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 .
Claims 1-11 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7/11/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement was considered and attached by the examiner.
Claim Rejections - 35 USC § 101
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 1-11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, 10, and 11 the limitations of “acquiring a learning document including text data written by a plurality of users”, “determining a first text included in the learning document based on a preset unit in which meaning of a text constituting the learning document is maintained”, “determining a predetermined number of second texts from the first text of the learning document based on a number of words included in the learning document written by each user”, “generating a first feature vector configured for each user based on a frequency count including each second text among learning documents written by each user”, “generating a second feature vector based on a frequency count including each style specific text among all the acquired learning documents for each class by using pre-stored information about a plurality of classes specifying an interior style and a style specific text mapped to each class”, “determining a similarity between the first feature vector and the second feature vector and labeling the first feature vector with a class of the second feature vector most similar to the first feature vector”, and “generating and training a machine learning-based neural network model that derives a correlation between the first feature vector and the class labeled for the first feature vector”, as drafted, are processes that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. More specifically, the mental process of a human reading a document, determining a first text based on maintaining the meaning in the mind, determining a second text based on number of words in the mind, writing the frequently used word on paper using a pen or pencil, writing style specific word that occur frequently from reading documents for each class, determining similarity of the words written on paper in the mind and labeling the first word with a class label, and adjusting a set of instructions or rules in the mind with the determined word and class label. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the --Mental Processes-- grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the recitation of an apparatus in claim 1 and 10, a computer readable recording medium in claims 11, reads to generalized computer components, based upon the claim interpretation wherein the structure is interpreted using P0079, P0088-P0072 in the specification. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract idea into a practical application, the additional element of using generalized computer components to read a document, determine a first text based on maintaining the meaning in the mind, determine a second text based on number of words in the mind, write the frequently used word on paper using a pen or pencil, write style specific word that occur frequently from reading documents for each class, determine similarity of the words written on paper in the mind and labeling the first word with a class label, and adjust a set of instructions or rules in the mind with the determined word and class label amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
With respect to claim 2, the claim recites “wherein the determining the first text includes”, “extracting a special character and an emoticon included in the learning document using a regular expression and removing a number and a repeated string included in the learning document”, “removing a postposition, an article, and a non-sentence included in the learning document based on a pre-stored stopword dictionary”, and “determining the first text by extracting a stem unit included in the learning document based on a predetermined morpheme analysis algorithm” which reads on a human determining the first text by making determinations and modifications of the read text in the mind. No additional limitations are present.
With respect to claim 3, the claim recites “wherein the determining the second text includes determining a number of words to be used for learning based on statistics of a number of words for each learning document written by each user, and determining the second text selected by the determined number of words from the first text included in each learning document written by each user”, which reads on a human determining the second text by determining a number of words in the mind. No additional limitations are present.
With respect to claim 4, the claim recites “wherein the determining the second text includes”, “when statistics are aggregated in an order of a largest number of words included in the learning document written by each user for all documents, determining a number of words at a point at which a top third quartile begins with a number of words included in the learning document from the statistics, as the predetermined number”, and “determining the second text corresponding to the predetermined number from the first text included in each learning document created by each user” which reads on a human determining second text by determining a number of words and statistics in the mind. No additional limitations are present.
With respect to claim 5, the claim recites “wherein the generating the first feature vector includes generating a first feature vector including as an element a value calculated according to [Equation Omitted]”, which reads on a human generating a word from a document using an equation that can be performed on paper using a pen or pencil. No additional limitations are present.
With respect to claim 6, the claim recites “wherein the generating the first feature vector includes: after the first feature vector is generated, when a special character or an emotion is used adjacent between words of a predetermined unit from the second text in a learning document in which the second text is used, updating an element value of the first feature vector by applying a preset weight to the element value calculated for the predetermined second text”, which reads on a human generating a word value according to special character or emotion in the mind. No additional limitations are present.
With respect to claim 7, the claim recites “wherein the generating the second feature vector includes generating a second feature vector including a value calculated according to [Equation Omitted]”, which reads on a human generating a word from a document using an equation that can be performed on paper using a pen or pencil. No additional limitations are present.
With respect to claim 8, the claim recites “wherein the labelling includes calculating a cosine similarity of the first feature vector and the second feature vector and labelling the first feature vector with a class of the second feature vector, a value of which is calculated for the first feature vector is closest to +1”, which reads on a human making calculations of similarity in word representations in the mind or on paper and labeling words with class label. No additional limitations are present.
