Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed on 09/08/2026 has been entered. Claims 1-3, 5-11, 14-19, 21-25, and 29-30 remain pending in this application. No claims are newly amended, cancelled, or are new.
Response to Arguments
Applicant’s arguments filed 09/08/2026 regarding prior art rejections have been fully considered but they are not persuasive. All prior art rejections are maintained for the same or similar reasoning as provided in the previous office action dated 07/07/2026.
Regarding Applicant’s arguments directed to the Rappaport reference, the Examiner maintains the use of Rappaport as proper in the prior art rejections of record including all previous responses to arguments.
Applicant argues that Rappaport fails to teach training a machine learning model, however, the teachings of Rappaport clearly disclose learning by a machine learning model, which is the same as training. The Examiner additionally cites [0053] “the exemplary system, method and computer-accessible medium can learn and determine what objects can exist in the channel, and where they can exist relative to other objects, based on time of flight of particular signatures measured in the channel.”. Additionally, the Applicant should also review at least Rappaport [0056]-[0065].
Additionally, the Examiner argues that a training location of the wireless device is disclosed by the combination of Rappaport and Butt. Rappaport discloses the transmission of location data corresponding to the wireless device while Butt more explicitly discloses a training location as having known locations. The detailed rejection is presented below.
Further, the Applicant appears to argue for a narrower definition of RFFP than is present in the claims and does not propose any distinctive qualities that separate the signals of Rappaport, Butt, and the claims of the proposed invention in any way. Therefore, the Examiner maintains that the transmitted/received signals of Rappaport and Butt are used identically to the proposed invention as or analogous to RFFP signals.
Therefore, the prior art rejections of all independent and dependent claims are maintained for the same or similar reasoning as provided in the previous action dated 07/07/2026.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 5-11, 14, 16, 25, 28, and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Rappaport (US 20200025911 A1), hereinafter Rappaport, in view of Butt (US 20220264514 A1), hereinafter Butt.
Regarding claim 1, Rappaport, as shown below, discloses a system comprising the following limitations:
receiving one or more training self-radio frequency fingerprint (self-RFFP) measurements obtained by an observer device based on reflections of one or more first reference signals transmitted by the observer device (See at least [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements);
receiving (See at least [0010] “Information regarding the second mmWave RF radiation can be transmitted to a further device, and the image(s) or the video(s) can be received from the further device.”), from a target device, one or more self-RFFP measurements (See at least [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements) obtained by the target device based on reflections of one or more second reference signals transmitted by the target device (See at least Fig. 3, [0054] FIG. 3 shows an exemplary diagram illustrating how the transmitted signal from the handheld device 105 can interact with the physical environment 305, facilitating the received version of the signal to be captured by the receiver in the handheld device”); and
determining a location of the target device based on applying the machine learning model to the one or more self-RFFP measurements (See at least [0010] “The image(s) or the video(s) can be generated based on the second mmWave RF radiation using a machine learning procedure.”, [0042] “incorporate and/or utilize movement of the exemplary mobile device (e.g., various positions and angles) in order to more accurately determine the position of the mobile device and/or generate an image of the surroundings.”)
Rappaport, does not explicitly disclose obtaining one or more training locations of the observer device, the one or more training locations being associated with the one or more training
obtaining one or more training locations of the observer device, the one or more training locations being associated with the one or more training (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.” The RFFP measurements of Butt are not explicitly “self-RFFP”, however because self-RFFP is disclosed by the primary reference, the teachings of Butt are a simple substitution with predictable results by using RFFP from another source.);
training a machine learning model based on training input data and reference output data, the training input data including the one or more training (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.”)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 2, The combination of Rappaport and Butt, as shown above, discloses all of the limitations of claim 1. Rappaport additionally discloses
obtaining one or more uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more uplink signals transmitted by the target device (See at least Fig. 3, [0045] “using wireless communication spectrum”, [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements),
wherein the location of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurements and the one or more UL- RFFP measurements (See at least [0010] “The image(s) or the video(s) can be generated based on the second mmWave RF radiation using a machine learning procedure.”, [0042] “incorporate and/or utilize movement of the exemplary mobile device (e.g., various positions and angles) in order to more accurately determine the position of the mobile device and/or generate an image of the surroundings.” The Examiner notes that Rappaport discloses a system where the UL-RFFP and self-RFFP may be the same signal.).
