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 .
Election/Restrictions
Applicant's election without traverse of group V corresponding to claims 8-15, 21-25, 28 and 30 along with generic claims 1-3, 16-18, 26-27 and 29 in the reply filed on 8/1/2017 is acknowledged. Claims 4-7 and 19-20 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected group. Claims 1-3, 8-18 and 21-30 are pending in this application.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 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.
Claim(s) 1, 3, 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raghothaman (US 2020/0260403) and further in view of Ghanbarinejad (US 2020/0067615).
Referring to claim 1, Raghothaman discloses a method of operating a network device (FIG. 2, Par. 16, “communication system … to determine a user's/wireless device's physical location within the site. At the same time (or nearly the same time), a signature vector may be determined for the user/wireless device at the site, e.g., based on SRSs received at the RPs from the user/wireless device”), comprising:
obtaining one or more uplink composite radio frequency fingerprint (RFFP) measurements (Par. 16, 21, “receive uplink RF signals transmitted by wireless devices 110”, “a signature vector may be determined for the user/wireless device at the site, e.g., based on SRSs received at the RPs from the user/wireless device”, “the signature vector 130 for a wireless device 110 may be based on: (1) the angle of arrival measured at different RPs 106, e.g., for an SRS 132”, “A signature vector 130 may be determined (e.g., periodically) for each wireless device 110 connected to the system”. Par. 47, “uplink SRS 132 sent by the wireless device 110 may reach the RPs 106”, Note that the signature vector is equivalent to radio frequency fingerprint (RFFP) and SRS is the uplink sounding reference signal because the SRS is transmitted by wireless device to the network on an uplink channel. Further note that RP or Radio Points receive the SRS from the wireless devices and obtains the uplink RF signature vector, which is equivalent to the RF fingerprint, based on the uplink reference signals (or SRS)
the uplink reference signals being transmitted by target device over an uplink reference signal resource (Par. 16, “of uplink-sounding reference signal (UL-SRS) transmitted from a UE”); and
determining estimated positions of the target devices based on applying a machine learning model to the one or more uplink composite RFFP measurements (Par. 16, 47, “determine a user's/wireless device's physical location based on an RF signature/fingerprint (e.g., signature vector)”. Par. 32, “machine learning computing”, “the RF fingerprinting component 198 may estimate the position of the UE based at least in part on matching the at least one second RF fingerprint to at least one of the plurality of first RF fingerprints”, “a machine learning model 124, e.g., that performs … location determination and/or tracking”. Par. 62, “signature vector 130 is determined, it may be transmitted to the machine learning computing system”, note that location determination system is a machine learning system based on evaluating radio fingerprinting).
Raghothaman does not explicitly disclose the uplink reference signals being transmitted by multiple target devices.
In an analogous art, Ghanbarinejad discloses uplink reference signals being transmitted by multiple target devices (Par. 133, “plurality of uplink composite reference signals from a UE or a plurality of UEs … TRP may perform measurements through different antenna panel combinations or different beam combinations and use the measurement results”, note that the uplink reference signals are transmitted by multiple UEs (or devices) so that the TRP can make measurements based on multiple uplink signals).
It would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to modify the invention of Raghothaman by incorporating the teachings of Ghanbarinejad for the purpose of determining the RF fingerprint/or signature based on a plurality of devices in the vicinity and thus providing a more reliable RF fingerprinting. Further, this an example of use of known technique to improve similar devices, methods or products in the same way. MPEP 2143.
Referring to claim 3, the combination of Raghothaman/Ghanbarinejad discloses the method of claim 1, wherein the uplink reference signals include a sounding reference signal (SRS), an uplink channel signal carrying data, or an uplink channel reference signal (Raghothaman, Par. 61, “a wireless device 110 may periodically transmit a Sounding Reference Signal (SRS) 132A-C, e.g., every 80-160 ms.”. Par. 49, “Each signature vector 130 for a wireless device 110 may be iteratively determined/updated (while that wireless device 110 is connected to the system 100A) based on SRSs 132 transmitted by the wireless device 110.”).