With respect to claim 9, the claim recites “wherein the generating and training the neural network model includes setting the first feature vector to be input to an input layer of a neural network designed based on a predetermined convolutional neural network (CNN), setting the class labeled for each first feature vector to be input to an output layer, and training a weight of a neural network that derives a correlation between the first feature vector and the class labeled for the first feature vector”, which reads on a human setting rules or instructions to derive a correlation between a word and class label in the mind. No additional limitations are present.
These claims further do not remedy the judicial exception being integrated into a practical application and further fail to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Double Patenting
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.
Claims 1-5 and 7-11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 5-12 of a copending Application No. 18/350,363 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the issued patent anticipate the claims of the instant application. Please see below for the mapping table, where the bolded limitations indicate the corresponding limitations between the reference application and instant application. With respect to the dependent claims, each of the claims map to a corresponding dependent claim of the co-pending application or are found within the scope of the independent claim.
With respect to each of the dependent claims and independent claims, each claim corresponds numerically. Please see the mapping that follows: Instant Application (I) : Reference Application (R). Claim 1, 10, 11 : Claim 1, 9. Claim 2 : Claim 5. Claim 3 : Claim 6. Claim 4 : Claim 7. Claim 5 : Claim 8. Claim 7 : Claim 10. Claim 8 : Claim 11. Claim 9 : Claim 12.
Instant Application: 18350060
Co-pending Application: 18350363
Claim 1: A style analysis model providing apparatus comprising:
one or more memories configured to store instructions to perform a predetermined operation; and one or more processors operatively connected to the one or more memories and configured to execute the instructions, wherein the operation performed by the processor includes:
acquiring a learning document including text data written by a plurality of users;
determining a first text included in the learning document based on a preset unit in which meaning of a text constituting the learning document is maintained;
determining a predetermined number of second texts from the first text of the learning document based on a number of words included in the learning document written by each user;
generating a first feature vector configured for each user based on a frequency count including each second text among learning documents written by each user;
generating a second feature vector based on a frequency count including each style specific text among all the acquired learning documents for each class by using pre-stored information about a plurality of classes specifying an interior style and a style specific text mapped to each class;
determining a similarity between the first feature vector and the second feature vector and labeling the first feature vector with a class of the second feature vector most similar to the first feature vector; and
generating and training a machine learning-based neural network model that derives a correlation between the first feature vector and the class labeled for the first feature vector.
Claim 1: A style analysis model providing server comprising:
one or more memories configured to store instructions to perform a predetermined operation; andone or more processors operatively connected to the one or more memories and configured to execute the instructions, wherein the operation performed by the processor includes:
Claim 9: acquiring a learning document including text data written by a plurality of users;
determining a first text included in the document based on a preset unit in which meaning of a text constituting the learning document is maintained;
determining a predetermined number of second texts from the first text of the learning document based on a number of words included in the learning document written by each user;
generating a first feature vector configured for each user based on a frequency count including each second text among learning documents written by each user;
generating a second feature vector based on a frequency count including each style specific text among all the acquired learning documents for each class by using pre-stored information about a plurality of classes specifying an interior style and a style specific text mapped to each class;
determining a similarity between the first feature vector and the second feature vector and labeling the first feature vector with a class of the second feature vector most similar to the first feature vector; and
generating and training a machine learning-based neural network model that derives a correlation between the first feature vector and the class labeled for the first feature vector.
The apparatus claims 1 and 10 of the instant application is rejected over the server claim 1 of the co-pending application using the same rationale as that provided in the table above for the server claims.
Regarding the differences between claim 1 and 10 of the instant application and the server claim 1 of the co-pending application, it would have been obvious to one of ordinary skill in the art that the server limitation of the co-pending application could be applied to a apparatus as presented in the instant application.
The computer readable recording medium claim 11 of the instant application is rejected over the server claim 1 of the co-pending application using the same rationale as that provided in the table above for the server claims.
Regarding the differences between claim 11 of the instant application and the server claim 1 of the co-pending application, it would have been obvious to one of ordinary skill in the art that the server limitation of the co-pending application could be applied to a computer readable recording medium as presented in the instant application.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL WONSUK CHUNG whose telephone number is (571)272-1345. The examiner can normally be reached Monday - Friday (7am-4pm)[PT].
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/DANIEL W CHUNG/Examiner, Art Unit 2659
/PIERRE LOUIS DESIR/Supervisory Patent Examiner, Art Unit 2659