DETAILED ACTION
Statement of claims
The present amended application includes:
Claims 1, 3-7, 9-13 and 15-18 were amended. New claims 19-20.
Claims 1-20 remain pending in the application. Claims 1-20 are being considered on the merits.
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
Claim Rejection(s) under 35 U.S.C. 103
Applicant argues that:
Point 1 “Applicant submits that Yu and Gunawardena, either alone or in combination, do not teach or suggest “a real-time Self Optimizing Network (SON) Virtual Network Function (VNF) included as part of the HNG”, “wherein a time-based recording of the data is stored in a data lake coupled to a SON of the HNG” as recited in Claim 1, as amended.".
In response, Applicant’s arguments have been considered but are moot in view of new ground rejection based on ASGHAR, Muhammad et al. WO 2019/034805 International Publication Date21 February 2019 (21.02.2019) and Gunawardena et al. (US 2016/0180030).
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over ASGHAR, Muhammad et al. WO 2019/034805 International Publication Date21 February 2019 (21.02.2019) in view Gunawardena et al. (US 2016/0180030, Gunawarden hereinafter).
As to claim 1, Muhammad teaches Heterogeneous Gateway (HNG) (e.g., see page 32, wherein “Figure 9 discloses an exemplary node device (90). As the node refers to a physical or virtual computational entity capable of managing virtual nodes associated with it. The computational entity may be a device 25 capable of handling data. It may be a server device, computer or like running a chat application or a game application etc. “. Thus, Heterogeneous Gateway (HNG)) comprising;
a real-time Self Optimizing Network (SON) Virtual Network Function (VNF) included as part of the HNG (e.g., pages 7 and 8, “The system comprises a SON Engine configured to maintain an up-to-date model of the self-organizing network, the up-to-date model being configured to predict behavior of the self-organizing network and customer behavior in real-time and the SON Engine is further configured to self-configure, self-optimize, self-heal and self-protect the cognitive self-organizing network on basis of the up-to-date model. “.
Also, see Figure 5, “VNF”), wherein the HNG includes a processor and a memory coupled to the processor (e.g., see page 33, “The memory (91) may comprise volatile or non-volatile memory, for example EEPROM, ROM, PROM, RAM, DRAM, SRAM, firmware, programmable logic, etc.The node device (90) further comprises one or more processor units (92) for processing the instructions and running computer programs and an interface unit (93) for sending and receiving messages.“) ;
wherein data from connected devices is forwarded to the HNG, for at least 2G and another radio access technology (e.g., see FIG. 8, page 31, “radio measurement logs”, and “In 2G networks, such as GSM and CDMA, the Received Signal Level (Rxlev) can be analyzed.” In page 15);
wherein the data is organized into virtualized containers (e.g., see FIG. 8);
wherein a time-based recording of the data is stored in a data lake coupled to a SON of the HNG (e.g., page 26, “Modeling of the unified real-time information in phase 623 is performed to extract system and customer behavior models on which a SON engine can perform SON functions using the key unified KPis (612), the mobility information (622) and the business intelligence information (632) either directly or after knowledge mining.”.
Thus, the “real-time information” include the a time-based recording of the data, therefore wherein a time-based recording of the data is stored in a data lake coupled to a SON of the HNG) ; and
wherein the data is processed and results are displayed to a user (e.g., see FIG. 5, “Dashboard”., see page 23, “the mobile operator will have the control over the final decisions and possible corrective actions on the live network. The policy and business targets are communicated to the CSON system through this interface. The Operator's Interface Layer (506) may provide a graphical dashboard (526) to the mobile operator's personnel”.
Thus, the “provide a graphical dashboard (526) include display , therefore wherein the data is processed and results are displayed to a use).
However, Muhammad does not teach the data is processed by agile analytics.
