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 .
Information Disclosure Statement
The information disclosure statement submitted on 12/04/2025, have been considered by the examiner and made of record in the application file.
Response to Amendment
This Office Action is in response to applicant’s amendment submitted on December 4, 2025.
Claims, 1-20 are now currently pending in the present application.
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.
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 non-obviousness.
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.
Claims 1-20 are rejected under U.S.C. 103 as being unpatentable by DAVIS et al. (DAVIS US 20100190509 A1, hereinafter DAVIS) in view of SUNDARARAJAN et al. (US 2024007988A1, hereinafter SUNDARARAJAN).
Consider Claim 1, DAVIS discloses a system, comprising:
at least one processor; and (fig. 2 paragraph 0049, example system 200 can include a processor 250, which is configured to confer, and confers, at least in part, the described functionality of the various components included in example system 200. Processor 250 can execute code instructions (not shown) stored in memory 240, or other memory(ies), to provide the described functionality).
at least one memory that stores executable instructions that, when executed by the at least one processor, facilitate performance of operations, the operations comprising: (Fig. 2 paragraph 0049, example system 200 can include a processor 250, which is configured to confer, and confers, at least in part, the described functionality of the various components included in example system 200. Processor 250 can execute code instructions (not shown) stored in memory 240, or other memory(ies), to provide the described functionality).
DAVIS discloses the claim invention obtaining round trip time data representative of a round trip time and angle of arrival data representative of an angle of arrival for a communication between a transmit- receive point of a group of transmit-receive points at a known location but fail to teach of respective known locations of respective transmit-receive points of the group of transmit-receive points, and a user equipment at an unknown location.
However, SUNDARARAJAN teaches (Paragraph 0077, known position-determination techniques include RTT, multi-RTT, OTDOA (also called TDOA and including UL-TDOA and DL-TDOA), Enhanced Cell Identification (E-CID), DL-AoD, UL-AoA, etc. RTT uses a time for a signal to travel from one entity to another and back to determine a range between the two entities. The range, plus a known location of a first one of the entities and an angle between the two entities (e.g., an azimuth angle) can be used to determine a location of the second of the entities. In multi-RTT (also called multi-cell RTT), multiple ranges from one entity (e.g., a UE) to other entities (e.g., TRPs) and known locations of the other entities may be used to determine the location of the one entity).
inputting the round trip time data and angle of arrival data to a round trip time correction module corresponding to the transmit-receive point; ( Fig. 8 paragraph 0040, calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT. See par 57, 66 and 67)
DAVIS discloses the claim invention modifying but fails to teach using a machine learning model of respective machine learning models, based on the angle of arrival, the round trip time for the communication into virtual line of sight round trip time data, wherein the respective machine learning models were trained for the respective transmit-receive points at the respective known locations, and based on respective round-trip times corresponding to the respective transmit-receive points, respective angle of arrival training datasets for respective training communications between the respective transmit-receive points, and respective device instances at respective known training locations.
However, SUNDARARAJAN teaches (paragraph 0109, the position information may be determined by an entity other than the server 400, e.g., a UE and/or a TRP. The server 400, and/or other entity, may employ a consistency algorithm to determine whether data are consistent or anomalous. Also or alternatively, the server 400 may employ machine learning (e.g., a neural network) to the measurement report information (e.g., based on training data) from one or more TRPs to determine position information for the UE 200. For example, the machine learning may use measurements as inputs and compare an estimated location with a verified location of the UE 200 to refine an algorithm for determining the location estimate based on the measurements such that location estimate accuracy may be improved).
combining the virtual line of sight round trip time data into a vector dataset, the vector dataset comprising the virtual line of sight round trip time data and respective other round trip time data based on respective other communications between respective other transmit-receive points and the user equipment; (Paragraph 0030, Path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay .DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
inputting the vector dataset to a line of sight-based position determination function; and (paragraph 0030, path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
obtaining, in response to the inputting of the vector dataset to the line of sight- based position determination function, an estimated location of the user equipment. (Paragraph 0030, Path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
Consider Claim 2, DAVIS discloses the system of claim 1, wherein the round trip time correction module comprises a look-up table that relates the round trip time data and the angle of arrival data to round trip time-related correction data. (Paragraph 0037, calibration platform 210 also includes analysis component 218 that can implement various algorithms, stored in algorithm storage 244, to characterize or evaluate features of location data, or estimates, generated by TOF component 212; location data can be retained in location intelligence storage 232. In an aspect, algorithms employed by analysis component 218 include statistical analysis methodologies; other analysis methodologies such as spectral analysis and time-series analysis also can be utilized).
