DETAILED ACTION
Introduction
1. This office action is in response to Applicant’s submission filed on 1/30/2025. Claims 1-20 are pending in the application and have been examined.
Notice of Pre-AIA or AIA Status
2. 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
3. The information disclosure statement (IDS) submitted on 9/16/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Statement Re: 35 USC 101
4. The pending independent claims recite “using a trained text reconstruction model” and “using a trained text detection model.” As these features are too complicated to be done in the human mind or with pen and paper, the pending claims comply with 35 USC 101.
Claim Rejections - 35 USC § 103
5. 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 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.
6. Claims 1-4 and 11-14 are rejected under 35 U.S.C. 103 as unpatentable over US Pat. App. Pub. No. 20230016365 (Qiu et al, hereinafter “Qiu”) in view of “Accurate Generated Text Detection Based on Deep Layer-wise Relevance Propagation” (Guo et al., hereinafter “Guo”).
With regard to Claim 1, Qiu describes:
“A text detection method, comprising:
determining a text feature of a target text; (Paragraph 44 describes determining semantic vectors of a target text, cited as “a text feature.”)
performing, by using a trained text reconstruction model, noise addition on the text feature to obtain a text feature after noise addition, wherein the noise addition comprises performing noise addition on a text feature corresponding to a text element in the target text; and (Paragraph 54 describes adding a perturbation to the text data, cited as “noise addition.”)
determining, by using a trained text detection model and based on the text feature after noise addition, a detection result of the target text.” (Paragraph 65 describes that a classification result is obtained by the device, which is cited as “a detection result.”)
Qiu does not explicitly describe “the detection result indicates whether the target text is generated by a machine learning model.”
However, Section IV(C) of Guo describes a device that detects if an input text was generated by a model or not.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the text generation source indicator as described by Guo into the method of Qiu to allow a user to evaluate the reliability of the text, as described in the Abstract of Guo.
With regard to Claim 2, Qui describes “determining, based on the text feature of the text element, semantic terms of the text element, wherein the semantic terms indicate a semantic feature of the text element, and the semantic terms at least comprise a first semantic term and a second semantic term; (Paragraph 52 describes generating semantic representations Ra of the text samples.)
performing the noise addition on the second semantic term to obtain a noise term; and (Paragraph 52 describes generating perturbations P, which are cited as “a noise term.)
determining, based on the first semantic term and the noise term, a noise addition result of the text element.” (Paragraph 53 describes determining the noise addition result.)
With regard to Claim 3, Qui describes “performing, by using a semantic mapper in the text reconstruction model, semantic feature mapping on the text feature of the text element to obtain a semantic mapping result; and (Paragraph 52 describes generating semantic representations Ra of the text samples.)
determining, by using a biaser in the text reconstruction model, a bias of the semantic mapping result to obtain the semantic terms of the text element.” (Paragraph 52 describes biasing the text samples with perturbation information P before generating the semantic representations Ra.)
With regard to Claim 4, Qui describes “the text reconstruction model and the text detection model are jointly trained.” Paragraph 90 describes joint training the model based on the classification loss and the contrastive loss.
With respect to Claims 11-14, device Claim 11 and method Claim 1 are related as a device programmed to perform the same steps as the method. Accordingly, Claims 11-14 are similarly rejected under the same rationale as applied above with respect to Claims 1-4.
Allowable Subject Matter
7. Claims 5-10 and 15-20 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.
The following is an examiner’s statement of reasons for allowance:
The prior art does not teach or suggest “the text reconstruction model to be trained comprises a first sub-model and a second sub-model with a same architecture, and the text reconstruction model is trained by:
performing, by using the text reconstruction model to be trained, noise addition on a sample text feature of sample text to obtain a sample text feature after noise addition, wherein the sample text feature after noise addition comprises a first sample feature obtained by performing, by the first sub-model, noise addition on the sample text feature, and a second sample feature obtained by performing, by the second sub-model, noise addition on the sample text feature;
determining, by using the text detection model and based on the sample text feature after noise addition, a sample detection result, wherein the sample detection result comprises a first detection result determined based on the first sample feature, and a second detection result determined based on the second sample feature;
training the text reconstruction model to be trained based on a difference to obtain the trained text reconstruction model, wherein the difference at least indicates a noise loss function between the sample text feature after noise addition and the sample text feature, and the noise loss function comprises a first noise loss function between the first sample feature and the sample text feature, and a second noise loss function between the second sample feature and the sample text feature; and
retaining one of the first sub-model or the second sub-model in the trained text reconstruction model.” as recited in Claims 5 and 15 and in combination with the features of the independent claims.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. Pat. App. Pub. No. 20210201196 (Liu et al.) also describes a device that adds noise text to a sample being evaluated.
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EDWARD TRACY whose telephone number is (571)272-8332. The examiner can normally be reached Monday-Friday 9 AM- 5PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bhavesh Mehta can be reached on 571-272-7453. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/EDWARD TRACY JR./Examiner, Art Unit 2656
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