DETAILED ACTION
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 .
Drawings
The drawings are objected to because in FIG. 8, some of the texts are not in English. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claim 2 is objected to because of the following informalities: “the acquiring the plurality of data item” should be “the acquiring the Appropriate correction is required.
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 7 and 16 are 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.
The term “high” in claims 7 and 16 is a relative term which renders the claim indefinite. The term “high” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The limitation “acquiring the accuracy of each of the plurality of machine learning algorithms, by using a combination of a certain upper number of columns with a high ratio to be included as the dataset” is rendered indefinite by the use of the term “high”.
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.
Claim(s) 1-2, 10-11 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Goto et al (US 20200279178) in view of Volodarskiy et al (US 20200175354).
As to claim 1, Goto discloses a non-transitory computer-readable storage medium storing an accuracy calculation program that causes at least one computer to execute a process (see [0142]), the process comprising:
acquiring data item that corresponds to a generation condition based on an input dataset that is a set of input data that includes at least one data item and the generation condition regarding a correspondence relationship between a combination of feature and label information, as selection target rules (FIGS. 2-3 and [0047], The information on hypothesis 142 is information that associates a combination of an objective variable [label information] and conditions regarding one or more explanatory variables corresponding to the objective variable with an importance degree; see [0052], the generation unit 151 generates combinations of the objective variable and conditions regarding one or more explanatory variables corresponding to the objective variable as hypotheses; see [0064], [0079]);
acquiring accuracy (see [0054], adjusts importance degrees of the hypotheses (knowledge chunks (hereinafter, sometimes simply described as “chunks”)) and constructs a classification model with high accuracy); and
outputting the accuracy (see [0080]).
Goto fails to explicitly disclose acquiring accuracy of each of a plurality of machine learning algorithms.
However, Volodarskiy teaches acquiring accuracy of each of a plurality of machine learning algorithms (see [0054]-[0057]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Goto using Volodarskiy’s teachings to acquiring accuracy of each of a plurality of machine learning algorithms in order to find more accurate models in bounded time frames (Volodarskiy; [0007])
As to claim 2, Goto modified by Volodarskiy further discloses wherein the input dataset is a set of the input data in which the at least one data item and the label information are set as columns (FIG. 2),
the acquiring the plurality of data item includes acquiring a combination of columns, in which column data of the label information that indicates to belong to a certain class and column data of the correlated data item are combined, as the selection target rules, from the input dataset, according to the generation condition (see [0052]-[0053]), and
the acquiring the accuracy includes:
generating the dataset that holds each data item used for each selection rule that satisfies a certain condition among the selection target rules (see [0054]-[0055]); and
acquiring accuracy of each of the plurality of machine learning algorithms, based on a result of executing each of the plurality of machine learning algorithms, by using data set to each data item in each selection rule as an explanatory variable and data set to the label information as a responsive variable (see [0054], [0077]).
As to claims 10-11, method claims 10-11 recite the same features as those recited in claims 1-2 and are therefore rejected for the same reasons of obviousness as used above.
As to claim 19, Goto discloses an information processing device (FIG. 27) comprising:
one or more memories (FIG. 27, memory 10c); and
one or more processors coupled to the one or more memories and the one or more processors (FIG. 27 and [0141], processor 10d) configured to:
acquire data item that corresponds to a generation condition based on an input dataset that is a set of input data that includes at least one data item and the generation condition regarding a correspondence relationship between a combination of feature and label information, as selection target rules (FIGS. 2-3 and [0047], The information on hypothesis 142 is information that associates a combination of an objective variable [label information] and conditions regarding one or more explanatory variables corresponding to the objective variable with an importance degree; see [0052], the generation unit 151 generates combinations of the objective variable and conditions regarding one or more explanatory variables corresponding to the objective variable as hypotheses; see [0064], [0079]);
acquire accuracy certain condition among the selection target rules (see [0054], adjusts importance degrees of the hypotheses (knowledge chunks (hereinafter, sometimes simply described as “chunks”)) and constructs a classification model with high accuracy); and
output the accuracy (see [0080]).
Goto fails to explicitly disclose acquiring accuracy of each of a plurality of machine learning algorithms.
However, Volodarskiy teaches acquiring accuracy of each of a plurality of machine learning algorithms (see [0054]-[0057]).
At the time before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skills in the art to modify Goto using Volodarskiy’s teachings to acquiring accuracy of each of a plurality of machine learning algorithms in order to find more accurate models in bounded time frames (Volodarskiy; [0007])
Allowable Subject Matter
Claims 3-9 and 12-18 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BOUBACAR ABDOU TCHOUSSOU whose telephone number is (571)272-7625. The examiner can normally be reached M-F 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chris Kelley can be reached at 5712727331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BOUBACAR ABDOU TCHOUSSOU/Primary Examiner, Art Unit 2482