DETAILED ACTION
This Office Action is in response to the Applicants' communication filed on October 23, 2025. Claims 1, 5, 6, 10, 11 and 16 are amended. Claims 1-20 are currently pending and have been examined.
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 Arguments
Applicant’s arguments/remarks made in an amendment filed October 23, 2025, have been fully considered. In view of the amended claims 1, 5, 6, 10, 11 and 16 and upon further consideration, a new ground(s) of rejection, necessitated by the amendments is made in view of different interpretation of the previously applied references as presented in this Office action. Applicant’s arguments with respect to claim(s) 1-20 are therefore moot.
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, 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 US 10498467 B1 (Ravkine) in view of US 20200134061 A1 (Gaber et al.) (hereinafter Gaber).
In re claims 1, 6, 11 and 16, Ravkine discloses a method (Fig. 10, Col 1, lines 44-65, “a motion detection system may be configured to detect motion in a space based on changes in wireless signals transmitted between devices through a space over a communication channel”. “In some cases, controlling which devices from which channel information is obtained improves the quality of data to be used in motion sensing applications, and may further improve the operation of motion detection systems, such as monitoring and alarm systems”) and a cloud-based computer system (Fig. 2, Col 7, lines 49-59, “In a mesh configuration (e.g., the motion detection system 202 with multiple APs 202 interconnected), sounding and beamforming is performed between APs 202 and their respective leaf devices 204, and motion information is determined at each of the APs 202. The motion information can then be sent to a hub device (e.g., one of the APs 202) or another device, such as a server, to analyze the motion information and make an overall determination of whether motion has occurred in the space, detect a location of detected motion, or both”. Col 6, lines 50-57, “The storage of data related to processes for identifying fixed leaf nodes, and/or classifying and selecting fixed leaf nodes for sounding in the monitoring system may be performed on a wireless communication device 102 configured as an AP device (e.g. gateway device), another type of computing device, in the motion detection system, or in some cases, may be performed in the cloud”) comprising: one or more processors (Fig. 11:1110); and memory storing instructions (Fig. 11, 1120, Col 20, lines 35-38, “The example processor 1110 can execute instructions, for example, to generate output data based on data inputs. The instructions can include programs, codes, scripts, modules, or other types of data stored in memory 1120”) that, when executed by the one or more processors, perform operations comprising: receiving motion sensing system data from a motion sensing system (Fig. 3, Col 4, lines 30-37, “For example, based on the received wireless signals, the wireless communication device 102C may generate motion data, presence data, or both. In some instances, the wireless communication device 102C may communicate the motion detection data, to another device or system, such as a security system, that may include a control center for monitoring movement within a space, such as a room, building, outdoor area, etc.”), the motion sensing system comprising a plurality of wireless communication devices (Fig. 2, Col 6, lines 24-30, “In some implementations, the wireless communication system 100 may be, or may include, a motion detection system. The motion detection system may include one or more of the wireless communication devices 102A, 102B, 102C and possibly other components. One or more wireless communication devices 102A, 102B, 102C, in the motion detection system may be configured for motion detection”), the plurality of wireless communication devices comprising an access point communicably coupled to a cloud-based computer system (Col 2, line 56 - Col 3, line 14, “In some cases, the wireless communication devices 102A, 102B, 102C may be Wi-Fi access points (APs) or another type of wireless access point (AP)”. Col 6, lines 50-57, “The storage of data related to processes for identifying fixed leaf nodes, and/or classifying and selecting fixed leaf nodes for sounding in the monitoring system may be performed on a wireless communication device 102 configured as an AP device (e.g. gateway device), another type of computing device, in the motion detection system, or in some cases, may be performed in the cloud”); the motion sensing system data comprising: the device information for the plurality of wireless communication devices (Col 4, lines 48-53, “The header may include additional information such as, for example, an indication of whether motion was detected by another device in the communication system 100, an indication of the modulation type, an identification of the device transmitting the signal, etc.”