DETAILED ACTION
Claims status
In response to the application filed on06/03/2024, claims 1-20 are currently pending for the examination. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice of Pre-AIA or AIA Status
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on06/03/2024 has been placed in the application file, and the information referred therein has been considered as to the merits.
Drawings
Drawing figures submitted on06/03/2024 have been reviewed and accepted.
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.
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 1-4, 7-10, 14-16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over XU et al. (US 2024/0244454 A1) in view of Tullberg et al. (US 2022/0322195 A1).
Regarding claim 1; XU teaches a wireless communication method, comprising:
obtaining, by a terminal device, a first model (See Fig. 8: At operation 805, the system receives, at a user equipment from a network, a configuration for a first type of machine learning (ML) model. ¶ [0108]);
receiving, by the terminal device, first model indication information from a second network device (See Fig. 8 and steps 805 and 810: sending the control information (i.e., first model indication) for receiving a first type of machine learning (ML) model from a network (i.e., second network device). ¶ [0108]-[0109]), wherein the first model indication information indicates a second model of the second network device (See Fig. 8 and step 810: wherein the control information (i.e., first model indication) indicates a first configuration for receiving a first type of machine learning model and a second configuration for receiving a second type of machine learning (i.e., the second model of the network device). ¶ [0109]); and
configuring, by the terminal device, second model indication information, wherein the second model indication information indicates an model of the first model and the second model (See Fig. 8: configuring a machine learning model (i.e., second model indication information) on the user equipment based on at least one of the configuration for the first type of machine learning model (i.e., first model) or the configuration for the second type of machine learning model (i.e., second model) and based on a type of the user equipment. ¶ [0110]).
XU doesn’t explicitly provide obtaining the first model of a first network device and an ensemble model of the first and second models.
However, Tullberg discloses obtaining the first model of a first network device (Tullberg-See Fig. 7: predicting a time for a UE handover to a target cell (i.e., first network device)_using the first sequential time-based machine learning model, radio link monitoring measurements for the UE, and geographic positions associated with the radio link monitoring measurements; determining whether enough time exists to perform the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell.
See Abstract), and an ensemble model of the first and second models (See Fig. 7: evaluating ML1 and ML2 before getting the final prediction of the ML3 or the ensemble model. ¶ [0083]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to provide obtaining the first model of a first network device and generating the ensemble model as taught by Tullberg to have incorporated in the system of XU, so that it would provide to predict handover in terms of what cell to switch to and when the handover will occur; and (c) a method and apparatus to maintain learning in real-time to ensure reliability. Tullberg: ¶ [0008].
Regarding claim 2: XU teaches the method further comprising: handing over the terminal device from the first network device to the second network device (Tullberg: performing the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell. Abstract).
Regarding claim 3: XU in view of Tullberg discloses the method wherein the second model indication information comprises a model parameter of the ensemble model (Tullberg-See Fig. 7: The predicted handover attributes are used input to ML 3 at step 76, which evaluates the input values and determines the final predicted handover attributes. ¶ [0084]).
Regarding claim 4; XU teaches the method wherein the method further comprises: sending, by the terminal device, third model indication information to the first network device, wherein the third model indication information indicates the ensemble model (Tullberg-See Fig. 7: The predicted handover attributes are used input to ML 3 at step 76, which evaluates the input values and determines the final predicted handover attributes. ¶ [0084]).
Regarding claim 7: XU teaches a communication apparatus, comprising at least one processor, and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to perform operations comprising:
obtaining, by a terminal device, a first model (See Fig. 8: At operation 805, the system receives, at a user equipment from a network, a configuration for a first type of machine learning (ML) model. ¶ [0108]);
receiving, by the terminal device, first model indication information from a second network device (See Fig. 8 and steps 805 and 810: sending the control information (i.e., first model indication) for receiving a first type of machine learning (ML) model from a network (i.e., second network device). ¶ [0108]-[0109]), wherein the first model indication information indicates a second model of the second network device (See Fig. 8 and step 810: wherein the control information (i.e., first model indication) indicates a first configuration for receiving a first type of machine learning model and a second configuration for receiving a second type of machine learning (i.e., the second model of the network device). ¶ [0109]); and
configuring, by the terminal device, second model indication information, wherein the second model indication information indicates an model of the first model and the second model (See Fig. 8: configuring a machine learning model (i.e., second model indication information) on the user equipment based on at least one of the configuration for the first type of machine learning model (i.e., first model) or the configuration for the second type of machine learning model (i.e., second model) and based on a type of the user equipment. ¶ [0110]).
XU doesn’t explicitly provide obtaining the first model of a first network device and an ensemble model of the first and second models.
