Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 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, 3, 8, 9, 14, 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.
As to claim 2, the limitations “wherein the one or more classification systems using supervised learning based on the operator-confirmed incident type labels and the subset of the data instances, and on the generated data instances which are not in the subset of the data instances and labeled with NULL incident type. wherein the one or more classification systems using supervised learning based on the operator-confirmed incident type labels and the subset of the data instances, and on the generated data instances which are not in the subset of the data instances and labeled with a NULL incident type” are not understood by the examiner. These limitations are a long run-on sentence and it isn’t clear what exactly the applicant is trying to claim. The examiner will interpret the claims as if any data instances that are not in the subset should be labeled with a NULL incident type.
As to claim 3, the limitations “wherein the first system is trained to generate an anomaly value for each metric value of a data instance, and wherein the incident type labels are automatically assigned to a subset of the data instances based on the anomaly values for each metric value of each data instance of the subset of the data instances and on the incident classification models.” are not understood by the examiner. These limitations are a long run-on sentence and it isn’t clear what exactly the applicant is trying to claim. The examiner will interpret the claims as incident type labels are automatically assigned based on subsets.
Claim 8 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons.
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reason
.
Claim 9 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons.
Claim 15 has similar limitations as claim 3. Therefore, the claim is rejected for the same reason
.
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 –18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
As to claim 1,
Step 2A, Prong One
The claim recites in part:
generate data instances from the time-series data, each data instance including a value of each of the plurality of metrics and associated with a respective time period;
determine an operator-confirmed incident type label for each presented data instance based on input received from the operator;
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Generating data instances and determining labels are mental processes and instances and labels are produced based on the humans knowledge and understanding. Humans have been determining labels before computers where ever invented.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
receive time-series data of each of a plurality of metrics;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
present the subset of the data instances and the assigned incident type labels to an operator;
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The claim further recites:
training the language model using the dataset train a first system using unsupervised learning to generate anomaly values based on the data instances;
automatically assign incident type labels to a subset of the data instances based on the anomaly values and on incident classification models;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The memory and processing unit are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
In addition, the recitation of unsupervised learning and supervised learning amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)). As such, the claim does not integrate the judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
receive time-series data of each of a plurality of metrics;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
present the subset of the data instances and the assigned incident type labels to an operator;
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The claim further recites:
training the language model using the dataset train a first system using unsupervised learning to generate anomaly values based on the data instances;
automatically assign incident type labels to a subset of the data instances based on the anomaly values and on incident classification models;
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The memory and processing unit are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
The recitation of unsupervised learning and supervised learning amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2,
Step 2A, Prong One
The claim recites in part:
label the generated data instances which are not in the subset of the data instances with a NULL incident type,
wherein the one or more classification systems using supervised learning based on the operator-confirmed incident type labels and the subset of the data instances, and on the generated data instances which are not in the subset of the data instances and labeled with a NULL incident type.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Determining labels is a mental processes and said labels are produced based on the humans knowledge and understanding. Humans have been determining labels before computers where ever invented.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determining” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 3,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the incident type labels are automatically assigned to a subset of the data instances based on the anomaly values for each metric value of each data instance of the subset of the data instances and on the incident classification models.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein the incident type labels are automatically assigned to a subset of the data instances based on the anomaly values for each metric value of each data instance of the subset of the data instances and on the incident classification models.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 4,
Step 2A, Prong One
The claim recites in part:
determine one or metrics correlated to each of the plurality of incident types based on the subset of the data instances and the assigned incident type labels,
wherein the one or more classification systems are trained using supervised learning based on the one or more correlated metrics of the subset of the data instances.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. Determining labels is a mental processes and said labels are produced based on the humans knowledge and understanding. Humans have been determining labels before computers where ever invented.
Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Determining” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein training the one or more classification systems comprises:
training a first classification system using supervised learning based on only values of the subset of the data instances of one or more metrics correlated with a first incident type;
training a second classification system using supervised learning based on only
values of the subset of the data instances of one or more metrics correlated with a second incident type.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. The limitations:
wherein training the one or more classification systems comprises:
training a first classification system using supervised learning based on only values of the subset of the data instances of one or more metrics correlated with a first incident type;
training a second classification system using supervised learning based on only values of the subset of the data instances of one or more metrics correlated with a second incident type.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
Claim 7 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons.
Claim 8 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons.
Claim 9 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons.
Claim 10 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons.
Claim 11 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons.
Claim 12 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons.
The computer-readable medium and computing system are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons.
Claim 15 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons.
Claim 16 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons.
Claim 17 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons.
Claim 18 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons.
Claims 13 – 18 recites a “computer-readable medium” that storea a software program performing a function. The Specification fails to expressly limit the recited “medium” to a statutory embodiment. Thus, the plain and ordinary meaning of the recited "medium" includes signals, carrier waves, etc.
Accordingly, the recited “computer-readable storage media’ are not a process, a machine, a manufacture or a composition of matter.
