DETAILED ACTION
Receipt of Applicant’s Preliminary Amendment, filed 07/02/2025 is acknowledged.
Claims 3, 8-11, 16-21-24, 29, and 34-37 were amended.
Claims 2, 5-7, 12-13, 15, 18-20, 25-26, 28, 30-33, and 38-39 were cancelled.
Claims 1, 3-4, 8-11, 14, 16-17, 21-24, 27, 29, 34-37 are pending in this office action.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Applicant’s claim for the benefit to 63/44707 filed February 10, 2023 is acknowledged.
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show “dashed line” as described in the specification. The instant specification recites:
“In the example of FIG. 1, the dashed lines represent unused validator modules 118 and validators 106. For example, validator modules 118 may be excluded because there are no columns of the relevant data type included in the dataset 109 or because they are manually excluded from the validation schema 103” (Paragraph [0017] of original specification)
“In the example of FIG. 1, the dataset 109 includes only multidimensional data. As such, only the multidimensional validator modules 118 (e.g., 118c, 118d, 118e, 118g, 118h, 118i, 118j, 118n, 118o, 118p) for the selected validators 106 (e.g., 106b, 106d, 106e) are selected by the data analysis engine 115 to analyze the data included in the dataset 109.” (Paragraph [0017] of original specification)
Figure 1 does not appear to contain any dashed lines. The specification suggests that there should be dashed lines between some of the elements 118 and 106, yet all of the lines within the instant Figure 1 (filed 07/02/2025) appear to be solid. For example, according to the above cited txt, 118a and 118b should be depicted with a dashed line.
Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). 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. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. 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 Interpretation
With regard to claims 1, 14, and 27, the claims recite the term “validator module”. This claim element has been construed in light of Paragraphs [0025], [0046] as comprising executable code.
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 27, 29, 34-37 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because the claim appears to be directed to software per sae. Claim 27 is directed to a “non-statutory, computer-readable medium… comprising machine-readable instructions”. One of ordinary skill in the art would recognize the machine-readable instructions as software per sae. Since the computer-readable medium is recited as comprising these ‘software’ instructions, one of ordinary skill in the art would identify the computer-readable medium as being software per sae. As such, the claim is directed to software per sae.
The recitation of “for facilitating customizable and modular data analysis” appears to recite an intended use for the computer-readable medium and does not recite any structural element.
The language “that, when executed by a processor of a computing device, cause the computing device to at least: …” is reciting a function that occurs when the instructions are executed. The recited ‘processor of a computing device’ is not part of the machine-readable instructions or the computer-readable medium. The processor and computing device are recited as being external to the claimed computer-readable medium. It is suggested that the claim be amended to clearly recite that the computer-readable medium comprises the processor. For example “A non-transitory, computer-readable medium for facilitating customizable and modular data analysis, the non-transitory, computer-readable medium comprising a processor and machine-readable instructions that, when executed by the processor of a computing device, cause the computing device to at least: …”.
Claim Objections
Claims 3, 16, 29, 8, 21, and 34 are objected to because of the following informalities. Appropriate correction is required.
With regard to claims 3, 16, and 29, claim 3 recites “a client device”. The parent claim has already recited “a client device” (see stanza labeled [a] in claim mapping below). It is unclear if applicant is attempting to recite a second client device or if applicant is attempting to refer to the previously recited client device. For examination purposes this claim limitation has been construed to mean --the client device--.
With regard to claims 8, 21, and 34, the claim recites “a data type”. The parent claim has already recited “a data type” (see stanza labeled [c]). It is unclear if applicant is attempting to recite a new data type or if applicant is attempting to refer to the previously recited data type. For examination purposes this claim limitation has been construed to mean --the data type--.
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, 11, 24, and 37 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.
With regard to claims 11, 24, and 37, claim 11 recites “transform at least a portion of the data in the dataset a different data format based at least in part on one or more transformation functions stored in a transformation library.” This claim limitation contains a grammatical issues which causes confusion regarding the scope of the claim. There appears to be a missing term, detailing the relationship between “the dataset” and the “a different data format” with regard to the transforming. Is the claimed device transform the portion of the data in the dataset to/from/in/into a different data format? For examination purposes this claim limitation has been construed to mean -- transform at least a portion of the data in the dataset to a different data format …-- (read in light of Paragraph [0056] of the original specification).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3-4, 8-11, 14, 16-17, 21-24, 27, 29, 34-37 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by GonzalezMacias [2022/02342868].