Referring to claim 26, Raghothaman discloses a network device (FIGS. 1A-1B, Par. 13, 20, base station … that employs at least one (and optionally multiple) baseband unit 104 and multiple (e.g., N=2-100) radio points (RPs) ), comprising: a memory; at least one transceiver; and at least one processor communicatively coupled to the memory and the at least one transceiver (Par. 116, “ a processor will receive instructions and data from a read-only memory and/or a random access memory. For example, where a computing device is described as performing an action, the computing device may carry out this action using at least one processor”), the at least one processor configured to:
obtain one or more uplink composite radio frequency fingerprint (RFFP) measurements (Par. 16, 21, “receive uplink RF signals transmitted by wireless devices 110”, “a signature vector may be determined for the user/wireless device at the site, e.g., based on SRSs received at the RPs from the user/wireless device”, “the signature vector 130 for a wireless device 110 may be based on: (1) the angle of arrival measured at different RPs 106, e.g., for an SRS 132”, “A signature vector 130 may be determined (e.g., periodically) for each wireless device 110 connected to the system”. Par. 47, “uplink SRS 132 sent by the wireless device 110 may reach the RPs 106”, Note that the signature vector is equivalent to radio frequency fingerprint (RFFP) and SRS is the uplink sounding reference signal because the SRS is transmitted by wireless device to the network on an uplink channel. Further note that RP or Radio Points receive the SRS from the wireless devices and obtains the uplink RF signature vector, which is equivalent to the RF fingerprint, based on the uplink reference signals (or SRS)
the uplink reference signals being transmitted by target device over an uplink reference signal resource (Par. 16, “of uplink-sounding reference signal (UL-SRS) transmitted from a UE”); and
determine estimated positions of the target devices based on applying a machine learning model to the one or more uplink composite RFFP measurements (Par. 16, 47, “determine a user's/wireless device's physical location based on an RF signature/fingerprint (e.g., signature vector)”. Par. 32, “machine learning computing”, “the RF fingerprinting component 198 may estimate the position of the UE based at least in part on matching the at least one second RF fingerprint to at least one of the plurality of first RF fingerprints”, “a machine learning model 124, e.g., that performs … location determination and/or tracking”. Par. 62, “signature vector 130 is determined, it may be transmitted to the machine learning computing system”, note that location determination system is a machine learning system based on evaluating radio fingerprinting).
Raghothaman does not explicitly disclose the uplink reference signals being transmitted by multiple target devices.
In an analogous art, Ghanbarinejad discloses uplink reference signals being transmitted by multiple target devices (Par. 133, “plurality of uplink composite reference signals from a UE or a plurality of UEs … TRP may perform measurements through different antenna panel combinations or different beam combinations and use the measurement results”, note that the uplink reference signals are transmitted by multiple UEs (or devices) so that the TRP can make measurements based on multiple uplink signals).
It would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to modify the invention of Raghothaman by incorporating the teachings of Ghanbarinejad for the purpose of determining the RF fingerprint/or signature based on a plurality of devices in the vicinity and thus providing a more reliable RF fingerprinting. Further, this an example of use of known technique to improve similar devices, methods or products in the same way. MPEP 2143.
Claim(s) 2 and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raghothaman (US 2020/0260403) and further in view of Ghanbarinejad (US 2020/0067615) and further in view of HUANG (US 2024/0188029).
Referring to claim 2, the combination of Raghothaman/Ghanbarinejad discloses the method of claim 1.
The above combination is not relied on for wherein the uplink reference signals are based on a same reference sequence.
In an analogous art, HUANG discloses uplink reference signals are based on a same reference sequence (Par. 536, “each triggered SRS resource set corresponds to the same sequence number,” note that SRS or sounding Reference Signal (RS) is defined as uplink reference signal transmitted by UE on the uplink to the base station and each SRS are at least based on the same sequence)
It would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to modify the combination by incorporating the teachings of HUANG for the purpose of allowing the uplink reference signals to be mapped to the sequences easily. Further, this an example of use of known technique to improve similar devices, methods or products in the same way. MPEP 2143.
Referring to claim 27, the combination of Raghothaman/Ghanbarinejad discloses the network device of claim 26.