Consider Claim 3, DAVIS discloses the system of claim 1, wherein the round trip time correction module comprises an analytical function that converts the round trip time data and the angle of arrival data to round trip time-related correction data. (Paragraph 0037, calibration platform 210 also includes analysis component 218 that can implement various algorithms, stored in algorithm storage 244, to characterize or evaluate features of location data, or estimates, generated by TOF component 212; location data can be retained in location intelligence storage 232. In an aspect, algorithms employed by analysis component 218 include statistical analysis methodologies; other analysis methodologies such as spectral analysis and time-series analysis also can be utilized).
Consider Claim 4, DAVIS discloses the system of claim 1, wherein the known location of the transmit-receive point is a first known location, and wherein the machine learning model trained with respective round trip time datasets and respective angle of arrival training datasets for respective communications between the transmit-receive point and a respective device instances at respective second known locations. (Paragraph, 0038, received location estimate(s) 215 are retained in location intelligence storage 242. It should be appreciated that based upon specific aspects of location source(s) platform 220, calibration platform 210 can receive location estimate(s) 215 over the air-interface (e.g., 182) via communication platform 230, or through a network management component such as a network server ;a radio network controller; or a network gateway or associated serving node(s), e.g., gateway mobile location center (GMLC) and related serving mobile location center (SMLC). Location source(s) platform 220 provides accurate location data based at least in part on GNSS, such as assisted GPS, and network planning information. In an aspect, location source(s) platform 220 is embodied in a set of mobile devices that support GNSS data reception and manipulation thereof, as illustrated in FIGS. 4A and 4B. Diagram 400 in FIG. 4A displays a snapshot at an instant .tau. 404 of a set 406 of eighteen mobile devices distributed through four TA bands 402.sub.1-402.sub.4 that communicate with a GNSS system 410 (e.g., GPS, Galileo, GLONASS) through a deep-space link 412. Mobile devices in set 406 receive timing signaling that allows determination, at least in part, of accurate position of each mobile the receives sufficient information (e.g., timing information from three or more satellites) for triangulation. It is noted that in AGPS the mobile devices in set 406 can receive timing messages via one or more components in network mobile platform(s) 108; in an aspect, the one or more components can include PDF(s), e.g., CGI+RTT, FL-TDOA or the like, and node(s) for generation or implementation thereof).
Consider Claim 5, DAVIS discloses the system of claim 4, wherein the respective device instances comprise respective positioning reference unit instances located at the respective second known locations. (Paragraph 0037, calibration platform 210 also includes analysis component 218 that can implement various algorithms, stored in algorithm storage 244, to characterize or evaluate features of location data, or estimates, generated by TOF component 212; location data can be retained in location intelligence storage 232. In an aspect, algorithms employed by analysis component 218 include statistical analysis methodologies; other analysis methodologies such as spectral analysis and time-series analysis also can be utilized).
Consider Claim 6, DAVIS discloses the system of claim 4, wherein the respective device instances comprise respective mobile device instances located at the respective second known locations and configured to report the respective second known locations via global positioning system data. (Paragraph, 0038, Location source(s) platform 220 provides accurate location data based at least in part on GNSS, such as assisted GPS, and network planning information. In an aspect, location source(s) platform 220 is embodied in a set of mobile devices that support GNSS data reception and manipulation thereof, as illustrated in FIGS. 4A and 4B. Diagram 400 in FIG. 4A displays a snapshot at an instant .tau. 404 of a set 406 of eighteen mobile devices distributed through four TA bands 402.sub.1-402.sub.4 that communicate with a GNSS system 410 (e.g., GPS, Galileo, GLONASS) through a deep-space link 412. Mobile devices in set 406 receive timing signaling that allows determination, at least in part, of accurate position of each mobile the receives sufficient information (e.g., timing information from three or more satellites) for triangulation).
