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
Amendment filed on 07/23/2026 is acknowledged. As a result, claims 1-20 are pending for examination.
Response to Arguments
Applicant’s arguments with respect to claims 1,19 and 20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. See below detailed rejection with new reference.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 19 and 20 and all their dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, the claim language recites “whether to use an artificial intelligence network model or an artificial intelligence network model parameter”. Since this is alternative language (using “or”), examiner may choose one of the two, e.g. examiner chooses model parameter. As a result, the later cited “whether to use the artificial intelligence network model to obtain” has lack of antecedent basis since the network model was not chosen.
In addition, the scope of “artificial intelligence network model (hereinafter a) or an artificial intelligence network model parameter (hereinafter b)” is uncertain as examiner is uncertain if 1) a and b must exist and the device has to choose either a or b; or 2) device is configured to have the capability to handle either a or b.
Fort above reasons, it is rejected under 112(b) for being indefinite.
Regarding Claim 19, the above mentioned reasonings apply here and therefore is rejected.
Regarding Claim 20, the above mentioned reasonings apply here and therefore is rejected.
Their dependent claims are rejected as they are indirectly or directly dependent upon the rejected base claim.
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 1,2, 9-10, 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over PEZESHKI et al. (US 20220294548 A1, hereinafter Pezeshki) in view of Sundararajan et al. (US 11889458 B2, hereinafter Sundararajan)
Regarding Claim 1, Pezeshki teaches a location method comprising,
determining, by a first communication device according to first information, whether to use an artificial intelligence network model or an artificial intelligence network model parameter;
(Pezeshki [0083, line 4] if a base station has gone through training a neural network with a first UE (e.g., UE 1), the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[0093 and FIG. 7] At 736, if a receiving UE receives the transmitted/broadcasted ML model parameter, the receiving UE may determine whether the ML model parameters are applicable to the receiving UE and/or the receiving UE may apply the transmitted/broadcasted ML model parameters. For example, after the second UE 706 receives the ML model parameters from the base station 702, the second UE 706 may apply the ML model parameters received after determining they are applicable to the second UE 706. As such, the second UE 706 may apply the received ML model parameters, or may determine that the received ML model parameters are applicable to it, based at least in part on the location of the second UE 706 being within the corresponding zone ID for the set of ML model parameters.)
(Note: “second UE” in the reference reads onto the “a first communication device” and received “transmitted/broadcasted ML model parameter” disclosed in the reference reads onto the “first information” in the claim limitation.)
or determining, by a first communication device according to the first information, an artificial intelligence network model or artificial intelligence network model parameter to be used
(Note: Examiner is required to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
wherein the artificial intelligence network model is
(Pezeshki [0083, line 6] the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[FIG. 7, 726 and 732] determines the artificial intelligence network model is used to obtain or optimize positioning signal measurement information of a target terminal UE2.)
or location information of the target terminal
(Pezeshki [0035] In certain aspects, the base station 102/180 may include a neural network sharing component 199 configured to receive and share trained neural network parameters from a UE with other UE(s) based on zone IDs, or other location information, associated with the UEs.)
the first information comprises configuration information
(Pezeshki [0101] Aspects presented herein may enable zone IDs to be associated with machine learnings, where each zone ID may have its own corresponding neural network configuration (ML parameters). As such, in some examples, a transition procedure may be defined for a neural network device (e.g., a UE with trained ML model(s) or ML model parameter(s)) when the neural network device is moving along different zone IDs. For example, a neural network device may be configured with a transition procedure that autonomously know to update the ML parameters when neural network device calculates or detects that the Zone ID has changed, where the new zone ID may have a different set of ML parameters according to the aforementioned tables (e.g., the Tables 728, 730, 828, 830))
the first information comprises configuration information , wherein the configuration information is used for indicating whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information of the target terminal or the location information of the target terminal,
(Pezeshki [0037] In certain aspects, the ML model sharing and application component 198 is configured to transmit/share trained neural network (e.g., trained ML parameters) with a base station. In one configuration, the ML model sharing and application component 198 may be configured to transmit, to a base station, a set of ML model parameters. In such configuration, the ML model sharing and application component 198 may transmit, to the base station, position information for the UE associated with the set of ML model parameters.
[0093 and FIG. 7] At 736, if a receiving UE receives the transmitted/broadcasted ML model parameter, the receiving UE may determine whether the ML model parameters are applicable to the receiving UE and/or the receiving UE may apply the transmitted/broadcasted ML model parameters.)
However, Pezeshki fails to teach
and reference information of a reference terminal
and the reference information of the reference terminal comprises location information of the reference terminal and measurement information of the reference terminal;
However, in a similar endeavor, Sundararajan teaches
and reference information of a reference terminal,
(Sundararajan [Col. 28, line 64] To support position estimates, the base stations 602 may be configured to broadcast reference RF signals.)
(Note: base station 602 is considered to be the “reference terminal” in the claim limitation.)
and the reference information of the reference terminal comprises location information of the reference terminal and measurement information of the reference terminal;
(Sundararajan [Col. 2, lines 16-18] In some aspects, the obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.
[Col. 28, line 64] To support position estimates, the base stations 602 may be configured to broadcast reference RF signals.)
(Note: base station 602 is considered to be the “reference terminal” in the claim limitation.)
