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 Amendment
This is in response to an amendment/response/communication filed 7/2/2024.
No claims have been cancelled.
No claims have been added.
Claims(s) 1-20 is/are currently pending.
Drawings
The drawings were received on 7/2/2024. These drawings are accepted.
Examiner’s Comments Regarding Subject Matter Eligibility
The abstract idea of “determine a mobility scenario of an environment in which the UE is situated” as noted in claim 1 and similarly in claim 11 are considered as being recited with additional elements which integrate the abstract idea into a practical application and the claims are therefore considered as eligible subject matter under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 6, 9, 11, 16 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anchan et al. US 20160302128 in view of Chen et al. WO 2025035367.
As to claim 1:
Anchan et al. discloses:
A method, comprising:
utilizing, by a processor of a user equipment (UE), an
adjusting, by the processor, one or more parameters used in a system selection according to a result of the determining.
(“In some aspects, the UE may identify the potential handoff using machine learning. For example, UE 710 may generate a model, may refine the model based on known training data (e.g., data where a handoff occurred, data where a handoff did not occur, etc.), and may feed in inputs (e.g., motion processor information, signal strength information, RAT signaling information, uplink and/or downlink parameters, a de-jitter buffer state parameter, one or more environment parameters, etc.) to the model to make a determination whether a potential handoff will occur. In this case, UE 710 may continue to update the model based on identifying potential handoffs and determining whether the potential handoffs occurred.”; Anchan et al.; 0120)
(“As shown in FIG. 7A, UE 710 and UE 730 may be connected in a communication (e.g., a video telephony communication via source RAT 720 and eNB 740). As shown by reference number 755, UE 710 may identify a potential handoff (e.g., from source RAT 720 to target RAT 750) during the communication. UE 710 may identify the potential handoff based on motion processor information (e.g., based on determining that UE 710 is being moved away from source RAT 720 and/or toward target RAT 750), based on signal strength information (e.g., based on a signal strength of source RAT 720 failing to satisfy a first threshold and/or based on a signal strength of target RAT 750 satisfying a second threshold), based on signaling information (e.g., based on receiving a particular signal from source RAT 720 and/or target RAT 750 indicating a potential handoff), or the like. Based on identifying the potential handoff, UE 710 may cause an adjustment to a first communication parameter or a first communication rate.”; Anchan et al.; 0091)
(where
See FIGs. 6 and 15 for “processor of a user equipment (UE)”
“the UE may identify the potential handoff using machine learning. For example, UE 710 may generate a model, may refine the model based on known training data (e.g., data where a handoff occurred, data where a handoff did not occur, etc.), and may feed in inputs (e.g., motion processor information, …one or more environment parameters, etc.) to the model to make a determination whether a potential handoff will occur” maps to “utilizing, by a processor of a user equipment (UE), an , where “UE 710” maps to “UE”, “using machine learning…may generate a model…refine the model” map to “utilizing…an , where “using” maps to “utilizing”, “machine learning…model” maps to “,
“determination whether a potential handoff will occur” maps to “to determine a mobility scenario”,
“UE…inputs …one or more environment parameters” maps to “of an environment in which the UE is situated”
“Based on identifying the potential handoff, UE 710 may cause an adjustment to a first communication parameter” maps to “adjusting, by the processor, one or more parameters used in a system selection according to a result of the determining”, where “adjustment to a first communication parameter” maps to “adjusting…one or more parameters”, “potential handoff” maps to “used in a system selection”, “Based on” maps to “according to a result of the determining”, “potential” maps to “result”
Anchan et al. teaches UE which executes an machine learning model to perform determination of a potential handoff of the UE where based on the determination, adjusted
Anchan et al. as described above does not explicitly teach:
artificial intelligence (AI)
However, Chen et al. further teaches an AI/ML capability which includes:
artificial intelligence (AI)
(“A user equipment (UE) mobility management method executable by a UE. The UE receives configuration of UE-based mobility management and transmits artificial intelligence (AI) /machine learning (ML) model-inferred one or more parameters of UE-based mobility management based on the configuration to assist a handover operation of the UE. The one or more parameters comprise UE-predicted UE trajectory of the UE.”; Chen et al.; Abstract)
(where
“artificial intelligence (AI) /machine learning (ML) model” maps to “artificial intelligence (AI)
Chen et al. teaches a UE performing AI/ML for assisting in handover of the UE by communicating parameters associated with mobility.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the AI/ML capability of Chen et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the AI/ML capability as taught by the processing/communications of Chen et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved service quality (Chen et al.; 0059) are achieved.
As to claim 6:
Anchan et al. discloses:
performing, by the processor, the system selection with the adjusted one or more parameters.
