding Claim 1, Lin teaches, a method of generating a model for predicting a material characteristic (Lin: Pg. 84 section 1 [1]: clustering of objects with similar characteristics), the method comprising:
acquiring a training dataset (Lin: Pg. 87 section 3.1 [1]: dataset D);
generating a trained clustering model, using the training dataset and a clustering model, and classifying the training dataset into N clusters, where N is an integer greater than 1 (Lin: Pg. 87 section 3.1 [1]: partition Dataset D into C clusters; Pg. 90 section 4.2: data into clusters);
calculating a distance between centroids of the clusters (Lin: Pg. 86 [3]: learning a distance function, Pg. 91-92 section 5.1 [2]: distance function, Pg. 88 [3]: discriminant function - implies measuring inter-clusters distance for feature selection);
calculating a weight between the clusters, using both the distance between the centroids of the clusters and a parameter representing a feature of the training dataset (Lin: Pg. 86 [3,5]: reweighting feature dimensions, merging via a voting scheme); and
generating, for the N clusters, respective trained prediction models {Mi} 1 <= i <= N, using the clusters and the weight (Lin: Pg. 87-88, section 3. 2 [1-2]: learning cluster-specific classifiers).
Regarding Claim 3, Lin teaches the limitations as claimed in claim 1 and, wherein the generating of the trained clustering model performs at least one or a plurality of clustering techniques among a K-means method, a Nearest Neighbor method, a hierarchical clustering method, a Gaussian mixture method, a DBSCAN method, a t-SNE method, and a self-organizing map method (Lin: Pg. 86 [1,3], Pg. 88 section 4 [1], Pg. 92 [last]: clustering methods such as k-means, gaussian mixture etc.)
Regarding Claim 4, Lin teaches the limitations as claimed in claim 1 and, the calculating of the distance includes using at least one or a combination of a plurality of methods among an Euclidean distance method, a Manhattan distance method, a Mahalanobis distance method, a Minkowski distance method, a cosine distance method, a shortest distance method, a longest distance method, a centroid method, a group average method, a Ward's method, a Kullback- Leibler divergence, a Jensen-Shannon divergence, a Dynamic time warping, and an Earth mover's distance (Lin: Pg. 95 [1]: Euclidean distance option).
Regarding Claim 6, Lin teaches the limitations as claimed in claim 1 and, wherein the calculating of the weight includes using at least one or a plurality of weighting functions among an exponential function type, a reciprocal function type, and a reciprocal power type (Lin: Pg. 87 equation 1: exponential function).
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.
Claim(s) 2, 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (Clustering Complex Data with Group-Dependent Feature Selection) in view of DeBruin (US 20110119212).
Regarding Claim 2, Lin teaches the limitations as claimed in claim 1 and
acquiring data used for the predicting (Lin: Pg. 91 section 5.1 [1]: dataset);
identifying, using the trained clustering model, that the data used for the predicting belongs to a cluster p among the N clusters (Lin: Pg. 92-93 section Quantitative results: number of clusters and clustering performance); and
determining a prediction value, using a trained prediction model Mp among the trained prediction models {Mi} 1 < I < N corresponding to the cluster p with the data used for the predicting as an input (Lin: Pg. 87-88 section 3.2 [1], Pg. 91 section 5 [1]: classifiers used during testing to output values);
Although Lin does not recite the words “prediction value”, it is obvious that a prediction value is indicated by a cluster specific classifier during testing of the model. Thus the limitations as claimed is obvious based on the teachings in Lin;
Nonetheless, DeBruin teaches,
determining a prediction value, using a trained prediction model (DeBruin: [76, 123]: prediction value; [42, 71, 75, 106]: building models using processed data which may be clustering of data);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lin and DeBruin because the combination would enable generating classifier output values indicating a prediction. One of ordinary skill in the art would have been motivated to combine the teachings because the combination enables improving existing processes by using and executing machine learning model outputs in relevant applications for “improved accuracy and efficiency of the prediction and estimation” (see DeBruin [15]).
Regarding Claim 7, Lin teaches A device of generating a model for predicting a material characteristic, the device comprising: a processor; and a memory storing program instructions that cause the processor to:
generate a trained clustering model, using a training dataset and a clustering model, and classify the training dataset into N clusters, where N is an integer greater than 1 (Lin: Pg. 87 section 3.1 [1]: partition Dataset D into C clusters; Pg. 90 section 4.2: data into clusters); and
calculate a distance between centroids of the clusters and a weight between the clusters, using both the calculated distance between the centroids of the clusters and a parameter representing a feature of the training dataset (Lin: Pg. 86 [3]: learning a distance function, Pg. 91-92 section 5.1 [2]: distance function, Pg. 88 [3]: discriminant function - implies measuring inter-clusters distance for feature selection); and
generate, for the N clusters, respective trained prediction models {Mi} 1 <= i <= N, using the clusters and the weight (Lin: Pg. 87-88, section 3. 2 [1-2]: learning cluster-specific classifiers);
Although it is obvious that implementing and executing and algorithm requires processor and memory, Lin does not specifically teach, the device comprising: a processor; and a memory storing program instructions;
DeBruin teaches, the device comprising: a processor; and a memory storing program instructions (DeBruin: [19, 69, 132]: algorithm loaded in computer);
The same motivation to combine as stated above applies.
