DETAILED ACTION
This action is in response to communication filed on 11/4/2024.
Claims 1-20 are pending.
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 (IDS) submitted on 11/4/2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-16 of U.S. Patent No. 12,184,631. Although the claims at issue are not identical, they are not patentably distinct from each other because performing CSI data collection/reporting and proximity determination at the computing device (active/passive sensing per Gandhi claims) is an obvious implementation/variation of the authentication service method of instructing a device to collect and report CSI for predefined gesture MFA comparison/grant-deny (as claimed in the ‘631 patent).
Claim Objections
Claim 2 is objected to because of the following informalities: On lines 5-6, “the communication links matches to” should be --communication links matches--. Appropriate correction is required.
Claim 9 is objected to because of the following informalities: On lines 5-6, “the communication links matches to” should be --communication links matches--. Appropriate correction is required.
Claim 16 is objected to because of the following informalities: On line 5, “the communication links matches to” should be --communication link matches--. Appropriate correction is required.
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 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 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 nonobviousness.
Claims 1-3, 5-6, 8-10, 12-13, 15-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (US 2022/0124154) in view of Colon et al. (US 2021/0056188).
Regarding claim 1, Zou discloses a computing device, comprising: one or more processors; and one or more non-transitory computer-readable media storing computer-executable instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving an instruction from a remote system to collect channel state information (CSI) data that is indicative of a gesture (Zou discloses RX device collects CSI continuously from data frames and transmits it to the server (remote system), and the server uses it for “gesture identification”; [0087] “By leveraging the disclosed CSI enabled IoT platform, a preliminary experiment may be conducted by using two TP-LINK N750 wireless routers (e.g., one as TX and another one as RX) to evaluate whether distinct CSI measurements can be revealed for human gesture identification. The two routers were put 1 m away on a table in a conference room. One volunteer performed six gestures, moving right and left, pushing and pulling, rolling right and left, near the line-of-sight of the TX-RX pair”);
collecting the CSI data for communication links established with one or more devices in an environment of the computing device, wherein the CSI data represents variations in properties of a radio signal caused by a user making the gesture through at least a portion of the radio signal (Zou discloses the RX device (computing device in the environment) collects CSI from TX-RX WiFi communication links; the CSI explicitly captures “amplitude attenuation and phase shift” variations caused by the user’s gesture moving through the radio signal paths; [0083] “WiFi signals propagate through multiple paths from a TX to an RX in indoor environments due to reflection, scattering, and diffraction introduced by walls, doors, and furniture, as well as the movements of occupants. Different from the RSS which only captures the superimposition of multipath signals, CSI reveals fine-grained information about how the signal is propagated and interfered, including different time delays, amplitude attenuation, and phase shift of multiple paths on each subcarrier. Analyzing these signal propagation variations caused by human motions makes device-free gesture recognition feasible”); and
providing the remote system with an indication of the gesture (Zou discloses the RX device sends the CSI (or processed gesture data) to the server (remote system), which then identifies the gesture; [0103] “After receiving the data frames from the TX, the RX may analyze the data packet, extract the CSI data, and forward them to a back-end computation unit through UDP”).
However, the prior art does not explicitly disclose a gesture used for authentication.
Colon in the field of the same endeavor discloses techniques for performing user authentication on a mobile device by detecting a specific touchscreen gesture on an authentication user interface. In particular, Colon teaches the following:
a gesture used for authentication (Colon discloses authentication server (remote system) issues the access request that triggers gesture collection specifically “for authentication”; [0045] “In block 402, in response to the request in block 401, the user may be presented with an authentication user interface via a display screen of the user device 130, prompting the user to input a predefined gesture in order to gain access to the selected mobile application or requested web site”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine the prior art with the teaching of Colon. One would have been motivated to adapt Zou’s platform so that CSI collection for gesture recognition is initiated upon receiving an instruction from the remote authentication server (as taught by Colon) rather than operating continuously yielding predictable improvement of a secure, multi-factor authentication system that leverages existing wireless infrastructure for contactless gesture verification.
