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 Office Action has been issued in response to Applicant’s Communication of application S/N 19/038,226 filed on April 21, 2025. Claims 28-47 are pending with this application.
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 28-30, 33-42, and 45-47 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
With respect to claim 28, the limitations directed towards determine, based on a priority assigned to a plurality of data points in a plurality of different software modules, a plurality of critical data points of the plurality of data points; identify a classifier based on the plurality of critical data points, the classifier bound to the plurality of critical data points according to one or more binding rules configured to map the classifier to one or more schemas associated with the plurality of critical data points; generate, using the classifier, a decay index for the plurality of critical data points based on one or more inconsistencies between the plurality of different software modules and output associated with the plurality of critical data points; determine, based on a comparison of the decay index and a threshold, a level of impact to one or more operations performed by the plurality of different software modules, the level of impact based on the priority and the one or more inconsistences in the plurality of critical data points, is a process that, under its broadest reasonably interpretation, covers performance of these limitations in the mind but for the recitation of generic computer components. That is, other than reciting a system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points, nothing in the claim precludes these steps from practically being performed in the mind and/or by a human with pen and paper.
For example, but for the limitation stating a system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points, the mention of determine, based on a priority assigned to a plurality of data points in a plurality of different software modules, a plurality of critical data points of the plurality of data points; identify a classifier based on the plurality of critical data points, the classifier bound to the plurality of critical data points according to one or more binding rules configured to map the classifier to one or more schemas associated with the plurality of critical data points; generate, using the classifier, a decay index for the plurality of critical data points based on one or more inconsistencies between the plurality of different software modules and output associated with the plurality of critical data points; determine, based on a comparison of the decay index and a threshold, a level of impact to one or more operations performed by the plurality of different software modules, the level of impact based on the priority and the one or more inconsistences in the plurality of critical data points encompasses a user mentally determining anomalies in data points associated with software. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application by additional elements. In particular, the claim recites a system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points. A system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points is recited at a high level of generality (i.e., as a generic computer performing a generic computer function of analyzing) such that it amounts to no more than mere instructions to apply the exception. A system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points, is interpreted by the examiner to be mere data gathering which the courts have found to be insignificant extra-solution activity. These elements do not integrate the abstract idea into a practical application because it does not impose a meaningful limit on the judicial exception and it merely confines the claim to a particular technological environment or field of use in conjunction with the abstract idea.
These claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, a system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points is recited at a high level of generality to apply the exception using generic components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The additional elements a system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points is interpreted to be well understood, routine and conventional activity (Storing and retrieving information in memory, Versata (see MPEP 2106.06(d))). To further elaborate, the additional limitations a system, comprising: one or more processors, coupled with memory, to transmit, for display on a graphical user interface, an indication of the plurality of critical data points does not impose a meaningful limit on the judicial exception and it merely confines the claims to a particular technological environment or field of use. Claim 1 is not patent eligible.
Claims 28 and 47 are similarly rejected because they are similar in scope.