Examiners Note: the labels (e.g. [a]) denoted below are merely to facilitate readability of the office action and do not impact the scope of the claim language in any way.
With regard to claim 1 GonzalezMacias teaches A system for facilitating customizable and modular data analysis, the system comprising:
a computing device as a system (GonzalezMacias, ¶99 “system comprising: one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations comprising any of those in embodiments 1-8.”) comprising a processor as processors (Id) and a memory as memory (Id); and
machine-readable instructions as the instructions (Id) stored in the memory that, when executed by the processor (Id), cause the computing device as the system (Id) to at least:
[a] receive a dataset (GonzalezMacias, ¶54 “Model selection system 102 may be configured to receive a timeseries dataset, for example, from data node 104. The timeseries dataset may include values and corresponding timestamps.”) and a validation schema as table 400 exists, meaning it was received (¶63 “For example, model matching subsystem 116 may traverse the temporal trait column in table 400 until the determined temporal trait matches a temporal trait in the temporal trait column.”) from a client device (GonzalezMacias, ¶163 “Anomaly detection system 902 may receive the request from a client (not shown) or from another source (e.g., data node 904 or alert processing system 906a”);
[b] select a validator as detecting a trait associated with the timeseries data (GonzalezMacias, ¶56 “Temporal trait detection subsystem 114 may determine a temporal trait associated with the timeseries dataset. As referred to herein, the temporal trait identifies a pattern within the timeseries dataset.”; Please note this claim imitation has been read in light of Paragraph [0038] of the instant specficiation.) to analyze as identify a pattern (Id) data in the dataset as within the timeseries dataset (Id) based at least in part on (GonzalezMacias, ¶63 “For example, model matching subsystem 116 may traverse the temporal trait column in table 400 until the determined temporal trait matches a temporal trait in the temporal trait column.”) the validation schema as the table with detected traits and associated model execution parameters (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”; Please note this claim limitation has been read in light of paragraph [0039] of the original specification as defining columns of the data set, and data used to select validator models);
[c] select a validator module as selecting an anomality detection model (GonzalezMacias, ¶62 “Model matching subsystem 116 may select, based on the temporal trait, from a plurality of anomaly detection models, an anomaly detection model for detecting anomalies in the timeseries dataset”) associated with the validator as matching with temporal traits (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) based at least in part on a data type as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) of the data in the dataset being analyzed as data included in the timeseries dataset (Id);
[d] execute the validator module (GonzalezMacias, ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset”) to determine a validation result as receiving one or more animalities (Id) associated with the data in the dataset being analyzed as associated with the timeseries dataset (Id);
[e] generate a validator results report as generating an alert (GonzalezMacias, ¶76 “Alerting subsystem 118 may generate an alert based on the one or more anomalies. For example, alerting subsystem 118 may generate one alert for each detected anomaly.”) including the validation result as for the detected anomaly (id); and
[f] transmit the validator results report to the client device (GonzalezMacias, ¶79 “When the alerts have been created, alerting subsystem 118 may pass the alert or alerts to communication subsystem 112. Communication subsystem 112 may transmit (e.g., via network 150) the alert or alerts to an appropriate alert processing system (e.g., alert processing system 106a).”).
With regard to claims 3, 16, and 27 GonzalezMacias further teaches wherein the validation schema as the table with detected traits and associated model execution parameters (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) is user-defined in response to a user interaction as the model selection system performing parameters adjustment/selection (GonzalezMacias, ¶9 “In some embodiments, the model selection system may perform parameter adjustment/selection by creating a grid of multiple sets of parameter values, and fitting the selected model for each element on the grid/set. Each element in the grid may include a unique set of parameter values.”) with a user interface (GonzalezMacias, ¶187 “I/O devices 1760 may include, for example, graphical user interface presented on displays”) rendered on a client device(GonzalezMacias, ¶163 “Anomaly detection system 902 may receive the request from a client (not shown) or from another source (e.g., data node 904 or alert processing system 906a”), the user interaction comprising a selection of a validator component as selecting one or mor execution parameters (GonzalezMacias, ¶8 “Thus, the model selection system may select one or more execution parameters based on the timeseries signal.”; ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset. In some embodiments, model matching subsystem 116 may input the model execution parameter into the anomaly detection model so that the execution of the model is modified, as discussed above.”) associated with the validator as the detected trait associated with the timeseries data (GonzalezMacias, ¶56).
