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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
3. The information disclosure statement(s) submitted on September 30, 2024 has/have been considered by the Examiner and made of record in the application file.
Specification
4. The amendments to the specification received on September 30, 2024. These amendments to the specification are accepted.
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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 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.
Claim Rejections - 35 USC § 103
5. 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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-3, 6, 8, 10-12, 16-23, 28, and 40 are rejected under 35 U.S.C. 103 as being unpatentable over Tullberg et al. (U.S. Patent Application Publication # 2022/0078637 A1) in view of Shori et al. (U.S. Patent Application Publication # 20230370338 A1).
Regarding claim 1, Tullberg et al. teach a method performed by a user equipment (UE) for detecting performance degradation of a machine learning (ML)-model utilizable by the UE (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120), the method comprising:
sending, to the network node (Fig(s).7 @ 110 and 8 @ 110), communication information (Fig(s).7 and 8 @ 803),
wherein the communication information is associated with communication performance of the UE with respect to the utilization of the ML model by the UE. (read as “The network node 110, 130, 143 receives, from the wireless device 120, information relating to at least one prediction of an operation of the wireless device 120 and to at least one result of the operation.”(Fig(s).7-8; Paragraph [0088]))
However, Tullberg et al. fail to explicitly teach sending, to a network node, at least one ML-model output;
Shori et al. teach a method for sending at least one ML-model output (Fig(s).2-4, and 5 @ 516);
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Regarding claim 16, Tullberg et al. teach a method performed by a network node (Fig(s).7 @ 110 and 8 @ 110) for detecting performance degradation for machine learning (ML)-model performance of a user equipment (UE) (Fig(s).7-8), the method comprising:
receiving, from the UE (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120), communication information associated with communication performance of the UE (Fig.8 @ 801); and
sending, to the UE (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120), degradation information associated with a cause of degraded performance of the UE (Fig.8 @ 805),
However, Tullberg et al. fail to explicitly teach receiving, from the UE, at least one ML-model output;
the cause of the degraded performance of the UE including at least one of a cause related to the ML-model or a cause unrelated to the ML model,
wherein the degradation information is determined based on the at least one ML-model output and the communication information associated with communication performance of the UE.
Shori et al. teach a method for receiving at least one ML-model output;
the cause of the degraded performance of the UE including at least one of a cause related to the ML-model or a cause unrelated to the ML model,
wherein the degradation information is determined based on the at least one ML-model output and the communication information associated with communication performance of the UE.
Shori et al. teach a method for receiving at least one ML-model output (Fig(s).2-4, and 5 @ 516);
the cause of the degraded performance of the UE including at least one of a cause related to the ML-model or a cause unrelated to the ML model (Fig(s).2-4, and 5 @ 514, 516),
wherein the degradation information is determined based on the at least one ML-model output and the communication information associated with communication performance of the UE. (Fig.5 @ 508, 512)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the network node as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Regarding claim 28, Tullberg et al. teach a network node (Fig.3 @ 110, 130, 143) for performing user equipment (UE) machine-learning (ML) model analysis (Fig(s).7-8), the network node (Fig.3 @ 110, 130, 143) comprising:
a transceiver (Fig.3 @ 302, 303),
a processor (Fig.3 @ 307), and
a memory (Fig.3 @ 306),
said memory (Fig.3 @ 306) containing instructions (read as program code (Paragraph [0143])) executable by the processor (Fig.3 @ 307) whereby the network node is operative to perform:
receiving, from the UE (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120), communication information associated with communication performance of the UE (Fig.8 @ 801); and
sending, to the UE (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120), degradation information associated with a cause of degraded performance of the UE (Fig.8 @ 805),
However, Tullberg et al. fail to explicitly teach receiving, from the UE, at least one ML-model output;
the cause of the degraded performance of the UE including at least one of a cause related to the ML-model or a cause unrelated to the ML model,
wherein the degradation information is determined based on the at least one ML-model output and the communication information associated with communication performance of the UE.
