DETAILED ACTION
This Action is responsive to Claims filed 10/04/2022.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/04/2022 was filed before the mailing date of the first action. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
Receipt is acknowledged of the drawings filed on 10/04/2022. These Drawings are acceptable.
Status of the Claims
Claims 1-17 are pending.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
It is unclear what is meant by the term “variable combination” and what the scope of said combination is. The Specification does not expand on the term other the repetition of claim language, without an explicit definition limiting a combination’s scale or scope. There is no definition of which, how many, what order, or whether there is duplication of variables in the use of “variable combination” within the Specification.
Claims 7-8, 10-11, 14, and 17 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 7-8, 10-11, and 14 recites the limitation "based on the selection probability of each variable combination and the target number of variable combinations to be dropped." (emphasis added). There is insufficient antecedent basis for this limitation in the claim. Nowhere in the claims on which these depend is a “target number of variable combinations to be dropped” determined. There is no reference to a dropout operation until claim 13, and there is still no determination of a target number of combinations to drop. Claim 17 does not address the deficiency of claim 14 and is therefore similarly rejected.
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 1-17 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more; and because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea, see Alice Corporation Pty. Ltd. v. CLS Bank International, et al, 573 U.S. (2014). In determining whether the claims are subject matter eligible, the Examiner applies the 2019 USPTO Patent Eligibility Guidelines. (2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50, Jan. 7, 2019.)
Step 1:
Claims 1-8 recite a preprocessing device for learning data of a classifier, which falls under the statutory category of a machine. Claims 9-11 recite a preprocessing device for learning data of a classifier, which falls under the statutory category of a machine. Claims 12-17 recite a preprocessing method of learning data of a classifier, which falls under the statutory category of a process.
Step 2A – Prong 1:
Claim 1 recites an abstract idea, law of nature, or natural phenomenon. The limitations “calculate an appearance frequency of each variable combination based on the data,”, “calculate a cumulative appearance frequency of each variable combination by cumulating and summing the appearance frequency of each variable combination;”, and “generate a prediction variable-target variable frequency matrix based on the appearance frequencies of each variable combination,” under the broadest reasonable interpretation, cover a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper. These limitations therefore fall within the mental process group.
Calculating a generic appearance frequency from generic data is practically performed within the human mind or with the aid of pen and paper. Cumulating and summing frequencies is practically performed within the human mind or with the aid of pen and paper. Generating a matrix is practically performed within the human mind or with the aid of pen and paper.
The limitation “apply Laplace smoothing to the frequency matrix according to the cumulative appearance frequency to calculate a probability value of each variable combination,” under the broadest reasonable interpretation, cover a mathematical calculation or relationship. This limitation therefore falls under the mathematical concept group.
Step 2A – Prong 2:
The additional elements of claim 1 do not integrate the abstract idea into a judicial exception. The claim recites the additional elements “A preprocessing device”, “a variable spatial information extractor”, and “a data preprocessor” are recognized as generic computer components recited at a high level of generality (the Specification does not indicate these elements are different from a typical processing unit). Although it has and executes instructions to perform the abstract idea itself, this also does not serve to integrate the abstract idea into a practical application as it merely amounts to instructions to "apply it." (See MPEP 2106.04(d)(2) indicating mere instructions to apply an abstract idea does not amount to integrating the abstract idea into a practical application).
The additional elements recited in the limitations “learning data” and “a classifier” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional elements “receive data for learning of the classifier,” and “provide the probability value of each variable combination to the classifier.” are found to be merely insignificant extra-solution activity steps (See MPEP 2106.05(g)(3)(iii)). These limitations are found to be recited at a high level of generality that does not impose a meaningful limit on the abstract idea.
