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 .
Election/Restrictions
Applicant has elected Group I (claims 1-6, 9-11,14-15, 21-23, 26, 29, and 30) without traverse.
Claim Interpretation
3. The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
4. The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means for” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Claim limitation of claim 29 have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because it uses/they use a generic placeholder “means for” coupled with functional language “monitoring" and “transmitting”, without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 29 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation, means for monitoring ([0194], “A first network entity, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: monitor performance of a machine learning positioning model based on one or more parameters, wherein the machine learning positioning model is used for positioning a user equipment (UE); and transmit, via the one or more transceivers, to a second network entity, a failure indication for the machine learning positioning model based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer”; [0255-0258]) and means for transmitting ([0194], “A first network entity, comprising: one or more memories; one or more transceivers; and one or more processors communicatively coupled to the one or more memories and the one or more transceivers, the one or more processors, either alone or in combination, configured to: monitor performance of a machine learning positioning model based on one or more parameters, wherein the machine learning positioning model is used for positioning a user equipment (UE); and transmit, via the one or more transceivers, to a second network entity, a failure indication for the machine learning positioning model based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer”; [0255-0258]).
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
Claim Rejections - 35 USC § 103
5. 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 (i.e., changing from AIA to pre-AIA ) 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.
6. 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.
7. Claims 1-2, 5-6, 11, 21-23, 26, and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over R1-2304921 ("Other aspects on AI-ML for positioning accuracy enhancement", 3GPP TSG RAN WG1 #113 Incheon, Korea, Baicells, R1-2304921, May 22nd – May 26th, 2023) in view of Zhuang ("Positioning Methods, Communication Equipment and Network Equipment", KR 20230002832 A, pub. date 2023-01-05), and further in view of Li ("UE Autonomous Actions Based on ML Model Failure Detection", WO 2023187687 A1, pub. date 2023-10-05).
Regarding claim 1, R1-2304921 teaches a method of communication performed by a first network entity (pg. 5, par. 3, “In Case 3a, the model is deployed at gNB side”), comprising:
monitoring performance of a machine learning positioning model based on one or more parameters (pg. 3, sect. 2.2, “Regarding monitoring for AI/ML based positioning, at least the following entities are identified to derive monitoring metric ... gNB at least for Case 3a (with gNB-side model)”; pg. 5, Proposal 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network”),
wherein the machine learning positioning model is used for positioning a user equipment (UE) (pg. 4, “periodically testing the model's performance on a test set and comparing the predicted UE positions to the actual UE positions”).
R1-2304921 does not explicitly teach transmitting, to a second network entity, a failure indication for the machine learning positioning model based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.
However, Zhuang teaches transmitting, to a second network entity, a failure indication for a machine learning positioning model (pg. 22, par. 6, “the network equipment 400 provides (~transmitting) first information to the communication equipment (~second network entity) - the first information includes first machine learning model information, first preprocessing and a transmitting module 401, configured to transmit: including at least one of model information and first error model information (~failure indication for a machine learning positioning model), wherein the first information is used by the communication equipment to determine information related to a location (~position) of the terminal equipment”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zhuang with the teaching of R1-2304921 in order to maintain a system reliability, trigger fallback positioning methods, and initiate model retraining.
The combination does not explicitly teach that the failure indication is based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.
However, Li teaches a failure indication is based on a number of failures of a machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer ([0087], “When the UE detects an ML model problem (e.g., indication internally at the UE of a MLMP (~machine learning model problem), or any other criterion according to the various embodiments of monitoring discussed above, the UE starts the failure timer and increments the failure counter. If the number of instances reaches the failure counter maximum value (~a number of failures of a machine learning positioning model satisfying a maximum number of failures) while the failure timer is running (~before expiration of a failure timer), the UE declares a failure in the ML model”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang in order to prevent premature network disconnections and unnecessary signaling overhead while ensuring reliable positioning tracking in mobile communication networks.
