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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Claims have priority of foreign filing date 05/12/2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 9/21/2023 is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-4, 8-11, 15-17, 22, 26-29, 31-34, and 35 are rejected under 35 U.S.C. 102(a)(2) as anticipated by US 20220229143 A1 DWIVEDI; Satyam et al.
Regarding Claims 1, 26, 34, and 35, DWIVEDI teaches A method of positioning performed by a network node (Fig. 8 RAN Node 800 ¶30; Fig. 5 BS), comprising: and A network node, comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor (Fig. 5 BS; Fig. 8 RAN Node 800 ¶30, processor 803, memory 805, transceiver 801) configured to: and A network node, comprising: means for(Fig. 5 BS; Fig. 8 RAN Node 800 ¶30, processor 803, memory 805, transceiver 801) and A non-transitory computer-readable medium storing computer-executable instructions that, when executed by a network node (¶125 “computer program product comprising a non-transitory storage medium including program code to be executed by processing circuitry (803, 903) of a network node (800, 900) configured to operate in a communication network, whereby execution of the program code causes the network node (800, 900) to perform operations according to any of embodiments 1-10.”), cause the network node to:
receiving, from a network entity, one or more parameters for a neural network (Fig. 11, 1101; [0095]” In operation 1101, the processing circuitry 803 may, via transceiver circuitry 801, receive a request to measure and report LOS detection measurements associated with a UE from a location server.” ; [0076]: “the network programs the neural network through signaling containing connection weights and connection bias for the neuron connections in the neural network.”)
performing one or more measurements of at least one reference signal from a target user equipment (UE) ([0096] “In operation 1103, the processing circuitry 803 may perform LOS detection measurements associated with the UE. . .. he LOS detection methods may be a channel impulse response at the UE, a time difference between signal detection and a time of arrival of a first peak of a power delay profile, PDP, of the channel impulse response, a relative strength of the first peak compared to later peaks of the PDP wherein the relative strength comprises one of a power variation and a signal magnitude, a dynamic range of the first peak of the PDP, a stability of an estimated Doppler spread of peaks of the PDP, an angle of arrivals from base stations of the neighboring base stations and a serving cell of the UE and angle of departure at the base stations of the serving cell, a time variation of a number of time of arrivals from the base stations of the neighboring base stations and the serving cell base station over a time period, and/or a received signal strength being within a margin of an expected received signal strength”; See also - [0044]-[0051]: LOS detection measurements (PDP peaks, TOA, dynamic range, Doppler spread, RSRP));
based on a measurement configuration for the at least one reference signal (¶79 “In operation 603 , the processing circuitry 803 may Transmit, via transceiver circuitry 801 , a request 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 request may specify which LOS detection methods Are to be used in measuring and reporting the LOS detection Measurement, which of the UE or base station is to use a LOS detection method..”);
generating one or more statistics of one or more features of the at least one reference signal based on the neural network and the one or more measurements ([0055]-[0064]: “The neural network cost function based on LOS/NLOS using measurements The trained neural network is used to discriminate between LOS and NLOS.”); and
reporting the one or more statistics to the network entity (¶75 “In one embodiment the measurement reports are first signaled by the gNB to the location server over LPPa and next signaled from the location server to the UE over LPP.”) to enable the network entity to estimate a location of the target UE based on the one or more statistics and a location of the network node ¶77 “ The location server may be a core network node (e.g., core network node 900) or a RAN network node (e.g., network node 800). And ¶84 estimate UE position based on LOS detection measurements by core network node 900 in view of ¶77)“
Regarding Claims 2 and 27, DWIVEDI teaches The method of claim 1, wherein the one or more parameters comprise one or more weights and one or more biases of the neural network ([0076]: “the network programs the neural network through signaling containing connection weights and connection bias for the neuron connections in the neural network.”).
Regarding Claims 3 and 28, DWIVEDI teaches The method of claim 1, further comprising: receiving, from the network entity, at least one message containing the measurement configuration ((¶79 “The request may specify which LOS detection methods Are to be used in measuring and reporting the LOS detection measurement, which of the UE or base station is to use a LOS detection method..”), wherein the one or more parameters are received from the network entity in the at least one message containing the measurement configuration (¶76 “the network programs the neural network (or machine learning mechanism) through signaling. This signaling is done via LPPe and may contain connection weights and connection bias for the neuron connections in the neural network.”).