Gunawardena teaches data is processed by agile analytics and results are displayed to a user (e.g., para [0082] Dashboards 312a-312n are the interactive user interfaces provided to the user to help them perform self-exploratory, agile analytics. This is where the user would be able to view the final form of the integrated medical services data set for their perusal. The data would be displayed in a format that is easy and covers the holistic revenue cycle management process and/or other related systems' processes. On the revenue cycle management angle, there would be the dashboard tab giving a snapshot of the practice performance, closely followed by the appointments tab that would display data pertaining to the appointments scheduled at the practice. Next it would be the submission and production data that would outline the claims submission data and the charges per claim.).
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Muhammad with those of Gunawardena because both references are directed to related systems addressing similar technical problems within the same field and seek to improve system performance, reliability, and efficiency.
Muhammad et al. disclose A Heterogeneous Gateway (HNG) comprising; a real-time Self Optimizing Network (SON) Virtual Network Function VNF) included as part of the HNG, wherein the HNG includes a processor and a memory coupled to the processor; wherein data from connected devices is forwarded to the HNG, for at least 2G and another radio access technology; wherein the data is organized into virtualized containers; wherein a time-based recording of the data is stored in a data lake coupled to a SON of the HNG while Gunawardena et al. teaches the data is processed by agile analytics and results are displayed to a user.
Incorporating the teachings of Gunawardena et al. into the system of Muhammad et al. would have been a predictable and logical modification, yielding improved operational robustness and efficiency without requiring undue experimentation.
Such a combination would merely involve the substitution or integration of known elements performing their established functions, as taught by Gunawardena et al., into the system of Muhammad et al., consistent with design incentives and market demands for improved performance and scalability. Moreover, Gunawardena et al. explicitly recognize benefits to enable “ dynamic and interactive dashboard user interfaces that allow for a user to answer nearly any question. (See Gunawardena, para 285) . —that would naturally be desirable in the system of Muhammad et al.
Accordingly, to one of ordinary skill in the art would have had a reasonable expectation of success in combining Muhammad et al. with Zhang et al., and the combination represents no more than the predictable use of prior art elements according to their known functions.
As to claim 2, Muhammad teaches wherein the data is used for at least one of planning (e.g., see page 19, “Dynamic Radio Configuration includes features enabling omitting of detailed radio planning, reducing labor intensive manual planning and more accurate parameter setting based on measurements from actual network. Parameters”), deployment, optimization and maintenance.
As to claim 3, Muhammad teaches wherein the SON VNF enables self-optimization and seamless mobility across any radio access technology (e.g., see FIG. 3, page 27, “The SON engine 300 runs different SON functions to produce new network parameters that are optimal for the network. The SON engine (300) comprises the self-configuration (301), self-optimization (302), self-healing (303) and self-protection (304) functionalities here illustrated with a common box of self-X functions. The SON engine also comprises policy (306) and coordination“ and “Information collected for the specific, recognized customer with the detailed radio measurement logs may comprise for example mobile terminal measurements, location information, context information, information about the customer's surroundings, including other users nearby, traffic load nearby).
As to claim 4, Muhammad teaches wherein the SON VNF enables self-optimization and seamless mobility across any haul (e.g., see page 19, wherein “self-optimization (302) means utilizing the network measurements and performance indicators collected from user equipment and base stations for auto-tuning the network settings. Self-optimization may be initiated and/or performed by the SON Engine, facilitated by the up-to-date model. Self-optimization may comprise for example tuning network parameters for improving network performance experienced by one or more users.“. Thus, wherein the SON VNF enables self-optimization and seamless mobility across any haul).
As to claim 5, Muhammad teaches wherein the SON VNF enables self-optimization and seamless mobility across any slice(e.g., see page 19, wherein “self-optimization (302) means utilizing the network measurements and performance indicators collected from user equipment and base stations for auto-tuning the network settings. Self-optimization may be initiated and/or performed by the SON Engine, facilitated by the up-to-date model. Self-optimization may comprise for example tuning network parameters for improving network performance experienced by one or more users.“. Thus, wherein the SON VNF enables self-optimization and seamless mobility across any slice).