Regarding claim 5, The combination of Rappaport and Butt as shown above, discloses all the limitations of claim 1. Rappaport does not explicitly disclose obtaining one or more training uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more other reference signals transmitted by the observer device, wherein the training input data further includes the one or more training UL- RFFP measurement. However, Butt, in the same or in a similar field of endeavor, discloses
obtaining one or more training uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more other reference signals transmitted by the observer device, wherein the training input data further includes the one or more training UL- RFFP measurement (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.”);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 6, The combination of Rappaport and Butt as shown above, discloses all the limitations of claims 1. Rappaport does not explicitly disclose receiving one of the one or more training locations of the observer device from the observer device. However, Butt, in the same or in a similar field of endeavor, discloses
receiving one of the one or more training locations of the observer device from the observer device. (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.”);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 7, The combination of Rappaport and Butt as shown above, discloses all the limitations of claim 1. Rappaport does not explicitly disclose determining one of the one or more training locations of the observer device, wherein the one of the one or more training locations of the observer device is determined based on an uplink time difference of arrival (UL-TDoA), an uplink angle- of-arrival (UL-AoA), or round-trip time (RTT) positioning, or a combination thereof. However, Butt, in the same or in a similar field of endeavor, discloses
determining one of the one or more training locations of the observer device, wherein the one of the one or more training locations of the observer device is determined based on an uplink time difference of arrival (UL-TDoA), an uplink angle- of-arrival (UL-AoA), or round-trip time (RTT) positioning, or a combination thereof (See at least [0044] “In an embodiment, a ray tracing tool 300 may be used, based on realistic maps, get measurements on each coordinate of the map of the selected area under study. Measurements of radio signals and satellite positioning data 302 may be utilised. The measurements may be obtained from user terminals in test or experimental mode in known locations.” Butt discloses a ‘ray tracing tool’ equivalent to an uplink angle-of-arrival);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 8, The combination of Rappaport and Butt, as shown above, discloses all of the limitations of claim 1. Rappaport additionally discloses
the machine learning model is trained based on one or more training self-RFFP measurements obtained by one or more observer devices, each one of the training self-RFFP measurements being obtained by a corresponding observer device based on reflections of a corresponding reference signal transmitted by the corresponding observer device. (See at least [0065] “More complex scattering and reflection can occur with rough surfaces, such as carpeting or plaster walls, or people, but the responses over a wide range of frequencies, polarizations, and incident/departure angles can be known, as a look up or pre-loaded, or can be learned or trained in the operation of the exemplary system”).
Regarding claim 9, The combination of Rappaport and Butt as shown above, discloses all the limitations of claims 1 and 8. Rappaport does not explicitly disclose the machine learning model is trained further based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices. However, Butt, in the same or in a similar field of endeavor, discloses
the machine learning model is trained further based on one or more training uplink RFFP (UL-RFFP) measurements obtained by the one or more observer devices (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.” Butt discloses data from NLOS sources, therefore disclosing reflection.);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 10, The combination of Rappaport and Butt, as shown above, discloses all of the limitations of claims 1 and 8. Rappaport additionally discloses
the target device is configured as an observer device (See at least Fig. 3, [0054] FIG. 3 shows an exemplary diagram illustrating how the transmitted signal from the handheld device 105 can interact with the physical environment 305, facilitating the received version of the signal to be captured by the receiver in the handheld device”)
Regarding claim 11, Rappaport, as shown below, discloses a system comprising the following limitations:
transmitting one or more first reference signals (See at least Fig. 3, [0054] FIG. 3 shows an exemplary diagram illustrating how the transmitted signal from the handheld device 105 can interact with the physical environment 305, facilitating the received version of the signal to be captured by the receiver in the handheld device”);
