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 .
This office action is in response to communication filed on 6/30/2026. Claim 2 has been canceled. Claims 10-21 have been added. Claims 1 and 3-21 are pending on this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 9/10/2026 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 § 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) 1, 4, 8-10, and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) in view of Galle et al (US20230109734).
Regarding claim 1, Altevogt teaches an information processing apparatus (fig. 1) comprising:
processing circuitry (11 in fig. 1) configured to:
divide a set of documents, collected in advance, into vocabulary units (fig. 3; para. [0022], The model training process uses model parameters and performs an analysis of the set of training documents for the building of grammar statistics; para. [0023]);
generate a reference distribution indicating a relation between an appearance frequency of each vocabulary unit included in the set of documents and a rank of the appearance frequency in the set of documents (fig. 4; para. [0022], The model training process uses model parameters and performs an analysis of the set of training documents for the building of grammar statistics in step 23. For example, a statistical analysis of word frequencies may be needed to check the conformance to Zipf's Law and Heap's law against desired values; 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);
divide text data into vocabulary units (para. [0023], In step 31 a parsing of the document takes place. Step 32 extracts the structure elements and steps 33 and 34 perform training the combined language and structure models used where step 33 performs the text model training and step 34 performs the training of the structure model);
generate a target distribution indicating a relation between an appearance frequency of each vocabulary unit included in the text data and a rank of the appearance frequency in the text data (para. [0024], The process starts with step 40 which obtains all n-gram probabilities of a set of training documents collected in step 20. In step 42 the quality of the generated documents is checked. 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); and
calculate an evaluation value relating to the text data, the evaluation value being based on a difference between the reference distribution and the target distribution (para. [0008], For securing the quality of the new documents it is determined if the deviation of the word frequency as a function of the word rank from Zipf's law and the deviation of the growth of the vocabulary as a function of the number of terms from the Heap's law are below user defined thresholds. Only those new documents are accepted which fulfil these conditions; para. [0024]).
Altevogt fails to teach receiving text data from a terminal apparatus communicable with the information processing apparatus via a network;
calculating an evaluation value relating to an unnaturalness of the text data.
However Galle teaches receiving text data from a terminal apparatus (para. [0032], Referring to FIG. 1, an example hardware platform for implementing a method of detecting machine-generated documents in a collection of documents) communicable with a information processing apparatus via a network (para. [0033]); and
calculating an evaluation value (para. [0047], Accordingly, classified documents having scores at or above a higher threshold (≥ 80%) are selected for the new dataset at 414 as being positive examples of machine-generated documents. Also, classified documents having scores at or below a lower threshold (≤ 20%) are selected for the new dataset at 414 as being negative examples of machine-generated documents. In turn the new dataset is fed to the classifiers, via 408, for retraining) relating to an unnaturalness of the text data (para. [0035], [0041]).
Therefore taking the combined teachings of Altevogt and Galle 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 Galle into the apparatus of Altevogt. The motivation to combine Galle and Altevogt would be to improve the precision of a plurality of classifiers (para. [0017] of Galle).
Regarding claim 4, the modified apparatus of Altevogt teaches an information processing apparatus wherein the text data is a set of text data (para. [0041] of Galle), and
the processing circuitry is configured to acquire the set of text data (para. [0042], [0049] of Galle), and
calculate the evaluation value for each of the text data included in the set of text data (para. [0045] of Galle).
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.
Regarding claim 10, the modified apparatus of Altevogt teaches an information processing apparatus wherein each of the vocabulary units is a word or a unit equivalent to a word (para. [0024] of Altevogt; para. [0004] of Galle).
Regarding claim 15, the modified apparatus of Altevogt teaches an information processing apparatus wherein the set of documents includes data collected from a website (para. [0041] of Altevogt).
Claim(s) 3 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Wyard et al (US6167398).
Regarding claim 3, the modified apparatus of Altevogt 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. [0008], [0024] of Altevogt).
The modified apparatus of Altevogt fails to teach normalizing each of the target distribution and the reference distribution, and
calculating 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 modified 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 modified Altevogt. He motivation to combine Wyard and modified Altevogt would be to improve a language model used in a speech application (col. 1 lines 64-67 of Wyard).
Regarding claim 21, the claim recites similar subject matter as claim 3 and is rejected for the same reasons as stated above.
Claim(s) 5-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Huang et al (US20230096821).
Regarding claim 5, the modified apparatus of Altevogt 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 modified Altevogt 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 modified Altevogt. The motivation to combine modified Altevogt and Huang would be to improve an automatic speech recognition model's recognition quality of rare words (para. [0025] of Huang).
Regarding claim 6, the modified apparatus of Altevogt 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 modified Altevogt 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 modified Altevogt. The motivation to combine modified Altevogt and Huang would be to improve an automatic speech recognition model's recognition quality of rare words (para. [0025] of Huang).
Regarding claim 7, the modified apparatus of Altevogt 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 modified Altevogt 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 modified Altevogt. The motivation to combine modified Altevogt and Huang would be to improve an automatic speech recognition model's recognition quality of rare words (para. [0025] of Huang).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Roy et al (US20220171925).
