DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to Application filed 01/29/2025.
Claims 1-20 are pending.
Priority
This application is claimed and considered as a continuation of U.S. Patent Application No. 18/362,956 filed on 07/31/2023, which is a continuation of U.S. Patent Application No. 17/002,744 filed on 08/25/2020, which is a continuation of U.S. Patent Application No. 15/790,453 filed on 10/23/2017. Therefore, the effective filing date of this application is 10/23/2017.
Information Disclosure Statement
The Information Disclosure Statement (IDS) filed by Applicant on 01/29/2025 has been considered. A copy of the considered IDS is enclosed with this Office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-5 and 12-15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2 and 12, it is unclear how obtaining the structured data comprises obtaining a plurality of instances of activity associated with an element of structured data as recited. According to the disclosure, a structured data includes a plurality of elements/attributes (see [0024]), the verifier can perform verification of structured data from one or more user devices (see [0023]), and each sample of the same structured data can be sourced from a different user device (see [0031]). The recitation raises question regarding what is the plurality of instances of activity associated with an element of the structured data as recited (e.g., what information? what activity?...included in the structured data or in an element of the structured data?) since the term “instance[s] of activity” is not used in the Specification.
Other dependent claims 3-5 and 13-15 are rejected as incorporating the deficiency of claims 2 and 12 upon which they depend correspondingly.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of analyzing and/or evaluating data without significantly more.
The claims recite an abstract idea of analyzing and/or evaluating data based on broadly recited steps of comparing, identifying, determining, generating, and updating, which are broadly recited steps/concepts that can be performed in the human mind or with the aid of pencil and paper and directed to mental processes grouping of abstract ideas . This judicial exception is not integrated into a practical application because other additional elements including genetic computer components and common computer functionality (e.g., accessing, storing, displaying, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering and displaying) for implementing the abstract idea are not sufficient to integrate the abstract idea into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements include only generic/common computer components (e.g., memory, processor, program instructions, etc.) and generic/common computer functions (e.g., accessing, storing, displaying, etc.) and/or insignificant extra-solution activity (e.g., mere data gathering and displaying), which are not sufficient to amount to significantly more than the recited abstract idea.
Abstract idea analysis as follows:
Step 1:
According to the first part of the analysis, in the instant claims, claims 1-10 are directed to a method (i.e. a process), and claim 11-20 are directed to a system (i.e., a machine). Thus, each of the claims falls within one of the four statutory categories (i.e. process, machine, manufacture or composition of matter).
Step 2a Prong 1 (claims 1 and 11):
The following limitations recited in claims 1 and 11 are abstract ideas that fall under mental processes:
comparing the structured data to the standard data (the step of comparing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment, and opinion));
based on comparing the structured data to the standard data, identifying a difference between the structured data and the standard data (the step of identifying a difference based on comparing as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment, and opinion));
determining, based on at least one rule, that the difference between the structured data and the standard data is unexpected (the step of determining as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment, and opinion));
based on determining that the difference between the structured data and the standard data is unexpected, generating a signal indicating that the difference between the structured data and the standard data is a result of malicious code (the step of generating as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment, and opinion)); and
updating the at least one rule based on the indication (the step of updating as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment, and opinion)).
All the limitations above are mental steps that can be performed in the human mind or with the aid of pencil and paper.
Step 2a Prong 2 (Claims 1 and 11):
The following limitations in claims 1 and 11 are additional elements:
obtaining structured data (the step of obtaining data as broadly recited is directed to mere data gathering recited at high level of generality, or being insignificant extra-solution activity);
obtaining standard data corresponding to the structured data (the step of obtaining data as broadly recited is directed to mere data gathering recited at high level of generality, or being insignificant extra-solution activity);
obtaining an indication that the structured data is not the result of malicious code (the step of obtaining an indication as broadly recited is directed to mere data gathering recited at high level of generality, or being insignificant extra-solution activity);
data processing hardware (these elements are directed to generic computer components for implementing or applying the abstract idea); and
memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising (these elements are directed to generic computer components and mere instructions for implementing or applying the abstract idea).
These are a generic computer and/or generic computer components used to perform generic computer functions and/or insignificant extra-solution activity for implementing or applying the abstract. Accordingly, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s).