Regarding claim 2, R1-2304921 in view of Zhuang, and further in view of Li teaches the method of claim 1,
wherein the one or more parameters comprise: one or more monitoring metrics (R1-2304921 pg. 4, “To conduct ground truth based monitoring, the performance metrics should be defined first. Different performance metrics can be used to evaluate the model's performance, such as mean absolute error (MAE) or root mean square error (RMSE)”),
one or more failure thresholds associated with the one or more monitoring metrics,
the failure timer, a failure counter for tracking the number of failures,
the maximum number of failures, or
any combination thereof.
Regarding claim 5, R1-2304921 in view of Zhuang, and further in view of Li teaches the method of claim 2.
The combination of R1-2304921 and Zhuang does not explicitly teach further comprising: resetting the failure counter based on expiration of the failure timer.
However, Li further teaches further comprising: resetting a failure counter based on expiration of a failure timer ([0087], “If the timer expires (i.e., before the number of ML model problem or problem instances (or MLMP indication(s)) reaches the failure counter maximum value), the UE resets the failure counter”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang and Li in order to allow a user or system another chance to try an action after a temporary lockout ends.
Regarding claim 6, R1-2304921 in view of Zhuang, and further in view of Li
teaches the method of claim 1,
further comprising: transmitting, to the second network entity, a capability message indicating one or more capabilities of the first network entity to monitor the performance of the machine learning positioning model (R1-2304921 pg. 3, sect. 2.2, “Regarding monitoring for AI/ML based positioning, at least the following entities are identified to derive monitoring metric ... gNB at least for Case 3a (with gNB-side model)”; pg. 5, Proposal 5, “If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side (~transmitting, to the second network entity)”, wherein the gNB (~first network entity) reporting of the switching/fallback/selection of monitoring model to the second network entity (~network side) reads on transmitting, to the second network entity, an indication of a capability of the first network entity to monitor the switched/fallbacked/selected).
Regarding claim 11, R1-2304921 in view of Zhuang, and further in view of Li teaches the method of claim 1,
further comprising: receiving, from the second network entity, a configuration for monitoring the performance of the machine learning positioning model (R1-2304921 pg. 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network ... Regarding AI/ML model inference, to study the potential specification impact (including the feasibility, and the necessity of specifying AI/ML model input and/or output) at least for the following aspects for AI/ML based positioning accuracy enhancement ... For AI/ML assisted positioning with UE-assisted (Case 2a) and NG-RAN node assisted positioning (Case 3a), measurement report to carry model output to LMF”; pg. 3, par. 2-4, “Observation 2: Assistance information such as time stamp can be collected to support training data association, false data removal, thus ensuring high-quality training dataset generation. Training data collection with the aid of time stamp can facilitate training data pre-processing (e.g. data binding) ... Time stamp for model inference/monitoring ... In positioning scenarios, positioning data are usually generated with temporal consistency. Therefore, time stamp information for continuously running model inference can be used to provide an effective means for error detection/model monitoring. For instance, by attaching time stamp with the model inference result, the monitoring component can detect a model malfunction by observing the discontinuities in time and space”).
Regarding claim 21, R1-2304921 teaches a first network entity (pg. 3, sect. 2.2, gNB), comprising:
one or more memories (pg. 3, sect. 2.2, gNB comprises one or more memories);
one or more transceivers (pg. 3, sect. 2.2, gNB comprises one or more transceivers); and
one or more processors communicatively coupled to the one or more memories and the one or more transceivers (pg. 3, sect. 2.2, gNB comprises one or more processors communicatively coupled to the one or more memories and the one or more transceivers),
the one or more processors, either alone or in combination (pg. 3, sect. 2.2, gNB comprises the one or more processors, either alone or in combination),
configured to: monitor performance of a machine learning positioning model based on one or more parameters (pg. 3, sect. 2.2, “Regarding monitoring for AI/ML based positioning, at least the following entities are identified to derive monitoring metric ... gNB at least for Case 3a (with gNB-side model)”; pg. 5, Proposal 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network”),
wherein the machine learning positioning model is used for positioning a user equipment (UE) (pg. 4, “periodically testing the model's performance on a test set and comparing the predicted UE positions to the actual UE positions”).
