DETAILED ACTION
Notice of 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 .
Response to Amendment
2. Amendment filed 06/10/2026 has been considered by Examiner. Claims 1, 2, 13, 17, 18, and 20 have been amended. Claims 4, 10, 11, and 12 have been cancelled. Claims 1-3, 5-9, and 13-20 are pending, and likewise Claims 1-3, 5-9, and 13-20 have been examined.
Response to Arguments
Applicant’s amendments and arguments filed 06/10/2026, with respect to claim(s) 1-20 have been fully considered.
Applicant’s arguments in pages 6-9, filed 06/10/2026, with respect to 35 U.S.C 101 rejections of Claims 1-20 have been fully considered but they are not persuasive. Applicant argued that the amended limitations “computing a rhetoric vector indicative of a rhetorical force of the content based on one or more language structure metrics determined by analyzing a language structure of the content, the rhetoric vector comprising a plurality of dimensions each representative of a rhetoric aspect of the language structure” and "dynamically modifying a display at a user device of a content consumer when it is determined that the content advisory should be associated with the content, by outputting the content advisory to the content consumer concurrently with the content or prior to giving access to the content to the content consumer", recite specific, technical processes that are not practically able to be performed in the human mind. Examiner respectfully disagrees. Computing a vector indicating the nature of the rhetoric of the content, based on the language structure of the content, such as word, n-gram count, distance between certain words, how many times certain word has been recited, could be performed by a human with pen and paper. The content advisory with the content can be write down in a paper and can be displayed to the customer. Display element or user device are just additional element, which does not integrate the exception into a practical application or provide significantly more. Throughout the amended claim 1, there is no specialized or unique technology have been mentioned to achieve the goal. The use of a computer does not preclude performance of the invention via pen and paper or in a person’s mind. Also, the use of a computer or other machinery in its ordinary capacity to perform a task or simply adding a general purpose computer to an abstract idea, does not integrate a judicial exception into a practical application. Here the computer is the machine that is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more. Thus, 35 U.S.C 101 rejections of Claims 1-3, 5-9, 13-20 have been maintained.
Applicant’s arguments filed in pages 9-13, filed 06/10/2026, with respect to amended independent claims 1, 18 and 20, under 35 U.S.C. 103 have been fully considered but not persuasive. Applicant argued that the combination of Galitsky and Rubin fails to teach or suggest the amended limitations of claim 1, which is 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. Therefore, the 35 U.S.C. 103 rejection using combination of Galitsky and Rubin has been withdrawn and replaced with the rejections under 35 U.S.C 103 over Galitsky, in view of Brestoff. Please see the rejections below.
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 1-3, 5-9, 13-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The Independent claims 1, 18 and 20 recite “receiving content to be analyzed”; “computing a rhetoric vector indicative of a rhetorical force of the content based on one or more language structure metrics determined by analyzing a language structure of the content, the rhetoric vector comprising one or more a plurality of dimensions each representative of a rhetoric aspect of the language structure”; “[[and]] classifying the rhetoric vector with a trained classifier to determine whether a content advisory should be associated with the content”; “and dynamically modifying a display at a user device of a content consumer when it is determined that the content advisory should be associated with the content, by outputting the content advisory to the content consumer concurrently with the content or prior to giving access to the content to the content consumer”. The limitations above as drafted, is a process that, under its broadest reasonable interpretation, covers a mental process, as this could be performed in the human mind or with the aid of pen and paper.
The limitation of " receiving ... ", "computing ... ", "classifying ... ",”modifying..” as drafted covers mental activities. More specifically, a human can obtain a content, which can be an email or dialogue; can compute, based on the different aspect/features of the language structure of the content, a rhetoric vector, which can be a number representation of the aspects/features of the content; with a trained classifier, which can be a predefined/premade table of rhetoric vector with a score or identifier, can classify the vector and can decide whether there should be a warning/advisory that the content contains high level of rhetoric, can write the advisory on a piece of paper with the content and display it, can change the advisory and the content, whenever there is any modification. The above steps, as drafted, is a process that under its broadest reasonable interpretation, covers performance of the limitation in the mind. That is, other than reciting “processor”, “non-transitory computer readable memory”, nothing in the claim element precludes the steps from practically being performed in the human mind. Additionally, the mere nominal recitation of a generic computer appliance does not take the claim limitations out of the mental processes grouping. Thus, the claims recite a mental process.
