DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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 § 102
2. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) The claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
3. Claims 1, 14-16, 28, 33 and 35 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by EP 3,897,033 A1 to Harada et al. (Harada).
As to Claims 1 and 33, Harada discloses a communication device, comprising:
processor (Fig. 6, ‘processor 1001’);
a memory storing a program executable by the processor (‘Fig. 6 illustrates an example of a hardware configuration of user terminals 1a and 1b and radio base station 2 according to an embodiment. Physically, user terminals 1a and 1b and radio base station 2 as described above may be a computer apparatus including processor 1001, memory 1002, storage 1003, communication apparatus 1004, input apparatus 1005, output apparatus 1006, bus 1007, and the like’, ¶ 0081), wherein the processor is configured to:
receive control information (‘radio base station 2 may explicitly or implicitly indicate to user terminals 1a and 1b whether optimization (parameter optimization) of the operation performed using the AI functions is on or off. For example, radio base station 2 may use RRC, MAC Control Element (MAC CE), or Downlink Control information (DCI) to explicitly transmit, to user terminals 1a and 1b, the information indicating whether or not autonomous parameter determination is permitted’, ¶ 0053), wherein the control information is at least configured to control an operation of a first prediction model in a User Equipment (UE), and the first prediction model is configured to obtain a prediction result of Radio Resource Management (RRM) (‘based on the information transmitted using the RRC, MAC CE, or DCI, user terminals 1a and 1b may switch on or off the optimization of the operation performed using the AI functions. The optimization of the operation using the AI functions may be, for example, optimization of the parameters relevant to one or both of the RRM and RLM’, ¶ 0053).
As to Claim 14, Harada further discloses wherein the prediction result comprises at least one of: a prediction result associated with UE's own RRM; a prediction result of RRM of a serving cell where the UE is located; or a prediction result of RRM of at least one neighbor cell of the UE (‘data samples relevant to learning by AI may be provided by radio base station 2 for user terminals 1a and 1b. The data samples for learning by AI may be, for example, data samples relevant to one or both of the RRM and RLM’, ¶ 0050).
As to Claim 15, Harada further discloses controlling the first prediction model to stop prediction for a predetermined prediction result type of a second cell, in response to the UE handing over from a serving cell to a target cell (‘on the other hand, for example, when the moving speed of user terminal 1a is high and/or when the user terminal is located at the cell end of the serving cell, it is considered to be likely for user terminal 1a to move from the serving cell to a neighboring cell. Accordingly, control section 12 (AI) may, for example, increase the measurement accuracy of the quality measurement for the neighboring cell, and determine parameters for a shorter measurement cycle of the quality measurement for the neighboring cell’, ¶ 0044).
As to Claims 16 and 35, Harada discloses a communication device, comprising: a processor (Fig. 6, ‘processor 1001’); and
a memory storing a program executable by the processor (‘Fig. 6 illustrates an
example of a hardware configuration of user terminals 1a and 1b and radio base station 2 according to an embodiment. Physically, user terminals 1a and 1b and radio base station 2 as described above may be a computer apparatus including processor 1001, memory 1002, storage 1003, communication apparatus 1004, input apparatus 1005, output apparatus 1006, bus 1007, and the like’, ¶ 0081),
wherein the processor is configured to sending control information (‘radio base station 2 may explicitly or implicitly indicate to user terminals 1a and 1b whether optimization (parameter optimization) of the operation performed using the AI functions is on or off. For example, radio base station 2 may use RRC, MAC Control Element (MAC CE), or Downlink Control information (DCI) to explicitly transmit, to user terminals 1a and 1b, the information indicating whether or not autonomous parameter determination is permitted’, ¶ 0053), wherein the control information is at least configured to control an operation of a first prediction model in a User Equipment (UE), and the first prediction model is configured to obtain a prediction result of Radio Resource Management (RRM) (‘based on the information transmitted using the RRC, MAC CE, or DCI, user terminals 1a and 1b may switch on or off the optimization of the operation performed using the AI functions. The optimization of the operation using the AI functions may be, for example, optimization of the parameters relevant to one or both of the RRM and RLM’, ¶ 0053).
As to Claim 28, Harada further discloses wherein the prediction result comprises at least one of: a prediction result associated with UE's own RRM; a prediction result of RRM of a serving cell where the UE is located; or a prediction result of RRM of at least one neighbor cell of the UE (‘data samples relevant to learning by AI may be provided by radio base station 2 for user terminals 1a and 1b. The data samples for learning by AI may be, for example, data samples relevant to one or both of the RRM and RLM’, ¶ 0050).