With respect to claims 29 and 42, the limitations are directed towards determine, based on the comparison of the decay index and the threshold, that the decay index exceeds a threshold; and transmit the indication of the plurality of critical data points responsive to the decay index exceeding the threshold. The elements directed to determine, based on the comparison of the decay index and the threshold, that the decay index exceeds a threshold further elaborate the abstract idea and the human mind and/or with pen and paper can determine, based on the comparison of the decay index and the threshold, that the decay index exceeds a threshold. The additional elements directed to transmit the indication of the plurality of critical data points responsive to the decay index exceeding the threshold further elaborate the abstract idea and merely confine the claim to a particular technological environment or field of use. Therefore, claims 29 and 42 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claim 30, the limitations are directed towards determine that a second decay index generated for a second plurality of critical data points does not exceed the threshold indicating the level of impact to a second operation performed by the plurality of different software modules. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can determine that a second decay index generated for a second plurality of critical data points does not exceed the threshold indicating the level of impact to a second operation performed by the plurality of different software modules. Therefore, claim 30 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claims 33 and 45, the limitations are directed towards identify the plurality of critical data points in the plurality of different software modules, each data point in the plurality of critical data points assigned with the priority, the priority indicating a first level of priority, a second level of priority, or a third level of priority. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can identify the plurality of critical data points in the plurality of different software modules, each data point in the plurality of critical data points assigned with the priority, the priority indicating a first level of priority, a second level of priority, or a third level of priority. Therefore, claims 33 and 45 do not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claims 34 and 46, the limitations are directed towards wherein to identify the classifier, the one or more processors further: identify a source table associated with the binding rules. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can identify a source table associated with the binding rules. Therefore, claims 34 and 46 do not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claim 35, the limitations are directed towards wherein the classifier comprises a domain specific classifier. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can determine a classifier is determine a classifier is domain specific classifier. Therefore, claim 35 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claim 36, the limitations are directed towards wherein the domain specific classifier is selected from at least one of a duplication classifier, cross domain classifier, knowledge based classifier, or format classifier. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can select the domain specific classifier from at least one of a duplication classifier, cross domain classifier, knowledge based classifier, or format classifier. Therefore, claim 36 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claim 37, the limitations are directed towards wherein the classifier comprises at least one of a dangling key classifier, a histogram classifier, a reporting tree classifier, an accuracy classifier, or a time continuation classifier. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can determine the at least one of a dangling key classifier, a histogram classifier, a reporting tree classifier, an accuracy classifier, or a time continuation classifier. Therefore, claim 37 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claim 38, the limitations are directed towards wherein the plurality of critical data points correspond to at least one of employee hiring, employment termination, employee transfer, position management, organizational changes, time off requests, benefits management, adding compensation, or payroll data. These elements further elaborate the abstract idea and the human mind and/or with pen and paper can correspond the plurality of critical data points to at least one of employee hiring, employment termination, employee transfer, position management, organizational changes, time off requests, benefits management, adding compensation, or payroll data. Therefore, claim 38 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claim 39, the limitations are directed towards bind the classifier to the plurality of critical data points using at least one of static mapping or a machine learning mapping. These elements further elaborate the abstract idea and merely confine the claim to a particular technological environment or field of use. Therefore, claim 39 does not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
With respect to claims 40, the limitations are directed towards generate a data quality report comprising the level of impact of the operations performed by the plurality of different software modules; and transmit, for display on the graphical user interface, the data quality report. These additional elements are interpreted to be well understood, routine and conventional activity (Storing and retrieving information in memory, Versata (see MPEP 2106.06(d))). Therefore, claims 40 do not recite additional limitations which tie the abstract idea into a practical application and amount to significantly more than the identified judicial exception.
Claim(s) 28-30, 33, 35-43, and 47 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinemeyer et al. (U.S. Publication No. 20210194924 A1) hereinafter Heinemeyer, in view of Zabel et al. (U.S. Classification No.: US 20210117436 A1) hereinafter Zabel, and further in view of Malka et al. (U.S. Publication No.: US 20130117203 A1) hereinafter Malka.
