DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The instant application No. 18672704 has claims 1-20 are pending.
2 The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5, 8, 10-14 and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Skog et al. (WO 2021/028063 A1; hereinafter Skog) in view of Dahan et al. (Pub. No. US 20240064076 A1; hereinafter Dahan)
Regarding claims 1 and 10, Skog in view of a computing system for adjusting network parameters in a wireless communication network, wherein the computing system comprises: one or more processing devices; (Page 16, computer readable code being executed by a processor or computer) and memory communicatively coupled with and readable by the one or more processing devices and having stored therein processor-readable instructions which, (Page 16, computer readable code being executed by a processor or computer) when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising: (Page 16, computer readable code being executed by a processor or computer) executing a machine learning model on the computing system, wherein the computing system operates on a centralized node of a wireless communication network; (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) detecting, using the machine learning model, an occurrence of one or more conditions in the user plane network traffic indicative of one or more network issues; (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) and adjusting one or more network parameters of the wireless communication network based on detecting the occurrence of the one or more conditions in the user plane network traffic. (Page 15, in the case that UPF 102 is an anchor (e.g. a reference for the traffic flow, where the mobile session terminates) it can apply local enforcements for the subscriber flows experiencing user plane congestion, e.g. it can shape or optimize those flows based on predefined policies.)
However, Skog fails to disclose monitoring, using the machine learning model, user plane network traffic associated with a user plane tunnel in the wireless communication network
Dahan discloses monitoring, using the machine learning model, user plane network traffic associated with a user plane tunnel in the wireless communication network; (20240064076-See ¶0035, a Smart Probe can be equipped with an Artificial Intelligence (AI) engine and/or a Machine Learning (ML) engine to make some decisions on what traffic will be monitored; See ¶0037, If issues are more User Plane (UP) related, the Smart Probe can decide to increase dynamically the amount of UP traffic collected; See ¶0092, PS gateway node(s) 518 can generate packet data protocol contexts when a data session is established; other data structures that facilitate routing of packetized data also can be generated. To that end, in an aspect, PS gateway node(s) 518 can comprise a tunnel interface)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify utilizing AI to detect occurrence of congestion in the network to include monitoring by using AI for user plane traffic. The motivation to combine is monitoring of only relevant data (as opposed to monitoring everything) see ¶0016.
Regarding claims 2, 11 and 18, Skog discloses the user plane network traffic is real-time general packet radio service (GPRS) tunneling protocol (GTP) user plane function (GTP-U) network traffic. (Page 14, user plane traffic is GPRS GTP-U network traffic)
Regarding claims 3, 12 and 19, Skog discloses the operations further comprise: analyzing, using the machine learning model, training data indicative of historical user plane network traffic associated with the user plane tunnel in the wireless communication network; (Page 9, training data is indicative of historical data set and training data is from user plane data) and identifying, using the machine learning model, the one or more conditions in the historical user plane network traffic. (Page 9, historical data set is used to train the machine learning model to identify congestion in the network)
Regarding claims 4 and 13, Skog discloses analyzing the training data comprises analyzing at least one of a traffic volume, (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) a packet loss rate, a latency, or a quality of service associated with the historical user plane network traffic.
Regarding claims 5 and 14, Skog discloses detecting the occurrence of the one or more conditions in the user plane network traffic comprises detecting at least one of an increase in traffic volume, (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) an increase in a packet loss rate, an increase in latency, or a decrease in a quality
of service in the user plane network traffic.
Regarding claim 8, Skog discloses adjusting the one or more network parameters comprises adjusting at least one of a network policy, (Page 15, in the case that UPF 102 is an anchor (e.g. a reference for the traffic flow, where the mobile session terminates) it can apply local enforcements for the subscriber flows experiencing user plane congestion, e.g. it can shape or optimize those flows based on predefined policies.) a network configuration, or a quality of service requirement associated with the user plane tunnel.
