DETAILED ACTION
The office action is in response to the application filed received on Aug. 8, 2024.
The Oath was received on Sept. 19, 2024.
Claims 1-19 are pending in this application, based on the claims on Aug. 8, 2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on Sept. 19, 2024 has been considered by the examiner.
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 .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-19 are rejected under U.S.C. 103 as being unpatentable over Kunjan Shah et. al (USPub. No.: US 20250300900 A1, hereinafter “Shah”) and in a view of Hasegawa, Fumihiro et. al (Int. Pub. No.: WO 2024211566 A2, hereinafter “Hasegawa”).
Regarding claim 1, Shah teaches that a first apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to perform: (Shah, in Fig. 1B and in Paragraphs [0056]-[0057] and [0060], teaches that the device 102 included include a processor 118, a transceiver 120, non-removable memory 130, removable memory 132, among others. To perform signal coding, data processing, input/output processing, and/or any other functionality, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and/or the removable memory 132.) transmitting test configuration information to a second apparatus, the test configuration information indicating a test mode of an artificial intelligence/machine learning (AI/ML) model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator, a test channel indicator indicating a probability of a communication channel between the second apparatus and a TRP being a line-of-sight channel or a non-line-of-sight channel; (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the first device transmits LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the first device transmits conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Although Shah further teaches testing AI/ML mode (method) in the second apparatus (WTRU: Wireless Transmission and Reception Unit) in Fig. 8, its verification is done by the second apparatus and the verification results is feedback to the first apparatus. While, Hasegawa teaches the feedback of the testing (predicted) results to the first apparatus and the validation for testing AI/ML mode in the first apparatus.
Hasegawa teaches receiving, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model; and determining a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated (Hasegawa, in Fig. 14 and in Paragraphs [0156]-[0158], teaches that based on the description in Paragraph [0084], the AIML models are testing or performing by the procedure in Fig. 14. Based on the selected AI/ML method, the WTRU (the second apparatus) is configured, to enhance the LOS indicator reliability, by a target TRP, a LOS indicator threshold (the required or test channel indicator (test LOS indicator) such as a required reliability value (described in Paragraphs [0101] and [0106]), a time threshold, etc. Since the WTRU has the capability for the validation, the WTRU validates the determined LOS indicator according to the LOS indicator threshold when the determined LOS indicator is less than the LOS indicator threshold. Namely, the determined LOS indicator is matched with the acceptance range. However, if the determined LOS indicator is above the threshold, the WTRU report the measurement, the WTRU location and/or the determined LOS indicator (based on the received PRS (Position Reference Signal)) associated with the target TRP to the network (the first apparatus). For this case, the network further verified the determined LOS indicator to confirm the tested AI/ML method.
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of receiving, from the second apparatus, at least one predicted channel indicator for the at least one TRP, the at least one predicted channel indicator being derived by the second apparatus using the AI/ML model; and determining a test result for the AI/ML model based on a comparison between the at least one predicted channel indicator and the at least one test channel indicator, the test result indicating whether the AI/ML model is validated of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 2, combination of Shah and Hasegawa teaches the features defined in the claim 1, -refer to the indicated claim for reference(s).
Shah further teaches that wherein the test configuration information comprises at least one of the following: an indication of the AI/ML model to be tested, an indication of the at least one TRP, or an indication of the test mode, a test command, or a test configuration. (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the first device transmits LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the first device transmits conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Regarding claim 3, combination of Shah and Hasegawa teaches the features defined in the claim 1, -refer to the indicated claim for reference(s).
Shah further teaches that wherein the first apparatus is further caused to perform: selecting the at least one test channel indicator for the at least one TRP; determining the test plan based on the at least one test channel indicator, the test plan comprising a test setup for a test channel indicator, a test setup being defined as a physical arrangement of a TRP with respect to the second apparatus and at least one signal reflector within the environment; and causing the at least one TRP to be arranged according to the test plan. (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the first device transmits LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the first device transmits conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Regarding claim 4, combination of Shah and Hasegawa teaches the features defined in the claim 3, -refer to the indicated claim for reference(s).