obtaining one or more training self-radio frequency fingerprint (self-RFFP) measurements (See at least [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements) for training a machine learning model (See at least [0010] “The image(s) or the video(s) can be generated based on the second mmWave RF radiation using a machine learning procedure.”, [0065] “More complex scattering and reflection can occur with rough surfaces, such as carpeting or plaster walls, or people, but the responses over a wide range of frequencies, polarizations, and incident/departure angles can be known, as a look up or pre-loaded, or can be learned or trained in the operation of the exemplary system”) based on reflections of the one or more first reference signals transmitted by the wireless device (See at least Fig. 3, [0054] FIG. 3 shows an exemplary diagram illustrating how the transmitted signal from the handheld device 105 can interact with the physical environment 305, facilitating the received version of the signal to be captured by the receiver in the handheld device”); and
transmitting, to a network entity: the one or more training self-RFFP measurements (See at least [0010] “Information regarding the second mmWave RF radiation can be transmitted to a further device, and the image(s) or the video(s) can be received from the further device.”); and
the (See at least Fig. 3, [0009] “The image(s) or the video(s) can be determined based on the information. A location or position of objects in the environment can be determined, which can include (i) obstructions, (ii) walls, (iii) objects of interest, or (iv) people” Rappaport discloses reporting a training location as reporting a co-located object. See also [0010]-[0011])
Rappaport, does not explicitly disclose determining a training location of the wireless device, associated with the one or more training self-RFFP measurements, for training the machine learning model; and
determining a training location of the wireless device, associated with the one or more training self-RFFP measurements, for training the machine learning model (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.” The RFFP measurements of Butt are not explicitly “self-RFFP”, however because self-RFFP is disclosed by the primary reference, the teachings of Butt are a simple substitution with predictable results by using RFFP from another source.); and
(See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.” The RFFP measurements of Butt are not explicitly “self-RFFP”, however because self-RFFP is disclosed by the primary reference, the teachings of Butt are a simple substitution with predictable results by using RFFP from another source.)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 14, applicant recites limitations of the same or substantially the same scope as claim 7. Accordingly, claim 14 is rejected in the same or substantially the same manner as claim 7, shown above.
Regarding claim 16, The combination of Rappaport and Butt, as shown above, discloses all of the limitations of claim 11. Rappaport additionally discloses
the one or more training self-RFFP measurements correspond to the reflections received by a single antenna or multiple antennas of the wireless device (See at least [0012] “provide the first mmWave RF radiation to an environment(s), receive, using the antenna array(s), a second mmWave RF radiation”).
Regarding claim 25, applicant recites limitations of the same or substantially the same scope as claim 11. Accordingly, claim 25 is rejected in the same or substantially the same manner as claim 11, shown above. Rappaport additionally discloses
a memory (See at least [0147] “As shown in FIG. 21, for example a computer-accessible medium 2115 (e.g., as described herein above, a storage device such as a hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof)”);
at least one transceiver (See at least Fig. 1, [0049] “The exemplary device can use a transceiver 115, or separate receiver and transmitter 115”); and
at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to (See at least [0147] “As shown in FIG. 21, for example a computer-accessible medium 2115 (e.g., as described herein above, a storage device such as a hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) can be provided (e.g., in communication with the processing arrangement 2105)”):
Regarding claim 28, applicant recites limitations of the same or substantially the same scope as claim 7. Accordingly, claim 28 is rejected in the same or substantially the same manner as claim 7, shown above.
Regarding claim 30, applicant recites limitations of the same or substantially the same scope as claim 16. Accordingly, claim 30 is rejected in the same or substantially the same manner as claim 16, shown above.
Claim 3, 15, 17-19, 21-24, and 29 is rejected under 35 U.S.C. 103 as being unpatentable over Rappaport, in view of Butt, in further view of Liu (EP 3958593 A1), hereinafter Liu.