Claim Rejections - 35 USC § 103
4. 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.
5. Claims 2-4, 10, 17-19 and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Harada, in view of Publication No.: US 2022/0335337 A1 to Kovacs et al. (Kovacs).
As to Claim 2, Harada does not expressly disclose wherein the control information comprises at least one of: a control instruction, wherein upon being received by the UE, the control instruction is configured to control starting or stopping of the first prediction model or configuration information, comprising threshold information for controlling the starting and/or the stopping of the first prediction model.
However, Kovacs discloses wherein the control information comprises at least one of: a control instruction, wherein upon being received by the UE, the control instruction is configured to control starting or stopping of the first prediction model or configuration information, comprising threshold information for controlling the starting and/or the stopping of the first prediction model (‘at 406 the network node 100 may send an activation request to the user node 200 to activate one or more of the machine learning based functionalities among the machine learning based functionalities (machine learning entity—machine learning mode) received from the user node 200 according to is radio resource management (RRM) needs’, ¶ 0157).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the control information comprises at least one of: a control instruction, wherein upon being received by the UE, the control instruction is configured to control starting or stopping of the first prediction model or configuration information, comprising threshold information for controlling the starting and/or the stopping of the first prediction model’ as disclosed by
Kovacs into Harada so as to effectively enable efficient configuration of machine learning based assistance to reduce overhead in wireless communication nodes, Kovacs ¶ 0004.
As to Claim 3, Harada does not expressly disclose wherein the threshold information indicates at least one of: a time threshold, comprising at least one of a time threshold for the starting of the first prediction model or a time threshold for the stopping of the first prediction model; a position threshold, comprising at least one of a position threshold for the starting of the first prediction model or a position threshold for the stopping of the first prediction model; a movement speed threshold, comprising at least one of a movement speed threshold for the starting of the first prediction model or a movement speed threshold for the stopping of the first prediction model; a signal quality threshold, comprising at least one of a signal quality threshold for the starting of the first prediction model or a signal quality threshold for the stopping of the first prediction model; or a prediction threshold, comprising at least one of a prediction threshold for the starting of the first prediction model or a prediction threshold for the stopping of the first prediction model, wherein the prediction threshold is configured to be compared with a prediction value of a second prediction model.
However, Kovacs discloses wherein the threshold information indicates at least one of: a time threshold, comprising at least one of a time threshold for the starting of the first prediction model or a time threshold for the stopping of the first prediction model; a position threshold, comprising at least one of a position threshold for the starting of the first prediction model or a position threshold for the stopping of the first prediction model; a movement speed threshold, comprising at least one of a movement speed threshold for the starting of the first prediction model or a movement speed threshold for the stopping of the first prediction model; a signal quality threshold, comprising at least one of a signal quality threshold for the starting of the first prediction model or a signal quality threshold for the stopping of the first prediction model; or a prediction threshold, comprising at least one of a prediction threshold for the starting of the first prediction model or a prediction threshold for the stopping of the first prediction model, wherein the prediction threshold is configured to be compared with a prediction value of a second prediction model (‘even if the example embodiments discussed herein may relate to a scenario in which the user node has the functionalities needed to provide ML-based assistance to the radio access network and where the functionalities may consist of, for example, predictions (forecasts) of certain events such as handover (HO), crossing reference symbol received power (RSRP) thresholds, quality of service variations, mobility state change, etc., these are only non-restrictive examples. Further, the employed ML algorithms providing/generating the user node machine learning based assistance information (for example, prediction of RSRP values, CSI, HO events) may have been optimized (trained and tested) under a comprehensive set of operating modes (input data) and may have the capability of delivering at least one of the typical ML/deep learning (DL)/artificial intelligence (AI) algorithm performance measures’, ¶ 0122).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the threshold information indicates at least one of: a time threshold, comprising at least one of a time threshold for the starting of the first prediction model or a time threshold for the stopping of the first prediction model; a position threshold, comprising at least one of a position threshold for the starting of the first prediction model or a position threshold for the stopping of the first prediction model; a movement speed threshold, comprising at least one of a movement speed threshold for the starting of the first prediction model or a movement speed threshold for the stopping of the first prediction model; a signal quality threshold, comprising at least one of a signal quality threshold for the starting of the first prediction model or a signal quality threshold for the stopping of the first prediction model; or a prediction threshold, comprising at least one of a prediction threshold for the starting of the first prediction model or a prediction threshold for the stopping of the first prediction model, wherein the prediction threshold is configured to be compared with a prediction value of a second prediction model’ as disclosed by Kovacs into Harada so as to effectively enable efficient configuration of machine learning based assistance to reduce overhead in wireless communication nodes, Kovacs ¶ 0004.