Shah further teaches that a first test setup for a test channel indicator indicating a probability of a communication channel between the second apparatus and a first TRP being a line-of-sight channel, the first test setup being defined as a physical arrangement of the first TRP towards the second apparatus, and a second test setup for a test channel indicator indicating a probability of a communication channel between the second apparatus and a second TRP being a non-line-of-sight channel, the second test setup being defined as a physical arrangement of the second TRP towards at least one signal reflector. (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the first device transmits LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0378], if the device determines that it is not associated with a corner based on the measurements of the TRP or TRPs associated with the comer, at
2035, the device may determine if it is in an NLOS environment. If the device determines that the number of LOS indicators received from the network and/or TRPs is below the LOS TRP threshold, the device determines that it is in an NLOS environment. Or, the device determines that the number of NLOS indicators received from the network and/or TRPs is above an NLOS TRP threshold, the device determines that it is in an NLOS environment. Depending on this decision, the testing is proceeded for the LOS TRP channel or the NLOS TRP channel, respectively.)
Regarding claim 5, combination of Shah and Hasegawa teaches the features defined in the claim 1, -refer to the indicated claim for reference(s).
Shah further teaches that causing the at least one TRP arranged within the environment to transmit reference signals, measurement results of the reference signals being measured by the second apparatus and used as inputs to the AI/ML model (Shah, in Fig. 20 and Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. Further, as described in Paragraphs [0373]-[0375], at 2015, the PRS (Position Reference Signal) configuration is received by the second apparatus and it is transmitted by a TRP, a network device such as LMF, gNB, etc, or as a response to the request message from the second apparatus with TRP locations, LOS indicators, and corner identifiers. At 2020, the PRS signal is measured from a plurality of TRP’s, in serial or in parallel and the PRS measurement is used as AI/ML model training for NLOS identification as described in Paragraph [0271].)
Regarding claim 6, combination of Shah and Hasegawa teaches the features defined in the claim 1, -refer to the indicated claim for reference(s).
Hasegawa further teaches that receiving, from the second apparatus, acknowledgement of the test configuration information; and based on receiving the acknowledgement of the test configuration information, causing the test plan to be executed (Hasegawa, in Fig. 14 and Paragraphs [0157], teaches that as described in Paragraph [0157], the second apparatus (the WTRU) receives, at 1405, a request to verify the LOS indicator for the configured target TRP. The WTRU send 1415 a message to the network accepting the request or acknowledging the LOS indicator verification request. The WTRU receives a SRS resource ID and when receiving a PRS, performs 1420 measurements on the PRS and determines the LOS indicator for the target TRP. Thus, the first apparatus receive acknowledgement of the test configuration information from the second apparatus and the second apparatus executes the test as requested.
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of receiving, from the second apparatus, acknowledgement of the test configuration information; and based on receiving the acknowledgement of the test configuration information, causing the test plan to be executed of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 7, combination of Shah and Hasegawa teaches the features defined in the claim 1, -refer to the indicated claim for reference(s).
Shah further teaches that based on a determination that the at least one predicted channel indicator matches with the at least one test channel indicator, determining the test result to indicate that the AI/ML model is validated; and based on a determination that the at least one predicted channel indicator mismatches with the at least one test channel indicator, determining the test result to indicate that the AI/ML model is invalidated. (Shah, in Paragraphs [0251], teaches that after fulfillment of training completion criteria, the training is stopped and returns the trained model to the first apparatus. For the fulfillment, if a difference between the estimated LOS indicator (predicted channel indicator) and reference LOS indicator (test channel indicator) is below a preconfigured threshold, a change between the estimated LOS indicator for consecutive occasions is below a preconfigured threshold, the trained AI/ML model is passed or validated. Otherwise, the trained AI/ML model is not validated)
Regarding claim 8, combination of Shah and Hasegawa teaches the features defined in the claim 1, -refer to the indicated claim for reference(s).
Shah further teaches that wherein the first apparatus comprises testing equipment, and the second apparatus comprises a device under test (Shah, in Paragraphs [0094], teaches that the emulation devices performs all functions while being fully or partially implemented and/or deployed as part of wireless communication network in order to test other devices within the communication network. Thus, during the test, the emulation device (testing equipment) is working as a wireless network device (the first apparatus) and the other wireless device such as UE or WTRU is tested as the second apparatus. )
Regarding claim 9, combination of Shah and Hasegawa teaches the features defined in the claim 8, -refer to the indicated claim for reference(s).