R1-2304921 does not explicitly teach transmitting, via one or more transceivers, to a second network entity, a failure indication for the machine learning positioning model.
However, Zhuang teaches transmitting, via one or more transceivers, to a second network entity, a failure indication for a machine learning positioning model (pg. 22, par. 6, “the network equipment 400 provides (~transmitting) first information to the communication equipment (~second network entity) - the first information includes first machine learning model information, first preprocessing and a transmitting module 401, configured to transmit: including at least one of model information and first error model information (~failure indication for a machine learning positioning model), wherein the first information is used by the communication equipment to determine information related to a location (~position) of the terminal equipment”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zhuang with the teaching of R1-2304921 in order to maintain a system reliability, trigger fallback positioning methods, and initiate model retraining.
The combination does not explicitly teach that the failure indication is based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.
However, Li teaches a failure indication is based on a number of failures of a machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer ([0087], “When the UE detects an ML model problem (e.g., indication internally at the UE of a MLMP (~machine learning model problem), or any other criterion according to the various embodiments of monitoring discussed above, the UE starts the failure timer and increments the failure counter. If the number of instances reaches the failure counter maximum value (~a number of failures of a machine learning positioning model satisfying a maximum number of failures) while the failure timer is running (~before expiration of a failure timer), the UE declares a failure in the ML model”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang in order to prevent premature network disconnections and unnecessary signaling overhead while ensuring reliable positioning tracking in mobile communication networks.
Regarding claim 22, R1-2304921 in view of Zhuang, and further in view of Li teaches the first network entity (R1-2304921 pg. 3, sect. 2.2, gNB) of claim 21,
wherein the one or more parameters comprise: one or more monitoring metrics (R1-2304921 pg. 4, “To conduct ground truth based monitoring, the performance metrics should be defined first. Different performance metrics can be used to evaluate the model's performance, such as mean absolute error (MAE) or root mean square error (RMSE)”),
one or more failure thresholds associated with the one or more monitoring metrics,
the failure timer,
a failure counter for tracking the number of failures,
the maximum number of failures, or
any combination thereof.
Regarding claim 23, R1-2304921 in view of Zhuang, and further in view of Li teaches the first network entity (R1-2304921 pg. 3, sect. 2.2, gNB) of claim 21,
wherein the one or more processors, either alone or in combination (R1-2304921 pg. 3, sect. 2.2, gNB comprises the one or more processors, either alone or in combination),
are further configured to: transmit, via the one or more transceivers, to the second network entity, a capability message indicating one or more capabilities of the first network entity to monitor the performance of the machine learning positioning model (R1-2304921 pg. 3, sect. 2.2, “Regarding monitoring for AI/ML based positioning, at least the following entities are identified to derive monitoring metric ... gNB at least for Case 3a (with gNB-side model)”; pg. 5, Proposal 5, “If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side (~transmitting, to the second network entity)”, wherein the gNB (~first network entity) reporting of the switching/fallback/selection of monitoring model to the second network entity (~network side) reads on transmitting, to the second network entity, an indication of a capability of the first network entity to monitor the switched/fallbacked/selected).
