DETAILED ACTION
Claims 1-20 are pending in this 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 .
Claim Rejections - 35 USC § 103
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 8-9, 11, 13-14 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lu et al. (US PGPUB No. 2025/0141857) [hereinafter “Lu”] in view of Vogel et al. (WO-2014039897-A1) [hereinafter “Vogel”] in further view of Todhunter et al. (US PGPUB No. 2006/0041424) [hereinafter “Todhunter”].
As per claim 1, Lu teaches a method, comprising: determining, by the computing device, a set of writing patterns of at least one data communication (Abstract and [0008], analyzing submitted material using various learned patterns); determining, by the computing device, a style on the set of writing patterns of the at least one data communication ([0040], classifiers are trained on various things including style of writing which are used to analyze the submitted material); providing, by the computing device, a writing profile model by training on the style and the set of writing patterns ([0046], training classifiers on submitted material and sentiment analysis with models on human and AI generation); comparing, by the computing device, a new data communication to the trained writing profile model (Abstract, comparing a second submission in a verification response); and flagging, by the computing device, the new data communication based on the comparison being over a threshold level ([0043], flagging the submitted material if there a significant difference in risk scores of the two submissions).
Lu does not explicitly teach determining a narrative style of at least one data communication. Vogel teaches a narrative style of at least one data communication (Page 2, lines 12-20, extracting narrative attributes from a writing document).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu with the teachings of Vogel, a narrative style of at least one data communication, to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
The combination of Lu and Vogel does not explicitly teach wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a cause and effect style using natural language processing (NLP). Todhunter teaches wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a cause and effect style using natural language processing (NLP) (Abstract, using linguistic models to recognize writing patterns in the form of cause-and-effect in semantic analysis of natural language).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu and Vogel with the teachings of Todhunter, wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a cause-and-effect style using natural language processing (NLP), to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
As per claim 8, the combination of Lu, Vogel and Todhunter teaches the method of claim 1, wherein the writing profile model comprises an artificial intelligence (AI) model which is further trained using historical data of the narrative style and historical data of the set of writing patterns (Lu; [0008], using as feedback data other AI-generated content and past human-authored pieces).
As per claim 9, the combination of Lu, Vogel and Todhunter teaches the method of claim 1, wherein the writing profile model comprises a machine learning (ML) model which is further trained using historical data of the narrative style and historical data of the set of writing patterns See id. (Examiner Note: reinforced learning is a type of machine learning which is also considered a subset of AI learning – Examiner suggests further details to explicitly distinguish this claim).
As per claim 11, the combination of Lu, Vogel and Todhunter teaches the method of claim 1, wherein flagging the new data communication based on the comparison over the threshold level comprises: determining a plurality of deltas based on the comparison of a plurality of writing profiles in the trained writing profile model to the new data communication (Lu; Abstract, comparing differences, i.e. deltas, between scores of two or more materials – score is some measured characteristic of a particular writing sample); and determining whether a set of the plurality of deltas is over the threshold level (Lu; [0043], determining whether the risk scores between the two materials are significantly different).
As per claim 13, the combination of Lu, Vogel and Todhunter teaches the method of claim 12, wherein flagging the new data communication based on the comparison over the threshold level further comprises: flagging the new data communication as a potential scam (Lu; Abstract, flagging the material as potentially AI generated and not human generated).
As per claim 14, Lu teaches a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: determine a set of writing patterns of at least one data communication (Abstract and [0008], analyzing submitted material using various learned patterns); determine a narrative writing style on the set of writing patterns of the at least one data communication; provide a writing profile model by training on the narrative writing style and the set of writing patterns ([0040], classifiers are trained on various things including style of writing which are used to analyze the submitted material); compare a new data communication to the trained writing profile model (Abstract, comparing a second submission in a verification response); and flag the new data communication based on the comparison being over a threshold level ([0043], flagging the submitted material if there a significant difference in risk scores of the two submissions),
Lu does not explicitly teach determine a narrative style of at least one data communication. Vogel teaches determine a narrative style of at least one data communication (Page 2, lines 12-20, extracting narrative attributes from a writing document).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu with the teachings of Vogel, determine a narrative style of at least one data communication, to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
The combination of Lu and Vogel does not explicitly teach wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a cause and effect style using natural language processing (NLP). Todhunter teaches wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a cause and effect style using natural language processing (NLP) (Abstract, using linguistic models to recognize writing patterns in the form of cause-and-effect in semantic analysis of natural language).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu and Vogel with the teachings of Todhunter, wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a cause-and-effect style using natural language processing (NLP), to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
As per claim 17, the substance of the claimed invention is identical or substantially similar to that of claim 8. Accordingly, this claim is rejected under the same rationale.
