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
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1-2, 3-9, 11-16 and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Dey et al (2019/0065467) in views of Ravindra et al (2020/0322361).
For claim 1, Dey teaches that An information analysis apparatus (abstract) comprising: at least one memory storing instructions (Dey teaches memory (RAM) with storing instrauction as Dey teaches in par.28 and 111); and at least one processor configured to execute the instructions (Dey teaches CPU executes instructions as Dey teaches in par.28 and 108) to: obtain a news article by accessing a news database or an expert information database via a network (Dey teaches that the set of articles to obtain a plurality of groups with each group comprising a plurality of news related to a common event information component, wherein the plurality of news are aggregated across date, sources of information and languages a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network as Dey teaches in par.6 and 75); based on determining that the news article comprises the example, extract feature information, from the news article (Dey teaches The Stanford NER extracts a set of named entities as Dey teaches in par.50); and extract, from a database storing technical information that has already occurred, technical information related to the feature information (Dey teaches set of articles are crawled (302) are pre-processed (304) and a set of metadata associated with the set of articles are extracted. Further, a set of crime information components are extracted (306) from a set of articles by utilizing the metadata. Further, a plurality of similar news from the set of articles are aggregated (308) across on date, sources of information and languages as Dey teaches in par.36 and 73), and associate the feature information with the technical information (Dey teaches that a set of crime components and a relationship associated with the set of crime components are imported form the crime ontology as Dey teaches in par.73 and 78).
Dey fails to teach determine that the news article comprises an example of damage from a cyberattack using a machine learning model, wherein the machine learning model is an unsupervised machine learning model trained using news articles; and indicating a characteristic item in the cyberattack and regarding a cyberattack.
Ravindra teaches, similar system, determine that the news article comprises an example of damage from a cyberattack using a machine learning model, wherein the machine learning model is an unsupervised machine learning model trained using news articles (Ravindra teaches that automatically infer temporal information associated with a cybersecurity event (e.g., an IoC) begins by extracting information about the event, typically from an source document that comprises unstructured security content, such as a news article, a blog, or some other security threat reporting source. Using natural language processing (NLP) or the like, one or more time expressions present in the text are detected and Machine Learning tasks are typically classified into the following three broad categories, depending on the nature of the learning signal or feedback available to a learning system: supervised learning, unsupervised learning, and reinforcement learning and In unsupervised machine learning, the algorithm trains on unlabeled data. The goal of these algorithms is to explore the data and find some structure within as Ravindra teaches in par.7, 70-73 and 92-93); and indicating a characteristic item in the cyberattack and regarding a cyberattack (Ravindra teaches Security event extraction is accomplished by identification of security entities (such as malware, cybercriminals, IoCs, etc.) and the relationship between and among the security objects as Ravindra teaches in par.73). It would have been obvious to one ordinary skill in the art to modify Dey to include wherein the machine learning model is an unsupervised machine learning model trained using news articles as taught and suggested by Ravindra for the purpose of automatically inferring temporal relationship data for security events By associating a time value marker for the event in this manner, more useful and accurate information about the cybersecurity event is then output (e.g., to other systems or security analysts), thereby improving the speed and accuracy with which the security events and incidents are managed (Ravindra, par.7).
For claims 2, 9 and 16, Dey, as modified by Ravindra, further teaches that at least one processor configured to execute the instructions to: extract at least one of a victim name, damage details, and a damage cost as the feature information from the news article (Dey teaches that pattern learning Victim Name* Named Entity extraction as Dey teaches in par.36 and table 2A and table 2B).
Dey fails to teach cyberattack.
Ravindra teaches cyberattack (Ravindra teaches Security event extraction is accomplished by identification of security entities (such as malware, cybercriminals, IoCs, etc.) and the relationship between and among the security objects as Ravindra teaches in par.73). It would have been obvious to one ordinary skill in the art to modify Dey to include cyberattack as taught and suggested by Ravindra for the purpose of automatically inferring temporal relationship data for security events By associating a time value marker for the event in this manner, more useful and accurate information about the cybersecurity event is then output (e.g., to other systems or security analysts), thereby improving the speed and accuracy with which the security events and incidents are managed (Ravindra, par.7).
For claims 4, 11 and 18, Dey, as modified by Ravindra, further teaches at least one processor configured to execute the instructions to: store, in a storage region of a storage device, the technical information and the feature information associated therewith in a state where the technical information and the feature information are associated with each other (Dey teaches of storing information as Dey teaches in par.77).
