Notice of Pre-AIA or AIA Status
This Non-Final communication is in response to Application No. 18/210,094 filed 06/15/2023 which claims priority to 63/352,693 filed on 06/16/2022. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description: Database 118 in paragraph [0040] and GUI 100A in paragraph [0066]. The drawings are also objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: 510 in figure 5 and 1002B in figure 10B. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a
judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly
more.
101 Subject Matter Eligibility Analysis
Step 1: Claims 1-18 are within the four statutory (a process, machine, manufacture or composition of
matter.) Claims 1-9 describe a process and 10-18 describes a machine.
With respect to claim 1:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
identifying, …, a pattern associated with the entity within the one or more derived data signals and the plurality of data signals, wherein the pattern corresponds to occurrence of two or more data signals within an overlap in the time dimensions associated with the two or more data signals; and (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
determining, …, occurrence of an event associated with the entity within a predefined time period, based on the identified pattern and the overlap in the time dimensions associated with the two or more data signals. (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
receiving a plurality of data signals associated with an entity from a plurality of data sources, wherein the plurality of data signals comprises structured data signals and unstructured data signals; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
deriving one or more data signals from the plurality of data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Artificial Intelligence (AI) based analysis technique, wherein each of the one or more data signals comprises an associated time dimension; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
by a trained Al model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception
The additional elements “ receiving…”, and “deriving…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “by a trained AI model” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 1 is ineligible.
With respect to claim 2:
Step 2A Prong 1: claim 2, which incorporates the rejection of claim 1, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
pre-processing each of the plurality of data signals received from the plurality of data sources based on pre-defined criteria; and (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
removing a set of data signals from the plurality of data signals in response to pre- processing, wherein each of the set of data signals corresponds to a noisy data signal. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional elements add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 2 is ineligible.
With respect to claim 3:
Step 2A Prong 1: claim 3, which incorporates the rejection of claim 1, recites an additional abstract idea:
converting each of the one or more data signals and the associated time dimension into a set of vectors; (This is an abstract idea of a "Mental Process." The "converting" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
determining, …, whether a relation exists between the two or more data signals from the one or more data signals based on a vector associated with each of the two or more data signals; and(This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
upon determining the relation, identifying,… , the pattern corresponding to the determined relation. (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
by the trained Al model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 3 is ineligible.
With respect to claim 4:
Step 2A Prong 1: claim 4, which incorporates the rejection of claim 3, does not recite an abstract idea.
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
the relation is determined based on at least one of a repeated occurrence of the two or more data signals over a period of time, a magnitude of an analogous pattern associated with two or more former data signals, and a weight associated the analogous pattern. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
Therefore, claim 4 is ineligible.
With respect to claim 5:
Step 2A Prong 1: claim 5, which incorporates the rejection of claim 3, recites an additional abstract idea:
upon identifying the pattern, identifying, …, one or more events mapped to the pattern; (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
determining, …, at least one event from the one or more events occurred within the predefined time period; (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
computing, …, a degree of confidence for each of the at least one event based on a set of secondary attributes; and (This is an abstract idea of a "Mental Process." The "computing" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
selecting, …, an event from each of the at least one event based on an associated degree of confidence. (This is an abstract idea of a "Mental Process." The "selecting" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The selection could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
by the trained Al model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 5 is ineligible.
With respect to claim 6:
Step 2A Prong 1: claim 6, which incorporates the rejection of claim 1, recites an additional abstract idea:
generating, …, a set of user actions based on the determined event; (This is an abstract idea of a "Mental Process." The "generating" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The generation could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
By an AI model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
rendering the set of user actions to the user. (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element “by an AI model” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
The additional element “rendering the set of user actions to the user” adds insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 6 is ineligible.
With respect to claim 7:
Step 2A Prong 1: The claim recites an abstract idea enumerated in the 2019 PEG
converting each of the one or more derived training data signals and the associated time dimension into a set of training vectors; and (This is an abstract idea of a "Mental Process." The "converting" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application
Additional elements:
extracting a plurality of training data signals associated with at least one entity from a plurality of data sources, wherein the plurality of training data signals comprises structured training data signals and unstructured training data signals, and wherein the plurality of training data signals is extracted from the plurality of data sources for a pre- defined time period; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
deriving one or more training data signals from the plurality of training data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Ai based analysis technique, wherein each of the one or more training data signals comprises an associated time dimension; (this limitation amounts to adding insignificant extra-solution activity to the judicial exception).
iteratively training the Al model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly
more than the judicial exception
The additional elements “ extracting…”, and “deriving…” add insignificant extra-solution activity to the judicial exception and cannot provide an inventive concept. Storing and retrieving information in memory is directed to a well understood routine conventional activity of data transmission (MPEP 2106.05(d)(II)(iv)).