Or the first information comprises configuration information and at least one of line of sight (LOS) indication information or environmental information of the target terminal, wherein the configuration information is used for indicating whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information of the target terminal or the location information of the target terminal.
(Note: Examiner is required to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki by incorporating Sundararajan to arrive at the invention.
The motivation of doing so would have enabled the determination of using AI/ML model or model parameter in position and location measurement accurately.
Regarding Claim 2, the combination of Pezeshki and Sundararajan teaches claim 1 and Pezeshki further teaches wherein the first information further comprises at least one of the following:
a preset condition;
a preset event;
priority information, wherein the priority information is used for agreeing event, condition or cell-defaulted or initially-activated or preferentially-used artificial intelligence network models or artificial intelligence network model parameters;
positioning signal measurement information of the target terminal;
or location information of the target terminal
(Pezeshki [0035] In certain aspects, the base station 102/180 may include a neural network sharing component 199 configured to receive and share trained neural network parameters from a UE with other UE(s) based on zone IDs, or other location information, associated with the UEs.)
(Note: Claim language requires examiner to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
Regarding Claim 9, the combination of Pezeshki and Sundararajan teaches the method of Claim 1 and Sundararajan further teaches, reporting, by the first communication device according to the first information, third information, wherein the third information comprises at least one of the following:
the positioning signal measurement information of the target terminal;
(Sundararajan [Column 2, line 54] In some aspects, the processing processes the positioning measurement data into a probability distribution associated with the set of positioning measurement features based on the at least one neural network function, and the reporting reports the probability distribution or metrics based on the probability distribution.)
the location information of the target terminal; error information, wherein the error information comprises at least one of the following: a location error value, a measurement error value, an artificial intelligence network model error value, or a parameter error value; indication information, used for indicating whether the positioning signal measurement information or the location information reported by the target terminal is obtained or optimized by using the artificial intelligence network model; or information of the artificial intelligence network model or artificial intelligence network model parameter;
(Note: Claim language requires examiner to find one of the five limitations in the reference. Examiner has elaborated one which are considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki by incorporating Sundararajan positioning signal measurement information of the target terminal.
The motivation of doing so would have identified the target terminal with more accuracy.
Regarding Claim 10 and as applied to Claim 9, Sundararajan further teaches reporting, by the first communication device, associated information of the LOS indication information, wherein the associated information comprises at least one of the following:
an LOS confidence;
(Sundararajan [Column 31, line 7] whereas the beams of interest for position estimation will be the beams carrying RF signals that excite the shortest path or LOS path (e.g., an LOS path 610).)
or second information for determining the LOS indication information.
(Note: Claim language requires examiner to find one of the two limitations in the reference. Examiner has elaborated one which is considered be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki by incorporating Sundararajan to get LOS indication information within the acquired information in claim 9.
The motivation of doing so would have further increased the accuracy of determination of the target terminal.
Regarding Claim 16, the combination of Pezeshki and Sundararajan teaches the method of Claim 1 and Sundararajan further teaches wherein the reference information of the reference terminal further comprises at least one of the following: identification information of the reference terminal;
(Sundararajan [Col. 29, line 22] A location server (e.g., location server 230) may send assistance data to the UE 604 that includes an identification of one or more neighbor cells of base stations 602 and configuration information for reference RF signals transmitted by each neighbor cell.)
error information of the reference terminal;
an artificial intelligence network model used by the reference terminal;
or an artificial intelligence network model parameter used by the reference terminal.
(Note: Claim language requires examiner to find one of the six limitations in the reference. Examiner has elaborated one which are considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki by incorporating Sundararajan to get LOS indication information within the acquired information in claim 9.
The motivation of doing so would have further enhanced the accuracy of determination of the target terminal.
Regarding Claim 18, the combination of Pezeshki and Sundararajan teaches the method of Claim 1 and Pezeshki further teaches
reporting, by the first communication device, capability information, wherein the capability information comprises at least one of the following: whether to support an artificial intelligence network model or an artificial intelligence network model parameter;
(Pezeshki [0083, line 4] if a base station has gone through training a neural network with a first UE (e.g., UE 1), the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[0093] At 736, if a receiving UE receives the transmitted/broadcasted ML model parameter, the receiving UE may determine whether the ML model parameters are applicable to the receiving UE and/or the receiving UE may apply the transmitted/broadcasted ML model parameters. For example, after the second UE 706 receives the ML model parameters from the base station 702, the second UE 706 may apply the ML model parameters received after determining they are applicable to the second UE 706.)
whether to support a plurality of artificial intelligence network models or a plurality of sets of artificial intelligence network model parameters; or whether to support using an artificial intelligence network model or an artificial intelligence network model parameter to obtain or optimize positioning signal measurement information.
