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 .
This application has been examined.
Claims 1-44 were originally filed. Claims 20-22, and 24-44 have been cancelled in a preliminary amendment. Claims 1-19, and 23 remain pending.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-19, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vaderna et al. (US Patent Application Publication 2014/0087716; hereinafter Vaderna) in view of De Buitleir (US Patent Application Publication 2020/0336373).
Regarding claim 1 Vaderna discloses an apparatus for a terminal device (fig. 4), comprising at least one processor and at least one memory including computer program code, wherein the at least one memory and computer program code are configured to, with the at least one processor (paragraph 0058), cause the apparatus to:
receive, from a network node, a request for performing a radio configuration test for selecting operational parameters of the terminal device for a radio connection of the terminal device (paragraphs 0063, 0069, 0080; a network node instructs the UE to perform passive/active measurements and the UE generates test traffic), the request comprising at least one information element indicating a target performance metric and at least one information element defining limitations to the operational parameters available for the radio configuration test (paragraphs 0009, 0028, 0030, 0072, 0089, 0096; instructing the UE to measure metrics as throughput, jitter, among others, which can also be restricted to a certain terminal type or period of time);
perform the radio configuration test by testing a plurality of different combinations of operational parameters of the terminal device, within the limitations received from the network node, and determining a performance metric of each of the plurality of different combinations of tested operational parameters (paragraphs 0072, 0089-0097; the UE performs the measurements and generates test traffic regarding multiple measurements that were triggered and generates logs that are collected by the network node and forwarded to NMS);
cause transmission of a test report to the network node, the test report indicating at least a combination of tested operational parameters that provides a performance metric closest to the target performance metric (paragraphs 0025, 0070, 0075, 0083; the UE reports the tested traffic to network node to be forwarded to NMS).
Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses after transmitting the test report, receive from the network node a radio configuration defining a set of operational parameters of the terminal devices selected by the network node for the radio connection (paragraphs 0035, 0047-0048; wherein a system recommendation is generated by the network node and is output to be implemented by the receiving node). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 2 the modified Vaderna discloses the apparatus of claim 1, wherein the plurality of different combinations of operational parameters comprises operational parameters on a plurality of protocol layers, and wherein the apparatus is configured to test performance of the plurality of different combinations of operational parameters on the plurality of protocol layers (paragraphs 0095-0096; metrics that can be reported only by the application layers).
Regarding claim 3 the modified Vaderna discloses the apparatus of claim 2, wherein the apparatus is configured to determine a performance metric for each of the plurality of protocol layers, to combine the performance metrics on the plurality of protocol layers into a single performance metric, and to add the single performance metric to the test report (paragraphs 0095-0105; the operator can control and collect service specific UE performance measurements and activate specific test traffic generation to/from UEs for service centric network management purposes. With the addition of service layer UE performance measurements used in combination with UE radio layer performance measurements and network measurements, the operator will have a full control over the performance data collection from low level radio performance up to service layer performance data).
Regarding claim 4 the modified Vaderna discloses the apparatus of claim 1, wherein the set of operational parameters selected by the network node comprises the same operational parameters as comprised in the test report (paragraphs 0095-0105; the operator can control and collect service specific UE performance measurements and activate specific test traffic generation to/from UEs for service centric network management purposes. With the addition of service layer UE performance measurements used in combination with UE radio layer performance measurements and network measurements, the operator will have a full control over the performance data collection from low level radio performance up to service layer performance data).
Regarding claim 5 the modified Vaderna discloses the apparatus of claim 1. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein at least one operational parameter is the same in the test report and in the set of operational parameters selected by the network node but has a different value in the test report than in the set of operational parameters selected by the network node (paragraph 0032; The individual recommendation may give a new value of the parameter to be configured or a value that is not changed compared to what is currently in operation. The second option indicates that no change is needed). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 6 the modified Vaderna discloses the apparatus of claim 1. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein the apparatus is configured to determine, during the testing that the apparatus cannot find a combination of operational parameters within the limitations that would meet the target performance metric and, in response to so determining, find a combination of operational parameters within the limitations that provides the closest match with the target performance metric, determine a performance metric for the combination of operational parameters that provides the closest match with the target performance metric and to insert the determined performance metric to the test report (paragraphs 0032, 0043, 0048; recommendations that are close together). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 7 the modified Vaderna discloses the apparatus of claim 1, wherein the apparatus is configured to transmit, before receiving the request, a message indicating capability for the radio configuration test to the network node (paragraph 0075; In order to provide service or application related quality reporting and active measurement capability in the UE).