With regard to claims 4 and 17 GonzalezMacias further teaches wherein the validator as the trait (GonzalezMacias, ¶63 “each temporal trait may have a corresponding machine learning model trained for detecting anomalies in timeseries datasets classified under each temporal trait.”) comprises a plurality of validator modules as the corresponding machine learning models trained for detecting anomalies (Id; ¶62; Please note this claim limitation has been read in light of Paragraph [0040]), individual validator modules of the plurality of validator modules as selecting an anomality detection model (GonzalezMacias, ¶62) being configured to analyze one or more respective data types as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) of a plurality of different data types (GonzalezMacias, ¶17 “timeseries datasets may include different types of data.”), the validator module being one of the plurality of validator modules (GonzalezMacias, ¶17 “The datasets with different types of data may be input into different anomaly detection models.”), and the plurality of validator modules associated with validator being unknown to a user as the model selection system (GonzalezMacias, ¶9 “In some embodiments, the model selection system may perform parameter adjustment/selection by creating a grid of multiple sets of parameter values, and fitting the selected model for each element on the grid/set. Each element in the grid may include a unique set of parameter values.”) defining the validation schema (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”).
With regard to claims 8, 21, and 34 GonzalezMacias further teaches wherein the validator module as selecting an anomality detection model (GonzalezMacias, ¶62 “Model matching subsystem 116 may select, based on the temporal trait, from a plurality of anomaly detection models, an anomaly detection model for detecting anomalies in the timeseries dataset”) is a first validator module as model 1 (GonzalezMacias, Figure 4) of a plurality of validator modules a multitude of models (GonzalezMacias, ¶6 “The model selection system may select a model from a multitude of models such that each model is matched with a corresponding temporal trait.”) associated with the validator as matching with temporal traits (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) and the validation result as receiving one or more anomalies (GonzalezMacias, ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset”) comprises a first validation result as a first of the one or more received anomalies (Id), and wherein, when executed, the machine-readable instructions (GonzalezMacias, ¶999 “system comprising: one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations comprising any of those in embodiments 1-8.”) further cause the computing device as the system (Id) to at least:
select a second validator module as model 2 (GonzalezMacias, Figure 4) from the plurality of validator modules a multitude of models (GonzalezMacias, ¶6 “The model selection system may select a model from a multitude of models such that each model is matched with a corresponding temporal trait.”) associated with the validator as matching with temporal traits (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) based at least in part on a data type as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) of the data in the dataset being analyzed as data included in the timeseries dataset (Id);
execute the second validator module(GonzalezMacias, ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset”) to determine a second validation result as receiving a second of the one or more animalities (Id) associated with the data in the dataset being analyzed as associated with the timeseries dataset (Id); and
generate an aggregated result as generating one alert for all detected anomalies (GonzalezMacias, ¶76 “The alert may include timeseries data associated with the timestamp for which the anomaly was detected. In some embodiments, alerting subsystem 118 may generate one alert for all detected anomalies and include the timeseries data associated with each timestamp.”) based at least in part on the first validation result as a first detected anomaly associated with the timestamp (Id) and the second validation result as a second detected anomaly associated with the timestamp (Id), wherein the validator results report comprises the aggregated result as one alert for all the detected anomalies for the same timestamp (Id).
With regard to claims 9, 22, and 35 further teaches wherein, when executed, the machine-readable instructions (GonzalezMacias, ¶999 “system comprising: one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations comprising any of those in embodiments 1-8.”) further cause the computing device (Id) to receive a data object as value (GonzalezMacias, ¶54 “Model selection system 102 may be configured to receive a timeseries dataset, for example, from data node 104. The timeseries dataset may include values and corresponding timestamps.”) associated with the dataset as the timeseries dataset (Id), the data object defining the dataset as the values of the dataset (Id) and one or more splits (GonzalezMacias, ¶27 “The anomaly detection system may divide, based on the dividing attribute, the dataset into multiple datasets.”) of the dataset as the dataset (Id), the one or more splits comprising at least one of a training dataset (GonzalezMacias, ¶63 “For example, a model to be used with trending datasets, is trained using datasets previously classified as having trend.”), a validation dataset as feedback information (GonzalezMacias, ¶70 “(e.g., alone or in conjunction with user indications of the accuracy of outputs, labels associated with the inputs, or with other reference feedback information).”), a testing dataset as the data to be tested (¶71 “During testing, an input without a known classification may be input into the input layer, and a determined classification may be output”), or an inference dataset as variation inference (GonzalezMacias, ¶73 “Alternatively, the machine learning model may include a Bayesian model configured to perform variational inference on a graph and/or vector.”).