Regarding claim 3, The combination of Rappaport and Butt, as shown above, discloses all the limitations of claim 1. The combination of Rappaport and Butt does not explicitly disclose the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data. However, Liu, in the same or in a similar field of endeavor, discloses
the one or more reference signals include a sounding reference signal (SRS), a sidelink positioning reference signal (SL-PRS), a sidelink synchronization signal block (SL-SSB), a sidelink channel state information reference signal (SL CSI-RS), an uplink channel reference signal, an uplink channel signal carrying data, a sidelink channel reference signal, or a sidelink channel signal carrying data. (See at least Figs. 1, 3, [0049] “Step 301: A first network device receives a sounding reference signal (sounding reference signal, SRS) and a cell identifier that are sent by a terminal.”).
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt with the signal system disclosed by Liu. One would have been motivated to do so in order to advantageously improve positioning accuracy (See at least “Because the multipath fingerprint combination includes more radio signal features, the method can improve the positioning accuracy.”).
Regarding claim 15, applicant recites limitations of the same or substantially the same scope as claim 3. Accordingly, claim 15 is rejected in the same or substantially the same manner as claim 3, shown above.
Regarding claim 17, Rappaport, as shown below, discloses a network entity comprising the following limitations:
receive one or more training self-radio frequency fingerprint (self-RFFP) measurements obtained by an observer device based on reflections of one or more first reference signals transmitted by the observer device (See at least [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements);
receive (See at least [0010] “Information regarding the second mmWave RF radiation can be transmitted to a further device, and the image(s) or the video(s) can be received from the further device.”), from a target device, one or more self-RFFP measurements (See at least [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements) obtained by the target device based on reflections of one or more second reference signals transmitted by the target device (See at least Fig. 3, [0054] FIG. 3 shows an exemplary diagram illustrating how the transmitted signal from the handheld device 105 can interact with the physical environment 305, facilitating the received version of the signal to be captured by the receiver in the handheld device”); and
determine a location of the target device based on applying the machine learning model to the one or more self-RFFP measurements (See at least [0010] “The image(s) or the video(s) can be generated based on the second mmWave RF radiation using a machine learning procedure.”, [0042] “incorporate and/or utilize movement of the exemplary mobile device (e.g., various positions and angles) in order to more accurately determine the position of the mobile device and/or generate an image of the surroundings.”)
Rappaport, does not explicitly disclose obtain one or more training locations of the observer device, the one or more training locations being associated with the one or more training measurements; train a machine learning model based on training input data and reference output data, the training input data including the one or more training
obtain one or more training locations of the observer device, the one or more training locations being associated with the one or more training (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.” The RFFP measurements of Butt are not explicitly “self-RFFP”, however because self-RFFP is disclosed by the primary reference, the teachings of Butt are a simple substitution with predictable results by using RFFP from another source.);
train a machine learning model based on training input data and reference output data, the training input data including the one or more training (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.”)
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
The combination of Rappaport and Butt does not explicitly disclose 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 configured to: receive,
a memory (See at least Fig. 2, “memory 232”);
at least one transceiver (See at least Fig. 2, “wireless network interfaces 250”); and
at least one processor communicatively coupled to the memory and the at least one transceiver, the at least one processor configured to (See at least Fig. 2, “central processing unit 222”):
receive (See at least Fig. 1),
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the machine learning training system disclosed by Butt with the signal system disclosed by Liu. One would have been motivated to do so in order to advantageously improve positioning accuracy (See at least “Because the multipath fingerprint combination includes more radio signal features, the method can improve the positioning accuracy.”).
Regarding claim 18, The combination of Rappaport, Butt, and Liu as shown above, discloses all of the limitations of claim 17. Rappaport additionally discloses
obtain one or more uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more uplink signals transmitted by the target device (See at least Fig. 3, [0045] “using wireless communication spectrum”, [0009] “Information related to a phase(s) of the second mmWave RF radiation, a time of arrival of the second mmWave RF radiation, a relative time of arrival of the second mmWave RF radiation, or an angle of arrival of the second mmWave RF radiation can be determined.” Rappaport discloses a second mmWave as a reflected signal of a first mmWave transmission wherein signal properties such as phases are used as RFFP measurements),
wherein the location of the target device is determined based on applying the machine learning model to the one or more self-RFFP measurements and the one or more UL- RFFP measurements (See at least [0010] “The image(s) or the video(s) can be generated based on the second mmWave RF radiation using a machine learning procedure.”, [0042] “incorporate and/or utilize movement of the exemplary mobile device (e.g., various positions and angles) in order to more accurately determine the position of the mobile device and/or generate an image of the surroundings.” The Examiner notes that Rappaport discloses a system where the UL-RFFP and self-RFFP may be the same signal.).
