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 .
Response to Arguments
Applicant’s arguments with respect to pending claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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.
Claims 1-5, 9, 10, 13, 14, 17, 18, 45 and 47 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden et al (US 2023/0016595 A1) in view of You et al(US 2024/0114408 A1).
Regarding claim 1, Ryden ‘595 teaches, a method for communication processing, performed by a user equipment (UE) ([0030], [0036], [0041] and Fig. 1, a UE performing method 100), comprising:
triggering the UE to perform an operation corresponding to a network configuration in response to an artificial intelligence (AI) prediction result satisfying a configuration condition related to the network configuration ([0046]-[0050] and Figs. 1-2, UE triggering handover or reporting to network node when a Machine Learning(ML) prediction result compared with configured network parameters( e.g. signal strength threshold) satisfies a reporting condition or threshold), wherein the AI prediction result comprises a prediction result for at least one prediction object and a prediction parameter([0040], [0046], [0046] and Fig. 1, ML prediction on a second network node and outputs may include best carrier index and best node index).
Ryden ‘595 does not explicitly teach, wherein the prediction parameter comprises at least one of a prediction parameter of radio resource management (RRM) of the UE or a prediction parameter of the UE continuing to camp on a serving cell.
Veijalainen ‘271 teaches, wherein the prediction parameter comprises at least one of a prediction parameter of radio resource management (RRM) of the UE or a prediction parameter of the UE continuing to camp on a serving cell ([0058], [0062], [0099] and Fig. 5, the UE is configured with an ML model which is responsible for radio resource management (RRM) function. RRM related predictions includes, a predictive handover, target cell prediction, mobility prediction, RSRP measurement/prediction, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘595, by incorporating the teaching of Veijalainen ‘271, since such modification would enable to determine behavior result based on network condition and behavior information, and evaluating validity of a predictive model based on prediction result, behavior result, and difference determination information, as suggested by Veijalainen ‘271([0026]).
Regarding claim 2, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein configuration information of the network configuration comprises at least one of:
a trigger event; wherein the trigger event comprises a trigger event for reporting or a trigger event for conditional handover (CHO)([0049]-[0050] and Fis. 1, reporting trigger conditions (below/above threshold));
a threshold value of the trigger event([0051]-[0052] and Fig. 1, reporting being triggered when the ML prediction result is above/below threshold values, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations);
a hysteresis value of a threshold in the trigger event;
a trigger time or a number of triggers; an entering condition for the trigger event; or
a leaving condition for the trigger event.
Regarding claim 3, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein triggering the UE to perform the operation corresponding to the network configuration in response to the artificial intelligence AI prediction result satisfying the network configuration, comprises:
triggering the UE to perform an operation corresponding to the trigger event in response to the AI prediction result satisfying the trigger event of the network configuration ([0046]-[0050] and Figs. 1-2, UE triggering handover or reporting to network node when a Machine Learning(ML) prediction result compared with configured network parameters( e.g. signal strength threshold) satisfies a reporting condition or threshold). .
