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 .
DETAILED ACTION
1. This office action is in response to the Applicant Election filled on 06/23/2026. Currently, claims 1-41 are pending in the application. Claims 29-40 are withdrawn from Consideration.
Election/Restrictions
2. Applicant's election with traverse of Invention I, claims 1-41 in the reply filed on 06/23/2026 is acknowledged.
The first traversal is on the ground(s) that the examination of 1-28 and 41 would not present an undue burden on the Examiner, and respectfully request reconsideration and withdrawal of the Restriction Requirement.
Since, claims 29-40 does not read on the elected Invention I, claims 29-40 are withdrawn from further consideration. Only claims 1-28 and 41 are elected by Examiner.
This is not found persuasive and the Examiner has already established burden (as defined in M.P.E.P. 808.02) in the restriction requirement dated 08/30/2024. There is a search and/or examination burden for the patentably distinct species or device/method claims, wherein they require a different field of search (e.g., searching different classes/subclasses or electronic resources or non-patent language, or deploying different search queries); and/or the prior art applicable to one invention would not likely be applicable to another; and/or the inventions are likely to raise different non-prior art issues under 35 U.S.C 101 and/or 35 U.S.C 112, first paragraph. Therefore, the requirement is still deemed proper and is therefore made FINAL.
Claim Rejections - 35 USC 101
3. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-28 and 41 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites a method for monitoring a property, the method comprising: receiving by a processor, from a Wi-Fi management system of the property, data relating to communication exchanged between one or more wireless-enabled devices on the property and one or more Wi-Fi access points on the property; based on the data, identifying: one or more baseline location-related attributes of the devices, and one or more characteristics and an estimated location of a particular one of the devices; and based on the baseline location-related attributes and the characteristics, outputting an alert indicating that the particular device may be unwanted at least at the estimated location.
The limitations of receiving by a processor, from a Wi-Fi management system of the property, data relating to communication exchanged between one or more wireless-enabled devices on the property and one or more Wi-Fi access points on the property; identifying: one or more characteristic and estimated location of the devices , and one or more characteristics to estimate location of a particular one of the devices in a room; and based on the characteristics, outputting an alert indicating that the particular device may be unwanted at least at the estimated location, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting "by a processor," nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the "by a processor" language, "receiving by a processor, from a Wi-Fi management system of the property, data relating to communication exchanged between one or more wireless-enabled devices on the property and one or more Wi-Fi access points on the property; based on the data, identifying: one or more characteristic and estimated location of the devices , and one or more characteristics to estimate location of a particular one of the devices in a room; and based on the characteristics, outputting an alert indicating that the particular device may be unwanted at least at the estimated location" in the context of this claim encompasses the user manually receiving data exchanged between one or more wireless-enabled devices on the property and one or more Wi-Fi access points on a property to determine whether to output an alert.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element - using a processor to perform the receiving, identifying, and output steps. The processor in the steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of the receiving, identifying, and output steps) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
The claim is directed to an abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform both the receiving, identifying, and output steps amounts to no more than mere instructions to apply the exception using a generic computer component.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Claim Rejections - 35 USC § 112
4. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Regarding claims 1-3 and 28 , the phrase "may be" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Double Patenting
5. Claims 1-28 and 41 of this application is patentably indistinct from claims 1-38 of Application No. 19/052346. Pursuant to 37 CFR 1.78(e) or pre-AIA 37 CFR 1.78(b), when two or more applications filed by the same applicant contain patentably indistinct claims, elimination of such claims from all but one application may be required in the absence of good and sufficient reason for their retention during pendency in more than one application. Applicant is required to either cancel the patentably indistinct claims from all but one application or maintain a clear line of demarcation between the applications. See MPEP § 822.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO internet Web site contains terminal disclaimer forms which may be used. Please visit http://www.uspto.gov/forms/. The filing date of the application will determine what form 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 http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claim Rejections - 35 USC § 103
6. 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.
7. 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(a) 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.