The claims recite the additional limitation of “trained classifier”, “user device” for performing the method, which is recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. The current specification in paragraph [0062] specifies “different types of classifiers may be used, such as Naive Bayes, Logistic Regression, Boosted Decision Tree, or Random Forest”, which is generic and not sufficient to amount to significantly more than the judicial exception. Claims 18 and 20 recite the additional limitation of “processor”, “non-transitory computer readable memory”. The current specification in paragraph [0030],[0032],[0053] specifies them as generic and not sufficient to amount to significantly more than the judicial exception. All those are recited at a high level of generality and are recited as performing generic computer functions routinely used in computer applications. This is no more than mere instructions to apply the exception using a generic computer component. Accordingly, this 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.
Thus, taken alone, the additional elements do not amount to significantly more than the above identified judicial exception (the abstract idea). Looking at the limitations as an ordered combination adds
nothing that is not already present when looking at the elements taken individually. There is no indication
that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Claims 1, 18 and 20 are therefore not drawn to eligible subject matter as this are directed to an abstract idea without
significantly more than the abstract idea.
Claim 2 recites the additional limitation of “wherein the one or more language structure metrics include a distance metric, a proportion metric, and a count metric” , where calculating different metrics for language structure, based on the specification para.[0072]-[0074], could be calculation of different linguistic features, which could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 2 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 3 recites “wherein computing the rhetoric vector further comprises computing a word count in the content”, computing a word count of content, could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 3 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 5 recites “further comprising preprocessing the content, wherein preprocessing the content comprises one or both of: extracting the content from extraneous content, and generating cleaned text from the content”, where pre-processing the content by taking out unrelated content and generating clean text , could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 5 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 6 recites “wherein the content is received in a textual format, and the trained classifier is trained for the textual content”, to determine that the content is in textual format and classifier is trained with textual content is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of trained classifier, which is specified in specification in paragraph [0062] as, “different types of classifiers may be used, such as Naive Bayes, Logistic Regression, Boosted Decision Tree, or Random Forest”, which is generic and not sufficient to amount to significantly more than the judicial exception. The claim 6 as drafted, is not patent eligible.
Claim 7 recites “wherein the content is received in an audio or video format, and the method further comprises converting the format to a textual format and analyzing the language structure of the textual format of the content”, where to determine that the content can be either in audio or video format is an observation, evaluation and could be performed in the human mind or with the aid of pen and paper. Converting the audio or video format to a textual format could also be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 7 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 8 recites “wherein the trained classifier is trained for audio or video content”, where determining that the classifier is trained with the audio or video content, could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of trained classifier, which is specified in specification in paragraph [0062] as, “different types of classifiers may be used, such as Naive Bayes, Logistic Regression, Boosted Decision Tree, or Random Forest”, which is generic and not sufficient to amount to significantly more than the judicial exception. The claim 8 as drafted, is not patent eligible.
Claim 9 recites “further comprising generating a content advisory for the content based on one or more dimensions of the rhetoric vector”, where generating an advisory or warning based on the linguistic features ( dimensions of the rhetoric vector) could be performed with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 9 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 13 recites “wherein the content advisory is output to the content consumer in association with a web search result returning the content”, to determine that the content advisory is delivered to a consumer as an output which is associated with a web search result, could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 13 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 14 recites “further comprising ranking a webpage containing the content using one or both of the rhetoric vector and a classification of the rhetoric vector by the trained classifier”, where a webpage can be ranked by using rhetoric vector and the classification by the trained classified which could be an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitation of trained classifier, which is specified in specification in paragraph [0062] as, “different types of classifiers may be used, such as Naive Bayes, Logistic Regression, Boosted Decision Tree, or Random Forest”, which is generic and not sufficient to amount to significantly more than the judicial exception. The claim 14 as drafted, is not patent eligible.
Claim 15 recites “further comprising adjusting a pay-per-click cost associated with the content based on one or both of the rhetoric vector and a classification of the rhetoric vector by the trained classifier”, where human can adjust manually a payment method for a content based on the classification of the rhetoric vector by the classifier. The claim recites additional limitation of trained classifier, which is specified in specification in paragraph [0062] as, “different types of classifiers may be used, such as Naive Bayes, Logistic Regression, Boosted Decision Tree, or Random Forest”, which is generic and not sufficient to amount to significantly more than the judicial exception. The claim 15 as drafted, is not patent eligible.