. Col 8, lines 29-35, “In some cases, the motion detection system observes that some leaf nodes sound poorly during the sounding process to collect channel information, resulting in bad channel information being provided to the motion detection system”); and environment information for an environment of the motion sensing system (Fig. 1, Col 4, line 54 – Col 5, line 10, “In some instances, wireless signals received at each of the wireless communication devices 102 may be analyzed to determine channel information for the different communication links in the network (e.g., between respective pairs of wireless communication devices in the network). The channel information may be representative of a physical medium that applies a transfer function to wireless signals that traverse the space. Channel response information may refer to known channel properties of a communication link, and may describe how a wireless signal propagates from a transmitter to a receiver, representing the combined effect of, for example, scattering, fading, and power decay within the space between the transmitter and receiver. In particular, a link may correspond to a receive (Rx)/transmit (Tx) antenna pair. Various configurations of the Rx/Tx antennas may be supported”. Col 5, lines 64-67, “In some instances, the motion detection zones 110 can include, for example, air, solid materials, liquids, or another medium through which wireless electromagnetic signals may propagate”); identifying a reference environment based on the environment information (Col 3, lines 27-37, “In some cases, the motion probe signals include reference signals known to all devices in the network. In some instances, one or more of the wireless communication devices may process motion detection signals, which are signals received based on motion probe signals transmitted through the space. For example, the motion detection signals may be analyzed to detect motion of an object in a space, lack of motion in the space, or presence or absence of an object in the space, based on changes (or lack thereof) detected in the communication channel”); receiving, from the cloud-based computer system, health score data for the plurality of wireless communication devices, wherein the health score data are provided by the cloud-based computer system in response to the motion sensing system data (Fig. 4, Col 12, lines 49-52, “In some implementations, the motion detection system classifies the quality of each AP-leaf node link by determining a score 440 (also referred to as a ‘health score’ herein) for each link, shown in table 480”); the health score data being determined based on health score statistics stored in a reference database and accessed by the cloud-based computer system, in the motion sensing system, determining health scores for the plurality of wireless communication devices based on the health score data from the cloud-based computer system (Col 12, lines 49-58, “For example, each AP-leaf node link may be assigned a value based on the link quality data in each network status report 310 for each calibration period 420. In the example shown in FIG. 4, the score 440 for each AP-leaf node link is compiled by adding the link quality values for each calibration period 420 across the calibration window 410 for the AP-leaf node link”. Col 13, lines 17-27, “Each calibration result 560 carries a numeric weight based on how desirable the leaf node is from a sounding priority perspective. The numeric weights are summed over a calibration window to derive a score (e.g., the score 440 shown in table 480 in FIG. 4). In some instances, a negative score indicates that sounding that leaf node is undesirable, while a positive score indicates that sounding the leaf node would contribute positively to the performance of the motion detection system. In some cases, the leaf nodes are ranked in priority based on the magnitude of the score, e.g., highest to lowest”); based on the health scores, selecting a subset of the plurality of wireless communication devices to use for motion detection in the motion sensing system (Fig. 10:1030, Col 2, lines 6-13, “a motion detection system may select which leaf node devices will be used for collecting channel information. In some cases, a fixed leaf node device may be selected based on link quality compared with other fixed leaf node devices”. Col 17, lines 59-67, “In some cases, a static leaf node is selected by deriving a link quality score for each static AP-leaf node link for the calibration window (e.g., the score for each calibration period are assigned in FIG. 5 and summed for the calibration window in the table in FIG. 4). In some instances, the static AP-leaf node links are prioritized according to their respective link quality scores, and the static leaf node having a static AP-leaf node link with the highest link quality score is selected (e.g., AP0-Leaf0 in FIG. 4)”); and updating the motion sensing system to use the selected subset of wireless communication devices for motion detection (Fig. 10:1040, Col 18, lines 59-60, “At 1040, the motion detection system is updated to use the selected one or more static leaf nodes for motion detection”. Col 3, line 57 – Col 4, line 5, “In some instances, the wireless communication device 102C (or another system or device) may perform one or more operations as described below with respect to any of FIGS. 3-8 or in the example processes described with respect to FIGS. 9-10, or another type of process for identifying and selecting fixed leaf nodes, and updating the motion detection system to use the selected fixed leaf nodes for motion detection”).
Ravkine does not explicitly disclose the health score data being determined based on health score statistics stored in a reference database and accessed by the cloud-based computer system.