Shori et al. teach a method for receiving at least one ML-model output (Fig(s).2-4, and 5 @ 516);
the cause of the degraded performance of the UE including at least one of a cause related to the ML-model or a cause unrelated to the ML model (Fig(s).2-4, and 5 @ 514, 516),
wherein the degradation information is determined based on the at least one ML-model output and the communication information associated with communication performance of the UE. (Fig.5 @ 508, 512)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the network node as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Regarding claim 40, Tullberg et al. teach a user equipment (UE) for performing user equipment (UE) machine-learning (ML) model analysis (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120), the UE comprising:
a transceiver (Fig.5 @ 502, 503),
a processor (Fig.5 @ 506), and
a memory (Fig.5 @ 505),
said memory (Fig.5 @ 505) containing instructions (read as computer program code (Paragraph [0192])) executable by the processor (Fig.5 @ 505) whereby the UE (Fig(s).5 @ 120, 7 @ 120, and 8 @ 120) is operative to perform:
sending, to the network node (Fig(s).7 @ 110 and 8 @ 110), communication information (Fig(s).7 and 8 @ 803),
wherein the communication information is associated with communication performance of the UE (read as “The network node 110, 130, 143 receives, from the wireless device 120, information relating to at least one prediction of an operation of the wireless device 120 and to at least one result of the operation.”(Fig(s).7-8; Paragraph [0088])),
However, Tullberg et al. fail to explicitly teach sending at least one ML-model output; and
wherein the at least one ML-model output and the communication information associated with communication performance of the UE facilitate determination of a cause of degraded performance of the UE including at least one of a cause related to the ML model or a cause unrelated to the ML model.
Shori et al. teach a method for sending at least one ML-model output (Fig(s).2-4, and 5 @ 516); and
wherein the at least one ML-model output and the communication information associated with communication performance of the UE facilitate determination of a cause of degraded performance of the UE including at least one of a cause related to the ML model or a cause unrelated to the ML model. (Fig.5)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Regarding claim 2, and as applied to claim 1 above, Tullberg et al., as modified by Shori et al., teach a method wherein the at least one ML-model output and the communication information associated with communication performance of the UE facilitate performance assessment of an ML-model operable by the UE.(Fig(s).4-8)
Regarding claim 3, and as applied to claim 1 above, Tullberg et al., as modified by Shori et al., teach a method further comprising:
receiving, from the network node (Fig.3 @ 110, 130, 143), degradation information associated with a cause of degraded performance of the UE including at least one of a cause related to the ML model or a cause unrelated to the ML model. (Fig(s).4-8)
Regarding claim 6, and as applied to claim 1 above, Tullberg et al., as modified by Shori et al., teach a method further comprising:
receiving, from the network node (Fig.3 @ 110, 130, 143), a first request for the at least one ML-model output. (Fig(s).4-8)
Regarding claim 8, and as applied to claim 6 above, Tullberg et al., as modified by Shori et al., teach a method wherein the first request is received with an Information Element (IE) or field including one or more parameters for at least one of channel state information (CSI) reporting or beam management reporting. (read as beam-forming (Paragraph [0140]))
Regarding claim 10, and as applied to claim 6 above, Tullberg et al., as modified by Shori et al., teach a method further comprising:
receiving, from the network node (Fig.3 @ 110, 130, 143), a second request for the communication information. (read as request (Fig.2 @ 201; Paragraph [0083]))
Regarding claim 11, and as applied to claim 10 above, Tullberg et al., as modified by Shori et al., teach a method wherein at least one of the first request or the second request is received in
a periodic,
aperiodic,
semi-persistent, or
event-triggered manner. (read as an event (Paragraph [0073]))
Regarding claim 12, and as applied to claim 10 above, Tullberg et al., as modified by Shori et al., teach a method wherein the second request is received with the first request. (read as request (Fig.2 @ 201; Paragraph [0083]))
Regarding claim 17, and as applied to claim 16 above, Tullberg et al., as modified by Shori et al., teach a method further comprising the step of:
detecting the degraded performance of the UE based on one or more of:
the UE's previous performance (Fig.8);
an average performance of one or more other UEs;
estimated performance or estimated report contents based on UE modeling in the network node;
an uncertainty indication signaled from the UE; and
a reliability indication signaled from the UE.
Regarding claim 18, and as applied to claim 17 above, Tullberg et al., as modified by Shori et al., teach clearly shows and discloses a method wherein detecting the degraded performance of the UE based on the UE's previous performance comprises detecting a change or inconsistency compared to the UE's previous reports.(Fig.8)
Regarding claim 19, and as applied to claim 16 above, Tullberg et al., as modified by Shori et al., teach a method further comprising the step of:
sending, to the UE (Fig.8 @ 120), a request for the communication information.(Fig.8 @ 805)
Regarding claim 20, and as applied to claim 19 above, Tullberg et al., as modified by Shori et al., teach a method wherein the request is sent when the network node detects degraded performance of the UE based on the at least one ML-model output. (Fig.8)
Regarding claim 21, and as applied to claim 19 above, Tullberg et al., as modified by Shori et al., teach a method wherein a configuration of the communication information is applicable to at least one of the following:
a future parameter measurement and reporting occasion;
a current parameter measurement and a future performance degradation occasion for the ML-mode of the UE (read as “the network node 110, 130, 143 may transmit the request for the information when it expects that a change in performance of the wireless communications system 10 is significant, e.g. above a threshold value, and one or more parameters of the first and/or second instances may need to be updated.”(Paragraph [0086])),
where the communication information comprises a future ML-based estimate and additional data pertaining to the current performance degradation occasion; or
an additional performance degradation occasion,
where the communication information contains additional data pertaining to the current performance degradation occasion. (read as “one or more parameters of the first and/or second instances may need to be updated.”(Paragraph [0086]))
Regarding claim 22, and as applied to claim 16 above, Tullberg et al., as modified by Shori et al., teach a method wherein the degradation information is determined by performing one or more of the following:
comparing the at least one ML-model output with the communication information (Fig.9);
comparing the at least one ML-model output and other information provided in the communication information;
evaluating link conditions based on the other information provided in the communication information; and
analyzing an uplink control information (UCI) false detection probability, based on a cyclic redundancy check (CRC) length for a first report payload.