8. Claims 1, 3-17, 19-28 and 41 are rejected under 35 U.S.C. 103(a) as being unpatentable over Alemi (US 20220109949 A1) (hereinafter Alemi) in view of Ergen (US 20210391072 A1) (hereinafter Ergen).
Regarding claim 1, Alemi discloses a method for monitoring a property having multiple rooms (para 09, Wi-Fi based approach that identifying potential contacts, locations within a building on different floor, para26, FIG. 3D is a map of Wi-Fi access point (AP) placement for the floorplan of FIG. 3A, para 38, FIG. 3A presents a sample floorplan of a public building, Each room is designated with a number ID, with a square footage given below the number ID. The different types of spaces have labels consistent with industry norms that will be recognized by those of skill in the art. For instance, corridors are labeled “CORR . . . ”, bathrooms “WOMENS . . . ” or “MENS . . . ”, stairs “STAIR . . . ”, and elevators “ELEV . . . ”.), the method comprising:
using a processor, ascertaining respective locations and types of the rooms by analyzing a floorplan of the property (para 75, processors cause a list of the respective persons too carry out aspects of invention, para 109, P3 method that models locations and movements prediction methods, detailed analysis of results indicated location as in FIG. 9 relationship between number of people in the building and of models);
receiving, from a Wi-Fi management system of the property, data relating to communication exchanged between one or more wireless-enabled devices on the property and one or more Wi-Fi access points on the property (para 97, The participants personal mobile devices while moving through scenarios, and devices were on and connected to Wi-Fi network and participants received access to parts of the building, para 102, Wi-Fi data retrieved during the scenarios were merged to allow for comparing real locations (as manually recorded by participants) and predicted locations)
based on the data, identifying one or more characteristics and an estimated location of one of the devices (para 17, employed real connection logs for participants accessing Wi-Fi networks at campus and, identifiable data from individuals were collected and cross-checked with real locations and time at those locations and location prediction to rudimentary Wi-Fi-based approaches to location tracing, para 42, system identify locations of people, including rooms, corridors, bathrooms, elevators, stairwells, lobbies, offices, communal workspaces, halls (e.g., lecture halls), garages, and the like, para 86, model predicts the locations of an individual and location-recall (L-R) specifies locations at which a person present and that correctly identified and location confidence (L-C) correctly identified locations by the model);
based on the estimated location of the device and the locations of the rooms, identifying the room in which the device is likely located (para 87, |predictions| indicates the number of predicted locations in which a person can be, para 07, RSSI information to run triangulation of multiple APs or Wi-Fi fingerprint to estimate a device's proximity, para 35, application of algorithms that estimate likely locations and likely contacts); and
based on the characteristics of the device and the type of the identified room (para 74, based on characteristics estimate values for different locations, and calibrate value in real-time, para 91, Wi-Fi access points in building were represented as AP-type nodes which were linked to location nodes which carry information about estimated RSSI in the building, para 95, estimating Wi-Fi usage would be to count people entering a building in a given period and compare that data to Wi-Fi usage).
Alemi specifically fails to disclose outputting an alert indicating that the device may be unwanted at least at the estimated location.
In analogous art, Ergen discloses outputting an alert indicating that the device may be unwanted at least at the estimated location (para 42, by using mobility pattern determination or monitoring module 214, authorities track each user's mobility pattern in case of an alert, such as when a curfew is declared, or in case a user is in a place/area that is restricted by authorities by specifying/separating places where user can go, para 47, users who are in the vicinity of risky area or approaching region may be alert, para 56, information such as, list of preferred/selected places to visit, and estimated crowd rate of selected places, are transmitted to guide or direct individuals).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen for tracking mobility patterns and contact points for the purpose of contact tracing, making use of wireless signals sent/received via a Software Development Kit (SDK) integrated into any client device application [Ergen, para 0013].
Regarding claim 3, Alemi discloses the method according to claim 1, wherein the alert indicates that the device may be lost, forgotten, or abandoned (para 14, log file include timestamps of users' association and disassociation (i.e., connection and disconnection) with an access point (AP), para 106, Wi-Fi access point information containing APs used for connecting to a network, one or more connection times of when respective mobile device connected to each of the one or more APs, and one or more disconnection, para 72, device can be reported as connected after it is no longer in range (lag in disconnect /switch to new AP)).