As per claim 18, the substance of the claimed invention is identical or substantially similar to that of claim 9. Accordingly, this claim is rejected under the same rationale.
Claims 2, 3 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel and Todhunter in further view of Al Badrashiny et al. (US Patent No. 8,577,898) [hereinafter “Al Badrashiny”].
As per claim 2, the combination of Lu, Vogel and Todhunter teaches the method of claim 1, further comprising creating a writing profile based on the narrative style, the set of writing patterns, and the trained writing profile model of the at least one data communication (Vogel; Page 12 lines 10-15, creating a isotone profile based on narrative dependencies in a collection of documents, semiotic patterns see Page 12 lines 23-25 and tone and style of individual documents see Page 11 lines 20-25) the determining the set of writing patterns of at least one data communication comprises images (Lu; [0034], determining generative style of images for comparison).
The combination of Lu, Vogel and Todhunter does not explicitly teach wherein the determining the set of writing patterns of at least one data communication comprises detecting spelling mistakes, different and mixed languages, images, a writing style, grammar, and a format using the NLP. Al Badrashiny teaches wherein the determining the set of writing patterns of at least one data communication comprises detecting spelling mistakes (Col. 1 lines 64-66, rating a text document on spelling accuracy), different and mixed languages (Abstract, evaluating writing style for many different languages), a writing style (Abstract, determining style score of text document), grammar (Abstract, determining punctuation score of text document), and a format using the NLP (Col. 9, lines 32-40, evaluating structure and organization through use of punctuation and symbols see also Col. 14 lines 13-15, analysis done using NLP).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel and Todhunter with the teachings of Al Badrashiny, wherein the determining the set of writing patterns of at least one data communication comprises detecting spelling mistakes, different and mixed languages, images, a writing style, grammar, and a format using the NLP, to provide a full comprehensive analysis regarding writing similarities which provides the most accurate determination regarding authorship and source.
As per claim 3, the combination of Lu, Vogel and Todhunter teaches the method of claim 2, further comprising storing the writing profile in a writing profile database (Vogel; Page 11 lines 20-25, tone and style of individual documents and other metrics are stored in databases see Page 8 lines 20-25).
As per claim 19, the substance of the claimed invention is identical or substantially similar to that of claim 3. Accordingly, this claim is rejected under the same rationale.
Claims 4 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel and Todhunter in further view of Lakshmanan et al. (US PGPUB No. 2022/0004935) [hereinafter “Lakshmanan”] in further view of Opos (US Patent No. 12,101,352).
As per claim 4, the combination of Lu, Vogel and Todhunter teaches the method of claim 1.
The combination of Lu, Vogel and Todhunter does not explicitly teach clustering the set of writing patterns using at least one of a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm and an affinity propagation clustering algorithm. Lakshmanan teaches clustering the set of writing patterns using at least one of a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm and an affinity propagation clustering algorithm ([0102], clustering deep features, like writing patterns taught in Lu and Vogel, using algorithms like BIRCH and affinity propagation).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel and Todhunter with the teachings of Lakshmanan, clustering the set of writing patterns using at least one of a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm and an affinity propagation clustering algorithm, to cluster and reduce the patterns in Lu and Vogel using various well-known algorithms like BIRCH and affinity clustering.
