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 .
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 32 and 33 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.
Claims 32 and 33 having phrase, “feature set lists comprise one or more identifiers per feature set of the plurality of feature sets” and “feature set lists further information on how to retrieve the plurality of feature sets” respectively. Here it is unclear what an applicants are trying to claim, accordingly makes the claims as being indefinite for failing to particularly point out and distinctly claim the subject matter.
The claims are rejected based on the understanding at the time of the examination.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 22 – 24, 27, 30, 31 and 33 – 39 are rejected under 35 U.S.C. 102(a)(1) as being unpatentable by
Sundararajan US PGPub: US 2022/0046577 A1 Feb. 10, 2022.
Regarding claims 22, 39, Sundararajan discloses,
an apparatus for wireless communication and a method (UE 302), comprising at least one processor (the processing systems 332, 384, and 394, cause the apparatuses 302, 304, and 306 to perform the functionality – paragraph 0138), and at least one memory storing instructions (the apparatuses 302, 304, and 306 include memory circuitry implementing memory components 340, 386, and 396 - e.g., each including a memory device, respectively – paragraph 0138) which, when executed by the at least one processor (a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures – ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 - 0020), cause the apparatus at least to:
receive, from a network element or a user device, one or more feature set lists (UE 302 - e.g., receiver 312, receiver 322, etc., obtains at least one neural network function configured to facilitate positioning measurement data processing at the UE. The at least one neural network function may be received from a network entity - e.g., BS 304. In some designs, the at least one neural network function may be generated by a network entity - e.g., network entity 306, such as an LMF or an external server, and then relayed to UE 302 via a serving BS – paragraph 0213)
each comprising a plurality of feature sets (the definition of the neural network function is provided with reference to Fig. 12 – Fig. 12, paragraphs 0240 – 0243)
in a priority order or rank order (an early, simplified set of positioning measurement features may first be obtained for determining a coarse location estimate of UE 302, followed by a more complex set of positioning measurement features for determining a more refined estimate of UE 302. Different neural network functions may be defined for each respective set of positioning measurement features in this case – paragraph 0215),
wherein the one or more feature set lists are based on at least one of the following: one or more bandwidth parts configured for the apparatus, one or more bandwidth capabilities of the apparatus, or one or more radio environment scenarios (the at least one neural network function may comprise a plurality of neural network functions that are each configured to facilitate positioning measurement data processing at the UE of a single positioning measurement type or a group of positioning measurement types. In other designs, the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing at the UE of a plurality of positioning measurement types. So, the neural network function may be implemented at different granularities in terms of location, gNB status - e.g., one neural network function for serving cell, another neural network function for non-serving cells, etc., Hence, a network operator can flexibly fine-tune the extent of a positioning feature reporting granularity to a desired level – paragraph 0222);
select a feature set from the plurality of feature sets based at least on the priority order or rank order (the at least one neural network function may comprise a first neural network function configured to process the positioning measurement data into a first respective set of positioning measurement features, and the at least one neural network function may comprise a second neural network function configured to process the positioning measurement data into a second respective set of respective set of positioning measurement features - i.e., a first neural network function is initially selected to obtain a coarse location estimate. – paragraph 0220); and
obtain the feature set from one or more received signals, wherein the feature set comprises one or more radio channel features (UE 302 - e.g., processing system 332, measurement module 342, etc., processes the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function – Fig. 9/930, paragraph 0215. The “positioning measurement features” are defined as Examples of positioning measurement features comprise a time-of-arrival - e.g., TOA TDOA, OTDOA, etc., reference signal time-difference, angle of departure AoD, angle of arrival AoA, timing and magnitude of a pre-defined number of peaks in the channel estimate, other channel estimate information such as a power delay profile PDP, etc., - paragraph 0211).
