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 § 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.
Claims 1-20 are rejected under 35 U.S.C. 102a1 as being anticipated by Tadayon (US-20210160712-A1).
Regarding claim 1, Tadayon discloses a method performed by a first wireless node for
enabling estimation of a position of a wireless terminal, the method comprising:
- receiving, from a positioning node (base stations 202, 204, 206, 208 in Fig. 2), a message indicative of a trained prediction model for determining a line-of-sight (LOS) path associated with the first wireless node during positioning of the first wireless node (prediction parameters 322 received, Fig. 3B)(paragraph 0111-0112),
- receiving, from a second wireless node, a reference signal (received NLos reference signal 324, Fig. 3B),
- determining, based on the reference signal and the trained prediction model, a line-of-sight (LoS) path between the first wireless node and the second wireless node (Fig. 3A),
wherein the wireless terminal is one of the first wireless node and the second
wireless node (estimation of terminal/UEs position 210, 212, 214 and 216 between the first and second wireless node).
Regarding claim 20, Tadayon discloses a method performed by a positioning node (see Fig. 1Cfor structure) for enabling positioning of a wireless terminal, the method comprising (a method performed by terminal with Fig. 1B in rejection of claim 1 is similarly applied here with Fig. 1C for method for positioning node):
- transmitting, to the first wireless node, a message indicative of the trained prediction model, to be used for determining a line-of-sight (LoS) path between the first wireless node and a second wireless node during positioning of the first wireless node, and
- receiving, from the first wireless node, a positioning measurement report, wherein the positioning measurement report is indicative of a determined LoS path between the first wireless node and the second wireless node, wherein the wireless terminal is one of the first wireless node and the second wireless node.
Regarding claim 2, Tadayon discloses wherein the method comprises:
- transmitting, to a positioning node, a positioning measurement report for the determined LoS path between the first wireless node and the second wireless node (UE location estimates may be generated based on a variety of different data sets, including uplink or downlink transmissions between the base station and UEs, receiving Global Positioning System (GPS) information from some of the UEs, and/or using other known location information, paragraph 0104).
Regarding claims 3-5, Tadayon discloses wherein determining comprises performing a channel measurement based on the received reference signal and the message indicate of the trained prediction model (receiving from a UE range estimates for reference signals received at the UE over an indirect propagation path from the base station, a received power for each received reference signal, and estimating locations of signal reflectors may further involve estimating locations of signal reflectors based on location information of the UE, a location of the base station, the received range estimates, and the received powers, paragraph 0030)(prediction model 502, Fig. 5 and Fig. 7).
Regarding claim 6, Tadayon discloses wherein the trained prediction model comprises a matrix of weights, and wherein applying the trained prediction model comprises applying the weights to the channel measurement (method may involve updating the shadowing map in response to receiving identifications of neighboring UEs and more recently received identifications of neighboring UEs may be assigned a greater weight than previously received identifications of neighboring UEs, paragraph 0022).
Regarding claims 7-8, Tadayon discloses wherein the message indicative of the trained prediction model is indicative of a full trained prediction model matrix or a part of the model matrix (network equipment may make a determination that a very strong LoS communication beam 1030 directed toward a UE 1028 from a base station 1026 is causing a reduction in MIMO channel matrix rank. The rank of the MIMO channel matrix is an indicator of how many data streams can be spatially multiplexed on the MIMO channel. The base station in FIG. 10D may respond by proactively directing one or more additional transmission beams 1032 and 1036 toward signal reflectors 1034 and 1038 that cause the additional beams to be redirected to the receiving UE. The transmission from the base station to the UE may then be multiplexed over the direct beam 1030 and the additional beams 1032 and 1036 to improve a MIMO matrix rank for the signal transmission, paragraph 0135).
Regarding claim 9, Tadayon discloses wherein the message indicative of the trained prediction model comprises a time parameter indicative of a time duration for which the prediction model is valid, and wherein determining comprises determining the Los path based on the trained prediction model for the time duration (When periodically updating the shadowing map in response to receiving ongoing identifications of neighboring UEs, the time-dependent factor causes more recently received identifications of neighboring UEs to be assigned a greater weight than previously received identifications of neighboring UEs, paragraph 0118).
Regarding claim 10, Tadayon discloses wherein the positioning measurement report is indicative of the trained prediction model based on which the Los path has been determined (see Fig. 5, NLOS with dash lines while LOS indicated with solid lines).
Regarding claim 11, Tadayon discloses wherein the message indicative of the trained prediction model is indicative of a structure of a predictive neural network used to train the trained prediction model (building 240, Fig. 2, paragraph 0099).
Regarding claim 12, Tadayon discloses wherein the message indicative of the trained prediction model is one or more of a positioning measurement request message, a ranging request, a positioning configuration information message, a positioning configuration update message, and an attach procedure message (retraining requested or initiated based on any one or more of: a threshold amount of time since previous training or retraining, movement of one or more UEs by more than a threshold distance, an error detection rate above a threshold, and/or any of various other factors, paragraph 0281).
Regarding claims 13-14, Tadayon discloses sending, to the positioning node, a message indicating a machine learning capability of the first wireless node or indicating the machine learning capability (a neural network, Fig. 23, paragraph -253) is indicative of one or more of:
- a matrix size,
- a type of prediction model,
- a number of prediction model layers (see layers in Fig. 23),
- a number of prediction model neurons, and
- a number of inferences per time unit for the trained prediction model that the first wireless node has capability to handle.
Regarding claims 15-17, Tadayon discloses wherein the first/second wireless node is the wireless terminal, and the second/first wireless node is a base station or between wireless terminals (see Fig. 2 uplink/downlink is performed between terminals and base stations).
Regarding claim 18, Tadayon discloses wherein the trained prediction model is a prediction model associated with one or more of (see Fig. 5, prediction is based on specific LOS/NLOS situation either on terminals or base stations):
- a specific base station,
- a specific wireless terminal,
- a specific area (based on network equipment).
Regarding claim 19, Tadayon discloses wherein the message indicative of the trained prediction model comprises an identifier, for identifying the specific base station and/or the specific area associated with the trained prediction model (inference pair , paragraph 0114)(identifies base station and terminal which also identifies area since base station has it unique ID in each area).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARRY K LIU whose telephone number is (571)270-1338. The examiner can normally be reached on every M-F 10 AM to 6:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, please leave a voice message with application serial number and nature of call, a response within 24 hours can be expected during regular business days. Also, the Examiner’s supervisor Hodge, Robert W. can be reached at (571) 272-6496. The fax phone number for the organization where this application or proceeding is assigned is 571-270-2338.
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/HARRY K LIU/Primary Examiner, Art Unit 3645 Tel: (571) 270-1338
Fax: (571) 270-2338
Email: harry.liu@uspto.gov