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 .
In response to Applicant’s claims filed on July 3, 2024 claims 1-15 are now pending for examination in the application.
Response to Arguments
“The 112 rejection under 35 USC 112 set forth in the 11/17/2025 office action is hereby withdrawn.”
Applicant’s arguments:
In regards to claim 1 on Page(s) 9, applicant argues “These technical elements go far beyond the generic "abstract idea" concept identified by the Examiner. They describe specific technological solutions that integrate classifying data, at least in part by identifying data as "blocked" or "non-blocked" content in novel ways. Under the August 2025 guidance, such specific technological implementations should not be categorized as abstract ideas, particularly where they involve computational processes that cannot practically be performed in the human mind..”
Examiner’s Reply:
The steps of preparing, assigning, and verifying can perform within the human mind. A human would be able to iteratively follow these steps along with any needed additional elements while using a computer as a tool (ie identifying content blocks). Classifying HTML does not improve the functioning of a computing system.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues “Specific Technological Improvements: The claims provide specific solutions to technical problems in classifying data. The system solves the technical challenge of classifying data at least by identifying data as blocked content or non-blocked content. Computer Functionality Improvements: Consistent with the Enfish decision, the claims improve computer functionality by enabling new forms of technological integration for classifying data. The specification clearly describes how these improvements address limitations in prior art systems. Non-Generic Implementation: The claims avoid the trap of generic computer implementation by reciting specific technical architectures, including a system that, in at least one aspect, executes a machine learning classification model. At least this system transforms the claims from an abstract concept into a concrete technological application..”
Examiner’s Reply:
If a claim limitation, under its broadest reasonable interpretation, covers a mental process (eg identifying block content), then it falls within the “Mental process” grouping of abstract ideas set forth in the 2019 PEG. Accordingly, the claim recites an abstract idea. The examiner notes that the computer as recited in the claims are being used for sending and receiving HTML (the computer is being used as a generic tool). Therefore, the abstract idea recited in the claims is generally linking it to a computer environment, and does not integrate the abstract idea into a practical application.
Applicant’s arguments:
In regards to claim 1 on Page(s) 10, applicant argues Step 2B: Inventive Concept Analysis IV. The claims contain inventive concepts that provide significantly more than any abstract idea. The classification of data solves the technical challenge of classifying data at least by identifying data as blocked content or non-blocked content and represents a non-conventional arrangement of technological elements that solves specific technical problems.”
Examiner’s Reply:
Collecting data for identification of content is well-understood, routine and conventional.
Applicant’s arguments:
In regards to claim 1 on Page(s) 16, applicant argues “Because the ground truth analyzer
of Bansal does not identify whether the data scraped from the 'merchant' is either a 'block' or 'non-block', Bansal cannot be found to provide, teach, or suggest this claim element.”
Examiner’s Reply:
Paragraph 63 discloses the privacy policy document may be processed to distinguish textual data from non-textual data. For example, a privacy policy manager machine learning engine may use one or more character recognition algorithms to identify and distinguish textual data and non-textual data content in the historical privacy policy documents used for training, although other manners of identifying the textual data and/or non-textual data may be used as is necessary and/or desired. Examples of non-textual content include graphical images, punctuation marks (e.g., periods, commas, parentheses, etc.), numerical digits, etc. Textual content may include words and letters, etc. A block of content using the Bri would have included a block content.
Applicant’s arguments:
In regards to claim 1 on Page(s) 16, applicant argues “Because the ground truth analyzer
of Bansal does not identify whether the data scraped from the 'merchant' is either a 'block' or 'non-block', Bansal cannot be found to provide, teach, or suggest this claim element whether the data received from the target is block content or non-block content." In contrast, Bansal does not describe the claimed preparation of the data collection response in any manner for the classification process. Bansal only classifies a 'merchant' as a restaurant, a grocery store, a bar, etc. Moreover, Bansal only teaches 'fetching' and "extracting' information, which cannot be considered comparable to the functions of the system as claimed.”