Regarding claim 23, Sundararajan discloses,
the apparatus according to claim 22, further being caused to: classify channel measurements or the one or more received signals as line-of-sight, LOS,
Regarding claim 24, Sundararajan discloses,
the apparatus according to claim 23, wherein the priority order or rank order of the plurality of feature sets is based on one or a combination of: a classification accuracy using each of the plurality of feature sets (identify the beam that best excite the LOS or shortest radio path between the UE 604 and the transmitting base stations 602. Identifying the LOS/shortest path beams is of interest not only because these beams can subsequently be used for OTDOA measurements between a pair of base stations 602, but also because identifying these beams can directly provide some positioning information based on the beam direction. Moreover, these beams can subsequently be used for other position estimation methods that require precise ToA, such as round-trip time estimation based methods – paragraph 0189),
a positioning accuracy using each of the plurality of feature sets (an early, simplified set of positioning measurement features may first be obtained for determining a coarse location estimate of UE 302, followed by a more complex set of positioning measurement features for determining a more refined estimate of UE 302. Different neural network functions may be defined for each respective set of positioning measurement features in this case – paragraph 0215), or
a separation or separability of the LOS and NLOS classes in a feature domain.
Regarding claim 27, Sundararajan discloses,
the apparatus according to claim 22, wherein the feature set is selected based further on a radio environment that the apparatus is in (the information and signals described below may be represented using any of a variety of different technologies and techniques – paragraph 0102. The different cells may be configured according to different protocol types - e.g., machine-type communication MTC, narrowband IoT NB-IoT, enhanced mobile broadband eMBB, or others that may provide access for different types of UEs. Because a cell is supported by a specific base station, the term “cell” may refer to either or both the logical communication entity and the base station that supports it, depending on the context– paragraph 0110. Different UEs 104/182 in a cell may have different downlink primary carriers – paragraph 0121).
Regarding claim 30, Sundararajan discloses,
the apparatus according to claim 22, further being caused to: transmit, to the network element or to the user device, an indication for updating the one or more feature set lists (UE 302 - e.g., receiver 312, receiver 322, etc., obtains at least one neural network function configured to facilitate positioning measurement data processing at the UE. The at least one neural network function may be received from a network entity - e.g., BS 304. In some designs, the at least one neural network function may be generated by a network entity - e.g., network entity 306, such as an LMF or an external server, and then relayed to UE 302 via a serving BS – paragraph 0213. The definition of the neural network function is provided with reference to Fig. 12 – Fig. 12, paragraphs 0240 – 0243).
Regarding claim 31, Sundararajan discloses,
the apparatus according to claim 22, wherein the one or more feature set lists are associated with a validity time based on a mobility condition of the apparatus, wherein the validity time indicates how long the one or more feature set lists are valid (some set of time/frequency resources in a particular frequency spectrum – paragraph 0132. A “PRS occasion” is one instance of a periodically repeated time window - e.g., a group of one or more consecutive slots where PRS are expected to be transmitted – paragraph 0166).
Regarding claim 33, Sundararajan discloses,
the apparatus according to claim 22, wherein the one or more feature set lists comprise one or more identifiers per feature set of the plurality of feature sets (UE 302 - e.g., receiver 312, receiver 322, etc., obtains at least one neural network function configured to facilitate positioning measurement data processing at the UE. The at least one neural network function may be received from a network entity - e.g., BS 304. In some designs, the at least one neural network function may be generated by a network entity - e.g., network entity 306, such as an LMF or an external server, and then relayed to UE 302 via a serving BS – paragraph 0213. The definition of the neural network function is provided with reference to Fig. 12 – Fig. 12, paragraphs 0240 – 0243).
Regarding claim 34, Sundararajan discloses,
the apparatus according to claim 22, wherein the one or more feature set lists further comprise information on how to retrieve the plurality of feature sets (UE 302 - e.g., receiver 312, receiver 322, etc., obtains at least one neural network function configured to facilitate positioning measurement data processing at the UE. The at least one neural network function may be received from a network entity - e.g., BS 304. In some designs, the at least one neural network function may be generated by a network entity - e.g., network entity 306, such as an LMF or an external server, and then relayed to UE 302 via a serving BS – paragraph 0213. The definition of the neural network function is provided with reference to Fig. 12 – Fig. 12, paragraphs 0240 – 0243).
Regarding claim 35, Sundararajan discloses,
the apparatus according to claim 22, further being caused to: transmit a message indicative of the one or more feature set lists via sidelink (D2D P2P link 192 and 194 i.e., UE 190 communicates with UE 104 through link 192 and UE 190 communicates with UE 152 through link 194 – Fig. 1, paragraph 0123).