As to Claim 4, Harada further disclose wherein the time threshold comprises at least one of: a time point threshold; or a time range threshold; the position threshold comprises :a distance threshold between the UE and a first reference position; and
the signal quality threshold comprises at least one of: a signal quality threshold in a first time domain range; a signal quality change threshold in the first time domain range; a signal quality threshold of at least one cell; or a signal quality threshold of at least one type (‘note that, the condition is a determination criterion used for judging whether or not user terminals 1a and 1b perform a certain operation, and is, for example, a threshold or the like. For example, when the quality of the serving cell falls below the threshold, user terminal 1a or 1b reports a result of the quality measurement for the neighboring cell to radio base station 2. The condition is the threshold that triggers reporting of the result of the quality measurement for the neighboring cell. In the present case, the condition may be understood as a part of the parameters’, ¶ 0048).
As to Claim 10, Harada further disclose wherein the prediction threshold is used for the UE to compare with the prediction value of the second prediction model, and control, based on a comparison result, starting and/or stopping of cell measurement for a first cell. (‘note that, the condition is a determination criterion used for judging whether or not user terminals 1a and 1b perform a certain operation, and is, for example, a threshold or the like. For example, when the quality of the serving cell falls below the threshold, user terminal 1a or 1b reports a result of the quality measurement for the neighboring cell to radio base station 2. The condition is the threshold that triggers reporting of the result of the quality measurement for the neighboring cell. In the present case, the condition may be understood as a part of the parameters’, ¶ 0048).
As to Claim 17, Harada does not expressly disclose wherein the control information comprises at least one of: a control instruction, wherein upon being received by the UE, the control instruction is configured to control starting or stopping of the first prediction model or configuration information, comprising threshold information for controlling the starting and/or the stopping of the first prediction model.
However, Kovacs discloses wherein the control information comprises at least one of: a control instruction, wherein upon being received by the UE, the control instruction is configured to control starting or stopping of the first prediction model or configuration information, comprising threshold information for controlling the starting and/or the stopping of the first prediction model (‘at 406 the network node 100 may send an activation request to the user node 200 to activate one or more of the machine learning based functionalities among the machine learning based functionalities (machine learning entity—machine learning mode) received from the user node 200 according to is radio resource management (RRM) needs’, ¶ 0157).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the control information comprises at least one of: a control instruction, wherein upon being received by the UE, the control instruction is configured to control starting or stopping of the first prediction model or configuration information, comprising threshold information for controlling the starting and/or the stopping of the first prediction model’ as disclosed by
Kovacs into Harada so as to effectively enable efficient configuration of machine learning based assistance to reduce overhead in wireless communication nodes, Kovacs ¶ 0004.
As to Claim 18, Harada does not expressly disclose wherein the threshold information indicates at least one of: a time threshold, comprising at least one of a time threshold for the starting of the first prediction model or a time threshold for the stopping of the first prediction model; a position threshold, comprising at least one of a position threshold for the starting of the first prediction model or a position threshold for the stopping of the first prediction model; a movement speed threshold, comprising at least one of a movement speed threshold for the starting of the first prediction model or a movement speed threshold for the stopping of the first prediction model; a signal quality threshold, comprising at least one of a signal quality threshold for the starting of the first prediction model or a signal quality threshold for the stopping of the first prediction model; or a prediction threshold, comprising at least one of a prediction threshold for the starting of the first prediction model or a prediction threshold for the stopping of the first prediction model, wherein the prediction threshold is configured to be compared with a prediction value of a second prediction model.