(“In some aspects, the UE may identify the potential handoff using machine learning. For example, UE 710 may generate a model, may refine the model based on known training data (e.g., data where a handoff occurred, data where a handoff did not occur, etc.), and may feed in inputs (e.g., motion processor information, signal strength information, RAT signaling information, uplink and/or downlink parameters, a de-jitter buffer state parameter, one or more environment parameters, etc.) to the model to make a determination whether a potential handoff will occur. In this case, UE 710 may continue to update the model based on identifying potential handoffs and determining whether the potential handoffs occurred.”; Anchan et al.; 0120)
(“As shown in FIG. 7A, UE 710 and UE 730 may be connected in a communication (e.g., a video telephony communication via source RAT 720 and eNB 740). As shown by reference number 755, UE 710 may identify a potential handoff (e.g., from source RAT 720 to target RAT 750) during the communication. UE 710 may identify the potential handoff based on motion processor information (e.g., based on determining that UE 710 is being moved away from source RAT 720 and/or toward target RAT 750), based on signal strength information (e.g., based on a signal strength of source RAT 720 failing to satisfy a first threshold and/or based on a signal strength of target RAT 750 satisfying a second threshold), based on signaling information (e.g., based on receiving a particular signal from source RAT 720 and/or target RAT 750 indicating a potential handoff), or the like. Based on identifying the potential handoff, UE 710 may cause an adjustment to a first communication parameter or a first communication rate.”; Anchan et al.; 0091)
As to claim 9:
Anchan et al. discloses:
wherein the performing of the system selection comprises performing a … or a cell selection.
(“In some aspects, the UE may identify the potential handoff based on motion processor information. For example, UE 710 may determine, based on inertial information, global positioning system (GPS) information, or the like, that UE 710 is being moved away from source RAT 720 and/or toward target RAT 750. In this case, UE 710 may determine that a potential handoff is imminent based on UE 710 being moved in a direction from a cell associated with source RAT 720 toward a cell associated with target RAT 750.”; Anchan et al.; 0117)
As to claim 11:
Anchan et al. discloses:
An apparatus implementable in a user equipment (UE), comprising:
a transceiver configured to communicate wirelessly; and
a processor coupled to the transceiver and configured to perform operations comprising:
(see FIG. 6 for “transceiver” and “processor”,
utilizing, by a processor of a user equipment (UE), an
adjusting, by the processor, one or more parameters used in a system selection according to a result of the determining.
(“In some aspects, the UE may identify the potential handoff using machine learning. For example, UE 710 may generate a model, may refine the model based on known training data (e.g., data where a handoff occurred, data where a handoff did not occur, etc.), and may feed in inputs (e.g., motion processor information, signal strength information, RAT signaling information, uplink and/or downlink parameters, a de-jitter buffer state parameter, one or more environment parameters, etc.) to the model to make a determination whether a potential handoff will occur. In this case, UE 710 may continue to update the model based on identifying potential handoffs and determining whether the potential handoffs occurred.”; Anchan et al.; 0120)
(“As shown in FIG. 7A, UE 710 and UE 730 may be connected in a communication (e.g., a video telephony communication via source RAT 720 and eNB 740). As shown by reference number 755, UE 710 may identify a potential handoff (e.g., from source RAT 720 to target RAT 750) during the communication. UE 710 may identify the potential handoff based on motion processor information (e.g., based on determining that UE 710 is being moved away from source RAT 720 and/or toward target RAT 750), based on signal strength information (e.g., based on a signal strength of source RAT 720 failing to satisfy a first threshold and/or based on a signal strength of target RAT 750 satisfying a second threshold), based on signaling information (e.g., based on receiving a particular signal from source RAT 720 and/or target RAT 750 indicating a potential handoff), or the like. Based on identifying the potential handoff, UE 710 may cause an adjustment to a first communication parameter or a first communication rate.”; Anchan et al.; 0091)
(where
See FIGs. 6 and 15 for “processor of a user equipment (UE)”
“the UE may identify the potential handoff using machine learning. For example, UE 710 may generate a model, may refine the model based on known training data (e.g., data where a handoff occurred, data where a handoff did not occur, etc.), and may feed in inputs (e.g., motion processor information, …one or more environment parameters, etc.) to the model to make a determination whether a potential handoff will occur” maps to “utilizing, by a processor of a user equipment (UE), an , where “UE 710” maps to “UE”, “using machine learning…may generate a model…refine the model” map to “utilizing…an , where “using” maps to “utilizing”, “machine learning…model” maps to “,
“determination whether a potential handoff will occur” maps to “to determine a mobility scenario”,
“UE…inputs …one or more environment parameters” maps to “of an environment in which the UE is situated”
“Based on identifying the potential handoff, UE 710 may cause an adjustment to a first communication parameter” maps to “adjusting, by the processor, one or more parameters used in a system selection according to a result of the determining”, where “adjustment to a first communication parameter” maps to “adjusting…one or more parameters”, “potential handoff” maps to “used in a system selection”, “Based on” maps to “according to a result of the determining”, “potential” maps to “result”
Anchan et al. teaches UE which executes an machine learning model to perform determination of a potential handoff of the UE where based on the determination, adjusted
Anchan et al. as described above does not explicitly teach:
artificial intelligence (AI)
However, Chen et al. further teaches an AI/ML capability which includes:
artificial intelligence (AI)
(“A user equipment (UE) mobility management method executable by a UE. The UE receives configuration of UE-based mobility management and transmits artificial intelligence (AI) /machine learning (ML) model-inferred one or more parameters of UE-based mobility management based on the configuration to assist a handover operation of the UE. The one or more parameters comprise UE-predicted UE trajectory of the UE.”; Chen et al.; Abstract)
(where
“artificial intelligence (AI) /machine learning (ML) model” maps to “artificial intelligence (AI)
Chen et al. teaches a UE performing AI/ML for assisting in handover of the UE by communicating parameters associated with mobility.