Regarding claim 36, Sundararajan discloses
an apparatus for communication (base station 304), comprising
at least one processor (the processing systems 332, 384, and 394, cause the apparatuses 302, 304, and 306 to perform the functionality – paragraph 0138), and
at least one memory storing instructions which, when executed by the at least one processor (the apparatuses 302, 304, and 306 include memory circuitry implementing memory components 340, 386, and 396 - e.g., each including a memory device, respectively – paragraph 0138) which, when executed by the at least one processor (a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE, the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures – ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 – 0020), cause the apparatus at least to:
determine one or more feature set lists (the at least one neural network function being generated dynamically based on machine-learning associated with one or more historical measurement procedures. The at least one neural network function may be generated by a network entity - e.g., network entity 306, such as an LMF or an external server. The one or more historical measurement procedures can be associated with different UEs - e.g., crowd-sourcing, and UE model type or operational conditions can also be used to filter the training data being fed to the machine-learning algorithm that generates the neural network function – paragraph 0218) based on at least one of the following: one or more bandwidth parts configured for one or more user devices, one or more bandwidth capabilities of the one or more user devices, or one or more radio environment scenarios associated with the one or more user devices (the at least one neural network function may comprise a plurality of neural network functions that are each configured to facilitate positioning measurement data processing at the UE of a single positioning measurement type or a group of positioning measurement types. In other designs, the at least one neural network function comprises a single neural network function configured to facilitate positioning measurement data processing at the UE of a plurality of positioning measurement types. So, the neural network function may be implemented at different granularities in terms of location, gNB status - e.g., one neural network function for serving cell, another neural network function for non-serving cells, etc., Hence, a network operator can flexibly fine-tune the extent of a positioning feature reporting granularity to a desired level – paragraph 0222),
wherein the one or more feature set lists comprise a plurality of feature sets (the definition of the neural network function is provided with reference to Fig. 12 – Fig. 12, paragraphs 0240 – 0243)
in a priority order or rank order (an early, simplified set of positioning measurement features may first be obtained for determining a coarse location estimate of UE 302, followed by a more complex set of positioning measurement features for determining a more refined estimate of UE 302. Different neural network functions may be defined for each respective set of positioning measurement features in this case – paragraph 0215); and
transmit the one or more feature set lists to the one or more user devices (UE 302 - e.g., receiver 312, receiver 322, etc., obtains at least one neural network function configured to facilitate positioning measurement data processing at the UE. The at least one neural network function may be received from a network entity - e.g., BS 304. In some designs, the at least one neural network function may be generated by a network entity - e.g., network entity 306, such as an LMF or an external server, and then relayed to UE 302 via a serving BS – paragraph 0213. BS 302 transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE – Fig. 10/1010, paragraph 0218),
wherein a feature set of the plurality of feature sets comprises one or more radio channel features (UE 302 - e.g., processing system 332, measurement module 342, etc., processes the positioning measurement data into a respective set of positioning measurement features based on the at least one neural network function – Fig. 9/930, paragraph 0215. The “positioning measurement features” are defined as Examples of positioning measurement features comprise a time-of-arrival - e.g., TOA TDOA, OTDOA, etc., reference signal time-difference, angle of departure AoD, angle of arrival AoA, timing and magnitude of a pre-defined number of peaks in the channel estimate, other channel estimate information such as a power delay profile PDP, etc., - paragraph 0211).
Regarding claim 37, Sundararajan discloses,
the apparatus according to claim 36, wherein the one or more feature set lists are determined based further on a computational capability of the one or more user devices (the one or more historical measurement procedures can be associated with different UEs - e.g., crowd-sourcing, and UE model type or operational conditions can also be used to filter the training data being fed to the machine-learning algorithm that generates the neural network function – paragraph 0218).