Regarding claim 4, Alemi discloses the method according to claim 1, wherein the property includes a hotel (para 42, identify all locations of people, including but not limited to rooms, corridors, bathrooms, elevators, stairwells, lobbies, offices, communal workspaces, halls (e.g., lecture halls), garages, and the like).
Regarding claim 5, Alemi discloses the method according to claim 1, further comprising, in response to ascertaining the respective types of the rooms, assigning respective sensitivity scores to the rooms, wherein outputting the alert comprises outputting the alert based on the sensitivity score of the identified room (para 98, calculate recall (sensitivity) of the Wi-Fi tracking, para 18, The system may then produce a ranked list of potential contacts, and associated scores, predicted location and predicted time of contact, para 73, Public health personnel can rank individuals by the scores to prioritize follow up, claim 2, initiating signals to transmit identification of respective persons whose contact scores are above a predetermined threshold).
Regarding claim 6, Alemi discloses the method according to claim 5, wherein assigning the sensitivity scores comprises assigning the sensitivity scores based on respective expected privacy levels associated with the types of the rooms (para 08, based on analysis of device association and disassociation time, which limits privacy concern of relying on RSSI data, claim 11, perform initiating signals to transmit identification of persons whose contact scores are above a predetermined threshold).
Regarding claim 7, Alemi discloses the method according to claim 1, further comprising, using the processor, obtaining the floorplan via an application programming interface of the Wi-Fi management system (para 37, process 101 comprises obtaining what floorplans 201 are available and extracting details such as room location, size, and capacity, para 50, FIG. 4 a method 400 for obtaining Wi-Fi connection information, para 91, Floorplans with Wi-Fi locations represented by a graph with 450 nodes with 392 nodes representing rooms, 53 nodes representing passages and hallways, 3 staircases, 2 elevators, and 667 edges connecting the nodes. 98 Wi-Fi access points in the building were represented as AP-type nodes which were linked to location nodes, which additionally carry information about estimated RSSI in the building).
Regarding claim 8, Alemi discloses the method according to claim 1, wherein the data includes the estimated location, and wherein identifying the estimated location comprises extracting the estimated location from the data (para 07, RSSI information to run triangulation of multiple APs or Wi-Fi fingerprint to estimate a device's proximity, para 35, application of algorithms that estimate likely locations and then likely contacts).
Regarding claim 9, Alemi discloses the method according to claim 1, wherein the data includes respective estimated distances from one or more of the Wi-Fi access points to the device, and wherein identifying the estimated location comprises computing the estimated location based on the estimated distances (para 74, based on spread characteristics one can estimate values for different locations, and calibrate value in real-time, estimate values for different locations, para 07, RSSI information to run triangulation of multiple APs or Wi-Fi fingerprint to estimate a device's proximity, para 35, application of algorithms that estimate likely locations).
Regarding claim 10, Alemi discloses the method according to claim 1, further comprising, based on the data, identifying an estimated location history of the device on the property, wherein the alert further indicates the estimated location history (para 95, The data used indicates that within a 31-day period in October/November 2020, the Wi-Fi network at the university campus (i.e. history of device in a property), para 69, Wi-Fi connection logs to further adjust or improve probabilities assigned to locations or adjust probabilities/weights assigned to locations include movement patterns from historical data, known office locations, class registration and meeting rosters).
Regarding claim 11, Alemi discloses the method according to claim 1, wherein outputting the alert comprises outputting the alert in response to the device likely having been in the room for longer than a predefined maximum duration (para 72, device in range of second access point while connected to first one, assume longest connection time when calculating time at location, para 61, locations determined by AP which last a predetermined duration of time, para 48, FIG. 1, location prediction 102 predicting location at any time, or multiple locations over a time period/ duration).