As to claim 28:
Heinemeyer discloses:
A system, comprising: one or more processors, coupled with memory, to: determine, based on a priority assigned to a plurality of data points in a plurality of different software modules, a plurality of critical data points of the plurality of data points [Paragraph 0024 teaches an AI adversary red team may refer to at least one or more of an apparatus, an appliance, a simulator, an extension (or agent), a service, a module, etc., configured as an attacking module or the like that may combine one or more cooperating modules, engines. Paragraph 0032 teaches these defense systems 100/115/125 may determine the unusual patterns by (i) filtering out what activities/events/alerts that fall within the window of what is the normal pattern of life for that network/system/entity/device/user under analysis. Paragraph 0045 teaches the AI adversary red team 105 may include and cooperate with one or more AI models trained with machine learning … these trained AI models may be configured to identify data points from the contextual knowledge of the organization and its entities. Paragraph 0055 teaches AI adversary red team 105 may include a collections module configured to monitor and collect specific organization-based data from multiple software processes executing on, for example, one of more of the host endpoint agents residing on the respective endpoint computing devices 101A-B in the cyber threat defense system 100. Paragraph 0099 teaches the gatherer modules may then filter or condense the mass of data down into the important or salient features of data. Note: Filtered (priority assignment) important or salient data points (critical data points) collected by an AI adversary red team as part of the defense systems from multiple software processes (software modules) reads on the claimed determining, by a rule engine, a number of critical data points in a number of different software modules.];
generate, using the classifier, a decay index for the plurality of critical data points based on one or more inconsistencies between the plurality of different software modules and output associated with the plurality of critical data points [Paragraph 0055 teaches AI adversary red team 105 may include a collections module configured to monitor and collect specific organization-based data from multiple software processes executing on, for example, one of more of the host endpoint agents residing on the respective endpoint computing devices 101A-B in the cyber threat defense system 100. Paragraph 0075 teaches cyber threat module may then determine, in accordance with the analyzed metrics and the moving benchmark used by the self-learning model of normal behavior of the entity, an anomaly score indicative of a likelihood of a harmful cyber threat and its severity. Paragraph 0128 teaches the autonomous email-report composer can compose an email threat report on cyber threats that is composed in a human-readable format with natural language prose, terminology, and level of detail on the cyber threats aimed at a target audience being able to understand the terminology and the detail. Such modules and AI models may cooperate with the autonomous email-report composer to indicate in the email threat report, for example, an email attack's purpose and/or targeted group (such as members of the finance team, or high-level employees). Note: An email-report containing a level of detail on the cyber threats such as an anomaly score (inconsistencies in the critical data points and decay index) that indicates the likelihood of a harmful cyber threat based on data collected from multiple software processes (the different software modules), wherein the cited benchmark is used as a threshold (decay index exceeds a threshold) to determine data inconsistencies (decay).] and
transmit, for display on a graphical user interface, an indication of the plurality of critical data points [Paragraph 0129 teaches the formatting module may format, present a rank for, and output the current email threat report, from a template of a plurality of report templates, that is outputted for a human user's consumption in a medium of, any of 1) a printable report, 2) presented digitally on a user interface, 3) in a machine readable format for further use in machine-learning reinforcement and refinement, and 4) any combination of the three.]
Heinemeyer discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose identify a classifier based on the plurality of critical data points, the classifier bound to the plurality of critical data points according to one or more binding rules configured to map the classifier to one or more schemas associated with the plurality of critical data points, and determine, based on a comparison of the decay index and a threshold, a level of impact to one or more operations performed by the plurality of different software modules, the level of impact based on the priority and the one or more inconsistences in the plurality of critical data points.
Zabel discloses:
identify a classifier based on the plurality of critical data points, the classifier bound to the plurality of critical data points according to one or more binding rules configured to map the classifier to one or more schemas associated with the plurality of critical data points [Paragraph 0016 teaches determine, based at least on the first plurality of data objects, a first data classifier corresponding to the first database schema. Note: A determination (a rule) based on first plurality of data objects (critical data points) that a classifier corresponds (maps) to a data schema reads on the claimed one or more binding rules configured to map the classifier to one or more schemas associated with the critical data points.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Heinemeyer, by incorporating a determination based on first plurality of data objects that a classifier corresponds to a data schema, as taught by Zabel (see Paragraph 0016), because both applications are directed to classifier processing; a determination based on first plurality of data objects that a classifier corresponds to a data schema provides improved data mapping (see Zabel Abstract).
Heinemeyer and Zabel discloses some of the limitations as set forth in claim 1 but does not appear to expressly disclose identify a classifier based on the plurality of critical data points, the classifier bound to the plurality of critical data points according to one or more binding rules configured to map the classifier to one or more schemas associated with the plurality of critical data points, and determine, based on a comparison of the decay index and a threshold, a level of impact to one or more operations performed by the plurality of different software modules, the level of impact based on the priority and the one or more inconsistences in the plurality of critical data points.