Hasegawa further teaches that wherein the testing equipment comprises a network entity, a base station, or a terminal device, and wherein the device under test comprises a network entity, a base station, or a terminal device, wherein the terminal device comprises one or more receivers (Hasegawa, in Paragraph [0099], teaches that as described in Paragraph [0099], since either the WTRU or the network device, or both are able to train AI/ML model with verified LOS indicators, both the WTRU such as UE or a terminal and the network device such as BS, or gNB can be either the first apparatus or the second apparatus, respectively. Thus, the testing equipment comprises the network device, BS, or a terminal device and the device under test comprises the network device, BS, or a terminal, too.
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of wherein the testing equipment comprises a network entity, a base station, or a terminal device, and wherein the device under test comprises a network entity, a base station, or a terminal device, wherein the terminal device comprises one or more receivers of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 10, Shah teaches that a second apparatus comprising: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the first apparatus at least to perform: (Shah, in Fig. 1B and in Paragraphs [0056]-[0057] and [0060], teaches that the device 102 included include a processor 118, a transceiver 120, non-removable memory 130, removable memory 132, among others. To perform signal coding, data processing, input/output processing, and/or any other functionality, the processor 118 may access information from, and store data in, any type of suitable memory, such as the non-removable memory 130 and/or the removable memory 132.) receiving test configuration information from a first apparatus, the test configuration information indicating a test mode of an artificial intelligence/machine learning (AI/ML) model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator, a test channel indicator indicating a probability of a communication channel between the second apparatus and a TRP being a line-of-sight channel or a non-line-of-sight channel; (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the second device receives AI/ML Model or Model indicator, TRP location information, and LOS indicators. the second device receives one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the second device receives TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the second device receives LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the second device receives conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Although Shah further teaches testing AI/ML mode (method) in the second apparatus (WTRU: Wireless Transmission and Reception Unit) in Fig. 8, its verification is done by the second apparatus and the verification results is feedback to the first apparatus. While, Hasegawa teaches the feedback of the testing (predicted) results of the AI/ML method to the first apparatus.
Hasegawa teaches based on the test configuration information, deriving at least one predicted channel indicator for the at least one TRP using the AI/ML model; and transmitting, to the first apparatus, at least one predicted channel indicator for the at least one TRP (Hasegawa, in Fig. 14 and in Paragraphs [0156]-[0158], teaches that based on the description in Paragraph [0084], the AIML models are testing or performing by the procedure in Fig. 14. Based on the selected AI/ML method, the WTRU (the second apparatus) is configured, to enhance the LOS indicator reliability, by a target TRP, a LOS indicator threshold (the required or test channel indicator (test LOS indicator) such as a required reliability value (described in Paragraphs [0101] and [0106]), a time threshold, etc. Since the WTRU has the capability for the validation, the WTRU validates the determined LOS indicator according to the LOS indicator threshold when the determined LOS indicator is less than the LOS indicator threshold. Namely, the determined LOS indicator is matched with the acceptance range. However, if the determined LOS indicator is above the threshold, the WTRU report the measurement, the WTRU location and/or the determined LOS indicator (based on the received PRS (Position Reference Signal)) associated with the target TRP to the network device (the first apparatus).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of based on the test configuration information, deriving at least one predicted channel indicator for the at least one TRP using the AI/ML model; and transmitting, to the first apparatus, at least one predicted channel indicator for the at least one TRP of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 11, combination of Shah and Hasegawa teaches the features defined in the claim 10, -refer to the indicated claim for reference(s).
Shah further teaches that wherein the test configuration information comprises at least one of the following: an indication of the AI/ML model to be tested, an indication of the at least one TRP, or an indication of the test mode, a test command, or a test configuration. (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the first device transmits LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the first device transmits conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Regarding claim 12, combination of Shah and Hasegawa teaches the features defined in the claim 10, -refer to the indicated claim for reference(s).
Hasegawa further teaches that transmitting, to the first apparatus, acknowledgement of the test configuration information, to trigger the test plan to be executed (Hasegawa, in Fig. 14 and Paragraphs [0157], teaches that as described in Paragraph [0157], the second apparatus (the WTRU) receives, at 1405, a request to verify the LOS indicator for the configured target TRP. The WTRU send 1415 a message to the network device(the first apparatus) accepting the request or acknowledging the LOS indicator verification request. The WTRU receives a SRS resource ID and when receiving a PRS, performs 1420 measurements on the PRS and determines the LOS indicator for the target TRP. Thus, the second apparatus transmits acknowledgement of the test configuration information to the first apparatus and the second apparatus triggers the test as requested.