Regarding claim 38, Sundararajan discloses,
the apparatus according to claim 36, wherein the transmission of the one or more feature set lists is triggered based on changes in a radio environment (the one or more historical measurement procedures can be associated with different UEs - e.g., crowd-sourcing, and UE model type or operational conditions can also be used to filter the training data being fed to the machine-learning algorithm that generates the neural network function – paragraph 0218).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 25, 26, 28, 29 and 32 are rejected under 35 U.S.C. 103 as being unpatentable over
Sundararajan US PGPub: US 2022/0046577 A1 Feb. 10, 2022 and in view of
Yerramalli US PGPub: US 2021/0321221 A1 Oct. 14, 2021.
Regarding claim 25, Sundararajan discloses all the claimed features,
but, does not disclose, the apparatus according to claim 22, wherein the feature set is selected based further on at least one of the following: a configured bandwidth part, a bandwidth capability, or a computational capability of the apparatus.
Yerrammalli teaches, neural network based line of sight detection for positioning a mobile device. A method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, 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 (ABSTRACT, Figs. 1, 5, 10, paragraphs 0006, 0010).
The receive configuration information may also be transceiver configuration information based on the capabilities of a device - i.e., a transceiver may include a combination of receiver and transmitter components (paragraph 0102).
The receiver configuration information may include an operating frequency and bandwidth. The neural network information may be stored on a network server. The neural network information may be stored on the mobile device. The neural network information may include an architecture of the neural network and its weight and bias matrices. A base station 502 may be configured to send a UE capability message 1002 to ascertain the capabilities to provide an LOS delay estimate based on a neural network. The UE 105 may be configured to respond with NN network information - e.g., based on configuration and/or Angle of Arrival capabilities in a NN report message 100 (Figs. 1, 5, 10/1002, 10/1004, paragraphs 0006, 0010, 0096, 0105).
The data structure may be constructed to enable a device, such as a network server - e.g. the server 400, LMF 120, etc., or a UE to associate configuration and operational parameters with one or more neural networks. Thus, a neural network may be selected based on the configuration of the UE, the configuration of a base station, or other combinations of network resources and operational parameters - e.g., proximity of neighbors, time of day, device density, network traffic, etc., (paragraph 0100).
A TRP 300 may receive SRS signals from the UE 200 (paragraph 0107).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE of Sundararajan (Sundararajan, ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 – 0020) wherein the system of Sundararajan, would have incorporated a neural network based line of sight detection for positioning a mobile device of Yerrammalli (Yerrammalli ABSTRACT, Figs. 1, 5, 10/1002, 10/1004, paragraphs 0096, 0100, 0102, 0105, 0107) for 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 (Yerrammalli, paragraph 0034).
Regarding claim 26, Sundararajan discloses all the claimed features,
but, does not disclose, the apparatus according to claim 25, wherein the plurality of feature sets are associated with a configured bandwidth part value or a bandwidth range per feature set of the plurality of feature sets.
Yerrammalli teaches, neural network based line of sight detection for positioning a mobile device. A method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, 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 (ABSTRACT, Figs. 1, 5, 10, paragraphs 0006, 0010).
The receive configuration information may also be transceiver configuration information based on the capabilities of a device - i.e., a transceiver may include a combination of receiver and transmitter components (paragraph 0102).
The receiver configuration information may include an operating frequency and bandwidth. The neural network information may be stored on a network server. The neural network information may be stored on the mobile device. The neural network information may include an architecture of the neural network and its weight and bias matrices. A base station 502 may be configured to send a UE capability message 1002 to ascertain the capabilities to provide an LOS delay estimate based on a neural network. The UE 105 may be configured to respond with NN network information - e.g., based on configuration and/or Angle of Arrival capabilities in a NN report message 100 (Figs. 1, 5, 10/1002, 10/1004, paragraphs 0006, 0010, 0096, 0105).
The data structure may be constructed to enable a device, such as a network server - e.g. the server 400, LMF 120, etc., or a UE to associate configuration and operational parameters with one or more neural networks. Thus, a neural network may be selected based on the configuration of the UE, the configuration of a base station, or other combinations of network resources and operational parameters - e.g., proximity of neighbors, time of day, device density, network traffic, etc., (paragraph 0100).