The additional element “iteratively training…” is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
When considered in combination, these additional elements represent insignificant extra-solution activity and mere instructions to apply an expectation, which do not provide an inventive concept.
Therefore, claim 7 is ineligible.
With respect to claim 8:
Step 2A Prong 1: claim 8, which incorporates the rejection of claim 7, recites an additional abstract idea:
determining, …, a relation between each of two or more training data signals of the one or more derived training data signals based on a vector associated with each of the two or more training data signals; (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
identifying, …, a plurality of training patterns corresponding to the determined relation, wherein each of the plurality of training patterns corresponds to occurrence of the two or more training data signals within an overlap in the time dimensions associated with the two or more training data signals; and (This is an abstract idea of a "Mental Process." The "identifying" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The identification could be made manually by an individual.)
determining, …, occurrence of the one or more events in response to identification of each of the plurality of training patterns. (This is an abstract idea of a "Mental Process." The "determining" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The determination could be made manually by an individual.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
by the Al model (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 8 is ineligible.
With respect to claim 9:
Step 2A Prong 1: claim 9, which incorporates the rejection of claim 8, recites an additional abstract idea:
comparing each of the one or more events with a corresponding actual event; (This is an abstract idea of a "Mental Process." The "comparing" step under its broadest reasonable interpretation, covers concepts that can be practically performed in the human mind. The comparison could be made manually by an individual.)
computing a confidence factor corresponding to determination of each of the one or more events, based on a pre-defined accuracy threshold in response to comparing; (This is an abstract idea of a "Mental Process." The "computing" step under its broadest reasonable interpretation, covers concepts that can be practically performed by a human using a pen and paper.)
Step 2A Prong 2: The judicial exception is not integrated into a practical application.
performing incremental training of the Al model corresponding to at least one event from the one or more events, wherein the confidence factor of the at least one event is below the pre-defined accuracy threshold. (This amounts to no more than mere instructions to “apply” the exception using a generic computer component.)
Step 2B: the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception
The additional element is recited in a generic level and they represent generic computer components to apply the abstract idea. Mere instructions to apply an exception cannot provide an inventive concept (MPEP 2106.05(f)).
Therefore, claim 9 is ineligible.
With respect to claim 10:
The claim recites similar limitations as corresponding to claim 1. Therefore, the same subject matter analysis that was utilized for claim 1, as described above, is equally applicable to claim 10. Therefore, claim 10 is ineligible.
With respect to claim 11:
The claim recites similar limitations as corresponding to claim 2. Therefore, the same subject matter analysis that was utilized for claim 2, as described above, is equally applicable to claim 11. Therefore, claim 11 is ineligible.
With respect to claim 12:
The claim recites similar limitations as corresponding to claim 3. Therefore, the same subject matter analysis that was utilized for claim 3, as described above, is equally applicable to claim 12. Therefore, claim 12 is ineligible.
With respect to claim 13:
The claim recites similar limitations as corresponding to claim 4. Therefore, the same subject matter analysis that was utilized for claim 4, as described above, is equally applicable to claim 13. Therefore, claim 13 is ineligible.
With respect to claim 14:
The claim recites similar limitations as corresponding to claim 5. Therefore, the same subject matter analysis that was utilized for claim 5, as described above, is equally applicable to claim 14. Therefore, claim 14 is ineligible.
With respect to claim 15:
The claim recites similar limitations as corresponding to claim 6. Therefore, the same subject matter analysis that was utilized for claim 6, as described above, is equally applicable to claim 15. Therefore, claim 15 is ineligible.
With respect to claim 16:
The claim recites similar limitations as corresponding to claim 7. Therefore, the same subject matter analysis that was utilized for claim 7, as described above, is equally applicable to claim 16. Therefore, claim 16 is ineligible.
With respect to claim 17:
The claim recites similar limitations as corresponding to claim 8. Therefore, the same subject matter analysis that was utilized for claim 8, as described above, is equally applicable to claim 17. Therefore, claim 17 is ineligible.
