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 .
This communication is in response to the amendment filed on 01/20/2026.
Claims 1-20 are pending and are rejected.
Response to Arguments
Applicant's arguments, with respect to claims 1 and 14 have been fully considered but they are not persuasive. Applicants are arguing in substance the following:
Arguments to claims 1 and 14: The cited prior art does not teach the limitation: “tag the operating data with metadata that identifies of the component of the RAN based mobile network.”
Response to the argument of claims 1 and 14:
Kancharla ([0059, fig. 3C) teaches that attribute ID 362 indicates an identifier of an attribute that corresponds the claimed: “metadata that identifies of the component”, wherein the operating environment (fig. 1) includes computing devices, such as smartphones, to communicate to other devices via network 150 that corresponds to the claimed element “mobile network”. Since Anan already taught mobile wireless telecommunications over a RAN. The combination of Anan and Kancharle teaches the entire limitation.
The rejection is maintained.
The Claim Objection and the rejection under 35 USC § 101 have been withdrawn.
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 1-2 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Ananthanarayanan et al. (US 20220417306 A1), hereafter Anan in view of Kancharla et al. (US 20220358123 A1), hereafter Kancharla.
Regarding claim 1, Anan teaches a data processing system implemented in a processing cloud having a plurality of components, the data processing system comprising a processor, input/output interfaces and non-transitory digital storage implementing a radio access network (RAN) based mobile network in a processing cloud ([0007] The RAN, in combination with a core network of a cloud service provider, represents a backbone network for mobile wireless telecommunications), the data processing system comprising:
a plurality components of the RAN based mobile network each residing in the non-transitory digital storage, wherein each of the components produces operating data during operation, and wherein a first one of the plurality of components of the RAN based mobile network produces the operating data in a first format and a second one of the plurality of components of the RAN based mobile network produces the operating data in a second format that is different from the first format ([0055] the first IoT device 502A and the second IoT device 502B are tasked with capturing videos of a street from different directions or angles and identifying regions in video frames where one or more cars appear. For example, the first IoT device 502A monitors the intersection from the north side (first format) while the second IoT device 502B monitors the same intersection from the south side (second format));
a plurality of real-time processing agents each executed by the processor to receive the operating data from the plurality of components of the RAN based mobile network ([0066] the model may be trained to receive two inputs (the first and second processing modules), each received from respective IoT devices; [0068] receives data from the IoT device. For example, the received data may be a textual description or a textual representation of a scene that has been captured by the IoT device), to convert both the operating data received from the first component of the RAN based mobile network in the first format and the operating data received from the second component of the RAN based mobile network in the second format into a common data format ([0058] The on-premises edge server then formats the aggregated first and second inference data based on the data streaming protocol (convert … into a common data format)), and to forward the operating data in the common data format for further processing ([0058] the aggregated first and second inference data and transmits the analysis and the aggregated first and second inference data to the cloud server);
a data collection system having data collection hardware in communication with the processing cloud, wherein the data collection system is configured to receive the operating data forwarded from each of the plurality of real-time processing agents and to amalgamate the forwarded operating data in the common format ([0058] The on-premises edge server 504 aggregates (532) the first inference data received from the first IoT device 502A and the second inference data received from the second IoT device 502B by confirming a number of cars passed through the intersection from both camera views; [0069] aggregate data “a number of automobile=1 at scene A” received from an IoT device and data “a number of automobile=1 at scene A” received from another IoT device); and
a data management system having data management hardware that is configured to receive the amalgamated operating data in the common data format from the data collection system ([0070] the generate operation generates textual data that describes a number of automobiles appearing in a video frame as inference data based on the captured video stream. For example, the generate operation generate a text data ““a number of automobile=1 at scenes A and B.”; [0071] Transmit operation 664 transmits the data to the network edge server), to store the amalgamated and tagged operating data in a database in the common data format to provide an output that describes the amalgamated operating data.
Anan does not explicitly teach
to tag the operating data with metadata that identifies of the component of the RAN based mobile network that produced the operating data, and
to store the amalgamated operating data in a database.
Kancharla teaches
to tag the operating data with metadata that identifies of the component of the RAN based mobile network that produced the operating data ([0022] elements may comprise attributes and/or metadata associated with the data source; [0059] Attribute ID indicates an identifier of an attribute (metadata that identifies of the component)), and
to store the amalgamated operating data in a database ([0078] the computing device may store the batch data and the converted (e.g., standardized) streaming data in a database).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan disclosure, store the converted data from different data sources into database, as taught by Kancharla. One would be motivated to do so to allow the creation of a single dataset that comprise data from multiple datasets having different cadences.
Regarding claims 2 and 15, Anan and Kancharla teach all limitations of parent claims 1 and 14 wherein Anan further teaches each of the plurality of real-time processing agents comprises data storage configured to cache the received operating data for at least a period of time ([0045], fig. 2, the model manager 262 may refresh the model cache 264 periodically based on an age of stored models in the model cache 264 and/or a memory capacity of the model cache).