Step 2b (Claims 1 and 11):
The following limitations in claims 1 and 11 are additional elements:
obtaining structured data (the step of obtaining data as broadly recited is directed to mere data gathering recited at high level of generality, or being insignificant extra-solution activity);
obtaining standard data corresponding to the structured data (the step of obtaining data as broadly recited is directed to mere data gathering recited at high level of generality, or being insignificant extra-solution activity);
obtaining an indication that the structured data is not the result of malicious code (the step of obtaining an indication as broadly recited is directed to mere data gathering recited at high level of generality, or being insignificant extra-solution activity);
data processing hardware (these elements are directed to generic computer components for implementing or applying the abstract idea); and
memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising (these elements are directed to generic computer components and mere instructions for implementing or applying the abstract idea).
These are a generic computer and/or generic computer components used to perform generic computer functions or well-understood, routine, conventional activity, and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 2 and 12, claims 2 and 12 depend on claims 1 and 11 respectively. As such, claims 2 and 12 recite the abstract idea as presented in claims 1 and 11.
In addition, claims 2 and 12 include following additional elements:
wherein obtaining the structured data comprises obtaining a plurality of instances of activity associated with an element of structured data (this step specifying obtaining the structured data by further specifying structured data, which is directed to mere data or an insignificant extra-solution activity).
These are additional elements directed to mere information and/or an insignificant extra-solution activity for implementing the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 3 and 13, claims 3 and 13 depend on claims 2 and 12 respectively. As such, claims 3 and 13 recite the abstract idea as presented in claims 2 and 12.
In addition, claims 3 and 13 include following additional elements:
identifying a hash corresponding to the element of structured data (this step of identifying as broadly recited can be mentally performed in a human mind or with the aid of pencil and paper (e.g., by observing data and recognizing or assigning some value/number for particular data element)).
These are additional elements directed to mental process or abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 4 and 14, claims 4 and 14 depend on claims 3 and 13 respectively. As such, claims 4 and 14 recite the abstract idea as presented in claims 3 and 13.
In addition, claims 4 and 14 include following additional elements:
wherein obtaining the plurality of instances of activity associated with the element is based on the hash (this step of obtaining is directed to mere data gathering recited as high level of generality or being insignificant extra-solution activity).
These are additional elements directed to insignificant extra-solution activity for implementing or applying the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 5 and 15, claims 5 and 15 depend on claims 2 and 12 respectively. As such, claims 5 and 15 recite the abstract idea as presented in claims 2 and 12.
In addition, claims 5 and 15 include following additional elements:
wherein each instance of the plurality of instances of activity is sourced from a different user device (this step specifying the source of instances of activity which is directed to descriptive information).
These are additional elements directed to mere descriptive data/information, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 6 and 16, claims 6 and 16 depend on claims 1 and 11 respectively. As such, claims 6 and 16 recite the abstract idea as presented in claims 1 and 11.
In addition, claims 6 and 16 include following additional elements:
wherein the structured data comprises binary data (this step specifying the structured data, which is directed to descriptive information).
These are additional elements directed to mere descriptive data/information for implementing or applying the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 7 and 17, claims 7 and 17 depend on claims 1 and 11 respectively. As such, claims 7 and 17 recite the abstract idea as presented in claims 1 and 11.
In addition, claims 7 and 17 include following additional elements:
wherein the structured data comprises at least one of creator information, version information, or data type (this step specifying the structured data, which is directed to descriptive information).
These are additional elements directed to mere descriptive data/information for implementing or applying the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 8 and 18, claims 8 and 18 depend on claims 1 and 11 respectively. As such, claims 8 and 18 recite the abstract idea as presented in claims 1 and 11.
In addition, claims 8 and 18 include following additional elements:
wherein determining that the difference between the structured data and the standard data is unexpected comprises determining that the difference between the structured data and the standard data satisfies a tolerance threshold (the step of determining the difference based on a threshold as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment and/or opinion)).
These are additional elements directed to mental process or the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 9 and 19, claims 9 and 19 depend on claims 8 and 18 respectively. As such, claims 9 and 19 recite the abstract idea as presented in claims 8 and 18.
In addition, claims 9 and 19 include following additional elements:
adjusting the tolerance threshold based on the indication (the step of adjusting the tolerance threshold without specifying how as broadly recited can be mentally performed in the human mind or with the aid of pencil and paper through mental processes (e.g., observation, evaluation, judgment and/or opinion)).
These are additional elements directed to mental process or the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
Regarding claims 10 and 20, claims 10 and 20 depend on claims 1 and 11 respectively. As such, claims 10 and 20 recite the abstract idea as presented in claims 1 and 11.
In addition, claims 10 and 20 include following additional elements:
storing the difference between the structured data and the standard data at a registry (the step of storing as broadly recited is directed to a generic/routine function of a generic computer (e.g., storing, displaying, etc.)).