Consider Claim 7, DAVIS discloses the system of claim 4, wherein the transmit-receive point at the first known location and the respective device instances at the respective second known locations are represented by a digital twin simulation of an environment, and wherein the respective round trip time datasets and the respective angle of arrival training datasets are based on the digital twin simulation. (Paragraph 0072, n AP 1005, memory 1045 can store data structures, code instructions and program modules, system or device information, code sequences for scrambling, spreading and pilot transmission, location intelligence storage, determined delay offset(s), over-the-air propagation models, and so on. Processor 1035 is coupled(twin simulation) to the memory 1045 in order to store and retrieve information necessary to operate and/or confer functionality to communication platform 1015, calibration platform 1012, and other components (not shown) of access point 1005. See par 35 and 53)
Consider Claim 8, DAVIS discloses the system of claim 1, wherein the transmit-receive point and the respective other transmit-receive points are spatially distributed in a deployment environment. (Paragraph 0030, in an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup (LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector; e.g., a first sector can be primarily densely populated while a neighboring sector can include a substantial area of public parks (e.g., dashed area(s) in diagrams 300 or 350)).
Consider Claim 9, DAVIS discloses the system of claim 8, wherein the transmit-receive point and the respective other transmit-receive points are evenly distributed. (Paragraph 0058, Such mapping enables generation of effective coverage pattern of sectors in deployed cells. It is noted that the effective coverage pattern is affected by substantially the same, or the same, wireless environment conditions described supra. It is further noted that the mapping also can include determining angular, azimuth, boundaries within a sector that enable, in conjunction with RTT or TA bands, determination of geographic tiles(squares) within an identified sector. Paragraph 0045, For nearly uniform propagation air-interface in which multi-path, strong reflection, and signal scattering is nearly uniform throughout a sector, the relationship among location estimates in the first set and TOF location estimates in the second set can be cast as .rho..sub.i=.rho..sub.i'+.DELTA..rho., where .DELTA..rho. is a uniform position offset).
Consider Claim 10, DAVIS discloses the claim invention a method comprising, but fails to teach inputting, to a machine learning model of respective machine learning models by a system comprising at least one processor, round trip time data and angle of arrival data for a communication between a transmit-receive point of a group of transmit-receive points at a known location of respective known locations and a user equipment at an unknown location wherein the respective machine learning models were trained for the respective transmit-receive points at the respective known locations, and based on respective round-trip times corresponding to the respective transmit-receive points, respective angle of arrival training datasets for respective training communications between the respective transmit-receive points, and respective device instances at respective known training locations.
However, SUNDARARAJAN teaches (paragraph 0109, the position information may be determined by an entity other than the server 400, e.g., a UE and/or a TRP. The server 400, and/or other entity, may employ a consistency algorithm to determine whether data are consistent or anomalous. Also or alternatively, the server 400 may employ machine learning (e.g., a neural network) to the measurement report information (e.g., based on training data) from one or more TRPs to determine position information for the UE 200. For example, the machine learning may use measurements as inputs and compare an estimated location with a verified location of the UE 200 to refine an algorithm for determining the location estimate based on the measurements such that location estimate accuracy may be improved).
modifying, by the machine learning model of the system based on the round trip time data and the angle of arrival data, the round trip time data for the communication into virtual line of sight round trip time data; ( Fig. 8 paragraph 0040, Calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT. See par 39, 57 and 66).
combining, by the system, the virtual line of sight round trip time data into a vector dataset, the vector dataset comprising the virtual line of sight round trip time data and respective other round trip time data based on respective other communications between respective other transmit-receive points and the user equipment; (Paragraph 0030, path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
inputting, by the system, the vector dataset to a line of sight-based position determination function; and (paragraph 0030, path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
obtaining, by the system, in response to the inputting of the vector dataset to the line of sight-based position determination function, an estimated location of the user equipment. (Paragraph 0030, path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
Consider Claim 11, DAVIS discloses the method of claim 10, wherein the vector dataset comprises at least four virtual line of sight values corresponding to at least four transmit-receive points. (Paragraph 0038, Location source(s) platform 220 provides accurate location data based at least in part on GNSS, such as assisted GPS, and network planning information. In an aspect, location source(s) platform 220 is embodied in a set of mobile devices that support GNSS data reception and manipulation thereof, as illustrated in FIGS. 4A and 4B. Diagram 400 in FIG. 4A displays a snapshot at an instant .tau. 404 of a set 406 of eighteen mobile devices distributed through four TA bands 402 sub 1-402 sub.4 that communicate with a GNSS system 410 (e.g., GPS, Galileo, GLONASS) through a deep-space link 412. Mobile devices in set 406 receive timing signaling that allows determination, at least in part, of accurate position of each mobile the receives sufficient information (e.g., timing information from three or more satellites) for triangulation).
