DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the Preliminary Amendment filed on 12/15/2023. Claims 1-10 are pending in the case. Claim 1 is an independent claim.
Drawings
New corrected drawings in compliance with 37 C.F.R. § 1.121(d) are required in this application because portions of . Applicant is advised to employ the services of a competent patent draftsperson outside the Office, as the U.S. Patent and Trademark Office no longer prepares new drawings. The corrected drawings are required in reply to the Office action to avoid abandonment of the application. The requirement for corrected drawings will not be held in abeyance.
INFORMATION ON HOW TO EFFECT DRAWING CHANGES
Replacement Drawing Sheets
Drawing changes must be made by presenting replacement sheets which incorporate the desired changes and which comply with 37 C.F.R. § 1.84. An explanation of the changes made must be presented either in the drawing amendments section, or remarks, section of the amendment paper. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 C.F.R. § 1.121(d). A replacement sheet must include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of the amended drawing(s) must not be labeled as “amended.” If the changes to the drawing figure(s) are not accepted by the examiner, applicant will be notified of any required corrective action in the next Office action. No further drawing submission will be required, unless applicant is notified.
Identifying indicia, if provided, should include the title of the invention, inventor’s name, and application number, or docket number (if any) if an application number has not been assigned to the application. If this information is provided, it must be placed on the front of each sheet and within the top margin.
Annotated Drawing Sheets
A marked-up copy of any amended drawing figure, including annotations indicating the changes made, may be submitted or required by the examiner. The annotated drawing sheet(s) must be clearly labeled as “Annotated Sheet” and must be presented in the amendment or remarks section that explains the change(s) to the drawings.
Timing of Corrections
Applicant is required to submit acceptable corrected drawings within the time period set in the Office action. See 37 C.F.R. § 1.85(a). Failure to take corrective action within the set period will result in ABANDONMENT of the application.
If corrected drawings are required in a Notice of Allowability (PTOL-37), the new drawings MUST be filed within the THREE MONTH shortened statutory period set for reply in the “Notice of Allowability.” Extensions of time may NOT be obtained under the provisions of 37 C.F.R. § 1.136 for filing the corrected drawings after the mailing of a Notice of Allowability.
Claim Interpretation
The following is a quotation of 35 U.S.C. § 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. § 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. § 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. § 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. § 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. § 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. § 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. § 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. § 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. § 112(f) because the claim limitations use a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “a knowledge base construction and update unit, building ... stored ...,” ““a classification model construction unit, constructing ...,” ““a query and aggregation unit, using ... querying ... using ...,” ““a loss calculation unit, using ... using ... calculating ...,” “a loss adjustment unit, constructing ... adjusting ...,” and “a parameter optimization, optimizing ...” in claim 6.
Because these claim limitations are being interpreted under 35 U.S.C. § 112(f) they are being interpreted to cover the corresponding structure described in the specification as performing the claimed functions, and equivalents thereof.
If applicant does not intend to have these limitations interpreted under 35 U.S.C. § 112(f) applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. § 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. § 112(f).
Claim Objections
Claim 6 is objected to because of the following informalities:
Claim 6 recites “The fine-tuning device” where “A fine-tuning device” was apparently intended.
Claim 6 recites “a parameter optimization” where “a parameter optimization unit” was apparently intended.
Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Independent claim 1 recites “the masked words.” There is insufficient antecedent basis for this limitation. For the purposes of prior art and subject matter eligibility analyses Examiner assumes some, but not all, words are masked. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claims 1-10 are rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention. Independent claim 1 recites “the masked words.” It is unclear which part of the invention performs the masking, when the masking is performed, how many times the masking is performed, or how it decides which words to mask. For the purposes of prior art and subject matter eligibility analyses Examiner assumes masking is performed once at the beginning and is random. Dependent claims inherit the same issue from parent claims and do not resolve it.
Claim Rejections - 35 U.S.C. § 102
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 (i.e., changing from AIA to pre-AIA ) 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 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 and 4-6 are rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Luong et al. (US 2021/0089724 A1, hereinafter Luong).