Regarding Claims 4 and 29, DWIVEDI teaches The method of claim 1, wherein the one or more parameters are received from the network entity (¶76 “the network programs the neural network (or machine learning mechanism) through signaling. This signaling is done via LPPe and may contain connection weights and connection bias for the neuron connections in the neural network.”) separately from the measurement configuration ((¶79 “The request may specify which LOS detection methods Are to be used in measuring and reporting the LOS detection measurement, which of the UE or base station is to use a LOS detection method..”).
Regarding Claims 8 and 31, DWIVEDI teaches The method of claim 1, wherein the one or more measurements comprise: I/Q samples of the at least one reference signal, a frequency-domain channel estimate of the at least one reference signal, or a time-domain channel estimate of the at least one reference signal (¶41-42 channel estimation, channel impulse response, power delay profile, different types of reference signals).
Regarding Claim 9, DWIVEDI teaches The method of claim 8, wherein the time-domain channel estimate comprises a power-delay-angle profile of the at least one reference signal (¶41-42 “the receiver is able to estimate the radio channel H(f, t, Ω) with some granularity in time t, frequency f, and angle Ω.” And also “power delay profile” and other measurements as taught).
Regarding Claims 10 and 32, DWIVEDI teaches The method of claim 1, wherein the one or more features comprise: a round-trip-time (RTT) measurement, a time difference of arrival (TDOA) measurement, a time of arrival (ToA) measurement, an angle of arrival (AoA) measurement, a zenith of arrival (ZoA) measurement, or any combination thereof (¶96 “The LOS detection methods may include all or a subset of the LOS detection methods described above. For example, the LOS detection methods may be a channel impulse response at the UE, a time difference between signal detection and a time of arrival of a first peak of a power delay profile, PDP, of the channel impulse response, a relative strength of the first peak compared to later peaks of the PDP wherein the relative strength comprises one of a power variation and a signal magnitude, a dynamic range of the first peak of the PDP, a stability of an estimated Doppler spread of peaks of the PDP, an angle of arrivals from base stations of the neighboring base stations and a serving cell of the UE and angle of departure at the base stations of the serving cell, a time variation of a number of time of arrivals from the base stations of the neighboring base stations and the serving cell base station over a time period, and/or a received signal strength being within a margin of an expected received signal strength” and combination with ¶55 neural network discriminator).
Regarding Claims 11 and 33, DWIVEDI teaches The method of claim 1, wherein the one or more statistics comprise: a mean of the one or more features, a confidence interval of the one or more features, a standard deviation of the one or more features, or any combination thereof (¶69 “the LOS detection methods may be weighted. The LOS detection methods can be combined based on their expected performance in detecting LOS.” Weighted combination teaching “mean of one or more features”).
Regarding Claim 22, DWIVEDI teaches The method of claim 1, wherein: the network node is a base station (Fig. 8 RAN node 800 ¶30), and the network entity is a location server (Fig. 9 CN Node 900 ¶31; ¶77 the location server may be a core network node (e.g., core network node 900) or a RAN network node (e.g., network node 800).)).
Regarding Claim 15, DWIVEDI teaches The method of claim 1, wherein the one or more statistics comprise one or more conditional statistics that are based on the location of the target UE (¶53 “The ninth LOS detection method is using coarse UE position in connection with a 3D model to determine LOS/NLOS paths.”).
Regarding Claim 16, DWIVEDI teaches The method of claim 15, wherein: the location of the target UE is estimated from the one or more statistics, or the location of the target UE is determined from a cell identity associated with the target UE ([0053] a first coarse positioning has been estimated in some way (e.g. via dedicated positioning reference signals in a cellular network, via GNSS or in some other way).
Regarding Claim 17, DWIVEDI teaches The method of claim 15, wherein the neural network further takes as input base station almanac (BSA) information (¶53 “assuming further that the network (or even UE) has access to a 3-dimensional model of the terrain and buildings, including the locations/heights of the BSs”).