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the AI/ML capability of Chen et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the AI/ML capability as taught by the processing/communications of Chen et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved service quality (Chen et al.; 0059) are achieved.
As to claim 16:
Anchan et al. discloses:
performing, by the processor, the system selection with the adjusted one or more parameters.
(“In some aspects, the UE may identify the potential handoff using machine learning. For example, UE 710 may generate a model, may refine the model based on known training data (e.g., data where a handoff occurred, data where a handoff did not occur, etc.), and may feed in inputs (e.g., motion processor information, signal strength information, RAT signaling information, uplink and/or downlink parameters, a de-jitter buffer state parameter, one or more environment parameters, etc.) to the model to make a determination whether a potential handoff will occur. In this case, UE 710 may continue to update the model based on identifying potential handoffs and determining whether the potential handoffs occurred.”; Anchan et al.; 0120)
(“As shown in FIG. 7A, UE 710 and UE 730 may be connected in a communication (e.g., a video telephony communication via source RAT 720 and eNB 740). As shown by reference number 755, UE 710 may identify a potential handoff (e.g., from source RAT 720 to target RAT 750) during the communication. UE 710 may identify the potential handoff based on motion processor information (e.g., based on determining that UE 710 is being moved away from source RAT 720 and/or toward target RAT 750), based on signal strength information (e.g., based on a signal strength of source RAT 720 failing to satisfy a first threshold and/or based on a signal strength of target RAT 750 satisfying a second threshold), based on signaling information (e.g., based on receiving a particular signal from source RAT 720 and/or target RAT 750 indicating a potential handoff), or the like. Based on identifying the potential handoff, UE 710 may cause an adjustment to a first communication parameter or a first communication rate.”; Anchan et al.; 0091)
As to claim 19:
Anchan et al. discloses:
wherein the performing of the system selection comprises performing a … or a cell selection.
(“In some aspects, the UE may identify the potential handoff based on motion processor information. For example, UE 710 may determine, based on inertial information, global positioning system (GPS) information, or the like, that UE 710 is being moved away from source RAT 720 and/or toward target RAT 750. In this case, UE 710 may determine that a potential handoff is imminent based on UE 710 being moved in a direction from a cell associated with source RAT 720 toward a cell associated with target RAT 750.”; Anchan et al.; 0117)
Claim(s) 2, 3, 10, 12, 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anchan et al. US 20160302128 in view of Chen et al. WO 2025035367 and in further view of Mwanje et al. WO 2022034114.
As to claim 2:
Anchan et al. as described above does not explicitly teach:
wherein the adjusting comprises loosening or decreasing a system search density in the system selection responsive to the mobility scenario being determined as a stable scenario corresponding to a low mobility of the UE.
However, Mwanje et al. further teaches an inference frequency capability which includes:
wherein the adjusting comprises loosening or decreasing a system search density in the system selection responsive to the mobility scenario being determined as a stable scenario corresponding to a low mobility of the UE.
(“Furthermore, the model may be used with minimal computational complexity on UE side. As the model learns trigger settings, the handover candidates, and handover points, inferences using the PACHO method may not need to be real-time, conserving UE resources, such as battery and computational power. Instead, PACHO proposes settings, which may then be fully compatible to the legacy systems to trigger the handovers. To further reduce computational resource use for both training and inference phases, the complexity of the model may be minimized by signaling a compressed model, minimizing processing requirements on the UE during inference, and bandwidth requirements when signaling the model to the UE. The model may also be trained to output recommended inference frequency. When the UE is slow-moving, or is not close to cell-border, a less frequent update of optimized parameters might suffice, conserving computation resources in the UE. Training may also be done in an offline maimer, during off-peak hours when the network has free resources. Thus, certain embodiments discussed below are directed to improvements in computer-related technology.”; Mwanje et al.; p.8, lines 8-21)
(“In some embodiments, the frequency of measuring these values may be configured by NE 130 differently for different user locations. For example, near a highway where UE 120 is likely to move at high speed, a higher frequency may be configured to ensure a good characterization of the path. On the other hand, a city’s business district may have many paths close to each other, so a high frequency may also be needed there to distinguish the different paths. Conversely, a mountain resort area where most users walk on foot and on a few paths may require only intermittent GPS records to adequately identify the paths and the proper settings on those paths.”; Mwanje et al.; p.11, lines 12-19)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inference frequency capability of Mwanje et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the inference frequency capability as taught by the processing/communications of Mwanje et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved labeling (Mwanje et al.; p.21, lines 10-15) are achieved.