Regarding claim 12, Alemi discloses the method according to claim 11, further comprising: based on the data and/or other data relating to other communication exchanged between one or more other wireless-enabled devices on at least one other property and one or more other Wi-Fi access points on the at least one other property, tracking movement of the other devices (para 66, AP allowing connection from multiple rooms, such as adjacent rooms or even rooms on different floors positioned above or below one another, para 69, Wi-Fi connection logs adjust or improve probabilities assigned to locations for specific individuals. Additional information that merged or adjust probabilities/weights assigned to locations include movement patterns from historical data, known office locations, para 49, tracking a mobile device” locations and movements of which correlate or match locations and movements of a person); and
defining the maximum duration based on the tracking (para 85, movement tracking methods comparing predictive approaches against predetermined participant movement paths, para 48, FIG. 1, location prediction stage 102 predicting location at any given time, or multiple locations over a time period/ duration).
Regarding claim 13, Alemi discloses the method according to claim 11, further comprising selecting the predefined maximum duration from multiple predefined maximum durations corresponding to different respective room types (para 48, FIG. 1, location prediction stage 102 predicting location at any given time, or multiple locations over a time period/ duration, para 61, P2 process locations determined by AP switches last a predetermined duration of time, movement from location A to location C to location B in rapid succession).
Regarding claim 14, Alemi discloses the method according to claim 11, further comprising selecting the predefined maximum duration from multiple predefined maximum durations corresponding to different respective geographic areas (para 102, Wi-Fi data retrieved to allow for comparing real and predicted locations, para 90, modeling openings that permit passage between locations. In this case, locations can be rooms, corridors or open areas as shown on the floorplan).
Regarding claim 15, Alemi fails to discloses the method according to claim 11, further comprising selecting the predefined maximum duration from multiple predefined maximum durations corresponding to different respective time slots.
In analogous art, Ergen discloses the method according to claim 11, further comprising selecting the predefined maximum duration from multiple predefined maximum durations corresponding to different respective time slots (para 16, Wi-Fi access point with respect to certain time periods for receiving/transmitting information for contact tracing, para 56, information such as preferred/selected places to visit, and estimated selected places, are transmitted to guide or direct individuals).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen to identifies client devices that the client device may have encountered based on analyzing the mobility patterns of the user of the client device and the recorded past and current signal data/measurements of the one or more Wi-Fi access points [Ergen, para 0013].
Regarding claim 16, Alemi fails to discloses the method according to claim 11, further comprising selecting the predefined maximum duration from multiple predefined maximum durations corresponding to different respective property types.
In analogous art, Ergen discloses the method according to claim 11, further comprising selecting the predefined maximum duration from multiple predefined maximum durations corresponding to different respective property types (para 40, Wi-Fi access points, Wi-Fi based sniffing to detect devices in vicinity of device 102A and/or GPS tracking. In this way, even indoors, room by room or floor by floor contact monitoring/tracing can be performed with higher accuracy, para 16, Wi-Fi access point with respect to certain time periods for receiving/transmitting information for contact tracing, para 56, information such as preferred/selected places to visit, and estimated selected places, are transmitted to guide or direct individuals).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen to performs sniffing to identify other client devices in the vicinity of client device over wireless interfaces such as, but not limited to, Wi-Fi and Bluetooth, to identify other mobile/fixed users who are in close proximity to client device [Ergen, para 021].
Regarding claim 17, Alemi discloses the method according to claim 1, wherein identifying the estimated location comprises: extracting, from the data, a preliminary estimated location of the device and respective signal strengths with which the communication from the device was received by the access points (para 07, RSSI information to run triangulation of multiple APs or Wi-Fi fingerprint to estimate a device's proximity, para 95, estimating Wi-Fi usage would be to count people entering a building in a given period and compare that data to the Wi-Fi usage, para 45, AP nodes with room nodes may additionally carry signal strength information (e.g., RSSI));
identifying, from the floorplan, respective materials of any signal-attenuating structures between the preliminary estimated location and the access points (para 91, AP-type nodes which were linked to location nodes within their coverage using edges, which additionally carry information about estimated RSSI in the building);
computing respective estimated distances between the device and the access points, based on the signal strengths and the materials of the signal-attenuating structures (para 92, Based on the distances to an access point, theoretical RSSI can be calculated given the transmit power of the access point, ; and
computing the estimated location based on the estimated distances (para 91, 99, AP-type nodes which were linked to location nodes within their coverage using edges, which additionally carry information about estimated RSSI in the building).