Regarding Claim 8, Lin and DeBruin teach the limitations as claimed in claim 7 and,
determine a prediction value, using a trained prediction model Mp among the trained prediction models {Mi} 1 <= i <= N with the data used for the predicting as an input, the trained prediction model Mp corresponding to the identified cluster p; and output the determined prediction value (Lin: Pg. 87-88 section 3.2 [1], Pg. 91 section 5 [1]: classifiers used during testing to output values) (DeBruin: [76, 123]: prediction value; [42, 71, 75, 106]: building models using processed data which may be clustering of data) .
Regarding Claim 9, Lin teaches A non-transitory computer-readable recording medium having stored therein a program of generating a model for predicting a material characteristic (Lin: Pg. 84 section 1 [1]: clustering of objects with similar characteristics), the program causing a computer to execute:
acquiring a training dataset (Lin: Pg. 87 section 3.1 [1]: dataset D);
generating a trained clustering model, using the training dataset and a clustering model, and classifying the training dataset into N clusters, where N is an integer greater than 1 (Lin: Pg. 87 section 3.1 [1]: partition Dataset D into C clusters; Pg. 90 section 4.2: data into clusters);
calculating a distance between centroids of the clusters (Lin: Pg. 86 [3]: learning a distance function, Pg. 91-92 section 5.1 [2]: distance function, Pg. 88 [3]: discriminant function - implies measuring inter-clusters distance for feature selection);
calculating a weight between the clusters, using both the distance between the centroids of the clusters and a parameter representing a feature of the training dataset (Lin: Pg. 86 [3,5]: reweighting feature dimensions, merging via a voting scheme); and
generating, for the clusters, respective trained prediction models {Mi} 1 <= i <= N, using the clusters and the weight (Lin: Pg. 87-88, section 3. 2 [1-2]: learning cluster-specific classifiers);
Although obvious that implementing and executing and algorithm may be done using a non-transitory computer-readable recording medium having stored therein a program, Lin does not specifically recite this;
DeBruin further teaches, a non-transitory computer-readable recording medium having stored therein a program (DeBruin: [19, 69, 132]: algorithm loaded in computer);
The same motivation to combine as stated above applies.
Regarding Claim 10, Lin and DeBruin teach the limitations as claimed in claim 9 and,
that the data used for the predicting belongs to a cluster p among the N clusters; and
determining, in response to the data used for the predicting being input, a prediction value, using a prediction model Mp among the trained prediction models {Mi} 1 <= i <= N, generated by the program stored in the non-transitory computer-readable recording medium as claimed in claim 9, the prediction model MP corresponding to the identified cluster p (Lin: Pg. 87-88 section 3.2 [1], Pg. 91 section 5 [1]: classifiers used during testing as per the input data) (DeBruin: [76, 123]: prediction value; [42, 71, 75, 106]: models using processed data which may be a cluster of data).
Although obvious that implementing and executing and algorithm may be done using a non-transitory computer-readable recording medium having stored therein a program, Lin does not specifically recite this;
DeBruin further teaches, a non-transitory computer-readable recording medium having stored therein a program (DeBruin: [19, 69, 132]: algorithm loaded in computer);
The same motivation to combine as stated above applies.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lin (Clustering Complex Data with Group-Dependent Feature Selection) in view of Yoo (US 12704594).
Regarding Claim 5, Lin teaches the limitations as claimed in claim 1 and, wherein, as the parameter representing the feature of the training dataset, at least one or a plurality of parameters among a systematic error, a standard deviation, a variance, a coefficient of variation, a quantile, kurtosis, and a skewness related to a characteristic value of the training dataset is used (Lin: Pg. 92 [1]: feature descriptors);
Although it is obvious that the functions in this limitation are used in the feature descriptors taught in Lin, Lin does not specifically mention, a systematic error, a standard deviation, a variance, a coefficient of variation, a quantile, kurtosis, and a skewness;
Nonetheless, Yoo teaches, plurality of parameters among a systematic error, a standard deviation, a variance, a coefficient of variation, a quantile, kurtosis, and a skewness related to a characteristic value of the training dataset (Yoo: col 12 lines 44-45, col 13 lines 4-5: features using “max, min, mean, skewness, kurtiosis, std”);
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Lin and Yoo because the combination would enable feature representation using parameters like kurtosis, skewness etc. One of ordinary skill in the art would have been motivated to combine the teachings because the combination enables using the commonly known functions in the art for feature representation of data set.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure in the attached 892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MANDRITA BRAHMACHARI whose telephone number is (571)272-9735. The examiner can normally be reached Monday to Friday, 11 am to 8 pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tamara Kyle can be reached at 571 272 4241. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Mandrita Brahmachari/Primary Examiner, Art Unit 2144
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