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 .
Information Disclosure Statement
The filed information disclosure statement (IDS) is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Is the claimed invention to a process, machine, manufacture or composition of matter?
The claimed invention, at independent claims 1, 6, and 11, is directed to a method (process), system (machine), and computer readable medium (manufacture) for in training a word splitter with a training data set including a plurality of training data pieces each associating a letter string data piece and a class data piece representing one of a plurality of classes that the letter string data piece pertains with each other, the word splitter outputting split letter string data including a plurality of letter strings obtained by splitting an inputted letter string data piece, the split letter string data serving as input data to be inputted into a machine- learning model that performs an inference process, training the word splitter based on biasedness of occurrence frequency in the plurality of classes for each ofa first plurality of letter strings included in a plurality of pieces of the split letter string data, the plurality of pieces of the split letter string data being obtained by inputting the letter string data piece into the word splitter and by splitting the letter string data piece in respective different splitting patterns corresponding to the letter string data piece..
Step 2A, prong 1: Does the claim recite an abstract idea, law or nature, or natural phenomenon?
Under the 35 U.S.C. 101 new guidelines, the broadest reasonable interpretation of the claims, the claimed steps fall within the “Mental Processes” grouping of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III.
The steps of collecting training data set including a plurality of training data pieces each associating a letter string data piece and a class data piece representing one of a plurality of classes that the letter string data piece pertains with each other, may be practically performed in the human mind using observation, evaluation, judgment, and opinion. For example, a human can collect training data with criteria as claimed without using a machine.
As to the step of training a word splitter, it encompasses mental processes practically performed in the human mind by observation, evaluation, judgment, and opinion. The plain meaning of training a model is gathering raw data and assigning meaningful labels or annotations to it. The claims do not provide any details about how the claimed machine learning model operates or how the training is made. See MPEP 2106.04(a)(2), subsection III.
Therefore, the claimed steps fall within the mental process grouping of abstract ideas
Step 2A, prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
The claim recites the additional elements of “a processor”, “machine learning”, are mere data gathering and manipulating recited at high level of generality and thus are insignificant extra-solution activity. The processor is recited at a high level of generality, and it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). The recitation of using “a machine learning” is at high level of generality. The mere nominal recitation of a generic network appliance does not take the claims limitations out of the mental processes grouping. Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application, and the claims are directed to the judicial exception.
Step 2B: Does the claim recite additional elements that amount to significantly more than the abstract idea?
As to whether the claims as a whole amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim (Step 2B), as explained above in Step 2A, Prong 2, the use of “machine learning model”, “processor” is at high level of generality, and even when considered in combination, these additional elements represent mere instructions to apply an exception and insignificant extra-solution activity, and therefore do not provide an inventive concept. Accordingly, the claims are ineligible.
Dependent claims 2-5, 7-10, and 12-13 further refer and describe the process of adjusting the score of each of the first plurality of letter strings, which encompasses a mental process that is practically performed in the human mind, as explained above in Step 2A, Prong 1. Accordingly, claims 1-13 are directed to an abstract idea, and are not patent eligible.
Claim Rejections - 35 USC § 103
4. 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 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 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Miura (US 2022/0180198) in view of Chen (US 20030093263).
As per claim 1, Miura teaches a non-transitory computer-readable recording medium having stored therein an information processing program ([0125]) that causes a computer to execute a process comprising:
in training a word splitter with a training data set including a plurality of training data pieces each associating a letter string data piece and a class data piece representing one of a plurality of classes that the letter string data piece pertains with each other (Fig. 4, [0039], training data to which a named entity tag (beginning-inside-outside (BIO) tag) is attached. [0047]- [0048], wherein training data is classified into different classes) the word splitter outputting split letter string data including a plurality of letter strings obtained by splitting an inputted letter string data piece , the split letter string data serving as input data to be inputted into a machine- learning model that performs an inference process ([0057], the pre-training unit 31 divides the text data into each word and inputs each word into the input layer. [0065], the pre-training unit 31 divides the paragraph text with noise into words. [0076], the prediction unit 40 divides text data which is the prediction data into words, inputs the words into the input layer of the multi-task learning model,…),
training the word splitter based on biasedness of occurrence frequency in the plurality of classes for each of a first plurality of letter strings included in a plurality of pieces of -the split letter string data, -the plurality of pieces of the split letter string data being obtained by inputting the letter string data piece into the word splitter and by splitting the letter string data piece in respective different splitting patterns corresponding to the letter string data piece (Fig. 11 and [0078]- [0082], training the word splitter in the plurality of classes) . Miura may not explicitly disclose training the word splitter based on biasedness of occurrence frequency. However, Chen in the same field of endeavor teaches training the word splitter based on biasedness of occurrence frequency ([0034]). Therefore, it would have been obvious at the time the application was filed to use the above feature of Chen with the system of Miura, in order to train the claimed splitter based on biasedness of occurrence frequency. This would enhance natural language understanding while addressing frequency effects and computational efficiency.
