DETAILED 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 .
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 – 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-12 and 14-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beckholm et al (US 2019/0174319) in view of Loehr et al (us 2020/0146045).
Regarding claim 1, Backholm teaches a computer system for using edge computing to enhance artificial intelligence and advanced analytics, comprising:
one or more processors [para 0016 & 0082; mobile device has processor] and
non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to [para 0016 & 0082; inherently in mobile device]:
provide a data layer including a data source [para 0082; data layer is met by data traffic or user detected behavior used from various sources; see upload events para 0062];
use an edge computing layer to pre-process data from the data source, wherein the edge computing layer includes machine learning models programmed to process some of the data without transmission to a remote location [para 0082; edge computing layer or mobile device processed data from data traffic or user behavior; it is noted applicant spec defines a edge computing layer to be located at an edge of network and not a central cloud; the mobile device meets this definition; pre-processing of data is met by data that is flagged see para 0090; at this stage, pre-processing is done locally and thus not sent to a remote location]
perform the artificial intelligence and advanced analytics on the data to form insights into the data [para 0016 - 0021; the mobile device uses machine learning to analyze indicators on the data to detect malware, form insights is met by analyzing indicators to detect malware; after the raw data is pre-processed by indicating flags or indicators, the data is then analyzed for malware; see para 0078 - 0082];
determine, by the artificial intelligence[para 0016 - 0021; the mobile device uses machine learning to analyze indicators on the data to detect malware] , which of the data requires transmission to the remote location for processing [see para 0082; dividing processing between mobile device and remote server… see “…the mobile device and cloud server(s) can use different processing techniques and/or algorithms to analyze data, given their different available processing resources and power constraints. The mobile device can perform different types and/or levels of analysis based on whether and how much additional processing is available from the cloud server(s)”; it is noted that a determination is made of how much additional processing is needed and/or available to be done at a cloud server];
provide the insights to a data center or a cloud computing environment located remotely from the data source[see para 0083; some or all of the processing is done at the remote server using machine learning or AI to identify indicators and determine whether an application is potentially harmful application] ; and
perform further artificial intelligence and advanced analytics on the insights at the data center or the cloud computing environment [see para 0083; some or all of the processing is done at the remote server using machine learning or AI to identify indicators and determine whether an application is potentially harmful application].
Backholm fails to teach which data requires transmission the remote location for processing based on: and urgency of data and filtering of the data to avoid sending unnecessary portions of the data to the remote location.
In analogous art, Loehr et al teaches transmission of high urgency/critical data [para 0083; it is noted that critical data is necessary data and since critical data is separated from non-critical data, the teaching of filtering to avoid sending unnecessary data is inherent/implicit]. Therefore, it would have been obvious at the time of filing of the invention to include the claimed transmitting data based on urgency of data and filtering of the data to avoid sending unnecessary portions of the data to the remote location for the benefit of minimizing bandwidth consumption and to ensure that the urgent and necessary portions of data are sent first.
[Examiner note: official notice could have been utilized to separately teach transmitting urgent and necessary portions of data as this would have been notoriously well known in the art].
Regarding claim 2, Backholm teaches the computer system of claim 1, wherein the data layer includes a plurality of different data sources [see para 0091: traffic can be flagged from suspicious destinations/origins; see para 0067: uploads; see para 0031: content provider, promotional content server, e-coupon server; it is implicitly taught that malware can be from different sources which would be included in the data layer].
Regarding claim 4, Backholm teaches the computer system of claim 1, comprising further instructions which, when executed by the one or more processors, causes the computer system to provide edge services in the cloud computing environment [see para 0082; dividing processing between mobile device and remote server… see “…the mobile device and cloud server(s) can use different processing techniques and/or algorithms to analyze data, given their different available processing resources and power constraints. The mobile device can perform different types and/or levels of analysis based on whether and how much additional processing is available from the cloud server(s)”; it is noted that a determination is made of how much additional processing is needed and/or available to be done at a cloud server; the edge services of malware detection/prevention for the user device are also provided in the cloud environment when additional processing is necessary].
