FINAL OFFICE ACTION
Status of the Claims
Claims 1, 3-13, and 15-20 are rejected under 35 U.S.C. 101
Claims 1, 3-13, and 15-20 are rejected under 35 U.S.C. 103
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-13, and 15-20 are rejected under 35 U.S.C. 101
Regarding Claim 1,
Step 2A Prong 1 Analysis: The Limitations:
Determining… based on the metadata, one or more first configurable items associated with one or more applications and/or services related to the current incident, MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
analyze empirical data that has linked a set of historical configurable items in the past,
assigning an association score to each of the one or more first configurable items based on a frequency for each of the one or more first configurable items historically being correlated to an application and/or service associated with the data object; MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determining, the association score for each of the one or more first configurable items is greater than a threshold value; MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determining, utilizing a second model and based on the metadata, one or more second configurable items associated with one or more products related to the current incident; MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determining, utilizing a third model and based on the metadata, one or more third configurable items associated with one or more lines of business and/or logical associations related to the current incident; MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
generating a list of configurable items by aggregating the one or more first configurable items, the one or more second configurable items, and the one or more third configurable items; MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
automatically filtering the list of configurable items to remove at least one redundant configurable item from the list; MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Step 2A Prong 2 Analysis:
Claim 1 additionally recites,
receiving a data object indicating an occurrence of a current incident, the data object including metadata, wherein the current incident is an occurrence that disrupts or causes a loss of operation, service, or function of a computer system MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity.
outputting the filtered list of configurable items excluding the at least one redundant configurable item to the computer system that automatically takes corrective action based on the filtered list MPEP 2106.05(g); This limitation recites additional elements that amount to no more than insignificant extra solution activity.
utilizing a first model, the first model including an association model that implements an algorithm including an Apriori, Eclat, or a Frequent Pattern Growth algorithm, MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
the association model including a machine learning model configured to MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
wherein the corrective action corrects a loss of operation, service, or function of the computer system, thereby improving operation of the computer system. MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “insignificant extra-solution activity” and “mere instructions to apply”. Mere instructions to apply an exception and insignificant extra-solution activity cannot provide an inventive concept. (See MPEP 2106.05(g)). The claim is not patent eligible.
Regarding Claim 3,
Step 2A Prong 1 Analysis: The Limitations:
confirming that the one or more lines of business are stored in association with the current incident. MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Regarding Claim 4,
Step 2A Prong 1 Analysis: The Limitations:
searching logical levels for the one or more lines of business to determine one or more businesses and/or applications related to the one or more lines of business. MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Regarding Claim 5,
Step 2A Prong 1 Analysis: The Limitations:
extracting one or more configurable items associated with the one or more businesses and/or applications. MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Regarding Claim 6,
Step 2A Prong 1 Analysis: The Limitations:
determine the one or more third configurable items, MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
determine configurable items that have been affected by past incidents that also affected the current incident. MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Step 2A Prong 2 Analysis:
Claim 6 additionally recites,
applying an association model to the extracted one or more configurable items associated with the one or more businesses and/or applications to MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
wherein the association model is configured to MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 7,
Step 2A Prong 1 Analysis: The Limitations:
analyzing historical incident data… to determine configurable items that were affected during one or more past incidents related to the one or more applications and/or services. MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Step 2A Prong 2 Analysis:
Claim 7 additionally recites,
utilizing an association model MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 8,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 7
Step 2A Prong 2 Analysis:
Claim 8 additionally recites,
wherein the historical incident data includes data that is input by a user to indicate applications and/or services related to past incidents. This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 9,
Step 2A Prong 1 Analysis: The Limitations:
analyzing historical incident data… to determine configurable items that were affected during one or more past incidents related to the one or more products. MPEP 2106.04(a)(2); This limitation covers performance in the mind in the form of evaluation and judgement with the assistance of pen and paper. Therefore, this limitation recites a mental process.
Step 2A Prong 2 Analysis:
Claim 9 additionally recites,
utilizing an association model MPEP 2106.05(f); This limitation recites additional elements that are mere instructions to apply an exception for the abstract ideas.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements are “mere instructions to apply”. Mere instructions to apply an exception cannot provide an inventive concept. The claim is not patent eligible.