Consider Claim 12, DAVIS discloses the method of claim 10, wherein at least one of the respective device instances comprises a positioning reference unit, and wherein the training process further comprises moving the positioning reference unit among at least two of the respective known training locations. (Paragraph 0038, received location estimate(s) 215 are retained in location intelligence storage 242. It should be appreciated that based upon specific aspects of location source(s) platform 220, calibration platform 210 can receive location estimate(s) 215 over the air-interface (e.g., 182) via communication platform 230, or through a network management component such as a network server ;a radio network controller; or a network gateway or associated serving node(s), e.g., gateway mobile location center (GMLC) and related serving mobile location center (SMLC). Location source(s) platform 220 provides accurate location data based at least in part on GNSS, such as assisted GPS, and network planning information. In an aspect, location source(s) platform 220 is embodied in a set of mobile devices that support GNSS data reception and manipulation thereof, as illustrated in FIGS. 4A and 4B).
Consider Claim 13, DAVIS discloses the method of claim 10, wherein at least one of the respective device instances comprises a mobile device that reports location coordinates, and wherein the training process further comprises moving the mobile device among at least two of the respective known training locations. (Paragraph 0038, a radio network controller; or a network gateway or associated serving node(s), e.g., gateway mobile location center (GMLC) and related serving mobile location center (SMLC). Location source(s) platform 220 provides accurate location data based at least in part on GNSS, such as assisted GPS, and network planning information. In an aspect, location source(s) platform 220 is embodied in a set of mobile devices that support GNSS data reception and manipulation thereof, as illustrated in FIGS. 4A and 4B. Diagram 400 in FIG. 4A displays a snapshot at an instant .tau. 404 of a set 406 of eighteen mobile devices distributed through four TA bands 402.sub.1-402.sub.4 that communicate with a GNSS system 410 (e.g., GPS, Galileo, GLONASS . . . ) through a deep-space link 412. Mobile devices in set 406 receive timing signaling that allows determination, at least in part, of accurate position of each mobile the receives sufficient information (e.g., timing information from three or more satellites) for triangulation).
Consider Claim 14, DAVIS discloses the method of claim 10, wherein the machine learning model is a first machine learning model, wherein the round trip time data is first round trip time data, wherein the angle of arrival data is first angle of arrival data for a first communication between a first transmit-receive point at a first known location and the user equipment, wherein the training process is a first training process comprising obtaining respective first round-trip time, angle of arrival training datasets for first respective training communications between the first transmit-receive point and first respective device instances at respective first known training locations, wherein the virtual line of sight round trip time data is first virtual line of sight round trip time data, wherein the respective other transmit-receive points comprise a second transmit-receive point, and further comprising: (Paragraph 0052, at act 610, a first set of location estimates from a location source is received. As described above, in an aspect, the location source can be embodied in one or more mobile handsets that support reception of GNSS data, such as assisted GPS (AGPS), and operation thereon (e.g., injection of GNSS data on location based applications that execute externally, or are native, to the mobile) or manipulation thereof such as delivery of location data. In an aspect, the first set of location estimates can be received at predetermined instants (e.g., .tau. 404) or time intervals that can be configured by a network operator. Additionally, received location estimates can be accumulated for a specific interval time interval (e.g., a second, a minute ) that also can be configured by a network operator).
inputting, by the system to a second machine learning model, second round trip time data and second angle of arrival data for a second communication between the second transmit-receive point at a second known location and the user equipment, the second machine learning model having been trained for the second transmit-receive point via a second training process comprising obtaining second respective round-trip time, angle of arrival training datasets for respective second training communications between the second transmit-receive point and second respective device instances at second respective known training locations; and (Paragraph 0053, in an aspect, the second set of location estimates is generated in response to receiving the first set of location estimates. In another aspect, the set of one or more probes, or wireless beacons, at known positions can be utilized to generate the second set of location estimates via a time of flight method. It is noted that a model utilized for RF signal propagation, or propagation of any EM radiation, in the wireless environment can affect the generation of locations through the TOF assessment. Accordingly, based at least on conditions in a wireless environment or changes thereof, e.g., foliage changes, landscape changes such as skyline changes, atmospheric conditions, which can affect absorption of propagated radiation and other scattering properties thereof, different wireless signal propagation models can be employed to generate the second set of location estimates).