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of transmitting, to the first apparatus, acknowledgement of the test configuration information, to trigger the test plan to be executed of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 13, combination of Shah and Hasegawa teaches the features defined in the claim 10, -refer to the indicated claim for reference(s).
Shah further teaches that measuring reference signals transmitted from the at least one TRP; and
providing the measurement results of reference signals as inputs to the AI/ML model to derive the at least one predicted channel indicator (Shah, in Fig. 20 and Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. Further, as described in Paragraphs [0373]-[0375], at 2015, the PRS (Position Reference Signal) configuration is received by the second apparatus and it is transmitted by a TRP, a network device such as LMF, gNB, etc, or as a response to the request message from the second apparatus with TRP locations, LOS indicators, and corner identifiers. At 2020, the PRS signal is measured from a plurality of TRP’s, in serial or in parallel and the PRS measurement is used as AI/ML model training for NLOS identification (predicted channel indicator) as described in Paragraph [0271].)
Regarding claim 14, combination of Shah and Hasegawa teaches the features defined in the claim 10, -refer to the indicated claim for reference(s).
Shah further teaches that wherein the first apparatus comprises testing equipment, and the second apparatus comprises a device under test (Shah, in Paragraphs [0094], teaches that the emulation devices perform all functions while being fully or partially implemented and/or deployed as part of wireless communication network in order to test other devices within the communication network. Thus, during the test, the emulation device (testing equipment) is working as a wireless network device (the first apparatus) and the other wireless device such as UE or WTRU is tested as the second apparatus. )
Regarding claim 15, combination of Shah and Hasegawa teaches the features defined in the claim 14, -refer to the indicated claim for reference(s).
Hasegawa further teaches that wherein the testing equipment comprises a network entity, a base station, or a terminal device, and wherein the device under test comprises a network entity, a base station, or a terminal device, wherein the terminal device comprises one or more receivers (Hasegawa, in Paragraph [0099], teaches that as described in Paragraph [0099], since either the WTRU or the network device, or both are able to train AI/ML model with verified LOS indicators, both the WTRU such as UE or a terminal and the network device such as BS, or gNB can be either the first apparatus or the second apparatus, respectively. Thus, the testing equipment comprises the network device, BS, or a terminal device and the device under test comprises the network device, BS, or a terminal, too.
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of wherein the testing equipment comprises a network entity, a base station, or a terminal device, and wherein the device under test comprises a network entity, a base station, or a terminal device, wherein the terminal device comprises one or more receivers of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 16, Shah teaches that a method for a second apparatus comprising: receiving test configuration information from a first apparatus, the test configuration information indicating a test mode of an artificial intelligence/machine learning (AI/ML) model with respect to at least one transmission and reception unit (TRP), the at least one TRP being arranged within an environment based on a test plan for at least one test channel indicator, a test channel indicator indicating a probability of a communication channel between the second apparatus and a TRP being a line-of-sight channel or a non-line-of-sight channel; (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the second device receives AI/ML Model or Model indicator, TRP location information, and LOS indicators. the second device receives one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the second device receives TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the second device receives LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the second device receives conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Although Shah further teaches testing AI/ML mode (method) in the second apparatus (WTRU: Wireless Transmission and Reception Unit) in Fig. 8, its verification is done by the second apparatus and the verification results is feedback to the first apparatus. While, Hasegawa teaches the feedback of the testing (predicted) results of the AI/ML method to the first apparatus.
Hasegawa teaches based on the test configuration information, deriving at least one predicted channel indicator for the at least one TRP using the AI/ML model; and transmitting, to the first apparatus, at least one predicted channel indicator for the at least one TRP (Hasegawa, in Fig. 14 and in Paragraphs [0156]-[0158], teaches that based on the description in Paragraph [0084], the AIML models are testing or performing by the procedure in Fig. 14. Based on the selected AI/ML method, the WTRU (the second apparatus) is configured, to enhance the LOS indicator reliability, by a target TRP, a LOS indicator threshold (the required or test channel indicator (test LOS indicator) such as a required reliability value (described in Paragraphs [0101] and [0106]), a time threshold, etc. Since the WTRU has the capability for the validation, the WTRU validates the determined LOS indicator according to the LOS indicator threshold when the determined LOS indicator is less than the LOS indicator threshold. Namely, the determined LOS indicator is matched with the acceptance range. However, if the determined LOS indicator is above the threshold, the WTRU report the measurement, the WTRU location and/or the determined LOS indicator (based on the received PRS (Position Reference Signal)) associated with the target TRP to the network device (the first apparatus).
