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 .
DETAILED ACTION
Claims 1 – 20 are pending.
Any references to applicant’s specification are made by way of applicant’s U.S. pre-grant printed patent publication.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
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Claim 1 – 8, 10 – 17, and 19 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 – 8, 11 – 18, and 20 of copending Application No. 18/619,802 (reference application). Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the reference application fully anticipate the claims of the instant application in the manner as shown within the table below.
This is a provisional nonstatutory double patenting rejection because the patentably indistinct claims have not in fact been patented.
Instant Application
Reference Application (18/619,802)
1. A computer-implemented method, comprising:
converting historical data into categorical time series data;
de-noising the categorical time series data by removing noisy transitions sets according to a coefficient of variation;
determining a likelihood of a category transition based on historical events using a Hawkes process to generate a relationship graph; determining relationships between pairs of nodes using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data;
and determining an anomaly threshold based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.
2. The method of claim 1, wherein converting the historical data into categorical time series data includes mapping numerical values onto categorical values.
3. The method of claim 1, wherein de-noising the categorical time series data includes determining a mean duration and standard deviation for an initial state of each transition set to calculate the coefficient of variation.
4. The method of claim 1, wherein the relationship graph identifies pair-wise relationship values for the nodes based on multiple types of input data.
5. The method of claim 4, wherein the multiple types of input data include a relationship value based on a correlation of categorical time series data for a pair of nodes.
6. The method of claim 5, wherein the multiple types of input data further include a distance between locations associated with the pair of nodes.
7. The method of claim 5, further comprising determining the correlation of categorical time series data for the pair of nodes using a neural network model.
8. The method of claim 5, further comprising determining the correlation of categorical time series data for the pair of nodes based on an occurrence of events in the time series data within a threshold time.
9.
10. A system, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to: convert historical data into categorical time series data; de-noise the categorical time series data by removing noisy transitions sets according to a coefficient of variation; determine a likelihood of a category transition based on historical events using a Hawkes process to generate a relationship graph; determine relationships between pairs of nodes using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data; and determine an anomaly threshold based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.
11. The system of claim 10, wherein the computer program further causes the hardware processor to map numerical values onto categorical values for the categorical time series data.
12. The system of claim 10, wherein the computer program further causes the hardware processor to determine a mean duration and standard deviation for an initial state of each transition set to calculate the coefficient of variation.
13. The system of claim 10, wherein the relationship graph identifies pair-wise relationship values for the nodes based on multiple types of input data.
14. The system of claim 13, wherein the multiple types of input data include a relationship value based on a correlation of categorical time series data for a pair of nodes.
15. The system of claim 14, wherein the multiple types of input data further include a distance between locations associated with the pair of nodes.
16. The system of claim 14, wherein the computer program further causes the hardware processor to determine the correlation of categorical time series data for the pair of nodes using a neural network model.
17. The system of claim 14, wherein the computer program further causes the hardware processor to determine the correlation of categorical time series data for the pair of nodes based on an occurrence of events in the time series data within a threshold time.
18.
19. A computer program product for event modeling, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a hardware processor to cause the hardware processor to: convert historical data into categorical time series data; de-noise the categorical time series data by removing noisy transitions sets according to a coefficient of variation; determine a likelihood of a category transition based on historical events using a Hawkes process to generate a relationship graph; determine relationships between pairs of nodes using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on de-noised categorical time-series data; and determine an anomaly threshold based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.
20.
1. A computer-implemented method of creating a model, comprising:
converting historical data into categorical time series data;
de-noising the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation;
generating a relationship graph that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events;
and determining an anomaly threshold based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.
2. The method of claim 1, wherein converting the historical data into categorical time series data includes mapping numerical values onto categorical values.
3. The method of claim 1, wherein de-noising the categorical time series data includes determining a mean duration and standard deviation for an initial state of each transition set to calculate the coefficient of variation.
4. The method of claim 1, wherein the relationship graph identifies pair-wise relationship values for the nodes based on multiple types of input data.
5. The method of claim 4, wherein the multiple types of input data include a relationship value based on a correlation of categorical time series data for a pair of nodes.
6. The method of claim 5, wherein the multiple types of input data further include a distance between locations associated with the pair of nodes.
7. The method of claim 5, further comprising determining the correlation of categorical time series data for the pair of nodes using a neural network model.
8. The method of claim 5, further comprising determining the correlation of categorical time series data for the pair of nodes based on an occurrence of events in the time series data within a threshold time.
9.
10.
11. A system for event modeling, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to: convert historical data into categorical time series data; de-noise the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation; generate a relationship graph that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events; and determine an anomaly threshold based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.
12. The system of claim 11, wherein the computer program further causes the hardware processor to map numerical values onto categorical values for the categorical time series data.
