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
This is in response to an amendment/response filed 8/5/2025.
No claims have been cancelled.
No claims have been added.
Claims 1-20 are now pending.
Response to Arguments
Applicant’s arguments with respect to independent claims 1, 11, and 20 (pages 6-7) in a reply filed 8/5/2025 have been considered but are moot because the arguments are based on newly changed limitations in the amendment and new ground of rejections using newly introduced references or a newly introduced portion of an existing reference are applied in the current rejection.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Otsuki et al. US 20240036227 (hereinafter “Otsuki”) in view of Janco et al. US 20220399920 (hereinafter “Janco”) and in further view of Zou et al. US 20220124154 (hereinafter “Zou”)
As to claim 1, 11, and 20 (claim 1 is the method claim for the system and computer-readable medium in claim 11 and 20 respectively):
Otsuki discloses:
A method for identifying a remote device, the method comprising: capturing a channel state information (CSI) packet, (“The capture device 103(1) and the capture device 103(2) are installed near a communication area including a communication area between the AP 101 and the STA 102, monitor wireless LAN frames communicated between the AP 101 and the STA 102, and capture a frame. The capture device 103(1) and the capture device 103(2) determine the VHT Compressed Beamforming Report frame transmitted from the STA 102 to the AP 101 among the wireless LAN frames communicated between the AP 101 and the STA 102 and capture the frame. A frame in which the captured compressed CSI is stored and information on the reception time of the frame are transmitted to both the detection device 104(1) and the detection device 104(2) which will be described later.”, Otsuki [0022]) sent from a receiver device in response to receiving a calibration packet, the calibration packet sent by the remote device via transmitter hardware; (“In FIG. 1, the AP 101 transmits a very high throughput null data packet (VHT NDP) to the STAs 102 as a reference signal for measuring a state of each propagation path between each antenna of the AP 101 and each of the STAs 102. Furthermore, the STA 102 calculates CSI indicating the state of the propagation path between each antenna of the AP 101 and the STA 102 from the VHT NDP, stores the result in a VHT Compressed Beam Forming Report frame, and transmits it to AP 101.”, Otsuki [0020])
extracting a feature set from the CSI packet captured, the feature set affected by characteristics of the transmitter hardware; (“the compressed CSI shown in FIG. 5 is an example. In the above case, φ11 indicates the phase difference when signals transmitted from AT(4) and AT(1) are received by the antenna of the STA 102. Similarly, φ21 indicates the phase difference between AT(4) and AT(2) and φ31 indicates the phase difference between AT(4) and AT(3). Note that φijε[0, 2π) in which i and j are positive integers. Also, ψ21 indicates a value, expressed in angle, of the amplitude ratio when the signals transmitted from AT(1) and AT(2) are received by the antenna of STA 102 (value of tan-1 of ratio of absolute value of amplitude). Similarly, ψ21 represents the amplitude ratio between AT(1) and AT(2) and ψ31 represents the amplitude ratio between AT(1) and AT(3). Note that ψijε[0, π/2) in which i and j are positive integers.”, Otsuki [0067])
Otsuki as described above does not explicitly teach:
producing a classified feature set by classifying the feature set extracted from the CSI packet captured;
and determining an identifier based on the classified feature set, the identifier corresponding to the remote device.