Regarding claim 23, and as applied to claim 16 above, Tullberg et al., as modified by Shori et al., teach a method further comprising the step of:
configuring a transmission scheme that is more reliable than a current transmission scheme for the UE to report the at least one ML-model output in accordance with a network node's determination that the cause of degraded performance of the UE is an erroneous reception communication performed by the UE.(Fig(s).9-10)
Claims 4 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Tullberg et al. (U.S. Patent Application Publication # 2022/0078637 A1), in view of Shori et al. (U.S. Patent Application Publication # 20230370338 A1), and Li et al. (U.S. Patent Application Publication # 20240349082 A1).
Regarding claim 4, and as applied to claim 1 above, Tullberg et al. teach “A network node (NN) and method therein for assisting a wireless device (UE) in updating a first instance of a machine learning model.”(Fig(s).4-8)
Shori et al. teach a “process for executing ML models to detect and/or repair service degradations and anomalies within a wireless network, …”(Fig.5; Paragraph [0008])
However, Tullberg et al. and Shori et al. fail to explicitly teach wherein the UE is configured to send the at least one ML-model output and the communication information in one report.
Li et al. teach a method wherein the UE is configured to send the at least one ML-model output and the communication information in one report. (Fig.1 @ 118)
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for generating a ML report as taught by Li et al. and the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Regarding claim 13, and as applied to claim 1 above, Tullberg et al. teach “A network node (NN) and method therein for assisting a wireless device (UE) in updating a first instance of a machine learning model.”(Fig(s).4-8)
Shori et al. teach a “process for executing ML models to detect and/or repair service degradations and anomalies within a wireless network, …”(Fig.5; Paragraph [0008])
However, Tullberg et al. and Shori et al. fail to explicitly teach clearly shows and discloses a method wherein the communication information associated with communication performance of the UE comprises at least one of the following:
radio measurements with different types comparing to the at least one ML-model output,
reference signals,
multiplexed uplink data with one or more ML-model outputs, and
one or more ML-model outputs configured with different transmission parameters compared to the at least one ML-model output.
Li et al. teach a method wherein the communication information associated with communication performance of the UE comprises at least one of the following:
radio measurements with different types comparing to the at least one ML-model output,
reference signals (read as CSI-RS (Paragraph [0093])),
multiplexed uplink data with one or more ML-model outputs, and
one or more ML-model outputs configured with different transmission parameters compared to the at least one ML-model output.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for generating/detecting a CSI-RS as taught by Li et al. and the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Tullberg et al. (U.S. Patent Application Publication # 2022/0078637 A1), in view of Shori et al. (U.S. Patent Application Publication # 20230370338 A1), and Orhan et al. (U.S. Patent Application Publication # 20220124543 A1).
Regarding claim 5, and as applied to claim 1 above, Tullberg et al. teach “A network node (NN) and method therein for assisting a wireless device (UE) in updating a first instance of a machine learning model.”(Fig(s).4-8)
Shori et al. teach a “process for executing ML models to detect and/or repair service degradations and anomalies within a wireless network, …”(Fig.5; Paragraph [0008])
However, Tullberg et al. and Shori et al. fail to explicitly teach wherein the communication information is sent after the UE transits to RRCCONNECTED from RRCIDLE or RRC_INACTIVE,
wherein the communication information comprises measurements collected when the UE is in RRCIDLE or RRCINACTIVE.