The combination of Lu, Vogel, Todhunter and Lakshmanan does not explicitly teach ranking the set of writing patterns based on a number of bold occurrences, a number of underline occurrences, a number of custom font occurrences, a number of misspelled words, and a number of rich text occurrences. Opos teaches ranking the set of writing patterns based on a number (Col. 8, lines 50-60, comparing text to a plurality of patterns in a database based on the number of similarities with each pattern) of bold occurrences, a number of underline occurrences, a number of custom font occurrences, a number of misspelled words, and a number of rich text occurrences (Col. 7, lines 32-50, similarities including bolding, underlining, font style, mis-spelled words and symbolic texts, i.e. rich text).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel, Todhunter and Lakshmanan with the teachings of Opos, ranking the set of writing patterns based on a number of bold occurrences, a number of underline occurrences, a number of custom font occurrences, a number of misspelled words, and a number of rich text occurrences, to provide a full comprehensive analysis regarding writing similarities which provides the most accurate determination regarding authorship and source.
As per claim 16, the substance of the claimed invention is identical or substantially similar to that of claim 4. Accordingly, this claim is rejected under the same rationale.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel, Todhunter and Lankshmanan in further view of Grushka et al. (US PGPUB No. 2024/0143666) [hereinafter “Grushka”].
As per claim 5, the combination of Lu, Vogel, Todhunter and Lankshmanan teaches the method of claim 4.
The combination of Lu, Vogel, Todhunter and Lankshmanan does not explicitly teach ranking and rating the clustered writing patterns. Grushka teaches ranking and rating the clustered writing patterns ([0055], scoring and ranking metrics, i.e. writing patterns taught by Lu and Vogel).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel, Todhunter and Lankshmanan with the teachings of Grushka, ranking and rating the clustered writing patterns, to find groups/clusters of more anomalous writing patterns, as taught in Lu and Vogel, to flag as potential issues.
As per claim 6, the combination of Lu, Vogel, Todhunter, Lakshmanan and Grushka teaches the method of claim 5, wherein the training is provided on the ranked and rated clustered writing patterns (Lankshamanan; Abstract, after ranking and rating clusters using the system in Grushka, the clusters would be fed into the deep feature learning system taught in Lankshamanan for model training).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel, Todhunter, Lankshmanan and Grushka in further view of Gelfand et al. (US PGPUB No. 2016/0357718) [hereinafter “Gelfand”].
As per claim 7, the combination of Lu, Vogel, Lakshmanan, Todhunter and Grushka teaches the method of claim 6.
The combination of Lu, Vogel, Todhunter, Lakshmanan and Grushka does not explicitly teach wherein the new data communication comprises an email message. Gelfand teaches wherein the new data communication comprises an email message ([0084], learning using an email how a user writes an email including style and patterns).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel, Todhunter, Lakshmanan and Grushka with the teachings of Gelfand, wherein the new data communication comprises an email message, to use a common and easy to transmit writing sample for verification and comparison of authorship and human/AI generated attributes.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel and Todhunter in further view of Gelfand in further view of Hao et al. (US Patent No. 11,080,486) [hereinafter “Hao”]
As per claim 10, the combination of Lu, Vogel and Todhunter teaches the method of claim 6.
The combination of Lu, Vogel and Todhunter does not explicitly teach wherein the new data communication comprises an email message. Gelfand teaches wherein the at least one data communication comprises an email message ([0084], learning using an email how a user writes an email including style and patterns).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel and Todhunter with the teachings of Gelfand, wherein the new data communication comprises an email message, to use a common and easy to transmit writing sample for verification and comparison of authorship and human/AI generated attributes.
The combination of Lu, Vogel, Todhunter and Gelfand does not explicitly teach wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a compare and contrast style using natural language processing (NLP). Hao teaches wherein the determining the narrative writing style o n the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a compare and contrast style (Col. 7, lines 25-40, determining a compare and contrast style in an input text document by analyzing its structure) using natural language processing (NLP) (Col. 7, lines 45-57. based on processing the terms and semantics of the terms, i.e. natural language).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel, Todhunter and Gelfand with the teachings of Hao, wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a compare and contrast style using natural language processing (NLP), to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel and Todhunter in further view of Pateromichelakis et al. (WO-2024027941-A1) [hereinafter “Pateromichelakis”].