However, Janco further teaches determining a fingerprint representing a STA based on the feature vector which includes:
and determining an identifier based on the classified feature set, the identifier corresponding to the remote device. (“calculate at least one feature vector from the monitored transmissions; produce a fingerprint representing the at least one STA, based on the calculated feature vector; and identify the at least one STA from a plurality of STAs, based on the produced fingerprint”, Janco [0026])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include producing a fingerprint representing a STA based on the feature vector as described in Janco into Otsuki. By modifying the method to include producing a fingerprint representing a STA based on the feature vector as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
The combination of Otsuki and Janco as described above does not explicitly teach:
producing a classified feature set by classifying the feature set extracted from the CSI packet captured;
However, Zou further teaches producing a unique representation based on extracted CSI data from captured CSI frames which includes:
producing a classified feature set by classifying the feature set extracted from the CSI packet captured; (“Since the disclosed CSI enabled IoT platform is generally able to capture CSI frames in a non-intrusive manner with high sampling rate, unlabeled data may be easily obtained in the target domain.”, Zou [0043]) (“The above observation implies that the unique gait information of each individual can be extracted from the CSI time series data and characterized by sub-sequences at critical times, known as shapelets. CSI shapelet analysis provides a sparse and unique representation of the high-resolution CSI data obtained from a person, like a fingerprint. Also, according to the biometric research, the gait cycle contains unique information that can be used as a biometric signature to identify the person. The above suggests mining CSI shapelets and storing them in a database to build classifiers for human identification.”, Zou [0054])
Otsuki, Zou, and Janco are analogous because they pertain to using CSI data for identification.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include producing a unique representation based on extracted CSI data from captured CSI frames as described in Zou into Otsuki as modified by Janco. By modifying the method to include producing a unique representation based on extracted CSI data from captured CSI frames as taught by Zou, the benefits of improved identification process (Otsuki [0006], Zou [0054], and Janco [0011]) are achieved.
As to claim 2 and 12 (claim 2 is the method claim for the system in claim 12):
Otsuki discloses:
The method of Claim 1, wherein the CSI packet is a non-encrypted packet (“FIG. 5 shows an example of a compressed CSI transmitted from the STA 102 to the AP 101. In FIG. 5, the number of transmitting antennas (the number of antennas of AP 101)×the number of receiving antennas (the number of antennas of STA 102), the number of compressed CSIs, and an example of compressed CSI are listed in order from the left column. Note that the number of transmission antennas is two or more.”, Otsuki [0064]) and wherein the CSI packet is a multi-user multi-input, multi-output (MU-MIMO) CSI packet. (“Furthermore, although FIG. 1 shows an example in which one STA 102 is provided, the embodiment can be applied even when multi user multiple input multiple output (MU-MIMO) transmission is performed between the AP 101 and a plurality of STAs 102.”, Otsuki [0019])
As to claim 3 and 13 (claim 3 is the method claim for the system in claim 13):
Otsuki discloses:
The method of Claim 1, wherein the characteristics represent at least one imperfection of the transmitter hardware of the remote device. (“the compressed CSI shown in FIG. 5 is an example. In the above case, φ11 indicates the phase difference when signals transmitted from AT(4) and AT(1) are received by the antenna of the STA 102. Similarly, φ21 indicates the phase difference between AT(4) and AT(2) and φ31 indicates the phase difference between AT(4) and AT(3). Note that φijε[0, 2π) in which i and j are positive integers. Also, ψ21 indicates a value, expressed in angle, of the amplitude ratio when the signals transmitted from AT(1) and AT(2) are received by the antenna of STA 102 (value of tan-1 of ratio of absolute value of amplitude). Similarly, ψ21 represents the amplitude ratio between AT(1) and AT(2) and ψ31 represents the amplitude ratio between AT(1) and AT(3). Note that ψijε[0, π/2) in which i and j are positive integers.”, Otsuki [0067])
As to claim 4 and 14 (claim 4 is the method claim for the system in claim 14):
Otsuki as described above does not explicitly teach:
The method of Claim 1, wherein the remote device is among a plurality of remote devices and wherein the identifier determined includes a) a unique device identifier, the unique device identifier distinguishing the remote device from the plurality of remote devices and b) a probability that the remote device sent the CSI packet.