As per claim 2, Miura teaches wherein the training comprises adjusting, based on the biasedness of the occurrence frequency in the plurality of classes for each of the first plurality of letter strings included in the plurality of pieces of split letter string data, a score of each of the first plurality of letter strings, the score pertaining to parameters of the word splitter ([0065], updating parameter (such as weights)s of the pre-trained model including the shared model (input layer and intermediate layer. [0074], updating parameters of the named entity extraction model including the shared model (input layer and intermediate layer). [0079], the training unit 30 executes update of parameters of the pre-trained model on the basis of the result of the restoration prediction. [0081], the training unit 30 executes update of parameters of the named entity extraction model on the basis of the result of the tagging prediction). Miura may not explicitly disclose training the word splitter and adjusting scores of the words based on biasedness of occurrence frequency. However, Chen in the same field of endeavor teaches training the word splitter based on biasedness of occurrence frequency ([0034]) and adjusting words probabilities ([0047]). Therefore, it would have been obvious at the time the application was filed to use the above features of Chen with the system of Miura, in order to train the claimed splitter and adjust scores of the words based on biasedness of occurrence frequency. This would enhance natural language understanding while addressing frequency effects and computational efficiency.
As per claim 3, Miura teaches wherein the adjusting comprises adjusting the score of each of the first plurality of letter strings such that a score of a letter string having a larger biasedness of the occurrence frequency in the plurality of classes comes to be higher among the first plurality of letter strings included in the plurality of pieces of split letter string data, and the word splitter outputs, based on the parameters, split letter string data maximizing a total sum of the scores of the plurality of letter strings for each of the plurality of pieces of letter string data inputted into the word splitter ([0101], the training device 10 acquires, as the prediction result, probabilities (likelihoods or probability scores) corresponding to a plurality of labels assumed in advance. Then, the training device 10 executes training by error back propagation so that a probability of the correct answer label is the highest among the plurality of labels assumed in advance. See also Chen [0047], [0049]).
As per claim 4, Miura teaches wherein the adjusting is performed on the parameters of the word splitter after being trained ([0065], [0074], [0074], [0079], [0100], updating parameters after training).
As per claim 5, Miura teaches wherein the adjusting is performed on the parameters when a machine learning process for the word splitter is being performed ([0121], [0065], updating parameter (such as weights) of the pre-trained model including the shared model (input layer and intermediate layer. [0074], updating parameters of the named entity extraction model including the shared model (input layer and intermediate layer).
As per claim 6-10, method claim 6-10 and apparatus claims 1-5 are related as method and apparatus of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 6-10 are similarly rejected under the same rationale as applied above with respect to apparatus claims 1-5.
As per claims 11-13, system claims 11-13 and method claims 6-10 are related as apparatus and the method of using same, with each claimed element's function corresponding to the claimed method step. Accordingly claims 11-13 are similarly rejected under the same rationale as applied above with respect to method claims 6-10. Furthermore, Miura teaches one or more processors; and memory storing thereon instructions, as claimed ([0125]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDELALI SERROU whose telephone number is (571)272-7638. The examiner can normally be reached M-F 9 Am - 5 PM.
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, Pierre-Louis Desir can be reached at 571-272-7799. 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.
/ABDELALI SERROU/Primary Examiner, Art Unit 2659
Prosecution Insights