Examiner’s Reply:
Paragraph 78 discloses Ground truth analyzer 320 may include software or hardware modules configured to collect and organize data that is associated with merchants 160. For example, ground truth analyzer 320 may be configured to collect hours of operation from online resources 140. Ground truth analyzer 320 may collect information using a “bot,” such as a web scraper, to automatically fetch and extract information from websites such as Yellowpages.com™, Google™, or Yelp™. In some embodiments, ground truth analyzer 320 may download source code of web pages and parse, search, reformat, and copy data. Ground truth analyzer 320 may sort information to select information about merchants 160. In web scraping an extracting information, one would be preparing the data for identification/classification.
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 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
Claim 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below, on Claim Rejections - 35 USC 101 accordance with the "2019 Revised Patent Subject Matter Eligibility Guidance" (published on 1/7/2019 in Fed, Register, Vol. 84, No. 4 at pgs. 50-57, hereinafter referred to as the "2019 PEG").
Step 1. in accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted the claim system (claims 1-15) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
Step 2A. In accordance with Step 2A, prong one of the 2019 PEG, it is noted that the independent claims recite an abstract idea falling within the Mental Processes enumerated groupings of abstract ideas set forth in the 2019 PEG. Examiner is of the position that independent claims 1 are directed towards the Mental Process Grouping of Abstract Ideas.
Independent claim(s) 1 recites the following limitations directed towards a Mental Processes:
to prepare HTML response data for classification by pre-processing the HTML response (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to prepare data);
to assign the classification of the HTML response (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to assign data);
to process the classification assigned at the scraping agent and to route the HTML response according to the classification (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to process data);
wherein an outcome of the classification verifies whether the data received from the target is block content or non-block content (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to classify data).
Step 2A. In accordance with Step 2A, prong two of the 2019 PEG, the judicial exception is not integrated into a practical application because of the recitation in claim(s) 1, 6, 7, and 9:
a processor (i.e., as a generic processor/component performing a generic computer function); and
at least one block detection unit, operable: to send a data collection request to a target with the request originating at a requesting user device (recites insignificant extra solution activity that amounts to transmitting data);
to receive a data collection response at a scraping agent (recites insignificant extra solution activity that amounts to receiving data);
to submit the HTML response for classification by the scraping agent (recites insignificant extra solution activity that amounts to submit data);
wherein the data collection response is received in Hypertext Markup Language (HTML) format as a HTML response (recites insignificant extra solution activity that amounts to receiving data);
to execute the machine learning classification model against the HTML response data (recites insignificant extra solution activity that amounts to modeling classification data);
to communicate the classification assigned to the scraping agent (recites insignificant extra solution activity that amounts to trasmit data);
Step 2B. Similar to the analysis under 2A Prong Two, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Because the additional elements of the independent claims amount to insignificant extra solution activity and/or mere instructions, the additional elements do not add significantly more to the judicial exception such that the independent claims as a whole would be patent eligible.
Therefore, independent claim(s) 1 is/are rejected under 35 U.S.C. 101.
