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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 2/24/2025 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 § 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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 recites the limitation "each lexicon". There is insufficient antecedent basis for this limitation in the claim.
Claim 3 is rejected for being dependent on rejected claim 2.
Claim Rejections - 35 USC § 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.
Claim(s) 1, 4, 8, and 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chhaya et al (US20200004820).
Regarding claim 1, Chhaya discloses an information processing apparatus comprising:
processing circuitry (fig. 4a) configured to:
acquire a reference distribution indicating a tendency of language information, the language information being extracted from text data (para. [0015], According to one embodiment of the present disclosure, a content variation generation system is disclosed that creates content to suit the mood and/or affect preferences of a target audience, more generally referred to herein as psycholinguistic preferences. In some such cases, affect distribution in historic content/data is analyzed to capture these psycholinguistic preferences; para. [0017], This may be achieved, for example, by analyzing historic content aimed at a particular audience segment and identifying optimal language distributions; para. [0020], historic affect distributions refer to probability distributions generated from historic content);
generate a target distribution related to the language information (para. [0018], an ideal affect distribution for a given target audience is determined; para. [0024]); and
calculate an evaluation value indicating a difference between the reference distribution and the target distribution (para. [0111], The optimization step is to minimize the distance between the affect of the input text and the affect of the target; para. [0113], In particular, the best possible word transformation is selected that reduces the distance between the current content affect and the target affect).
Regarding claim 4, Chhaya discloses an information processing apparatus wherein the text data is a set of text data (para. [0023], historic data 132, further comprising user interaction data 180 and messages 182 are received and processed to generate audience affect model map 186. User interaction data 180 represents historic data characterizing interaction between users in exchanging documents such as text messages, e-mails, etc),
wherein the processing circuitry is configured to acquire the set of text data (para. [0023], historic data 132, further comprising user interaction data 180 and messages 182 are received), and
wherein the evaluation value calculation unit is configured to calculate the evaluation value for each of the text data included in the set of text data (para. [0111], The optimization step is to minimize the distance between the affect of the input text and the affect of the target; para. [0113], In particular, the best possible word transformation is selected that reduces the distance between the current content affect and the target affect).
Regarding claim 8, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Regarding claim 9, the claim recites similar subject matter as claim 1 and is rejected for the same reasons as stated above.
Claim Rejections - 35 USC § 103
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.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chhaya et al (US20200004820) in view of Altevogt et al (US20060155530).
Regarding claim 2, Chhaya fails to teach an information processing apparatus wherein the language information indicates a relation between an appearance frequency of each lexicon and a rank of the appearance frequency of each lexicon (para. [0024], For this purpose it is determined whether the deviations of word frequency as a function of the word rank (Zipf's law) and the growth of the vocabulary as a function of the number of terms (Heap's law) are below user defined thresholds).
Therefore taking the combined teachings of Chhaya and Altevogt as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Altevogt into the apparatus of Chhaya. He motivation to combine Chhaya and Altevogt would be to model and analyze text documents and for generating large amounts of new documents having the essential properties of natural text document collections (para. [0007] of Altevogt).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chhaya et al (US20200004820) and Altevogt et al (US20060155530) in view of Wyard et al (US6167398).
Regarding claim 3, the modified apparatus of Chhaya teaches an information processing apparatus wherein the processing circuitry is configured to calculate a difference between the target distribution and the reference distribution as the evaluation value (para. [0111], [0113] of Chhaya).
The modified apparatus of Chhaya fails to teach wherein the processing circuitry is configured to normalize each of the target distribution and the reference distribution, and
wherein the processing circuitry is configured to calculate a difference between the normalized target distribution and the normalized reference distribution as the evaluation value.
However Wyard teaches normalizing each of a target distribution and a reference distribution (col. 8 lines 47-51), and
calculating a difference between the normalized target distribution and the normalized reference distribution as the evaluation value (col. 4 lines 26-30; col. 8 lines 51-55).
Therefore taking the combined teachings of Chhaya and Altevogt with Wyard as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Wyard into the apparatus of Chhaya and Altevogt. He motivation to combine Chhaya, Wyard and Altevogt would be to improve a language model used in a speech application (col. 1 lines 64-67 of Wyard).
Claim(s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chhaya et al (US20200004820) in view of Huang et al (US20230096821).
Regarding claim 5, Chhaya fails to teach an information processing apparatus wherein the processing circuitry is configured to generate a distribution related to the language information extracted from the set of text data as the reference distribution.
However Huang teaches generating a distribution related to the language information extracted from the set of text data as the reference distribution (para. [0064]-[0065]; equation 4).
Therefore taking the combined teachings of Chhaya and Huang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Huang into the apparatus of Chhaya. He motivation to combine Chhaya and Huang would be to improve an automatic speech recognition model's recognition quality of rare words (para. [0025] of Huang).
Regarding claim 6, Chhaya fails to teach an information processing apparatus wherein the processing circuitry is configured to delete the text data having an evaluation value higher than a predetermined value from the set of text data.
However Huang teaches deleting text data having an evaluation value higher than a predetermined value from the set of text data (equation 7; para. [0067], This contrastive selection is calculated for each training text sample in the set of low frequency training text samples. The contrastive filter 440 then may discard a training text sample 422 that is above a threshold).
Therefore taking the combined teachings of Chhaya and Huang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Huang into the apparatus of Chhaya. He motivation to combine Chhaya and Huang would be to improve an automatic speech recognition model's recognition quality of rare words (para. [0025] of Huang).
Regarding claim 7, Chhaya fails to teach an information processing apparatus wherein the processing circuitry is configured to divide the set of text data based on the evaluation value.
However Huang teaches dividing a set of text data based on an evaluation value (equation 7; para. [0067], The contrastive filter 440 then may discard a training text sample 422 that is above a threshold, to identify the subset of target domain training texts 442 from the set of low frequency training text samples 422 that are below the threshold).
Therefore taking the combined teachings of Chhaya and Huang as a whole, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to incorporate the features of Huang into the apparatus of Chhaya. He motivation to combine Chhaya and Huang would be to improve an automatic speech recognition model's recognition quality of rare words (para. [0025] of Huang).
Related Art
Nam et al (US20220147823) – see figs. 3-4
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663
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