Claim 16 recites “wherein the method is performed at a server providing access to the content”, determining that the method is performed at a server could be an observation, evaluation and could be performed in the human mind or with the aid of pen and paper. The claim recites additional limitations of server which is recited as performing generic computer functions, which is not sufficient to amount to significantly more than the judicial exception. The claim 16 as drafted, is not patent eligible.
Claim 17 recites “wherein the method is performed at a user device attempting to access to the content”, to determine that the method is performed at a user device is an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 17 does not recite any additional limitations. The claim as drafted, is not patent eligible.
Claim 19 recites “further comprising a database for storing the content in association with the content advisory when generated.”, to determine that the content is stored with the advisory in a database is s an evaluation, observation and could be performed in the human mind or with the aid of pen and paper. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception, as claim 19 does not recite any additional limitations. The claim as drafted, is not patent eligible.
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 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 of this title, 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, 6, 9, 16-20 are rejected under 35 U.S.C. 103 as being unpatentable over Galitsky et al. ( US 20230376693 A1), hereinafter referenced as Galitsky, in view of Brestoff et al. ( US 9552548 B1), hereinafter referenced as Brestoff.
Regarding Claim 1, Galitsky teaches a content analysis method, comprising:
receiving content to be analyzed ( Galitsky: Para.[0577],[0579], Figs. 1, 47, a process 4700 which is implemented on rhetoric classification computing device 101 by rhetoric classification application 102, is used to detect the presence of deception or fake content . At block 4701, receives text to be analyzed including sentence fragments, which can include, utterances from the user 160 ) ;
[[and]] classifying the rhetoric vector with a trained classifier to determine whether a content advisory should be associated with the content ( Galitsky: Para.[ 0096], [0616], Figs.1, 47, using a trained rhetoric agreement classifier 120, rhetoric classification application 102 determines whether the content/text is above a threshold level of matching. At block 4710, process 4700 involves a response to determining that the complexity score exceeds the threshold, identifying the text as including deceptive content. Hi complexity score can indicate a text that is unnaturally emotionally charged, with an invented overly complex mental state. Such a high complexity value indicates that the text cannot be trusted at all and is associated with deception or lies. The rhetoric classification application 102 can provide a response to a user device by indicating deception or fake content ( warning or advisory)).
Galitsky, while teaching the method of Claim 1, fails to explicitly teach the claimed, computing a rhetoric vector indicative of a rhetorical force of the content based on one or more language structure metrics determined by analyzing a language structure of the content, the rhetoric vector comprising one or more a plurality of dimensions each representative of a rhetoric aspect of the language structure; and dynamically modifying a display at a user device of a content consumer when it is determined that the content advisory should be associated with the content, by outputting the content advisory to the content consumer concurrently with the content or prior to giving access to the content to the content consumer
However, Brestoff does teach the claimed, computing a rhetoric vector indicative of a rhetorical force of the content based on one or more language structure metrics determined by analyzing a language structure of the content, the rhetoric vector comprising a plurality of dimensions each representative of a rhetoric aspect of the language structure ( Brestoff: Column 3, lines 64-67, column 5, lines 31-50, Fig. 1 illustrates a process 100 for using classified text and deep learning algorithms to identify risk and provide early warning. The process 100 begins at block 102 with mining of data for training one or more deep learning algorithms. The process of aggregating this training material and providing it to a deep learning engine involves creating a "vector" for each word in the block in relation to the two or three words before and after it. Each word vector has its own context, and that context is meaningful in connection with the type of case (and the type of risk) for which the deep learning algorithm is being trained);
and dynamically modifying a display at a user device of a content consumer when it is determined that the content advisory should be associated with the content, by outputting the content advisory to the content consumer concurrently with the content or prior to giving access to the content to the content consumer ( Brestoff: Column 6, lines 46-65, column 8, lines 14-20, Figs. 1, 2, an enterprise may configure the system to send its "early warning" alert to a list of devices owned by the enterprise where the communication of the output may be encrypted using appropriate security measures. When a scored Email is reported to a designated enterprise employee, that employee may be enabled to review the Email in one or more of several modes, e.g., the scored text in a spreadsheet format and/or a bar graph distribution. Column 11, lines 53-67, the trained deep learning system and the algorithm itself would scan internal emails and would run in the background. The system would output an alert to a privileged list of in-house personnel, likely in-house counsel or employees and they would be able to see a spreadsheet with a score in column A, the related text in column B, along with a bar chart for context, and then would be enabled to call forward the Emails of interest with early warning).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brestoff’s teaching of identifying risk and providing early warning using classified text and deep learning algorithm, into the system and method of detecting deceptive or fake content in text, taught by Galitsky, because, this would detect the risk of litigation and would provide early warning to the appropriate personnel. (Brestoff, Column 2).