13. The system of claim 11, wherein the computer program further causes the hardware processor to determine a mean duration and standard deviation for an initial state of each transition set to calculate the coefficient of variation.
14. The system of claim 11, wherein the relationship graph identifies pair-wise relationship values for the nodes based on multiple types of input data.
15. The system of claim 14, wherein the multiple types of input data include a relationship value based on a correlation of categorical time series data for a pair of nodes.
16. The system of claim 15, wherein the multiple types of input data further include a distance between locations associated with the pair of nodes.
17. The system of claim 15, wherein the computer program further causes the hardware processor to determine the correlation of categorical time series data for the pair of nodes using a neural network model.
18. The system of claim 15, wherein the computer program further causes the hardware processor to determine the correlation of categorical time series data for the pair of nodes based on an occurrence of events in the time series data within a threshold time.
19.
20. A computer program product for event modeling, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a hardware processor to cause the hardware processor to: convert historical data into categorical time series data; de-noise the categorical time series data by organizing events into transition sets and removing noisy transitions sets according to a coefficient of variation; generate a relationship graph that determines relationships between pairs of nodes, where the nodes relate to respective data sources and where the relationships indicate a degree of correlation between nodes based on de-noised categorical time-series data, using a Hawkes process that determines a likelihood of a category transition based on historical events; and determine an anomaly threshold based on anomaly scores for a validation dataset using the relationship graph, wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly.
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 non-statutory subject matter.
Regarding claims 1 – 20, it is noted that the claimed invention is directed to a judicial exception, i.e. an abstract idea, without significantly more.
Specifically, regarding claim 1, it essentially comprise the limitations for determining an “anomaly threshold” based upon calculations upon a set of time-series data and observations of relationships within the time-series data. Thus, the claims are directed towards the judicial exception of an abstract idea, without any integration into a practical application.
Regarding claims 2 – 8, the examiner notes that they essentially comprise recitations further directed towards mental processes, such as mapping of time-series data, calculations of standard deviations, identifications of relationship values, correlations of time-series data and distances, and modeling according to neural networks. These claims fail to integrate the abstract idea into any practical implementation.
Regarding claim 9, the examiner notes that the recitation of “monitoring a cyber-physical system (CPS) to determine the anomaly” still does not comprise any integration of the abstract idea into a practical application because the recitation of “monitoring …to determine the anomaly” is still limited to the realm of mental processes (i.e. observation, evaluation, etc…) without requiring any specific implementation into a practical application (e.g. a specific computing structure for performing the claimed process).
Regarding claims 10 – 20, they are rejected for essentially the same reasons as claims 1 – 9 because the examiner notes that, but for the additional recitations of generic computing elements (e.g. processor, memory, instructions), the claims fail to recite additional elements or combination of elements that impose any meaningful limits on the judicial exception so as to integrate the abstract idea into a practical implementation, but rather, simply represent the use of a generic computer as a tool to perform the abstract idea.
Furthermore, it is noted, regarding claims 19 and 20, these claims do not fall within at least one of the four categories of patent eligible subject matter because they are broadly limited to information (i.e. “program instructions”) embodied upon signals (i.e. “computer readable storage medium”).
Specifically, the examiner notes that the applicant’s specification defines the term “medium” as comprising “propagation”, “electromagnetic”, or “infrared” media – thus “signals” per se. (e.g. see Specification - [0078] “…The medium can be … electromagnetic, infrared, … or a propagation medium.”). Thus, the applicant’s claims do not appear to preclude signals per se. from the broadest reasonable interpretation.
Furthermore, the regarding the claimed term “computer readable storage medium”, the examiner points out that the applicant only provides an open-ended example of what may be considered “storage” media – and the open-ended example does not preclude signals per se. from the scope of the term. As applicants (those having ordinary skill in the art) commonly disclose signals as media which may be said to embody or “store” computer code, the examiner respectfully recommends that the applicant amend the claims to recite a “non-transitory” storage medium or to amend the claims so as to explicitly exclude “signals”.
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 – 6, 8 – 15, and 17 – 21 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (Tang), US 2018/0032724 A1, in view of Pincus, US 5,846,189.