Step 2B:
The only limitation on the performance of the described method is a limitation reciting “A preprocessing device”, “a variable spatial information extractor”, and “a data preprocessor” (the Specification does not indicate these elements are different from a typical processing unit) These elements are insufficient to transform a judicial exception to a patentable invention because the recited elements are considered insignificant extra-solution activity (generic computer system, processing resources, links the judicial exception to a particular, respective, technological environment). The claim thus recites computing components only at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components; mere instructions to apply an exception using a generic computer component cannot provide an inventive concept (see MPEP 2106.05(f)).
The additional elements recited in the limitations “learning data” and “a classifier” are recognized as non-generic computer components, however, they are found to generally link the abstract idea to a particular technological field (See MPEP 2106.05(h)).
The additional elements “receive data for learning of the classifier,” and “provide the probability value of each variable combination to the classifier.” is acknowledged to be well-understood, routine, conventional activity (see, e.g., court recognized WURC examples in MPEP 2106.05(d)(II), third list (iv)).
Taken alone or in ordered combination, these additional elements do not amount to significantly more than the above-identified abstract idea. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
For the reasons above, claim 1 is rejected as being directed to non-patentable subject matter under §101. This rejection applies equally to independent claims 9 and 12.
Claim 9 recites similar limitations to Claim 1 with the exception of the additional abstract idea mental process step “select a variable combination as a learning target of the classifier, based on the cumulative appearance frequency,” before the value is provided to the classifier (extra-solution/WURC activity); therefore, both claims are similarly rejected.
Claim 12 recites similar limitations to claim 1, with the exception of the additional abstract idea mental process step “a confidence-based smoothing operation of generating a prediction variable- target variable frequency matrix based on the appearance frequencies of each variable combination,” and the instructions to apply (See MPEP 2106.05(f)) step “a classifier learning operation of providing the probability value of each variable combination to the classifier.” therefore, both claims are similarly rejected.
Dependent Claims:
Claim 2 (Claim 13) recites similar abstract idea and extra-solution/WURC activity steps to the limitations of Claim 9.
Claim 3 recites refinements to the extra-solution/WURC activity of claim 1.
Claim 4 recites refinements to the abstract idea mental process steps of claim 1.
Claim 5 (Claim 15) recites abstract idea mental process steps “derives a probability distribution for the appearance frequency of each variable combination from the data, and the data preprocessor generates the frequency matrix based on the probability distribution.”
Claim 6 (Claim 16) recites refinements to the mathematical relationship of claim 1.
Claim 7 (Claims 10 and 14) recites abstract idea mental process steps “calculates a selection probability of each variable combination based on the cumulative appearance frequency, and selects a variable combination as a learning target of the classifier based on the selection probability of each variable combination and the target number of variable combinations to be dropped.”
Claim 8 (Claims 11 and 17) recites refinements to the abstract idea mental process steps of claims 1 and 2.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-6, 9, 12-13, and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Juan et al. (Reversing and Smoothing the Multinomial Naive Bayes Text Classifier, 2002), hereinafter Juan, and Meng et al. (Frailty Classification Based on Artificial Intelligence, 2021), hereinafter Meng.
In regards to claim 1: The present invention claims: “…receive data for learning of the classifier,” Juan’s method pertains to data input for classification by a classifier (Section 3, Pages 4-5).
“calculate an appearance frequency of each variable combination based on the data, and calculate a cumulative appearance frequency of each variable combination by cumulating and summing the appearance frequency of each variable combination;” Juan teaches “To estimate the set of parameters {α} that best fits a given sequence of i.i.d. training data {(xn, cn)}, n = 1, . . . ,N, we consider the maximization of the log-probability function: [Equation 12] where Ncd is the number of occurrences of word d in training documents of class
c, and the maximization is subject to the equality constraints (10)-(11).” (Page 5, mapping to a cumulating and summing of variable occurrences). Juan also teaches “In a more general setting, bigram or trigram counts can also be added to (the text document representation and) the model.” (Page 4, mapping to the BRI of a ”variable combination” if multiple words of an n-gram are used).