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.
Claim(s) 5-7 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220229143 A1 DWIVEDI; Satyam et al. in view of US 20190277957 A1 Chandrasekhar; Vikram et al.
Regarding Claims 5 and 30, DWIVEDI teaches The method of claim 1 (see rejection claim 1).
DWIVEDI does not explicitly disclose wherein the one or more parameters are customized for the network node, the target UE, or both based on one or more conditions.
Chandrasekhar teaches wherein the one or more parameters are customized for the network node, the target UE, or both based on one or more conditions. (Fig. 7 ¶79 “he AI system generates the labeled data i.e., collection of speed classes and associated features for training the neural network classifier. In one embodiment, the UE speed class for training the neural network could be obtained by simultaneously collecting uplink SRS measurements and (time-stamped) positions of the terminal and measuring the absolute rate of change of the terminal position”; ¶113 “ the AI classifier can automatically learn the features of channel measurements and store these learned features into the weight parameters of the neural network”)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of DWIVEDI to include the noted teachings of Chandrasekhar in order to categorize or determine the speed of a UE based on uplink SRS channel measurement inputs (Chandrasekhar ¶4).
Regarding Claim 6, The combination teaches The method of claim 5, wherein the one or more conditions comprise: a device model of the network node, a modem version of the network node, processing capability of the network node, network node channel measurements derived on different frequency bands, a type of region in which the target UE is located, an indoor or outdoor condition of the target UE, or any combination thereof ([0134] In step 1322, the deep neural network architecture obtains a signal measurement (e.g., estimate an SNR level) or feedback from the mobile client device. [0135] In step 1324, the deep neural network architecture selects one set of neural network layer weights, among different sets of neural network layer weights, based on a certain signal measurement or feedback from the mobile client device, and apply the selected set of neural network layer weights to each layer of the multiple CNN layer” where signal measurement or feedback (SNR) teaches structure of indicator of type of region, frequency band, or indoor/outdoor target).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of DWIVEDI to include the noted teachings of Chandrasekhar in order to categorize or determine speed of a UE based on uplink SRS channel measurement inputs (Chandrasekhar ¶4).
Regarding Claim 7, The combination teaches The method of claim 5, wherein: the neural network comprises a plurality of layers (Chandrashekar ¶129 deep neural networks Fig.s 13A, 13B with multiple layers), parameters of one or more layers of the plurality of layers are common across all of the one or more conditions, and for each condition of the one or more conditions, at least one remaining layer of the plurality of layers is specific to the condition ([0134] In step 1322, the deep neural network architecture obtains a signal measurement (e.g., estimate an SNR level) or feedback from the mobile client device. [0135] In step 1324, the deep neural network architecture selects one set of neural network layer weights, among different sets of neural network layer weights, based on a certain signal measurement or feedback from the mobile client device, and apply the selected set of neural network layer weights to each layer of the multiple CNN layers.” Where condition is the SNR)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of DWIVEDI to include the noted teachings of Chandrasekhar in order to categorize or determine the speed of a UE based on uplink SRS channel measurement inputs (Chandrasekhar ¶4).
Claim(s) 12-14 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220229143 A1 DWIVEDI; Satyam et al. in view of US 20230007615 A1 KEATING; Ryan et al.
Regarding Claim 12, DWIVEDI teaches The method of claim 1 (see rejection claim 1).
DWIVEDI does not explicitly disclose wherein the one or more statistics comprise: probability distribution of each of the one or more features, a joint probability distribution of the one or more features, or any combination thereof.
KEATING teaches wherein the one or more statistics comprise: probability distribution of each of the one or more features, a joint probability distribution of the one or more features, or any combination thereof. (Keating [0024] “the network node may determine at least one estimation of a NLOS bias distribution and at least one statistical parameter associated with representing The NLOS bias distribution” and [0025]-[0027], [0039]-[0041] generating statistical parameters of signal derived features (Gaussian mixture parameters, means, variances, weights) for positioning.; See also Keating [0022]-[0024]: network node receives MDT reports, performs RSTD/TOA related processing. - Keating Fig. 1, steps 110-112: PRS reception and measurement.)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the invention of DWIVEDI to include the noted teachings of KEATING in order to learn the distribution of the bias of the time of arrival caused by NLOS conditions and may use this to improve the positioning accuracy (KEATING ¶19).