Malka discloses:
determine, based on a comparison of the decay index and a threshold, a level of impact to one or more operations performed by the plurality of different software modules, the level of impact based on the priority and the one or more inconsistences in the plurality of critical data points [Paragraph 0112 teaches quality analysis module 260 can evaluate one or more of the quality metrics and determine whether correction of the data is necessary. In some cases, if the quality metrics indicate that the data has a quality level above a threshold level (e.g., 98%, 95%, and so on), quality analysis module 260 might determine that correction of the data is not to be performed. However, if the quality level is below the threshold level, then quality analysis module 260 can recommend correction of the data (e.g., by data enhancement component 215). Note: Determining that data quality levels based on comparing the quality levels to thresholds requires correction must include determining a level of impact reads on the claims.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Heinemeyer and Zabel, by incorporating determining that data quality levels based on comparing the quality levels to thresholds requires correction must include determining a level of impact, as taught by Malka (see Paragraph 0112), because the three applications are directed to classifier processing; incorporating determining that data quality levels based on comparing the quality levels to thresholds requires correction must include determining a level of impact improves quality as compared to the original set of data (see Malka Paragraph 0075).
As to claim 29:
Heinemeyer discloses:
The system of claim 28, wherein the one or more processors further: determine, based on the comparison of the decay index and the threshold, that the decay index exceeds a threshold [Paragraph 0055 teaches AI adversary red team 105 may include a collections module configured to monitor and collect specific organization-based data from multiple software processes executing on, for example, one of more of the host endpoint agents residing on the respective endpoint computing devices 101A-B in the cyber threat defense system 100. Paragraph 0075 teaches cyber threat module may then determine, in accordance with the analyzed metrics and the moving benchmark used by the self-learning model of normal behavior of the entity, an anomaly score indicative of a likelihood of a harmful cyber threat and its severity. Paragraph 0128 teaches the autonomous email-report composer can compose an email threat report on cyber threats that is composed in a human-readable format with natural language prose, terminology, and level of detail on the cyber threats aimed at a target audience being able to understand the terminology and the detail. Such modules and AI models may cooperate with the autonomous email-report composer to indicate in the email threat report, for example, an email attack's purpose and/or targeted group (such as members of the finance team, or high-level employees). Note: An email-report containing a level of detail on the cyber threats such as an anomaly score (inconsistencies in the critical data points) that indicates the likelihood of a harmful cyber threat based on data collected from multiple software processes (the different software modules), wherein the cited benchmark is used as a threshold (decay index exceeds a threshold) to determine data inconsistencies (decay).]; and transmit the indication of the plurality of critical data points responsive to the decay index exceeding the threshold [Paragraph 0129 teaches the formatting module may format, present a rank for, and output the current email threat report, from a template of a plurality of report templates, that is outputted for a human user's consumption in a medium of, any of 1) a printable report, 2) presented digitally on a user interface, 3) in a machine readable format for further use in machine-learning reinforcement and refinement, and 4) any combination of the three.]
Claim 42 recites similar limitations as in claim 29. Therefore claim 42 is rejected for the same reasons as set forth above. See claim 29 for analysis.
As to claim 30:
Heinemeyer discloses:
The system of claim 28, wherein the one or more processors further: determine that a second decay index generated for a second plurality of critical data points does not exceed the threshold indicating the level of impact to a second operation performed by the plurality of different software modules [Paragraph 0055 teaches AI adversary red team 105 may include a collections module configured to monitor and collect specific organization-based data from multiple software processes executing on, for example, one of more of the host endpoint agents residing on the respective endpoint computing devices 101A-B in the cyber threat defense system 100. Paragraph 0075 teaches cyber threat module may then determine, in accordance with the analyzed metrics and the moving benchmark used by the self-learning model of normal behavior of the entity, an anomaly score indicative of a likelihood of a harmful cyber threat and its severity. Paragraph 0128 teaches the autonomous email-report composer can compose an email threat report on cyber threats that is composed in a human-readable format with natural language prose, terminology, and level of detail on the cyber threats aimed at a target audience being able to understand the terminology and the detail. Such modules and AI models may cooperate with the autonomous email-report composer to indicate in the email threat report, for example, an email attack's purpose and/or targeted group (such as members of the finance team, or high-level employees). Note: An email-report containing a level of detail on the cyber threats such as an anomaly score (inconsistencies in the critical data points and second data index) that indicates the likelihood of a harmful cyber threat based on data collected from multiple software processes (the different software modules), wherein the cited benchmark is used as a threshold (decay index exceeds a threshold) to determine data inconsistencies (decay).]
