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 .
Priority
Applicant has no priority data.
Information Disclosure statements
Applicant has no Information Disclosure statements (IDS) on file.
Claim Rejections - 35 USC § 103
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 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pusuluri et al (US 2022/0415177) in view of Burns et al (US 2014/0082099).
Regarding claim 1, Pusuluri et al discloses one or more computing systems (fig. 3, computing system), comprising (Data processing system 302, 304 may be representative of a computer system, [0040], lines 2-3):
memory storing computer program instructions (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4); and
at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4):
monitor a location, an acceleration, and/or a speed of a mobile device (sensor detects (monitor) a location and speed data of a mobile device, [0013], lines 1-5),
provide information pertaining to the location, the acceleration, and/or the speed of the mobile device to one or more artificial intelligence (AI) / machine learning (ML) models as input (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12),
receive and analyze output from the one or more AI/ML models (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12),
detect, based on the analysis, that the location and/or movement of the mobile device is anomalous (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed (location and/or movement) data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1), and
Pusuluri et al does not specifically disclose concept of send a message pertaining to the anomalous location and/or movement of the mobile device via a roaming network or an Internet Service Provider (ISP) to a home network core.
However, Burns et al specifically teaches concept of send a message pertaining to the anomalous location and/or movement of the mobile device via a roaming network or an Internet Service Provider (ISP) to a home network core (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120, [0047], lines 1-3 and lines 5-7).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of send a message pertaining to the anomalous location and/or movement of the mobile device via a roaming network or an Internet Service Provider (ISP) to a home network core of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1)
Regarding claim 2, Pusuluri et al discloses one or more computing systems (fig. 3, computing system),
Pusuluri et al does not specifically disclose concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof.
However, Burns et al specifically teaches concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9[0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 3, Pusuluri et al discloses one or more computing systems (fig. 3, computing system), wherein at least one of the one or more AI/ML models is trained to determine speeds, locations, and/or accelerations that are anomalous based on training data and provide the output based on the determination (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1).
Regarding claim 4, Pusuluri et al discloses one or more computing systems (fig. 3, computing system),
Pusuluri et al does not specifically disclose concept of wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion.
However, Burns et al specifically teaches concept of wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120; thus is seen as the notification is prioritized to be sent using selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core) with no interruptions, [0047], lines 1-3 and lines 5-7).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 5, Pusuluri et al discloses one or more computing systems (fig. 3, computing system),
Pusuluri et al does not specifically disclose concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages.
However, Burns et al specifically teaches concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9[0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 6, Pusuluri et al discloses one or more computing systems (fig. 3, computing system), wherein at least one of the one or more AI/ML models is located on the mobile device or a computing system of the home network core (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12).
Regarding claim 7, Pusuluri et al discloses one or more computing systems, wherein the one or more AI/ML models comprise at least two AI/ML models, at least one AI/ML model is located on the mobile device, and at least one other AI/ML model is located on a computing system of the home network core (Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models, [0046], lines 1-5).
Regarding claim 8, Pusuluri et al discloses one or more computing systems (fig. 3, computing system), wherein the computer program instructions are further configured to cause the at least one processor to (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4):
receive the message and determine from the communication that the location and/or movement of the mobile device is anomalous (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed (location and/or movement) data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1);
Pusuluri et al does not specifically disclose concept of perform a lookup of one or more contacts for the user of the mobile device; and
send one or more notifications to respective mobile devices of the one or more contacts indicating that the location and/or movement of the mobile device is anomalous.
However, Burns et al specifically teaches concept of perform a lookup of one or more contacts for the user of the mobile device (misplacement analyzer program 130 operates to identify the most suitable person (contact affiliated with the first user) to notify of the misplaced mobile device. Notification manager program 132 operates to transmit a notification to the identified most suitable or best contact indicating that the first user has misplaced her mobile device. It is noted that terms "most suitable contact" and "best contact" may be used interchangeably herein, [0024], lines 3-6); and
send one or more notifications to respective mobile devices of the one or more contacts indicating that the location and/or movement of the mobile device is anomalous (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120, [0047], lines 1-3 and lines 5-7).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of perform a lookup of one or more contacts for the user of the mobile device; and send one or more notifications to respective mobile devices of the one or more contacts indicating that the location and/or movement of the mobile device is anomalous of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 9, Pusuluri et al discloses one or more computing systems (fig. 3, computing system),
Pusuluri et al does not specifically disclose concept of wherein the one or more notifications comprise a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, a communication via a third party application running on the respective mobile devices of the one or more contacts, or any combination thereof.