Regarding Claim 13, The combination teaches The method of claim 12, wherein: the probability distribution of each of the one or more features comprises a Gaussian function, and the one or more statistics further comprise a mean, a covariance, at least one weight, or any combination thereof of each Gaussian function (Keating [0024] “the network node may determine at least one estimation of a NLOS bias distribution and at least one statistical parameter associated with representing The NLOS bias distribution” and [0025]-[0027], [0039]-[0041] generating statistical parameters of signal derived features (Gaussian mixture parameters, means, variances, weights) for positioning.; See also Keating [0022]-[0024]: network node receives MDT reports, performs RSTD/TOA related processing. - Keating Fig. 1, steps 110-112: PRS reception and measurement.).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the COMBINATION to include the noted teachings of KEATING in order to learn the distribution of the bias of the time of arrival caused by NLOS conditions and may use this to improve the positioning accuracy (KEATING ¶19).
Regarding Claim 14, combination teaches The method of claim 12, wherein the joint probability distribution comprises a multi-dimensional mixture of Gaussian functions of the one or more features (Keating [0025]-[0027], [0039]-[0041] generating statistical parameters of signal derived features (i.e. ¶26-27 multidimension Gaussian mixture parameters, means, variances, weights) for positioning).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the COMBINATION to include the noted teachings of KEATING in order to learn the distribution of the bias of the time of arrival caused by NLOS conditions and may use this to improve the positioning accuracy (KEATING ¶19).
Claim(s 18-21 and 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over US 20220229143 A1 DWIVEDI; Satyam et al.
Regarding Claim 18, Dwivedi teaches The method of claim 1, wherein: the neural network is specific to the target UE, and the neural network is associated with the location of the target UE (Dwivedi ¶75 “[0075] The results of the LOS detection methods described above (or a subset of the LOS detection methods) that are performed by the network (e.g. the gNB designated as BS in FIG. 5)..” and ¶212 “Moreover, the functionality of a given block of the flowcharts and/or block diagrams may be separated into multiple blocks and/or the functionality of two or more blocks of the flowcharts and/or block diagrams may be at least partially integrated. “ thus ordinary and within the scope of teaching to distribute gNB over multiple units).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the teaching of Dwivedi to include ordinary variations and modifications can be made to the embodiments without substantially departing from the principles of the present inventive concepts (Dwivedi ¶19).
Regarding Claim 19, Dwivedi teaches The method of claim 18, wherein: the location of the target UE corresponds to a distributed unit of the network node to which the target UE is connected, or the location of the target UE corresponds to a central unit of the network node to which the target UE is connected (Dwivedi ¶75 “[0075] The results of the LOS detection methods described above (or a subset of the LOS detection methods) that are performed by the network (e.g. the gNB designated as BS in FIG. 5)..” and ¶212 “Moreover, the functionality of a given block of the flowcharts and/or block diagrams may be separated into multiple blocks and/or the functionality of two or more blocks of the flowcharts and/or block diagrams may be at least partially integrated. “ thus ordinary and within the scope of teaching to distribute gNB over multiple units).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the teaching of Dwivedi to include ordinary variations and modifications can be made to the embodiments without substantially departing from the principles of the present inventive concepts (Dwivedi ¶19).
Regarding Claim 20, AAA teaches The method of claim 1, further comprising: receiving a New Radio positioning protocol type A (NRPPa) Measurement Request (Fig. 5 NRPPA interface between BS (RAN node Fig. 8) and LS (CN node Fig. 9) indicating that the network node is expected to report the one or more statistics instead of the one or more features ((¶79 “The request may specify which LOS detection methods Are to be used in measuring and reporting the LOS detection measurement, which of the UE or base station is to use a LOS detection method..”).