Regarding claim 4, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein triggering the UE to perform the operation corresponding to the trigger event in response to the AI prediction result satisfying the trigger event of the network configuration, comprises at least one of:
triggering the UE to report first information required by a network in response to determining that the AI prediction result satisfies the trigger event for reporting of the network configuration ([0048]- [0052] and Fig. 1, reporting being triggered when the ML prediction result is above/below threshold values, and the UE send a message (e.g. report) comprising the predicted conditions to a node in the communications network); or
triggering the UE to perform a CHO procedure in response to determining that the AI prediction result satisfies the trigger event for the CHO of the network configuration([0046]-[0047], the UE obtains prediction condition from ML( e.g. signal strength of a reference signal, the best carrier index) and using predicted conditions to determine whether to perform a handover procedure to the second network node, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claim 5, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein the first information comprises at least one of: the AI prediction result or a result of radio resource management (RRM) measurement([0046], [0048] , UE reporting predicted conditions outputted from the ML (e.g. signal strength, SIR, best carrier index) ,notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claim 9, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein the AI prediction result comprises at least one of: at least part of an AI prediction result of all available cells( [0006], [0009], UE using ML models to predict condition on one or more possible nodes to which the UE could handover to) ;
at least part of an AI prediction result of a corresponding cell satisfying a trigger event ([0009], [0050]- [0051], UE sending a report if the predicted conditions on a second node satisfy the reporting condition (e.g. signal strength threshold));
at least part of an AI prediction result of a cell configured by a network;
at least part of a best AI prediction result among AI prediction results in cells configured by the network; or
at least part of an AI prediction result of a prediction parameter of a service type of the UE in prediction parameters (notice, the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claim 10, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein the trigger event comprises at least one of:
a trigger event for a comparison of the AI prediction result to at least one threshold ([0049]-[0051], UE comparing the ML models output (predicted signal strength) to a coverage threshold, and reporting when the model output is above/below threshold); or
a trigger event for a comparison of different AI prediction results (notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claim 13, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein triggering the UE to report the first information required by the network, comprises at least one of:
triggering the UE to report the first information required by the network once; or triggering the UE to periodically report the first information required by the network ([0009], [0050]- [0051], UE sending a report once if the predicted conditions on a second node satisfy the reporting condition (e.g. signal strength threshold), notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Regarding claim 14, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein triggering the UE to report the first information required by the network in response to determining that the AI prediction result satisfies the trigger event for reporting of the network configuration, comprises one of:
in response to an AI prediction result of a prediction object satisfying the entering condition for the trigger event within the trigger time or the number of triggers, reporting the corresponding AI prediction result of the prediction object satisfying the entering condition([0049]-[0051], UE comparing the ML models output (predicted signal strength) to a coverage threshold, and reporting when the model output is above/below threshold).
Regarding claim 17, Ryden ‘595 teaches, a method for communication processing)( [0030], [0036], [0041] and Fig. 1, method for communication disclosed ) wherein the network configuration and an artificial intelligence AI prediction result obtained by a user equipment UE are used by the UE for triggering the UE to perform an operation corresponding to the network configuration in response to determining that the AI prediction result satisfies a configuration condition related to the network configuration ( [0046]-[0050] and Figs. 1-2, UE triggering handover or reporting to network node when a Machine Learning(ML) prediction result compared with configured network parameters( e.g. signal strength threshold) satisfies a reporting condition or threshold), wherein the AI prediction result comprises a prediction result for at least one prediction object and a prediction parameter([0040], [0046], [0046] and Fig. 1, ML prediction on a second network node and outputs may include best carrier index and best node index).
Ryden ‘595 does not explicitly teach, performed by a network device, comprising: transmitting a network configuration, wherein the prediction parameter comprises at least one of a prediction parameter of radio resource management (RRM) of the UE or a prediction parameter of the UE continuing to camp on a serving cell.
Veijalainen ‘271 teaches, performed by a network device, comprising: transmitting a network configuration ([0059], [0136] and Fig. 10, network configures the UE with the predictive handover model) wherein the prediction parameter comprises at least one of a prediction parameter of radio resource management (RRM) of the UE or a prediction parameter of the UE continuing to camp on a serving cell ([0058], [0062], [0099] and Fig. 5, the UE is configured with an ML model which is responsible for radio resource management (RRM) function. RRM related predictions includes, a predictive handover, target cell prediction, mobility prediction, RSRP measurement/prediction, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘595, by incorporating the teaching of Veijalainen ‘271, since such modification would enable to determine behavior result based on network condition and behavior information, and evaluating validity of a predictive model based on prediction result, behavior result, and difference determination information, as suggested by Veijalainen ‘271([0026]).
Regarding claim 18, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above, Ryden ‘595 further teaches, wherein transmitting the network configuration comprises: transmitting configuration information of the network configuration; wherein the configuration information comprises one or more of:
a trigger event; wherein the trigger event comprises a trigger event for reporting or a trigger event for conditional handover (CHO)) ([0049]-[0050] and Fis. 1, reporting trigger conditions (below/above threshold));
a threshold value of the trigger event; ([0051]-[0052] and Fig. 1, reporting being triggered when the ML prediction result is above/below threshold values, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations);
a hysteresis value of a threshold in the trigger event;
a trigger time or a number of triggers;
an entering condition for the trigger event;
a leaving condition for the trigger event; or
a candidate cell identifier of the CHO.