With respect to claim 18:
The claim recites similar limitations as corresponding to claim 9. Therefore, the same subject matter analysis that was utilized for claim 9, as described above, is equally applicable to claim 18. Therefore, claim 18 is ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ryan (US 2019/0379589 A1) in view of Wilson (US 11,663,405).
Regarding claim 1, Ryan teaches
A method for determining occurrence of events based on data signals, the method comprising: ([0009] “According to one implementation of the present disclosure, a method for pattern detection is provided.”)
receiving a plurality of data signals associated with an entity from a plurality of data sources, wherein the plurality of data signals comprises structured data signals and unstructured data signals; ([0046] “Time-series data can also be one-dimensional or multi-dimensional. For example, multiple sensors can provide data at about the same time, whereby this sensor data can be stacked together to provide a time-series that has multiple types of measurements associated with each time point.”)
deriving one or more data signals from the plurality of data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Artificial Intelligence (AI) based analysis technique, wherein each of the one or more data signals comprises an associated time dimension; ([0078] “processing time-series data and creating an input for pattern detection;” and [0081] “Sliding windows 50 are stepped through/passed over the time-series 52 resulting in a sequence of related, overlapping windows.” The forming of the sliding windows is a type of deriving.)
identifying, by a trained Al model, a pattern associated with the entity within the one or more derived data signals and the plurality of data signals, wherein the pattern corresponds to occurrence of two or more data signals within an overlap in the time dimensions associated with the two or more data signals; and ([0051] “During training, pattern detection for threshold crossing forecasting examines historical time-series (e.g., of SNRs) to discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.”)
Ryan does not teach:
determining, by the trained Al model, occurrence of an event associated with the entity within a predefined time period, based on the identified pattern and the overlap in the time dimensions associated with the two or more data signals.
However Wilson does:
determining, by the trained Al model, occurrence of an event associated with the entity within a predefined time period, based on the identified pattern and the overlap in the time dimensions associated with the two or more data signals. (Col 13 lines 42-55 “For each community that is currently bursting (BET=Current Time, BL>=1), burst detector 262 may identify overlapping cliques with bursty communities from the previous iteration. If there is overlap, then the clique is determined to be the same event or issue. Otherwise, a new event or issue is identified. Accordingly, event detector 260 is enabled to quickly and reliably identify issues from sequence stream sources. Such event or issue detection ability may be a valuable resource for service and product providers, such as but not limited to online help or assistant tools (e.g., a virtual assistant). In various embodiments disclosed herein, the sequence stream sources may be analyzed to identify events indicative of new or emerging issues with products and services.”)
Ryan and Wilson are considered analogous art to the claimed invention because they are in the same field of endeavor being event detection. 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 overall system/method and pattern detection of Ryan with the temporal event detection of Wilson. One would want to do this for more efficient event detection (Wilson Col 1 lines 59-62).
Regarding claim 2, Ryan in view of Wilson teaches claim 1 as outlined above. Wilson further teaches:
pre-processing each of the plurality of data signals received from the plurality of data sources based on pre-defined criteria; and removing a set of data signals from the plurality of data signals in response to pre- processing, wherein each of the set of data signals corresponds to a noisy data signal. (Col 22 lines 58-67 “Turning now to FIG. 5A, a flow diagram is provided that illustrates a process 500 for detecting and/or identifying events or issues of the present disclosure. At least portions of process 500 may be carried out by event detector 260 and/or be discussed in conjunction with event detector 260. Process 500 begins, at block 502, where the received sequences are pre-processed. In various embodiments, pre-processing a sequence may include cleaning and tokenizing the sequence For example, pre-processing can include stop-word removal, lower-casing, and lemmatization of a NL phrase. At block 502, sequence may be collected and pre-processed for a rolling period of time such as a one month rolling feedback period.”)