Regarding claim 19, Alemi fails to discloses the method according to claim 1, wherein the characteristics include a type of the device.
In analogous art, Ergen discloses the method according to claim 1, wherein the characteristics include a type of the device (para 40, Wi-Fi access points, Wi-Fi based sniffing to detect devices in vicinity of device 102A, room by room or floor by floor contact monitoring/tracing can be performed with higher accuracy, Abstract, SDK then identifies other client devices that the client device may have encountered based on analyzing the mobility patterns).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen for analyzing the recorded past and current signal data/measurements of the one or more Wi-Fi access points to derive mobility patterns of the user of client device using a mobility pattern determination/monitoring module [Ergen, para 028].
Regarding claim 20, Alemi fails to discloses the method according to claim 19, wherein the data includes an identifier of the device communicated by the device, and wherein identifying the type of the device comprises identifying the type of the device based on the identifier.
In analogous art, Ergen discloses the method according to claim 19, wherein the data includes an identifier of the device communicated by the device, and wherein identifying the type of the device comprises identifying the type of the device based on the identifier (para 40, Wi-Fi access points, Wi-Fi based sniffing to detect devices in vicinity of device 102A, para 16, SDK identifies a total number of client devices connected to the one or more Wi-Fi access points, performs network sniffing over a Wi-Fi interface to identifiy mobile/fixed users of other client devices).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen to identify a number of client devices connected to the one or more Wi-Fi access points, a number of client devices the one or more Wi-Fi access points receives signals from, MAC addresses of those client devices in close proximity to client device [Ergen, para 029].
Regarding claim 21, Alemi fails to discloses the method according to claim 20, wherein the identifier includes a media access control address, and wherein identifying the type of the device comprises identifying the type of the device by querying a database, in which multiple organizationally unique identifiers are associated with respective device types, for the device type associated with the organizationally unique identifier of the media access control address.
In analogous art, Ergen discloses the method according to claim 20, wherein the identifier includes a media access control address, and wherein identifying the type of the device comprises identifying the type of the device by querying a database, in which multiple organizationally unique identifiers are associated with respective device types, for the device type associated with the organizationally unique identifier of the media access control address (para 40, Wi-Fi access points, Wi-Fi based sniffing to detect devices in vicinity of device 102A , para 16, SDK identifies a total number of client devices connected to the one or more Wi-Fi access points, performs network sniffing over a Wi-Fi interface to identify mobile/fixed users of other client devices, para 44, identification and tracing module 216 holds information such as, but not limited to, MAC addresses of paired client devices, their mobile numbers).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen to identify a number of client devices connected to the one or more Wi-Fi access points, a number of client devices the one or more Wi-Fi access points receives signals from, MAC addresses of those client devices in close proximity to client device [Ergen, para 029].
Regarding claim 22, Alemi discloses the method according to claim 1, wherein the characteristics include behavioral characteristics describing a manner in which the device communicates (para 85, tracking methods use by comparing predictive approaches against predetermined participant movement paths, para 49, tracking of a mobile device that connects to Wi-Fi tracking of person who carries mobile device).
Regarding claim 23, Alemi discloses the method according to claim 1, further comprising, based on the data, identifying a respective room signature for each room of at least some of the rooms, each room signature including those of the devices are usually co-located in the room, wherein outputting the alert comprises outputting the alert in response to the room signature for the identified room not including the device (para 36, computer graphics, stored as jpg or tiff files for instance, or else pdf or pdf-like file types. The number of rooms shown, the size of the rooms, the relative arrangement of the rooms and paths between them, para 38, Each room is designated with a number ID, with a square footage given below the number ID. The different types of spaces have labels consistent with industry norms that will be recognized by those of skill in the art. For instance, corridors are labeled “CORR . . . ”, bath rooms “WOMENS . . . ” or “MENS . . . ”, stairs “STAIR . . . ”, and elevators “ELEV . . . ”).