These are additional elements directed to routine computer function or insignificant extra-solution activity for implementing or applying the abstract idea, which do not integrate the judicial exception into a practical application and do not amount to significantly more, see MPEP 2106.05(d)(II).
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.
Claims 1-20 (effective filing date 10/23/2017) are rejected under 35 U.S.C. 103 as being unpatentable over Chinthagunti (U.S. Patent No. 10,324,923, effectively filed date 11/10/2014), and further in view of Jang et al. (U.S. Publication No. 2009/0133126, Publication date 05/21/2009).
As to claim 1, Chinthagunti teaches:
“A computer-implemented method executed by data processing hardware that causes the data processing hardware to perform operations comprising” (see Chinthagunti, Abstract and Fig. 1):
“obtaining structured data” (see Chinthagunti, [column 4, lines 1-20] for generating/obtaining metadata 114 that describes the data 106, wherein the metadata as disclosed can be interpreted as structured data as recited; also see [column 4, lines 43-48] for generating/obtaining metadata 114(2), wherein metadata 114(2) can be interpreted as structured data as recited;
“obtaining standard data corresponding to the structured data” (see Chinthagunti, [column 4, lines 20-28] for generating/obtaining metadata 114(1) wherein metadata 114(1) or baseline metadata can be interpreted as standard data as recited);
“comparing the structured data to the standard data” (see Chinthagunti, [column 4, lines 61-67] for comparing the metadata 114(1) (i.e., the standard data) to the metadata 114(2) (i.e., the structured data) to identify differences between the metadata 114(1) and the metadata 114(2));
“based on comparing the structured data to the standard data, identifying a difference between the structured data and the standard data” (see Chinthagunti, [column 4, lines 61-67] for comparing the metadata 114(1) (i.e., the standard data) to the metadata 114(2) (i.e., the structured data) to identify differences between the metadata 114(1) and the metadata 114(2));
“determining, based on at least one rule, that the difference between the structured data and the standard data is unexpected” (see Chinthagunti, [column 8, lines 34-56] and [column 12, lines 57-61] for determining whether the difference is acceptable/unacceptable or expected/unexpected based on exceptions wherein each exception as disclosed can be interpreted as a rule as recited; also see [column 13, lines 63-67] and [column 14, lines 35-56] for using a classifier to determine whether differences between metadata 114(1) and metadata 114(2) are acceptable/unacceptable (i.e., expected/unexpected), the classifier or machine learning process as disclosed (e.g., if a current difference is not included in the previous set of difference(s) or is not described in the exception information, the machine learning process may classify the current difference as an aberration to be further investigates) can be interpreted as equivalent to a rule as recited);
“based on determining that the difference between the structured data and the standard data is unexpected, generating a signal indicating that the difference between the structured data and the standard data is [unexpected]” (see Chinthagunti, [column 13, lines 8-22] for generating a notification if one or more differences between the metadata 114(1) and the metadata 114(2) are not described in the exception information (i.e., the difference(s) are unexpected);
“obtaining an indication that the structured data is not [unexpected]” (see Chinthagunti, [column 14, lines 17-34] wherein the difference(s) may be manually analyzed to determine whether the difference(s) are significant (i.e., indicating a potential problem or unexpected) or not (i.e., indicating the differences are acceptable or expected); and the acceptable or expected differences can be incorporated into training dataset to train a classifier); and
“updating the at least one rule based on the indication” (see Chinthagunti, [column 14, lines 17-56] for incorporating the acceptable or expected difference(s), which are manually determined/indicated, into training dataset to train the classifier (e.g., updating the rule used by the classifier or the machine learning method; also see [column 8, lines 51-53] wherein exception information (i.e., rule(s)) can be vary based on the preferences of individual(s); also see [column 11, lines 40-45] wherein exception information can be determined manually (i.e., updating by user input/indication)).
Thus, Chinthagunti teaches identifying unexpected differences between structured data and standard data (see [column 12, line 53 to column 13, line 22]).
However, Chinthagunti does not explicitly teach the difference(s) or unexpected difference(s) is a result of malicious code.
On the other hand, Jang et al. explicitly teaches the difference(s) or unexpected difference(s) is a result of malicious code (see Jang et al., [0041]-[0042] for determining malicious DDL (i.e., malicious code) based on difference(s) between explicit DDL (i.e. structured data) and standard data from the profiling DB).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jang et al.’s teaching to Chinthagunti’s system by applying Chinthagunti’s method/process of identifying differences between structured data and standard data into any different types of data in a system including executable code as disclosed by Jang et al. to identify malicious code by determining differences caused by malicious code. Ordinarily skilled artisan would have been motivated to do so to provide Chinthagunti’s system with an effective way to identify differences/issues caused by malicious code. In addition, both of the references (Chinthagunti and Jang et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, a system for identifying issues/problems in a system by comparing data to baseline/known data to identify differences or unexpected differences. This close relation between both of the references highly suggests an expectation of success when combined.