Regarding claim 3, Ryan in view of Wilson teaches claim 1 as outlined above. Ryan further teaches:
converting each of the one or more data signals and the associated time dimension into a set of vectors; ([0084] “In addition, FIG. 5B illustrates the process of obtaining two-dimensional windows from time-series data. The time-series is sampled with even samples that are Δ seconds apart. A time window 62 of length m is stepped through/passed over the time-series with a lag l, obtaining a series of horizontal vectors with length m. The horizontal vectors are grouped in groups of n (where n=2 in the example of the two-dimensional matrices) and then stacked to obtain matrices of size m×n. A matrix is obtained for every lag, resulting in a series of overlapping matrices i.sub.k, which can be referred to as images and can be processed using image processing techniques.”)
determining, by the trained Al model, whether a relation exists between the two or more data signals from the one or more data signals based on a vector associated with each of the two or more data signals; and ([0051] “For example, an operator needs to be confident that increasing the rate will not result in an outage sometime in the future, due to SNR dropping below a Forward Error Correction (FEC) limit for the higher rate modulation. During training, pattern detection for threshold crossing forecasting examines historical time-series (e.g., of SNRs) to discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.”)
upon determining the relation, identifying, by the trained Al model, the pattern corresponding to the determined relation. ([0051] “For example, an operator needs to be confident that increasing the rate will not result in an outage sometime in the future, due to SNR dropping below a Forward Error Correction (FEC) limit for the higher rate modulation. During training, pattern detection for threshold crossing forecasting examines historical time-series (e.g., of SNRs) to discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.”)
Regarding claim 4, Ryan in view of Wilson teaches claim 3 as outlined above. Ryan further teaches:
the relation is determined based on at least one of a repeated occurrence of the two or more data signals over a period of time, a magnitude of an analogous pattern associated with two or more former data signals, and a weight associated the analogous pattern. ([0051] “During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.” Magnitude can be consider the strength of the pattern. )
Regarding claim 5, Ryan in view of Wilson teaches claim 3 as outlined above. Wilson further teaches:
upon identifying the pattern, identifying, by the trained Al model, one or more events mapped to the pattern; (Col 13 lines 42-47 “For each community that is currently bursting (BET=Current Time, BL>=1), burst detector 262 may identify overlapping cliques with bursty communities from the previous iteration. If there is overlap, then the clique is determined to be the same event or issue. Otherwise, a new event or issue is identified.”)
determining, by the trained Al model, at least one event from the one or more events occurred within the predefined time period; (Col 13 lines 61-64 “In some embodiments, sequence stream sources may be analyzed using a dynamic monitoring period. Events or issues may be identified over different periods of time rather than a fixed time period.”)
computing, by the trained Al model, a degree of confidence for each of the at least one event based on a set of secondary attributes; and (Col 18 lines 61-67 “The components of the event classifier vector corresponds to probabilities, likelihoods, or confidences that the sequence is classified as the corresponding event or issue. For example, in a embodiment that include five possible values of classification, the output event classifier may be e.sub.i=[0.1, 0.4, 0.2, 0.3, 0.0]. In this example, the input sequence has a classification confidence scores of 0.1, 0.4, 0.2, 0.3, and 0.0 for the respective five possible categories. In some embodiments, the sequence is classified or categories as the most likely category. In the above example, the classification of the sequence would be the second possible category, with a confidence score of 0.4. In some embodiments, the possible categorizations are ranked via the corresponding confidence scores.”)
selecting, by the trained Al model, an event from each of the at least one event based on an associated degree of confidence. (Col 19 lines 27-31 “In real-time, the NL phrase can be classified and information regarding the classification can be provided to the user. UI 350 shows a ranking of possible issues or events related to the NL phrase, as a first possible issue 370 and a second possible issue 380.”)
Regarding claim 6, Ryan in view of Wilson teaches claim 1 as outlined above. Ryan further teaches:
generating, by an Al model, a set of user actions based on the determined event; and rendering the set of user actions to the user. ([0054] “A special case of alarm forecasting is if an alarm is triggered due to a threshold crossing, which could be accomplished by using a threshold forecast (see above). However, the advantage of this more general approach is that it is not dependent on the simple well-known causes of alarms and can therefore discover more complex non-obvious network patterns that result in alarms. As an example, the alarm may indicate a Loss of Signal (LOS), which is due to equipment failure. During training, pattern detection uses historical network measurements to discover patterns associated with future loss of signal alarms. During the online phase, pattern detection searches incoming network performance measurements for the previously found patterns and notifies the user if one is found.”)