However, Janco further teaches unique device identifier and probability that the CSI packet was sent by the remote device which includes:
The method of claim 1, wherein the remote device is among a plurality of remote devices (“identify the at least one STA from a plurality of STAs, based on the produced fingerprint”, Janco [0026]) and wherein the identifier determined includes a) a unique device identifier, the unique device identifier distinguishing the remote device from the plurality of remote devices (“uniquely identifying a wireless communication device at the physical level”, Janco [0011]) and b) a probability that the remote device sent the CSI packet. (“method for training one or more machine learning models to predict whether a received data transmission was transmitted from one or more specific wireless device, based, at least in part, on features extracted from and/or representing dataframes of the standard beamforming protocol”, Janco [0058])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include unique device identifier and probability that the CSI packet was sent by the remote device as described in Janco into Otsuki. By modifying the method to include unique device identifier and probability that the CSI packet was sent by the remote device as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 5 and 15 (claim 5 is the method claim for the system in claim 15):
Otsuki as described above does not explicitly teach:
The method of Claim 1, wherein the calibration packet is sent from a beamformer to a beamformee, wherein the CSI packet represents beamforming feedback information, and wherein the method further comprises capturing the CSI packet by monitoring a wireless channel between the beamformer and the beamformee.
However, Janco further teaches CSI packet representing the wireless channel between the beamformer and beamformee which includes:
The method of claim 1, wherein the calibration packet is sent from a beamformer to a beamformee (“As shown in block 202, a beamformer device, such as an AP, may transmit a Null Data Packet (NDP, denoted herein as NDP 200a) to a beamformee (e.g., an STA device) over a wireless channel”, Janco [0072]), wherein the CSI packet represents beamforming feedback information (“CSI matrix, into eigen components to produce one or more respective “feedback” matrices”, Janco [0075]), and wherein the method further comprises capturing the CSI packet by monitoring a wireless channel between the beamformer and the beamformee. (“system 302 may include an RF monitoring module 312, that may be configured to receive and/or monitor RF transmissions over a wireless channel between a beamformer device (e.g., AP) and at least one beamformee device”, Janco [0104])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include CSI packet representing the wireless channel between the beamformer and beamformee as described in Janco into Otsuki. By modifying the method to include CSI packet representing the wireless channel between the beamformer and beamformee as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 6 and 16 (claim 6 is the method claim for the system in claim 16):
Otsuki as described above does not explicitly teach:
The method of claim 5, wherein the feature set extracted includes beamforming feedback matrices computed by the beamformee, wherein the classifying is based on beamforming feedback angles, and wherein the beamforming feedback angles are derived from the beamforming feedback matrices.
However, Janco further teaches beamforming feedback matrices which includes:
The method of claim 5, wherein the feature set extracted includes beamforming feedback matrices computed by the beamformee, wherein the classifying is based on beamforming feedback angles, and wherein the beamforming feedback angles are derived from the beamforming feedback matrices. (“The beamformee (e.g., STA device) may produce a compressed form V.sup.c of feedback matrix {tilde over (V)}, that may be comprised of feedback angles ?.sub.(1,i) and ?.sub.(1,i), denoted herein as “feedback angle parameters 200b′” or “angle parameters 200b′” for short”, Janco [0092])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include beamforming feedback matrices as described in Janco into Otsuki. By modifying the method to include beamforming feedback matrices as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 7:
Otsuki as described above does not explicitly teach:
The method of claim 1, wherein the classifying includes employing a machine learning model to produce the classified feature set.
However, Janco further teaches using machine learning to classify a feature which includes:
The method of claim 1, wherein the classifying includes employing a machine learning model to produce the classified feature set. (“one computing device may identify the at least one beamformee by introducing at least one feature vector, corresponding to the at least one beamformee to an ML-based model, trained to identify the at least one beamformee based on the at least one feature vector”, Janco [0030])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include using machine learning to classify a feature as described in Janco into Otsuki. By modifying the method to include using machine learning to classify a feature as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 8:
Otsuki as described above does not explicitly teach:
The method of Claim 1, wherein the CSI packet includes physical layer (PHY) level information and wherein the classifying includes demodulating the PHY-level information and processing, via the machine learning model, the PHY-level information demodulated.