Regarding claim 26, R1-2304921 in view of Zhuang, and further in view of Li teaches the first network entity (R1-2304921 pg. 3, sect. 2.2, gNB) of claim 21,
wherein the one or more processors, either alone or in combination (R1-2304921 pg. 3, sect. 2.2, gNB comprises the one or more processors, either alone or in combination),
are further configured to: receive, via the one or more transceivers (R1-2304921 pg. 3, sect. 2.2, gNB comprises one or more transceivers), from the second network entity, a configuration for monitoring the performance of the machine learning positioning model (pg. 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network (~second network entity) ... Regarding AI/ML model inference, to study the potential specification impact (including the feasibility, and the necessity of specifying AI/ML model input and/or output) at least for the following aspects for AI/ML based positioning accuracy enhancement ... For AI/ML assisted positioning with UE-assisted (Case 2a) and NG-RAN node assisted positioning (Case 3a), measurement report to carry model output to LMF”; pg. 3, par. 2-4, “Observation 2: Assistance information such as time stamp can be collected to support training data association, false data removal, thus ensuring high-quality training dataset generation. Training data collection with the aid of time stamp can facilitate training data pre-processing (e.g. data binding) ... Time stamp for model inference/monitoring ... In positioning scenarios, positioning data are usually generated with temporal consistency. Therefore, time stamp information for continuously running model inference can be used to provide an effective means for error detection/model monitoring. For instance, by attaching time stamp with the model inference result, the monitoring component can detect a model malfunction by observing the discontinuities in time and space”).
Regarding claim 29, R1-2304921 teaches a first network entity (pg. 3, sect. 2.2, gNB), comprising:
means for monitoring performance of a machine learning positioning model based on one or more parameters (pg. 3, sect. 2.2, “Regarding monitoring for AI/ML based positioning, at least the following entities are identified to derive monitoring metric ... gNB at least for Case 3a (with gNB-side model)”; pg. 5, Proposal 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network”),
wherein the machine learning positioning model is used for positioning a user equipment (UE) (pg. 4, “periodically testing the model's performance on a test set and comparing the predicted UE positions to the actual UE positions”.
R1-2304921 does not explicitly teach means for transmitting, to a second network entity, a failure indication for the machine learning positioning model.
However, Zhuang teaches means for transmitting, to a second network entity, a failure indication for a machine learning positioning model (pg. 22, par. 6, “the network equipment 400 provides (~transmitting) first information to the communication equipment (~second network entity) - the first information includes first machine learning model information, first preprocessing and a transmitting module 401, configured to transmit: including at least one of model information and first error model information (~failure indication for a machine learning positioning model), wherein the first information is used by the communication equipment to determine information related to a location (~position) of the terminal equipment”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zhuang with the teaching of R1-2304921 in order to maintain a system reliability, trigger fallback positioning methods, and initiate model retraining.
The combination does not explicitly teach that the failure indication is based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.
However, Li teaches a failure indication is based on a number of failures of a machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer ([0087], “When the UE detects an ML model problem (e.g., indication internally at the UE of a MLMP (~machine learning model problem), or any other criterion according to the various embodiments of monitoring discussed above, the UE starts the failure timer and increments the failure counter. If the number of instances reaches the failure counter maximum value (~a number of failures of a machine learning positioning model satisfying a maximum number of failures) while the failure timer is running (~before expiration of a failure timer), the UE declares a failure in the ML model”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang in order to prevent premature network disconnections and unnecessary signaling overhead while ensuring reliable positioning tracking in mobile communication networks.
Regarding claim 30, R1-2304921 teaches a non-transitory computer-readable medium storing computer-executable instructions that (pg. 3, sect. 2.2, gNB comprises a non-transitory computer-readable medium storing computer-executable instructions that),
when executed by a first network entity (pg. 3, sect. 2.2, when executed by a gNB (~first network entity)),
cause the first network entity to (pg. 3, sect. 2.2, cause the gNB (~first network entity to)):
monitor performance of a machine learning positioning model based on one or more parameters (pg. 3, sect. 2.2, “Regarding monitoring for AI/ML based positioning, at least the following entities are identified to derive monitoring metric ... gNB at least for Case 3a (with gNB-side model)”; pg. 5, Proposal 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network”),
wherein the machine learning positioning model is used for positioning a user equipment (UE) (pg. 4, “periodically testing the model's performance on a test set and comparing the predicted UE positions to the actual UE positions”).
R1-2304921 does not explicitly teach transmitting, to a second network entity, a failure indication for the machine learning positioning model based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.