Regarding claim 11, the modified apparatus of Altevogt fails to teach an information processing apparatus wherein the reference distribution and the target distribution are each expressed using a logarithm of the appearance frequency and a logarithm of the rank of the appearance frequency.
However Roy teaches wherein a reference distribution and a target distribution are each expressed using a logarithm of an appearance frequency and a logarithm of a rank of the appearance frequency (figs. 6A-6D; para. [0029]-[0030]).
Therefore taking the combined teachings of modified Altevogt and Roy 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 Roy into the apparatus of modified Altevogt. The motivation to combine modified Altevogt and Roy would be to minimize standard error (para. [0030] of Roy).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Roy et al (US20220171925).
Regarding claim 12, the modified apparatus of Altevogt fails to teach an information processing apparatus wherein the processing circuitry is configured to generate the reference distribution by arranging the appearance frequency of each vocabulary included in the set of documents in descending order.
However Rickard teaches arranging the appearance frequency of each vocabulary included in a set of documents in descending order (para. [0061]).
Therefore taking the combined teachings of modified Altevogt and Rickard 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 Rickard into the apparatus of modified Altevogt. The motivation to combine modified Altevogt and Rickard would be to enable a user to search for non-textual data at a relatively higher contextual level having more practical significance or meaning (para. [0006] of Rickard).
Claim(s) 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530), Galle et al (US20230109734) and Wyard et al (US6167398) in view of Gupta et al (US20170031883).
Regarding claim 13, the modified apparatus of Altevogt fails to teach an information processing apparatus, wherein the difference is a sum of absolute values of differences in the appearance frequency for each rank.
However Gupta teaches wherein a difference is a sum of absolute values of differences in the appearance frequency for each rank (para. [0092]-[0093]).
Therefore taking the combined teachings of modified Altevogt with Gupta 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 Gupta into the apparatus of modified Altevogt. The motivation to combine Gupta and modified Altevogt would be to produce an accurate content genre identifier (para. [0089] of Gupta).
Regarding claim 14, the modified apparatus of Altevogt fails to teach information processing apparatus wherein the difference is a sum of squares of differences in the appearance frequency for each rank.
However Gupta teaches wherein a difference is a sum of squares of differences in the appearance frequency for each rank (para. [0094]).
Therefore taking the combined teachings of modified Altevogt with Gupta 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 Gupta into the apparatus of modified Altevogt. The motivation to combine Gupta and modified Altevogt would be to produce an accurate content genre identifier (para. [0089] of Gupta).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Semenov (US20230138491).
Regarding claim 16, the modified apparatus of Altevogt fails to teach an information processing apparatus wherein the set of documents includes data acquired from a printed document by optical character recognition.
However Semenov teaches wherein a set of documents includes data acquired from a printed document by optical character recognition (para. [0032], [0080]).
Therefore taking the combined teachings of modified Altevogt with Semenov 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 Semenov into the apparatus of modified Altevogt. The motivation to combine Semenov and modified Altevogt would be to improve the quality of detection results (para. [0056] of Semenov).
Claim(s) 17-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Kilari et al (US11450225).
Regarding claim 17, the modified apparatus of Altevogt fails to teach an information processing apparatus wherein the processing circuitry is configured to transmit, to the terminal apparatus, screen data for displaying a result screen indicating an evaluation result of the text data.
However Kilari teaches transmitting, to a terminal apparatus, screen data for displaying a result screen indicating an evaluation result of the text data (216 in fig. 2; col. 15 lines 37-44).
Therefore taking the combined teachings of modified Altevogt with Kilari 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 Kilari into the apparatus of modified Altevogt. The motivation to combine Kilari and modified Altevogt would be to provide feedback for answers in real-time in a cost-effective way (col. 3 lines 35-42 of Kilari).
Regarding claim 18, the modified apparatus of Altevogt teaches an information processing apparatus wherein the result screen (col. 15 lines 37-44 of Kilari) indicates an unnaturalness score acquired by classifying the unnaturalness of the text data into a plurality of levels (para. [0047] of Galle).
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Altevogt et al (US20060155530) and Galle et al (US20230109734) in view of Hashimoto et al (US20190074004).
Regarding claim 20, the modified apparatus of Altevogt fails to teach an information processing apparatus wherein the processing circuitry is configured to divide the set of text data into a plurality of subsets by comparing the evaluation value with an upper limit value and a lower limit value determined for each of the plurality of subsets.
However Hashimoto teaches dividing a set of text data into a plurality of subsets by comparing an evaluation value with an upper limit value and a lower limit value determined for each of the plurality of subsets (para. [0049]-[0050], [0076]).
Therefore taking the combined teachings of modified Altevogt with Hashimoto 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 Hashimoto into the apparatus of modified Altevogt. The motivation to combine Hashimoto and modified Altevogt would be to more accurately determine whether the conversation is natural or unnatural (para. [0072] of Hashimoto).
Allowable Subject Matter
Claim 19 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LEON VIET Q NGUYEN/ Primary Examiner, Art Unit 2663