Gaber discloses the health score data (Fig. 4, “Health Score 480”, [0026], “The IoT edge devices 110-1 through 110-N comprise, for example, household appliances, sensors, actuators and other devices that produce information or get commands to control the environment they are in”. [0017], “One or more embodiments of the disclosure empower a human user to automatically keep track and trustfulness of one or more agent devices that perform one or more actions on behalf of the user... The learned IoT activity profile is monitored over time to identify an anomaly with respect to an action performed by an agent device, for example, using a health score indicating a deviation from the learned patterns in the IoT activity profile”. [0004], “In one embodiment, an exemplary method comprises obtaining data from a plurality of Internet of Things (IoT) devices of a user; applying the obtained data to a feature engineering module to convert the obtained data into a plurality of time-series features that capture one or more of behavior of an IoT environment of the user and applying the plurality of time-series features to an analytic engine comprising a multi-variate anomaly detection method that learns one or more patterns in an IoT activity profile of the user for a normal state and identifies an anomaly with respect to an action performed by the agent device based on a health score indicating a deviation from the learned one or more patterns”) being determined based on health score statistics (Fig. 4:450, [0005], “In some embodiments, the multi-variate anomaly detection method comprises a Gaussian Mixture Model that calculates a multi-dimensional probability distribution function describing the IoT activity profile of the user, wherein each dimension captures a pattern in a corresponding monitored feature. The Gaussian Mixture Model optionally calculates, for the plurality of time-series features corresponding to a new data point, a weighted distance from a plurality of centers of the multi-dimensional probability distribution. An anomaly alert is optionally generated when the weighted distance exceeds a predefined threshold”. [0021], “The decision of fraud/no fraud is made based on labeled past example transactions. To identify fraud involving IoT automatically triggered transactions, the temporal dynamics of the individual IoT profile needs to be considered in addition to static attributes”. [0023], “A multi-dimensional probability distribution function needs to be calculated over multiple aspects of the user IoT profile simultaneously, in some embodiments, such that it can alert on non-trivial anomalies once the user drifts from the center of mass of the distribution”) stored in a reference database (Fig. 1:170, [0025], “FIG. 1 illustrates an exemplary IoT system 100, according to one embodiment of the disclosure. Generally, IoT systems, such as the exemplary IoT system 100, typically consolidate edge devices to gateways and then to a central backend datacenter where most of the processing is done”. [0039], “the collected features are time-series related features so each feature value can be crossed with any other feature values at the same point in time to generate new meaningful engineered features, while it can also be related and modeled by previous points in time, in a known manner” (shows analytics based on saved or stored data and comparison for anomaly detection) and accessed by the cloud-based computer system (Fig. 6, [0060], “FIG. 6 shows an example processing platform comprising cloud infrastructure 600”. [0065], “The cloud infrastructure 600 shown in FIG. 6 may represent at least a portion of one processing platform. Another example of such a processing platform is processing platform 700 shown in FIG. 7”. [0056], “One or more system components such as a cloud-based IoT activity anomaly detection engine 200”. [0057], “The cloud-based systems can include object stores such as Amazon S3, GCP Cloud Storage, and Microsoft Azure Blob Storage”. [0058], “For example, a given container of cloud infrastructure illustratively comprises a Docker container or other type of Linux Container (LXC)”. [0026], “The regional gateways can be connected in some embodiments to country clouds/gateways that are connected to a global cloud”. [0042], “In one or more embodiments, the disclosed IoT activity anomaly detection techniques should be deployed in an accessible environment of the user, such as private machines with sufficient storage and computing power or in a public cloud or in some hybrid fashion” (database accessed by cloud-based computer system)).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Ravkine with Gaber to provide a system and method wherein a cloud-based computer system can amass data from various IoT devices in the network and analysis the data to detect anomalies and improve operations of the devices using health scores and data extracted over a period of time. As the number of client devices increase exponentially, the advantage finds use in many applications such as fraud detection, malicious agents, naive bugs, security systems and improving quality of life.
In re claims 2, 7, 13 and 18, Ravkine discloses the method of claim 1, the motion sensing system of claim 6, the method of claim 11 and the cloud-based computer system of claim 16, wherein the device information comprises, for each wireless communication device, at least one of: a device type of the wireless communication device; a manufacturer of the wireless communication device; a model of the wireless communication device; or a version of the wireless communication device (Col 2, lines 19-23, “The example wireless communication system 100 may include additional wireless communication devices 102 and/or other components (e.g., one or more network servers, network routers, network switches, cables, or other communication links, etc.)”. Col 4, lines 48-53, “The header may include additional information such as, for example, an indication of whether motion was detected by another device in the communication system 100, an indication of the modulation type, an identification of the device transmitting the signal, etc.”).
In re claims 3, 8, 14 and 19, Ravkine discloses the method of claim 1, the motion sensing system of claim 6, the method of claim 11 and the cloud-based computer system of claim 16, wherein the environment information comprises at least one of: a geographic location of the motion sensing system; a type of structure where the motion sensing system resides; or a size of a space where the motion sensing system resides (Col 18, lines 1-4, “In some implementations, the maximum number of leaf nodes per AP are selected to sound for a next time period based on a link quality score and location of identified static leaf nodes”. Col 2, line 56 - Col 13, line14, “In some cases, the wireless communication devices 102A, 102B, 102C may be Wi-Fi access points (APs) or another type of wireless access point (AP). The wireless communication devices 102A, 102B, 102C may be configured to perform one or more operations as described herein that are embedded as instructions (e.g., software or firmware) on the wireless communication devices. In some cases, the wireless communication devices 102A, 102B, 102C may be nodes of a wireless mesh network. A wireless mesh network may refer to a decentralized wireless network whose nodes (e.g., wireless communication devices 102) communicate directly in a point-to-point manner without using a central access point, base station or network controller, for example. Wireless mesh networks may include mesh clients, mesh routers, or mesh gateways” (for stationary wireless communication devices, access points are at known locations, hence their geographic locations are known with the environment data sent)).