However, Zhuang teaches transmitting, to a second network entity, a failure indication for a machine learning positioning model (pg. 22, par. 6, “the network equipment 400 provides (~transmitting) first information to the communication equipment (~second network entity) - the first information includes first machine learning model information, first preprocessing and a transmitting module 401, configured to transmit: including at least one of model information and first error model information (~failure indication for a machine learning positioning model), wherein the first information is used by the communication equipment to determine information related to a location (~position) of the terminal equipment”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Zhuang with the teaching of R1-2304921 in order to maintain a system reliability, trigger fallback positioning methods, and initiate model retraining.
The combination does not explicitly teach that the failure indication is based on a number of failures of the machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer.
However, Li teaches a failure indication is based on a number of failures of a machine learning positioning model satisfying a maximum number of failures before expiration of a failure timer ([0087], “When the UE detects an ML model problem (e.g., indication internally at the UE of a MLMP (~machine learning model problem), or any other criterion according to the various embodiments of monitoring discussed above, the UE starts the failure timer and increments the failure counter. If the number of instances reaches the failure counter maximum value (~a number of failures of a machine learning positioning model satisfying a maximum number of failures) while the failure timer is running (~before expiration of a failure timer), the UE declares a failure in the ML model”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang in order to prevent premature network disconnections and unnecessary signaling overhead while ensuring reliable positioning tracking in mobile communication networks.
8. Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over R1-2304921 in view of Zhuang, further in view of Li, and further in view of Lee (US 2023/0403587 A1).
Regarding claim 3, R1-2304921 in view of Zhuang, and further in view of Li teaches the method of claim 2.
The combination of R1-2304921 and Zhuang does not explicitly teach wherein monitoring the performance of the machine learning positioning model comprises:
incrementing the failure counter based on a monitoring metric of the one or more monitoring metrics.
However, Li further teaches wherein monitoring a performance of a machine learning positioning model comprises: incrementing a failure counter based on a monitoring metric of one or more monitoring metrics ([0087], “When the UE detects an ML model problem (e.g., indication internally at the UE of a MLMP, or any other criterion according to the various embodiments of monitoring discussed above, the UE starts the failure timer and increments the failure counter”; [0085], “UE monitors each instance that a ML model problem occurs and increments the failure counter ... upon the occurrence of a ML model performance problem, such as the generation of a MLMP indication, the failure counter is incremented”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang and Li in order to ensure operational safety, maintain location accuracy, and trigger automated fallback or retraining mechanisms before bad predictions cause real-world failures.
The combination does not explicitly teach that the monitoring metrics satisfy a corresponding failure threshold of one or more failure thresholds.
However, Lee teaches monitoring metrics satisfying a corresponding failure threshold of one or more failure thresholds ([0082], “configuration information for evaluating the AI model transmitted from the base station to the UE includes at least one of ... the threshold value and timer for triggering the indication of the AI model failure”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Lee with the teaching of R1-2304921 as modified by Zhuang and Li in order to automatically detect, alert, and respond to system degradation before it causes a critical outage.
Regarding claim 4, R1-2304921 in view of Zhuang, further in view of Li, and further in view of Lee teaches the method of claim 3.
The combination of R1-2304921, Zhuang, and Lee does not explicitly teach further comprising: starting, in response to the failure counter being incremented, the failure timer based on the failure timer not being started.
However, Li further teaches further comprising: starting, in response to a failure counter being incremented, a failure timer based on the failure timer not being started ([0090], “UE increments the failure counter and, if the number of instances reaches the failure counter maximum value the failure timer is started. Failure Timer b. Failure Counter, and Recovery Counter”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Li with the teaching of R1-2304921 as modified by Zhuang, Li, and Lee in order to prevent multiple overlapping timers and establish a controlled window for error tracking.
9. Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over R1-2304921 in view of Zhuang, further in view of Li, and further in view of R1-2304686 ("Other Aspects on ML for Positioning Accuracy Enhancement", 3GPP TSG RAN WG1 #113 Meeting, Incheon, Korea, Nokia, Nokia Shanghai Bell, 22 – 26 May, 2023, R1-2304686).