A TRP 300 may receive SRS signals from the UE 200 (paragraph 0107).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE of Sundararajan (Sundararajan, ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 – 0020) wherein the system of Sundararajan, would have incorporated a neural network based line of sight detection for positioning a mobile device of Yerrammalli (Yerrammalli ABSTRACT, Figs. 1, 5, 10/1002, 10/1004, paragraphs 0096, 0100, 0102, 0105, 0107) for 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 (Yerrammalli, paragraph 0034).
Regarding claim 28, Sundararajan discloses all the claimed features,
but, does not disclose, the apparatus according to claim 22, wherein the one or more feature set lists are based further on one or more frequency ranges, and wherein the feature set is selected based further on a frequency range used by the apparatus.
Yerrammalli teaches, neural network based line of sight detection for positioning a mobile device. A method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, 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 (ABSTRACT, Figs. 1, 5, 10, paragraphs 0006, 0010).
The receive configuration information may also be transceiver configuration information based on the capabilities of a device - i.e., a transceiver may include a combination of receiver and transmitter components (paragraph 0102).
The receiver configuration information may include an operating frequency and bandwidth. The neural network information may be stored on a network server. The neural network information may be stored on the mobile device. The neural network information may include an architecture of the neural network and its weight and bias matrices. A base station 502 may be configured to send a UE capability message 1002 to ascertain the capabilities to provide an LOS delay estimate based on a neural network. The UE 105 may be configured to respond with NN network information - e.g., based on configuration and/or Angle of Arrival capabilities in a NN report message 100 (Figs. 1, 5, 10/1002, 10/1004, paragraphs 0006, 0010, 0096, 0105).
The data structure may be constructed to enable a device, such as a network server - e.g. the server 400, LMF 120, etc., or a UE to associate configuration and operational parameters with one or more neural networks. Thus, a neural network may be selected based on the configuration of the UE, the configuration of a base station, or other combinations of network resources and operational parameters - e.g., proximity of neighbors, time of day, device density, network traffic, etc., (paragraph 0100).
A TRP 300 may receive SRS signals from the UE 200 (paragraph 0107).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE of Sundararajan (Sundararajan, ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 – 0020) wherein the system of Sundararajan, would have incorporated a neural network based line of sight detection for positioning a mobile device of Yerrammalli (Yerrammalli ABSTRACT, Figs. 1, 5, 10/1002, 10/1004, paragraphs 0096, 0100, 0102, 0105, 0107) for 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 (Yerrammalli, paragraph 0034).
Regarding claim 29, Sundararajan discloses all the claimed features,
but, does not disclose, the apparatus according to claim 22, further being caused to: transmit, to the network element or to the user device, a request for the one or more feature set lists; and receive the one or more feature set lists in response to transmitting the request.
Yerrammalli teaches, neural network based line of sight detection for positioning a mobile device. A method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, 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 (ABSTRACT, Figs. 1, 5, 10, paragraphs 0006, 0010).
The receive configuration information may also be transceiver configuration information based on the capabilities of a device - i.e., a transceiver may include a combination of receiver and transmitter components (paragraph 0102).
The receiver configuration information may include an operating frequency and bandwidth. The neural network information may be stored on a network server. The neural network information may be stored on the mobile device. The neural network information may include an architecture of the neural network and its weight and bias matrices. A base station 502 may be configured to send a UE capability message 1002 to ascertain the capabilities to provide an LOS delay estimate based on a neural network. The UE 105 may be configured to respond with NN network information - e.g., based on configuration and/or Angle of Arrival capabilities in a NN report message 100 (Figs. 1, 5, 10/1002, 10/1004, paragraphs 0006, 0010, 0096, 0105).
The data structure may be constructed to enable a device, such as a network server - e.g. the server 400, LMF 120, etc., or a UE to associate configuration and operational parameters with one or more neural networks. Thus, a neural network may be selected based on the configuration of the UE, the configuration of a base station, or other combinations of network resources and operational parameters - e.g., proximity of neighbors, time of day, device density, network traffic, etc., (paragraph 0100).
