DETAILED ACTION
Double Patenting
1. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
2. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,288,550 in view of Kyunghi et al. (KR 0220105792A). Although the claims at issue are not identical, they are not patentably distinct from each other because all the claimed limitations recited in the present application are transparently found in the U.S. Patent 12,288,550 with obvious wording variations. In re KARLSON (CCPA) 136 USPA 184 (1963).
U.S. Patent Application 19/085,675
U.S. Patent 12,288,550
1.A computer implemented method, comprising:
1.A computer implemented method, comprising:
obtaining a machine learning model pre-trained for language modeling;
obtaining a machine learning model pre-trained for language modeling;
performing an iterative hypertuning process comprising:
(a)selecting one or more original auxiliary tasks from a pool of auxiliary tasks based one or more relationships of the one or more original auxiliary tasks to a downstream task,
(b)assigning hyperparameters to the machine learning model,
(c)post-training the machine learning model for the one or more auxiliary tasks using labeled data associated with the one or more auxiliary tasks and the assigned hyperparameters, wherein the post-training comprises performing iterative training operations to optimized model parameters of the machine learning model and generate a focused machine learning model,
(d)obtaining, using the focused machine learning model, output associated with performance of the one or more auxiliary tasks, the downstream task, or both,
(e) determining a performance metric based on the output, and
(f)performing (a)-(e) based on the performance metric to optimize selecting the one or more auxiliary tasks and assigning the hyperparameters, wherein (a)-(e) are repeated through n number of iterations until an optimal combination of the one or more auxiliary tasks and the hyperparameters are found to solve an optimization or search problem; and
post-training the machine learning model for multiple tasks to generate a focused machine learning model, wherein the post-training comprises:
training the machine learning model using an unlabeled set of training data, wherein the unlabeled set of training data pertains to a task of the multiple tasks, the machine learning model is pre-trained for the tasks as part of the language modeling, and the unlabeled set of training data pertains to a target domain, a target task, or a target language,
wherein said training comprises performing iterative training operations to further optimize model parameters of the machine learning model to encode information related to the target domain, the target task, or the target language,
further training the machine learning model using a labeled set of training data, wherein the labeled set of training data pertains to another task of the multiple tasks, the another task being an auxiliary task that is related to a downstream task to be performed using the machine learning model or output from the machine learning model, and
wherein said further training comprises performing iterative training operations to further optimize the model parameters of the machine learning model to encode auxiliary information related to the downstream task; and
providing the focused machine learning model comprising the optimized model parameters.
providing the focused machine learning model comprising the optimized model parameters.
Claim 1 of U.S. Patent No. 12,288,550 does not teach hypertuning process and tasks using labeled data. Kyunghi teaches unsupervised and supervised learning topic labeling (multiclass text classification); Hyper-tuning data to provide strong application efficiency (description-of-embodiments, 5th paragraph). It would have been obvious to incorporate the hypertuning process and labeling as taught by Kyunghi into the teaching of Claim 1 of U.S. Patent No. 12,288,550 for the purpose of providing strong application efficiency.
The Examiner also notes that claims 8, 15 of the ‘675 Patent Application corresponds to claims 8, 15 of the 12,288,550 patent.
Allowable Subject Matter
3. Claims 1-20 would be allowable if rewritten or amended to overcome the double patenting rejection(s), set forth in this Office action.
The following is an examiner’s statement of reasons for allowance:
Wang (2022/0101060) teaches text partitioning method, text classifying method, apparatus, device and storage medium. In an embodiment of the disclosure, the pre-training language model is a pre-training language representation model.
Wei (US Patent 12,062,375) teaches systems and methods for separating and identifying audio in an audio file using machine learning. The language model trained to calculate one or more language model scores. The training datasets used as an input to train the language model comprise any sequence of class labels mapped to audio.
As to claims 1, 8, and 15, prior of records fail to teach, or render obvious, alone or in combination a computer implemented method, a system, and a computer program product tangibly embodied in one or more non-transitory machine readable media, including instructions configured to cause one or more data processors to perform processing comprising the claimed components, relationships, and functionalities as specifically recited in the 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
4. Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUYNH H NGUYEN whose telephone number is (571)272-7489. The examiner can normally be reached Monday-Friday 7:30AM-3:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ahmad Matar can be reached on 571-272-7488. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/QUYNH H NGUYEN/Primary Examiner, Art Unit 2693
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