Regarding claim 45, Ryden ‘595 teaches, a user equipment (UE)([0103] user equipment 600), comprising: a processor([0103] user equipment 600 comprising processor 602); a memory([0103] user equipment 600 comprising memory 604) for storing instructions executable by the processor; wherein the processor is configured to: trigger the UE to perform an operation corresponding to a network configuration in response to an artificial intelligence (AI) prediction result satisfying a configuration condition related to the network configuration ([0046]-[0050] and Figs. 1-2, UE triggering handover or reporting to network node when a Machine Learning(ML) prediction result compared with configured network parameters( e.g. signal strength threshold) satisfies a reporting condition or threshold), wherein the AI prediction result comprises a prediction result for at least one prediction object and a prediction parameter([0040], [0046], [0046] and Fig. 1, ML prediction on a second network node and outputs may include best carrier index and best node index).
Ryden ‘595 does not explicitly teach, wherein the prediction parameter comprises at least one of a prediction parameter of radio resource management (RRM) of the UE or a prediction parameter of the UE continuing to camp on a serving cell.
Veijalainen ‘271 teaches, wherein the prediction parameter comprises at least one of a prediction parameter of radio resource management (RRM) of the UE or a prediction parameter of the UE continuing to camp on a serving cell ([0058], [0062], [0099] and Fig. 5, the UE is configured with an ML model which is responsible for radio resource management (RRM) function. RRM related predictions includes, a predictive handover, target cell prediction, mobility prediction, RSRP measurement/prediction, notice the claim limitation is written in alternative form thus examiner is required to show only one of the alternative claim limitations).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘595, by incorporating the teaching of Veijalainen ‘271, since such modification would enable to determine behavior result based on network condition and behavior information, and evaluating validity of a predictive model based on prediction result, behavior result, and difference determination information, as suggested by Veijalainen ‘271([0026]).
Regarding claim 47, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all claim limitations, Ryden ‘595 teaches, a network device([0093]-[0095] and Fig. 5, network node 500), comprising: a processor([0093]-[0097] and Fig. 5, network node 500 comprising processor 502 ),; a memory([0093]-[0097] and Fig. 5, network node 500 comprising memory 504)for storing instructions executable by the processor; wherein the processor is configured to implement the method for communication processing according to claim 17( see claim 17 rejection).
Claims 7, 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden ‘595 and Veijalainen ‘271 as applied to claims above, and further in view of You et al(US 2024/0114408 A1).
Regarding claim 7, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above except, wherein the prediction object comprises at least one of:
a cell identity or a frequency point; wherein the prediction parameter further comprises; a prediction parameter of the UE accessing a neighboring cell.
You ‘408 teaches, wherein the prediction object comprises at least one of:
a cell identity or a frequency point ([0098], [0146]-[0149], [0158], a target cell identifier);
wherein the prediction parameter further comprises; a prediction parameter of the UE accessing a neighboring cell ([0157]- [0159], [0177]- [0178], firs AI model predicting neighboring cell information).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the modified communication system of Ryden ‘595, by incorporating the teaching of You ‘408 since such modification would allow a UE to determine at least one handover information in a cell handover process based on autonomous decision-making of an AI model, and then complete the cell handover autonomously, thereby improving the efficiency of the cell handover, as suggested by you ‘408([0006]).
Regarding claim 15, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above except, wherein the configuration information further comprises: a candidate cell identifier of the CHO.
You ‘408 teaches, wherein the configuration information further comprises: a candidate cell identifier of the CHO ([0098], [0146]-[0149], [0158], network configuration information including target cell identifier for CHO).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘595, by incorporating the teaching of You ‘408 since such modification would allow a UE to determine at least one handover information in a cell handover process based on autonomous decision-making of an AI model, and then complete the cell handover autonomously, thereby improving the efficiency of the cell handover, as suggested by you ‘408([0006]).
