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 .
Claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/13/2026 has been entered.
Response to Arguments
Applicant’s arguments filed, with respect to the amended independent claims (Claims 1, 9 and 15) and the previously cited prior art, have been fully considered and are persuasive. Therefore, the rejection of Claims 1-2, 6, 9, 12, 15 and 19 under 35 USC § 102, and Claims 8 and 14 under 35 USC § 103, has been withdrawn. However, upon further consideration, a new ground of rejection is made in view of Meeks et al. (US Pub. 2024/0265204 A1).
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.
Claims 1-2, 8-9, 12, 14-15 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Anisimov et al. (US Pub. 2020/0364539 A1) in view of Meeks et al. (US Pub. 2024/0265204 A1).
Regarding Claims 1, 9 and 15, Anisimov teaches a method (see Fig.4 (400) and paragraph [0129]), comprising:
accessing, by a training module from a memory device, training data, the training data comprising a text input (see Fig.4 (401,402), paragraph [0129] and paragraph [0211], text to be read by a user for training the model);
generating, by the training module, from the training data, a trained scanpath generation model (see Fig.4 (403,405,406), Fig.5 (501,502,506, paragraph [0126], paragraph [0129] and paragraphs [0210-0211], a training model trained user data to generate a machine learning model for eye tracking), wherein a scanpath comprises a sequence of words and a corresponding sequence of fixation durations (see paragraph [0211]), wherein the sequence of words comprises one or more words comprising the text input (see paragraph [0211);
and outputting, by the training module, the trained scanpath generation model (see Fig.4 (406), paragraph [0129] and paragraph [0211]).
Anisimov fails to teach wherein training module comprises an adversarial training neural network trained to generate the scanpath generation model.
Meeks, however, teaches a generative adversarial neural network model that is trained to generate a content generation model (see Fig.1 (106) and paragraph [0064]).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to Anisimov’s training module an adversarial training neural network trained to generate a scanpath generation model. The motivation would be to use unsupervised machine learning to generate a trained content generation model.
Regarding Claim 2, Anisimov further teaches wherein the training data comprises a set of text inputs, the text inputs having an associated set of recorded scanpaths, the recorded scanpaths representing ground truth eye tracking recordings based on the set of text inputs (see Fig.4 (403,405) and paragraph [0211]).
Regarding Claims 6, 12 and 19, Anisimov further teaches augmenting one or more natural language processing ("NLP") models using the trained scanpath generation model (see paragraph [0211] and paragraph [0215]), wherein the one or more NLP models include sentiment analysis (see paragraph [0211] and paragraph [0215]); and training the one or more NLP models using generated scanpaths generated by the trained scanpath generation model to improve the performance of the one or more augmented NLP models (see Fig.4 (400), paragraph [0129] and paragraph [0211]).
Claims 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Anisimov et al. (US Pub. 2020/0364539 A1) in view of Meeks et al. (US Pub. 2024/0265204 A1), and in further view of Kobren et al. (US Patent 12,242,568 B2).
Regarding Claims 8 and 14, Anisimov teaches outputting, by the training module, the scanpath generation model but Anisimov and Meeks fail to teach accessing feedback from one or more client devices training the scanpath generation model based on the feedback from the one or more client devices.
Kobren, however, teaches updating a machine learning model based on user feedbacks obtained from client devices (see Fig.2 (203) and Col.6, Line 30-52).
It would have been obvious for one skilled in the art, before the effective filing date of the application, to include to the method of Claim 1 the steps for accessing feedback from one or more client devices training the scanpath generation model based on the feedback from the one or more client devices. The motivation would be to update the scanpath generation model based on use experience data.
Claim Objections
Claims 3-5, 7, 10-11, 13 16-18 and 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.
Regarding Claims 3-5, 7, 10-11, 13 16-18 and 20, the following is a statement of reasons for the indication of allowable subject matter: The prior art of record does not teach, disclose or suggest the claimed limitations of (in combination with all of the limitations of the base claim and any intervening claims) “wherein: the training data comprises a set of text inputs, the text inputs having an associated set of recorded scanpaths; and generating, by a training module, the scanpath generation model further comprises: receiving, by a conditional generator and from the training data, the text input; transforming, by the conditional generator, the text input into a generated scanpath; receiving, by a discriminator and from the training data, the text input; receiving, by the discriminator, the generated scanpath associated with the text input and the recorded scanpath associated with the text input; generating, by the discriminator, a first probability that the generated scanpath is a recorded scanpath and a second probability that the recorded scanpath is a recorded scanpath; training the conditional generator using the first probability, the second probability, the recorded scanpath, and the generated scanpath; and training the discriminator using the first probability and the second probability”, as found in Claim 3. Similar features are claimed in Claims 4-5, 7, 10-11, 13, 16-18 and 20.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to VU B HANG whose telephone number is (571)272-0582.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Hai Phan, can be reached at (571)272-6338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/VU B HANG/Primary Examiner, Art Unit 2654