As to independent claim 1, Luong discloses a fine-tuning method for classification model of knowledge representation decoupling, comprising the following steps:
step 1: building a knowledge base for retrieval, where multiple instance phrases are stored (“Given an input x=[x1; x2; . . . ; xn],” paragraph 0050 line 2) in the form of key value pair, the key stores an embedding vector of the instance phrase, and the value stores a true value of label of the instance phrase (“For a given position t, the discriminator 12 predicts whether the token x.sub.t is ‘real,’ i.e., that it comes from the data distribution rather than the generator distribution (e.g., a noise distribution),” paragraph 0049 lines 6-9);
step 2, constructing a classification model that comprises a pre-trained language model and a prediction and classification module (“the output of the trained language encoder model can be input into one or more neural network layers to perform a natural language processing task, such as classification, question answering or natural language generation. The one or more neural network layers can then output the result of the natural language task (e.g. a classification). A natural language model for the specific natural language task may be trained by fine-tuning the pre-trained language encoder model. The parameters of the pre-trained language encoder model can be input into the untrained natural language model (e.g. a classification model) at initialization,” paragraph 0060 lines 1-12);
step 3: using the pre-trained language model to extract a first embedding vector of the masked words in the input instance text, and using this first embedding vector as a first query vector, for each label class, querying multiple instance phrases closest to the first query vector from the knowledge base as a first neighboring instance phrase, using an aggregation result obtained by aggregating all the first neighboring instance phrases with the first query vector as an input data for the pre-trained language model (“The generator 14 can be trained to perform masked language modeling. Given an input x=[x1; x2; . . . ; xn], masked language modeling first selects a random set of positions (integers between 1 and n) to mask out m=[m1; . . . ; mk]. The tokens in the selected positions are replaced with a [MASK] token: which can be denoted as this as xmasked=REPLACE(x; m; [MASK]). The generator 14 can then learn to maximize the likelihood of the masked-out tokens. The discriminator 12 can be trained to distinguish tokens in the data from tokens sampled from the generator 14. More specifically, a ‘noised’ example xnoised 22 can be created by replacing the masked-out tokens 20a and 20b with generator samples. The discriminator 12 can then be trained to predict which tokens in xnoised 22 do not match the original input x 18,” paragraph 0050 lines 1-15);
step 4: using the pre-trained language model to extract a second embedding vector of the masked words in the input data, and using the prediction and classification module to classify and predict the second embedding vector to obtain a classification and prediction probability, based on the classification and prediction probability and the true value of label of the masked words, calculating a classification loss (“For a given position t, the discriminator 12 predicts whether the token x.sub.t is ‘real,’ i.e., that it comes from the data distribution rather than the generator distribution (e.g., a noise distribution),” paragraph 0049 lines 6-9; “a loss function 26 that evaluates the plurality of predictions 24 produced by the machine-learned language encoder model 12,” paragraph 0039 lines 3-5);
step 5, constructing a weight factor based on the true value of label of the masking word, and adjusting the classification loss based on the weight factor to make the classification loss more focused on misclassified instances (“a second loss function 28 that evaluates a difference between the one or more replacement tokens 23a and 23b and the one or more tokens selected to serve as masked tokens,” paragraph 0041 lines 4-7);
step 6, optimizing parameters of the classification model by using the adjusted classification loss to obtain a classification model after parameters optimization (“one example learning objective is to minimize the combined loss,” paragraph 0052 lines 1-2).
As to dependent claim 4, Luong further discloses a method comprising calculating the classification loss based on the cross entropy of the classification and prediction probability (“cross entropy loss,” paragraph 0073 line 10) and the true value of label of masked words (“For a given position t, the discriminator 12 predicts whether the token x.sub.t is ‘real,’ i.e., that it comes from the data distribution rather than the generator distribution (e.g., a noise distribution),” paragraph 0049 lines 6-9).
As to dependent claim 5, Luong further discloses a method comprising a first embedded vector extracted from the pre-trained language model and its corresponding true value of label form a new instance phrase, which is asynchronously updated to the knowledge base (“replacing, by the computing system, the one or more masked tokens in the original language input with the one or more replacement tokens to form a noised language input that includes a plurality of updated input tokens. For example, the plurality of updated input tokens can include a mixture of the one or more replacement tokens and the plurality of original input tokens that were not selected to serve as masked tokens,” paragraph 0006 lines 12-21).