With respect to claim(s) 2:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein when the classification denotes proper content, the response data is routed to the requesting user device (recites insignificant extra solution activity that amounts to routing data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 3:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein when the classification denotes a response containing the block content, the data collection request is re-submitted as a subsequent request (recites insignificant extra solution activity that amounts to re-submitting data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 4:
Step 2A, prong one of the 2019 PEG:
wherein preparation of the HTML response data for classification by pre-processing comprises:
extracting text blocks of the prepared HTML response (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to extract text);
parsing text within the HTML response extracted (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to parse text); and,
tokenizing the text parsed (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to tokenize text).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 5:
Step 2A, prong one of the 2019 PEG:
wherein the program instructions cause the processor to execute at least one of a highest-priority data extraction operation, a priority change operation, or an operation content concealing operation,
in the highest-priority data extraction operation, the program instructions cause the processor to acquire highest-priority data and position information of the highest-priority data by the highest-priority data reference operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by extracting data), and
delete data at a position indicated by the position information by the delete operation in the priority change operation, the program instructions cause the processor to delete data at a change target position by the delete operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by deleting data),
insert a changed priority and the data by the insertion operation (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by inserting data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 6:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the requesting user device submits preferences as to whether classification functionality is required, via parameters of the request (recites insignificant extra solution activity that amounts to submit data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 7:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the machine learning model employed is one of the following:
bag of words (recites insignificant extra solution activity that amounts to modeling classification data);
naïve bayes algorithm (recites insignificant extra solution activity that amounts to modeling classification data);
support vector machines (recites insignificant extra solution activity that amounts to modeling classification data);
logistic regression (recites insignificant extra solution activity that amounts to modeling classification data);
random forest classifier (recites insignificant extra solution activity that amounts to modeling classification data);
extreme gradient boosting model (recites insignificant extra solution activity that amounts to modeling classification data);
convolutional neural network (recites insignificant extra solution activity that amounts to modeling classification data); or
recurrent neural network (recites insignificant extra solution activity that amounts to modeling classification data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 8:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein a classification decision at a classification platform is submitted for quality assurance wherein the classification assigned is examined and confirmed (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to submit data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 9:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein the classification decision subjected to quality assurance is categorized as correct, becomes a part of future machine learning classification model training, and is incorporated into the corresponding training set (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to submit data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 10:
Step 2A, prong one of the 2019 PEG:
wherein content delivered within non-textual information may be processed by the classification model (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to processing data).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 11:
Step 2A, prong one of the 2019 PEG:
wherein the response classified as a block triggers re-submitting of the request as a data collection request, the re-submitting performed at the scraping agent and comprising at least one of the following:
acquiring a new scraping strategy at a scraping strategy selection unit (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to acquire a strategy);
acquiring a new proxy (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to acquire a proxy); or
Step 2A Prong Two Analysis:
submitting the request without adjustments (recites insignificant extra solution activity that amounts to resubmitting data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 12:
Step 2A, prong one of the 2019 PEG:
wherein the response is verified against a static ruleset before submitting the response for classification, the verification comprising:
identifying, in the response, technical protocol errors listed in the static ruleset (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to identifying errors), and
identifying, in the response, HTML elements listed in the static ruleset as witnessing a mangled content (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to identifying HTML elements).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 13:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein when verification against the static ruleset detects a block within the response, the response is not submitted for classification and the request is re-submitted as a data collection request (recites insignificant extra solution activity that amounts to re-submitting data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 14:
Step 2A, prong one of the 2019 PEG:
Examiner is of the position the dependent claim is directed toward additional elements.
Step 2A Prong Two Analysis:
wherein when verification against the static ruleset does not detect a block, the response is submitted to the block detection unit for classification (recites insignificant extra solution activity that amounts to submitting data).
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
With respect to claim(s) 15:
Step 2A, prong one of the 2019 PEG:
where the static ruleset can be updated with rules submitted by the requesting user devices along or within the parameters of the data collection request (The limitation recites a mental process of observation and/or evaluation capable of being performed by the human mind by using computer as a tool to updating rules).
Step 2A Prong Two Analysis:
This judicial exception is not integrated into a practical application because there are no
additional elements to provide practical application.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
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.
Claim(s) 1-7, and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et al. (US Pub. No. 20180308019) in view of Bonat et al. (US Pub. No. 20200380171).