Regarding claim 17, Skog discloses One or more non-transitory, computer-readable storage media having computer-readable instructions thereon which, when executed by one or more processing devices, cause the one or more processing devices to perform operations comprising: (Page 16, computer readable code being executed by a processor or computer) executing a machine learning model on the computing system, wherein the computing system operates on a centralized node of a wireless communication network; (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) detecting, using the machine learning model, an occurrence of one or more conditions in the user plane network traffic indicative of one or more network issues; (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) and adjusting one or more network parameters of the wireless communication network based on detecting the occurrence of the one or more conditions in the user plane network traffic. (Page 15, in the case that UPF 102 is an anchor (e.g. a reference for the traffic flow, where the mobile session terminates) it can apply local enforcements for the subscriber flows experiencing user plane congestion, e.g. it can shape or optimize those flows based on predefined policies.)
However, Skog fails to disclose monitoring, using the machine learning model, user plane network traffic associated with a user plane tunnel in the wireless communication network
Dahan discloses monitoring, using the machine learning model, user plane network traffic associated with a user plane tunnel in the wireless communication network; (20240064076-See ¶0035, a Smart Probe can be equipped with an Artificial Intelligence (AI) engine and/or a Machine Learning (ML) engine to make some decisions on what traffic will be monitored; See ¶0037, If issues are more User Plane (UP) related, the Smart Probe can decide to increase dynamically the amount of UP traffic collected; See ¶0092, PS gateway node(s) 518 can generate packet data protocol contexts when a data session is established; other data structures that facilitate routing of packetized data also can be generated. To that end, in an aspect, PS gateway node(s) 518 can comprise a tunnel interface)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify utilizing AI to detect occurrence of congestion in the network to include monitoring the using AI for user plane traffic. The motivation to combine is monitoring of only relevant data (as opposed to monitoring everything) see ¶0016.
Regarding claim 20, Skog discloses analyzing the training data comprises analyzing at least one of a traffic volume, (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) a packet loss rate, a latency, or a quality of service associated with the historical user plane network traffic, and wherein detecting the occurrence of the one or more conditions in the user plane network traffic comprises detecting at least one of an increase in the traffic volume, (WO 2021028063 A1-Page 8, the data from the UPF is related to traffic through the UPF and corresponding ground truth congestion levels; Page 13, the UPF determines in real time the user plane congestion associated with a location and a subscriber using trained machine learning model) an increase in the packet loss rate, an increase in the latency, or a decrease in the quality of service in the user plane network traffic.
Allowable Subject Matter
Claims 6-7, 9 and 15-16 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Saghir et al. (Pub. No. US 2021/0058769 A1)-See ¶0030, UPF 335 may receive user plane data (e.g., voice call traffic, data traffic, etc.), destined for UE 305, from DN 350, and may forward the user plane data toward UE 305 (e.g., via RAN 310, SMF 320, and/or one or more other devices). In some embodiments, multiple UPFs 335 may be deployed (e.g., in different geographical locations), and the delivery of content to UE 305 may be coordinated via the N9 interface (e.g., as denoted in FIG. 3 by the line marked “N9” originating and terminating at UPF 335). Similarly, UPF 335 may receive traffic from UE 305 (e.g., via RAN 310, SMF 320, and/or one or more other devices), and may forward the traffic toward DN 350. In some embodiments, UPF 335 may communicate (e.g., via the N4 interface) with SMF 320, regarding user plane data processed by UPF 335. In some embodiments, UPF 335 may be provisioned or configured to intercept some or all user plane traffic associated with UE 305 (e.g., based on an intercept request, as received by GLIS 105), and provide the intercepted traffic to GLIS 105 and/or some other system or device.
Han et al. (Pub. No. US 2023/0037685 A1)-See ¶0048, A user plane function (UPF) plays a role of processing actual user data, and plays a role of processing packets so that packets generated by the terminal may be transmitted to an external data network or data introduced from an external data network may be transmitted to the terminal. Main functions provided by the UPF may include, for example, functions such as performing an anchor role between radio access technologies, providing connectivity to PDU sessions and external data networks, packet routing and forwarding, packet inspection, and user plane policy application, traffic usage report generation, and buffering. In relation to the virtual network, the UPF plays a role of transmitting traffic received from the virtual network member to other members. In this case, the member of the virtual network may be positioned in the same mobile communication network or an external data network such as the Internet.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TEJIS DAYA whose telephone number is (571)270-7817. The examiner can normally be reached 6:30-4:30.
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, Nicholas Jensen can be reached at 571-270-5443. 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.
/Tejis Daya/Primary Examiner, Art Unit 2472
Prosecution Insights