Regarding Claim 10,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 9
Step 2A Prong 2 Analysis:
Claim 10 additionally recites,
wherein the historical incident data includes data that is input by a user to indicate products related to past incidents. This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 11,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 11 additionally recites,
wherein the data object is representative of a configurable item. This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Regarding Claim 12,
Step 2A Prong 1 Analysis: See corresponding analysis of Claim 1
Step 2A Prong 2 Analysis:
Claim 12 additionally recites,
historical incident data for the data object; and a line of business for the data object, the line of business including an association logic linking the line of business with one or more of: a business service, a service offering, an application, an application instance or web service, a server, or a service. This limitation recites additional elements that do not apply an exception for the abstract ideas in a meaningful way.
Step 2B Analysis:
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to the integration of the abstract ideas into a practical application, all of the additional elements do not apply the exception in a meaningful way. The claim is not patent eligible.
Claims 13 and 20 recite a shift of statutory category and are rejected under 35 U.S.C. 101 under the same grounds of rejection as claim 1.
Claims 15-19 are rejected under 35 U.S.C. 101 under the same grounds of rejection as claims 3-7 respectively.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-13, and 15-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schreiber et al. (U.S. Patent No. 11,595,243 B1), hereinafter referred to as Schreiber, in view of Castrejon, III et al. (U.S. Publication No. 2025/0016158 A1), hereinafter referred to as Castrejon.
Regarding Claim 1, Schreiber teaches:
A computer implemented method the method comprising:
receiving a data object indicating an occurrence of a current incident, the data object including metadata, (Col. 4, lines 61-64; regarding, “incident orchestrator 112 can receive data from one or more monitoring services 108 which includes various performance metrics which characterize the performance of one or more services 110 of provider network 100.”; Col. 5, lines 14-23; regarding, “At the beginning of an incident, the metrics of one or more services may begin to degrade. Based on these metrics (e.g., a specific metric value, a metric value relative to a minimum threshold or maximum threshold, depending on the metric, etc.), at numeral 2, the incident orchestrator 112 may determine that an incident is occurring. At numeral 3, while the incident is occurring, the incident orchestrator 112 can retrieve past incident data from incident data repository 114 and state change information from a prior time period that is maintained in state data repository 115.”);
wherein the current incident is an occurrence that disrupts or causes a loss of operation, service, or function of a computer system; (Col. 2, lines 29-33; regarding, “If an incident occurs that affects one or more of these provider network services, then the applications and services of a large number of customers are potentially affected.”)
determining, utilizing a first model, the first model including an association model… based on the metadata, one or more first configurable items associated with one or more applications and/or services related to the current incident, (Col. 5, lines 59-65; regarding, “The incident model 116 may be trained on the prior incident data that has been labeled with its resolution information. This enables the incident model 116 to learn to associate specific inputs (e.g., a description of the incident, metric data associated with affected services, etc.) with the mitigation actions and/or teams that were deployed to resolve the past incidents.”);
the association model including a machine learning model configured to analyze empirical data that has linked a set of historical configurable items in the past, (Col. 5, lines 50-53; regarding, “incident models 116 may include machine learning models, such as deep learning models, tree models, statistical models, etc., which have been trained to identify likely causes of an incident based on current and past performance data from the provider network 100”; Col. 5-6, lines 65-67, 1-4; regarding, “When data associated with a current incident is received, the incident model 116 can then infer the likely root cause of the incident and its most relevant details given the incidents data known at the start of the incidents and within its first minutes (e.g., impact description and metrics, and the list of events in the recent 24 hours).”);
determining, utilizing a second model and based on the metadata, one or more second configurable items associated with one or more products related to the current incident; (Col. 8, lines 11-19; regarding, “the service model 202 can be invoked to identify the products, services, or systems of the provider network or of the customer's infrastructure (e.g., applications, instances, etc.) most likely to be responsible for the incident. The service model can receive the metrics and state data obtained by the incident orchestrator 112, as described above, and can output values for various products, services, or systems which indicate how likely that particular product, service, or system is responsible for the current incident.”)