As to dependent claim 6, Luong further discloses a method comprising:
a knowledge base construction and update unit, building a knowledge base for retrieval, where multiple instance phrases are stored (“Given an input x=[x1; x2; . . . ; xn],” paragraph 0050 line 2) in the form of key value pair, the key stores an embedding vector of the instance phrase, and the value stores a true value of label of the instance phrase (“For a given position t, the discriminator 12 predicts whether the token x.sub.t is ‘real,’ i.e., that it comes from the data distribution rather than the generator distribution (e.g., a noise distribution),” paragraph 0049 lines 6-9);
a classification model construction unit, constructing a classification model that comprises a pre-trained language model and a prediction and classification module (“the output of the trained language encoder model can be input into one or more neural network layers to perform a natural language processing task, such as classification, question answering or natural language generation. The one or more neural network layers can then output the result of the natural language task (e.g. a classification). A natural language model for the specific natural language task may be trained by fine-tuning the pre-trained language encoder model. The parameters of the pre-trained language encoder model can be input into the untrained natural language model (e.g. a classification model) at initialization,” paragraph 0060 lines 1-12);
a query and aggregation unit, using the pre-trained language model to extract a first embedding vector of the masked words in the input instance text, and using this first embedding vector as a first query vector, for each label class, querying multiple instance phrases closest to the first query vector from the knowledge base as a first neighboring instance phrase, using an aggregation result obtained by aggregating all the first neighboring instance phrases with the first query vector as an input data for the pre-trained language model (“The generator 14 can be trained to perform masked language modeling. Given an input x=[x1; x2; . . . ; xn], masked language modeling first selects a random set of positions (integers between 1 and n) to mask out m=[m1; . . . ; mk]. The tokens in the selected positions are replaced with a [MASK] token: which can be denoted as this as xmasked=REPLACE(x; m; [MASK]). The generator 14 can then learn to maximize the likelihood of the masked-out tokens. The discriminator 12 can be trained to distinguish tokens in the data from tokens sampled from the generator 14. More specifically, a ‘noised’ example xnoised 22 can be created by replacing the masked-out tokens 20a and 20b with generator samples. The discriminator 12 can then be trained to predict which tokens in xnoised 22 do not match the original input x 18,” paragraph 0050 lines 1-15);
a loss calculation unit, using the pre-trained language model to extract a second embedding vector of the masked words in the input data, and using the prediction and classification module to classify and predict the second embedding vector to obtain a classification and prediction probability, based on the classification and prediction probability and the true value of label of the masked words, calculating a classification loss (“For a given position t, the discriminator 12 predicts whether the token x.sub.t is ‘real,’ i.e., that it comes from the data distribution rather than the generator distribution (e.g., a noise distribution),” paragraph 0049 lines 6-9; “a loss function 26 that evaluates the plurality of predictions 24 produced by the machine-learned language encoder model 12,” paragraph 0039 lines 3-5);
a loss adjustment unit, constructing a weight factor based on the true value of label of the masking word, and adjusting the classification loss based on the weight factor to make the classification loss more focused on misclassified instances (“a second loss function 28 that evaluates a difference between the one or more replacement tokens 23a and 23b and the one or more tokens selected to serve as masked tokens,” paragraph 0041 lines 4-7);
a parameter optimization, optimizing parameters of the classification model by using the adjusted classification loss to obtain a classification model after parameters optimization (“one example learning objective is to minimize the combined loss,” paragraph 0052 lines 1-2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Adelani (“TOKEN is a MASK: Few-shot Named Entity Recognition with Pre-trained Language Models,” 15 June 2022, https://arxiv.org/abs/2206.07841) disclosing masking tokens to refine a classifier language model
Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ryan Barrett whose telephone number is 571 270 3311. The examiner can normally be reached 9:00am to 5:30pm.
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/Ryan Barrett/
Primary Examiner, Art Unit 2148
Prosecution Insights