“…generate a prediction variable-target variable frequency matrix based on the appearance frequencies of each variable combination, apply Laplace smoothing to the frequency matrix according to the cumulative appearance frequency to calculate a probability value of each variable combination,” Juan teaches applying smoothing to the results of each word or n-gram count/frequency in order to avoid 0 values (Section 5, Pages 8-9).
“and provide the probability value of each variable combination to the classifier.” Section 7 of Juan, as well as Figures 1 and 2, show the output of the classifiers as a result of the aforementioned method (Pages 12-13).
While Juan teaches the above method of extracting features and smoothing the probabilities of a word/n-gram for classification, both word-conditional and class-conditional, Juan fails to explicitly teach the modules recited in:
“A preprocessing device for learning data of a classifier, comprising: a variable spatial information extractor configured to…”
and
“and a data preprocessor configured to…” However, Meng, in a similar field of endeavor of machine learning classifier training, teaches extracting features from radar input as part of preprocessing the stream of data before classification (Pages 3-4, Sections 2 and 3.1). It is also noted that Meng teaches cumulating/summing binarized feature point numbers as part of the preprocessing.
Meng demonstrates that preprocessing of stream data like radar data would have been known in the art at the time of the Applicant’s filing. Juan illustrates how their method, including smoothing of a probability or frequency distribution, has benefits for classification accuracy (Sections 7 and 8). It would have been obvious to one of ordinary skill in the art at the time of the Applicant’s filing to combine known methods of classification preprocessing such as in Juan to stream data such as in Meng to produce known benefits in the classification of said data.
In regards to claim 2: The present invention claims: “wherein the data preprocessor selects a variable combination as a learning target of the classifier based on the cumulative appearance frequency, and provides the probability value of each variable combination corresponding to the selected variable combination to the classifier.” See above where Juan teaches counting/cumulating/summing each word/n-gram and smoothing the probability distribution in a document for classification.
In regards to claim 3: The present invention claims: “wherein the variable spatial information extractor receives, as data for the learning of the classifier, data according to any one data type of instance and mini-batch.” See above how Juan teaches receiving data as a document or splitting into n-grams. Meng utilizes radar stream data. (mapping to the broad recitation of “any one data type”).
In regards to claim 4: The present invention claims: “wherein the variable spatial information extractor calculates the appearance frequency of each variable combination by binarizing the data.” While Juan does not directly reference binarization, Meng teaches “Then feature point numbers are counted in every separated part after binarization for creating the eight features. In binarization, optimized thresholds are obtained by sliding the threshold in experimentation.” (Abstract).
Techniques of binarization would have been known to one skilled in the art at the time of the Applicant’s filing when handling data more complex than that used in Juan, for example.
In regards to claim 5: The present invention claims: “wherein the variable spatial information extractor derives a probability distribution for the appearance frequency of each variable combination from the data, and the data preprocessor generates the frequency matrix based on the probability distribution.” See above where a combination of Juan and Meng would reasonably read on a probability distribution based on counting appearances of words/n-grams and a preprocessing system for said feature extraction.
In regards to claim 6: The present invention claims: “wherein the data preprocessor calculates a Laplace smoothing parameter based on the cumulative appearance frequency and a critical appearance frequency, and applies the Laplace smoothing to the frequency matrix according to the Laplace smoothing parameter to calculate the probability value of each variable combination” Juan determines a smoothed probability distribution with one of two ways described in Section 5 “The idea behind these two alternative techniques is known as absolute discounting. Instead of using artificial pseudo-counts, we gain “free” probability mass by discounting a small constant to every count associated with a seen event (positive count). The gained probability mass is then distributed among events in accordance with a generalized distribution” (Page 9), which is then applied to some or all events. (mapping the use of seen events to the broad recitation of “a critical appearance frequency”).
In regards to claim 9: Claim 9 recites similar limitations to claim 1, with the exception of “select a variable combination as a learning target of the classifier, based on the cumulative appearance frequency,” which the combination of Juan and Meng broadly reads on by training their models/classifying their data. Therefore, both claims are similarly rejected.