As to claim 33:
Heinemeyer discloses:
The system of claim 28, wherein the one or more processors further: identify the plurality of critical data points in the plurality of different software modules, each data point in the plurality of critical data points assigned with the priority, the priority indicating a first level of priority, a second level of priority, or a third level of priority [Paragraph 0130 teaches the modules can repetitively go through these steps and re-duplicate steps to filter and rank the one or more supported possible cyber threat hypotheses from the possible set of cyber threat hypotheses and/or compose the detailed information to populate into the email threat report. Note: Repeating filtering out data at various stages of processing reads on the claims.]
Claim 45 recites similar limitations as in claim 33. Therefore claim 45 is rejected for the same reasons as set forth above. See claim 33 for analysis.
As to claim 35:
Heinemeyer discloses:
The system of claim 28, wherein the classifier comprises a domain specific classifier [Paragraph 0042 teaches more than one or more of the entities 130-142 may be associated with its own internal client network (not shown), where each client network may represent an organizational sub-section, department, peer group/team. Paragraph 0045 teaches trained AI models may be configured to identify data points from the contextual knowledge of the organization and its entities, which may include, but is not limited to, language-based data, email/network connectivity and behavior pattern data, and/or historic knowledgebase data. Paragraph 0046 teaches the list of specific organization-based classifiers may be configured based on the organization. Paragraph 0122 teaches specific devices associated with compromising specific users, such as finance or human resources. Note: Classifiers that are organization-based (domain specific) read on the claimed classifier is a domain specific classifier because organizations are associated entities in organizational sub-sections or departments such as human resources.]
As to claim 36:
Heinemeyer discloses:
The system of claim 35, wherein the domain specific classifier is selected from at least one of a knowledge based classifier [Paragraph 0046 teaches the list of specific organization-based classifiers implemented by the AI adversary red team 105 may include a historic knowledgebase classifier.], duplication classifier, cross domain classifier, or format classifier.
As to claim 37:
Heinemeyer discloses:
The system of claim 28, wherein the classifier comprises at least one of a accuracy classifier [Paragraph 0044 teaches as shown in FIG. 1, the various cooperating modules residing in the AI adversary red team 105 may include, but are not limited to, an AI classifier with a list of specified classifiers. Paragraph 0045 teaches these trained AI models may be configured to identify data points from the contextual knowledge of the organization and its entities, which may include, but is not limited to, language-based data, email/network connectivity and behavior pattern data, and/or historic knowledgebase data. As noted above, the AI adversary red team 105 may use the trained AI models to cooperate with one or more AI classifier(s) by producing a list of specific organization-based classifiers for the AI classifier. Figure 4 and Paragraph 0099 teaches a gather module may be triggered by specific events and/or alerts of anomalies… inline data may be gathered on the deployment from a data store when the traffic is observed. The scope and wide variation of data available in the data store results in good quality data for analysis. Note: The trained AI models identifying data points such as language-based data, email/network connectivity, behavior pattern data, and historic knowledgebase data (data types) to produce a list of AI classifiers working with a gather module (accuracy classifier) to ensure good data quality regarding collected data points reads on the claimed identifying , by the one or more processors, a classifier based on a data type of the critical data points, wherein the classifier is at least one of a an accuracy classifier. The examiner interprets the claimed accuracy classifier to be a classifier verifying data quality.] dangling key classifier, a histogram classifier, a reporting tree classifier, an, or a time continuation classifier.
As to claim 38:
Heinemeyer discloses:
The system of claim 28, wherein the plurality of critical data points correspond to at least one of position management, employee hiring, organizational changes employment termination, employee transfer[Paragraph 0046 teaches the hierarchical relationship classifier may be configured to use text trained on identifying a high-level/low-level employee structure (e.g., management employees vs. entry (or subordinate) employees) based on all of the collected employee titles and roles for that organization. Paragraph 0067 teaches the AI adversary red team 105 may then locate all identifiable employees via public open sources databases/servers 122, such as LinkedIn, industry group and team pages on a company's website, Google, press releases, etc. and then retrieves the employee names, job titles, and another needed/desired inputs … AI adversary red team 105 may use one or more of its modules, AI models, and AI classifiers to attempt to automatically identify employee relationships and hierarchical structures for that organization based on retrieved names, job titles. Note: Text and data (data points) related to employee structure and organizational employee layout (organizational changes) associated reasonably includes employees that are/were hired (employee hiring), employee’s position in the organization (position management).] time off requests, benefits management, adding compensation, or payroll data.