As to claim 3:
Anchan et al. as described above does not explicitly teach:
wherein the adjusting comprises boosting or increasing a system search density in the system selection responsive to the mobility scenario being determined as an unstable scenario corresponding to a high mobility of the UE
However, Mwanje et al. further teaches an inference frequency capability which includes:
wherein the adjusting comprises boosting or increasing a system search density in the system selection responsive to the mobility scenario being determined as an unstable scenario corresponding to a high mobility of the UE
(“Furthermore, the model may be used with minimal computational complexity on UE side. As the model learns trigger settings, the handover candidates, and handover points, inferences using the PACHO method may not need to be real-time, conserving UE resources, such as battery and computational power. Instead, PACHO proposes settings, which may then be fully compatible to the legacy systems to trigger the handovers. To further reduce computational resource use for both training and inference phases, the complexity of the model may be minimized by signaling a compressed model, minimizing processing requirements on the UE during inference, and bandwidth requirements when signaling the model to the UE. The model may also be trained to output recommended inference frequency. When the UE is slow-moving, or is not close to cell-border, a less frequent update of optimized parameters might suffice, conserving computation resources in the UE. Training may also be done in an offline maimer, during off-peak hours when the network has free resources. Thus, certain embodiments discussed below are directed to improvements in computer-related technology.”; Mwanje et al.; p.8, lines 8-21)
(“In some embodiments, the frequency of measuring these values may be configured by NE 130 differently for different user locations. For example, near a highway where UE 120 is likely to move at high speed, a higher frequency may be configured to ensure a good characterization of the path. On the other hand, a city’s business district may have many paths close to each other, so a high frequency may also be needed there to distinguish the different paths. Conversely, a mountain resort area where most users walk on foot and on a few paths may require only intermittent GPS records to adequately identify the paths and the proper settings on those paths.”; Mwanje et al.; p.11, lines 12-19)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inference frequency capability of Mwanje et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the inference frequency capability as taught by the processing/communications of Mwanje et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved labeling (Mwanje et al.; p.21, lines 10-15) are achieved.
As to claim 10:
Anchan et al. as described above does not explicitly teach:
providing, by the processor, a feedback to the AI model upon performing the system selection with the adjusted one or more parameters
However, Mwanje et al. further teaches a feedback/CIO/update model/event capability which includes:
providing, by the processor, a feedback to the AI model upon performing the system selection with the adjusted one or more parameters
(“In various embodiments, training may be performed in a supervised-leaming-form using the sequences of RSRP values before and after the HO event. UE 130 may send the data to UE 120 which NE 130 may aggregate to perform batched learning. The model may then learns the profile of the RSRP values along each path across a given cell-pair boarder. Using the learned RSRP profiles, the solution may compute optimal TTT and CIO values for the different paths, which may then be forwarded to any other UEs to be used to trigger handovers along those paths. Training may also be performed actively, such as with reinforcement learning, based on the feedback of UE 120 concerning events observed by UE 120. If an event occurs, UE 120 may report the event together with the path or location sequence and RSRP values at which the event occurred. NE 130 may then update the model for the specific path or location and/or compute new HO settings (TTT and CIO) for the path/location. These settings may then be transmitted to other UE along a similar path for its handover evaluation.”; Mwanje et al.; p.21, lines 26-31 and p.22, lines 1-7)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inference frequency capability of Mwanje et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the inference frequency capability as taught by the processing/communications of Mwanje et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved labeling (Mwanje et al.; p.21, lines 10-15) are achieved.
As to claim 12:
Anchan et al. as described above does not explicitly teach:
wherein the adjusting comprises loosening or decreasing a system search density in the system selection responsive to the mobility scenario being determined as a stable scenario corresponding to a low mobility of the UE.
However, Mwanje et al. further teaches an inference frequency capability which includes:
wherein the adjusting comprises loosening or decreasing a system search density in the system selection responsive to the mobility scenario being determined as a stable scenario corresponding to a low mobility of the UE.
(“Furthermore, the model may be used with minimal computational complexity on UE side. As the model learns trigger settings, the handover candidates, and handover points, inferences using the PACHO method may not need to be real-time, conserving UE resources, such as battery and computational power. Instead, PACHO proposes settings, which may then be fully compatible to the legacy systems to trigger the handovers. To further reduce computational resource use for both training and inference phases, the complexity of the model may be minimized by signaling a compressed model, minimizing processing requirements on the UE during inference, and bandwidth requirements when signaling the model to the UE. The model may also be trained to output recommended inference frequency. When the UE is slow-moving, or is not close to cell-border, a less frequent update of optimized parameters might suffice, conserving computation resources in the UE. Training may also be done in an offline maimer, during off-peak hours when the network has free resources. Thus, certain embodiments discussed below are directed to improvements in computer-related technology.”; Mwanje et al.; p.8, lines 8-21)
(“In some embodiments, the frequency of measuring these values may be configured by NE 130 differently for different user locations. For example, near a highway where UE 120 is likely to move at high speed, a higher frequency may be configured to ensure a good characterization of the path. On the other hand, a city’s business district may have many paths close to each other, so a high frequency may also be needed there to distinguish the different paths. Conversely, a mountain resort area where most users walk on foot and on a few paths may require only intermittent GPS records to adequately identify the paths and the proper settings on those paths.”; Mwanje et al.; p.11, lines 12-19)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inference frequency capability of Mwanje et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the inference frequency capability as taught by the processing/communications of Mwanje et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved labeling (Mwanje et al.; p.21, lines 10-15) are achieved.