(Note: Claim language requires examiner to find one of the three limitations in the reference. Examiner has elaborated one which are considered to be sufficient)
Regarding Claim 19, Pezeshki teaches a communication device, comprising a memory storing a computer program;
(Pezeshki [0062] The controller/processor 359 can be associated with a memory 360 that stores program codes and data.
determining, by a first communication device according to first information, whether to use an artificial intelligence network model or an artificial intelligence network model parameter;
(Pezeshki [0083, line 4] if a base station has gone through training a neural network with a first UE (e.g., UE 1), the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[0093 and FIG. 7] At 736, if a receiving UE receives the transmitted/broadcasted ML model parameter, the receiving UE may determine whether the ML model parameters are applicable to the receiving UE and/or the receiving UE may apply the transmitted/broadcasted ML model parameters. For example, after the second UE 706 receives the ML model parameters from the base station 702, the second UE 706 may apply the ML model parameters received after determining they are applicable to the second UE 706. As such, the second UE 706 may apply the received ML model parameters, or may determine that the received ML model parameters are applicable to it, based at least in part on the location of the second UE 706 being within the corresponding zone ID for the set of ML model parameters.)
(Note: “base station 702” in the reference reads onto the “a first communication device” and received “transmitted/broadcasted ML model parameter” disclosed in the reference reads onto the “first information” in the claim limitation.)
or determining, by a first communication device according to the first information, an artificial intelligence network model or artificial intelligence network model parameter to be used
(Note: Examiner is required to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
wherein the artificial intelligence network model is
(Pezeshki [0083, line 6] the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[FIG. 7, 726 and 732] determines the artificial intelligence network model is used to obtain or optimize positioning signal measurement information of a target terminal UE2.)
or location information of the target terminal
(Pezeshki [0035] In certain aspects, the base station 102/180 may include a neural network sharing component 199 configured to receive and share trained neural network parameters from a UE with other UE(s) based on zone IDs, or other location information, associated with the UEs.)
the first information comprises configuration information
(Pezeshki [0101] Aspects presented herein may enable zone IDs to be associated with machine learnings, where each zone ID may have its own corresponding neural network configuration (ML parameters). As such, in some examples, a transition procedure may be defined for a neural network device (e.g., a UE with trained ML model(s) or ML model parameter(s)) when the neural network device is moving along different zone IDs. For example, a neural network device may be configured with a transition procedure that autonomously know to update the ML parameters when neural network device calculates or detects that the Zone ID has changed, where the new zone ID may have a different set of ML parameters according to the aforementioned tables (e.g., the Tables 728, 730, 828, 830))
the first information comprises configuration information , wherein the configuration information is used for indicating whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information of the target terminal or the location information of the target terminal,
(Pezeshki [0037] In certain aspects, the ML model sharing and application component 198 is configured to transmit/share trained neural network (e.g., trained ML parameters) with a base station. In one configuration, the ML model sharing and application component 198 may be configured to transmit, to a base station, a set of ML model parameters. In such configuration, the ML model sharing and application component 198 may transmit, to the base station, position information for the UE associated with the set of ML model parameters.)
However, Pezeshki fails to teach
and reference information of a reference terminal
and the reference information of the reference terminal comprises location information of the reference terminal and measurement information of the reference terminal;
However, in a similar endeavor, Sundararajan teaches
and reference information of a reference terminal,
(Sundararajan [Col. 28, line 64] To support position estimates, the base stations 602 may be configured to broadcast reference RF signals.)
(Note: base station 602 is considered to be the “reference terminal” in the claim limitation.)
and the reference information of the reference terminal comprises location information of the reference terminal and measurement information of the reference terminal;
(Sundararajan [Col. 2, lines 16-18] In some aspects, the obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.
[Col. 28, line 64] To support position estimates, the base stations 602 may be configured to broadcast reference RF signals.)
(Note: base station 602 is considered to be the “reference terminal” in the claim limitation.)
Or the first information comprises configuration information and at least one of line of sight (LOS) indication information or environmental information of the target terminal, wherein the configuration information is used for indicating whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information of the target terminal or the location information of the target terminal.
(Note: Examiner is required to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki by incorporating Sundararajan to arrive at the invention.
The motivation of doing so would have enabled the determination of using AI/ML model or model parameter in position and location measurement accurately.
Regarding Claim 20, Pezeshki teaches A non-transitory computer-readable storage medium, storing a computer program
Pezeshki [0033] Accordingly, in one or more example embodiments, the functions described may be implemented in hardware, software, or any combination thereof. If implemented in software, the functions may be stored on or encoded as one or more instructions or code on a computer-readable medium. Computer-readable media includes computer storage media.)
when the computer program is executed by a processor of a communication device, causes the processor to perform operations comprising
(Pezeshki[0059] FIG. 3 is a block diagram of a base station 310 in communication with a UE 350 in an access network. In the DL, IP packets from the EPC 160 may be provided to a controller/processor 375.)
determining, by a first communication device according to first information, whether to use an artificial intelligence network model or an artificial intelligence network model parameter;
(Pezeshki [0083, line 4] if a base station has gone through training a neural network with a first UE (e.g., UE 1), the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[0093 and FIG. 7] At 736, if a receiving UE receives the transmitted/broadcasted ML model parameter, the receiving UE may determine whether the ML model parameters are applicable to the receiving UE and/or the receiving UE may apply the transmitted/broadcasted ML model parameters. For example, after the second UE 706 receives the ML model parameters from the base station 702, the second UE 706 may apply the ML model parameters received after determining they are applicable to the second UE 706. As such, the second UE 706 may apply the received ML model parameters, or may determine that the received ML model parameters are applicable to it, based at least in part on the location of the second UE 706 being within the corresponding zone ID for the set of ML model parameters.)