Regarding claim 9, R1-2304921 in view of Zhuang, and further in view of Li teaches the method of claim 6.
The combination does not explicitly teach further comprising: receiving, from the second network entity, a request for the one or more capabilities.
However, R1-2304686 teaches further comprising: receiving, from a second network entity, a request for the one or more capabilities (Sect. 3, Fig. 6, Server sending RequestCapabilities message to Target).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of R1-2304686 with the teaching of R1-2304921 as modified by Zhuang and Li in order to establish a compatible, optimized, and secure communication link between them.
10. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over R1-2304921 in view of Zhuang, further in view of Li, further in view of R1-2304686, and further in view of Kumar ("Method and Apparatus for 5G Positioning Accuracy Improvement in Presence of Phase Noise", WO 2021071581 A1, pub. date 2021-04-15).
Regarding claim 10, R1-2304921 in view of Zhuang, further in view of Li, and further in view of R1-2304686 teaches the method of claim 9.
The combination does not explicitly teach wherein:
the request for the one or more capabilities is included in a first New Radio positioning protocol type A (NRPPa) message, and the capability message is a second NRPPa message.
However, Kumar teaches wherein: a request for one or more capabilities is included in a first New Radio positioning protocol type A (NRPPa) message ([00154], “In the signaling flow 1200, it is assumed that the UE 115 and location server 1201 communicate using the LPP positioning protocol referred to earlier, although use of NPP or a combination of LPP and NPP or other future protocol, such as NRPPa, is also possible”), and
a capability message is a second NRPPa message ([00154-00156], “In the signaling flow 1200, it is assumed that the UE 115 and location server 1201 communicate using the LPP positioning protocol referred to earlier, although use of NPP or a combination of LPP and NPP or other future protocol, such as NRPPa, is also possible. At stage 1, the location server 1201 sends a Request Capabilities message to the UE 115, e.g., to request the positioning capabilities of the UE 115. At stage 2, the UE 115 returns a Provide Capabilities message to the location server 1201 to provide the positioning capabilities of the UE 115. The positioning capabilities may include, e.g., the minimum number of resource blocks required by the UE 115 for positioning measurements or a desired positioning accuracy, which may be provided to the serving base station 105-a”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Kumar with the teaching of R1-2304921 as modified by Zhuang, Li, and R1-2304686 in order to provide a direct network-to-core positioning coordination, enable advanced 5G native positioning methods, highly efficient resource and signal management, and seamless support for hybrid and assisted location data by effective transfer of positioning-related information and measurement data.
11. Claims 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over R1-2304921 in view of Zhuang, further in view of Li, and further in view of Kumar.
Regarding claim 14, R1-2304921 in view of Zhuang, and further in view of Li teaches the method of claim 11,
further comprising: the configuration for monitoring the performance of the machine learning positioning model (R1-2304921 pg. 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network ... Regarding AI/ML model inference, to study the potential specification impact (including the feasibility, and the necessity of specifying AI/ML model input and/or output) at least for the following aspects for AI/ML based positioning accuracy enhancement ... For AI/ML assisted positioning with UE-assisted (Case 2a) and NG-RAN node assisted positioning (Case 3a), measurement report to carry model output to LMF”; pg. 3, par. 2-4, “Observation 2: Assistance information such as time stamp can be collected to support training data association, false data removal, thus ensuring high-quality training dataset generation. Training data collection with the aid of time stamp can facilitate training data pre-processing (e.g. data binding) ... Time stamp for model inference/monitoring ... In positioning scenarios, positioning data are usually generated with temporal consistency. Therefore, time stamp information for continuously running model inference can be used to provide an effective means for error detection/model monitoring. For instance, by attaching time stamp with the model inference result, the monitoring component can detect a model malfunction by observing the discontinuities in time and space”).
