DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
On pg. 13, par. 1, lines 9-11 of Applicant’s Response, applicant argues that Ponnuswamy never teaches that the features extracted depend upon, or are selected according to, a specific "reference profile type" that corresponds to the particular profile being determined at that moment.
Examiner respectfully disagrees with applicant’s argument.
The features extracted (Ponnuswamy [0051], noise level, interference level, and etc. are features extracted) depend upon, or are selected according to, a specific "reference profile type" (Ponnuswamy [0062], attributes is a specific reference profile type - behavior is another specific reference profile type) that corresponds to the particular profile being determined at that moment (Ponnuswamy [0051], the particular profile (~attributes profile - collection of attributes) comprises a noise level, an interference level, a number of wireless devices operating within a wireless range of the target device, a number of APs within a wireless range of the target device, and a number of channels utilized by the APs within a wireless range of the target device).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 5, 16-17, and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ponnuswamy (US 2018/0225592 A1).
Regarding claim 1, Ponnuswamy teaches a method implemented by a network device, for determining a terminal profile ([0025], “determining a target device profile including an expected behavior for a target device. The target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”), the method comprising:
obtaining a plurality of groups of profile detection data (different types of attributes and behaviors (~plurality of groups) for a client device; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors of the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”),
wherein the plurality of groups of profile detection data ([0062], first, second, third, and fourth client device with types of attributes and behaviors (~plurality of groups of profile detection data); [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and (f) response to a particular radio-frequency (RF) environment”; [0062], “a global dataset 212 includes multiple sets of device data 204c-d. A particular set of device data (illustrated as device data 204a, device data 204b, device data 204c, or device data 204d) includes attributes and behaviors of a particular client device. For example, device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”) are based on detection of one or more first terminals for a plurality of times (detection of different types of attributes and behaviors of a client device (~first terminal) requires detection of the client device (~first terminal) for a plurality of times; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”); and
determining a reference terminal profile based on the plurality of groups of profile detection data ([0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”; Applicant’s published specification in par. 6 defines “reference terminal profile” as “for one first terminal, the reference terminal profile is a terminal profile corresponding to the terminal”)
by: extracting corresponding key feature data from each of the plurality of groups of profile detection data based on a reference profile type corresponding to a to-be-determined terminal profile (key feature data are extracted from each of the plurality of groups of profile detection data ([0062], “global dataset 212 includes multiple sets of device data 204c-d”) based on different types of attributes and behaviors ([0051] and [0039-0045], lists various types of attributes and behaviors) which are reference profile types corresponding to a to-be-determined terminal profile ([0039], “A target device profile 104 includes expected behaviors 106 for the target device”); [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and (f) response to a particular radio-frequency (RF) environment”; [0062], “a global dataset 212 includes multiple sets of device data 204c-d. A particular set of device data (illustrated as device data 204a, device data 204b, device data 204c, or device data 204d) includes attributes and behaviors of a particular client device. For example, device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”; [0039], “A target device profile 104 includes expected behaviors 106 for the target device. The expected behaviors 106 may be determined based on actual behaviors of the target device and/or other client devices”); and
determining the reference terminal profile based on a plurality of pieces of extracted key feature data ([0025], “determining a target device profile (~reference terminal profile) including an expected behavior for a target device. The target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”),
wherein the reference profile type is a link quality measurement type ([0051], “Attributes of an RF environment include a noise level (~noise level/floor (link quality measurement type) is a foundational component used to measure wireless network link quality); an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”), a roaming steering type or a network detection type.
Regarding claim 2, Ponnuswamy teaches the method according to claim 1, wherein the one or more first terminals belong to a same category ([0123], “identifying clusters that share at least one device attribute with the target device (also referred to as “relevant clusters”)”), and
wherein the reference terminal profile references the terminal profile corresponding to a terminal that has reference category information (Clusters (~terminals/client devices) with terminal profile corresponding to a terminal that has reference category (~class/device type - see [0061]) information is referenced to the reference terminal profile (~target profile); [0027], “Clusters that share a device attribute with a target device are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device. The method used for determining an expected behavior for the target device depends on whether the behavior is common to multiple relevant clusters or is unique to a single relevant cluster”; [0061], “A classifier function 216 is applied to a set of device data to determine a particular class, of a candidate set of classes, for the set of device data. Each class includes device data associated with at least one common device attribute. In an embodiment, each class includes device data associated with the same device type. Examples of device types include Apple iPhone 7, Apple iPhone 6, and Samsung Galaxy S6. In other embodiments, each class includes device data associated with another common device attribute, such as the same manufacturer, the same operating channel, and/or the same multiple-input and multiple-output (MIMO) setting. As illustrated, a classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”).