(Note: “base station 702” in the reference reads onto the “a first communication device” and received “transmitted/broadcasted ML model parameter” disclosed in the reference reads onto the “first information” in the claim limitation.)
or determining, by a first communication device according to the first information, an artificial intelligence network model or artificial intelligence network model parameter to be used
(Note: Examiner is required to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
wherein the artificial intelligence network model is
(Pezeshki [0083, line 6] the first UE may upload/share the trained neural network (e.g., the trained ML model and/or ML model parameters) to the base station, and then the base station may transmit/share information about the trained neural network (e.g., trained neural network parameters) with a second UE (e.g., UE 2).
[FIG. 7, 726 and 732] determines the artificial intelligence network model is used to obtain or optimize positioning signal measurement information of a target terminal UE2.)
or location information of the target terminal
(Pezeshki [0035] In certain aspects, the base station 102/180 may include a neural network sharing component 199 configured to receive and share trained neural network parameters from a UE with other UE(s) based on zone IDs, or other location information, associated with the UEs.)
the first information comprises configuration information
(Pezeshki [0101] Aspects presented herein may enable zone IDs to be associated with machine learnings, where each zone ID may have its own corresponding neural network configuration (ML parameters). As such, in some examples, a transition procedure may be defined for a neural network device (e.g., a UE with trained ML model(s) or ML model parameter(s)) when the neural network device is moving along different zone IDs. For example, a neural network device may be configured with a transition procedure that autonomously know to update the ML parameters when neural network device calculates or detects that the Zone ID has changed, where the new zone ID may have a different set of ML parameters according to the aforementioned tables (e.g., the Tables 728, 730, 828, 830))
the first information comprises configuration information , wherein the configuration information is used for indicating whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information of the target terminal or the location information of the target terminal,
(Pezeshki [0037] In certain aspects, the ML model sharing and application component 198 is configured to transmit/share trained neural network (e.g., trained ML parameters) with a base station. In one configuration, the ML model sharing and application component 198 may be configured to transmit, to a base station, a set of ML model parameters. In such configuration, the ML model sharing and application component 198 may transmit, to the base station, position information for the UE associated with the set of ML model parameters.)
However, Pezeshki fails to teach
and reference information of a reference terminal
and the reference information of the reference terminal comprises location information of the reference terminal and measurement information of the reference terminal;
However, in a similar endeavor, Sundararajan teaches
and reference information of a reference terminal,
(Sundararajan [Col. 28, line 64] To support position estimates, the base stations 602 may be configured to broadcast reference RF signals.)
(Note: base station 602 is considered to be the “reference terminal” in the claim limitation.)
and the reference information of the reference terminal comprises location information of the reference terminal and measurement information of the reference terminal;
(Sundararajan [Col. 2, lines 16-18] In some aspects, the obtaining obtains the positioning measurement data by performing a set of positioning measurements on a reference signal for positioning.
[Col. 28, line 64] To support position estimates, the base stations 602 may be configured to broadcast reference RF signals.)
(Note: base station 602 is considered to be the “reference terminal” in the claim limitation.)
Or the first information comprises configuration information and at least one of line of sight (LOS) indication information or environmental information of the target terminal, wherein the configuration information is used for indicating whether to use the artificial intelligence network model to obtain or optimize the positioning signal measurement information of the target terminal or the location information of the target terminal.
(Note: Examiner is required to find at least one limitation in the reference. Examiner has elaborated one and that is considered to be sufficient.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki by incorporating Sundararajan to arrive at the invention.
The motivation of doing so would have enabled the determination of using AI/ML model or model parameter in position and location measurement accurately.
Claims 3-6, 8, 11, 12-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over PEZESHKI et al. (CN116074813A, hereinafter Pezeshki) in view of Sundararajan et al. (US 11889458 B2, hereinafter Sundararajan) and in further view of YERRAMALLI et al. (US 20210321221 A1, hereinafter Yerramalli)
Regarding Claim 3, the combination of Pezeshki and Sundararajan teach the method of Claim 1 but fail to teach wherein the positioning signal measurement information of the target terminal comprises at least one of the following: channel response information of a positioning signal; a reference signal time difference (RSTD) measurement result; round trip time (RTT); multi-round trip time; an angle of arrival (AOA) measurement result; an angle of departure (AOD) measurement result; or reference signal received power (RSRP).
However, in a similar endeavor, Yerramalli teaches wherein the positioning signal measurement information of the target terminal comprises at least one of the following: channel response information of a positioning signal;
(Yerramalli [0008, line 3] The reference signal information may be a positioning reference signal. The reference signal information may be a channel state information reference signal. [Fig 13B, 1324])
a reference signal time difference (RSTD) measurement result; round trip time (RTT);
(Yerramalli [0050, line 5] the location measurements may include one or more of a Received Signal Strength Indication (RSSI), Round Trip signal propagation Time (RTT), Reference Signal Time Difference (RSTD))
multi-round trip time; an angle of arrival (AOA) measurement result; an angle of departure (AOD) measurement result; or reference signal received power (RSRP).
(Yerramalli [0075] One or more of many different techniques may be used to determine position of an entity such as the UE 105. For example, known position-determination techniques include RTT, multi-RTT
[0077, line 10] angle of arrival (AoA) measured by the base station 502.