Regarding claim 2, Zou-Colon discloses the computing device of claim 1, the operations further comprising:
receiving predefined CSI data from the remote system (Colon [0049] “Gesture verification may include comparing the gesture shape or characteristics to the user's predefined gestures. Additionally, in various embodiments, the gesture verification in block 403 may be performed by the server system 110 (e.g., authentication server 112), the user device 130, or by a combination of client-side and server-side gesture verification techniques”), wherein the predefined CSI data is associated with a predefined authenticating gesture (Zou [0091] “Step 1: in the original environment (e.g., source domain), a source encoder and a source classifier are generated with the labeled source CSI frames. Suppose L CSI frames X.sub.s with labels Y.sub.s (the ground truth of gesture type) are collected in an environment (referred to as the original environment, source domain)”);
comparing the CSI data to the predefined CSI data (Zou [0100-0101] “During the implementation phase, the real-time CSI frames may be mapped to the shared feature space through the target encoder M.sub.t constructed in Step 2 firstly, and then the pre-trained source gesture classifier C.sub.s may be adopted to identify the gesture in the new environment (e.g., target domain)… In the phase of implementation, the trained target encoder M.sub.t may be used to map the target CSI frame to the latent feature space and directly use the source classifier C.sub.s to identify various gestures”);
determining that the CSI data that was collected for the communication links matches to the predefined CSI data (Zou [0044] “To minimize the domain discrepancy distance between source and target domains, a domain-adversarial objective function may be implemented to train a generator (e.g., target encoder) to map the target data to the domain invariant latent feature space so that a domain discriminator cannot distinguish the domain labels of the data. After that, the trained target encoder may be used to map the real-time target CSI frame to latent space and use source classifier to identify various gestures”); and
providing the remote system with the indication that the user made the gesture (Zou [0097-0100] “Adversarial adaptation may be performed by learning a target representation mapping (e.g., target encoder) M.sub.t such that a discriminator D cannot distinguish the domain label of encoded source and target samples…Step 3: the trained target encoder maps the target CSI frames to the domain invariant latent feature space and the source classifier recognize gestures during the implementation”) used for authentication (Colon [0049] “The gesture data structure then may be transmitted to the server system 110 (e.g., authentication server 112) for verification, by comparing the gesture to the user's pre-stored gestures, using one or more similarity thresholds”).
Regarding claim 3, Zou-Colon discloses the computing device of claim 1, wherein providing the remote system with the indication of the gesture comprises sending the CSI data to the remote system (Zou [0103] “After receiving the data frames from the TX, the RX may analyze the data packet, extract the CSI data, and forward them to a back-end computation unit through UDP”).
Regarding claim 5, Zou-Colon discloses the computing device of claim 1, the operations further comprising:
determining a proximity of the user to the computing device (Zou discloses the RX computing device analyzes CSI amplitude attenuation and phase-shift variations across multiple TX-RX links, larger variations occur when the user (object) is closer (higher proximity) because the gesture perturbs a greater portion of the signal paths. This directly determines proximity via the strength of CSI perturbation; [0083] “WiFi signals propagate through multiple paths from a TX to an RX in indoor environments due to reflection, scattering, and diffraction introduced by walls, doors, and furniture, as well as the movements of occupants. Different from the RSS which only captures the superimposition of multipath signals, CSI reveals fine-grained information about how the signal is propagated and interfered, including different time delays, amplitude attenuation, and phase shift of multiple paths on each subcarrier. Analyzing these signal propagation variations caused by human motions makes device-free gesture recognition feasible”);
receiving a predefined proximity threshold from the remote system (Colon discloses the remote authentication server sends the access request (which implicitly carries or precedes the authentication criteria/threshold for success) that triggers the device to collect and evaluate data for authentication. One of ordinary skill would modify Zou’s CSI analysis to treat a server-provided value as the “predefined proximity threshold (e.g., minimum variance magnitude required); [0049] “The gesture data structure then may be transmitted to the server system 110 (e.g., authentication server 112) for verification, by comparing the gesture to the user's pre-stored gestures, using one or more similarity thresholds. Assuming the authentication server 112 determines that the user's gesture sufficiently matches the previously stored gestures for that user, the user device 130 may be granted the access to the requested application/resources in accordance with the user's permissions and authorization level”);
comparing the proximity to the predefined proximity threshold (Colon [0049] “The gesture data structure then may be transmitted to the server system 110 (e.g., authentication server 112) for verification, by comparing the gesture to the user's pre-stored gestures, using one or more similarity thresholds. Assuming the authentication server 112 determines that the user's gesture sufficiently matches the previously stored gestures for that user, the user device 130 may be granted the access to the requested application/resources in accordance with the user's permissions and authorization level”); and
in response to determining that the proximity within the environment is less than the predefined proximity threshold, allowing a user to perform an action with respect to an application service ((Colon [0049] “The gesture data structure then may be transmitted to the server system 110 (e.g., authentication server 112) for verification, by comparing the gesture to the user's pre-stored gestures, using one or more similarity thresholds. Assuming the authentication server 112 determines that the user's gesture sufficiently matches the previously stored gestures for that user, the user device 130 may be granted the access to the requested application/resources in accordance with the user's permissions and authorization level”); or
in response to determining that the proximity within the environment is greater than the predefined proximity threshold, denying the user to perform the action with respect to the application service.