With regard to claims 10, 33, 36 GonzalezMacias further teaches wherein the validator is one of a plurality of validators as different temporal traits (GonzalezMacias, ¶64 “In some embodiments, different types of anomaly detection models may be used for timeseries datasets associated with different traits.”), and when executed, the machine-readable instructions further cause the computing device (GonzalezMacias, ¶999 “system comprising: one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations comprising any of those in embodiments 1-8.”) to:
receive a request to create a new validator module from the client device as receiving data for a trait not encountered before (¶146 “If the value has not been encountered before, the anomaly detection system may generate a new data structure for entries having that value.”), the request including an identification of a particular validator of the plurality of validators such as when the system is being implemented (GonzalezMacias, ¶56 “Temporal trait detection subsystem 114 may determine a temporal trait associated with the timeseries dataset. As referred to herein, the temporal trait identifies a pattern within the timeseries dataset.”), one or more data types as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) to be supported by the new validator module as the anomality detection model that is set as being associate with the trait (GonzalezMacias, ¶62 “Model matching subsystem 116 may select, based on the temporal trait, from a plurality of anomaly detection models, an anomaly detection model for detecting anomalies in the timeseries dataset”), and data call information for the new validator module as building the database table associating the traits with the models (¶14 “if the temporal traits are stored in a database table with a corresponding anomaly detection model, the model selection system may identify the table entry that matches the identified temporal trait.”);
create the validator module based at least in part on the request (¶146 “If the value has not been encountered before, the anomaly detection system may generate a new data structure for entries having that value.”); and
store the new validator module in association with the particular validator (GonzalezMacias, ¶106 “For example, dataset processing subsystem 914 may copy the data from the first set into a newly generated data structure.”).
With regard to claims 11, 24, and 37 GonzalezMacias further teaches wherein, when executed, the machine-readable instructions further cause the computing device to at least:
transform at least a portion of the data in the dataset [to] (This limitation has been construed in light of Paragraph [0056]. Please see 112b above.) a different data format based at least in part on one or more transformation functions as transforming the data by applying different transformations (GonzalezMacias, ¶64 “In addition, model matching subsystem 116 may apply different transformation or transformations during preprocessing based on the temporal trait and/or the model used.”; ¶191) stored in a transformation library (¶67 “In yet another example, if the timeseries dataset is approximately constant, model matching subsystem 116 may perform Gaussian/KDE transformation.”; Claim 1 “transform, based on a time interval using a transformation function, the chronologically ordered dataset into an anomaly timeseries dataset”).