[0115, line 8] line the output 816 may include an angle of arrival estimate or an angle of departure estimate.
[0050, line 8] Reference Signal Received Power (RSRP))
(Note: Claim language requires examiner to find one of the seven limitations in the reference. Examiner has elaborated all seven)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the terminal to utilize any of the mentioned measurements that can help to select, update or send a better AI/ML model or parameters.
Regarding Claim 4 and as applied to Claim 2, the combination of Pezeshki and Sundararajan fail to teach wherein the positioning signal measurement information is associated with or comprises at least one piece of LOS indication information.
However, in a similar endeavor, Yerramalli further teaches wherein the positioning signal measurement information is associated with or comprises at least one piece of LOS indication information.
(Yerramalli [0077] Referring to FIG. 6, with further reference to FIG. 5, a conceptual diagram of an example position determination based on a line of sight signal is shown. LOS delay estimation is the first step in positioning for several methods such as ToA, TDoA and RTT based methods.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the system to have one LOS information while determining the positioning signal measurement for accuracy.
Regarding Claim 5 and as applied to Claim 2, the combination of Pezeshki and Sundararajan fail to teach wherein the positioning signal measurement information comprises positioning signal measurement information of at least one path; or the positioning signal measurement information comprises at least one of the following: angle information of a path ;time information of a path; energy information of a path; or LOS indication information.
However, in a similar endeavor, Yerramalli further teaches wherein the positioning signal measurement information comprises positioning signal measurement information of at least one path;
(Yerramalli [0077, line 4] The diagram 500 includes a simplified multi-path scenario where the base station 502 and the UE 105 may communicate with one another via a LOS path 506 or one or more non-LOS (NLOS) paths such as a first NLOS path 508 and a second NOS path 510.)
or the positioning signal measurement information comprises at least one of the following: angle information of a path;
(Yerramalli [0077, line 6] The position of the UE 105 along the circumference of the circle 602 may be based on uplink (UL) angle of arrival (AoA) measured by the base station 502. [0095] While the output 816 in FIG. 8 indicates a LOS delay estimate, the NN 800 architecture may be adapted for other inputs such as angle of arrival (AoA) and/or angle of departure (AoD) estimates.)
time information of a path;
(Yerramalli [0077, line 3] LOS delay estimation is the first step in positioning for several methods such as ToA, TDoA and RTT based methods.)
energy information of a path;
(Yerramalli [0086, line 19] In general, in realistic channels, a large fraction of the energy in the CIR is contained within a few time-domain samples.)
or LOS indication information.
(Yerramalli [0077] Referring to FIG. 6, with further reference to FIG. 5, a conceptual diagram of an example position determination based on a line of sight signal is shown. LOS delay estimation is the first step in positioning for several methods such as ToA, TDoA and RTT based methods. For example, the LOS delay associated with the LOS path 506 may be used to determine a radius of a circle 602 around the base station 502.)
(Note: Claim language requires examiner to find one of the five limitations in the reference. Examiner has elaborated all five)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the system to switch between different AI/ML models depending on propagation conditions.
Regarding Claim 6, the combination of Pezeshki and Sundararajan teach the method of claim 1 but fail to teach wherein the LOS indication information is used for indicating one of the following: an LOS condition between the target terminal and a target transmitting receiving point (TRP); an LOS condition of the target terminal; or an LOS condition between one or more location reference signal resources of the target terminal and a target TRP.
However, in a similar endeavor, Yerramalli further teaches wherein the LOS indication information is used for indicating one of the following: an LOS condition between the target terminal and a target transmitting receiving point (TRP);
(Yerramalli [0076] Referring to FIG. 5, with further reference to FIGS. 1-4, a conceptual diagram 500 of an example line of sight between a base station 502 and a mobile device (e.g., the UE 105) is shown. The base station may be a TRP 300 such as the eNB 110a. [76, line 13] The diagram 500 includes a simplified multi-path scenario where the base station 502 and the UE 105 may communicate with one another via a LOS path 506)
an LOS condition of the target terminal;
(Yerramalli [0076, line 13] The diagram 500 includes a simplified multi-path scenario where the base station 502 and the UE 105 may communicate with one another via a LOS path 506)
or an LOS condition between one or more location reference signal resources of the target terminal and a target TRP.
(Note: Claim language requires examiner to find one of the three limitations in the reference. Examiner has elaborated all three.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the system to switch between different AI/ML models depending on whether the radio path is LOS or NLOS.
Regarding Claim 8 and as applied to Claim 2, the combination of Pezeshki and Sundararajan fail to teach wherein the determining, by a first communication device, an artificial intelligence network model or an artificial intelligence network model parameter according to first information further comprises: determining, by the terminal, LOS indication information based on a second artificial intelligence network model.
However, in a similar endeavor, Yerramalli further teaches wherein the determining, by a first communication device, an artificial intelligence network model or an artificial intelligence network model parameter according to first information further comprises: determining, by the terminal, LOS indication information based on a second artificial intelligence network model.
(Yerramalli [0095] While the output 816 in FIG. 8 indicates a LOS delay estimate, the NN 800 architecture may be adapted for other inputs such as angle of arrival (AoA) and/or angle of departure (AoD) estimates. [0089] Referring to FIG. 8, with further reference to FIG. 7, a block diagram of an example neural network (NN) 800 for determining a line of sight delay estimate 816 is shown.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the system to switch between different AI/ML models depending on whether the radio path is LOS or NLOS.