As to claim 13:
Anchan et al. as described above does not explicitly teach:
wherein the adjusting comprises boosting or increasing a system search density in the system selection responsive to the mobility scenario being determined as an unstable scenario corresponding to a high mobility of the UE
However, Mwanje et al. further teaches an inference frequency capability which includes:
wherein the adjusting comprises boosting or increasing a system search density in the system selection responsive to the mobility scenario being determined as an unstable scenario corresponding to a high mobility of the UE
(“Furthermore, the model may be used with minimal computational complexity on UE side. As the model learns trigger settings, the handover candidates, and handover points, inferences using the PACHO method may not need to be real-time, conserving UE resources, such as battery and computational power. Instead, PACHO proposes settings, which may then be fully compatible to the legacy systems to trigger the handovers. To further reduce computational resource use for both training and inference phases, the complexity of the model may be minimized by signaling a compressed model, minimizing processing requirements on the UE during inference, and bandwidth requirements when signaling the model to the UE. The model may also be trained to output recommended inference frequency. When the UE is slow-moving, or is not close to cell-border, a less frequent update of optimized parameters might suffice, conserving computation resources in the UE. Training may also be done in an offline maimer, during off-peak hours when the network has free resources. Thus, certain embodiments discussed below are directed to improvements in computer-related technology.”; Mwanje et al.; p.8, lines 8-21)
(“In some embodiments, the frequency of measuring these values may be configured by NE 130 differently for different user locations. For example, near a highway where UE 120 is likely to move at high speed, a higher frequency may be configured to ensure a good characterization of the path. On the other hand, a city’s business district may have many paths close to each other, so a high frequency may also be needed there to distinguish the different paths. Conversely, a mountain resort area where most users walk on foot and on a few paths may require only intermittent GPS records to adequately identify the paths and the proper settings on those paths.”; Mwanje et al.; p.11, lines 12-19)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inference frequency capability of Mwanje et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the inference frequency capability as taught by the processing/communications of Mwanje et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved labeling (Mwanje et al.; p.21, lines 10-15) are achieved.
As to claim 20:
Anchan et al. as described above does not explicitly teach:
providing, by the processor, a feedback to the AI model upon performing the system selection with the adjusted one or more parameters
However, Mwanje et al. further teaches a feedback/CIO/update model/event capability which includes:
providing, by the processor, a feedback to the AI model upon performing the system selection with the adjusted one or more parameters
(“In various embodiments, training may be performed in a supervised-leaming-form using the sequences of RSRP values before and after the HO event. UE 130 may send the data to UE 120 which NE 130 may aggregate to perform batched learning. The model may then learns the profile of the RSRP values along each path across a given cell-pair boarder. Using the learned RSRP profiles, the solution may compute optimal TTT and CIO values for the different paths, which may then be forwarded to any other UEs to be used to trigger handovers along those paths. Training may also be performed actively, such as with reinforcement learning, based on the feedback of UE 120 concerning events observed by UE 120. If an event occurs, UE 120 may report the event together with the path or location sequence and RSRP values at which the event occurred. NE 130 may then update the model for the specific path or location and/or compute new HO settings (TTT and CIO) for the path/location. These settings may then be transmitted to other UE along a similar path for its handover evaluation.”; Mwanje et al.; p.21, lines 26-31 and p.22, lines 1-7)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the inference frequency capability of Mwanje et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the inference frequency capability as taught by the processing/communications of Mwanje et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved labeling (Mwanje et al.; p.21, lines 10-15) are achieved.
Claim(s) 4 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anchan et al. US 20160302128 in view of Chen et al. WO 2025035367 and in further view of Tripathi et al. US 20210099942.
As to claim 4:
Anchan et al. as described above does not explicitly teach:
wherein the adjusting comprises:
defining a plurality of combinations of parameters, including at least a first
combination of the parameters having first settings and a second combination of the parameters having second settings different from the first settings; and
applying one of the combinations of the parameters corresponding to the
determined mobility scenario.
However, Tripathi et al. further teaches a context/parameters/combination/mobility capability which includes:
defining a plurality of combinations of parameters, including at least a first
combination of the parameters having first settings and a second combination of the parameters having second settings different from the first settings; and
applying one of the combinations of the parameters corresponding to the
determined mobility scenario.
(“According to embodiments of the present disclosure, mobility management configurations for a UE are customized based on the context for that UE. In certain embodiments, the context of a UE is characterized by parameters such as the UE's absolute or pseudo location, a traveling speed (such as miles per hour) of the UE, and direction of travel (such as east and north-east) of the UE and the like. The context of a UE can also include a Quality of Service metric. The mobility profile of a UE includes the actual speed of the UE or speed categories such as low-speed, medium-speed, and high-speed. The context of a UE may also include the network (e.g., LTE and its variants including LTE-Machine type communications (LTE-M)), Narrowband Internet of Things (NB-IoT) and 5G), and spectrum capabilities of the UE (e.g., sub-7 GHz and mmW bands). The context parameters are combined in a suitable manner to customize handover related parameters for the device. Customization of handover parameters based on the context characterization for a UE at a given instant results in faster and more reliable handover. The customized handover can minimize any service interruption as well as increase performance and service experience as compared to a case when such customization is absent.”; Tripathi et al.; 0030)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the context/parameters/combination/mobility capability of Tripathi et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the context/parameters/combination/mobility capability as taught by the processing/communications of Tripathi et al., the benefits of improved rate adaption (Anchan et al.; 0003) with reduced RRM (Tripathi et al.; 0099) are achieved.