With regard to claim 14 GonzalezMacias teaches A method (GonzalezMacias, ¶196 “It should also be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods.”) for facilitating customizable and modular data analysis, the method comprising:
[a] receiving, by at least one computing device (GonzalezMacias, ¶99), a dataset (GonzalezMacias, ¶54 “Model selection system 102 may be configured to receive a timeseries dataset, for example, from data node 104. The timeseries dataset may include values and corresponding timestamps.”) and a validation schema as table 400 exists, meaning it was received (¶63 “For example, model matching subsystem 116 may traverse the temporal trait column in table 400 until the determined temporal trait matches a temporal trait in the temporal trait column.”) from a client device (GonzalezMacias, ¶163 “Anomaly detection system 902 may receive the request from a client (not shown) or from another source (e.g., data node 904 or alert processing system 906a”);
[b] selecting, by at least one computing device (GonzalezMacias, ¶99) a validator as detecting a trait associated with the timeseries data (GonzalezMacias, ¶56 “Temporal trait detection subsystem 114 may determine a temporal trait associated with the timeseries dataset. As referred to herein, the temporal trait identifies a pattern within the timeseries dataset.”; Please note this claim imitation has been read in light of Paragraph [0038] of the instant specficiation.) to analyze as identify a pattern (Id) data in the dataset as within the timeseries dataset (Id) based at least in part on (GonzalezMacias, ¶63 “For example, model matching subsystem 116 may traverse the temporal trait column in table 400 until the determined temporal trait matches a temporal trait in the temporal trait column.”) the validation schema as the table with detected traits and associated model execution parameters (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”; Please note this claim limitation has been read in light of paragraph [0039] of the original specification as defining columns of the data set, and data used to select validator models);
[c] selecting, by at least one computing device (GonzalezMacias, ¶99) a validator module as selecting an anomality detection model (GonzalezMacias, ¶62 “Model matching subsystem 116 may select, based on the temporal trait, from a plurality of anomaly detection models, an anomaly detection model for detecting anomalies in the timeseries dataset”) associated with the validator as matching with temporal traits (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) based at least in part on a data type as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) of the data in the dataset being analyzed as data included in the timeseries dataset (Id);
[d] executing, by at least one computing device (GonzalezMacias, ¶99) the validator module (GonzalezMacias, ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset”) to determine a validation result as receiving one or more animalities (Id) associated with the data in the dataset being analyzed as associated with the timeseries dataset (Id);
[e] generating, by at least one computing device (GonzalezMacias, ¶99) a validator results report as generating an alert (GonzalezMacias, ¶76 “Alerting subsystem 118 may generate an alert based on the one or more anomalies. For example, alerting subsystem 118 may generate one alert for each detected anomaly.”) including the validation result as for the detected anomaly (id); and
[f] transmitting, by at least one computing device (GonzalezMacias, ¶99) the validator results report to the client device (GonzalezMacias, ¶79 “When the alerts have been created, alerting subsystem 118 may pass the alert or alerts to communication subsystem 112. Communication subsystem 112 may transmit (e.g., via network 150) the alert or alerts to an appropriate alert processing system (e.g., alert processing system 106a).”).
With regard to claim 27 GonzalezMacias teaches A non-transitory, computer-readable medium for facilitating customizable and modular data analysis, the non-transitory, computer-readable medium comprising machine-readable instructions (GonzalezMacias, ¶99 “system comprising: one or more processors; and memory storing instructions that, when executed by the processors, cause the processors to effectuate operations comprising any of those in embodiments 1-8.”) that, when executed by a processor as processor (Id) of a computing device as the system (Id), cause the computing device to at least:
[a] receive a dataset (GonzalezMacias, ¶54 “Model selection system 102 may be configured to receive a timeseries dataset, for example, from data node 104. The timeseries dataset may include values and corresponding timestamps.”) and a validation schema as table 400 exists, meaning it was received (¶63 “For example, model matching subsystem 116 may traverse the temporal trait column in table 400 until the determined temporal trait matches a temporal trait in the temporal trait column.”) from a client device (GonzalezMacias, ¶163 “Anomaly detection system 902 may receive the request from a client (not shown) or from another source (e.g., data node 904 or alert processing system 906a”);
[b] select a validator as detecting a trait associated with the timeseries data (GonzalezMacias, ¶56 “Temporal trait detection subsystem 114 may determine a temporal trait associated with the timeseries dataset. As referred to herein, the temporal trait identifies a pattern within the timeseries dataset.”; Please note this claim imitation has been read in light of Paragraph [0038] of the instant specficiation.) to analyze as identify a pattern (Id) data in the dataset as within the timeseries dataset (Id) based at least in part on (GonzalezMacias, ¶63 “For example, model matching subsystem 116 may traverse the temporal trait column in table 400 until the determined temporal trait matches a temporal trait in the temporal trait column.”) the validation schema as the table with detected traits and associated model execution parameters (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”; Please note this claim limitation has been read in light of paragraph [0039] of the original specification as defining columns of the data set, and data used to select validator models);
[c] select a validator module as selecting an anomality detection model (GonzalezMacias, ¶62 “Model matching subsystem 116 may select, based on the temporal trait, from a plurality of anomaly detection models, an anomaly detection model for detecting anomalies in the timeseries dataset”) associated with the validator as matching with temporal traits (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) based at least in part on a data type as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) of the data in the dataset being analyzed as data included in the timeseries dataset (Id);
[d] execute the validator module (GonzalezMacias, ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset”) to determine a validation result as receiving one or more animalities (Id) associated with the data in the dataset being analyzed as associated with the timeseries dataset (Id);
[e] generate a validator results report as generating an alert (GonzalezMacias, ¶76 “Alerting subsystem 118 may generate an alert based on the one or more anomalies. For example, alerting subsystem 118 may generate one alert for each detected anomaly.”) including the validation result as for the detected anomaly (id); and
[f] transmit the validator results report to the client device (GonzalezMacias, ¶79 “When the alerts have been created, alerting subsystem 118 may pass the alert or alerts to communication subsystem 112. Communication subsystem 112 may transmit (e.g., via network 150) the alert or alerts to an appropriate alert processing system (e.g., alert processing system 106a).”).