Regarding claim 24, Alemi discloses the method according to claim 1, further comprising, based on the data, identifying respective person signatures for one or more people on the property, each person signature including those of the devices are usually co-located on a respective one of the people, wherein outputting the alert comprises outputting the alert based on the person signatures (para 12, identifying individuals contact tracing, para 17, identifiable data from individuals were collected and cross-checked with the individual's locations and time at those locations by using Wi-Fi-based approaches to location tracing).
Regarding claim 25, Alemi discloses the method according to claim 24, wherein outputting the alert comprises outputting the alert in response to the device being included in one of the person signatures but not being co- located with one or more others of the devices included in the person signature (para 16, Wi-Fi access point (AP) information for a plurality of mobile devices, each of the plurality of mobile devices being associated with a different individual of a plurality of people, para 75, processors cause push notifications sent to respective first mobile devices of respective persons to alert).
Regarding claim 26, Alemi discloses the method according to claim 24, wherein outputting the alert comprises: ascertaining, based on the person signatures, whether the room is likely occupied (para 53, When room occupancy information is available, occupancy of the given room, with the assumption that there are more people in larger rooms, para 42, identify all possible locations of people, including corridors, bath rooms, elevators, stairwells, lobbies, offices, communal workspaces, halls (e.g., lecture halls), garages, and the like); and
outputting the alert in response to ascertaining whether the room is likely occupied (para 56, rooms identifiable as Room 1 through Room 300, and Person A's mobile device is connected to an AP with coverage in Rooms 37, 39, and 40, each of equal occupancy and probability of Person A being in rooms 37, 39, and 40, para 75, The output resulting from such a query may be a list of locations where the target person visited during the time period and a list of contacts at those locations).
Regarding claim 27, Alemi discloses the method according to claim1,whereinoutputting the alert comprises: inputting the characteristics of the device to a machine-learned model trained to identify unwanted devices (para 85, movement tracking by comparing predictive approaches against predetermined participant movement paths and Wi-Fi data modeling was implemented using machine learning and model evaluation, and graph modeling and analysis), and
outputting the alert based on output from the machine-learned model (para 88, machine learning software that can output more than one predicted class in output).
Regarding claim 28, Alemi discloses a system for monitoring a property having multiple rooms (para 09, Wi-Fi based approach that identifying potential contacts, locations within a building on different floor, para26, FIG. 3D is a map of Wi-Fi access point (AP) placement for the floorplan of FIG. 3A, para 38, FIG. 3A presents a sample floorplan of a public building, Each room is designated with a number ID, with a square footage given below the number ID. The different types of spaces have labels consistent with industry norms that will be recognized by those of skill in the art. For instance, corridors are labeled “CORR . . . ”, bathrooms “WOMENS . . . ” or “MENS . . . ”, stairs “STAIR . . . ”, and elevators “ELEV . . . ”.), the system comprising:
a communication interface; and a processor, configured to: ascertain respective locations and types of the rooms by analyzing a floorplan of the property (para 75, one or more processor cause a list of the respective persons o carry out aspects of the present invention, para 109, P3 method that models locations and movements prediction methods, detailed analysis of results indicated location as in FIG. 9 shows relationship between number of people in the building and AUC of models),
receive via the communication interface, from a Wi-Fi management system of the property, data relating to communication exchanged between one or more wireless- enabled devices on the property and one or more Wi-Fi access points on the property (para 97, The participants personal mobile devices while moving through scenarios, and devices were on and connected to Wi-Fi network and participants received access to parts of the building, para 102, Wi-Fi data retrieved during scenarios were merged to allow for comparing real locations and predicted locations),
based on the data, identify one or more characteristics and an estimated location of one of the devices (para 17, employed real connection logs for participants accessing Wi-Fi networks at campus and, identifiable data from individuals were collected and cross-checked with real locations and time at those locations and location prediction to rudimentary Wi-Fi-based approaches to location tracing, para 42, system identify locations of people, including rooms, corridors, bathrooms, elevators, stairwells, lobbies, offices, communal workspaces, halls (e.g., lecture halls), garages, and the like, para 86, model predicts the locations of an individual and location-recall (L-R) specifies locations at which a person present and that correctly identified and location confidence (L-C) correctly identified locations by the model),
based on the estimated location of the device and the locations of the rooms, identify the room in which the device is likely located (para 87, |predictions| indicates the number of predicted locations in which a person can be, para 07, RSSI information to run triangulation of multiple APs or Wi-Fi fingerprint to estimate a device's proximity, para 35, application of algorithms that estimate likely locations and likely contacts), and
based on the characteristics of the device and the type of the identified room (para 74, based on characteristics estimate values for different locations, and calibrate value in real-time, para 91, Wi-Fi access points in building were represented as AP-type nodes which were linked to location nodes which carry information about estimated RSSI in the building, para 95, estimating Wi-Fi usage would be to count people entering a building in a given period and compare that data to Wi-Fi usage).