As to claim 14:
Anchan et al. as described above does not explicitly teach:
wherein the adjusting comprises:
defining a plurality of combinations of parameters, including at least a first
combination of the parameters having first settings and a second combination of the parameters having second settings different from the first settings; and
applying one of the combinations of the parameters corresponding to the
determined mobility scenario.
However, Tripathi et al. further teaches a context/parameters/combination/mobility capability which includes:
defining a plurality of combinations of parameters, including at least a first
combination of the parameters having first settings and a second combination of the parameters having second settings different from the first settings; and
applying one of the combinations of the parameters corresponding to the
determined mobility scenario.
(“According to embodiments of the present disclosure, mobility management configurations for a UE are customized based on the context for that UE. In certain embodiments, the context of a UE is characterized by parameters such as the UE's absolute or pseudo location, a traveling speed (such as miles per hour) of the UE, and direction of travel (such as east and north-east) of the UE and the like. The context of a UE can also include a Quality of Service metric. The mobility profile of a UE includes the actual speed of the UE or speed categories such as low-speed, medium-speed, and high-speed. The context of a UE may also include the network (e.g., LTE and its variants including LTE-Machine type communications (LTE-M)), Narrowband Internet of Things (NB-IoT) and 5G), and spectrum capabilities of the UE (e.g., sub-7 GHz and mmW bands). The context parameters are combined in a suitable manner to customize handover related parameters for the device. Customization of handover parameters based on the context characterization for a UE at a given instant results in faster and more reliable handover. The customized handover can minimize any service interruption as well as increase performance and service experience as compared to a case when such customization is absent.”; Tripathi et al.; 0030)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the context/parameters/combination/mobility capability of Tripathi et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the context/parameters/combination/mobility capability as taught by the processing/communications of Tripathi et al., the benefits of improved rate adaption (Anchan et al.; 0003) with reduced RRM (Tripathi et al.; 0099) are achieved.
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anchan et al. US 20160302128 in view of Chen et al. WO 2025035367 and in further view of Tripathi et al. US 20210099942 and Gill et al. US 20250317704.
As to claim 5:
Anchan et al. as described above does not explicitly teach:
wherein the parameters comprise some or all of a sniffer interval, …, and a type of search
However, Tripathi et al. further teaches a context/parameters/combination/mobility/speed capability which includes:
wherein the parameters comprise some or all of a …, …, and a type of search
(“According to embodiments of the present disclosure, mobility management configurations for a UE are customized based on the context for that UE. In certain embodiments, the context of a UE is characterized by parameters such as the UE's absolute or pseudo location, a traveling speed (such as miles per hour) of the UE, and direction of travel (such as east and north-east) of the UE and the like. The context of a UE can also include a Quality of Service metric. The mobility profile of a UE includes the actual speed of the UE or speed categories such as low-speed, medium-speed, and high-speed. The context of a UE may also include the network (e.g., LTE and its variants including LTE-Machine type communications (LTE-M)), Narrowband Internet of Things (NB-IoT) and 5G), and spectrum capabilities of the UE (e.g., sub-7 GHz and mmW bands). The context parameters are combined in a suitable manner to customize handover related parameters for the device. Customization of handover parameters based on the context characterization for a UE at a given instant results in faster and more reliable handover. The customized handover can minimize any service interruption as well as increase performance and service experience as compared to a case when such customization is absent.”; Tripathi et al.; 0030)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the context/parameters/combination/mobility/speed capability of Tripathi et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the context/parameters/combination/mobility/speed capability as taught by the processing/communications of Tripathi et al., the benefits of improved rate adaption (Anchan et al.; 0003) with reduced RRM (Tripathi et al.; 0099) are achieved.
However, Gill et al. further teaches a monitoring period capability which includes:
sniffer interval
(“In some aspects, a server (e.g., a cloud server) may request a network (e.g., a network entity, base station, and/or network TRP) or multiple networks to monitor or track a particular device along a route or route segments. For instance, a server may transmit, to a network entity or TRP, a request to monitor one or more devices (e.g., tracking devices) along one or more route segments. The request to monitor the devices may indicate to the network to monitor/track, via sensing, a particular transport vehicle along a particular route or route segment. The transport vehicle may be number of devices, such as a moving device (e.g., a vehicle, an automobile, a boat, an airplane, etc.), a tracking device, and/or cargo/packages on a particular device. Also, a server may provide the network with information associated with a route or route segments (i.e., route information) along with device information associated with the devices. In some instances, the request to monitor may include device information associated with the devices and/or route information associated with the route or route segments. The device information may include a device identifier (ID) for each of the devices, and the route information may include a configuration of the route segments or one or more conditions of the route segments. Further, the server may indicate (e.g., indicate in the request to monitor) a number of different factors regarding the network operation during the monitoring of the devices. For example, the request to monitor may indicate a time period (i.e., a monitoring period) for monitoring the devices, a periodicity for monitoring the devices, a distance for monitoring the devices, and/or an accuracy level for monitoring the devices. In some instances, if a particular network operator does not have coverage to monitor the devices, the server can engage another network operator in order to handoff the monitoring operation (e.g., a sensing track operation) from an initial network operator to another network operator. For instance, the server may re-engage the other network operator in a similar manner to the initial network operator, such as by transmitting a request to monitor the devices. Also, the server may provide a precise position of a device, along with particular features that either identify the device (e.g., a transport vehicle) or disambiguate the device from the other nearby devices or vehicles.”; Gill et al.; 0101)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the monitoring period capability of Gill et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the monitoring period capability as taught by the processing/communications of Gill et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved resource utilization (Gill et al.; 0129) are achieved.