Regarding claim 7, Ryan teaches:
A method of training an Artificial Intelligence (AI) model for determining occurrence of events, the method comprising: ([0009] “The method also includes training a deep neural network with the one-dimensional or multi-dimensional windows utilizing historical and/or simulated data to provide a neural network model. The method further includes processing ongoing data from a network with the neural network model to detect one or more patterns of a particular category in the ongoing data and localizing the one or more patterns in time.”)
extracting a plurality of training data signals associated with at least one entity from a plurality of data sources, wherein the plurality of training data signals comprises structured training data signals and unstructured training data signals, and wherein the plurality of training data signals is extracted from the plurality of data sources for a pre- defined time period; ([0063] “ A pattern detection model that is modeled from the historical training data can be used with new data for predicting when the SNR curve crosses over a threshold 22. Using the pattern detection model, new data can be plotted, and patterns may be detected to predict when the SNR in the future may cross the threshold 22. Pattern detection may include analyzing an upward slope pattern 24 or other curve characteristic to predict a future result 26 of a threshold crossing.”)
deriving one or more training data signals from the plurality of training data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Ai based analysis technique, wherein each of the one or more training data signals comprises an associated time dimension; ([0078] “processing time-series data and creating an input for pattern detection;” [0081] “Sliding windows 50 are stepped through/passed over the time-series 52 resulting in a sequence of related, overlapping windows.” The forming of the sliding windows is a type of deriving.)
converting each of the one or more derived training data signals and the associated time dimension into a set of training vectors; and ([0084] “In addition, FIG. 5B illustrates the process of obtaining two-dimensional windows from time-series data. The time-series is sampled with even samples that are Δ seconds apart. A time window 62 of length m is stepped through/passed over the time-series with a lag l, obtaining a series of horizontal vectors with length m. The horizontal vectors are grouped in groups of n (where n=2 in the example of the two-dimensional matrices) and then stacked to obtain matrices of size m×n. A matrix is obtained for every lag, resulting in a series of overlapping matrices i.sub.k, which can be referred to as images and can be processed using image processing techniques.”)
Ryan does not teach:
iteratively training the Al model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals.
However Wilson does:
iteratively training the Al model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals. (Col 19 lines 38-48 “Classifier-training engine 270 may train classifier 286 via supervised machine learning (ML) methods. The received sequences used to detect the events, via event detector 260, may be employed as labelled training data. The categories of the received sequences may be labeled as the event or issue that the sequence was employed to detect or identify. The classifier-training engine 280 may employ the labeled training data to train the NN via supervised ML, e.g., via the labels (or classifications) included in the training data, a defined loss function, backpropagation, and stochastic gradient descent methods.”
Ryan and Wilson are considered analogous art to the claimed invention because they are in the same field of endeavor being event detection. 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 overall system/method training and pattern detection of Ryan with the temporal event detection of Wilson. One would want to do this for more efficient event detection (Wilson Col 1 lines 59-62).
Regarding claim 8, Ryan in view of Wilson teaches claim 7 as outlined above. Ryan further teaches:
determining, by the Al model, a relation between each of two or more training data signals of the one or more derived training data signals based on a vector associated with each of the two or more training data signals; ([0051] “During training, pattern detection for threshold crossing forecasting examines historical time-series (e.g., of SNRs) to discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.”)
identifying, by the Al model, a plurality of training patterns corresponding to the determined relation, wherein each of the plurality of training patterns corresponds to occurrence of the two or more training data signals within an overlap in the time dimensions associated with the two or more training data signals; and ([0051] “During training, pattern detection for threshold crossing forecasting examines historical time-series (e.g., of SNRs) to discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.”)
Wilson further teaches:
determining, by the Al model, occurrence of the one or more events in response to identification of each of the plurality of training patterns. (Col 13 lines 61-64 “In some embodiments, sequence stream sources may be analyzed using a dynamic monitoring period. Events or issues may be identified over different periods of time rather than a fixed time period.”)