Claims 13 – 18 depend on claim 13, therefore the claims are also rejected.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 4, 7, 9,10, 13, 15, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over KAUL et al (2023/0275915) in view of ABDULAAL et al (US 2020/0226490)
As to claim 1, KAUL et al teaches a system (paragraph [0076]…computing system) comprising:
a memory (paragraph [0076]…memory) storing processor-executable program code; and
at least one processing unit (paragraph [0076]…processor) to execute the processor-executable program code to cause the system to:
receive time-series data of each of a plurality of metrics (paragraph [0070]… obtaining unlabeled training datasets based on one or more instances of logon event data received from one or more devices (1010) ; Examiner’s Note: “obtaining unlabeled training datasets” reads on “receive time-series data” ; “logon on event data” reads on “plurality of metrics”);
generate data instances from the time-series data, each data instance including a value of each of the plurality of metrics and associated with a respective time period (paragraph [0030]… The logon event data may be a dataset that associates the user ID of the relevant device with a specific instance of a logon event that occurs for that device. In other words, every time that that one of the devices 110-113 logs onto or logs off from the network 100, logon event data is generated for that logon event. Thus, depending on how the user IDs are associated with the devices 110-113, each of the logon event data may be associated with a single user ID or it may be associated with multiple user IDs ; Examiner’s Note: “user ID” reads on “value” ; “logon event” reads on “time period”);
train a first system using unsupervised learning to generate anomaly values based on the data instances (paragraph [0072]… the method 1000 includes training an unsupervised machine-learning model using the unlabeled training datasets such that the unsupervised machine-learning model is configured to generate a probability score based on the plurality of features, the probability score indicating whether an instance of the logon event data is anomalous (1030) ; paragraph [0075]… plurality of features cause the instances of the logon event data ; Examiner’s Note: “probability score” reads on “anomaly values”);
automatically assign incident type labels to a subset of the data instances based on the anomaly values and on incident classification models (paragraph [0072]… the trained unsupervised machine-learning model 142, 240, or 334 to be configured to generate a probability score 332 for each instance of the logon event data based on the extracted features.; Examiner’s Note: “anomalous or not anomalous” reads on “labels”);
train one or more classification systems using supervised learning based on incident type labels and the subset of the data instances (paragraph [0075]… the method 1000 includes training a supervised machine-learning model using the labeled training datasets such that the supervised machine-learning model is configured to generate a contribution score based on the plurality of features, the contribution score specifying which of the plurality of features are likely to have caused the instance of the logon event data to be labeled as anomalous. For example, as previously described the supervised machine-learning model 152, 540, or 634 is trained by the machine-learning module 530 using a subset of the labeled training datasets 141, 510, or 550 having a label 557 ; Examiner’s Note: “what caused the labeling of “anomalous or not anomalous” reads on “incident type labels” ; “a subset of the labeled training datasets” reads on “subset of the data instances” ).
KAUL et al teaches fails to explicitly show/teach present the subset of the data instances and the assigned incident type labels to an operator;
determine an operator-confirmed incident type label for each presented data instance based on input received from the operator
However, ABDULAAL et al teaches present the subset of the data instances and the assigned incident type labels to an operator (paragraph [0030]…additional training data is obtained through human confirmation 114 or rejection of classifications made by the ML classifier 104. In these configurations, an interface can be provided through which a user can confirm or reject classifications of real-time machine metrics 108 made by the ML classifier 104. For example, and without limitation, data identifying real-time machine metrics 108 classified as an anomaly cluster of high incident likelihood can be presented in a UI ; Examiner’s Note: “data identifying real-time machine metrics” reads on “subset of the data instances”; “anomaly cluster of high incident likelihood” reads on “assigned incident type labels”);
determine an operator-confirmed incident type label for each presented data instance based on input received from the operator (paragraph [0030]…a user can then provide an indication by way of the UI indicating whether the real-time machine metrics 108 indicate or do not indicate an incident. This indication can be utilized to perform additional supervised training of the ML classifier 104 such as, for example, updating 116 incident probability inferences generated using Bayesian learning. Additional details regarding this process will be provided below with regard to FIG. 6 ; Examiner’s Note: “human confirmation classifications” reads on “operator-confirmed incident type label” ; “a user indication” reads on “input received from operator”).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for KAUL et al to have present the subset of the data instances and the assigned incident type labels to an operator; determine an operator-confirmed incident type label for each presented data instance based on input received from the operator, as in ABDULAAL et al, for the purpose of the human operator's to provide label training data, guid the model’s learning, evaluate performance, and correct errors.
As to claim 3, KAUL et al teaches the system, wherein the first system is trained to generate an anomaly value for each metric value of a data instance, and wherein the incident type labels are automatically assigned to a subset of the data instances based on the anomaly values for each metric value of each data instance of the subset of the data instances and on the incident classification models (paragraph [0067]…the method 900 includes generating a contribution score based on the plurality of features, using one or more supervised machine-learning models that have been trained using a subset of the one or more labeled datasets, the contribution score specifying which of the plurality of features are likely to have caused the instance of the logon event data to be labeled as anomalous (960). For example, as previously described the supervised machine-learning model 152 or 634 is trained using a subset of the labeled datasets 141, 342, or 550 having the label 557. The score generator 630 uses the trained supervised machine-learning model 152 or 634 to generate a contribution score 632 for each instance of the logon event data based on the extracted features. The contribution score 632 specifies which of the extracted features are likely to have caused the instance of the logon event data to be labeled as anomalous).