As to claim 15:
Anchan et al. as described above does not explicitly teach:
wherein the parameters comprise some or all of a sniffer interval, …, and a type of search
However, Tripathi et al. further teaches a context/parameters/combination/mobility/speed capability which includes:
wherein the parameters comprise some or all of a …, …, and a type of search
(“According to embodiments of the present disclosure, mobility management configurations for a UE are customized based on the context for that UE. In certain embodiments, the context of a UE is characterized by parameters such as the UE's absolute or pseudo location, a traveling speed (such as miles per hour) of the UE, and direction of travel (such as east and north-east) of the UE and the like. The context of a UE can also include a Quality of Service metric. The mobility profile of a UE includes the actual speed of the UE or speed categories such as low-speed, medium-speed, and high-speed. The context of a UE may also include the network (e.g., LTE and its variants including LTE-Machine type communications (LTE-M)), Narrowband Internet of Things (NB-IoT) and 5G), and spectrum capabilities of the UE (e.g., sub-7 GHz and mmW bands). The context parameters are combined in a suitable manner to customize handover related parameters for the device. Customization of handover parameters based on the context characterization for a UE at a given instant results in faster and more reliable handover. The customized handover can minimize any service interruption as well as increase performance and service experience as compared to a case when such customization is absent.”; Tripathi et al.; 0030)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the context/parameters/combination/mobility/speed capability of Tripathi et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the context/parameters/combination/mobility/speed capability as taught by the processing/communications of Tripathi et al., the benefits of improved rate adaption (Anchan et al.; 0003) with reduced RRM (Tripathi et al.; 0099) are achieved.
However, Gill et al. further teaches a monitoring period capability which includes:
sniffer interval
(“In some aspects, a server (e.g., a cloud server) may request a network (e.g., a network entity, base station, and/or network TRP) or multiple networks to monitor or track a particular device along a route or route segments. For instance, a server may transmit, to a network entity or TRP, a request to monitor one or more devices (e.g., tracking devices) along one or more route segments. The request to monitor the devices may indicate to the network to monitor/track, via sensing, a particular transport vehicle along a particular route or route segment. The transport vehicle may be number of devices, such as a moving device (e.g., a vehicle, an automobile, a boat, an airplane, etc.), a tracking device, and/or cargo/packages on a particular device. Also, a server may provide the network with information associated with a route or route segments (i.e., route information) along with device information associated with the devices. In some instances, the request to monitor may include device information associated with the devices and/or route information associated with the route or route segments. The device information may include a device identifier (ID) for each of the devices, and the route information may include a configuration of the route segments or one or more conditions of the route segments. Further, the server may indicate (e.g., indicate in the request to monitor) a number of different factors regarding the network operation during the monitoring of the devices. For example, the request to monitor may indicate a time period (i.e., a monitoring period) for monitoring the devices, a periodicity for monitoring the devices, a distance for monitoring the devices, and/or an accuracy level for monitoring the devices. In some instances, if a particular network operator does not have coverage to monitor the devices, the server can engage another network operator in order to handoff the monitoring operation (e.g., a sensing track operation) from an initial network operator to another network operator. For instance, the server may re-engage the other network operator in a similar manner to the initial network operator, such as by transmitting a request to monitor the devices. Also, the server may provide a precise position of a device, along with particular features that either identify the device (e.g., a transport vehicle) or disambiguate the device from the other nearby devices or vehicles.”; Gill et al.; 0101)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the monitoring period capability of Gill et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the monitoring period capability as taught by the processing/communications of Gill et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved resource utilization (Gill et al.; 0129) are achieved.
Claim(s) 7, 8, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anchan et al. US 20160302128 in view of Chen et al. WO 2025035367 and in further view of Song et al. WO 2024256902.
As to claim 7:
Anchan et al. as described above does not explicitly teach:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a static scenario; and
adjusting the search policy while the UE stays in a stable scenario.
However, Song et al. further teaches a different steps/configurations/quasi-stationary capability which includes:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a static scenario; and
adjusting the search policy while the UE stays in a stable scenario.
(“The gNB may be configured to determine one or more configuration parameters for the prediction window and/or its update. The parameters may comprise at least one of: 1) a prediction interval, 2) a prediction window length/duration ^.sub.^^^^, 3) updating criteria to adapt the prediction window length based on some constraints. When determining the prediction window length update, both rules/policy-based mechanism and ML-based functionality can be used with different implementation steps and signaling configurations. [0089] The prediction window update procedure for rule-based mechanism may be conducted at both the UE 102 and gNB 106 in static and/or semi-static way.”; Song et al.; 0088-0089)
(“System state based adaptivity considers for how long the future prediction window should be from a perspective of system performance. Shorter prediction windows may be sufficient for high-speed mobility as more handover (HO) related events may happen in a shorter time span. However, a functional relation may need to be established. On contrary, longer prediction windows can be applied in a quasi-stationary/slow moving environment, for example.”; Song et al.; 0074)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the different steps/configurations/quasi-stationary capability of Song et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the different steps/configurations/quasi-stationary capability as taught by the processing/communications of Song et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved adjustments (Song et al.; 0076) are achieved.