The above combination is not relied on for wherein the uplink reference signals are based on a same reference sequence.
In an analogous art, HUANG discloses uplink reference signals are based on a same reference sequence (Par. 536, “each triggered SRS resource set corresponds to the same sequence number,” note that SRS or sounding Reference Signal (RS) is defined as uplink reference signal transmitted by UE on the uplink to the base station and each SRS are at least based on the same sequence)
It would have been obvious to one skilled in the art, before the effective filing date of the claimed invention, to modify the combination by incorporating the teachings of HUANG for the purpose of allowing the uplink reference signals to be mapped to the sequences easily. Further, this an example of use of known technique to improve similar devices, methods or products in the same way. MPEP 2143.
Allowable Subject Matter
Claim(s) 8-15 and 28 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is the examiner’s statement of reasons for allowance:
Regarding Claims 8 and 28:
The prior art fails to disclose or suggest the limitation “obtaining a sidelink composite RFFP measurement, the sidelink composite RFFP measurement being based on multiple sidelink reference signals observed at a first target device of the target devices, the multiple sidelink reference signals being transmitted by two or more target devices of the target devices different from the first target device over a sidelink reference signal resource, wherein the determining the estimated positions of the target devices is based on applying the machine learning model to the one or more uplink composite RFFP measurements and the sidelink composite RFFP measurement”, as recited in claims 8 and 28, along with the limitations of the intermediate and/or base claims.
Regarding claims 9-15:
Claims 9-15 depend on allowable subject matter of claim 8, thus, they are allowable for being dependent upon allowable claims.
Claims 16-18, 21-25 and 29-30 are allowed.
The following is the examiner’s statement of reasons for allowance:
Regarding Independent claims 16 and 29:
The prior art fails to disclose or suggest the limitations “obtaining one or more training uplink composite radio frequency fingerprint (RFFP) measurements, the one or more training uplink composite RFFP measurements being based on multiple training uplink reference signals observed at one or more Transmission / Reception Points(TRPs), the training uplink reference signals being transmitted by multiple observed devices over an uplink reference signal resource; obtaining training positions of the observed devices, the training positions being associated with the one or more training uplink composite RFFP measurements; and training a machine learning model based on training input data and reference output data, the training input data including the one or more training uplink composite RFFP measurements, and the reference output data including the training positions of the observed devices, wherein estimated positions of multiple target devices are determinable based on applying the machine learning model to one or more uplink composite RFFP measurements, the one or more uplink composite RFFP measurements being based on multiple uplink reference signals transmitted by the target devices”, as recited in claims 16 and 29 along with the limitations of the claims.
Claims 17-18, 21-25 and 30 depend on allowable subject matter of claims 16 and 29, thus, they are allowable for being dependent upon allowable claims.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Wang et al. U.S. Pub. No. 20250015993 A1 discloses the features of the geographic position feed is generated to identify a geographic position of a receiver by comparing a measured radio signature in the form of a unique composite identifier to radio signatures in a signature lookup table (e.g., lookup table 450 of FIG. 4). The unique composite identifier included a plurality of measured signal qualities that collectively represent a frequency spectrum. Each measured signal quality in the plurality of measured signal qualities corresponds to a portion of the frequency spectrum. In some embodiments, the agent facilitates comparing the unique composite identifier in the form of the measured radio signature with any of a plurality of predetermined radio signatures found in the lookup table. In some such embodiments, the respective geographic position of each of the radio signatures in the plurality of predetermined radio signatures was known. Therefore, when a match is found between the unique composite identifier and a radio signature found in lookup table , the location of the receiver, and, therefore, the appliance, is determined to be the geographic location of the matching radio signature in lookup table (see Par. 385).
Applicant is invited to contact the examiner to discuss claim amendments in order to place claims in condition for allowance and thereby expedite prosecution.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Fred Casca, whose telephone number is (571) 272-7918. The examiner can normally be reached on Monday through Friday from 9 to 5. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Kathy Wang-Hurst, can be reached at (571) 270-5371. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/FRED A CASCA/Primary Examiner, Art Unit 2644