Orhan et al. teach a method wherein the communication information is sent after the UE transits to RRCCONNECTED from RRCIDLE or RRC_INACTIVE (read as “The serving cell ID-UE change of status includes an association between a serving cell ID and a UE state or status. The UE state or status may include, for example, radio resource control (RRC) connection status (or RRC protocol state) such as an idle state (RRC_IDLE) or NR-RRC idle, an inactive state (RRC_INACTIVE) or NR-RRC Inactive, and an active or connected state (RRC_CONNECTED) or NR-RRC Connected (DRB active user).”(Paragraph [0103])),
wherein the communication information comprises measurements collected when the UE is in RRCIDLE or RRCINACTIVE. (read as “The signal/channel measurements/metrics may include, for example, RSRP, RSRQ, SNR, SINR (e.g., SINR related to channel quality indicator (CQI)), wideband CQI, RSRQ, E.sub.cN.sub.0, and/or any other suitable signal/channel measurements/metrics …”(Fig(s).4-5; Paragraph [0103]) For example, “an ML/AI-based framework for load-aware connection management and/or handover management to optimize user associations/connections and load balancing to fulfill QoS requirements.”(Fig(s).4-5; Paragraph [0021]))
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for UE to change RRC status and the function for connection management using ML/AI framework to optimize user association(s) as taught by Orhan et al. and the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Tullberg et al. (U.S. Patent Application Publication # 2022/0078637 A1), in view of Shori et al. (U.S. Patent Application Publication # 20230370338 A1), and Guan et al. (U.S. Patent Application Publication # 20230328492 A1).
Regarding claim 7, and as applied to claim 6 above, Tullberg et al. teach “A network node (NN) and method therein for assisting a wireless device (UE) in updating a first instance of a machine learning model.”(Fig(s).4-8)
Shori et al. teach a “process for executing ML models to detect and/or repair service degradations and anomalies within a wireless network, …”(Fig.5; Paragraph [0008])
However, Tullberg et al. and Shori et al. fail to explicitly teach wherein the first request is received in a radio resource control (RRC) message corresponding to at least one of the following:
an RRC RESUME message used when the UE transits from RRC_INACTIVE to RRC_CONNECTED,
an RRC Setup message when the UE transits from RRC_IDLE to RRC_CONNECTED, or
an RRC Reconfiguration message when the UE is in RRC_CONNECTED.
Guan et al. teach wherein the first request is received in a radio resource control (RRC) message corresponding to at least one of the following:
an RRC RESUME message used when the UE transits from RRC_INACTIVE to RRC_CONNECTED,
an RRC Setup message when the UE transits from RRC_IDLE to RRC_CONNECTED (read as RRC setup request (Paragraph [0151])), or
an RRC Reconfiguration message when the UE is in RRC_CONNECTED. (read as RRC reconfiguration (Paragraph [0170]))
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for generating an RRC message (e.g.: RRC setup request, RRC configuration) as taught by Orhan et al. and the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Regarding claim 9, and as applied to claim 6 above, Tullberg et al. teach “A network node (NN) and method therein for assisting a wireless device (UE) in updating a first instance of a machine learning model.”(Fig(s).4-8)
Shori et al. teach a “process for executing ML models to detect and/or repair service degradations and anomalies within a wireless network, …”(Fig.5; Paragraph [0008])
However, Tullberg et al. and Shori et al. fail to explicitly teach wherein the first request is received in a medium access control (MAC)-control element (CE) or a downlink control information (DCI) message.
Guan et al. teach a method wherein the first request is received in a medium access control (MAC)-control element (CE) or a downlink control information (DCI) message.(read as DCI (Paragraph [0068]))
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to employ the function for generating a DCI as taught by Orhan et al. and the function for detecting service degradations based on machine learning (ML) model output as taught by Shori with the UE as taught by Tullberg et al. for the purpose of improving forecasting of network degradations based on performance metrics by devices in a communication network.
Conclusion
6. The prior art made of record and not relied upon is considered pertinent to Applicant’s disclosure:
Lara Mauri and Ernesto Damiani (“Estimating Degradation of Machine Learning Data Assets”, December 2021) teach “a quantitative technique for assessing the degradation of ML models’ data assets due to injection of spurious training data.”(page 12)
Wang et al. (U.S. Patent Application Publication # 2026/0046219 A1) teach “methods, devices and a computer storage medium for communication, especially for reporting similarity information between training/predicted/preconfigured beam information and field/actual beam information.”(Paragraph [0003])
Any response to this Office Action should be faxed to (571) 273-8300 or mailed to:
Commissioner for Patents
P.O. Box 1450
Alexandria, VA 22313-1450
Any inquiry concerning this communication or early communications from the Examiner should be directed to Salvador E. Rivas whose telephone number is (571) 270-1784. The examiner can normally be reached on Monday-Friday from 7:00AM to 3:30PM.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, Un C. Cho can be reached on (571) 272- 7919. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/SALVADOR E RIVAS/Primary Examiner, Art Unit 2413
October 13, 2026