However, Janco further teaches CSI packet with PHY level information which includes:
The method of claim 1, wherein the CSI packet includes physical layer (PHY) level information (“beamforming protocol dataframes may include data elements that are derived from, or correlate to physical layer data, including for example channel estimation information (e.g., CSI)”, Janco [0015]) and wherein the classifying includes demodulating the PHY-level information and processing, via the machine learning model, the PHY-level information demodulated. (“machine learning module 318 may be configured to train and inference one or more machine learning models configured to predict whether a received data transmission was transmitted from one or more specific wireless devices, based, at least in part, on features extracted from and/or representing dataframes (such as dataframe 200b shown in FIG. 2) of the standard beamforming protocol transmitted by the specific device”, Janco [0127])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include CSI packet with PHY level information as described in Janco into Otsuki. By modifying the method to include CSI packet with PHY level information as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 9 and 18 (claim 9 is the method claim for the system in claim 18):
Otsuki as described above does not explicitly teach:
The method of claim 1, wherein the remote device is wireless device and wherein the wireless device is Wi-Fi compliant.
However, Janco further teaches Wi-Fi compliance of the wireless device which includes:
The method of claim 1, wherein the remote device is wireless device and wherein the wireless device is Wi-Fi compliant. (“CSI data may be accessible via a proprietary software patch that may be applied to Wi-Fi communication cards, or via Application Programming Interfaces (APIs) that may be provided by Wi-Fi silicon providers”, Janco [0008]
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include Wi-Fi compliance of the wireless device as described in Janco into Otsuki. By modifying the method to include Wi-Fi compliance of the wireless device as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 10 and 19 (claim 10 is the method claim for the system in claim 19):
Otsuki as described above does not explicitly teach:
The method of claim 1, further comprising employing the identifier to authenticate the remote device or outputting the identifier to a system, the system configured to authenticate the remote device based on the identifier output.
However, Janco further teaches network device authentication which includes:
The method of claim 1, further comprising employing the identifier to authenticate the remote device or outputting the identifier to a system, the system configured to authenticate the remote device based on the identifier output. (“the present technique may be used in the context of a wireless communications network, for identifying and authenticating network devices”, Janco [0061])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include network device authentication as described in Janco into Otsuki. By modifying the method to include network device authentication as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
As to claim 17:
Otsuki as described above does not explicitly teach:
The system of Claim 10, wherein the classifier is further configured to employ a machine learning model to produce the classified feature set, wherein the CSI packet includes physical layer (PHY) level information, and wherein the classifier is further configured to demodulate the PHY-level information and process, via the machine learning model, the PHY-level information demodulated
However, Janco further teaches using machine learning to produce a feature set and deriving data that correlate to physical layer data which includes:
The system of claim 10, wherein the classifier is further configured to employ a machine learning model to produce the classified feature set (“one computing device may identify the at least one beamformee by introducing at least one feature vector, corresponding to the at least one beamformee to an ML-based model, trained to identify the at least one beamformee based on the at least one feature vector”, Janco [0030]), wherein the CSI packet includes physical layer (PHY) level information (“beamforming protocol dataframes may include data elements that are derived from, or correlate to physical layer data, including for example channel estimation information (e.g., CSI)”, Janco [0015]), and wherein the classifier is further configured to demodulate the PHY-level information and process, via the machine learning model, the PHY-level information demodulated (“machine learning module 318 may be configured to train and inference one or more machine learning models configured to predict whether a received data transmission was transmitted from one or more specific wireless devices, based, at least in part, on features extracted from and/or representing dataframes (such as dataframe 200b shown in FIG. 2) of the standard beamforming protocol transmitted by the specific device”, Janco [0127])
Otsuki and Janco are analogous because they pertain to identifying an object using RF signals.
Thus it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include using machine learning to produce a feature set and deriving data that correlate to physical layer data as described in Janco into Otsuki. By modifying the method to include using machine learning to produce a feature set and deriving data that correlate to physical layer data as taught by Janco, the benefits of improved identification process (Otsuki [0006] and Janco [0011]) are achieved.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW C KIM whose telephone number is (703)756-5607. The examiner can normally be reached M-F 9AM - 5PM (PST).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sujoy K Kundu can be reached at (571) 272-8586. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/A.C.K./
Examiner
Art Unit 2471
/SUJOY K KUNDU/Supervisory Patent Examiner, Art Unit 2471 August 26, 2025