Regarding claim 19, applicant recites limitations of the same or substantially the same scope as claim 3. Accordingly, claim 19 is rejected in the same or substantially the same manner as claim 3, shown above.
Regarding claim 21, The combination of Rappaport, Butt, and Liu as shown above, discloses all the limitations of claim 17. Rappaport does not explicitly disclose obtain one or more training uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more other reference signals transmitted by the observer device, wherein the training input data further includes the one or more training UL- RFFP measurement. However, Butt, in the same or in a similar field of endeavor, discloses
obtain one or more training uplink RFFP (UL-RFFP) measurements based on the one or more reference signals or one or more other reference signals transmitted by the observer device, wherein the training input data further includes the one or more training UL- RFFP measurement (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.”);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the signal system disclosed by Liu with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 22, The combination of Rappaport, Butt, and Liu as shown above, discloses all the limitations of claim 17. Rappaport does not explicitly disclose receive one of the one or more training locations of the observer device from the observer device. However, Butt, in the same or in a similar field of endeavor, discloses
receive one of the one or more training locations of the observer device from the observer device. (See at least [0044] “The measurements may be obtained from user terminals in test or experimental mode in known locations. […] This generated RSRP data may then be used as an input for ML training where an ML model 308 for enabling UE positioning based on radio characteristics is created.”);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the signal system disclosed by Liu with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 23, The combination of Rappaport, Butt, and Liu as shown above, discloses all the limitations of claim 17. Rappaport does not explicitly disclose determine one of the one or more training locations of the observer device, wherein the one of the one or more training locations of the observer device is determined based on an uplink time difference of arrival (UL-TDoA), an uplink angle- of-arrival (UL-AoA), or round-trip time (RTT) positioning, or a combination thereof. However, Butt, in the same or in a similar field of endeavor, discloses
determine one of the one or more training locations of the observer device, wherein the one of the one or more training locations of the observer device is determined based on an uplink time difference of arrival (UL-TDoA), an uplink angle- of-arrival (UL-AoA), or round-trip time (RTT) positioning, or a combination thereof (See at least [0044] “In an embodiment, a ray tracing tool 300 may be used, based on realistic maps, get measurements on each coordinate of the map of the selected area under study. Measurements of radio signals and satellite positioning data 302 may be utilised. The measurements may be obtained from user terminals in test or experimental mode in known locations.” Butt discloses a ‘ray tracing tool’ equivalent to an uplink angle-of-arrival);
Furthermore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the network system disclosed by Rappaport with the signal system disclosed by Liu with the machine learning training system disclosed by Butt. One would have been motivated to do so in order to advantageously increase mapping accuracy (See at least [0004] “Intelligent computing algorithms utilising ML help in increasing accuracy to map characteristics of RF data to physical locations.”).
Regarding claim 24, The combination of Rappaport, Butt, and Liu, as shown above, discloses all of the limitations of claim 17. Rappaport additionally discloses
the machine learning model is trained based on one or more training self-RFFP measurements obtained by one or more observer devices, each one of the training self-RFFP measurements being obtained by a corresponding observer device based on reflections of a corresponding reference signal transmitted by the corresponding observer device. (See at least [0065] “More complex scattering and reflection can occur with rough surfaces, such as carpeting or plaster walls, or people, but the responses over a wide range of frequencies, polarizations, and incident/departure angles can be known, as a look up or pre-loaded, or can be learned or trained in the operation of the exemplary system”).
Regarding claim 29, applicant recites limitations of the same or substantially the same scope as claim 3. Accordingly, claim 29 is rejected in the same or substantially the same manner as claim 3, shown above.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNETH W GOOD whose telephone number is (571)272-4186. The examiner can normally be reached Mon - Thu 7:30 am - 5:00 pm.
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/KENNETH W GOOD/Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