As per claim 12, the combination of Lu, Vogel and Todhunter teaches the method of claim 11
The combination of Lu, Vogel and Todhunter does not explicitly teach wherein flagging the new data communication based on the comparison over the threshold level further comprises: determining the set comprises at least a specified percentage of the plurality of deltas. Pateromichelakis teaches flagging the new data communication based on the comparison over the threshold level ([0111], flagging data from a data source based on comparison to a threshold) further comprises: determining the set comprises at least a specified percentage of the plurality of deltas ([0114], expressing the deviations in the data from the data source(s) as a percentage, i.e. accuracy) (Examiner Note: To expedite prosecution, Examiner suggests explaining the relationship how the percentage is used in the comparing/flagging, etc.).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel and Todhunter with the teachings of Pateromichelakis, wherein flagging the new data communication based on the comparison over the threshold level further comprises: determining the set comprises at least a specified percentage of the plurality of deltas, to reflect the delta changes or deviations in various forms allowing for better understanding by a user or easier subsequent processing.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Lu, Vogel and Todhunter in further view of Lankshmanan in further view of Al Badrashiny in further view of Hao.
As per claim 15, the combination of Lu, Vogel and Todhunter teaches the computer program product of claim 14.
The combination of Lu, Vogel and Todhunter does not explicitly teach clustering the set of writing patterns using a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm. Lakshmanan teaches clustering the set of writing patterns using a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm ([0102], clustering deep features, like writing patterns taught in Lu and Vogel, using algorithms like BIRCH and affinity propagation).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel and Todhunter with the teachings of Lakshmanan, clustering the set of writing patterns using a balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm, to cluster and reduce the patterns in Lu and Vogel using various well-known algorithms like BIRCH and affinity clustering.
The combination of Lu, Vogel, Todhunter and Lakshmanan does not explicitly teach wherein the determining the set of writing patterns of at least one data communication comprises detecting spelling mistakes, different and mixed languages, images, a writing style, grammar, and a format using the NLP. Al Badrashiny teaches wherein the determining the set of writing patterns of at least one data communication comprises detecting spelling mistakes (Col. 1 lines 64-66, rating a text document on spelling accuracy), different and mixed languages (Abstract, evaluating writing style for many different languages), a writing style (Abstract, determining style score of text document), grammar (Abstract, determining punctuation score of text document), and a format using the NLP (Col. 9, lines 32-40, evaluating structure and organization through use of punctuation and symbols see also Col. 14 lines 13-15, analysis done using NLP).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel, Todhunter and Lakshmanan with the teachings of Al Badrashiny, wherein the determining the set of writing patterns of at least one data communication comprises detecting spelling mistakes, different and mixed languages, images, a writing style, grammar, and a format using the NLP, to provide a full comprehensive analysis regarding writing similarities which provides the most accurate determination regarding authorship and source.
The combination of Lu, Vogel, Todhunter, Lakshmanan and Al Badrashinv does not explicitly teach wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a compare and contrast style using natural language processing (NLP). Hao teaches wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a compare and contrast style (NLP) (Col. 7, lines 25-40, determining a compare and contrast style in an input text document by analyzing its structure) using natural language processing (NLP) (Col. 7, lines 45-57. based on processing the terms and semantics of the terms, i.e. natural language).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Vogel, Todhunter, Lakshmanan and Al Badrashinv, with the teachings of Hao, wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is a compare and contrast style using natural language processing (NLP), to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Lu in view of Tran et al. (US PGPUB No. 2022/0164532) in further view of Vogel in further view of Plotkin (US PGPUB No. 2025/0190693) in further view of Amick et al. (WO-2007149220-A2) [hereinafter “Amick”].
As per claim 20, Lu teaches a system comprising: a processor, a computer readable memory, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to: receive at least one incoming email from a user device (Abstract and [0008], analyzing submitted material using various learned patterns); determine at least one writing pattern based on the at least one incoming email; cluster the at least one writing pattern using at least one algorithm ([0040], classifiers are trained on various things including style of writing which are used to analyze the submitted material); rate the at least one writing pattern ([0040], scoring a submitted material for style and AI generation); determine a narrative writing style based on the rated at least one writing pattern ([0040], classifiers are trained on various things including style of writing which are used to analyze the submitted material); provide a writing profile model by training on the narrative writing style and the rated at least one writing pattern ([0046], training classifiers on submitted material and sentiment analysis with models on human and AI generation); compare a new email to the trained writing profile model (Abstract, comparing a second submission in a verification response); and flag the new email based on the comparison being over a threshold level ([0043], flagging the submitted material if there a significant difference in risk scores of the two submissions).