As to claim 39:
Heinemeyer discloses:
The system of claim 28, wherein the one or more processors further: bind the classifier to the plurality of critical data points using at least one of static mapping or a machine learning mapping [Paragraph 0045 teaches the AI adversary red team 105 may include and cooperate with one or more AI models trained with machine learning on the contextual knowledge of the organization… These trained AI models may be configured to identify data points from the contextual knowledge of the organization and its entities, which may include, but is not limited to, language-based data, email/network connectivity and behavior pattern data, and/or historic knowledgebase data… the AI adversary red team 105 may use the trained AI models to cooperate with one or more AI classifier(s) by producing a list of specific organization-based classifiers for the AI classifier. Paragraph 0046 teaches the list of specific organization-based classifiers may be configured based on the organization, the available OS data, the various customizable scenario attacks and phishing emails, and so on. In some embodiments, the specific organization-based classifiers may include one or more default (or predetermined) classifiers in conjunction with one or more customized classifiers. Furthermore, in several embodiments, the list of specific organization-based classifiers implemented by the AI adversary red team 105 may include, but are not limited to, (i) a content classifier, (ii) a natural language classifier, (iii) a historic knowledgebase classifier, (iv) an OS classifier, (v) an industry group classifier, (vi) a domain classifier, (vii) an attack vectors classifier, and (viii) a hierarchical relationship classifier. Note: Configuring (mapping) the AI classifiers to work with natural language data, historic data, and industry group data by utilizing machine learning and AI reads on the claimed wherein binding the classifier to the critical data points comprises machine learning mapping.]
As to claim 40:
Heinemeyer, Zabel, and Malka discloses all of the limitations as set forth in claim 28.
Malka also discloses:
The system of claim 28, wherein the one or more processors further: generate a data quality report comprising the level of impact of the operations performed by the plurality of different software modules; and transmit, for display on the graphical user interface, the data quality report [Paragraph 0178 teaches the request can indicate the quality issue that was discovered and also provide a recommended solution to the problem. In an example, the request can be output as a prompt in a display on a user device asking the user to review the data and provide additional information that can be utilized to correct the problem.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Heinemeyer and Zabel, by incorporating request can indicate the quality issue that was discovered and also provide a recommended solution to the problem and displaying the results of that request, as taught by Malka (see Paragraph 0112), because the three applications are directed to classifier processing; incorporating request can indicate the quality issue that was discovered and also provide a recommended solution to the problem and displaying the results of that request improves quality as compared to the original set of data (see Malka Paragraph 0075).
Claim(s) 31, 32, 43, and 44 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinemeyer et al. (U.S. Publication No. 20210194924 A1) hereinafter Heinemeyer, in view of Zabel et al. (U.S. Classification No.: US 20210117436 A1) hereinafter Zabel, in view of Malka et al. (U.S. Publication No.: US 20130117203 A1) hereinafter Malka, in view of Basu et al. (U.S. Publication No.: US 20220300557 A1) hereinafter Basu, and further in view of Bohannon et al. (U.S. Publication No.: US 20080027930 A1) hereinafter Bohannon.