Regarding Claim 11, the combination of Pezeshki and Sundararajan teach the method of Claim 1 but fail to teach wherein the artificial intelligence network model parameter comprises at least one of the following: a structure of the artificial intelligence network model; a multiplicative coefficient, an additive coefficient, or an activation function of each neuron of the artificial intelligence network model; complexity information of the artificial intelligence network model; an expected training number of the artificial intelligence network model; an application document of the artificial intelligence network model; an input format of the artificial intelligence network model; or an output format of the artificial intelligence network model.
However, in a similar endeavor, Yerramalli teaches
wherein the artificial intelligence network model parameter comprises at least one of the following: a structure of the artificial intelligence network model;
(Yerramalli [0024] FIG. 7 is a flow diagram of an example process for generating a channel impulse response input for a neural network. [0026] FIG. 9A is a block diagram of an example pointwise convolution layer in a neural network.
[0027] FIG. 9B is a block diagram of an example depth wise convolution layer in a neural network. [0029] FIG. 11 includes example message flows between a base station and a mobile device for retraining a neural network.
[0030] FIG. 12 is an example data structure for neural network models.)
a multiplicative coefficient, an additive coefficient, or an activation function of each neuron of the artificial intelligence network model;
complexity information of the artificial intelligence network model;
an expected training number of the artificial intelligence network model;
an application document of the artificial intelligence network model;
an input format of the artificial intelligence network model;
or an output format of the artificial intelligence network model.
(Note: Claim language requires examiner to find one of the 7 limitations in the reference. Examiner has elaborated one which is considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the system to decide when and what AI/ML to use.
Regarding Claim 12 and as applied to Claim 2, the combination of Pezeshki and Sundararajan fail to teach wherein the determining, by a first communication device according to the first information, an artificial intelligence network model or artificial intelligence network model parameter to be used
However, in a similar endeavor, Yerramalli further teaches wherein the determining, by a first communication device according to the first information, an artificial intelligence network model or artificial intelligence network model parameter to be used
(Yerramalli [0008, line 8] Determining the neural network may be based at least in part on a receiver configuration. Refer to [Fig 10, 1008]) ([0008, line 12] Determining the neural network may include transmitting the neural network information from a network to a mobile device. Refer to [Fig 10, 1010] Determining the neural network may include transmitting an indication of a selected neural network from a list of neural networks available at a mobile device. Refer to [Fig 10, 1016]) [0007, line 4] determining a channel frequency response or a channel impulse response based on the reference signal information, processing the channel frequency response or the channel impulse response with a neural network, and determining the line of sight delay, the angle of arrival, or the angle of departure value based on an output of the neural network. Refer to [Fig 14, 1404]
[Note: neural network is a subset of machine learning, which is, in turn, a subset of Artificial Intelligence.])
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the adaptive choice of AI approach.
Regarding Claim 13 and as applied to Claim 12, the combination of Pezeshki and Sundararajan fail to teach wherein the indicating, configuring or activating, by the first communication device, a target artificial intelligence network model or a target artificial intelligence network model parameter according to the first information comprises one of the following: indicating, configuring or activating, by the first communication device, a first target artificial intelligence network model or a first target artificial intelligence network model parameter in response to the LOS indication information indicating being an LOS; indicating, configuring or activating, by the first communication device, a second target artificial intelligence network model or a second target artificial intelligence network model parameter in response to the LOS indication information indicating being an NLOS; indicating, configuring or activating, by the first communication device, the first target artificial intelligence network model or the first target artificial intelligence network model parameter in response to that a probability of being an LOS indicated by the LOS indication information is greater than or equal to a first threshold; or indicating, configuring or activating, by the first communication device, the second target artificial intelligence network model or the second target artificial intelligence network model parameter in response to that a probability of being an LOS indicated by the LOS indication information is less than or equal to a second threshold.
However, in a similar endeavor, Yerramalli teaches
wherein the indicating, configuring or activating, by the first communication device, a target artificial intelligence network model or a target artificial intelligence network model parameter according to the first information comprises one of the following: indicating, configuring or activating, by the first communication device, a first target artificial intelligence network model or a first target artificial intelligence network model parameter in response to the LOS indication information indicating being an LOS;
(Yerramalli [0034] Techniques are discussed herein for neural network based positioning of a mobile device. For example, the disclosure addresses the problem of accurate line-of-sight (LOS) delay estimation in a wireless channel using deep neural networks (NN), which can be used as a building block to derive accurate position estimates. A NN may be used to exploit the properties of the wireless channel to estimate a LOS delay. The proposed NN shows improved performance in the presence of weak LOS signals and dense multipath, which are typically challenging scenarios for traditional signal processing algorithms.)
indicating, configuring or activating, by the first communication device, a second target artificial intelligence network model or a second target artificial intelligence network model parameter in response to the LOS indication information indicating being an NLOS;
indicating, configuring or activating, by the first communication device, the first target artificial intelligence network model or the first target artificial intelligence network model parameter in response to that a probability of being an LOS indicated by the LOS indication information is greater than or equal to a first threshold;
or indicating, configuring or activating, by the first communication device, the second target artificial intelligence network model or the second target artificial intelligence network model parameter in response to that a probability of being an LOS indicated by the LOS indication information is less than or equal to a second threshold.