Regarding claim 9, Ryan in view of Wilson teaches claim 8 as outlined above. Ryan further teaches:
comparing each of the one or more events with a corresponding actual event; ([0111] “The best model determines the best data transformation, or best combination of data transformations. The best model is selected based on a key performance indicator (KPI) relevant to how the model is going to be used for prediction/classification (e.g. smallest false positive rate, smallest prediction latency, highest true positive rate for a given maximum false positive rate, etc.).”)
computing a confidence factor corresponding to determination of each of the one or more events, based on a pre-defined accuracy threshold in response to comparing; and ([0111] “The best model determines the best data transformation, or best combination of data transformations. The best model is selected based on a key performance indicator (KPI) relevant to how the model is going to be used for prediction/classification (e.g. smallest false positive rate, smallest prediction latency, highest true positive rate for a given maximum false positive rate, etc.).”)
performing incremental training of the Al model corresponding to at least one event from the one or more events, wherein the confidence factor of the at least one event is below the pre-defined accuracy threshold. ([0111] “The best model determines the best data transformation, or best combination of data transformations. The best model is selected based on a key performance indicator (KPI) relevant to how the model is going to be used for prediction/classification (e.g. smallest false positive rate, smallest prediction latency, highest true positive rate for a given maximum false positive rate, etc.). It is noted that selecting the model in this way is in fact searched over a hyper-parameter space of models and results in the “optimal” model for the machine learning task at hand. The selection may be performed during the validation stage of the training. Finally, anomalies are detected (block 198) using the best model.”)
Regarding claim 10, Ryan teaches:
A system for determining occurrence of events based on data signals, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: ([0158] (FIG. 32 is a block diagram of a server 500 which may be used to implement the systems and methods described herein. The server 500 can implement the various processes associated with the systems and methods described herein. The server 500 may be a digital computer that, in terms of hardware architecture, generally includes a processor 502, input/output (I/O) interfaces 504, a network interface 506, a data store 508-1, and memory 510.”)
receive a plurality of data signals associated with an entity from a plurality of data sources, wherein the plurality of data signals comprises structured data signals and unstructured data signals; ([0046] “Time-series data can also be one-dimensional or multi-dimensional. For example, multiple sensors can provide data at about the same time, whereby this sensor data can be stacked together to provide a time-series that has multiple types of measurements associated with each time point.”)
derive one or more data signals from the plurality of data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Artificial Intelligence (AI) based analysis technique, wherein each of the one or more data signals comprises an associated time dimension; ([0078] “processing time-series data and creating an input for pattern detection;” and [0081] “Sliding windows 50 are stepped through/passed over the time-series 52 resulting in a sequence of related, overlapping windows.” The forming of the sliding windows is a type of deriving.)
identify, by a trained Al model, a pattern associated with the entity within the one or more derived data signals and the plurality of data signals, wherein the pattern corresponds to occurrence of two or more data signals within an overlap in the time dimensions associated with the two or more data signals; and ([0051] “During training, pattern detection for threshold crossing forecasting examines historical time-series (e.g., of SNRs) to discover patterns during a time interval, associated with values of the time-series dropping below the threshold at a later time. If there is a correlation between measurements and subsequent threshold crossings, machine learning may be used to discover this correlation and associate the correlation with a pattern. During online usage of new data, pattern detection functions include examining the time-series to find the previously discovered patterns. If a pattern associated with threshold crossing is not found with high confidence, the threshold crossings will not be detected in the future.”)
Ryan does not teach:
determine, by the trained Al model, occurrence of an event associated with the entity within a predefined time period, based on the identified pattern and the overlap in the time dimensions associated with the two or more data signals.
However Wilson does:
determine, by the trained Al model, occurrence of an event associated with the entity within a predefined time period, based on the identified pattern and the overlap in the time dimensions associated with the two or more data signals. (Col 13 lines 42-55 “For each community that is currently bursting (BET=Current Time, BL>=1), burst detector 262 may identify overlapping cliques with bursty communities from the previous iteration. If there is overlap, then the clique is determined to be the same event or issue. Otherwise, a new event or issue is identified. Accordingly, event detector 260 is enabled to quickly and reliably identify issues from sequence stream sources. Such event or issue detection ability may be a valuable resource for service and product providers, such as but not limited to online help or assistant tools (e.g., a virtual assistant). In various embodiments disclosed herein, the sequence stream sources may be analyzed to identify events indicative of new or emerging issues with products and services.”)
Ryan and Wilson are considered analogous art to the claimed invention because they are in the same field of endeavor being event detection. 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 overall system/method and pattern detection of Ryan with the temporal event detection of Wilson. One would want to do this for more efficient event detection (Wilson Col 1 lines 59-62).
Regarding claim 11, Ryan in view of Wilson teaches claim 10 as outlined above. Claim 11 recites similar limitations corresponding to claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale.
Regarding claim 12, Ryan in view of Wilson teaches claim 10 as outlined above. Claim 12 recites similar limitations corresponding to claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale.