.
As to claim 6, Muhammad teaches wherein the SON VNF enables self-optimization and seamless mobility across any service (e.g., see page 19, wherein “self-optimization (302) means utilizing the network measurements and performance indicators collected from user equipment and base stations for auto-tuning the network settings. Self-optimization may be initiated and/or performed by the SON Engine, facilitated by the up-to-date model. Self-optimization may comprise for example tuning network parameters for improving network performance experienced by one or more users.“. Thus, wherein the SON VNF enables self-optimization and seamless mobility across any service).
.
As to claim 7, see rejection of claims1 above.
As to claims 8-12, see rejection of claims 2-6 above.
As to claim 13, see rejection of claim1 above . Muhammad teaches further a non-transitory computer-readable medium containing instructions, which, when executed, cause a system to perform steps ( e.g., see page 32-33, “The node device (90), comprises a memory (91) for storing information relating e.g. to the virtual nodes associated with it, instructions how to handle messages The memory (91) may comprise volatile or non-volatile memory, for example EEPROM, ROM, PROM, RAM, DRAM, SRAM, firmware, programmable logic, etc. The node device (90) further comprises one or more processor units (92)for processing the instructions and running computer programs and an interface unit (93) for sending and receiving messages.
As to claims 14-18, see rejection of claims 2-6 above.
As to claim 19, wherein bidirectional communication between machine learning/artificial intelligence and the data lake is used for at least one of network operation or configuration (e.g., page 22, “The SON Engine (514) deals with the network problems using Artificial Intelligence (AI) and machine learning algorithms”) .
As to claim 20, see rejection of claim 19 above.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Grayson et al. (US 2017/0353983) discloses method is provided in one example embodiment and may include subscribing to a key distribution service by a plurality of Wi-Fi access points belonging to a same mobility domain; receiving a request from a user equipment to connect to a first Wi-Fi access point of the plurality of Wi-Fi access points belonging to the same mobility domain; determining one or more second Wi-Fi access points of the plurality of Wi-Fi access points belonging to the same mobility domain that neighbor the first Wi-Fi access points; and distributing keying parameters to each of the one or more second Wi-Fi access points. The keying parameters can be associated with 802.11r pairwise master key (PMK) keying parameters.
Da silva et al. (US 2015/0382265) discloses to identifying stale failure reports in a cellular communications network. In one embodiment, a node in a cellular communications network receives a failure report associated with a connection failure for a user equipment and determines when the connection failure occurred with respect to a most recent mobility adjustment made by the node. If the connection failure occurred before the most recent mobility adjustment made by the node, the node classifies the failure report as a stale failure report. In one embodiment, if the failure report is classified as a stale failure report, the node discards the failure. In another embodiment, if the failure report is classified as a stale failure report, the node considers the failure report with reduced relevance for a next iteration of a process to determine whether new mobility adjustments are desired.
Iwai et al. (US 2015/0282009) discloses a radio access network apparatus (100) is configured to send, to a core network (30), RAN terminal information regarding a mobile terminal (200). The RAN terminal information includes at least one of: (a) measurement information regarding the mobile terminal (200) acquired in a RAN (20); (b) history information regarding the mobile terminal (200) acquired in the RAN (20); and (c) configuration information regarding the mobile terminal (200) determined in the RAN (20). Accordingly, it is, for example, possible to contribute to continuous use by a radio access network (RAN) of RAN terminal information (e.g., measurement information or history information) for a time period over a plurality of CONNECTED-IDLE transitions. .
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDOU K SEYE whose telephone number is (571)270-1062. The examiner can normally be reached M-F 9-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre Vital can be reached at 5712724215. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ABDOU K SEYE/Examiner, Art Unit 2198
/PIERRE VITAL/Supervisory Patent Examiner, Art Unit 2198
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