As to claim 11, Chinthagunti teaches:
“A system” (see Chinthagunti, Abstract and Fig. 1) comprising:
“data processing hardware” (see Chinthagunti, Fig. 5 for processor(s) 502); and
“memory hardware in communication with the data processing hardware, the memory hardware storing instructions that when executed on the data processing hardware cause the data processing hardware to perform operations comprising” (see Chinthagunti, Fig. 5 for memory 510):
“obtaining structured data” (see Chinthagunti, [column 4, lines 1-20] for generating/obtaining metadata 114 that describes the data 106, wherein the metadata as disclosed can be interpreted as structured data as recited; also see [column 4, lines 43-48] for generating/obtaining metadata 114(2), wherein metadata 114(2) can be interpreted as structured data as recited;
“obtaining standard data corresponding to the structured data” (see Chinthagunti, [column 4, lines 20-28] for generating/obtaining metadata 114(1) wherein metadata 114(1) or baseline metadata can be interpreted as standard data as recited);
“comparing the structured data to the standard data” (see Chinthagunti, [column 4, lines 61-67] for comparing the metadata 114(1) (i.e., the standard data) to the metadata 114(2) (i.e., the structured data) to identify differences between the metadata 114(1) and the metadata 114(2));
“based on comparing the structured data to the standard data, identifying a difference between the structured data and the standard data” (see Chinthagunti, [column 4, lines 61-67] for comparing the metadata 114(1) (i.e., the standard data) to the metadata 114(2) (i.e., the structured data) to identify differences between the metadata 114(1) and the metadata 114(2));
“determining, based on at least one rule, that the difference between the structured data and the standard data is unexpected” (see Chinthagunti, [column 8, lines 34-56] and [column 12, lines 57-61] for determining whether the difference is acceptable/unacceptable or expected/unexpected based on exceptions wherein each exception as disclosed can be interpreted as a rule as recited; also see [column 13, lines 63-67] and [column 14, lines 35-56] for using a classifier to determine whether differences between metadata 114(1) and metadata 114(2) are acceptable/unacceptable (i.e., expected/unexpected), the classifier or machine learning process as disclosed (e.g., if a current difference is not included in the previous set of difference(s) or is not described in the exception information, the machine learning process may classify the current difference as an aberration to be further investigates) can be interpreted as equivalent to a rule as recited);
“based on determining that the difference between the structured data and the standard data is unexpected, generating a signal indicating that the difference between the structured data and the standard data is [unexpected]” (see Chinthagunti, [column 13, lines 8-22] for generating a notification if one or more differences between the metadata 114(1) and the metadata 114(2) are not described in the exception information (i.e., the difference(s) are unexpected);
“obtaining an indication that the structured data is not [unexpected]” (see Chinthagunti, [column 14, lines 17-34] wherein the difference(s) may be manually analyzed to determine whether the difference(s) are significant (i.e., indicating a potential problem or unexpected) or not (i.e., indicating the differences are acceptable or expected); and the acceptable or expected differences can be incorporated into training dataset to train a classifier); and
“updating the at least one rule based on the indication” (see Chinthagunti, [column 14, lines 17-56] for incorporating the acceptable or expected difference(s), which are manually determined/indicated, into training dataset to train the classifier (e.g., updating the rule used by the classifier or the machine learning method; also see [column 8, lines 51-53] wherein exception information (i.e., rule(s)) can be vary based on the preferences of individual(s); also see [column 11, lines 40-45] wherein exception information can be determined manually (i.e., updating by user input/indication)).
Thus, Chinthagunti teaches identifying unexpected differences between structured data and standard data (see [column 12, line 53 to column 13, line 22]).
However, Chinthagunti does not explicitly teach the difference(s) or unexpected difference(s) is a result of malicious code.