modifying, by the second machine learning model of the system based on the second round trip time data and the second angle of arrival data, the second round trip time data for the second communication into second virtual line of sight round trip time data, wherein the other round trip time data comprises the second virtual line of sight round trip time data. (Paragraph 0058, at act 650, sector identification (ID) information extracted from the first set of location estimates is mapped to sector ID data obtained from the second set of location estimates. Such mapping enables generation of effective coverage pattern of sectors in deployed cells. It is noted that the effective coverage pattern is affected by substantially the same, or the same, wireless environment conditions described supra. It is further noted that the mapping also can include determining angular, azimuth, boundaries within a sector that enable, in conjunction with RTT or TA bands, determination of geographic tiles within an identified sector).
Consider Claim 15, DAVIS discloses a non-transitory machine-readable medium, comprising executable instructions that, when executed by at least one processor, facilitate performance of operations, the operations comprising:
DAVIS discloses the claim invention obtaining a first dataset comprising first round trip time data representative of a first round trip time and first angle of arrival data representative of a first angle of arrival for first communications between a first transmit-receive point of a group of transmit-receive points at a first known location of the respective known locations and a user equipment at an unknown location.
However, SUNDARARAJAN teaches (Paragraph 0077, known position-determination techniques include RTT, multi-RTT, OTDOA (also called TDOA and including UL-TDOA and DL-TDOA), Enhanced Cell Identification (E-CID), DL-AoD, UL-AoA, etc. RTT uses a time for a signal to travel from one entity to another and back to determine a range between the two entities. The range, plus a known location of a first one of the entities and an angle between the two entities (e.g., an azimuth angle) can be used to determine a location of the second of the entities. In multi-RTT (also called multi-cell RTT), multiple ranges from one entity (e.g., a UE) to other entities (e.g., TRPs) and known locations of the other entities may be used to determine the location of the one entity).
inputting the first round trip time data and first angle of arrival data into a first round trip time correction module corresponding to the first transmit-receive point; ( Fig. 8 paragraph 0040, Calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT See par 39, 57 and 66).
DAVIS discloses the claim invention modifying but fails to teach using a first machine learning model of the respective models and based on the first angle of arrival, the first round trip time for the first communications into first virtual line of sight round trip time data, wherein the respective machine learning models were trained for the respective transmit-receive points at the respective known locations, and based on respective round-trip times corresponding to the respective transmit-receive points, respective angle of arrival training datasets for respective training communications between the respective transmit-receive points, and respective device instances at respective known training locations.
However, SUNDARARAJAN teaches (paragraph 0109, the position information may be determined by an entity other than the server 400, e.g., a UE and/or a TRP. The server 400, and/or other entity, may employ a consistency algorithm to determine whether data are consistent or anomalous. Also or alternatively, the server 400 may employ machine learning (e.g., a neural network) to the measurement report information (e.g., based on training data) from one or more TRPs to determine position information for the UE 200. For example, the machine learning may use measurements as inputs and compare an estimated location with a verified location of the UE 200 to refine an algorithm for determining the location estimate based on the measurements such that location estimate accuracy may be improved).
DAVIS discloses the claim invention obtaining a second dataset comprising second round trip time data but fails to teach representative of a second round trip time and second angle of arrival data representative of a second angle of arrival for second communications between a second transmit-receive point of the group of transmit-receive points at a second known location of the respective known locations and the user equipment at the unknown location;
However, SUNDARARAJAN teaches (Paragraph 0077, known position-determination techniques include RTT, multi-RTT, OTDOA (also called TDOA and including UL-TDOA and DL-TDOA), Enhanced Cell Identification (E-CID), DL-AoD, UL-AoA, etc. RTT uses a time for a signal to travel from one entity to another and back to determine a range between the two entities. The range, plus a known location of a first one of the entities and an angle between the two entities (e.g., an azimuth angle) can be used to determine a location of the second of the entities. In multi-RTT (also called multi-cell RTT), multiple ranges from one entity (e.g., a UE) to other entities (e.g., TRPs) and known locations of the other entities may be used to determine the location of the one entity).