Claim 18 is a content analysis system claim, comprising: a processor ( Galitsky: Para.[0721], Fig. 51, processor in the processing unit 5104); and a non-transitory computer-readable memory having computer-executable instructions stored thereon, which when executed by the processor ( Galitsky: Para.[0722],[0730], Fig. 51, non-transitory computer-readable storage media in storage media 5122. Processing unit 5104 can execute a variety of programs in response to program code and can maintain multiple concurrently executing programs or processes), configure the content analysis system to perform the steps in method claim 1 above and as such, claim 18 is similar in scope and content to claim 1 and therefore, claim 18 is rejected under similar rationale as presented against claim 1 above.
Claim 20 is a non-transitory computer readable memory claim, having computer-executable instructions stored thereon, which when executed by a processor ( Galitsky: Para.[0722],[0730], Fig. 51, non-transitory computer-readable storage media in storage media 5122. Processing unit 5104 can execute a variety of programs in response to program code and can maintain multiple concurrently executing programs or processes), configure the processor to perform the steps in method claim 1 above and as such, claim 20 is similar in scope and content to claim 1 and therefore, claim 20 is rejected under similar rationale as presented against claim 1 above.
Regarding Claim 6, Galitsky in view of Brestoff teach the method of claim 1. Galitsky further teaches, wherein the content is received in a textual format, and the trained classifier is trained for the textual content ( Galitsky: Para.[0100], Rhetoric classification application 102 analyzes textual content received from a user device. Para.[0172], rhetoric classification application 102 uses training data 125 to train rhetoric agreement classifier 120. Training data 125 can include a positive training set and a negative training set ( text)).
Regarding Claim 9, Galitsky in view of Brestoff teach the method of claim 1. Galitsky further teaches, further comprising generating a content advisory for the content based on one or more dimensions of the rhetoric vector ( Galitsky: Para.[0616], Fig. 47, at 4710 rhetoric classification application 102 can generate an indication of deception or fake content based on the counting of words ( dimension of rhetoric vector). Depending on the genre of the text, e.g., opinionated text, truthful text that is emotionally charged may have a complexity above 10 for 100 words of text. A complexity density above 30 can indicate a text that is unnaturally emotionally charged, with an invented overly complex mental state).
Regarding Claim 16, Galitsky in view of Brestoff teach the method of claim 1. Galitsky further teaches, wherein the method is performed at a server providing access to the content ( Galitsky: Para.[0677], Fig. 49, server 4912 may be communicatively coupled with remote client computing devices).
Regarding Claim 17, Galitsky in view of Brestoff teach the method of claim 1. Galitsky further teaches, wherein the method is performed at [[a]] the user device attempting to access to the content ( Galitsky: Para.[0093], Figs. 1, 50, Examples of rhetoric classification computing device 101 include client computing devices 5002, 5004, 5006, and 5008 depicted in FIG. 50).
Regarding Claim 19, Galitsky in view of Brestoff teach the content analysis system of claim 18. Brestoff further teaches, further comprising a database for storing the content in association with the content advisory when generated ( Brestoff: Column 7, lines 10-13, Fig.1, at step 112, a determination is made that an identified email is a true positive ( contain early warning topic), a copy of that Email may be placed in a True Positive database at step 114).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Brestoff’s teaching of identifying risk and providing early warning using classified text and deep learning algorithm, into the system and method of detecting deceptive or fake content in text, taught by Galitsky, because, this would detect the risk of litigation and would provide early warning to the appropriate personnel. (Brestoff, Column 2).
Claims 2, 3, 13, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Galitsky et al. ( US 20230376693 A1), hereinafter referenced as Galitsky, in view of Brestoff et al. ( US 9552548 B1), hereinafter referenced as Brestoff, further in view of Najork et al. ( US 7962510 B2), hereinafter referenced as Najork.
Regarding Claim 2, Galitsky in view of Brestoff teach the method of claim 1. Galitsky further teaches, wherein the one or more language structure metrics include a distance metric,[ a proportion metric, and a count metric] ( Galitsky: Para.[0171], Fig. 1, Rhetoric classification application 102 can process two pairs at a time, for example <q1, a1> and <q2, a2> and compares q1 with q2 and a1 with a2. Such a comparison allows a determination of whether an unknown question/answer pair contains a correct answer or not by assessing a distance from another question/answer pair with a known label ( distance metric)).