Regarding claim 5, Ponnuswamy teaches the method according to claim 1, wherein after determining the reference terminal profile ([0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”; Applicant’s published specification in par. 6 defines “reference terminal profile” as “for one first terminal, the reference terminal profile is a terminal profile corresponding to the terminal”),
the method further comprising:
obtaining terminal feature data describing a feature of one target terminal or a common feature of a plurality of target terminals ([0027], “Clusters that share a device attribute with a target device (~feature of one target terminal) are identified as “relevant clusters.””);
determining, from a plurality of pieces of stored feature data, target feature data that is most similar to the terminal feature data, wherein each of the plurality of pieces of feature data corresponds to a particular terminal profile ([0027], “Clusters that share a device attribute with a target device (~target feature data that is most similar to the terminal feature data) are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device”; [0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”, wherein each of the plurality of pieces of feature data corresponds to a particular terminal profile; [0029-0031], “(a) an attribute type of the attribute that is shared between a particular relevant cluster and the target device; (b) a behavior type of the common behavior shared across the relevant clusters; (c) a number of device attributes that are shared between a particular relevant cluster and the target device”), and
wherein terminal profiles corresponding to the plurality of pieces of feature data comprise the reference terminal profile ([0027], “Clusters that share a device attribute with a target device (~terminal profiles corresponding to the plurality of pieces of feature data comprise the reference terminal profile) are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device”; [0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”, wherein each of the plurality of pieces of feature data corresponds to the plurality of pieces of feature data comprise the reference terminal profile); and
determining a first terminal profile corresponding to the target feature data as a second terminal profile of the one or the plurality of target terminals ([0027], “Clusters that share a device attribute with a target device (~a first terminal profile corresponding to the target feature data is determined as a second terminal profile of the one or the plurality of target terminals) are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device”; [0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”, wherein a first terminal profile corresponding to the target feature data is determined as a second terminal profile of the one or the plurality of target terminals).
Regarding claim 16, Ponnuswamy teaches an apparatus for determining a terminal profile ([0206], “the techniques herein are performed by computer system 800 in response to processor 804 executing one or more sequences of one or more instructions contained in main memory 806”; [0025], “determining a target device profile including an expected behavior for a target device. The target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”), wherein the apparatus comprises:
a memory configured to store instructions ([0206], “one or more sequences of one or more instructions contained in main memory 806”); and
a processor coupled to the memory and configured to execute the instructions to cause the apparatus to ([0206], “processor 804 executing one or more sequences of one or more instructions contained in main memory 806”):
obtain a plurality of groups of profile detection data (different types of attributes and behaviors (~plurality of groups) for a client device; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors of the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”),
based on the one or more first terminals (detection of different types of attributes and behaviors of a client device (~first terminal) requires detection of the client device (~first terminal); [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”); and
determine a reference terminal profile based on the plurality of groups of profile detection data ([0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”; Applicant’s published specification in par. 6 defines “reference terminal profile” as “for one first terminal, the reference terminal profile is a terminal profile corresponding to the terminal”)
by: extracting corresponding key feature data from each of the plurality of groups of profile detection data based on a reference profile type corresponding to a to-be-determined terminal profile (key feature data are extracted from each of the plurality of groups of profile detection data ([0062], “global dataset 212 includes multiple sets of device data 204c-d”) based on different types of attributes and behaviors ([0051] and [0039-0045], lists various types of attributes and behaviors) which are reference profile types corresponding to a to-be-determined terminal profile ([0039], “A target device profile 104 includes expected behaviors 106 for the target device”); [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and (f) response to a particular radio-frequency (RF) environment”; [0062], “a global dataset 212 includes multiple sets of device data 204c-d. A particular set of device data (illustrated as device data 204a, device data 204b, device data 204c, or device data 204d) includes attributes and behaviors of a particular client device. For example, device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”; [0039], “A target device profile 104 includes expected behaviors 106 for the target device. The expected behaviors 106 may be determined based on actual behaviors of the target device and/or other client devices”); and
determining the reference terminal profile based on a plurality of pieces of extracted key feature data ([0025], “determining a target device profile (~reference terminal profile) including an expected behavior for a target device. The target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”),
wherein the reference profile type is a link quality measurement type ([0051], “Attributes of an RF environment include a noise level (~noise level/floor (link quality measurement type) is a foundational component used to measure wireless network link quality); an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”), a roaming steering type or a network detection type.