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which said subject matter pertains, to modify Claims 1, 10 and 15 by incorporating the teachings of DAVIS relates to wireless communications and, more particularly, to correction of propagation delay offsets of wireless signals with the method of reporting measurements(machine learning) of an uplink positioning reference signal includes receiving and determining a plurality of measurement values of the uplink positioning reference signal of SUNDARARAJAN. The motivation to do so would be to develop an expanded and improved wireless communication system or multiple point round trip time positioning algorithm to determine location of user equipment (UE). This method enables easily compensating the propagation delay such that accuracy of time-of-flight (TOF) locations and radio network performance can be improved for a base station for reporting measurements of uplink positioning reference signal in a wireless communication system.
inputting the second round trip time data and second angle of arrival data into a second round trip time correction module corresponding to the second transmit-receive point; (paragraph 0030, path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
modifying, using a second model of the respective machine learning models and based on the second angle of arrival, the second round trip time for the second communications into second virtual line of sight round trip time data;
combining the first virtual line of sight round trip time data and the second virtual line of sight round trip time data into a vector dataset; (Paragraph 0055, At act 630, RF signal propagation delay is determined based at least in part on the received first set of location estimates and the generated second set of location estimates. In an aspect, statistical analysis of the data, e.g., received location estimates and generated location estimates, is utilized to establish a correlation between the first set of location estimates and the second set of location estimates. Such correlation evaluates a degree of linear dependency, or co-linearity, among the first set and second set of location estimates.
inputting the vector dataset to a line of sight-based position determination function; and (paragraph 0030, path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup.(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
obtaining, in response to the inputting of the vector dataset, an estimated location of the user equipment. (Paragraph 0030, Path delay 186 typically is caused by various source, e.g., mismatches (e.g., impedance mismatch) among electronic elements and components, stray capacitances and inductances, length of the antenna(s) cable(s) in base station(s); tower height of base station, whereas timing delay spread 188 generally originates from any signal path scattering, or "signal bounces," such as multipath, strong reflections, etc.; and the like. It should be appreciated that timing delay spread 188 is largely stochastic and affected by complex and substantially unknowable sources or variables. In an aspect of the subject innovation, contribution of path delay and timing delay spread to propagation timing can be compensated, at least in part, and thus propagation timing can be employed for accurate location determination, since calibrated propagation timing substantially reveals LOS timing delay DELTA tau sup(LOS). It is noted that compensation of propagation timing delay offsets can depend on coverage sector, since structure of wireless channel 182, or wireless environment, typically depends on covered sector. See par 40 and 60).
Consider Claim 16, DAVIS discloses the non-transitory machine-readable medium of claim 15, wherein the operations further comprise combining third round trip time data corresponding to third communications with a third transmit-receive point into the vector dataset prior to the inputting of the vector dataset to the line of sight-based position determination function. (Paragraph 0029, time of flight measurements probe time of arrival, which is the propagation timing, or round trip time which includes propagation timing from the handset to the radio equipment. As illustrated in FIG. 1B, propagation timing 185, effective total timing delay, includes path delay 186 (DELTA tau sup.(path)) and over-the-air-interface (e.g., wireless channel 182) propagation which includes line-of-sight (LOS) timing delay 188 (DELTA tau sup.(LOS)) and timing delay spread 190 (DELTA tau sup.(spread))).
Consider Claim 17, DAVIS discloses the non-transitory machine-readable medium of claim 15, wherein the first round trip time correction module comprises at least one of: a look-up table that relates the first round trip time data and the first angle of arrival data to first round trip time-related correction data, or an analytical function that converts the first round trip time data and the first angle of arrival data to the first round trip time-related correction data. (Paragraph 0037, calibration platform 210 also includes analysis component 218 that can implement various algorithms, stored in algorithm storage 244, to characterize or evaluate features of location data, or estimates, generated by TOF component 212; location data can be retained in location intelligence storage 232. In an aspect, algorithms employed by analysis component 218 include statistical analysis methodologies; other analysis methodologies such as spectral analysis and time-series analysis also can be utilized).