(Note: Claim language requires examiner to find one of the four limitations in the reference. Examiner has elaborated one which is considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the system to switch between different AI/ML models depending on whether the radio path is LOS.
Regarding Claim 14 and as applied to Claim 2, the combination of Pezeshki and Sundararajan fail to teach wherein the preset condition comprises at least one of the following: a channel model is an LOS; a probability of an LOS is greater than or equal to a first threshold; an RSRP of a target cell is greater than or equal to a third threshold; an Rx Timing or TOA of the target cell is less than or equal to a fourth threshold; a difference between the Rx Timing or TOA of the target cell and a serving cell is less than or equal to a fifth threshold; a multi-path distribution satisfies a first condition; a related bandwidth is greater than or equal to a sixth threshold; or a multi-antenna measurement result satisfies a second condition; or, the preset condition comprises at least one of the following: a channel model is an NLOS; a probability of an LOS is less than or equal to a second threshold; an RSRP of a target cell is less than or equal to a seventh threshold; an Rx Timing or TOA of the target cell is greater than or equal to an eighth threshold; a difference between the Rx Timing or TOA of the target cell and a serving cell is greater than or equal to a ninth threshold; a multi-path distribution does not satisfy a first condition; a related bandwidth is less than or equal to a tenth threshold; or a multi-antenna measurement result does not satisfy a second condition.
However, in a similar endeavor Yerramalli teaches wherein the preset condition comprises at least one of the following: a channel model is an LOS;
(Yerramalli [0034, line 6] A NN may be used to exploit the properties of the wireless channel to estimate a LOS delay. The proposed NN shows improved performance in the presence of weak LOS signals.
[0077, line 10] The NN based estimator described herein provides improved LOS delay estimation for a variety of weak LOS and multipath scenarios as compared to conventional algorithms such as Matrix Pencil delay estimation and Threshold peak detection and interpolation.)
a probability of an LOS is greater than or equal to a first threshold; an RSRP of a target cell is greater than or equal to a third threshold;
an Rx Timing or TOA of the target cell is less than or equal to a fourth threshold;
a difference between the Rx Timing or TOA of the target cell and a serving cell is less than or equal to a fifth threshold;
a multi-path distribution satisfies a first condition;
(Yerramalli [0076, line 13] line The diagram 500 includes a simplified multi-path scenario where the base station 502 and the UE 105 may communicate with one another via a LOS path 506 or one or more non-LOS (NLOS) paths such as a first NLOS path 508 and a second NOS path 510.)
a related bandwidth is greater than or equal to a sixth threshold; or
a multi-antenna measurement result satisfies a second condition; or
the preset condition comprises at least one of the following: a channel model is an NLOS;
a probability of an LOS is less than or equal to a second threshold;
an RSRP of a target cell is less than or equal to a seventh threshold;
an Rx Timing or TOA of the target cell is greater than or equal to an eighth threshold;
a difference between the Rx Timing or TOA of the target cell and a serving cell is greater than or equal to a ninth threshold;
a multi-path distribution does not satisfy a first condition;
a related bandwidth is less than or equal to a tenth threshold;
or a multi-antenna measurement result does not satisfy a second condition.
(Note: Claim language requires examiner to find one of the 18 limitations in the reference. Examiner has elaborated two which are considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan by incorporating Yerramalli to arrive at the invention.
The motivation of doing so would have enabled the communication device to choose, activate, configure, or update an AI network model or model parameters based on first information such as LOS/NLOS indication, environmental classification, preset events, or configuration rules.
Regarding Claim 15, the combination of Pezeshki and Sundararajan teach the method of Claim 2 and Sundararajan further teaches wherein the preset event comprises at least one of the following:
a quality of service (QoS) event;
(Sundararajan [Column 16, line 56] Functions of the UPF include acting as an anchor point for intra-/inter-RAT mobility (when applicable), acting as an external protocol data unit (PDU) session point of interconnect to the data network (not shown), providing packet routing and forwarding, packet inspection, user plane policy rule enforcement (e.g., gating, redirection, traffic steering), lawful interception (user plane collection), traffic usage reporting, quality of service (QoS) handling for the user plane.)
a periodic event;
an event in which an absolute location variance is greater than or equal to an eleventh threshold;
an event in which a multi-measurement variance is greater than or equal to a twelfth threshold;
a radio link failure (RLF) event; a radio resource management (RRM) event;
a beam failure (BF) event;
a beam failure recover (BFR) event;
timing measurement;
(Yerramalli [0053] Information provided by the gNBs 110a, 110b, and/or the ng-eNB 114 to the LMF 120 using NRPPa may include timing and configuration information.)
timing advance (TA) measurement;
(Yerramalli [0075, line 31] E-CID uses the identity of a serving cell, the timing advance (i.e., the difference between receive and transmit times at the UE), estimated timing and power of detected neighbor cell signals, and possibly angle of arrival (e.g., of a signal at the UE from the base station or vice versa) to determine location of the UE.)
an event in which a round trip time (RTT) measurement error or variance is excessive;
an event in which an observed time difference of arrival (OTDOA) measurement error or variance is excessive;
an event in which a time difference of arrival (TDOA) measurement error or variance is excessive;
an event in which an RSRP measurement error or variance is excessive;
an event in which an RSRP measurement is lower than a thirteenth threshold;
an event in which a measurement error or variance of a reference terminal is excessive;
report failure of a reference terminal;
or an event in which a location error or variance of a reference terminal is excessive.