Regarding claim 1, Tang discloses:
A computer-implemented method (e.g. Tang, claim 10), comprising:
converting historical data into categorical time series data (e.g. Tang, fig. 3:304, 306; par. 22, 26, 31, 33 – series calculation of historical event data);
determining a likelihood of a category transition based on historical events using a Hawkes process to generate a relationship graph (e.g. Tang, fig. 3:302; par. 24, 27, 40 – construction of a directed graph of event correlations, i.e. graph of category transitions);
determining relationships between pairs of nodes using the relationship graph, where the relationships indicate a degree of correlation between the nodes based on … (e.g. Tang, par. 42 – herein path distances between correlated events within the directed graph are determined);
and determining an anomaly threshold (e.g. Tang, par. 19) based on anomaly scores for a validation dataset using the relationship graph (e.g. Tang, par. 37, 40), wherein a likelihood output of the Hawkes process that exceeds the anomaly threshold indicates an anomaly (e.g. Tang, claim 10, par. 24, 40 – malicious events, i.e. “anomalies” are determined using anomaly labels of events that exceed the threshold).
Tang discloses collecting a set of time series data for the analysis of anomalies, but does not appear to explicitly teach “de-noising” the collected time series data. Pincus, like Tang, discloses a system that collects time series data, but furthermore teaches that it is important to remove the noise from the collected time series data so as to make meaningful observations (e.g. Pincus, 2:8-28, 53-67).
It would have been obvious to one of ordinary skill in the art to employ the teachings of Pincus, for de-noising the time series data, because one of ordinary skill in the art would have been motivated by the teachings that removing the noise from collected time series data improves the usefulness of the data for many different data applications (e.g. Pincus, 6:12-21; 12:61-64).
Thus, the combination enables:
de-noising the categorical time series data by removing noisy transitions sets according to a coefficient of variation (e.g. Pincus, 18:17-29; 25:41-46), and …
determining relationships between pairs of nodes … based on denoised categorical time-series data (e.g. Tang, par. 42; Pincus, 18:17-29; 18:30-35; 25:41-46).
Regarding claim 2, the combination enables:
wherein converting the historical data into categorical time series data includes mapping numerical values onto categorical values (e.g. Tang, fig. 3:306; par. 31).
Regarding claim 3, the combination enables:
wherein de-noising the categorical time series data includes determining a mean duration and standard deviation for an initial state of each transition set to calculate the coefficient of variation (e.g. Pincus, 18:30-35).
Regarding claim 4, the combination enables:
wherein the relationship graph identifies pair-wise relationship values for the nodes based on multiple types of input data (e.g. Tang, par. 31, 39).
Regarding claim 5, the combination enables:
wherein the multiple types of input data include a relationship value based on a correlation of categorical time series data for a pair of nodes (e.g. Tang, par. 31, 39; claim 3).
Regarding claim 6, the combination enables:
wherein the multiple types of input data further include a distance between locations associated with the pair of nodes (e.g. Tang, claim 3).
Regarding claim 8, the combination enables:
further comprising determining the correlation of categorical time series data for the pair of nodes based on an occurrence of events in the time series data within a threshold time (e.g. Tang, 19, 37).
Regarding claim 9, the combination enables:
further comprising monitoring a cyber-physical system (CPS) to determine the anomaly (e.g. Tang, Abstract; par. 2, 5, 6).
Regarding claims 10 – 15 and 17 – 20, they are apparatus and medium claims essentially corresponding to the claims above, and they are rejected, at least, for the same reasons, and furthermore because combination enables:
A system, comprising: a hardware processor; and a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to (e.g. Tang, par. 49) … A computer program product for event modeling, the computer program product comprising a computer readable storage medium having program instructions embodied therewith (e.g. Tang, par. 48) …
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tang et al. (Tang), US 2018/0032724 A1, in view of Pincus, US 5,846,189, in view of Wu et al. (Wu), US 2025/0005403 A1.
Regarding claims 7 and 16, Tang discloses using a learning model to determine correlations within time series data (e.g. Tang, par. 26, 30). However, Tang does not appear to explicitly recite using a “neural network” learning model.
However, like Tang, Wu discloses using a learning model to analyze time series data (e.g. Wu, Abstract), and furthermore teaches that such a model may comprise a “neural network” (e.g. Wu, par. 23, 38, 40, 49-51).
It would have been obvious to one of ordinary skill in the art to incorporate the neural network learning model teachings of Wu within the system of Tang for employing a learning model. This would have been obvious because one of ordinary skill in the art would have been motivated by the teachings that the use of “neural network” learning model enhances the modeling of time series data such that dependencies involving a plurality of event instances may be better captured (e.g. Wu, par. 23, 38, 40, 49-51).
Thus, the combination enables:
further comprising determining the correlation of categorical time series data for the pair of nodes using a neural network model (e.g. Tang, par. 26, 30; e.g. Wu, par. 23, 38, 40, 49-51).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
See Notice of References Cited.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFFERY L WILLIAMS whose telephone number is (571)272-7965. The examiner can normally be reached on 7:30 am - 4:00 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Farid Homayounmehr can be reached on 571-272-3739. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JEFFERY L WILLIAMS/Primary Examiner, Art Unit 2495