determining, utilizing a third model and based on the metadata, one or more third configurable items associated with one or more lines of business and/or logical associations related to the current incident; (Col. 8, lines 23-27; regarding, “State change model 205 can receive the state data obtained from incident orchestrator 112 and output values for various state changes that have occurred within the most recent window of time since the incident was detected.”);
generating a list of configurable items by aggregating the one or more first configurable items, the one or more second configurable items, and the one or more third configurable items; (Col. 8, lines 2-4; regarding, “the results of multiple models may be combined by metamodel 200 to produce the predicted results 208.”);
and outputting the filtered list of configurable items excluding the at least one redundant configurable item to the computer system that automatically takes corrective action based on the filtered list. (Col. 7, lines 43-46; regarding, “The incident orchestrator then obtains past incident data and state change data from incident data repository 114 and state data repository 115. In some embodiments, separate incident data and/or state data repositories may be maintained for each customer. The past incident data, current incident data, and state change data is then provided to incident models 116. In particular, incident models 116 may include a customer-specific model which has been trained on past customer incident data to determine the likely causes of a customer's incident. Based on the results from the customer incident model, the appropriate teams and/or state change rollbacks may be automatically engaged.”);
wherein the corrective action corrects a loss of operation, service, or function of the computer system, thereby improving operation of the computer system. (Col. 2, lines 36-41; regarding, “the incident management service 102 may automatically execute one or more mitigation actions, such as engaging technical teams to begin mitigating the incident, rolling back changes to the provider network that may be responsible for the incident, etc.”).
Schreiber fails to explicitly disclose while Castrejon teaches:
determining, utilizing a first model…that implements an algorithm including an Apriori, Eclat, or a Frequent Pattern Growth algorithm… ([0086]; regarding, “The machine learning algorithms contemplated, described, and/or used herein include… an associated rule learning algorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.)”);
wherein determining the one or more first configurable items comprises: assigning an association score to each of the one or more first configurable items based on a frequency for each of the one or more first configurable items historically being correlated to an application and/or service associated with the data object; ([0051]; regarding, “embodiments of the present disclosure may employ AI/ML to identify a likelihood of a case contributing to a loss based on a database of historical cases. By utilizing case data (e.g., date, geographic location, line of business, communication channel, or the like) from historical cases that are known to have caused a loss, AI/ML models can analyze historical patterns”; [0052]; regarding, “By comparing the likelihood scores generated by the AI/ML models… organizations can determine whether a case's likelihood level necessitates immediate remedial action.”);
and determining, the association score for each of the one or more first configurable items is greater than a threshold value; ([0052]; regarding, “threshold can be assigned to serve as a decision-making criterion. This threshold value represents a specific level of likelihood that the entity deems significant enough to warrant intervention. By comparing the likelihood scores generated by the AI/ML models against the predefined threshold, organizations can determine whether a case's likelihood level necessitates immediate remedial action.”);
automatically filtering the list of configurable items to remove at least one redundant configurable item from the list; ([0082]; regarding, “The stream processing engine 212 may be used to… filter the incoming data to retain specific portions that are deemed useful by aggregating, analyzing, transforming, and ingesting the data.”; [0083]; regarding, “The data pre-processing engine 216 may implement advanced integration and processing steps needed to prepare the data for machine learning execution. This may include modules to perform any upfront, data transformation to consolidate the data into alternate forms by changing the value, structure, or format of the data using generalization, normalization, attribute selection, and aggregation, data cleaning by filling missing values, smoothing the noisy data, resolving the inconsistency, and removing outliers, and/or any other encoding steps as needed.”).
Therefore, it would have been obvious before the effective filing date of the claimed invention to one of ordinary skill in the art to which said subject matter pertains to combine Schreiber with the teachings of Castrejon. Doing so can improve speed and efficiency of the process and conserve computing resources (Castrejon, [0056]).