Regarding Claim 21, AAA teaches The method of claim 20, wherein the NRPPa Measurement Request (Fig. 5 NRPPA interface between BS (RAN node Fig. 8) and LS (CN node Fig. 9) indicating that the network node is expected to report the one or more statistics instead of the one or more features comprises: the NRPPa Measurement Request including a measurement type field, and the measurement type field indicating that the network node is expected to execute the neural network and report the one or more statistics (¶79 “The request may specify which LOS detection methods Are to be used in measuring and reporting the LOS detection measurement, which of the UE or base station is to use a LOS detection method..”; [0055] neural network-based LOS discriminator).
Examiner further notes no tangible effect or improvement from structure of the measurement request is claimed thus considered non-functional descriptive material. See MPEP 2111.05
Regarding Claim 23, Dwivedi teaches The method of claim 22, wherein: the base station is associated with a central unit and a plurality of co-located distributed units, the plurality of co-located distributed units is associated with a corresponding plurality of neural networks, and the neural network is one of the plurality of neural networks (Dwivedi ¶75 “[0075] The results of the LOS detection methods described above (or a subset of the LOS detection methods) that are performed by the network (e.g. the gNB designated as BS in FIG. 5)..” and ¶212 “Moreover, the functionality of a given block of the flowcharts and/or block diagrams may be separated into multiple blocks and/or the functionality of two or more blocks of the flowcharts and/or block diagrams may be at least partially integrated. “ thus ordinary and within the scope of teaching to distribute gNB over multiple units)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the teaching of Dwivedi to include ordinary variations and modifications can be made to the embodiments without substantially departing from the principles of the present inventive concepts (Dwivedi ¶19).
Regarding Claim 24, Dwivedi teaches The method of claim 22, wherein: the base station is associated with a central unit and a plurality of co-located distributed units, the central unit is associated with a single neural network, and the neural network is the single neural network (Dwivedi ¶75 “[0075] The results of the LOS detection methods described above (or a subset of the LOS detection methods) that are performed by the network (e.g. the gNB designated as BS in FIG. 5)..” and ¶212 “Moreover, the functionality of a given block of the flowcharts and/or block diagrams may be separated into multiple blocks and/or the functionality of two or more blocks of the flowcharts and/or block diagrams may be at least partially integrated.“ thus ordinary and within the scope of teaching to distribute gNB over multiple units)
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the teaching of Dwivedi to include ordinary variations and modifications can be made to the embodiments without substantially departing from the principles of the present inventive concepts (Dwivedi ¶19).
Regarding Claim 25, Dwivedi teaches The method of claim 1, wherein: the network node is a UE in communication with the target UE over a sidelink, and the network entity is a serving base station of the UE ([0076] In one embodiment the LOS decision is performed by an artificial neural network algorithm or other type of machine learning algorithm in the UE. In one embodiment the network programs the neural network (or machine learning mechanism) through signaling).
It would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art, to modify the teaching of Dwivedi to include ordinary variations and modifications can be made to the embodiments without substantially departing from the principles of the present inventive concepts (Dwivedi ¶19).
Pertinent Prior Art(s)
The prior art made of record though not relied upon in the current rejection is considered pertinent to applicant's disclosure:
KR 102036442 B1 강홍구 et al. Apparatus and method for estimating location of user terminal based on deep learning
KR 102036442 B1 discloses a method of positioning performed by a network node (par. 17), comprising:
receiving, from a network entity, one or more parameters for a neural network;
performing one or more measurements of at least one reference signal from a target user equipment UE based on a measurement configuration for the at least one reference signal (par. 22-23, 25, 46);
generating one or more statistics of one or more features of the at least one reference signal based on the neural network and the one or more measurements (par. 22-23, 31-33); and
reporting the one or more statistics to the network entity to enable the network entity to estimate a location of the target UE based on the one or more statistics and a location of the network node (par. 31-33, 46-49).
Claims 26, 51, 76 correspond to claim 1 and are also considered not new.
CN 109996168 A LI, YANG et al. Method and device for obtaining terminal position
(par. 5-49, 65-68).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UMAIR AHSAN whose telephone number is (571)272-1323. The examiner can normally be reached Monday - Friday 10-5 PM EST or by emailing UMAIR.AHSAN@USPTO.GOV.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alison Slater can be reached at (571) 270-0375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/UMAIR AHSAN/Primary Examiner, Art Unit 2647