With respect to claim 1, Bansal et al. teaches a system for processing a data collection response from a network employing a machine learning classification model including a non-transitory computer-readable medium comprising computer readable instructions that, when executed by a processor, configure the processor:
to send a data collection request to a target with the request originating at a requesting user device (Paragraph 78 discloses collect information using a “bot,” such as a web scraper);
to receive a data collection response at a scraping agent (Paragraph 100 discloses receive requests for hours of operation from client devices);
wherein the data collection response is received in Hypertext Markup Language (HTML) format as a HTML response (Paragraph 79 discloses a web scraper may be configured to recognize labels in html code);
to submit the HTML response for classification by the scraping agent (Paragraph 78 discloses a web scraper, to automatically fetch and extract information from websites and Paragraph 118 discloses determine a merchant classification);
to prepare HTML response data for classification by pre-processing the HTML response (Paragraph 78 discloses fetch and extract information from websites such as Yellowpages.com™, Google™, or Yelp™);
to execute the machine learning classification model against the HTML response data (Paragraph 85 discloses model builder 346 may use association rule mining, artificial neural networks, and/or deep learning algorithms to develop models);
to assign the classification of the HTML response (Paragraph 118 discloses determine whether there is a classification associated with the merchant);
to communicate the classification assigned to the scraping agent; to process the classification assigned at the scraping agent and to route the HTML response according to the classification (Paragraph 119 discloses Model generator 120 may respond with one or more prediction models for the merchant based on, for example, the classification for the merchant and the day of the week. For instance, model generator 120 may send to prediction system 110 a group of prediction models specifically for restaurants open on Saturdays). Bansal et al. does not disclose wherein an outcome of the classification verifies whether the data received from the target is block content or non-block content.
However, Bonat et al. teaches wherein an outcome of the classification verifies whether the data received from the target is block content or non-block content (Paragraph 63 discloses distinguish textual data and non-textual data content).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Bansal et al. with Bonat et al. to include wherein an outcome of the classification verifies whether the data received from the target is block content or non-block content. This would have facilitated data classification that would have been modified using machine learning. See Bonat et al. Paragraphs 6-21.
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 2, Bansal et al. teaches the system of claim 1 wherein when the classification denotes proper content, the response data is routed to the requesting user device (Paragraph 78 discloses a web scraper, to automatically fetch and extract information from websites and Paragraph 118 discloses determine a merchant classification).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 3, Bansal et al. teaches the system of claim 1 wherein when the classification denotes a response containing the block content, the data collection request is re-submitted as a subsequent request (Paragraph 78 discloses In some embodiments, ground truth analyzer 320 may download source code of web pages and parse, search, reformat, and copy data. Ground truth analyzer 320 may sort information to select information about merchants 160).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 4, Bansal et al. teaches the system of claim 1 wherein preparation of the HTML response data for classification by pre-processing comprises:
extracting text blocks of the prepared HTML response (Paragraph 78 discloses In some embodiments, ground truth analyzer 320 may download source code of web pages and parse, search, reformat, and copy data. Ground truth analyzer 320 may sort information to select information about merchants 16);
parsing text within the HTML response extracted (Paragraph 78 discloses In some embodiments, ground truth analyzer 320 may download source code of web pages and parse, search, reformat, and copy data. Ground truth analyzer 320 may sort information to select information about merchants 16); and,
tokenizing the text parsed (Paragraph 78 discloses In some embodiments, ground truth analyzer 320 may download source code of web pages and parse, search, reformat, and copy data. Ground truth analyzer 320 may sort information to select information about merchants 16).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 4. With respect to claim 5, Bansal et al. teaches the system of claim 4 wherein the pre-processing additionally comprises at least one of the following:
detecting a language of the text parsed (Paragraph 78 discloses In some embodiments, ground truth analyzer 320 may download source code of web pages and parse, search, reformat, and copy data. Ground truth analyzer 320 may sort information to select information about merchants 16);
eliminating low-benefit text elements from the text parsed;
eliminating stopwords from the text tokenized;
translating tokenized text, if language detection detected multiple language, into the identified primary language; or
stemming text elements within the tokenized text.
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 6, Bansal et al. teaches the system of claim 1 wherein the requesting user device submits preferences as to whether classification functionality is required, via parameters of the request (Paragraph 100 discloses receive requests for hours of operation from client devices).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 7, Bansal et al. teaches the system of claim 1 wherein the machine learning model employed is one of the following:
bag of words (Paragraph 73 discloses a bag-of-words);
naïve bayes algorithm;
support vector machines;
logistic regression;
random forest classifier;
extreme gradient boosting model;
convolutional neural network; or
recurrent neural network (”machine learning techniques such that the quality of their analysis improves over time as the total number of user-generated content entries analyzed increases,” See Paragraph 26). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Bansal et al. reference and the Bonat et al. reference is applicable to dependent claim 7.