Alemi specifically fails to disclose output an alert indicating that the device may be unwanted at least at the estimated location.
In analogous art, Ergen discloses output an alert indicating that the device may be unwanted at least at the estimated location (para 42, by using mobility pattern determination or monitoring module 214, authorities track each user's mobility pattern in case of an alert, such as when a curfew is declared, or in case a user is in a place/area that is restricted by authorities by specifying/separating places where user can go, para 47, users who are in the vicinity of risky area or approaching region may be alert, para 56, information such as, list of preferred/selected places to visit, and estimated crowd rate of selected places, are transmitted to guide or direct individuals).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen for tracking mobility patterns and contact points for the purpose of contact tracing, making use of wireless signals sent/received via a Software Development Kit (SDK) integrated into any client device application [Ergen, para 0013].
Regarding claim 41, Alemi discloses a method for monitoring a property (para 09, Wi-Fi based approach that identifying potential contacts, locations within a building on different floor, para26, FIG. 3D is a map of Wi-Fi access point (AP) placement for the floorplan of FIG. 3A, para 38, FIG. 3A presents a sample floorplan of a public building, Each room is designated with a number ID, with a square footage given below the number ID. The different types of spaces have labels consistent with industry norms that will be recognized by those of skill in the art. For instance, corridors are labeled “CORR . . . ”, bathrooms “WOMENS . . . ” or “MENS . . . ”, stairs “STAIR . . . ”, and elevators “ELEV . . . ”.), the method comprising:
receiving by a processor, from a Wi-Fi management system of the property, a preliminary estimated location of a wireless-enabled device on the property and respective signal strengths with which communication from the device was received by one or more Wi-Fi access points on the property (para 75, one or more processor cause a list of the respective persons to carry out aspects of the present invention, para 109, P3 method that models locations and movements prediction methods, detailed analysis of results indicated location as in FIG. 9 shows relationship between number of people in the building and AUC of models, para 97, The participants personal mobile devices while moving through scenarios, and devices were on and connected to Wi-Fi network and participants received access to parts of the building, para 102, Wi-Fi data retrieved during the scenarios were merged to allow for comparing real locations (as manually recorded by participants) and predicted locations);
identifying by the processor, from a floorplan of the property, respective materials of any signal-attenuating structures between the preliminary estimated location and the access points (para 17, employed real connection logs for participants accessing Wi-Fi networks at campus and, identifiable data from individuals were collected and cross-checked with real locations and time at those locations and location prediction to rudimentary Wi-Fi-based approaches to location tracing, para 42, system identify locations of people, including rooms, corridors, bathrooms, elevators, stairwells, lobbies, offices, communal workspaces, halls (e.g., lecture halls), garages, and the like, para 86, model predicts the locations of an individual and location-recall (L-R) specifies locations at which a person present and that correctly identified and location confidence (L-C) correctly identified locations by the model);
computing respective estimated distances between the device and the access points, based on the signal strengths and the materials of the signal-attenuating structures (para 87, |predictions| indicates the number of predicted locations in which a person can be, para 07, RSSI information to run triangulation of multiple APs or Wi-Fi fingerprint to estimate a device's proximity, para 35, application of algorithms that estimate likely locations and likely contacts);
computing an estimated location of the device based on the estimated distances (para 74, based on characteristics estimate values for different locations, and calibrate value in real-time, para 91, Wi-Fi access points in building were represented as AP-type nodes which were linked to location nodes which carry information about estimated RSSI in the building, para 95, estimating Wi-Fi usage would be to count people entering a building in a given period and compare that data to Wi-Fi usage).