Galitsky in view of Brestoff while teaching the method of claim 2, fail to explicitly teach the claimed, wherein the one or more language structure metrics include [a distance metric,] a proportion metric, and a count metric.
However, Najork does teach the claimed, wherein the one or more language structure metrics include [a distance metric,] a proportion metric, and a count metric ( Najork: Column 5, lines 40-56, Fig. 3, when the web pages are evaluated using such a metric ( e.g., in step 220), if the number of words of the web page fall above a threshold value ( count metric), the web page can be identified as web spam pending the results of any other evaluations that may be based on additional metrics. Column 6, lines 41-48, Fig. 8, The zipRatio of a page is determined by dividing the size (in bytes) of uncompressed visible text by the size (in bytes) of compressed visible text. When a web page is evaluated using such a potential metric (e.g., in step 220), if the zipRatio ( proportional metric) of the web page lies above a threshold (e.g., 2.0 here), then the web page can be identified as web spam pending the results of any other evaluations that may be based on additional metrics).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Najork’s teaching of using content analysis to detect spam web pages, into the system and method, taught by Galitsky in view of Brestoff, because, by using a classifier to combine different metrics to detect spam pages, can improve the prediction accuracy (Najork, Column 4, lines, 11-26).
Regarding Claim 3, Galitsky in view of Brestoff teach the method of claim 1. Galitsky in view of Brestoff fail to teach the claimed, wherein computing the rhetoric vector further comprises computing a word count in the content.
However, Najork does teach the claimed, wherein computing the rhetoric vector further comprises computing a word count in the content ( Najork: Column 5, lines 40-56, Fig. 3, if the number of words of the web page fall above a threshold value, the web page can be identified as web spam pending the results of any other evaluations that may be based on additional metrics).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Najork’s teaching of using content analysis to detect spam web pages, into the system and method, taught by Galitsky in view of Brestoff, because, by using a classifier to combine different metrics to detect spam pages, can improve the prediction accuracy (Najork, Column 4, lines, 11-26).
Regarding Claim 13, Galitsky in view of Brestoff teach the method of claim [[11]] 1. Galitsky in view of Brestoff fail to explicitly teach the claimed, wherein the content advisory is output to the content consumer in association with a web search result returning the content.
However, Najork does teach the claimed, wherein the content advisory is output to the content consumer in association with a web search result returning the content ( Najork: Column 4, lines 51-67,Fig. 2A, a search engine receives contents by crawling a set of web pages at step 210. The crawled web pages are evaluated (via a processor, also referred to as a classifier) using one or more metrics at step 220. The result(s) of the metrics are compared against one or more thresholds to determine whether web spam is present (or likely present) at step 230 ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Najork’s teaching of using content analysis to detect spam web pages, into the system and method, taught by Galitsky in view of Brestoff, because, by using a classifier to combine different metrics to detect spam pages, can improve the prediction accuracy (Najork, Column 4, lines, 11-26).
Regarding Claim 14, Galitsky in view of Brestoff teach the method of claim 1. Galitsky in view of Brestoff fail to explicitly teach the claimed, further comprising ranking a webpage containing the content using one or both of the rhetoric vector and a classification of the rhetoric vector by the trained classifier.
However, Najork does teach the claimed, further comprising ranking a webpage containing the content using one or both of the rhetoric vector and a classification of the rhetoric vector by the trained classifier ( Najork: Column 4, lines 36-50,Fig. 1 illustrates an example spam web page. Spam web page 10 includes keywords, search terms, and links ( classification), each of which can be generated by an SEO ( search engine optimizer) to enhance the ranking of a web site in a search results list from a search engine or the like ).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Najork’s teaching of using content analysis to detect spam web pages, into the system and method, taught by Galitsky in view of Brestoff, because, by using a classifier to combine different metrics to detect spam pages, can improve the prediction accuracy (Najork, Column 4, lines, 11-26).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Galitsky et al. ( US 20230376693 A1), hereinafter referenced as Galitsky, in view of Brestoff et al. ( US 9552548 B1), hereinafter referenced as Brestoff, further in view of Chandramouli et al. ( US 20150254566 A1), hereinafter referenced as Chandramouli.
Regarding Claim 5, Galitsky in view of Brestoff teach the method of claim 1. Galitsky in view of Brestoff fail to explicitly teach the claimed, further comprising preprocessing the content, wherein preprocessing the content comprises one or both of: extracting the content from extraneous content, and generating cleaned text from the content.
