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 .
Response to Arguments
Applicant’s arguments with respect to claims 1, 3-8, 10-15 and 17-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
Claims 1, 3-8, 10-15 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being described by Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition by Chen et al.
Chen teaches claims 1, 8 and 15. A method, the method comprising:
performing, using a prompt encoder, prompt learning on an input data set to generate a revised text pattern; (Chen sec. 4.1 “Figure 2, given a sentence x = {x1,x2,...,xN} of length N concatenated with a task prefix that specifies the source and target styles of transfer, the generator Gθ encodes it into a sequence of latent representations z… and then decodes these representations into its paraphrase ˆy… of length M which has the same meaning as x but a different style.” Is the input data set, paraphrase y is the revised text pattern.)
processing, using a text generative adversarial network, the revised text pattern based on an existing data set to generate a fused data set, wherein the performing and processing operations further comprise preserving an original meaning of the input data set while utilizing a style of the existing data set, and wherein the style corresponds to a data distribution and representation format of the existing data set; and (Chen sec. 4 p. 1829 “We propose an adversarial learning framework to generate a paraphrase of the source data in a high resource domain whose style conforms to the target data in a low-resource domain.” Chen fig. 2 shows the generative adversarial with generator a and discriminator b. Chen fig. 1 “For each sample from the source domain, we transfer it to the target domain by changing its style-related attributes as synthetic data, and then use it to train NER models.” Data distribution and representation format are not terms of art, and they are not defined in the specification. For purposes of examination, representation format will be length of the sentence, taught by Chen sec. 3.1 “length M”. The probability distribution in sec. 4.1 makes it so the style corresponds to a data distribution of the existing data set.)
updating a machine learning system with the fused data set. (Chen fig. 1 “For each sample from the source domain, we transfer it to the target domain by changing its style-related attributes as synthetic data, and then use it to train NER models.” The fused data set includes paraphrases P of the source data Dsrc, and some number of samples from the target data Dtgt, Chen sec. 5.1 e.g. “We randomly select samples from Dtgt and ensure that each entity class contains K ∼ 2K examples.”)
Chen teaches claims 3, 10 and 17. The method of claim 1, wherein the performance of the prompt learning matches the input data set with the existing data set using a user-defined or auto-generated template. (Chen sec. 3 “Given an input sequence x = {x1,x2,...,xN} of length N… ˆy = {ˆy1, ˆy2,..., ˆyM} is the generated sentence of length M that has the same meaning as x but a different style… we also prepend task prefixes to the beginning of the sentences to guide the direction of style transfer, where a prefix is a sequence of words for task de scription specifying the source and target styles of transfer (e.g., “transfer source to target: ").” A prefix is a user-defined or auto-generated template.)
Chen teaches claims 4, 11 and 18. The method of claim 1, wherein the updating operation further comprises obtaining corresponding meanings of new words of the input data set based on the fused data set. (Chen fig. 1 “For each sample from the source domain, we transfer it to the target domain by changing its style-related attributes as synthetic data, and then use it to train NER models.” The trained NER model then takes new words from the source and puts their meaning in the target based on the fused data set. The meaning is part of the updating because the style transfer, throughout Chen, relies on the output sentence having “the same meaning as x but a different style.” Chen sec. 3.)
Chen teaches claims 5, 12 and 19. The method of claim 1, wherein the processing the revised text pattern further comprises fine-tuning the revised text pattern to generate the fused data set. (Chen sec. 5.2 “Using Same Amount of Pseudo Data Here, we randomly select 1K, 2K, 3K, and 4K samples generated by each method as the training data to fine-tune the model. The baseline is established by fine-tuning the model on the same amount of data from the source domain.”)
Chen teaches claims 6, 13 and 20. The method of claim 1, further comprising constructing the prompt encoder and configuring the prompt encoder to construct the revised text pattern according to new words in the input data set, (Chen fig. 2 “GEθ and GDθ refer to the encoder and decoder of the generator Gθ…”) the revised text pattern enabling a pre-trained language model to ascertain a specific meaning of the new words. (Chen sec. 3 “to provide supervision signals for style transfer and a pre-trained generative model Gθ based on an encoder-decoder… architecture…. the generated sentence of length M that has the same meaning as x but a different style.”)
Chen teaches claims 7 and 14. The method of claim 1, wherein the performing, processing, and updating steps are carried out without feature engineering by a human expert. (Chen does not mention feature engineering, so this claim element is taught.)
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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