For claims 5, 12 and 19, Dey, as modified by Ravindra, wherein associating the feature information with the technical information comprises: comparing a date provided to the technical information in the database storing technical information with a publication date and time of the news article and associating the feature information with the technical information if a difference between the date provided to the technical information and the publication date and time of the news article is within a set range (Dey teaches that wherein the plurality of news articles are aggregated across date, sources of information and languages as Dey teaches in par.76 and 93).
For claims 6, 13 and 20, Dey, as modified by Ravindra, further teaches wherein the technical information comprises at least one of information regarding vulnerability of an attacked system, a name of software used
Dey fails to teach in a cyberattack, or cyberattack tactics, techniques, and procedures (TTPs).
Ravindra teaches a cyberattack, or cyberattack tactics, techniques, and procedures (TTPs) (Ravindra teaches Security event extraction is accomplished by identification of security entities (such as malware, cybercriminals, IoCs, etc.) and the relationship between and among the security objects as Ravindra teaches in par.73). It would have been obvious to one ordinary skill in the art to modify Dey to include cyberattack as taught and suggested by Ravindra for the purpose of automatically inferring temporal relationship data for security events By associating a time value marker for the event in this manner, more useful and accurate information about the cybersecurity event is then output (e.g., to other systems or security analysts), thereby improving the speed and accuracy with which the security events and incidents are managed (Ravindra, par.7).
For claims 7, 14 and 21, Dey, as modified by Ravindra, further teaches at least one processor configured to execute the instructions to: specify, if the technical information comprises information regarding vulnerability, an event that is caused by the vulnerability, and associate feature information that comprises the specified event with the technical information that comprises the information regarding vulnerability (Dey teaches in par.32 and 33).
For claim 8, Dey teaches An information analysis method (abstract) comprising:
obtaining a news article by accessing a news database or an expert information database via a network (Dey teaches that the set of articles to obtain a plurality of groups with each group comprising a plurality of news related to a common event information component, wherein the plurality of news are aggregated across date, sources of information and languages a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network as Dey teaches in par.6 and 75);
based on determining that the news article comprises the example, extracting feature information indicating a characteristic item, from a news article (Dey teaches The Stanford NER extracts a set of named entities as Dey teaches in par.50); and extracting, from a database storing technical information that has already occurred, associating the technical information related to the feature information (Dey teaches set of articles are crawled (302) are pre-processed (304) and a set of metadata associated with the set of articles are extracted. Further, a set of crime information components are extracted (306) from a set of articles by utilizing the metadata. Further, a plurality of similar news from the set of articles are aggregated (308) across on date, sources of information and languages as Dey teaches in par.36 and 73), and associating the feature information with the technical information (Dey teaches that a set of crime components and a relationship associated with the set of crime components are imported form the crime ontology as Dey teaches in par.73 and 78).
Dey fails to teach determining that the news article comprises an example of damage from a cyberattack using a machine learning model, wherein the machine learning model is an unsupervised machine learning model trained using news articles; and extracting feature information indicating a characteristic item in the cyberattack, from a news article.
Ravindra teaches, similar system, determining that the news article comprises an example of damage from a cyberattack using a machine learning model, wherein the machine learning model is an unsupervised machine learning model trained using news articles (Ravindra teaches that automatically infer temporal information associated with a cybersecurity event (e.g., an IoC) begins by extracting information about the event, typically from an source document that comprises unstructured security content, such as a news article, a blog, or some other security threat reporting source. Using natural language processing (NLP) or the like, one or more time expressions present in the text are detected and Machine Learning tasks are typically classified into the following three broad categories, depending on the nature of the learning signal or feedback available to a learning system: supervised learning, unsupervised learning, and reinforcement learning and In unsupervised machine learning, the algorithm trains on unlabeled data. The goal of these algorithms is to explore the data and find some structure within as Ravindra teaches in par.7, 70-73 and 92-93); and extracting feature information indicating a characteristic item in the cyberattack, from a news article (Ravindra teaches Security event extraction is accomplished by identification of security entities (such as malware, cybercriminals, IoCs, etc.) and the relationship between and among the security objects as Ravindra teaches in par.73). It would have been obvious to one ordinary skill in the art to modify Dey to include wherein the machine learning model is an unsupervised machine learning model trained using news articles as taught and suggested by Ravindra for the purpose of automatically inferring temporal relationship data for security events By associating a time value marker for the event in this manner, more useful and accurate information about the cybersecurity event is then output (e.g., to other systems or security analysts), thereby improving the speed and accuracy with which the security events and incidents are managed (Ravindra, par.7).