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 10, Bonat et al. teaches the system of claim 1 wherein content delivered within non-textual information may be processed by the classification model (Paragraph 81 discloses textual data may be distinguished from non-textual data). The motivation to combine statement previously provided in the rejection of independent claim 1 provided above, combining the Bansal et al. reference and the Bonat et al. reference is applicable to dependent claim 10.
Claim(s) 8-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et al. (US Pub. No. 20180308019) and Bonat et al. (US Pub. No. 20200380171) in further view of Guan et al. (US Pub. No. 20210042767).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 8, Bansal et al. reference as modified by Bonat et al. does not disclose the quality assurance.
However, Guan et al. teaches the method of claim 1 wherein a classification decision at a classification platform is submitted for quality assurance wherein the classification assigned is examined and confirmed (“It should be appreciated that various AI techniques or machine learning may be performed on the crawled data. For example, this may include Automated Classification/Topic Modeling, which in turn may involve the following: (i) Importing packages, preparing stopwords, remove special characters, tokenizing keywords; (ii) Creating multigram models, lemmatizing multigrams; (iii) Building LDA classifier with dictionary and corpus, measuring model Quality (e.g., Perplexity and Coherence, Interactive Topic Visualization); and (iv) Optimizing LDA Classifier, visualizing and Extracting the Most Optimal Model,” See Paragraph 127).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Bansal et al. and Bonat et al. with Guan et al. to include quality assurance. This would have facilitated digital content management. See Guan et al. Paragraph(s) 2-5.
The Bansal et al. reference as modified by Bonat et al. and Guan et al. teaches all the limitations of claim 8. With respect to claim 9, Guan et al. teaches the method of claim 8 wherein the classification decision subjected to quality assurance is categorized as correct and becomes a part of future machine learning classification model training and is incorporated into the corresponding training set (“It should be appreciated that various AI techniques or machine learning may be performed on the crawled data. For example, this may include Automated Classification/Topic Modeling, which in turn may involve the following: (i) Importing packages, preparing stopwords, remove special characters, tokenizing keywords; (ii) Creating multigram models, lemmatizing multigrams; (iii) Building LDA classifier with dictionary and corpus, measuring model Quality (e.g., Perplexity and Coherence, Interactive Topic Visualization); and (iv) Optimizing LDA Classifier, visualizing and Extracting the Most Optimal Model,” See Paragraph 127).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et al. (US Pub. No. 20180308019) and Bonat et al. (US Pub. No. 20200380171) in further view of Mukherjee et al. (US Pub. No. 20200310599).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 11, Bansal et al. as modified by Bonat et al. does not disclose a new scraping strategy.
However, Mukherjee et al. teaches the system of claim 1 wherein the response classified as a block triggers re-submitting of the request as a data collection request, the re-submitting performed at the scraping agent and comprising at least one of the following:
acquiring a new scraping strategy at a scraping strategy selection unit;
acquiring a new proxy; or
submitting the request without adjustments (“different scraping strategies,” See Paragraph 129).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Bansal et al. and Bonat et al. with Mukherjee et al. to include a new scraping strategy. This would have facilitated digital content classification. See Mukherjee et al. Paragraph(s) 2-7. In addition, all references teach features that are directed to analogous art and they are directed to the same field of endeavor: machine learning.
Claim(s) 12-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bansal et al. (US Pub. No. 20180308019) and Bonat et al. (US Pub. No. 20200380171) in further view of Newman (US Pub. No. 20200074300).
The Bansal et al. reference as modified by Bonat et al. teaches all the limitations of claim 1. With respect to claim 12, Bansal et al. as modified by Bonat et al. does not disclose identifying, in the response, technical protocol errors listed in the static ruleset, and identifying, in the response, HTML elements listed in the static ruleset as witnessing a mangled content.
However, Newman teaches the method of claim 1 wherein the response is verified against a static ruleset before submitting the response for classification, the verification comprising:
identifying, in the response, technical protocol errors listed in the static ruleset, and identifying, in the response, HTML elements listed in the static ruleset as witnessing a mangled content (“the classification system continuously retrains the AI such as the neural networks. After a dataset is processed by the neural networks, the results thereof are crosschecked and verified for accuracy. Necessary corrections are applied in the data storage facility. Then, the entire knowledge base is used to retrain the neural networks thereby allowing for a rapid increase in accuracy,” See Paragraph 32).