A TRP 300 may receive SRS signals from the UE 200 (paragraph 0107).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE of Sundararajan (Sundararajan, ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 – 0020) wherein the system of Sundararajan, would have incorporated a neural network based line of sight detection for positioning a mobile device of Yerrammalli (Yerrammalli ABSTRACT, Figs. 1, 5, 10/1002, 10/1004, paragraphs 0096, 0100, 0102, 0105, 0107) for 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 (Yerrammalli, paragraph 0034).
Regarding claim 32, Sundararajan discloses all the claimed features,
but, does not disclose, the apparatus according to claim 22, further being caused to: report, to the network element or to the user device, the feature set selected by the apparatus.
Yerrammalli teaches, neural network based line of sight detection for positioning a mobile device. A method for determining a line of sight delay, an angle of arrival, or an angle of departure value, according to the disclosure includes receiving reference signal information, 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 (ABSTRACT, Figs. 1, 5, 10, paragraphs 0006, 0010).
The receive configuration information may also be transceiver configuration information based on the capabilities of a device - i.e., a transceiver may include a combination of receiver and transmitter components (paragraph 0102).
The receiver configuration information may include an operating frequency and bandwidth. The neural network information may be stored on a network server. The neural network information may be stored on the mobile device. The neural network information may include an architecture of the neural network and its weight and bias matrices. A base station 502 may be configured to send a UE capability message 1002 to ascertain the capabilities to provide an LOS delay estimate based on a neural network. The UE 105 may be configured to respond with NN network information - e.g., based on configuration and/or Angle of Arrival capabilities in a NN report message 100 (Figs. 1, 5, 10/1002, 10/1004, paragraphs 0006, 0010, 0096, 0105).
The data structure may be constructed to enable a device, such as a network server - e.g. the server 400, LMF 120, etc., or a UE to associate configuration and operational parameters with one or more neural networks. Thus, a neural network may be selected based on the configuration of the UE, the configuration of a base station, or other combinations of network resources and operational parameters - e.g., proximity of neighbors, time of day, device density, network traffic, etc., (paragraph 0100).
A TRP 300 may receive SRS signals from the UE 200 (paragraph 0107).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify a network component transmits, to a UE, at least one neural network function configured to facilitate processing of positioning measurement data into one or more positioning measurement features at the UE of Sundararajan (Sundararajan, ABSTRACT, Figs. 9, 10, 12, paragraphs 0006 – 0020) wherein the system of Sundararajan, would have incorporated a neural network based line of sight detection for positioning a mobile device of Yerrammalli (Yerrammalli ABSTRACT, Figs. 1, 5, 10/1002, 10/1004, paragraphs 0096, 0100, 0102, 0105, 0107) for 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 (Yerrammalli, paragraph 0034).
The prior arts made of record and not relied upon are considered pertinent to applicants disclosure.
Kirmaz US PGPub: US 2024/0283551 A1 Aug. 22, 2024.
Bandwidth based and/or scenario based feature selection for high line-of-sight/non-line-of-sight classification accuracy.
A method performing by an apparatus, including: receiving (501), from a network entity or transmitting to a user device, one or more feature set lists each including a plurality of feature sets possibly in a priority order or rank order associated with the apparatus or the user device, wherein the one or more feature set lists are based on at least one of the following: one or more bandwidth parts configured for the apparatus or the user device, one or more bandwidth capabilities of the apparatus or the user device, or one or more radio environment scenarios; and classifying or selecting channel measurements of the one or more received signals per measurement by the apparatus or by the user device, to estimate a likelihood of a propagation condition classification including one of: line-of-sight (LOS), non-line-of-sight (NLOS) and obstructed line-of-sight, based on the feature sets to determine positioning of the apparatus or the user device.
Dwivedi US PGPub: US 2022/0229143 A1 Jul. 21, 2022.
A method in a network node and a network is provided to determine line of sight, LOS, base stations for a user equipment (UE) is provided. A request is provided to at least one of the UE and a plurality of base stations to measure and report LOS detection measurements, wherein the plurality of base stations includes base stations of a serving cell of the UE. The LOS detection measurements are received from the at least one of the UE and the plurality of base stations. LOS base stations for the UE are determined based on the LOS detection measurements. An indication of the LOS base stations is transmitted to the UE.
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/NIMESH PATEL/Primary Examiner, Art Unit 2642