Lu does not explicitly teach rank the clustered at least one writing pattern. Tran teaches rank the clustered at least one writing pattern ([0071], ranking a number of writing patterns based on past user writings) (Examiner Note: Examiner suggests including what the purpose of the ranking is for and/or what the ranking is based on to overcome this current rejection).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu with the teachings of Tran, rank the clustered at least one writing pattern, to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
The combination of Lu and Tran does not explicitly teach determine a narrative style of at least one data communication. Vogel teaches determine a narrative style of at least one data communication (Page 2, lines 12-20, extracting narrative attributes from a writing document).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu and Tran with the teachings of Vogel, determine a narrative style of at least one data communication, to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
The combination of Lu, Tran and Vogel does not explicitly teach wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is one of a cause-and-effect style or a compare and contrast style. Plotkin teaches wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is one of a cause-and-effect style (Examiner Note: this is an optional feature but may overcome the current rejection if included as a require feature) or a compare and contrast style using natural language processing (NLP) ([0182], implementing a compare and contrast pattern/style to process natural language content) (Examiner Note: this is supported by [0063] of the provisional application 63/588,835, filed 10/9/2023).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Tran and Vogel with the teachings of Plotkin, wherein the determining the narrative writing style on the set of writing patterns of the at least one data communication comprises determining that the set of writing patterns of the at least one data communication is one of a case-and-effect style or a compare and contrast style, to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
The combination of Lu, Tran, Vogel and Plotkin does not explicitly teach ranking a writing pattern based on at least one of a number of bold occurrences, a number of underline occurrences, a number of custom font occurrences, a number of misspelled words, or a number of rich text occurrences. Amick teaches ranking a writing pattern based on at least one of a number of bold occurrences, a number of underline occurrences, a number of custom font occurrences, a number of misspelled words, or a number of rich text occurrences (Page 6, lines 8-15, scoring text from reading material based on “readability” which includes the number of misspelled words – scoring is interpreted to be a form ranking).
At the time of filing, it would have been obvious to one of ordinary skill in the art to combine Lu, Tran, Vogel and Plotkin with the teachings of Amick, ranking a writing pattern based on at least one of a number of bold occurrences, a number of underline occurrences, a number of custom font occurrences, a number of misspelled words, or a number of rich text occurrences, to include all relevant characteristic attributes of human writing style which will produce a more accurate review.
Response to Arguments
Applicant’s arguments with respect to the rejection of claims 1-20 under 35 U.S.C. 103 have been fully considered. In light of the latest amendments, Examiner has introduced new prior art references, Todhunter, Hao and Amick, to teach the new features.
To expedite prosecution, Examiner is open to conducting an after-final interview to discuss claim amendments to overcome the current rejection and/or place the application in condition for allowance.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Shukla (US PGPUB No. 2025/0117665), Grushka et al. (US PGPUB No. 2024/0143666), Denholm et al. (US PGPUB No. 2021/0133654), Rehberg et al. (US PGPUB No. 2007/0214404), Yang et al. ("Modifying AI, Enhancing Essays: How Active Engagement with Generative AI Boosts Writing Quality," arXiv:2412.07200, December 10, 2024), Lo et al. ("Scaffolding Support Mechanism for Writing Cause and Effect Essays," 2009 International Conference on Computational Intelligence and Software Engineering, Wuhan, China, 2009, pp. 1-4, doi: 10.1109/CISE.2009.5366690) and Wei et al ("A Hybrid Natural Language Generation System Integrating Rules and Deep Learning Algorithms," arXiv:2006.09213, June 17, 2020) all disclose various aspects of the claimed invention including detecting and profiling writing styles of data communications.
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 PETER C SHAW whose telephone number is (571)270-7179. The examiner can normally be reached Max Flex.
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/PETER C SHAW/Primary Examiner, Art Unit 2493 April 17, 2026
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