Regarding claim 5, Backholm teaches the computer system of claim 4, wherein the edge services are positioned between the data source in the cloud computing environment and the data center [para 0082; edge computing layer or mobile device processed data from data traffic or user behavior; it is noted applicant spec defines a edge computing layer to be located at an edge of network and not a central cloud; the mobile device meets this definition; pre-processing of data is met by data that is flagged see para 0090; at this stage, pre-processing is done locally and thus not sent to a remote location; see para 0082; dividing processing between mobile device and remote server… see “…the mobile device and cloud server(s) can use different processing techniques and/or algorithms to analyze data, given their different available processing resources and power constraints. The mobile device can perform different types and/or levels of analysis based on whether and how much additional processing is available from the cloud server(s)”; it is noted that a determination is made of how much additional processing is needed and/or available to be done at a cloud server; it is noted since uploads are from sources to a mobile device and the mobile device sends flags and indicators to cloud the central cloud, it is inherent that the edge services are positioned between data source and the cloud or data center].
Regarding claim 6, Backholm teaches the computer system of claim 1, wherein the cloud computing environment is positioned at a location remote from the data center [see para 0082 & 0083; the cloud computer is positioned at different location than the mobile device].
Regarding claim 7, Backholm teaches the computer system of claim 1, wherein the data source is unstructured, semi- structured, or structured data including customer data [see para 0018: uploaded data from a data source is personal information; see para 0090: URL can be a suspicious data source].
Regarding claim 8, Backholm teaches the computer system of claim 7, comprising further instructions which, when executed by the one or more processors, causes the computer system to use the edge computing layer to predict customer behavior as part of the insight [see fig 2a and para 0131: user activity module predicts or anticipates user behavior].
Regarding claim 9, Backholm teaches the computer system of claim 1, wherein the artificial intelligence and advanced analytics are programmed to predict, classify, and detect anomalies associated with the data [see fig 2a and para 0131: user activity module predicts or anticipates user behavior; see para 0155: malware applications can be blacklisted (meets the classify limitation); see para 0083 & 0084: artificial intelligence is used to analyze the data to detect whether the application is potentially harmful (meets detect anomalies limitation;] .
Regarding claim 10, Blackholm teaches the computer system of claim 1, wherein the artificial intelligence and advanced analytics are programmed to provide predictive modeling [see fig 2a and para 0131: user activity module predicts or anticipates user behavior; para 0083: data analysis is performed by AI; see para 0052: user behavior is processed and analyzed by AI] .
Regarding claims 11 -12 and 14-20, since claims 11-12 and 14-20 are the system claims of method claims 1-2 and 4-10 and recites the same limitations, the rejection for claims 1-2 and 4-10 applies [claims 11-12 and 14-20 are rejected based on the reasoning provided in rejecting claims 1-2 and 4-10].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Beckholm in view of Loehr et al, and further in view of Kavanagh (US 2010/0189052).
Regarding claims 3 and 13, Backholm teaches edge computing layer that services a user device and teaches a population of mobile devices can more useful in detecting a potentially harmful application [see para 0083] and further suggests the local proxy can be bundled into a firewall or router [see para 0119] but fails to teach the computer system of claim 1, wherein the edge computing layer includes a plurality of different edge computers to pre-process the data.
In analogous art, Kavanagh teaches a residential gateway which acts as a firewall for a plurality of local devices [see para 0035]. Therefore, based on the suggestion of Beckholm and the teaching of Kavanagh, it would have been obvious to modify Beckholm and the time of the filing of the invention to include the edge computing layer in a local gateway or router to enable detecting, monitoring and preventing malware from a centralized local location. The motivation for doing so would enable protecting multiple devices and would also enable a single edge computing layer instead of needing multiple edge computing layers for each device.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/VIVEK SRIVASTAVA/ Supervisory Patent Examiner, Art Unit 2449
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