Regarding claim 13, Ryan in view of Wilson teaches claim 12 as outlined above. Claim 13 recites similar limitations corresponding to claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale.
Regarding claim 14, Ryan in view of Wilson teaches claim 12 as outlined above. Claim 14 recites similar limitations corresponding to claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Regarding claim 15, Ryan in view of Wilson teaches claim 10 as outlined above. Claim 15 recites similar limitations corresponding to claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Regarding claim 16, Ryan teaches:
A system for training an Artificial Intelligence (AI) model for determining occurrence of events, the system comprising: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to: ([0158] (FIG. 32 is a block diagram of a server 500 which may be used to implement the systems and methods described herein. The server 500 can implement the various processes associated with the systems and methods described herein. The server 500 may be a digital computer that, in terms of hardware architecture, generally includes a processor 502, input/output (I/O) interfaces 504, a network interface 506, a data store 508-1, and memory 510.”)
extract a plurality of training data signals associated with at least one entity from a plurality of data sources, wherein the plurality of training data signals comprises structured training data signals and unstructured training data signals, and wherein the plurality of training data signals is extracted from the plurality of data sources for a pre- defined time period; ([0063] “A pattern detection model that is modeled from the historical training data can be used with new data for predicting when the SNR curve crosses over a threshold 22. Using the pattern detection model, new data can be plotted, and patterns may be detected to predict when the SNR in the future may cross the threshold 22. Pattern detection may include analyzing an upward slope pattern 24 or other curve characteristic to predict a future result 26 of a threshold crossing.”)
derive one or more training data signals from the plurality of training data signals via at least one of a pre-configured algorithm, a statistical analysis technique, or an Ai based analysis technique, wherein each of the one or more training data signals comprises an associated time dimension; ([0078] “processing time-series data and creating an input for pattern detection;” [0081] “Sliding windows 50 are stepped through/passed over the time-series 52 resulting in a sequence of related, overlapping windows.” The forming of the sliding windows is a type of deriving.)
convert each of the one or more derived training data signals and the associated time dimension into a set of training vectors; and ([0084] “In addition, FIG. 5B illustrates the process of obtaining two-dimensional windows from time-series data. The time-series is sampled with even samples that are Δ seconds apart. A time window 62 of length m is stepped through/passed over the time-series with a lag l, obtaining a series of horizontal vectors with length m. The horizontal vectors are grouped in groups of n (where n=2 in the example of the two-dimensional matrices) and then stacked to obtain matrices of size m×n. A matrix is obtained for every lag, resulting in a series of overlapping matrices i.sub.k, which can be referred to as images and can be processed using image processing techniques.”)
Ryan does not teach:
iteratively train the Al model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals.
However Wilson does:
iteratively train the Al model based on the set of training vectors and the one or more derived training data signals for determining occurrence of one or more events for each of the plurality of training data signals. (Col 19 lines 38-48 “Classifier-training engine 270 may train classifier 286 via supervised machine learning (ML) methods. The received sequences used to detect the events, via event detector 260, may be employed as labelled training data. The categories of the received sequences may be labeled as the event or issue that the sequence was employed to detect or identify. The classifier-training engine 280 may employ the labeled training data to train the NN via supervised ML, e.g., via the labels (or classifications) included in the training data, a defined loss function, backpropagation, and stochastic gradient descent methods.”
Ryan and Wilson are considered analogous art to the claimed invention because they are in the same field of endeavor being event detection. 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 overall system/method training and pattern detection of Ryan with the temporal event detection of Wilson. One would want to do this for more efficient event detection (Wilson Col 1 lines 59-62).
Regarding claim 17, Ryan in view of Wilson teaches claim 16 as outlined above. Claim 17 recites similar limitations corresponding to claim 8 and is rejected for similar reasons as claim 8 using similar teachings and rationale.
Regarding claim 18, Ryan in view of Wilson teaches claim 17 as outlined above. Claim 18 recites similar limitations corresponding to claim 9 and is rejected for similar reasons as claim 9 using similar teachings and rationale.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL PATRICK GRUSZKA whose telephone number is (571)272-5259. The examiner can normally be reached M-F 9:00 AM - 6:00 PM ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL GRUSZKA/Examiner, Art Unit 2121
/Li B. Zhen/Supervisory Patent Examiner, Art Unit 2121