However, Kovacs discloses wherein the threshold information indicates at least one of: a time threshold, comprising at least one of a time threshold for the starting of the first prediction model or a time threshold for the stopping of the first prediction model; a position threshold, comprising at least one of a position threshold for the starting of the first prediction model or a position threshold for the stopping of the first prediction model; a movement speed threshold, comprising at least one of a movement speed threshold for the starting of the first prediction model or a movement speed threshold for the stopping of the first prediction model; a signal quality threshold, comprising at least one of a signal quality threshold for the starting of the first prediction model or a signal quality threshold for the stopping of the first prediction model; or a prediction threshold, comprising at least one of a prediction threshold for the starting of the first prediction model or a prediction threshold for the stopping of the first prediction model, wherein the prediction threshold is configured to be compared with a prediction value of a second prediction model (‘even if the example embodiments discussed herein may relate to a scenario in which the user node has the functionalities needed to provide ML-based assistance to the radio access network and where the functionalities may consist of, for example, predictions (forecasts) of certain events such as handover (HO), crossing reference symbol received power (RSRP) thresholds, quality of service variations, mobility state change, etc., these are only non-restrictive examples. Further, the employed ML algorithms providing/generating the user node machine learning based assistance information (for example, prediction of RSRP values, CSI, HO events) may have been optimized (trained and tested) under a comprehensive set of operating modes (input data) and may have the capability of delivering at least one of the typical ML/deep learning (DL)/artificial intelligence (AI) algorithm performance measures’, ¶ 0122).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the threshold information indicates at least one of: a time threshold, comprising at least one of a time threshold for the starting of the first prediction model or a time threshold for the stopping of the first prediction model; a position threshold, comprising at least one of a position threshold for the starting of the first prediction model or a position threshold for the stopping of the first prediction model; a movement speed threshold, comprising at least one of a movement speed threshold for the starting of the first prediction model or a movement speed threshold for the stopping of the first prediction model; a signal quality threshold, comprising at least one of a signal quality threshold for the starting of the first prediction model or a signal quality threshold for the stopping of the first prediction model; or a prediction threshold, comprising at least one of a prediction threshold for the starting of the first prediction model or a prediction threshold for the stopping of the first prediction model, wherein the prediction threshold is configured to be compared with a prediction value of a second prediction model’ as disclosed by Kovacs into Harada so as to effectively enable efficient configuration of machine learning based assistance to reduce overhead in wireless communication nodes, Kovacs ¶ 0004.
As to Claim 19, Harada further disclose wherein the time threshold comprises at least one of: a time point threshold; or a time range threshold; the position threshold comprises :a distance threshold between the UE and a first reference position; and
the signal quality threshold comprises at least one of: a signal quality threshold in a first time domain range; a signal quality change threshold in the first time domain range; a signal quality threshold of at least one cell; or a signal quality threshold of at least one type (‘note that, the condition is a determination criterion used for judging whether or not user terminals 1a and 1b perform a certain operation, and is, for example, a threshold or the like. For example, when the quality of the serving cell falls below the threshold, user terminal 1a or 1b reports a result of the quality measurement for the neighboring cell to radio base station 2. The condition is the threshold that triggers reporting of the result of the quality measurement for the neighboring cell. In the present case, the condition may be understood as a part of the parameters’, ¶ 0048).
As to Claim 24, Harada further disclose wherein the prediction threshold is used for the UE to compare with the prediction value of the second prediction model, and control, based on a comparison result, starting and/or stopping of cell measurement for a first cell. (‘note that, the condition is a determination criterion used for judging whether or not user terminals 1a and 1b perform a certain operation, and is, for example, a threshold or the like. For example, when the quality of the serving cell falls below the threshold, user terminal 1a or 1b reports a result of the quality measurement for the neighboring cell to radio base station 2. The condition is the threshold that triggers reporting of the result of the quality measurement for the neighboring cell. In the present case, the condition may be understood as a part of the parameters’, ¶ 0048).
6. Claims 11 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Harada, in view of Publication No.: US 2022/0150727 A1 to Pezeshki et al. (Pezeshki).
As to Claim 11, Harada does not expressly disclose receiving a control instruction carrying indication information indicating a second prediction type set, wherein the control instruction indicates the UE to control starting or stopping of the first prediction model; and determining the prediction result, by the first prediction model run by the UE, based on a prediction result type in a second prediction type set, wherein the second prediction type set comprises at least one prediction result type of at least one prediction object.
However, Pezeshki discloses receiving a control instruction carrying indication information indicating a second prediction type set, wherein the control instruction indicates the UE to control starting or stopping of the first prediction model; and determining the prediction result, by the first prediction model run by the UE, based on a prediction result type in a second prediction type set, wherein the second prediction type set comprises at least one prediction result type of at least one prediction object (‘in another example, the first notification is transmitted via an uplink control information (UCI). The UE then disables the first machine learning model and activates the second machine learning model. The UE uses the second machine learning model, which is applicable for the second TRP’, ¶ 0134).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘receiving a control instruction carrying indication information indicating a second prediction type set, wherein the control instruction indicates the UE to control starting or stopping of the first prediction model; and determining the prediction result, by the first prediction model run by the UE, based on a prediction result type in a second prediction type set, wherein the second prediction type set comprises at least one prediction result type of at least one prediction object’ as disclosed by Pezeshki into Harada so as to effectively share machine learning models between wireless communication nodes, Pezeshki ¶ 0006.