As to claim 31:
Heinemeyer discloses:
The system of claim 28, wherein the one or more processors further: and scan the plurality of critical data points bound to the classifier that supplies the transformation of the input and the output to identify one or more inconsistencies between the plurality of different software modules and the input or the output associated with the plurality of critical data points [Paragraph 0055 teaches AI adversary red team 105 may include a collections module configured to monitor and collect specific organization-based data from multiple software processes executing on, for example, one of more of the host endpoint agents residing on the respective endpoint computing devices 101A-B in the cyber threat defense system. Paragraph 0065 teaches the host endpoint agents 211A-D may be configured to reside on their respective endpoint devices 201A-D… monitor the “pattern of life” of their respective endpoint devices 201A-D (e.g., including monitoring at least one or more of: (a) process behavior (use of network, filesystem, etc.), (b) relationships between processes (parent/child, shared files, IPC, etc.), and/or (c) user behavior (applications commonly used, IT habits, etc.)… audit system details, for example installed operating systems, installed software, software versioning, security update status, etc.; (b) gather system usage activity such as shutdown periods, login failures, file modifications, network connections, etc.. lastly react autonomously to anomalies in pattern of life. Note: Gathering data (scanning) indicating the “pattern of life” and detecting and reacting to anomalies (inconsistencies) in pattern of life reads on the claimed scanning the critical data points comprises checking for inconsistencies in data points between the different software modules.]
Heinemeyer, Zabel, and Malka discloses some of the limitations as set forth in claim 28 and some of 31 but does not appear to expressly disclose bind the classifier to the plurality of critical data points based on: i) identification of an input associated with of the plurality of critical data points and the output associated with of the plurality of critical data points, and ii) supply of a transformation of the input and the output based on one or more binding rules that identify a source table and are configured to map the classifier to one or more schemas associated with the plurality of critical data points.
Basu discloses:
bind the classifier to the plurality of critical data points based on: i) identification of an input associated with of the plurality of critical data points and the output associated with of the plurality of critical data points, and ii) supply of a transformation of the input and the output based on one or more binding rules [Paragraph 0001 teaches the term “classifier” may refer to any computing platform, system, application, program, model, function, routine, and/or subroutine that takes an input “datapoint” and outputs a “label” for the datapoint. Paragraph 0015 teaches A classifier (or classifier model) “classifies” an input datapoint (e.g., an input vector that encodes an object such as an image or document) by assigning one or more possible labels to the data, via the mapping functionality of the classifier. Note: Datapoints (critical data point) input into the classifier resulting in labels (output) for the data points reads on the claimed an input and an output of the critical data points and supplying a transformation function. The examiner interprets transforming datapoints into the labels for data points to be a transformation function.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Heinemeyer, Zabel, and Malka, by incorporating datapoints (critical data point) input into the classifier resulting in labels (output) for the data points, as taught by Basu (see Paragraph 0001 and 0015), because the four applications are directed to classifier processing; incorporating datapoints (critical data point) input into the classifier resulting in labels (output) for the data points improves the performance of a classifier (see Basu Paragraph 0008).
Heinemeyer, Zabel, Malka, and Basu discloses some of the limitations as set forth in claim 28 and some of 31 but does not appear to expressly disclose identify a source table and are configured to map the classifier to one or more schemas associated with the plurality of critical data points.
Bohannon discloses:
identify a source table and are configured to map the classifier to one or more schemas associated with the plurality of critical data points [Paragraph 0002 teaches a schema mapping is a data transformation that, given an instance conforming to a source schema, will produce an instance that conforms to a target schema while preserving the appropriate information content of the source. Finding schema mappings is a common task in a wide variety of data exchange and integration scenarios. Paragraph 0006 teaches according to one aspect of the invention, at least one source table is mapped to at least one target table. A list of matches are generated between the at least one source table and the at least one target table. Paragraph 0007 teaches or example, by (i) creating a set of views for categorical attributes in the tables and adding a view for each partitioning of the values of the attributes in the tables; (ii) using a classifier built on target attribute values; or (iii) evaluating internal features of a source table to identify candidate logical conditions by rating one or more attributes on an ability of the one or more rated attributes to classify values of other attributes.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Heinemeyer, Zabel, Malka, and Basu, by incorporating utilizing a classifier to identify a source table associated with schemas using data attributes (critical data points), as taught by Bohannon (see Paragraph 0002, 0006, and 0007), because the five applications are directed to classifier processing; incorporating utilizing a classifier to identify a source table associated with schemas using data attributes (critical data points) improves matching quality (see Bohannon Paragraph 0029).