Regarding Claim 3, Schreiber in view of Castrejon teach the method of claim 1 as referenced above. Schreiber in view of Castrejon further teach:
wherein determining the one or more third configurable items includes confirming that the one or more lines of business are stored in association with the current incident. (Col. 4, lines 39-47; regarding, “a past incident's data may include: its start time and timeline of events; an impact description including impacted services, metrics and locations; a list of engaged groups and experts; state changes that occurred to the provider network 100 within 24 hours before the incident, including code changes and deployments, customer behavior changes, infrastructure issues, etc., with a timeline of each such event; post-incident analysis indicating the root cause of the incident and other relevant details.”).
Regarding Claim 4, Schreiber in view of Castrejon teach the method of claim 3 as referenced above. Schreiber in view of Castrejon further teach:
wherein determining the one or more third configurable items further includes: searching logical levels for the one or more lines of business to determine one or more businesses and/or applications related to the one or more lines of business. (Col. 12, lines 6-14; regarding, “the operations include receiving a request from a monitoring service of a provider network to identify a cause of an incident affecting performance of one or more services or components, the request including performance metric data associated with the provider network, obtaining state data from an incident data repository, the state data indicating changes to the provider network over a previous time period prior to the incident…”).
Regarding Claim 5, Schreiber in view of Castrejon teach the method of claim 4 as referenced above. Schreiber in view of Castrejon further teach:
wherein determining the one or more third configurable items further includes: extracting one or more configurable items associated with the one or more businesses and/or applications. (Col. 5, lines 6-10; regarding, “data may include monitoring and operational data in the form of logs, metrics, and events, providing performance data for computing resources, applications, and services that run on provider network-managed resources.”).
Regarding Claim 6, Schreiber in view of Castrejon teach the method of claim 5 as referenced above. Schreiber in view of Castrejon further teach:
wherein determining the one or more third configurable items further includes: applying an association model to the extracted one or more configurable items associated with the one or more businesses and/or applications to determine the one or more third configurable items, wherein the association model is configured to determine configurable items that have been affected by past incidents that also affected the current incident. (Col. 5-6, lines 59-67, 1-4; regarding, “The incident model 116 may be trained on the prior incident data that has been labeled with its resolution information. This enables the incident model 116 to learn to associate specific inputs (e.g., a description of the incident, metric data associated with affected services, etc.) with the mitigation actions and/or teams that were deployed to resolve the past incidents. When data associated with a current incident is received, the incident model 116 can then infer the likely root cause of the incident and its most relevant details given the incidents data known at the start of the incidents and within its first minutes (e.g., impact description and metrics, and the list of events in the recent 24 hours).”).
Regarding Claim 7, Schreiber in view of Castrejon teach the method of claim 1 as referenced above. Schreiber in view of Castrejon further teach:
wherein determining the one or more first configurable items includes analyzing historical incident data utilizing an association model to determine configurable items that were affected during one or more past incidents related to the one or more applications and/or services. (Col. 5, lines 59-63; regarding, “The incident model 116 may be trained on the prior incident data that has been labeled with its resolution information. This enables the incident model 116 to learn to associate specific inputs (e.g., a description of the incident, metric data associated with affected services, etc.)”).
Regarding Claim 8, Schreiber in view of Castrejon teach the method of claim 7 as referenced above. Schreiber in view of Castrejon further teach:
wherein the historical incident data includes data that is input by a user to indicate applications and/or services related to past incidents. (Castrejon, [0051]; regarding, “By utilizing case data (e.g., date, geographic location, line of business, communication channel, or the like) from historical cases that are known to have caused a loss, AI/ML models can analyze historical patterns and trends to predict potential vulnerabilities. User input labeling known cases that resulted in a loss can help train the AI/ML models…”).
Regarding Claim 9, Schreiber in view of Castrejon teach the method of claim 1 as referenced above. Schreiber in view of Castrejon further teach:
wherein determining the one or more second configurable items includes analyzing historical incident data utilizing an association model to determine configurable items that were affected during one or more past incidents related to the one or more products. (Col. 10, lines 19-28; regarding, “For example, in FIG. 4, product A and its service/component 1 have been flagged with high confidence 408 because the are impacted by the incident and are associated with recent changes. Service/component 2 is flagged for being helpful in past similar incidents. In some embodiments, the reason 406 is provided based on the results of the incident model(s). In some embodiments, one incident model may be trained to identify the reasons for a given product/component/etc. based on the results of other incident models.”).