However, Burns et al specifically teaches concept of wherein the one or more notifications comprise a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, a communication via a third party application running on the respective mobile devices of the one or more contacts, or any combination thereof (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9 [0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the one or more notifications comprise a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, a communication via a third party application running on the respective mobile devices of the one or more contacts, or any combination thereof of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 10, Pusuluri et al discloses one or more computing systems (fig. 3, computing system), wherein the computer program instructions are configured to cause the at least one processor to (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4):
Pusuluri et al does not specifically disclose concept of send one or more additional notifications to domestic and/or international authorities pertaining to the anomalous location and/or movement of the mobile device.
However, Burns et al specifically teaches concept of send one or more additional notifications to domestic and/or international authorities pertaining to the anomalous location and/or movement of the mobile device (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120, [0047], lines 1-3 and lines 5-7).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of send one or more additional notifications to domestic and/or international authorities pertaining to the anomalous location and/or movement of the mobile device of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 11, Pusuluri et al discloses One or more non-transitory computer-readable media storing computer program instructions, wherein the computer program instructions are configured to cause at least one processor to (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4):
monitor a location, an acceleration, and/or a speed of a mobile device (sensor detects (monitor) a location and speed data of a mobile device, [0013], lines 1-5),
provide information pertaining to the location, the acceleration, and/or the speed of the mobile device to one or more artificial intelligence (AI) / machine learning (ML) models as input (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12),
receive and analyze output from the one or more AI/ML models (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12),
detect, based on the analysis, that the location and/or movement of the mobile device is anomalous (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1), and
at least one of the one or more AI/ML models is trained to determine speeds, locations, and/or accelerations that are anomalous based on training data and provide the output based on the determination (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1)
Pusuluri et al does not specifically disclose concept of send a message pertaining to the anomalous location and/or movement of the mobile device via a roaming network or an Internet Service Provider (ISP) to a home network core.
However, Burns et al specifically teaches concept of send a message pertaining to the anomalous location and/or movement of the mobile device via a roaming network or an Internet Service Provider (ISP) to a home network core (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120, [0047], lines 1-3 and lines 5-7)
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of send a message pertaining to the anomalous location and/or movement of the mobile device via a roaming network or an Internet Service Provider (ISP) to a home network core of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 12, Pusuluri et al discloses one or more non-transitory computer-readable media (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4),
Pusuluri et al does not specifically disclose concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof.
However, Burns et al specifically teaches concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9[0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 13, Pusuluri et al discloses one or more non-transitory computer-readable media (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4),
Pusuluri et al does not specifically disclose concept of wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion.
However, Burns et al specifically teaches concept of wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120; thus is seen as the notification is prioritized to be sent using selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core) with no interruptions, [0047], lines 1-3 and lines 5-7).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 14, Pusuluri et al discloses one or more non-transitory computer-readable media (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4),
Pusuluri et al does not specifically disclose concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages.
However, Burns et al specifically teaches concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9[0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 15, Pusuluri et al discloses one or more non-transitory computer-readable media (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4), wherein at least one of the one or more AI/ML models is located on the mobile device or a computing system of the home network core (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1).
Regarding claim 16, Pusuluri et al discloses one or more non-transitory computer-readable media (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4), wherein the one or more AI/ML models comprise at least two AI/ML models, at least one AI/ML model is located on the mobile device, and at least one other AI/ML model is located on a computing system of the home network core (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12).