Therefore, it would have been obvious before the effective filing data of invention was made to a person having ordinary skill in the art to modify Bansal et al. and Bonat et al. with Newman to include verifying a response. This would have facilitated improved classification. See Newman Paragraph(s) 2-8.
The Bansal et al. reference as modified by Bonat et al. and Newman teaches all the limitations of claim 12. With respect to claim 13, Newman teaches the method of claim 13 wherein when verification against the static ruleset detects a block within the response, the response is not submitted for classification and the request is resubmitted as a data collection request (“the classification system continuously retrains the AI such as the neural networks. After a dataset is processed by the neural networks, the results thereof are crosschecked and verified for accuracy. Necessary corrections are applied in the data storage facility. Then, the entire knowledge base is used to retrain the neural networks thereby allowing for a rapid increase in accuracy,” See Paragraph 32). The motivation to combine statement previously provided in the rejection of dependent claim 13 provided above, combining the Bansal et al. reference and the Bonat et al. reference is applicable to dependent claim 10.
The Bansal et al. reference as modified by Bonat et al. and Newman teaches all the limitations of claim 12. With respect to claim 14, Newman teaches the method of claim 13 wherein when verification against the static ruleset does not detect a block, the response is submitted to the block detection unit for classification (“the classification system continuously retrains the AI such as the neural networks. After a dataset is processed by the neural networks, the results thereof are crosschecked and verified for accuracy. Necessary corrections are applied in the data storage facility. Then, the entire knowledge base is used to retrain the neural networks thereby allowing for a rapid increase in accuracy,” See Paragraph 32).
The Bansal et al. reference as modified by Bonat et al. and Newman teaches all the limitations of claim 12. With respect to claim 15, Newman teaches the method of claim 13 where the static ruleset can be updated with rules submitted by the requesting user devices along or within the parameters of the data collection request (“the classification system continuously retrains the AI such as the neural networks. After a dataset is processed by the neural networks, the results thereof are crosschecked and verified for accuracy. Necessary corrections are applied in the data storage facility. Then, the entire knowledge base is used to retrain the neural networks thereby allowing for a rapid increase in accuracy,” See Paragraph 32).
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PG-Pub. No. 20200193153 is directed to MULTI-SEGMENT TEXT SEARCH USING MACHINE LEARNING MODEL FOR TEXT SIMILARITY: [0053] A scraper server 121 may run a scraping computer program to access data stored on the external data storage 111 and download it. The scraper server 121 may store the downloaded data in raw data storage 122. Optionally, the scraper server 121 may store the downloaded data in raw form in the raw data storage 122 meaning that the data is stored in the form it is downloaded without transforming it. Text similarity storage 123 may store data identifying similar and dissimilar text segments. In an embodiment, the text similarity storage 123 comprises a database of relationships, such as in the form (text1, text2, sim) where text1 is a first text segment, text2 is a second text segment, and sim is an indication of whether text1 is similar or not to text2. In some embodiments, sim may be a binary value such as 0 for dissimilar or 1 for similar. In some embodiments, sim may be a floating or real value representing the degree of similarity between text1 and text2. For example, sim may take values between 0 and 1 where values closer to 1 indicate a higher degree of similarity and values closer to 0 indicate a lower degree of similarity. Document storage 124 may store electronic documents. Application server 125 may serve an online application to clients. For example, application server 125 may serve a web application or a mobile application. Machine learning server 126 may perform machine learning functionality. In some embodiments, the application server 125 may perform application programming interface (API) calls to machine learning server 126 to request the machine learning server 126 to perform machine learning tasks. The machine learning server 126 may return results of the API call that the application server 125 may incorporate in results returned to clients.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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/N.E.A/Examiner, Art Unit 2154
/SYED H HASAN/Primary Examiner, Art Unit 2154