Regarding claim 8 the modified Vaderna discloses the apparatus of claim 1, wherein the apparatus is configured to store a database comprising various combinations of operational parameters used in earlier radio connections and, for each combination of operational parameters, at least one performance metric (paragraphs 0093-0096; to collect legacy trace logs). Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses to perform the radio configuration test by searching, from the database, the combination of tested operational parameters that provides a performance metric closest to the target performance metric (paragraphs 0075; The recommendations of the current population of machine-learning processes may be weighted by one or more factors. The examples of weighting factors include machine-learning process age, confidence level, and historical records (i.e. how good were their earlier recommendations). In one embodiment, the weighted recommendations of the current population of machine-learning processes are averaged to produce a single output recommendation). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 9 the modified Vaderna discloses the apparatus of claim 1. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein the request comprises a plurality of target performance metrics (paragraph 0032; a plurality of machine-learning processes, wherein an individual machine-learning process operates based on a data model and decision-making rules. These machine-learning processes form a population of machine-learning processes. In order to get diverse recommendations and from them select an output recommendation the plurality of machine-learning processes operate based on a plurality of different data models and a plurality of different decision-making rules), and wherein the apparatus is configured to test the plurality of different combinations of operational parameters, within the limitations received from the network node, to determine performance metrics of each of the plurality of different combinations of operational parameters, and to insert to the test report at least the combination of tested operational parameters that provides performance metrics closest to the target performance metrics (paragraphs 0032-0040; The machine-learning processes then choose an action from a set of actions, where each action produces a recommended value of a parameter to be configured (or multiple values of parameters to be configured). When new values for the KPIs become available, they are used to determine which ones of the machine-learning processes receive the energy reward ). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 10 the modified Vaderna discloses the apparatus of claim 1, wherein the apparatus is configured to receive, from the network node, further requests for performing the radio configuration test for different target performance metrics, to perform the radio configuration test for the different target performance metrics and to perform the reporting separately for each request (paragraphs 0075, 0077, 0083, 0088; tests triggered and performed for each individual trigger).
Regarding claim 11 the modified Vaderna discloses the apparatus of claim 1, wherein the request is a part of reconfiguration of the radio connection and the target performance metric indicates a purpose of the reconfiguration, wherein the radio configuration also indicates the purpose, and wherein the apparatus is configured to store the radio configuration as linked to the purpose and use the store configuration and the purpose in a later radio configuration test indicating the same purpose (paragraphs 0075, 0077, 0083, 0088, 0106; control and collect service specific UE performance measurements and activate specific test traffic generation to/from UEs for service centric network management purposes. With the addition of service layer UE performance measurements used in combination with UE radio layer performance measurements and network measurements, the operator will have a full control over the performance data collection from low level radio performance up to service layer performance data. This facilitates to omit drive tests not only for radio measurement purposes but also for application performance collection purposes).
Regarding claim 12 Vaderna discloses an apparatus for a network node (fig. 3), comprising at least one processor and at least one memory including computer program code, wherein the at least one memory and computer program code are configured to, with the at least one processor (paragraph 0058), cause the apparatus to:
transmit, to a terminal device, a request for performing a radio configuration test for selecting operational parameters of the terminal device for a radio connection of the terminal device (paragraphs 0063, 0069, 0080; a network node instructs the UE to perform passive/active measurements and the UE generates test traffic), the request comprising at least one information element indicating a target performance metric and at least one information element defining limitations to the operational parameters available for the radio configuration test (paragraphs 0009, 0028, 0030, 0072, 0089, 0096; instructing the UE to measure metrics as throughput, jitter, among others, which can also be restricted to a certain terminal type or period of time); and
receive, from the terminal device, a test report indicating at least a combination of tested operational parameters that provides a performance metric closest to the target performance metric (paragraphs 0025, 0070, 0075, 0083; the UE reports the tested traffic to network node to be forwarded to NMS).
Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses select, on the basis of the test report, a radio configuration defining a set of operational parameters for the radio connection (paragraphs 0032-0033; individual recommendations are generated to achieve a particular goal or desired optimization); and cause transmission of at least one radio configuration message to the terminal device, the at least one radio configuration message comprising the set of operational parameters selected for the radio connection (paragraphs 0035, 0047-0048; wherein a system recommendation is generated by the network node and is output to be implemented by the receiving node). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 13 the modified Vaderna discloses the apparatus of claim 12, wherein the limitations limit the operational parameters available for the radio configuration test on a plurality of protocol layers (paragraphs 0095-0096; metrics that can be reported only by the application layers).
