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 .
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)(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.
Claims 1 – 3, 5 – 12, 14 – 20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Huang et al. (Response Enhanced Semi-supervised Dialogue Query Generation, March 2024).
As per claims 1, 10, and 18, Huang et al. teach a method for improving a query producer, the method comprising:
constructing a set of training samples comprising a first dialogue corpus and corresponding queries by, for each dialogue in a second dialogue corpus (we select high-quality RA-generated queries, whose similarity score exceeds a pre-determined threshold α, to construct pseudo instances with the corresponding dialogue histories.”; page 18310, col.1, lines 4 – 7) :
predicting a plurality of first queries with a query producer based on a dialogue history of the dialogue, and predicting a query with a response-augmented query producer based on the dialogue history and a dialogue response of the dialogue (“we train a standard query producer (QP) and a response-augmented query producer (RA) on a labeled dataset via supervised learning. In Stage 2, both QP and RA generate pseudo queries for an unlabeled dialogue corpus.”; page 18309, col.1, paragraphs 2, 3),
quantifying a maximum similarity score between the predicted query and each query of the first queries, determining whether the maximum similarity score is larger than or equal to a pre-defined threshold (we quantify the quality of RA-generated query ¯q by…the following similarity score denotes a text similarity function that re turns the score of a specific quantitative metric.. we select high-quality RA-generated queries,
whose similarity score exceeds a pre-determined threshold α, to construct pseudo instances with the corresponding dialogue histories.”; pages 18309, 18310), and
in response to determining that the maximum similarity score is larger than or equal to the pre-defined threshold, constructing the training samples by including the dialogue history of the dialogue into the first dialogue corpus and including the predicted query as the dialogue's corresponding query (“we select high-quality RA-generated queries, whose similarity score exceeds a pre-determined threshold α, to construct pseudo instances with the corresponding dialogue histories. Next, we use these pseudo instances to further train QP using the CE loss again page 18310) ; and
training the query producer with the dialogue history of the first dialogue corpus and the corresponding queries to improve the query producer (“we train a standard query producer (QP) and a response-augmented query producer (RA) on a labeled
dataset via supervised learning…enhance the query producer(QP)with the guidance of the response-augmented query producer (RA).”; pages 18309, col.1, paragraph 2; 18313, conclusion);
wherein each of the query producer and the response-augmented query producer comprises a text-to-text transformer (“A fine-tuned T5-base model”; page 18311, col.1).
As per claims 2, 11, Huang et al. further disclose the text-to-text transformer comprises a neural network (“neural machine translation…A fine-tuned T5-base model”; pages 18308, 18311, col.1).
As per claims 3, 12, Huang et al. further disclose pre-training the query producer with a third dialogue corpus and labeled queries; and pre-training the response-augmented query producer with the third dialogue corpus and labeled queries (“A model initialized from the original T5-base parameters and trained on the QP-labeled pseudo instances following He et al. (2020). • Self-training(QP) A model initialized from trained QP in Stage 1 and then tuned on self-labeled pseudo in stances.”; pages 18310,18311).
As per claims 5, 14, Huang et al. further disclose the second dialogue corpus comprises a plurality of unlabeled dialogues; and each dialogue of the plurality of unlabeled dialogues comprises the dialogue history and the dialogue response (“for each instance in an unlabeled dialogue corpus, we first sample Nc candidate queries from the predictive distribution of QP”; pages 18309, 18310).
As per claims 6, 15, Huang et al. further disclose predicting a query with the trained query producer based on an input dialogue, wherein the predicted query is for predicting a response to the input dialogue (“we train a response-augmented query producer (RA) to provide rich and effective training signals for QP. We first apply a similarity-based query selection strategy to select high-quality RA-generated pseudo queries, which are used to construct pseudo instances for training QP and RA.”; page 18307).
As per claims 7, 16, and 19, Huang et al. further disclose training the response-augmented query producer with the dialogue history and the dialogue response of the first dialogue corpus and corresponding queries to improve the response-augmented query producer (“we train a standard query producer (QP) and a response-augmented query producer (RA) on a labeled dataset via supervised learning. In Stage 2, both QP and RA generate pseudo queries for an unlabeled dialogue corpus.”; pages 18307 -18309).
As per claims 8, 17, and 20, Huang et al. further disclose training the query producer with reinforcement learning by: predicting a query with the query producer based on a dialogue history of an input dialogue, producing a reinforcement score with the response-augmented query producer based on the predicted query and the dialogue history and a dialogue response of the input dialogue, and training the query producer based on the produced reinforcement score, the dialogue history of the input dialogue, and the predicted query (“in Stage 3, we employ reinforcement learning to further improve QP with RA providing rewards as fine-grained training signals.”; page 18309).
As per claim 9, Huang et al. further disclose during training the query producer based on the produced reinforcement score, the dialogue history of the input dialogue, and the predicted query, modifying a loss function according to the produced reinforcement score(“in Stage 3, we employ reinforcement learning to further improve QP with RA providing rewards as fine-grained training signals… For each instance, we take the cross-entropy loss (CE) as the training objective:”; page 18309).
Allowable Subject Matter
Claims 4, 13 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 a statement of reasons for the indication of allowable subject matter:
As to claims 4, 13, Huang et al. further disclose each dialogue of the third dialogue corpus comprises a dialogue history and a dialogue response; the pre-training the query producer with the third dialogue corpus and the labeled queries comprises: pre-training the query producer with the dialogue history of the third dialogue corpus and the labeled queries; and the pre-training the response-augmented query producer with the third dialogue corpus and the labeled queries comprises: pre-training the response-augmented query producer with the dialogue history and the dialogue response of the third dialogue corpus and the labeled queries.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Dangoor et al. teach INTELLIGENT QUERY AUTO-COMPLETION SYSTEMS AND METHODS. Halabi et al. teach rewriting queries. Chechik teaches determining query suggestions. Zheng et al. teach methods for query engine analysis.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEONARD SAINT-CYR whose telephone number is (571)272-4247. The examiner can normally be reached Monday- Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Richemond Dorvil can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LEONARD SAINT-CYR/ Primary Examiner, Art Unit 2658