However, Chandramouli does teach the claimed, further comprising preprocessing the content, wherein preprocessing the content comprises one or both of: extracting the content from extraneous content, and generating cleaned text from the content ( Chandramouli: Para.[0238]-[0244], the preprocessing steps were implemented for all the data sets are tokenization, stemming, pruning and no punctuation (NOP), tab line and paragraph indicators, to have a clean dataset for the detection accuracy).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chandramouli’s teaching of an automated detection of deception in short and multilingual electronic messages, into the system and method, taught by Galitsky in view of Brestoff, because, this would improve the detection of deception in electronic text communications with accuracy. (Chandramouli, Para.[0004]-[0007]).
Claims 7, 8 are rejected under 35 U.S.C. 103 as being unpatentable over Galitsky et al. ( US 20230376693 A1), hereinafter referenced as Galitsky, in view of Brestoff et al. ( US 9552548 B1), hereinafter referenced as Brestoff, further in view of Li et al. ( US 11282509 B1), hereinafter referenced as Li.
Regarding Claim 7, Galitsky in view of Brestoff teach the method of claim 1. Galitsky in view of Brestoff fail to explicitly teach the claimed, wherein the content is received in an audio or video format, and the method further comprises converting the format to a textual format and analyzing the language structure of the textual format of the content .
However, Li does teach the claimed, wherein the content is received in an audio or video format, and the method further comprises converting the format to a textual format and analyzing the language structure of the textual format of the content ( Li: Column 20, lines 44-66, column 21, lines 1-9, Fig. 2, transcription component 218 generate text representation of speech utterance. The transcription 226 may be a textual representation of speech utterances in the audio signal included in the item of content 202. Transcription 226 is analyzed by text classifier 228 for the semantic meanings of the utterance).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Li’s teaching of classifiers for media content, into the system and method, taught by Galitsky in view of Brestoff, because, by detecting and classifying different content types using the described techniques may improve content recommendations and/or search results by identifying, from what is included in the video or audio itself, what the content is "about," along with other content having similar characteristics. (Li, Column 2).
Regarding Claim 8, Galitsky in view of Brestoff, further in view of Li teach the method of claim 7. Li further teaches, wherein the trained classifier is trained for audio or video content ( Li: Column 9, lines 6-17, Fig. 1, The audio classifier 112 may be a machine-learned model trained to detect and/or classify events in an audio signal included in the content received from the computing device 104(1)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Li’s teaching of classifiers for media content, into the system and method, taught by Galitsky in view of Brestoff, because, by detecting and classifying different content types using the described techniques may improve content recommendations and/or search results by identifying, from what is included in the video or audio itself, what the content is "about," along with other content having similar characteristics. (Li, Column 2).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Galitsky et al. ( US 20230376693 A1), hereinafter referenced as Galitsky, in view of Brestoff et al. ( US 9552548 B1), hereinafter referenced as Brestoff, further in view of Chino et al. ( US 20160371361 A1), hereinafter referenced as Chino.
Regarding Claim 15, Galitsky in view of Brestoff teach the method of claim 1. Galitsky in view of Brestoff fail to explicitly teach the claimed, further comprising adjusting a pay-per-click cost associated with the content based on one or both of the rhetoric vector and a classification of the rhetoric vector by the trained classifier.
However, Chino does teach the claimed, further comprising adjusting a pay-per-click cost associated with the content based on one or both of the rhetoric vector and a classification of the rhetoric vector by the trained classifier ( Chino: Para.[0072], A click will not be counted as a financial event by the system unless it is preceded or accompanied by another such event that informs the system that a human being is conducting the click ( adjusting). This solves the long-standing problem of click fraud within the Internet advertising industry by changing the payment trigger from a cost per click metric to a cost per engagement metric).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Chino’s teaching of method and system which includes a user interface module and accessible by a plurality of user computers operated by a plurality of users over the network and is operative to respond to user requests for web pages or other selections of content, where user inputs of one or more collections can be obtained, into the system and method, taught by Galitsky in view of Brestoff, because, this would optimize monetization for users who create collections, as well as enabling such users to submit requests via the browser or application to a server to select the advertising location and market participants within their collections. (Chino, Para.[0002]).
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 extension fee 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 date of this final action.
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/NADIRA SULTANA/Examiner, Art Unit 2653
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
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