For claim 15, Dey teaches A non-transitory computer-readable recording medium that comprises a program recorded thereon (Dey teaches a non-transitory computer readable medium having a computer readable program embodied therein as Dey teaches in par.8), the program including instructions that cause a computer to carry out the steps of: obtaining a news article by accessing a news database or an expert information database via a network (Dey teaches that the set of articles to obtain a plurality of groups with each group comprising a plurality of news related to a common event information component, wherein the plurality of news are aggregated across date, sources of information and languages a distributed computing environment where functions are performed by remote processing devices that are linked through a communication network as Dey teaches in par.6 and 75);
based on determining that the news article comprises the example, extracting feature information indicating a characteristic item, from a news article (Dey teaches The Stanford NER extracts a set of named entities as Dey teaches in par.50); and extracting, from a database storing technical information that has already occurred, associating the technical information related to the feature information (Dey teaches set of articles are crawled (302) are pre-processed (304) and a set of metadata associated with the set of articles are extracted. Further, a set of crime information components are extracted (306) from a set of articles by utilizing the metadata. Further, a plurality of similar news from the set of articles are aggregated (308) across on date, sources of information and languages as Dey teaches in par.36 and 73), and associating the feature information with the technical information (Dey teaches that a set of crime components and a relationship associated with the set of crime components are imported form the crime ontology as Dey teaches in par.73 and 78).
Dey fails to teach determining that the news article comprises an example of damage from a cyberattack using a machine learning model, wherein the machine learning model is an unsupervised machine learning model trained using news articles; and extracting feature information indicating a characteristic item in the cyberattack, from a news article.
Ravindra teaches, similar system, determining that the news article comprises an example of damage from a cyberattack using a machine learning model, wherein the machine learning model is an unsupervised machine learning model trained using news articles (Ravindra teaches that automatically infer temporal information associated with a cybersecurity event (e.g., an IoC) begins by extracting information about the event, typically from an source document that comprises unstructured security content, such as a news article, a blog, or some other security threat reporting source. Using natural language processing (NLP) or the like, one or more time expressions present in the text are detected and Machine Learning tasks are typically classified into the following three broad categories, depending on the nature of the learning signal or feedback available to a learning system: supervised learning, unsupervised learning, and reinforcement learning and In unsupervised machine learning, the algorithm trains on unlabeled data. The goal of these algorithms is to explore the data and find some structure within as Ravindra teaches in par.7, 70-73 and 92-93); and extracting feature information indicating a characteristic item in the cyberattack, from a news article (Ravindra teaches Security event extraction is accomplished by identification of security entities (such as malware, cybercriminals, IoCs, etc.) and the relationship between and among the security objects as Ravindra teaches in par.73). It would have been obvious to one ordinary skill in the art to modify Dey to include wherein the machine learning model is an unsupervised machine learning model trained using news articles as taught and suggested by Ravindra for the purpose of automatically inferring temporal relationship data for security events By associating a time value marker for the event in this manner, more useful and accurate information about the cybersecurity event is then output (e.g., to other systems or security analysts), thereby improving the speed and accuracy with which the security events and incidents are managed (Ravindra, par.7).
Response to Amendments/Arguments
Applicant’s arguments with respect to claim(s) 1-2, 3-9, 11-16 and 18-21 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Based the amendments to claims 1, 8 and 15, the 101 rejections have been withdrawn.
The applicant’s arguments regarding new amendment limitations in claims 1, 8 and 15, has been considered but is moot, because the examiner applied new art, Ravindra et al (2020/0322361), that covers newly claimed limitation.
Regarding dependent claims arguments, said arguments are moot because the applied references are not considered to have alleged differences, and therefore are considered to properly show that for which they were cited.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AYUB A MAYE whose telephone number is (571)270-5037. The examiner can normally be reached Monday-Friday 9AM-5PM.
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/AYUB A MAYE/Examiner, Art Unit 2436
/TRONG H NGUYEN/Primary Examiner, Art Unit 2436
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