Regarding claim 14, Anan teaches an automated process performed by a data processing system comprising a plurality of processing modules residing in a data processing cloud, the automated process comprising:
receiving operating data from each of a plurality of processing modules executing in the data processing cloud at a plurality of real-time processing agents also executing in the data processing cloud ([0066] the model may be trained to receive two inputs, each received from respective IoT devices; [0068] receives data from the IoT device. For example, the received data may be a textual description or a textual representation of a scene that has been captured by the IoT device), wherein each of the processing modules implements a component of a radio access network (RAN) based mobile network within the data processing cloud ([0007] The RAN, in combination with a core network of a cloud service provider, represents a backbone network for mobile wireless telecommunications);
converting the received operating data received from each of the components of the RAN based mobile network from a component-specific format produced by the component of the RAN based mobile network to a common format ([0058] The on-premises edge server then formats the aggregated first and second inference data based on the data streaming protocol);
forwarding the operating data in the common format from each of the real-time processing agents to a data collection system associated with the data processing cloud ([0058] The on-premises edge server 504 aggregates (532) the first inference data received from the first IoT device 502A and the second inference data received from the second IoT device 502B by confirming a number of cars passed through the intersection from both camera views; [0069] aggregate data “a number of automobile=1 at scene A” received from an IoT device and data “a number of automobile=1 at scene A” received from another IoT device);
providing an output that describes the amalgamated operating data ([0070] the generate operation generates textual data that describes a number of automobiles appearing in a video frame as inference data based on the captured video stream. For example, the generate operation generate a text data ““a number of automobile=1 at scenes A and B.”; [0071] Transmit operation 664 transmits the data to the network edge server).
Anan does not explicitly teach
tagging the operating data with metadata that identifies of the component of the RAN based mobile network that produced the operating data, and
storing the amalgamated operating data based upon the operating data of the plurality of components of the RAN-based mobile network in the common data format in a database;
Kancharla teaches
tagging the operating data with metadata that identifies of the component of the RAN based mobile network that produced the operating data ([0022] elements may comprise attributes and/or metadata associated with the data source; [0059] Attribute ID indicates an identifier of an attribute (metadata that identifies of the component)), and
storing the amalgamated operating data based upon the operating data of the plurality of components of the RAN-based mobile network in the common data format in a database ([0078] the computing device may store the batch data and the converted (e.g., standardized) streaming data in a database).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan disclosure, store the converted data from different data sources into database, as taught by Kancharla. One would be motivated to do so to allow the creation of a single dataset that comprise data from multiple datasets having different cadences.
Claims 3-13, 16-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Anan in view of Kancharla and further in view of Srivastava et al. (US 20160065428 A1), hereafter Srivastava.
Regarding claim 3, Anan and Kancharla teach the cloud-based data processing system of claim 1 wherein Kancharla further teaches each of the plurality of real-time processing agents are configured to filter the operating data received from the one or more components of the RAN based mobile network ([0062] filter a dataset based on one or more conditions).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan disclosure, filtering dataset based on condition, as taught by Kancharla. One would be motivated to do so to allow the creation of a single dataset that comprise data from multiple datasets having different cadences.
Anan and Kancharla do not explicitly teach
to immediately generate an alert message if the filtering identifies an alert condition.
Srivastava teaches
to immediately generate an alert message if the filtering identifies an alert condition ([0038] the user may indicate that the network analysis application is to send notifications to the user or to others associated with user device).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan and Kancharla disclosure, generate a notification when condition is met, as taught by Srivastava. One would be motivated to do so for monitoring and managing network devices of a network, which may significantly reduce costs for the service provider.
Regarding claim 4, Anan, Kancharla, and Srivastava teach the cloud-based data processing system of claim 3 wherein Srivastava further teaches the alert condition is based upon anomalous behavior of one of the plurality of processing components of the RAN based mobile network ([0054] As shown in FIG. 5B, the user may identify preferences for sending notifications about anomalies, associated with network devices 230, in a third configuration section 540).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan and Kancharla disclosure, generate a notification based on a detection of anomalous, as taught by Srivastava. One would be motivated to do so for monitoring and managing network devices of a network, which may significantly reduce costs for the service provider.
Regarding claim 5, Anan, Kancharla, and Srivastava teach the cloud-based data processing system of claim 4 wherein Srivastava further teaches the alert message is a text message sent in response to the anomalous behavior ([0038] sending notifications associated with anomalies detected for network devices. For example, the user may indicate that the network analysis application is to send notifications to the user or to others associated with user device (e.g., via a text message)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan and Kancharla disclosure, notification message is a text message, as taught by Srivastava. One would be motivated to do so for monitoring and managing network devices of a network, which may significantly reduce costs for the service provider.