Regarding claim 17, Ponnuswamy teaches the apparatus according to claim 16,
wherein the one or more first terminals belong to a same category ([0123], “identifying clusters that share at least one device attribute with the target device (also referred to as “relevant clusters”)”), and
wherein the reference terminal profile references a terminal profile corresponding to a terminal that has reference category information (Clusters (~terminals/client devices) with terminal profile corresponding to a terminal that has reference category (~class/device type - see [0061]) information is referenced to the reference terminal profile (~target profile); [0027], “Clusters that share a device attribute with a target device are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device. The method used for determining an expected behavior for the target device depends on whether the behavior is common to multiple relevant clusters or is unique to a single relevant cluster”; [0061], “A classifier function 216 is applied to a set of device data to determine a particular class, of a candidate set of classes, for the set of device data. Each class includes device data associated with at least one common device attribute. In an embodiment, each class includes device data associated with the same device type. Examples of device types include Apple iPhone 7, Apple iPhone 6, and Samsung Galaxy S6. In other embodiments, each class includes device data associated with another common device attribute, such as the same manufacturer, the same operating channel, and/or the same multiple-input and multiple-output (MIMO) setting. As illustrated, a classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”).
Regarding claim 20, Ponnuswamy teaches the apparatus according to claim 16, wherein the processor is further configured to execute the instructions to cause the apparatus to ([0206], “the techniques herein are performed by computer system 800 in response to processor 804 executing one or more sequences of one or more instructions contained in main memory 806”):
obtain terminal feature data, describing a feature of one target terminal or a common feature of a plurality of target terminals ([0027], “Clusters that share a device attribute with a target device (~feature of one target terminal) are identified as “relevant clusters.””);
determine, from a plurality of pieces of stored feature data, target feature data that is most similar to the terminal feature data, wherein each of the plurality of pieces of feature data corresponds to a particular terminal profile ([0027], “Clusters that share a device attribute with a target device (~target feature data that is most similar to the terminal feature data) are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device”; [0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”, wherein each of the plurality of pieces of feature data corresponds to a particular terminal profile), and
wherein terminal profiles corresponding to the plurality of pieces of feature data comprise the reference terminal profile ([0027], “Clusters that share a device attribute with a target device (~terminal profiles corresponding to the plurality of pieces of feature data comprise the reference terminal profile) are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device”; [0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”, wherein each of the plurality of pieces of feature data corresponds to the plurality of pieces of feature data comprise the reference terminal profile); and
determine a first terminal profile corresponding to the target feature data as a second terminal profile of the one or the plurality of target terminals ([0027], “Clusters that share a device attribute with a target device (~a first terminal profile corresponding to the target feature data is determined as a second terminal profile of the one or the plurality of target terminals) are identified as “relevant clusters.” Behaviors associated with the relevant clusters are used to determine expected behaviors for the target device”; [0025], “target device profile is determined by applying an unsupervised machine learning algorithm to different datasets. First, a global dataset includes multiple sets of device data, each set of device data including device attributes and behaviors corresponding to a different client device”, wherein a first terminal profile corresponding to the target feature data is determined as a second terminal profile of the one or the plurality of target terminals).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Ponnuswamy in view of Yoran (US 2010/0312886 A1).