The combination does not explicitly teach that the configuration is a request for the configuration transmitting to the second network entity.
However, Kumar teaches further comprising: transmitting, to a second network entity, a request for a configuration ([0127-0128]“in stage 7b, one or more base stations 105 may send a Provide Transmission Configuration message to the location server 1001 to request that PTRS is provided for positioning or that a mix of PTRS and UL-PRS signals are provided for positioning. The transmission configuration may additionally or alternatively include a request a specific PRS frame structure, e.g., a comb value of 2 or 1, which will reduce the impact of phase noise. At stage 9a, the location server 1001 sends a Transmission Configuration message to the gNBs 105 that includes, e.g., an indication to transmit PTRS, a mix of PTRS with DL-PRS, or a specific PRS frame structure requested at stage 8a”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Kumar with the teaching of R1-2304921 as modified by Zhuang and Li in order to dynamically obtain operational parameters, network settings, or security policies required.
Regarding claim 15, R1-2304921 in view of Zhuang, further in view of Li, and further in view of Kumar teaches the method of claim 14, wherein: the configuration for monitoring a performance of a machine learning positioning model (R1-2304921 pg. 5, “In Case 3a, the model is deployed at gNB side ... If model monitoring is deployed at the same side, gNB can make decisions (e.g. model switching/fallback/selection) based on the monitoring metrics and report to network side. To make the decision, gNB may need assistance information from the network ... Regarding AI/ML model inference, to study the potential specification impact (including the feasibility, and the necessity of specifying AI/ML model input and/or output) at least for the following aspects for AI/ML based positioning accuracy enhancement ... For AI/ML assisted positioning with UE-assisted (Case 2a) and NG-RAN node assisted positioning (Case 3a), measurement report to carry model output to LMF”; pg. 3, par. 2-4, “Observation 2: Assistance information such as time stamp can be collected to support training data association, false data removal, thus ensuring high-quality training dataset generation. Training data collection with the aid of time stamp can facilitate training data pre-processing (e.g. data binding) ... Time stamp for model inference/monitoring ... In positioning scenarios, positioning data are usually generated with temporal consistency. Therefore, time stamp information for continuously running model inference can be used to provide an effective means for error detection/model monitoring. For instance, by attaching time stamp with the model inference result, the monitoring component can detect a model malfunction by observing the discontinuities in time and space”).
The combination of R1-2304921, Zhuang, and Li does not explicitly teach wherein: the configuration is a request for the configuration included in a first NRPPa message, and the configuration is included in a second NRPPa message.
However, Kumar further teaches wherein: a request for a configuration is included in a first NRPPa message ([0102-103], “In the signaling flow 900, it is assumed that the UE 115 and location server 901 communicate using the LPP positioning protocol referred to earlier, although use of NPP or a combination of LPP and NPP or other future protocol, such as NRPPa, is also possible. At stage 1, the location server 901 sends a Request Transmission Configuration message to the UE 115 (~via gNB2 and gNB1), e.g., to request transmission configuration from the UE 115”), and
the configuration is included in a second NRPPa message ([00102-00104], “In the signaling flow 900, it is assumed that the UE 115 and location server 901 communicate using the LPP positioning protocol referred to earlier, although use of NPP or a combination of LPP and NPP or other future protocol, such as NRPPa ... At stage 2, the UE 115 returns a Provide Transmission Configuration message to the location server (~via gNB1 and gNB2) 901 to provide a request that PTRS is provided for positioning or that a mix of PTRS and DL-PRS signals are provided for positioning”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Kumar with the teaching of R1-2304921 as modified by Zhuang, Li, and Kumar in order to provide a direct network-to-core positioning coordination, enable advanced 5G native positioning methods, highly efficient resource and signal management, and seamless support for hybrid and assisted location data by effective transfer of positioning-related information and measurement data.
Conclusion
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/ALEXANDER YI/
Examiner, Art Unit 2643
/JINSONG HU/ Supervisory Patent Examiner, Art Unit 2643
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