In regards to claims 12-13 and 15-16: Claims 12-13 and 15-16 recites similar limitations to claims 1-2 and 5-6, with the exception of “a confidence-based smoothing operation of generating a prediction variable- target variable frequency matrix based on the appearance frequencies of each variable combination,” (read on by the smoothing within a combination of Juan and Meng) and “a classifier learning operation of providing the probability value of each variable combination to the classifier.” (read on by the probability/frequency determination within a combination of Juan and Meng) of claim 12; therefore, both sets of claims are similarly rejected.
Claim(s) 7-8, 10-11, 14, and 17 s/are rejected under 35 U.S.C. 103 as being unpatentable over Juan and Meng as applied to claim 1 and 2 above, and further in view of Watanabe et al. (US 9,442,785 B2), hereinafter Watanabe.
In regards to claim 7: The present invention claims: “wherein the data preprocessor calculates a selection probability of each variable combination based on the cumulative appearance frequency, and selects a variable combination as a learning target of the classifier based on the selection probability of each variable combination and the target number of variable combinations to be dropped.” The combination of Juan and Meng fails to explicitly teach the above limitations, however, Watanabe, in a similar field of endeavor of classifier training, teaches “An information processing apparatus determines first message types that are not used for learning, on the basis of the appearance frequencies of individual message types obtained using a set of collected messages, and learns a message pattern which appears when a fault occurs and from which the first message types have been removed, from the set of messages and fault information. The information processing apparatus determines second message types that are not used for detection, on the basis of the appearance frequencies of individual message types obtained using a set of collected messages, generates a message pattern, from which the second message types have been removed, from the set of messages, and compares the message patterns with each other to detect a fault symptom in a system.” (Abstract) and “The frequency calculation unit 121 registers the total count and appearance count of each type, calculated at step S12, in the frequency table of the frequency information storage unit 123. The frequency calculation unit 121 calculates the frequency and score for each type on the basis of the total count and the appearance counts of the individual types, and registers them in the frequency table. For example, the frequency is calculated as "frequency"="appearance count" +"total count", and the score is calculated as the reciprocal of the frequency.” (Column 15, Lines 50-59, mapping to the broad recitation of using the frequencies to determine combinations to drop).
Watanabe details how noisy data may affect fault detection or the predictive ability of a classifier if not properly handled (Column 2, Lines 5-32). It would have been obvious to one of ordinary skill in the art combining Juan and Meng to incorporate elements similar to those used in Watanabe in order to further improve the smoothed frequency distribution accuracy for classification.
In regards to claim 8: The present invention claims: “wherein the data preprocessor calculates a selection probability of each variable combination based on a reciprocal of the cumulative appearance frequency, and selects a variable combination as a learning target of the classifier based on the selection probability of each variable combination and the target number of variable combinations to be dropped.” See above how Watanabe uses the frequency distribution and the reciprocal of the frequency.
In regards to claim 10-11: Claims 10-11 recite similar limitations to claims 7-8, with the exception of “select a variable combination as a learning target of the classifier, based on the cumulative appearance frequency,” of claim 9, which the combination of Juan and Meng broadly reads on by training their models/classifying their data. Therefore, both sets of claims are similarly rejected.
In regards to claims 14 and 17: Claims 14 and 17 recite similar limitations to claims 7-8, with the exception of “a confidence-based smoothing operation of generating a prediction variable- target variable frequency matrix based on the appearance frequencies of each variable combination,” (read on by the smoothing within a combination of Juan and Meng) and “a classifier learning operation of providing the probability value of each variable combination to the classifier.” (read on by the probability/frequency determination within a combination of Juan and Meng) of claim 12; therefore, both sets of claims are similarly rejected.
References not Cited
The following prior art was found to be relevant to the present application, but was not cited in the rejection
Kaga et al. (WO 2015155881 A1, 2015)
Conclusion
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/GRIFFIN TANNER BEAN/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121