Regarding claim 6, Ponnuswamy teaches the method according to claim 1, wherein prior to obtaining the plurality of groups of profile detection data (different types of attributes and behaviors (~plurality of groups) for a client device; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors of the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”),
the method further comprising:
obtaining reference category information ([0061], “applied to a set of device data to determine a particular class, of a candidate set of classes, for the set of device data ... each class includes device data associated with the same device type”);
determining, based on the reference category information, a reference terminal identification identifier referencing a terminal identification identifier corresponding to the one or more first terminals ([0061], “classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”; [0062], “device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”); and
detecting the one or more first terminals for the plurality of times based on the reference terminal identification identifier(detection of different types of attributes and behaviors of a client device (~first terminal) requires detection of the client device (~first terminal) for a plurality of times; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”).
Ponnuswamy does not explicitly teach that the reference terminal identification is an identifier referencing a terminal identification identifier corresponding to the one or more first terminals.
However, Yoran teaches an identifier referencing a terminal identification identifier corresponding to one or more first terminals ([0017], “It is further intended that the identification mean is linked to a personal profile of the respective member. In addition the identification mean is allocated to a communication mean, preferably a telecommunication device, mobile, PDA, notebook or a Smart Phone. It is a purpose that the identification mean is preferably located in spatial vicinity of the member and has an identification number. It is intended that the identification number is a Bluetooth identification number, MAC-ID, telephone number, IMEI or any other numerical, clearly allocatable identifier”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Yoran with the teaching of Ponnuswamy in order to uniquely distinguish, track, and manage specific, individual devices within a broader, often multi-device, network.
Regarding claim 7, Ponnuswamy in view of Yoran teaches the method according to claim 6, wherein determining the reference terminal identification identifier based on the reference category information (Ponnuswamy [0061], “classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”; [0062], “device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”) comprises:
determining, from a plurality of pieces of stored category information, target category information that is most similar to the reference category information, wherein each of the plurality of pieces of stored category information corresponds to a particular terminal identification identifier (Ponnuswamy [0061], “classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”; [0062], “device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”); and
determining the particular terminal identification identifier corresponding to the target category information as the reference terminal identification identifier (Ponnuswamy [0061], “classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”; [0062], “device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”).
Ponnuswamy does not explicitly teach that the terminal identification is a terminal identification identifier.
However, Yoran further teaches a terminal identification identifier ([0017], “It is further intended that the identification mean is linked to a personal profile of the respective member. In addition the identification mean is allocated to a communication mean, preferably a telecommunication device, mobile, PDA, notebook or a Smart Phone. It is a purpose that the identification mean is preferably located in spatial vicinity of the member and has an identification number. It is intended that the identification number is a Bluetooth identification number, MAC-ID, telephone number, IMEI or any other numerical, clearly allocatable identifier”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Yoran with the teaching of Ponnuswamy as modified by Yoran in order to uniquely distinguish, track, and manage specific, individual devices within a broader, often multi-device, network.
Claims 23 and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Ponnuswamy in view of Shimizu (US 2013/0059542 A1).
Regarding claim 23, Ponnuswamy teaches the method according to claim 6, wherein detecting the one or more first terminals for the plurality of times comprises (detection of different types of attributes and behaviors of a client device (~first terminal) requires detection of the client device (~first terminal) for a plurality of times; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”):
Ponnuswamy does not explicitly teach determining whether the one or more first terminals meet a detection protection condition, wherein the detection protection condition comprises at least one of a first signal strength of a source access point being within a reference strength range, a difference between the first signal strength and a second signal strength of a destination access point being greater than a strength threshold, or a number of steering times for the one or more first terminals being less than or equal to a quantity threshold; and performing the detecting only when the detection protection condition is met.
