DETAILED ACTION
Remarks
This non-final office action is in response to the application filled on 07/16/2024. Claims 1-4 are pending and examined below.
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
As of date of this action, IDS filled has been annotated and considered.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by US 2021/0012267 (“Fawaz”).
Regarding claim 1, Fawaz discloses a trained model verification system comprising (see at least fig 2 and [0015], where a trained model is verified):
a rule model convertor to convert a trained model to be verified into a rule model having an input/output relationship equivalent to that of the trained model (see at least [0015], where “The training data is inputted into the filtering model, and the performance of the trained filtering model is validated using the rules. For example, the filtering model is trained to predict labels in the training data given features for the corresponding candidate-job pairs. After training is complete, the filtering model is applied to test and/or validation data to verify that the filtering model has learned all of the rules.”; see also [0088] and fig 2, block 204);
a verification data set generator to generate a verification data set for the rule model (see at least [0060], where “model-creation apparatus 210 obtains and/or creates a test or validation data set containing candidate-job features 224 for candidate-job pairs that match different subsets and/or combinations of rules 222. Model-creation apparatus 210 uses filtering model 208 to generate scores 214 from the test or validation data set and verifies that the outputted scores 214 are indicative of labels 212 and/or negative outcomes 226 for the candidate-job pairs.”); and
a verificator to verify the rule model or the trained model using the verification data set (see at least [0015] and [0060]).
Regarding claim 2, Fawaz further discloses a system comprising: a violation degree calculator to calculate an evaluation index of the rule model or the trained model using the verification data set (see at least [0072], [0063] and [0074], where score is interpreted as evaluation index and negative outcome is violation degree); and
a verification result output interface to convert the evaluation index into a format in which the evaluation index can be displayed as a diagram, a table, or a graph and to output the converted evaluation index (see at least [0073] and [0034]).
Regarding claim 3, Fawaz further discloses a system wherein the format includes a form of a tree (see at least [0071] and [0077]).
Regarding claim 4, Fawaz further discloses a system wherein the rule model convertor can set an upper limit value of the number of rules as a design parameter (see at least [0047], where “upper bound”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SOHANA TANJU KHAYER whose telephone number is (408)918-7597. The examiner can normally be reached on Monday - Thursday, 7 am-5.30 pm, PT.
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/SOHANA TANJU KHAYER/Primary Examiner, Art Unit 3657
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