(Note: Claim language requires examiner to find one out of eighteen limitations. Examiner has elaborated three which are considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan and by incorporating Yerramalli by incorporating Sundararajan to identify the present event with one of the several events disclosed.
The motivation of doing so would have enhance the information gathering process for calculating better position of the target terminal.
Regarding Claim 17 and as applied to Claim 2, Sundararajan further teaches wherein the priority information comprises at least one of the following:
preferential use of first-ranked artificial intelligence network models or artificial intelligence network model parameters;
(Sundararajan [column 33, line 61] In some designs, such aspects may facilitate various technical advantages, such as more flexibility in terms of feature reporting (e.g., need not use a set of pre-defined feature rules, such that network can generate more sophisticated rules based on operating conditions, device information or configuration, etc.) as well as more accurate UE positioning estimates while also keeping signaling overhead at a manageable level (e.g., by using ML-techniques to filter out certain positioning measurement data in a manner that does not introduce inaccuracies in a UE positioning estimate))
preferential use of specified artificial intelligence network models or artificial intelligence network model parameters;
preferential use of associated artificial intelligence network models or artificial intelligence network model parameters;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having small identifiers (ID);
preferential use of artificial intelligence network models or artificial intelligence network model parameters having large IDs;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having large data volume;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having small data volume;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having complex model structures;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having simple model structures;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having many model layers;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having few model layers;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having high quantification levels; preferential use of artificial intelligence network models or artificial intelligence network model parameters having low quantification levels;
preferential use of artificial intelligence network models or artificial intelligence network model parameters having fully connected neural network structures; or
preferential use of artificial intelligence network models or artificial intelligence network model parameters having convolutional neural network structures.
(Note: Claim language requires examiner to find one of the fifteen limitations in the reference. Examiner has elaborated one which is considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Yerramalla by incorporating Sundararajan to identify the priority information with one of the several options disclosed.
The motivation of doing so would have enhance the information gathering process to determine preferential use of artificial intelligence network models or artificial intelligence network model parameter.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over PEZESHKI et al. (CN116074813A, hereinafter Pezeshki) in view of Sundararajan et al. (US 11889458 B2, hereinafter Sundararajan) and in further view of Calcev (US 20200045608 A1, hereinafter Clacev)
Regarding Claim 7, the combination of Pezeshki and Sundararajan teach the method of Claim 1 but fail to teach wherein the LOS indication information comprises at least one of the following: a first bit for indicating being an LOS or a non-line of sight (NLOS); a second bit for indicating a probability of being an LOS; or a third bit for indicating a confidence of being an LOS.
However, in a similar endeavor, Calcev teaches wherein the LOS indication information comprises at least one of the following: a first bit for indicating being an LOS or a non-line of sight (NLOS);
(Calcev[0007] According to other embodiment, there is provided a method performed by a device, comprising: sending a LOS determination request to a peer device which communicates with the device, wherein the LOS determination request includes an indication of which a dual polarization procedure for Line of Sight (LOS) is used to indicate that a same sequence of bits is sent at two polarizations to the same direction;
[0060] The message indicating the receiver 104 or the transmitter 102 supports the LOS determination may be an enhanced directional multi-gigabit (EDMG) beam refinement protocol (BRP) request.
[0061] As an example, the EDMG request may comprise the following element in table 1:
TABLE-US-00001 TABLE 1 B0 B7 B16 B23 B32 B39 B40 B50 B51 B52 B53 B56 B57 B58 Element B8 B15 Element ID B24 B31 L-TX- TX EDMG EDMG TRN- EDMG TRN- ID Length Extension L-RX RX Sector ID TRN-Unit P Unit M Unit N Bits: 8 8 8 8 8 11 2 4 2 B76 B83 B84 B59 B60 B61 B69 B70 B75 TX First B85 TXSS- TXSS-REQ- TXSS- BRP Antenna Path LOS REQ RECIPROCAL SECTORS CDOWN Mask Training Training B86 B87 Bits: 1 1 9 6 8 1 1 3)
a second bit for indicating a probability of being an LOS; or
a third bit for indicating a confidence of being an LOS.
(Note: Claim language requires examiner to find one of the three limitations in the reference. Examiner has elaborated one which is considered to be sufficient)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the examined application to have modified Pezeshki and Sundararajan and by incorporating Calcev and in further teaching of Choi to identify the types of indication information.
The motivation of doing so would have enabled the target terminal location information accurately through the use of artificial intelligence network model or artificial intelligence network parameter model.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RANA HASSAN MAHMUD whose telephone number is (571)272-8939. The examiner can normally be reached Mon-Friday.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kathy Wang-Hurst can be reached at 5712705371. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RANA H MAHMUD/Examiner, Art Unit 2644
/KATHY W WANG-HURST/Supervisory Patent Examiner, Art Unit 2644
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