On the other hand, Jang et al. explicitly teaches the difference(s) or unexpected difference(s) is a result of malicious code (see Jang et al., [0041]-[0042] for determining malicious DDL (i.e., malicious code) based on difference(s) between explicit DDL (i.e. structured data) and standard data from the profiling DB).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Jang et al.’s teaching to Chinthagunti’s system by applying Chinthagunti’s method/process of identifying differences between structured data and standard data into any different types of data in a system including executable code as disclosed by Jang et al. to identify malicious code by determining differences caused by malicious code. Ordinarily skilled artisan would have been motivated to do so to provide Chinthagunti’s system with an effective way to identify differences/issues caused by malicious code. In addition, both of the references (Chinthagunti and Jang et al.) teach features that are directed to analogous art and they are directed to the same field of endeavor, such as, a system for identifying issues/problems in a system by comparing data to baseline/known data to identify differences or unexpected differences. This close relation between both of the references highly suggests an expectation of success when combined.
As to claims 2 and 12, these claims are rejected based on the same arguments as above to reject claims 1 and 11 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein obtaining the structured data comprises obtaining a plurality of instances of activity associated with an element of structured data” (see Chinthagunti et al., Fig. 3 and [column 3, line 66 to column 4, line 20] for generating/obtaining metadata (i.e., structured data) including obtaining different types of data/metadata).
As to claims 3 and 13, these claims are rejected based on the same arguments as above to reject claims 2 and 12 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein the operations further comprise identifying a hash corresponding to the element of structured data” (see Chinthagunti, Fig. 3 and [column 7, line 60 to column 8, line 15] wherein metadata is associated with different characteristics/attributes/elements, wherein each name associated with each characteristic/attribute/element (e.g., source name, table name, column name, etc.) can be interpreted as equivalent to a hash as recited).
As to claims 4 and 14, these claims are rejected based on the same arguments as above to reject claims 3 and 13 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein obtaining the plurality of instances of activity associated with the element is based on the hash” (see Chinthagunti, [column 14, lines 35-56] for obtaining previous set of previous difference(s) (i.e., instances of activity) between the current metadata (i.e., structured data) and baseline metadata (i.e., standard data)).
As to claims 5 and 15, these claims are rejected based on the same arguments as above to reject claims 2 and 12 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein each instance of the plurality of instances of activity is sourced from a different user device” (see Chinthagunti, [column 4, line 61 to column 5, line 14] wherein the data/metadata differences generated from comparison (i.e., instance of activity) are from source process on the host device(s)).
As to claims 6 and 16, these claims are rejected based on the same arguments as above to reject claims 1 and 11 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein the structured data comprises binary data” (see Chinthagunti, Fig. 3; also see Jang et al., [0034] for a portable executable (PE) file in a binary file format).
As to claims 7 and 17, these claims are rejected based on the same arguments as above to reject claims 1 and 11 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein the structured data comprises at least one of creator information, version information, or data type” (see Chinthagunti, Fig. 3 for source name (i.e., creator information); also see Jang et al., [0042] for VERSONINFO denotes information on a company manufacturing a DDL (i.e., creator information)).
As to claims 8 and 18, these claims are rejected based on the same arguments as above to reject claims 1 and 11 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein determining that the difference between the structured data and the standard data is unexpected comprises determining that the difference between the structured data and the standard data satisfies a tolerance threshold” (see Chinthagunti, [column 14, lines 25-56] if a current difference is not included in the previous set of difference(s) and is not described in the exception information, the machine learning process may classify the current difference as an aberration to be further investigated; also see [column 8, lines 34-53] for exception information that can be set by individual(s) (e.g., range of values, number of distinct value, etc.) to identify differences as acceptable/unacceptable or expected/unexpected).
As to claims 9 and 19, these claims are rejected based on the same arguments as above to reject claims 8 and 18 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein the operations further comprise adjusting the tolerance threshold based on the indication” (see Chinthagunti, [column 14, lines 17-56] for incorporating the acceptable or expected difference(s), which are manually determined/indicated, into training dataset to train the classifier (e.g., updating the rule used by the classifier or the machine learning method; also see [column 8, lines 51-53] wherein exception information (i.e., rule(s) or threshold number/value) can be vary based on the preferences of individual(s); also see [column 11, lines 40-45] wherein exception information can be determined manually (i.e., updating by user input/indication)).
As to claims 10 and 20, these claims are rejected based on the same arguments as above to reject claims 1 and 11 respectively, and are similarly rejected including the following:
Chinthagunti as modified by Jang et al. teaches:
“wherein the operations further comprise storing the difference between the structured data and the standard data at a registry” (see Chinthagunti, [column 13, lines 23-29] for storing the comparison results information (e.g., difference(s) between current metadata and baseline metadata) in a database (e.g., a registry); also see [column 14, lines 13-56] for using differences between metadata in training dataset and machine learning process).
Conclusion
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/Phuong Thao Cao/Primary Examiner, Art Unit 2164