Claim 43 recites similar limitations as in claim 31. Therefore claim 43 is rejected for the same reasons as set forth above. See claim 31 for analysis.
As to claim 32:
Heinemeyer, Zabel, Malka, Basu, and Bohannon discloses all of the limitations as set forth in claim 28 and 31.
Heinemeyer also discloses:
The system of claim 31, wherein to scan the plurality of critical data points, the one or more processors further: check for the one or more inconsistences in the plurality of data points between the plurality of different software modules. [Paragraph 0055 teaches AI adversary red team 105 may include a collections module configured to monitor and collect specific organization-based data from multiple software processes executing on, for example, one of more of the host endpoint agents residing on the respective endpoint computing devices 101A-B in the cyber threat defense system. Paragraph 0065 teaches the host endpoint agents 211A-D may be configured to reside on their respective endpoint devices 201A-D… monitor the “pattern of life” of their respective endpoint devices 201A-D (e.g., including monitoring at least one or more of: (a) process behavior (use of network, filesystem, etc.), (b) relationships between processes (parent/child, shared files, IPC, etc.), and/or (c) user behavior (applications commonly used, IT habits, etc.)… audit system details, for example installed operating systems, installed software, software versioning, security update status, etc.; (b) gather system usage activity such as shutdown periods, login failures, file modifications, network connections, etc.. lastly react autonomously to anomalies in pattern of life. Note: Gathering data (scanning) indicating the “pattern of life” and detecting and reacting to anomalies (inconsistencies) in pattern of life reads on the claimed scanning the critical data points comprises checking for inconsistencies in data points between the different software modules.]
Claim 44 recites similar limitations as in claim 32. Therefore claim 44 is rejected for the same reasons as set forth above. See claim 32 for analysis.
Claim(s) 34 and 46 is/are rejected under 35 U.S.C. 103 as being unpatentable over Heinemeyer et al. (U.S. Publication No. 20210194924 A1) hereinafter Heinemeyer, in view of Zabel et al. (U.S. Classification No.: US 20210117436 A1) hereinafter Zabel, in view of Malka et al. (U.S. Publication No.: US 20130117203 A1) hereinafter Malka, and further in view of Bohannon et al. (U.S. Publication No.: US 20080027930 A1) hereinafter Bohannon.
As to claim 34:
Heinemeyer, Zabel, and Malka discloses some of the limitations as set forth in claim 28 but does not appear to expressly disclose wherein to identify the classifier, the one or more processors further: identify a source table associated with the binding rules
Bohannon discloses:
The system of claim 28, wherein to identify the classifier, the one or more processors further: identify a source table associated with the binding rules [Paragraph 0002 teaches a schema mapping is a data transformation that, given an instance conforming to a source schema, will produce an instance that conforms to a target schema while preserving the appropriate information content of the source. Finding schema mappings is a common task in a wide variety of data exchange and integration scenarios. Paragraph 0006 teaches according to one aspect of the invention, at least one source table is mapped to at least one target table. A list of matches are generated between the at least one source table and the at least one target table. Paragraph 0007 teaches or example, by (i) creating a set of views for categorical attributes in the tables and adding a view for each partitioning of the values of the attributes in the tables; (ii) using a classifier built on target attribute values; or (iii) evaluating internal features of a source table to identify candidate logical conditions by rating one or more attributes on an ability of the one or more rated attributes to classify values of other attributes.]
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, to combine the teaching of the cited references and modify the invention as taught by Heinemeyer, Zabel, and Malka, by incorporating utilizing a classifier to identify a source table associated with schemas using data attributes (critical data points), as taught by Bohannon (see Paragraph 0002, 0006, and 0007), because the four applications are directed to classifier processing; incorporating utilizing a classifier to identify a source table associated with schemas using data attributes (critical data points) improves matching quality (see Bohannon Paragraph 0029).
Claim 46 recites similar limitations as in claim 34. Therefore claim 46 is rejected for the same reasons as set forth above. See claim 34 for analysis.
Conclusion
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/EARL LEVI ELIAS/Examiner, Art Unit 2169
/SHERIEF BADAWI/Supervisory Patent Examiner, Art Unit 2169
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