Regarding Claim 10, Schreiber in view of Castrejon teach the method of claim 9 as referenced above. Schreiber in view of Castrejon further teach:
wherein the historical incident data includes data that is input by a user to indicate products related to past incidents. (Castrejon, [0051]; regarding, “By utilizing case data (e.g., date, geographic location, line of business, communication channel, or the like) from historical cases that are known to have caused a loss, AI/ML models can analyze historical patterns and trends to predict potential vulnerabilities. User input labeling known cases that resulted in a loss can help train the AI/ML models…”).
Regarding Claim 11, Schreiber in view of Castrejon teach the method of claim 1 as referenced above. Schreiber in view of Castrejon further teach:
wherein the data object is representative of a configurable item. (Col. 4-5, lines 65-67, 1-13; regarding, “the performance metrics (also referred to as performance data) may include CPU, memory usage, latency, response time, etc. and may also include availability, health, or other service quality indicators or aggregations of any or all of these metrics. In some embodiments, the monitoring service provides data which allow a user to monitor their applications, respond to system-wide performance changes, optimize resource utilization, and get a unified view of operational health. The data may include monitoring and operational data in the form of logs, metrics, and events, providing performance data for computing resources, applications, and services that run on provider network-managed resources. The monitoring service 108 may additionally, or alternatively, include a configuration service that assesses, audits, and evaluates the configurations of computing resources in provider network 100.”).
Regarding Claim 12, Schreiber in view of Castrejon teach the method of claim 1 as referenced above. Schreiber in view of Castrejon further teach:
wherein the metadata includes:
historical incident data for the data object; (Castrejon, [0051]; regarding, “embodiments of the present disclosure may employ AI/ML to identify a likelihood of a case contributing to a loss based on a database of historical cases.”);
and a line of business for the data object, the line of business including an association logic linking the line of business with one or more of: a business service, a service offering, an application, an application instance or web service, a server, or a service. (Castrejon, [0051]; regarding, “By utilizing case data (e.g., date, geographic location, line of business, communication channel, or the like) from historical cases that are known to have caused a loss, AI/ML models can analyze historical patterns and trends to predict potential vulnerabilities.”).
Claims 13, and 15-19 are rejected under 35 U.S.C. 103 under the same grounds of rejection as claims 1, and 3-7 respectively.
Claim 20 is rejected under 35 U.S.C. 103 under the same grounds of rejection as claim 1.
Response to Arguments
Applicant’s arguments filed 02/06/2026 have been fully considered.
Regarding the 35 U.S.C. 101 rejection applicant argues that the claims recite an inventive concept and an improvement to technology. Examiner respectfully disagrees. Even if the claims did recite an improvement, as written, it would be an improvement to the abstract idea. The MPEP notes that it is important to keep in mind that an improvement in the abstract idea itself is not an improvement in technology. MPEP 2106.05(a)(II).
Regarding the 35 U.S.C. 103 rejection applicant argues that the cited art fails to explicitly disclose the automatic filtering and outputting as claimed. Examiner respectfully disagrees. As referenced above Castrejon teaches filtering data, further Castrejon teaches “perform any upfront, data transformation to consolidate the data into alternate forms by changing the value, structure, or format of the data using generalization, normalization, attribute selection, and aggregation, data cleaning by filling missing values, smoothing the noisy data, resolving the inconsistency, and removing outliers, and/or any other encoding steps as needed.” ([0083]). Schreiber teaches providing models incident data and based on the results from the model, automatically engaging the appropriate response as referenced above. Castrejon further teaches “machine learning models trained using supervised learning algorithms may be used to structure computations in terms of categorized outputs (e.g., C_1, C_2 . . . . C_n 238) or observations based on defined classifications, represent possible solutions to a decision based on certain conditions, model complex relationships between inputs and outputs to find patterns in data or capture a statistical structure among variables with unknown relationships, and/or the like.” ([0088]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/M.D.G./Examiner, Art Unit 2113
/BRYCE P BONZO/Supervisory Patent Examiner, Art Unit 2113
Prosecution Insights