However, Tullberg discloses obtaining the first model of a first network device (Tullberg-See Fig. 7: predicting a time for a UE handover to a target cell (i.e., first network device)_using the first sequential time-based machine learning model, radio link monitoring measurements for the UE, and geographic positions associated with the radio link monitoring measurements; determining whether enough time exists to perform the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell.
2. The method according to claim 1, further comprising: handing over the terminal device from the first network device to the second network device. See Abstract), and an ensemble model of the first and second models (See Fig. 7: evaluating ML1 and ML2 before getting the final prediction of the ML3 or the ensemble model. ¶ [0083]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to provide obtaining the first model of a first network device and generating the ensemble model as taught by Tullberg to have incorporated in the system of XU, so that it would provide to predict handover in terms of what cell to switch to and when the handover will occur; and (c) a method and apparatus to maintain learning in real-time to ensure reliability. Tullberg: ¶ [0008].
Regarding claim 8: XU teaches the method further comprising: handing over the terminal device from the first network device to the second network device (Tullberg: performing the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell. Abstract).
Regarding claim 9: XU in view of Tullberg discloses the method wherein the second model indication information comprises a model parameter of the ensemble model (Tullberg-See Fig. 7: The predicted handover attributes are used input to ML 3 at step 76, which evaluates the input values and determines the final predicted handover attributes. ¶ [0084]).
Regarding claim 10; XU teaches the method wherein the method further comprises: sending, by the terminal device, third model indication information to the first network device, wherein the third model indication information indicates the ensemble model (Tullberg-See Fig. 7: The predicted handover attributes are used input to ML 3 at step 76, which evaluates the input values and determines the final predicted handover attributes. ¶ [0084]).
Regarding claim 14: XU teaches a communication apparatus comprising at least one processor, and one or more memories coupled to the at least one processor and storing programming instructions for execution by the at least one processor to perform operations comprising:
sending first model indication information from a second network device (See Fig. 8 and steps 805 and 810: sending the control information (i.e., first model indication) for receiving a first type of machine learning (ML) model from a network (i.e., second network device). ¶ [0108]-[0109]), wherein the first model indication information indicates a second model of the second network device (See Fig. 8 and step 810: wherein the control information (i.e., first model indication) indicates a first configuration for receiving a first type of machine learning model and a second configuration for receiving a second type of machine learning (i.e., the second model of the network device). ¶ [0109]); and
configuring, by the terminal device, second model indication information, wherein the second model indication information indicates an model of the first model and the second model (See Fig. 8: configuring a machine learning model (i.e., second model indication information) on the user equipment based on at least one of the configuration for the first type of machine learning model (i.e., first model) or the configuration for the second type of machine learning model (i.e., second model) and based on a type of the user equipment. ¶ [0110]).
XU doesn’t explicitly provide obtaining the first model of a first network device and an ensemble model of the first and second models.
However, Tullberg discloses obtaining the first model of a first network device (Tullberg-See Fig. 7: predicting a time for a UE handover to a target cell (i.e., first network device)_using the first sequential time-based machine learning model, radio link monitoring measurements for the UE, and geographic positions associated with the radio link monitoring measurements; determining whether enough time exists to perform the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell.
2. The method according to claim 1, further comprising: handing over the terminal device from the first network device to the second network device. See Abstract), and an ensemble model of the first and second models (See Fig. 7: evaluating ML1 and ML2 before getting the final prediction of the ML3 or the ensemble model. ¶ [0083]).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention was made to provide obtaining the first model of a first network device and generating the ensemble model as taught by Tullberg to have incorporated in the system of XU, so that it would provide to predict handover in terms of what cell to switch to and when the handover will occur; and (c) a method and apparatus to maintain learning in real-time to ensure reliability. Tullberg: ¶ [0008].
Regarding claim 15: XU teaches the communication apparatus further comprising: handing over the terminal device from the first network device to the second network device (Tullberg: performing the UE handover before the predicted handover time; and upon determining enough time exists, performing the UE handover to the target cell. Abstract).
Regarding claim 16: XU in view of Tullberg discloses the communication apparatus wherein the second model indication information comprises a model parameter of the ensemble model (Tullberg-See Fig. 7: The predicted handover attributes are used input to ML 3 at step 76, which evaluates the input values and determines the final predicted handover attributes. ¶ [0084]).
Regarding claim 17; XU teaches the method wherein the communication apparatus further comprises: wherein the first model indication information comprising a model ensembling parameter of the second network device (Tullberg-See Fig. 7: The predicted handover attributes are used input to ML 3 at step 76, which evaluates the input values and determines the final predicted handover attributes. ¶ [0084]).
Allowable Subject Matter
Claims 5-6, 11-12, and 17-19 are objected to as being dependent upon the rejected base claims 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.
Ryden et al. (US 2023/0276263 A1).
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/SAI AUNG/
Primary Examiner, Art Unit 2416
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