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of based on the test configuration information, deriving at least one predicted channel indicator for the at least one TRP using the AI/ML model; and transmitting, to the first apparatus, at least one predicted channel indicator for the at least one TRP of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 17, combination of Shah and Hasegawa teaches the features defined in the claim 16, -refer to the indicated claim for reference(s).
Shah further teaches that wherein the test configuration information comprises at least one of the following: an indication of the AI/ML model to be tested, an indication of the at least one TRP, or an indication of the test mode, a test command, or a test configuration. (Shah, in Fig. 20 and in Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. As described in Paragraph [0370], the first device transmits LOS indicators (considered as test channel indicator that indicated LOS or NLOS) associated with each TRP identifier that include binary flag (1 or 0), data strings or other predetermined signals. Further, as described in Paragraph [0254]-[0255], LOS probability of each neighboring TRP is obtained. Based on LOS probability of the different TRPs, the first device selects neighboring TRPs to transmit PRS (Positioning Reference Signal) and measure TDOA (Time Difference of Arrival). Further, as described in Paragraphs [0371]-[0372], the first device transmits conner identifier and/or threshold associated with TRP identification to identify the wall, reflectors, or obstacles. Thus, it is also one of information for the test channel indicator to indicate LOS or NLOS.)
Regarding claim 18, combination of Shah and Hasegawa teaches the features defined in the claim 16, -refer to the indicated claim for reference(s).
Hasegawa further teaches that transmitting, to the first apparatus, acknowledgement of the test configuration information, to trigger the test plan to be executed (Hasegawa, in Fig. 14 and Paragraphs [0157], teaches that as described in Paragraph [0157], the second apparatus (the WTRU) receives, at 1405, a request to verify the LOS indicator for the configured target TRP. The WTRU send 1415 a message to the network device (the first apparatus) accepting the request or acknowledging the LOS indicator verification request. The WTRU receives a SRS resource ID and when receiving a PRS, performs 1420 measurements on the PRS and determines the LOS indicator for the target TRP. Thus, the second apparatus transmits acknowledgement of the test configuration information to the first apparatus and the second apparatus triggers the test as requested.
It would have been obvious for one of ordinary skill in the art, before the effective filing date of the claimed invention, to combine Shah and Hasegawa to include the technique of transmitting, to the first apparatus, acknowledgement of the test configuration information, to trigger the test plan to be executed of Hasegawa in the system of Shah to provide the verification procedure for AIML position and measurement procedure for wireless network by increasing the reliability of the LOS indicators. (Hasegawa, see Paragraphs [0003] and [0084]).).
Regarding claim 19, combination of Shah and Hasegawa teaches the features defined in the claim 16, -refer to the indicated claim for reference(s).
Shah further teaches that measuring reference signals transmitted from the at least one TRP; and
providing the measurement results of reference signals as inputs to the AI/ML model to derive the at least one predicted channel indicator (Shah, in Fig. 20 and Paragraphs [0367]-[0382], teaches that in Fig. 20 shows the AI/ML-assisted position estimation method. In Paragraph [0368], at 2005, the first device transmits (the second device receives) AI/ML Model or Model indicator, TRP location information, and LOS indicators. The first device transmits one or more models (modes) for AI/ML assisted positioning, such as a single TRP fingerprinting model or multi TRP fingerprinting model. As described in Paragraph [0369], the first device transmits TRP location information (TRP arrangement information in the environment) that is associated with each TRP according to a TRP identifier. Further, as described in Paragraphs [0373]-[0375], at 2015, the PRS (Position Reference Signal) configuration is received by the second apparatus and it is transmitted by a TRP, a network device such as LMF, gNB, etc, or as a response to the request message from the second apparatus with TRP locations, LOS indicators, and corner identifiers. At 2020, the PRS signal is measured from a plurality of TRP’s, in serial or in parallel and the PRS measurement is used as AI/ML model training for NLOS identification (predicted channel indicator) as described in Paragraph [0271].)
Conclusion
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/JAEYOUNG KWAK/Examiner, Art Unit 2472
/KEVIN T BATES/Supervisory Patent Examiner, Art Unit 2472
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