As to claim 8:
Anchan et al. as described above does not explicitly teach:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a non-static scenario; and
adjusting the search policy while the UE stays in an unstable scenario.
However, Song et al. further teaches a different steps/configurations/high-speed capability which includes:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a non-static scenario; and
adjusting the search policy while the UE stays in an unstable scenario.
(“The gNB may be configured to determine one or more configuration parameters for the prediction window and/or its update. The parameters may comprise at least one of: 1) a prediction interval, 2) a prediction window length/duration ^.sub.^^^^, 3) updating criteria to adapt the prediction window length based on some constraints. When determining the prediction window length update, both rules/policy-based mechanism and ML-based functionality can be used with different implementation steps and signaling configurations. [0089] The prediction window update procedure for rule-based mechanism may be conducted at both the UE 102 and gNB 106 in static and/or semi-static way.”; Song et al.; 0088-0089)
(“System state based adaptivity considers for how long the future prediction window should be from a perspective of system performance. Shorter prediction windows may be sufficient for high-speed mobility as more handover (HO) related events may happen in a shorter time span. However, a functional relation may need to be established. On contrary, longer prediction windows can be applied in a quasi-stationary/slow moving environment, for example.”; Song et al.; 0074)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the different steps/configurations/high-speed capability of Song et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the different steps/configurations/high-speed capability as taught by the processing/communications of Song et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved adjustments (Song et al.; 0076) are achieved.
As to claim 17:
Anchan et al. as described above does not explicitly teach:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a static scenario; and
adjusting the search policy while the UE stays in a stable scenario.
However, Song et al. further teaches a different steps/configurations/quasi-stationary capability which includes:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a static scenario; and
adjusting the search policy while the UE stays in a stable scenario.
(“The gNB may be configured to determine one or more configuration parameters for the prediction window and/or its update. The parameters may comprise at least one of: 1) a prediction interval, 2) a prediction window length/duration ^.sub.^^^^, 3) updating criteria to adapt the prediction window length based on some constraints. When determining the prediction window length update, both rules/policy-based mechanism and ML-based functionality can be used with different implementation steps and signaling configurations. [0089] The prediction window update procedure for rule-based mechanism may be conducted at both the UE 102 and gNB 106 in static and/or semi-static way.”; Song et al.; 0088-0089)
(“System state based adaptivity considers for how long the future prediction window should be from a perspective of system performance. Shorter prediction windows may be sufficient for high-speed mobility as more handover (HO) related events may happen in a shorter time span. However, a functional relation may need to be established. On contrary, longer prediction windows can be applied in a quasi-stationary/slow moving environment, for example.”; Song et al.; 0074)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the different steps/configurations/quasi-stationary capability of Song et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the different steps/configurations/quasi-stationary capability as taught by the processing/communications of Song et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved adjustments (Song et al.; 0076) are achieved.
As to claim 18:
Anchan et al. as described above does not explicitly teach:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a non-static scenario; and
adjusting the search policy while the UE stays in an unstable scenario.
However, Song et al. further teaches a different steps/configurations/high-speed capability which includes:
wherein the performing of the system selection comprises:
starting the system selection with a search policy associated with one or more parameters corresponding to a non-static scenario; and
adjusting the search policy while the UE stays in an unstable scenario.
(“The gNB may be configured to determine one or more configuration parameters for the prediction window and/or its update. The parameters may comprise at least one of: 1) a prediction interval, 2) a prediction window length/duration ^.sub.^^^^, 3) updating criteria to adapt the prediction window length based on some constraints. When determining the prediction window length update, both rules/policy-based mechanism and ML-based functionality can be used with different implementation steps and signaling configurations. [0089] The prediction window update procedure for rule-based mechanism may be conducted at both the UE 102 and gNB 106 in static and/or semi-static way.”; Song et al.; 0088-0089)
(“System state based adaptivity considers for how long the future prediction window should be from a perspective of system performance. Shorter prediction windows may be sufficient for high-speed mobility as more handover (HO) related events may happen in a shorter time span. However, a functional relation may need to be established. On contrary, longer prediction windows can be applied in a quasi-stationary/slow moving environment, for example.”; Song et al.; 0074)
Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to implement the different steps/configurations/high-speed capability of Song et al. into Anchan et al. By modifying the processing/communications of Anchan et al. to include the different steps/configurations/high-speed capability as taught by the processing/communications of Song et al., the benefits of improved rate adaption (Anchan et al.; 0003) with improved adjustments (Song et al.; 0076) are achieved.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 20230010095 – teaches prediction of future selected carriers based on parameter associated with handover (see para. 0048).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL K PHILLIPS whose telephone number is (571)272-1037. The examiner can normally be reached M-F 8am-10am, 1pm-5pm.
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MICHAEL K. PHILLIPS
Examiner
Art Unit 2464
/MICHAEL K PHILLIPS/Examiner, Art Unit 2464
Prosecution Insights