In re claims 4, 9, 15 and 20, Ravkine discloses the method of claim 1, the motion sensing system of claim 6, the method of claim 11 and the cloud-based computer system of claim 16, wherein the plurality of wireless communication devices communicate in a wireless communication network, and the motion sensing system data further comprises: a number of mesh access point devices in the wireless communication network (Fig. 1, Col 5, lines 31-34, “In the example shown in FIG. 1, the wireless communication system 100 is illustrated as a wireless mesh network, with wireless communication links between each of the respective wireless communication devices 102”. Col 19, lines 17-20, “In some instances, the example wireless communications device may be configured as an access point (AP), or as a hub in a mesh network comprising multiple APs”); a network type of the wireless communication network; or a type of wireless protocol used by the wireless communication network (Fig. 2, Col 7, lines 1-6, “In some instances, the motion detection system includes multiple APs 202 (e.g. wireless communication devices 102 described in FIG. 1) communicating according to a wireless mesh protocol, with one or more leaf nodes 204 connected to each of the APs 202, as shown in FIG. 2”).
In re claims 5 and 10, Ravkine discloses the method of claim 1 and the motion sensing system of claim 6, wherein determining the health scores for the plurality of wireless communication devices based on the health score data comprises setting an initial health score for a first wireless communication device equal to a health score value provided in the health score data, and the operations comprise updating the health score for the first wireless communication device based on quality testing of the first wireless communication device (Col 12, lines 61-64, “However, in other implementations, other values may be assigned to represent link quality and the score may be calculated in a different manner, e.g. a lower score may represent a higher quality”. Col 15, lines 32-47, “After the maximum number of leaf nodes for each AP are selected, at 635, it is determined whether at least one of the candidate leaf nodes is a newly identified static leaf node with a link quality that exceeds minimum link quality score (e.g., SCORE_THRES). An example of the scores for each AP-leaf node link are described in FIG. 4 (e.g., scores 440 in table 480). In some instances, no static leaf nodes meet the quality score criteria, in which case, at 660, the accumulators are reset (e.g. scores 440, points 450, and range 460 in FIG. 4), and at 665, aggregate statistics for the just-completed calibration period are updated for all AP-leaf node links, such as, presence, successful sounding rate (‘prate’), and failed sounding rate (Irate), and any other link statistics being tracked, such as average RSSI and motion detection failure rate. After the accumulators are updated, the system waits to receive the next status report at 610”).
In re claims 12 and 17, Ravkine discloses the method of claim 11 and the cloud-based computer system of claim 16, wherein the health score statistics comprise an average health score and a standard deviation for each wireless communication device, and the health score for each wireless communication device is based on the average health score and the standard deviation (Col 9, lines 3-5, “The network status report 310 for each AP in the motion detection system comprises statistics for each active AP-leaf node link during the defined time interval”. Col 9, lines 16-19, “In some instances, the status reports 310 from multiple time intervals are aggregated to derive statistics for each AP-leaf node link over a calibration period”. Col 9, lines 36-52, “In some implementations, for each AP-leaf node link, a successful sounding metric 326 is calculated indicating the average successful channel frequency response (CFR) sounding rate (range 0-100%), and the failed sounding metric 327 is calculated indicating the average failed CFR sounding rate (range 0-100%)”. Col 12, lines 1-11, “In an implementation, the range 460 for an AP-leaf node link is determined by the age of the oldest calibration period 420 for which data is available to use in the calibration window 410, relative to the most recent calibration period...”. Col 12, lines 61-64, “However, in other implementations, other values may be assigned to represent link quality and the score may be calculated in a different manner, e.g. a lower score may represent a higher quality” (implicitly discloses wherein the health score statistics comprise an average health score and a standard deviation for each wireless communication device, and the health score for each wireless communication device is based on the average health score and the standard deviation)).
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.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SWATI JAIN whose telephone number is (571)270-0699. The examiner can normally be reached Mon - Fri (830 am - 530 pm).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pan Yuwen can be reached on 5712727855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SWATI JAIN/Examiner, Art Unit 2649 /YUWEN PAN/Supervisory Patent Examiner, Art Unit 2649
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