As to Claim 25, Harada does not expressly disclose sending a control instruction carrying indication information indicating a second prediction type set, wherein the control instruction indicates the UE to control starting or stopping of the first prediction model; wherein the prediction result is determined, by the first prediction model run by the UE, based on a prediction result type in the second prediction type set; and the second prediction type set comprises at least one prediction result type of at least one prediction object.
However, Pezeshki discloses sending a control instruction carrying indication information indicating a second prediction type set, wherein the control instruction indicates the UE to control starting or stopping of the first prediction model; wherein the prediction result is determined, by the first prediction model run by the UE, based on a prediction result type in the second prediction type set; and the second prediction type set comprises at least one prediction result type of at least one prediction object
(‘in another example, the first notification is transmitted via an uplink control information (UCI). The UE then disables the first machine learning model and activates the second machine learning model. The UE uses the second machine learning model, which is applicable for the second TRP’, ¶ 0134).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘sending a control instruction carrying indication information indicating a second prediction type set, wherein the control instruction indicates the UE to control starting or stopping of the first prediction model; wherein the prediction result is determined, by the first prediction model run by the UE, based on a prediction result type in the second prediction type set; and the second prediction type set comprises at least one prediction result type of at least one prediction object’ as disclosed by Pezeshki into Harada so as to effectively share machine learning models between wireless communication nodes, Pezeshki ¶ 0006.
7. Claims 7 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Harada, in view of Kovacs and further in view of Publication No.: US 2022/0150727 A1 to Pezeshki et al. (Pezeshki).
As to Claim 7, Harada in view of Kovacs do not expressly disclose wherein the prediction value of the second prediction model is determined by running the second prediction model by the UE based on a prediction value type in a first prediction type set: the first prediction type set comprises at least one prediction value type of at least one prediction object; and the method further comprises one of: controlling the first prediction model to start in response to the second prediction model not being started; and controlling the first prediction model to stop in response to the second prediction model not being started.
However, Pezeshki discloses wherein the prediction value of the second prediction model is determined by running the second prediction model by the UE based on a prediction value type in a first prediction type set: the first prediction type set comprises at least one prediction value type of at least one prediction object; and the method further comprises one of: controlling the first prediction model to start in response to the second prediction model not being started; and controlling the first prediction model to stop in response to the second prediction model not being started (‘in another example, the first notification is transmitted via an uplink control information (UCI). The UE then disables the first machine learning model and activates the second machine learning model. The UE uses the second machine learning model, which is applicable for the second TRP’, ¶ 0134).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the prediction value of the second prediction model is determined by running the second prediction model by the UE based on a prediction value type in a first prediction type set: the first prediction type set comprises at least one prediction value type of at least one prediction object; and the method further comprises one of: controlling the first prediction model to start in response to the second prediction model not being started; and controlling the first prediction model to stop in response to the second prediction model not being started’ as disclosed by Pezeshki into Harada in view of Kovacs so as to effectively share machine learning models between wireless communication nodes, Pezeshki ¶ 0006.
As to Claim 22, Harada in view of Kovacs do not expressly disclose wherein the prediction value of the second prediction model is determined by running the second prediction model by the UE based on a prediction value type in a first prediction type set; and wherein the first prediction type set comprises at least one prediction value type of at least one prediction object.
However, Pezeshki discloses wherein the prediction value of the second prediction model is determined by running the second prediction model by the UE based on a prediction value type in a first prediction type set; and wherein the first prediction type set comprises at least one prediction value type of at least one prediction object (‘in certain aspects, the BS sends to the UE a notification (e.g., a second notification) to switch to the second machine learning model when the UE moves from the first TRP to the second TRP. Based on the notifications, the UE disables the first machine learning model and activates the second machine learning model for the second TRP. The UE uses (and executes) the second machine learning model when the UE is associated with the second TRP’, ¶ 0144).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to provide ‘wherein the prediction value of the second prediction model is determined by running the second prediction model by the UE based on a prediction value type in a first prediction type set; and wherein the first prediction type set comprises at least one prediction value type of at least one prediction object’ as disclosed by Pezeshki into Harada in view of Kovacs so as to effectively share machine learning models between wireless communication nodes, Pezeshki ¶ 0006.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to GBEMILEKE J ONAMUTI whose telephone number is (571)270-5619. The examiner can normally be reached 8:00 AM - 5:00 PM.
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/GBEMILEKE J ONAMUTI/Primary Examiner, Art Unit 2463