Regarding claim 16, the combination of Ryden ‘595, Veijalainen ‘271, and You ‘408 teaches all of the claim limitations, You ‘408 further teaches, wherein triggering the UE to perform the CHO procedure in response to determining that the AI prediction result satisfies the trigger event for the CHO of the network configuration ([0157]- [0159], [0177]- [0178], UE performing conditional handover based on firs AI model prediction), comprises:
in response to an AI prediction result of the candidate cell satisfying the entering condition for the trigger event within the trigger time or the number of triggers, triggering the UE to perform the CHO procedure based on the candidate cell ([0157]-[0159], [0177]-[0178], based on first AI Model prediction result satisfying the condition the UE triggering CHO).
Claims 11, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ryden ‘595 and Veijalainen ‘271 as applied to claims above, and further in view of Madadi et al(US 2022/0338189 A1).
Regarding claim 11, the combination of Ryden ‘595 and Veijalainen ‘271 teaches all the claim limitations above except, wherein triggering the UE to report the first information required by the network, comprises: triggering the UE to report first information indicated to be reported in the configuration information.
Madadi ‘189 teaches, wherein triggering the UE to report the first information required by the network, comprises: triggering the UE to report first information indicated to be reported in the configuration information([0117]-[0118], [0125]-[0127] and Figs. 6, 8, UE receiving configuration information from BS, the configuration information indicating which measurements/parameters to be reported when to be reported and the UE reporting when the conditions received from the BS is satisfied).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘595, by incorporating the teaching of Madadi ‘189, since such modification would enable to incorporate application of machine learning in communications equipment, to improved channel state information feedback, as suggested by Madadi ‘189 ([0002]).
Regarding claim 12, the combination of Ryden ‘595, Veijalainen ‘271 and Madadi ‘189 teaches all of the claim limitations, Madadi ‘189 further teaches, wherein the configuration information comprises: a number of reports, indicating that the number of reports is N, wherein the N is an integer greater than 0; and an interval indication, indicating an interval time for reporting ([0116]-[0118], [0125]-[0127] and Figs. 6, 8, the UE receiving configuration information that indicates the number of AI triggered reports need to be transmitted within a period of time(periodicity of reporting).
Regarding claim 19, the combination of Ryden ‘595, Veijalainen ‘271 teaches all of the claim limitations, Ryden ‘595 further teaches, wherein, the trigger event and the AI prediction result obtained by the UE are used by the UE for triggering the UE to perform an operation corresponding to the trigger event in response to determining that the AI prediction result satisfies the trigger event( [0046]-[0050] and Figs. 1-2, UE triggering handover or reporting to network node when a Machine Learning(ML) prediction result compared with configured network parameters( e.g. signal strength threshold) satisfies a reporting condition or threshold); wherein the method further comprises:
receiving first information required by a network reported by the UE in response to determining that the AI prediction result satisfies the trigger event( [0046]-[0050] and Figs. 1-2, UE reporting to network node when a Machine Learning(ML) prediction result compared with configured network parameters( e.g. signal strength threshold) satisfies a reporting condition or threshold).
The combination of Ryden ‘595 and Veijalainen ‘271 does not explicitly teach, wherein the configuration information comprises: a number of reports, indicating that the number of reports is N, wherein the N is an integer greater than 0
Madadi ‘189 teaches, wherein the configuration information comprises: a number of reports, indicating that the number of reports is N, wherein the N is an integer greater than 0([0116]-[0118], [0125]-[0127] and Figs. 6, 8, the UE receiving configuration information that indicates the number of AI triggered reports need to be transmitted within a period of time(periodicity of reporting).; and
an interval indication, indicating an interval time for reporting ([0116]-[0118], [0125]-[0127] and Figs. 6, 8, the UE receiving configuration information that indicates periodicity of reporting).
Therefore, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the communication system of Ryden ‘595, by incorporating the teaching of Madadi ‘189, since such modification would enable to incorporate application of machine learning in communications equipment, to improved channel state information feedback, as suggested by Madadi ‘189 ([0002]).
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.
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/AWET HAILE/ Primary Examiner, Art Unit 2474
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