With regard to claim 29 GonzalezMacias further teaches wherein the validation schema as the table with detected traits and associated model execution parameters (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”) is user-defined in response to a user interaction as the model selection system performing parameters adjustment/selection (GonzalezMacias, ¶9 “In some embodiments, the model selection system may perform parameter adjustment/selection by creating a grid of multiple sets of parameter values, and fitting the selected model for each element on the grid/set. Each element in the grid may include a unique set of parameter values.”) with a user interface (GonzalezMacias, ¶187 “I/O devices 1760 may include, for example, graphical user interface presented on displays”) rendered on a client device (GonzalezMacias, ¶163 “Anomaly detection system 902 may receive the request from a client (not shown) or from another source (e.g., data node 904 or alert processing system 906a”), the user interaction comprising a selection of a validator component as selecting one or mor execution parameters (GonzalezMacias, ¶8 “Thus, the model selection system may select one or more execution parameters based on the timeseries signal.”; ¶68 “Model matching subsystem 116 may input the timeseries dataset into the anomaly detection model, and receive, from the anomaly detection model, one or more anomalies associated with the timeseries dataset. In some embodiments, model matching subsystem 116 may input the model execution parameter into the anomaly detection model so that the execution of the model is modified, as discussed above.”) associated with the validator as the detected trait associated with the timeseries data (GonzalezMacias, ¶56), and
wherein the validator as the trait (GonzalezMacias, ¶63 “each temporal trait may have a corresponding machine learning model trained for detecting anomalies in timeseries datasets classified under each temporal trait.”) comprises a plurality of validator modules as the corresponding machine learning models trained for detecting anomalies (Id; ¶62; Please note this claim limitation has been read in light of Paragraph [0040]), individual validator modules of the plurality of validator modules as selecting an anomality detection model (GonzalezMacias, ¶62) being configured to analyze one or more respective data types as type of data, e.g. ‘timeseries signal’ (GonzalezMacias, ¶61 “In some embodiments, the schema may include a flag indicating the type of data that is included in the timeseries dataset sometimes referred to as timeseries signal.”) of a plurality of different data types (GonzalezMacias, ¶17 “timeseries datasets may include different types of data.”), the validator module being one of the plurality of validator modules (GonzalezMacias, ¶17 “The datasets with different types of data may be input into different anomaly detection models.”), and the plurality of validator modules associated with validator being unknown to a user as the model selection system (GonzalezMacias, ¶9 “In some embodiments, the model selection system may perform parameter adjustment/selection by creating a grid of multiple sets of parameter values, and fitting the selected model for each element on the grid/set. Each element in the grid may include a unique set of parameter values.”) defining the validation schema (GonzalezMacias, Figure 4, ¶39; ¶62 “FIG. 4 illustrates a table with temporal traits matching different anomaly detection models. Rows 402 illustrates that Model 1 will be used for timeseries datasets that have trending data while row 404 illustrates that Model 2 will be used for timeseries datasets that exhibit heteroskedasticity”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grooarke [2025/0124216]. The data in the message is type agnostic (Paragraph [0060]. The system performs strip validation (Figure 3, 302), and files are split into complex Types (Figure 3, 304).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA WILLIS whose telephone number is (571)270-7691. The examiner can normally be reached Monday-Friday 8am-2pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at 571-272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/AMANDA L WILLIS/Primary Examiner, Art Unit 2156
Prosecution Insights