Regarding claim 6, Zou-Colon discloses the computing device of claim 1, wherein:
the CSI data is collected according to a passive policy such that a secondary device need not be detected in the environment to collect the CSI data (Zou discloses the RX (computing device) collects CSI passively from “regular data frames transmitted in the existing traffic” on ordinary COTS routers operating in normal AP client mode with background networks present; [0071] “implementing AutoID using two TP-LINK N750 routers” and [0085] “To overcome this bottleneck, a CSI enabled IoT platform may be implemented such that the CSI measurements from regular data frames transmitted in the existing traffic can be obtained directly from the COTS IoT devices, such as commodity WiFi routers”); and
collecting the CSI data is performed absent detection of the secondary device and prior to receiving the instruction to collect the CSI data (Zou [0071] “Existing WiFi networks such as a campus network were operated as usual and other WiFi MDs coexisted during the entire experiments. The sampling rate was 700 packets/s and linear interpolation was adopted to ensure the stationary interval of consecutive CSI values when there was a packet loss”).
Regarding claim(s) 8-10, 12-13 and 15-17 and 19 do(es) not teach or further define over the limitation in claim(s) 1-3, 5-6 respectively. Therefore claim(s) 8-10, 12-13 and 15-17 and 19 is/are rejected for the same rationale of rejection as set forth in claim(s) 1-3, 5-6 respectively.
Further, for claim 15, Zou further teaches the limitation that are not recited in claim 1 as follows:
“establishing a communication link with a secondary device in an environment of the computing device” and “collecting channel state information (CSI) data for the communication link” are disclosed in Zou [0050-0051] “Most existing CSI-based sensing systems adopt the Intel 5300 NIC tool to extract the CSI data from laptops with external WiFi NIC cards. Requiring laptops as receivers severely limit them from large-scale implementation…At each time instance, each TX-RX pair may be able to provide N.sub.TX×N.sub.RX×114 CSI amplitude and phase measurements, where N.sub.TX and N.sub.RX represent the number of TX and RX antennas, respectively”
“providing a remote system with at least one of the CSI data or an indication of the gesture” is disclosed by Zou; the RX device sends the CSI (or processed gesture data) to the server (remote system), which then identifies the gesture; [0103] “After receiving the data frames from the TX, the RX may analyze the data packet, extract the CSI data, and forward them to a back-end computation unit through UDP”).
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (US 2022/0124154) in view of Colon et al. (US 2021/0056188) in view of Omer et al. (US 2023/0125109).
Regarding claim 4, Zou-Colon discloses the computing device of claim 1, the operations further comprising:
receiving predefined CSI data from the remote system (Colon [0049] “Gesture verification may include comparing the gesture shape or characteristics to the user's predefined gestures. Additionally, in various embodiments, the gesture verification in block 403 may be performed by the server system 110 (e.g., authentication server 112), the user device 130, or by a combination of client-side and server-side gesture verification techniques”), wherein the predefined CSI data is associated with a predefined authenticating gesture (Zou [0091] “Step 1: in the original environment (e.g., source domain), a source encoder and a source classifier are generated with the labeled source CSI frames. Suppose L CSI frames X.sub.s with labels Y.sub.s (the ground truth of gesture type) are collected in an environment (referred to as the original environment, source domain)”);
comparing the CSI data to the predefined CSI data (Zou [0044] “To minimize the domain discrepancy distance between source and target domains, a domain-adversarial objective function may be implemented to train a generator (e.g., target encoder) to map the target data to the domain invariant latent feature space so that a domain discriminator cannot distinguish the domain labels of the data. After that, the trained target encoder may be used to map the real-time target CSI frame to latent space and use source classifier to identify various gestures”); and
determining whether the secondary CSI data matches the predefined CSI data associated with the predefined authenticating gesture (Zou [0044] “To minimize the domain discrepancy distance between source and target domains, a domain-adversarial objective function may be implemented to train a generator (e.g., target encoder) to map the target data to the domain invariant latent feature space so that a domain discriminator cannot distinguish the domain labels of the data. After that, the trained target encoder may be used to map the real-time target CSI frame to latent space and use source classifier to identify various gestures”).