Alemi specifically fails to disclose outputting an output based on the estimated location.
In analogous art, Ergen discloses outputting an output based on the estimated location (para 42, by using mobility pattern determination or monitoring module 214, authorities track each user's mobility pattern in case of an alert, such as when a curfew is declared, or in case a user is in a place/area that is restricted by authorities by specifying/separating places where user can go, para 47, users who are in the vicinity of risky area or approaching region may be alert, para 56, information such as, list of preferred/selected places to visit, and estimated crowd rate of selected places, are transmitted to guide or direct individuals).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific for predicting locations and movements using Wi-Fi access data and other types of information, and in particular, to contact tracing disclosed by Alemi to integrated into client device in a Wi-Fi network for current signal data/measurements of one or more Wi-Fi access points encountered by the client device as taught by Ergen for tracking mobility patterns and contact points for the purpose of contact tracing, making use of wireless signals sent/received via a Software Development Kit (SDK) integrated into any client device application [Ergen, para 0013].
10. Claims 2 and 18 are rejected under 35 U.S.C. 103(a) as being unpatentable over Alemi (US 20220109949 A1) (hereinafter Alemi) in view of Ergen (US 20210391072 A1) (hereinafter Ergen) and further in view of Baxley (US 20140031062 A1) (hereinafter Baxley).
Regarding claim 2, Alemi and Ergen fails to discloses the method according to claim 1, wherein the alert indicates that the device may be a malicious surveillance device.
In analogous art, Baxley discloses the method according to claim 1, wherein the alert indicates that the device may be a malicious surveillance device (para 44, The malicious agent may be configured as a rogue wireless access point to target other wireless devices 110, wireless device 110 may automatically, or manually, connect to the malicious Wi-Fi network, para 45, determine that malicious device is broadcasting signal to create an unauthorized wireless network, para 58, building sensors to disguise an unauthorized entry into the building , to cause a false alarm, or otherwise interfere with building security systems functionality).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific to predict locations of individuals in buildings over time based on Wi-Fi access point connectivity and other reference information disclosed by Alemi and Ergen to prevent wireless device to automatically, or manually, connect to the malicious Wi-Fi network after assuming it to be trustworthy as taught by Baxley to include Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence [Baxley, paragraph 0044].
Regarding claim 18, Alemi and Ergen fails to discloses the method according to claim 17, further comprising identifying, from the floorplan, respective thicknesses of the signal-attenuating structures, wherein computing the estimated distances comprises computing the estimated distances based on the thicknesses.
In analogous art, Baxley discloses the method according to claim 17, further comprising identifying, from the floorplan, respective thicknesses of the signal-attenuating structures, wherein computing the estimated distances comprises computing the estimated distances based on the thicknesses (para 41, sensors 120 may be altered to accommodate structures within environment such as walls, stairwells, and so forth, para 290, geolocation module 355 can leverage improved environment that incorporate and account for walls, obstructions, conductive objects, shadows, attenuating, reflections, multipath, radio interference, or other such factors).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify teaching of user-specific to predict locations of individuals in buildings over time based on Wi-Fi access point connectivity and other reference information disclosed by Alemi and Ergen to prevent wireless device to automatically, or manually, connect to the malicious Wi-Fi network after assuming it to be trustworthy as taught by Baxley to include Security administrators may use a visualization console to monitor for wireless security threats and to visualize images or videos overlaid with radio frequency intelligence [Baxley, paragraph 0044].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mirza Alam whose telephone number is (469) 295-9286. The examiner can be reached on Monday-Thursday 7:30AM-6:00PM (EST).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached on 571-270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MIRZA F ALAM/Primary Examiner, Art Unit 2688
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