Regarding claim 6, Anan, Kancharla, and Srivastava teach the cloud-based data processing system of claim 4 wherein Srivastava further teaches the alert message is an email message sent in response to the anomalous behavior ([0038] sending notifications associated with anomalies detected for network devices. For example, the user may indicate that the network analysis application is to send notifications to the user or to others associated with user device (e.g., an email message))..
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan and Kancharla disclosure, notification message is an email message, as taught by Srivastava. One would be motivated to do so for monitoring and managing network devices of a network, which may significantly reduce costs for the service provider.
Regarding claim 7, Anan, Kancharla, and Srivastava teach the cloud-based data processing system of claim 4 wherein Anan further teaches each real-time processing agent resides upon data processing hardware ([0030] the on-premises edges 110A-B, which are closer to the cell towers 102A-C and to the video cameras 104A-C (or IoT devices) than the cloud 150, may provide real-time processing) and digital storage within the processing cloud ([0007] The term “on-premises edge” may refer to a datacenter at a remote location at the far-edge of a private cloud; [0042] The network edge server includes a model cache).
Regarding claims 8 and 20, Anan, Kancharla, and Srivastava teach all limitations of parent claims 7 and 14 wherein Anan further teaches the real-time processing agent is further configured receive the operating data via a RAN interface associated with each of the components of the RAN based mobile network ([0007] cell towers may receive and transmit radio signals to communicate with IoT devices (e.g., video cameras) over a RAN).
Regarding claims 9 and 16, Anan and Kancharla teach all limitations of parent claims 1 and 14, Anan does not explicitly teach
wherein the real-time processing agent is further configured to filter the operating data received from the one or more components of the data processing system and to identify an alert condition based upon the filtered operating data indicating anomalous behavior by one or more of the components of the RAN based mobile network identified by the metadata.
Srivastava teaches
wherein the real-time processing agent is further configured to filter the operating data received from the one or more components of the data processing system and to identify an alert condition based upon the filtered operating data indicating anomalous behavior by one or more of the processing modules ([0064] Analysis server may compare current network information with the determined normal behavior patterns (filter the operating data) in order to detect anomalous network devices and/or to predict abnormal behavior of network devices before the abnormal behavior occurs).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan disclosure, anomalous is detected based on the behavior patterns, as taught by Srivastava. One would be motivated to do so for monitoring and managing network devices of a network, which may significantly reduce costs for the service provider.
Regarding claims 12 and 18, Anan, Kancharla, and Srivastava teach all limitations of parent claims 1 and 14 wherein Srivastava further teaches the metadata further comprises a date and a time that the operating data was produced ([0080] User interface includes a section that displays alerts associated with particular network devices at particular times; [0082] the third section may include dates associated with when the anomalous network devices are detected (e.g., Aug. 7, 2013, Aug. 6, 2013, etc.)).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan and Kancharla disclosure, metadata includes date and time, as taught by Srivastava. One would be motivated to do so for monitoring and managing network devices of a network, which may significantly reduce costs for the service provider.
Regarding claim 13, Anan, Kancharla, and Srivastava teach the cloud-based data processing system of claim 12 wherein Kancharla further teaches the data management system is configured to store the metadata in the database with the amalgamated operating data ([0032] such metadata may itself be stored in a data store, such as database).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan and Srivastava disclosure, metadata are stored in database, as taught by Kancharla. One would be motivated to do so to allow the creation of a single dataset that comprise data from multiple datasets having different cadences.
Claim 19 is rejected under 35 U.S.C. 103 as being unpatentable over Anan in view of Kancharla in view of Srivastava and further in view of Liang et al. (US 20250150323 A1), hereafter Liang.
Regarding claim 19, Anan, Kancharla, and Srivastava teach the automated process of claim 18 Anan does not explicitly teach wherein each of the plurality of real-time processing agents subscribes to one or more KAFKA feeds that deliver the operating data in real-time from the one or more processing modules of the RAN based mobile network.
Liang teaches
each of the plurality of real-time processing agents subscribes to one or more KAFKA feeds that deliver the operating data in real-time from the one or more processing modules of the RAN based mobile network ([0164] In order to achieve the real time data collection and storage, FA makes use of the Apache Kafka and Apache Flink for streaming data processing. Apache Kafka is a distributed publish-subscribe messaging system and a robust queue that can handle a high volume of data and enables user to pass messages from one end-point to another).
It would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention made to include in the Anan disclosure, the Apache Kafka is used for streaming data, as taught by Liang. One would be motivated to do so to isolate the problem to be a private network problem or a public network problem.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANH NGUYEN whose telephone number is (571)270-0657. The examiner can normally be reached M-F.
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/ANH NGUYEN/Primary Examiner, Art Unit 2458