However the prior art does not explicitly disclose the following:
instructing a mobile device to collect a secondary CSI data for a duration of time;
receiving the secondary CSI data collected by the mobile device;
Omer in the field of the same endeavor discloses techniques for recognizing gestures (e.g., human gestures) based on wireless signals. In particular, Omer teaches the following:
instructing a mobile device to collect a secondary CSI data for a duration of time (Omer [0021] “one or more of the wireless communication devices 102 is a mobile device (e.g., a smartphone, a smart watch, a tablet, a laptop computer, etc.), an IoT device (e.g., a Wi-Fi enabled thermostat, a Wi-Fi enabled lighting control, a Wi-Fi enabled camera, a smart TV, a Wi-Fi enabled doorbell), or another type of device that communicates in a wireless network” and [0045] “changes to the steering or feedback properties used in the beamforming process indicate changes, which may be caused by moving objects, in the space accessed by the wireless communication system. For example, motion may be detected by substantial changes in the communication channel, e.g. as indicated by a channel response, or steering or feedback properties, or any combination thereof, over a period of time”);
receiving the secondary CSI data collected by the mobile device;
(Omer [0018] “a gesture recognition engine receives channel information from one or more of the wireless communication devices 102A, 102B, 102C, which collect the channel information based on wireless signals transmitted through the physical environment of the wireless communication network 100” and [0047] “The channel information may be collected by a channel sounding procedure (e.g., according to a Wi-Fi protocol) or another type of process”).
Therefore, it would have been obvious to a person of ordinary skill in the art at the time the invention was effectively filed to combine the prior art with Omer. One would have been motivated because incorporating gesture recognition engine and action initiation to network connected devices would predictably enable reliable, gesture triggered multi-factor authentication without unexpected results.
Regarding claim(s) 11 and 18, do(es) not teach or further define over the limitation in claim(s) 4 respectively. Therefore claim(s) 11 and 18 is/are rejected for the same rationale of rejection as set forth in claim(s) 4 respectively.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Zou et al. (US 2022/0124154) in view of Colon et al. (US 2021/0056188) in view of Chien et al. (US 2017/0086256).
Regarding claim 7, Zou-Colon discloses the computing device of claim 1, however, the prior art does not explicitly disclose wherein:
the CSI data is collected according to an active policy such that the user makes the gesture while holding a secondary device in the environment to collect the CSI data; and
the radio signal is associated with a communication link established between the computing device and the secondary device.
Chen in the field of the same endeavor discloses techniques for using Wi-Fi signals as a human control interface by determining the amount of noise in the wireless network, selecting an appropriate HCI sounding technique. In particular, Chen teaches the following:
the CSI data is collected according to an active policy such that the user makes the gesture while holding a secondary device in the environment to collect the CSI data (Chen discloses the “active policy” maps directly to the noise-based selection of sounding technique that triggers collection only when user input/gesture occurs (the process is initiated and adapted for the gesture performance). The secondary device is the “HCI Device”/STA, explicitly described as a cell phone or similar Wi-Fi enabled device that the user would hold while making the gesture in the environment, the channel data is collected precisely because of the gesture affecting the link; [0032] “the AP 110 may select one of a plurality of HCI sounding techniques to be used for detecting the user activity 101, for example, based on an amount of noise in the wireless channel 150”, [0081-0082] “The AP 110 selects an HCI sounding technique based, at least in part, on the detected amount of noise (520)…The AP 110 then detects a pattern of Doppler shifts in wireless signals received from an HCI device (e.g., the STA 120) using the selected HCI sounding technique (530)”); and
the radio signal is associated with a communication link established between the computing device and the secondary device (Chen [0030-0031] “the AP 110 may detect user activity 101 in the wireless channel 150 based on Doppler shifts in a set of wireless signals received by the AP 110. The user activity 101 may correspond to any type of gesture (e.g., such as the user waving a hand, raising an arm, etc.) or interaction with the wireless channel 150 that causes a detectable pattern of Doppler shifts in wireless signals propagating through the wireless channel 150… The AP 110 may then provide the HCI input 102 to the STA 120 (e.g., as a user input) to operate and/or control one or more functions of the STA 120”).
Therefore, it would have been obvious for a person of ordinary skill in the art at the time the invention was effectively filed to combine the prior art with Chen. One would have been motivated because using variation in wireless channel properties for gesture recognition in indoor environment with commodity device would predictably yield more granular data for gesture-based authentication without producing unexpected results.
Regarding claim(s) 14, do(es) not teach or further define over the limitation in claim(s) 7 respectively. Therefore claim(s) 14 is/are rejected for the same rationale of rejection as set forth in claim(s) 7 respectively.
Conclusion
For the reason above, claims 1-20 have been rejected and remain pending.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIMMY H TRAN whose telephone number is (571)270-5638. The examiner can normally be reached Monday-Friday 9am-5pm PST.
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JIMMY H TRAN
Primary Examiner
Art Unit 2451
/JIMMY H TRAN/Primary Examiner, Art Unit 2451
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