Regarding claim 14 the modified Vaderna discloses the apparatus of claim 12. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein the apparatus is configured to select the combination of tested operational parameters reported by the terminal device to provide the performance metric closest to the target performance metric as at least a part of the set of operational parameters for the radio connection (paragraphs 0032, 0043, 0048; recommendations that are close together). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 15 the modified Vaderna discloses the apparatus of claim 12. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein the apparatus is configured to select, as the set of operational parameters selected by the network node, at least partially different operational parameters than the combination of tested operational parameters reported by the terminal device to provide the performance metric closest to the target performance metric (paragraphs 0032-0040; The machine-learning processes then choose an action from a set of actions, where each action produces a recommended value of a parameter to be configured (or multiple values of parameters to be configured). When new values for the KPIs become available, they are used to determine which ones of the machine-learning processes receive the energy reward ). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 16 the modified Vaderna discloses the apparatus of claim 12. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein the test report comprises, in addition to the combination of tested operational parameters, a performance metric indicating performance of the combination of tested operational parameters with respect to the target performance metric, wherein the performance metric differs from the target performance metric, and wherein the apparatus is configured to select the set of operational parameters for the connection further on the basis of the reported performance metric (paragraphs 0075; The recommendations of the current population of machine-learning processes may be weighted by one or more factors. The examples of weighting factors include machine-learning process age, confidence level, and historical records (i.e. how good were their earlier recommendations). In one embodiment, the weighted recommendations of the current population of machine-learning processes are averaged to produce a single output recommendation). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 17 the modified Vaderna discloses the apparatus of claim 12, wherein the apparatus is configured to receive, before transmitting the request, a message indicating capability of the terminal device for the radio configuration test, and to transmit the request in response to receiving the message (paragraph 0075; In order to provide service or application related quality reporting and active measurement capability in the UE).
Regarding claim 18 the modified Vaderna discloses the apparatus of claim 12. Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses wherein the request comprises a plurality of target performance metrics (paragraph 0032; a plurality of machine-learning processes, wherein an individual machine-learning process operates based on a data model and decision-making rules. These machine-learning processes form a population of machine-learning processes. In order to get diverse recommendations and from them select an output recommendation the plurality of machine-learning processes operate based on a plurality of different data models and a plurality of different decision-making rules), and wherein the test report comprises at least the combination of tested operational parameters that provides performance metrics closest to the target performance metrics (paragraphs 0075; The recommendations of the current population of machine-learning processes may be weighted by one or more factors. The examples of weighting factors include machine-learning process age, confidence level, and historical records (i.e. how good were their earlier recommendations). In one embodiment, the weighted recommendations of the current population of machine-learning processes are averaged to produce a single output recommendation). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Regarding claim 19 the modified Vaderna discloses the apparatus of claim 12, wherein the apparatus is configured to transmit, to the terminal device, further requests for performing the radio configuration test for different target performance metrics, and to receive the test report separately for each request (paragraphs 0075, 0077, 0083, 0088; tests triggered and performed for each individual trigger).
Regarding claim 23 Vaderna discloses a method comprising:
receiving, by a terminal device from a network node, a request for performing a radio configuration test for selecting operational parameters of the terminal device for a radio connection of the terminal device (paragraphs 0063, 0069, 0080; a network node instructs the UE to perform passive/active measurements and the UE generates test traffic), the request comprising at least one information element indicating a target performance metric and at least one information element defining limitations to the operational parameters available for the radio configuration test (paragraphs 0009, 0028, 0030, 0072, 0089, 0096; instructing the UE to measure metrics as throughput, jitter, among others, which can also be restricted to a certain terminal type or period of time);
performing, by the terminal device the radio configuration test by testing a plurality of different combinations of operational parameters of the terminal device, within the limitations received from the network node, and determining, by the terminal device, a performance metric of each of the plurality of different combinations of tested operational parameters (paragraphs 0072, 0089-0097; the UE performs the measurements and generates test traffic regarding multiple measurements that were triggered and generates logs that are collected by the network node and forwarded to NMS); and
transmitting, by the terminal device, a test report to the network node, the test report indicating at least a combination of tested operational parameters that provides a performance metric closest to the target performance metric (paragraphs 0025, 0070, 0075, 0083; the UE reports the tested traffic to network node to be forwarded to NMS).
Vaderna fails to explicitly disclose but De Buitleir, in the same field of endeavor related to using machine-learning for network optimization, discloses after transmitting the test report, receiving by the terminal device from the network node a radio configuration defining a set of operational parameters of the terminal devices selected by the network node for the radio connection (paragraphs 0035, 0047-0048; wherein a system recommendation is generated by the network node and is output to be implemented by the receiving node). Therefore, it would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to modify the teachings of Vaderna with the teachings of De Buitleir, in order to optimize network performance (De Buitleir: paragraph 0001).
Citation of Pertinent Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US PGPUB 2026/0025321 to Malboubi et al. – that discloses processing system to facilitate performance of operations, receive a request from a mobile network entity for a key performance indicator (KPI) prediction over a portion of a mobile network and obtain a group of identifiers associated with the mobile network entity. The processor determines a KPI prediction associated with the mobile network entity based on the group of KPIs and allocates a group of network resources to the mobile network entity based on the KPI prediction, where the KPI prediction is based on a short-term KPI prediction, channel quality indicator (CQI) prediction, long-term KPI prediction and cell-based KPI prediction.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Aixa A Guadalupe-Cruz whose telephone number is (571)270-7523. The examiner can normally be reached Monday - Thursday 6AM - 4:00PM.
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/Aixa Guadalupe-Cruz/
Examiner
Art Unit 2466
/FARUK HAMZA/Supervisory Patent Examiner, Art Unit 2466