However, Shimizu teaches determining whether one or more first terminals meet a detection protection condition, wherein the detection protection condition comprises at least one of a first signal strength of a source access point being within a reference strength range ([0105], “detecting an AP in which the radio field strength of the reception signal is greater than a predetermined threshold as an AP that is associated with an exhibition that is compatible with the audio”), a difference between the first signal strength and a second signal strength of a destination access point being greater than a strength threshold, or a number of steering times for the one or more first terminals being less than or equal to a quantity threshold; and
performing the detecting only when the detection protection condition is met ([0105], “detecting an AP in which the radio field strength of the reception signal is greater than a predetermined threshold (~only when the detection protection condition is met) as an AP that is associated with an exhibition that is compatible with the audio”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Shimizu with the teaching of Ponnuswamy in order to filter out irrelevant devices and accurately identify real threats such as rogue access points or malicious hotspots while preventing false alarms caused by distant, harmless neighboring networks.
Regarding claim 26, Ponnuswamy teaches the apparatus according to claim 16,
wherein prior to obtaining the plurality of groups of profile detection data (different types of attributes and behaviors (~plurality of groups) for a client device; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors of the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”),
the processor is further configured to execute the instructions to cause the apparatus to ([0206], “the techniques herein are performed by computer system 800 in response to processor 804 executing one or more sequences of one or more instructions contained in main memory 806”):
obtaining reference category information ([0061], “applied to a set of device data to determine a particular class, of a candidate set of classes, for the set of device data ... each class includes device data associated with the same device type”);
determining, based on the reference category information, a reference terminal identification identifier referencing a terminal identification identifier corresponding to the one or more first terminals ([0061], “classifier function 216 divides a global dataset 212 into multiple device type datasets 220a-b”; [0062], “device data 204a includes attributes 206a and behaviors 208a of one client device. Device data 204b includes attributes and behaviors of another client device. Device data 204c includes attributes and behaviors of another client device. Device data 204d includes attributes and behaviors of another client device”); and
detecting the one or more first terminals for the plurality of times based on the reference terminal identification identifier (detection of different types of attributes and behaviors of a client device (~first terminal) requires detection of the client device (~first terminal) for a plurality of times; [0051], “Attributes of an RF environment include a noise level; an interference level; a number of wireless devices operating within a wireless range of the target device; a number of APs within a wireless range of the target device; and a number of channels utilized by the APs within a wireless range of the target device”; [0039-0045], “actual behaviors the target device and/or other client devices. Types of behaviors for a client device include but are not limited to: (a) protocol behavior; (b) connection behavior; (c) power-save behavior; (d) roaming behavior; (e) response to a particular configuration algorithm; and [0045] (f) response to a particular radio-frequency (RF) environment”).
Ponnuswamy does not explicitly teach by: determining whether the one or more first terminals meet a detection protection condition, wherein the detection protection condition comprises at least one of a first signal strength of a source access point being within a reference strength range, a difference between the first signal strength and a second signal strength of a destination access point being greater than a strength threshold, or a number of steering times for the one or more first terminals being less than or equal to a quantity threshold; and performing the detecting only when the detection protection condition is met.
However, Shimizu teach by: determining whether one or more first terminals meet a detection protection condition, wherein detection protection condition comprises at least one of a first signal strength of a source access point being within a reference strength range ([0105], “detecting an AP in which the radio field strength of the reception signal is greater than a predetermined threshold as an AP that is associated with an exhibition that is compatible with the audio”), a difference between the first signal strength and a second signal strength of a destination access point being greater than a strength threshold, or a number of steering times for the one or more first terminals being less than or equal to a quantity threshold; and
performing the detecting only when the detection protection condition is met ([0105], “detecting an AP in which the radio field strength of the reception signal is greater than a predetermined threshold (~only when the detection protection condition is met) as an AP that is associated with an exhibition that is compatible with the audio”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of Shimizu with the teaching of Ponnuswamy in order to filter out irrelevant devices and accurately identify real threats such as rogue access points or malicious hotspots while preventing false alarms caused by distant, harmless neighboring networks.
Allowable Subject Matter
10. Claims 4, 8-9, 19, 21-22, and 24-25 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
11. THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER J. YI whose telephone number is (571)270-7696. The examiner can normally be reached Monday thru Friday: 8:00AM to 5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jinsong Hu can be reached at (571)272-3965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEXANDER J YI/Examiner, Art Unit 2643
/JINSONG HU/ Supervisory Patent Examiner, Art Unit 2643
Prosecution Insights