Consider Claim 18, DAVIS discloses the non-transitory machine-readable medium of claim 15, wherein the first machine learning model was trained with respective round trip time, angle of arrival training datasets for respective communications between the first transmit-receive point and respective training device instances at respective training device instance locations. (Paragraph, 0038, received location estimate(s) 215 are retained in location intelligence storage 242. It should be appreciated that based upon specific aspects of location source(s) platform 220, calibration platform 210 can receive location estimate(s) 215 over the air-interface (e.g., 182) via communication platform 230, or through a network management component such as a network server ;a radio network controller; or a network gateway or associated serving node(s), e.g., gateway mobile location center (GMLC) and related serving mobile location center (SMLC). Location source(s) platform 220 provides accurate location data based at least in part on GNSS, such as assisted GPS, and network planning information. In an aspect, location source(s) platform 220 is embodied in a set of mobile devices that support GNSS data reception and manipulation thereof, as illustrated in FIGS. 4A and 4B. Diagram 400 in FIG. 4A displays a snapshot at an instant .tau. 404 of a set 406 of eighteen mobile devices distributed through four TA bands 402.sub.1-402.sub.4 that communicate with a GNSS system 410 (e.g., GPS, Galileo, GLONASS . . . ) through a deep-space link 412. Mobile devices in set 406 receive timing signaling that allows determination, at least in part, of accurate position of each mobile the receives sufficient information (e.g., timing information from three or more satellites) for triangulation. It is noted that in AGPS the mobile devices in set 406 can receive timing messages via one or more components in network mobile platform(s) 108; in an aspect, the one or more components can include PDF(s), e.g., CGI+RTT, FL-TDOA or the like, and node(s) for generation or implementation thereof).
Consider Claim 19, DAVIS discloses the non-transitory machine-readable medium of claim 18, wherein the respective training device instances at the respective training device instance locations comprise at least one of: a positioning reference unit, or a mobile device. (Paragraph 0038, it is noted that in AGPS the mobile devices in set 406 can receive timing messages via one or more components in network mobile platform(s) 108; in an aspect, the one or more components can include PDF(s), e.g., CGI+RTT, FL-TDOA or the like, and node(s) for generation or implementation thereof. Mobiles in set 406 also can receive assisted timing information from mobile network platform(s) 108 through base station 110 serving sector 405; mobile network platform(s) 108 received timing information from GNSS 410 through deep-space link 414. Mobiles in set 406 deliver accurate position data to base station 110, which receives the data through antenna(s) 112 and radio component(s) 114 and conveys such data to a calibration platform (e.g., 210). In block diagram 420 in FIG. 4B, mobile(s) 430 can be embodied in the set of mobile devices 406).
Consider Claim 20, DAVIS discloses the non-transitory machine-readable medium of claim 15, wherein the operations further comprise:
obtaining a third dataset comprising third round trip time data representative of a third round trip time and third angle of arrival data representative of a third angle of arrival for third communications between a third transmit-receive point at a third known location and the user equipment at the unknown location; (Paragraph 0040, Calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT). (Paragraph 0040, Calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT. See par 39, 57 and 66).
inputting the third round trip time data and third angle of arrival data into a third round trip time correction module corresponding to the third transmit-receive point; ( Fig. 8 paragraph 0040, Calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT). See par 39, 57 and 66).
modifying, based on the third angle of arrival, the third round trip time for the third communications into third virtual line of sight round trip time data; and ( Fig. 8 paragraph 0040, Calibration of ETTD and associated timing delay error for range or distance from a base station, and sector bearing or angular position within a served cell or sector can enable refinement of location services based at least in part on CGI+RTT, for example, and improvement of conventional methods that may refine CGI+RTT. See par 39, 57 and 66)
combining the third virtual line of sight round trip time data into the vector dataset prior to the inputting of the vector dataset to the line of sight-based position determination function. (Paragraph 0055, At act 630, RF signal propagation delay is determined based at least in part on the received first set of location estimates and the generated second set of location estimates. In an aspect, statistical analysis of the data, e.g., received location estimates and generated location estimates, is utilized to establish a correlation between the first set of location estimates and the second set of location estimates. Such correlation evaluates a degree of linear dependency, or co-linearity, among the first set and second set of location estimates).
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.
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/MICHELE C DOUGLAS/Examiner, Art Unit 2646
/MATTHEW D. ANDERSON/Supervisory Patent Examiner, Art Unit 2646