As to claim 4, KAUL et al teaches the system, the at least one processing unit to execute the processor-executable program code to cause the system to:
determine one or metrics correlated to each of the plurality of incident types based on the subset of the data instances and the assigned incident type labels (paragraph [0008]…the output data is used to determine if the logon event data that has been labeled as anomalous is malicious or benign. In some embodiments, the various features comprise one or more of (1) a device name or identification, (2) an indication of a successful or unsuccessful logon, (3) an IP address from where the logon occurred, (4) a number of times a logon is successful or unsuccessful, (5) an account name, (6) an organization name, (7) a day of a week the logon occurred, (8) a time of a day the logon occurred, (9) an owner type, (10) a service type, (11) a domain type, (12) an operating system of the device, (13) whether this is a first logon attempt, (14) a location from where the logon occurs, or (15) whether a new IP host or service was used at the logon time),
wherein the one or more classification systems are trained using supervised learning based on the one or more correlated metrics of the subset of the data instance (paragraph [0011]…a supervised learning-model that uses the labeled data as input to determine features related to the logon event data that cause instances of the logon data to be labeled as anomalous).
Claim 7 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons.
Claim 9 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons.
Claim 10 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons.
Claim 12 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons.
Claim 13 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons.
Claim 15 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons.
Claim 16 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons.
Claim 18 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons.
Claim(s) 2, 5, 8, 11, 14, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over KAUL et al (2023/0275915) in view of ABDULAAL et al (US 2020/0226490) and in further view of Conort et al (US 2024/0256920).
As to claim 2, KAUL et al teaches automatically assign incident type labels to a subset of the data instances based on the anomaly values and on incident classification models (paragraph [0072]… the trained unsupervised machine-learning model 142, 240, or 334 to be configured to generate a probability score 332 for each instance of the logon event data based on the extracted features.; Examiner’s Note: “anomalous or not anomalous” reads on “labels”).
KAUL et al and ABDULAAL et al both fail to show label the generated data instances which are not in the subset of the data instances with a NULL incident type,
wherein the one or more classification systems using supervised learning based on the
operator-confirmed incident type labels and the subset of the data instances, and on the
generated data instances which are not in the subset of the data instances and labeled with a NULL incident type NULL.
Conort et al teaches the label the generated data instances which are not in the subset of the data instances with a NULL incident type, wherein the one or more classification systems using supervised learning based on the operator-confirmed incident type labels and the subset of the data instances, and on the generated data instances which are not in the subset of the data instances and labeled with a NULL incident type NULL (paragraph [0181]… In some contexts (e.g., the classification of past events, as in fraud detection), the concept of an inference period may not apply (and can be considered as NULL) because the goal may be to classify an event (e.g., identify a fraudulent transaction) as it occurs or after it has occurred, rather than predicting the occurrence of the event over a future time frame)
Therefore, it would have been obvious for one having ordinary skill in the art at the time the invention was made for KAUL et alto show label the generated data instances which are not in the subset of the data instances with a NULL incident type,
wherein the one or more classification systems using supervised learning based on the
operator-confirmed incident type labels and the subset of the data instances, and on the
generated data instances which are not in the subset of the data instances and labeled with a NULL incident type NULL, as in Conort et al, for the purpose of signifying the intentional absence of a value representing missing, unknown, or inapplicable data,.
As to claim 5, Conort et al teaches the system, wherein training the one or more classification systems comprises training a single classification system using supervised learning based on the operator-confirmed incident type labels and on only values of the subset of the data instances of the one or more correlated metrics (paragraph [0011]…supervised machine learning can be performed on the cluster-labeled training data to train the ML classifier. In some configurations, Bayesian learning is also performed on the cluster-labeled training data to assign incident probability inferences to the clustered training data. The incident probability inferences can be generated or updated through an offline user input as described in later sections. The incident probability inferences can be utilized to trigger remedial actions or other specified actions, including alerting human operators. Such a trigger can initiate cluster splits or merges during a periodic model update process, described below).
It would have been obvious for the system, wherein training the one or more classification systems comprises training a single classification system using supervised learning based on the operator-confirmed incident type labels and on only values of the subset of the data instances of the one or more correlated metrics, for the same reasons as above.
Claim 8 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons.
Claim 11 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons.
Claim 14 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons.
Claim 17 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons.
Allowable Subject Matter
Claims 6, 12, and 18 are objected to as being dependent upon a rejected base claim, but would be potentially allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims as well as overcoming the 112 Rejection and the 101 Rejection.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128