Regarding claim 17, Pusuluri et al discloses mobile device (fig. 1 item 102, mobile device), comprising:
memory storing computer program instructions (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4); and
at least one processor configured to execute the computer program instructions, wherein the computer program instructions are configured to cause the at least one processor to (system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention, [0016], lines 1-4):
monitor text messages sent and/or received by a mobile device, audio recorded by a microphone of the mobile device, or both,
provide the text messages and/or recorded audio to one or more artificial intelligence (AI) / machine learning (ML) models as input,
receive and analyze output from the one or more AI/ML models (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models), [0031], lines 5-12),
detect, based on the analysis, that a user of the mobile device is potentially experiencing an issue or is engaged in unpermitted behavior (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly (unpermitted behavior), [0031], lines 5-12, [0032], line 1), and
at least one of the one or more AI/ML models is trained to determine speeds, locations, and/or accelerations that are anomalous based on training data and provide the output based on the determination (The sensor data 118 may be collected from mobile devices. For example, all of the mobile devices located within the radius of 1000 feet may transmit their sensor location and speed data to server 112. The anomaly detection program 110A, 110B from server 112 may analyze the location and speed data to identify the vehicle using a machine learning algorithm. For example, the anomaly detection program 110A, 110B may use a trained neural network (artificial intelligence (AI) / machine learning (ML) models). Next, at 204, the anomaly detection program 110A, 110B analyzes the sensor location and speed data to identify an anomaly, [0031], lines 5-12, [0032], line 1)
Pusuluri et al does not specifically disclose concept of send a message pertaining to the potential issue or the unpermitted behavior via a roaming network or an Internet Service Provider (ISP) to a home network core, wherein
the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion.
However, Burns et al specifically teaches concept of send a message pertaining to the potential issue or the unpermitted behavior via a roaming network or an Internet Service Provider (ISP) to a home network core (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120, [0047], lines 1-3 and lines 5-7), wherein
the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion (fig. 6, notification manager program 132 may generate a notification message. The notification message may indicate that the first user has misplaced (anomalous location) her device and have some details on the, for example, determined current location of the first mobile device 136. At 508, notification manager program 132 may send the generated notification message to the identified recipient (best contact). The notification may be sent using the selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core), or the like. The second user may receive the notification via his mobile device (second mobile device 140) or client computer 120; thus is seen as the notification is prioritized to be sent using selected notification delivery channel, such as SMS, MMS, email (Internet Service Provider (ISP) to a home network core) with no interruptions, [0047], lines 1-3 and lines 5-7).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of send a message pertaining to the potential issue or the unpermitted behavior via a roaming network or an Internet Service Provider (ISP) to a home network core, wherein the message is sent with a sufficiently high priority that packets associated with the message are not dropped by the roaming network, the home network core, and/or the ISP due to congestion of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 18, Pusuluri et al discloses mobile device (fig. 1 item 102, mobile device),
Pusuluri et al does not specifically disclose concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof.
However, Burns et al specifically teaches concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9[0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the message comprises a voice call with a recording, a voicemail message, a Short Message Service (SMS) message, an email, or any combination thereof of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 19, Pusuluri et al discloses mobile device (fig. 1 item 102, mobile device),
Pusuluri et al does not specifically disclose concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages.
However, Burns et al specifically teaches concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages (If first mobile device 136 and second mobile device 140 are wireless VoIP (Voice over Internet Protocol) phones or have Unlicensed Mobile Access (UMA)/General Access Network (GAN) capability, they may also communicate with access points 104 and 105. At 504, notification manager program 132 may dynamically determine the best notification delivery channel based on, for example, contact profile information stored in database 124. Various notification delivery channels may include, but not limited to, SMS (short message service) messages, MMS (multimedia message service) messages, email messages, and social networking based channels, such as Twitter.RTM. and Facebook.RTM, [0020], lines 7-9, [0047], lines 1-3 and lines 5-8).
At the time the invention was filed, it would have been obvious for one of ordinary skill in the art to have modified system of Pusuluri et al with concept of wherein the high priority of the packets of the message is indicated by a Quality of Service (QoS) Class Identifier (QCI) of 1 for a voice call, 5 for a Short Message Service (SMS) message, or 6 for Internet Protocol (IP) voice calls or messages of Burns et al. One of ordinary skill in the art would have been motivated to make this modification in order to improve the process of physically locating a mobile device, (Burns et al, [0001], line 1).
Regarding claim 20, Pusuluri et al discloses mobile device (fig. 1 item 102, mobile device), wherein at least one of the one or more AI/ML models is located on the mobile device (Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models, [0046], lines 1-5).
Conclusion
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/FRANTZ BATAILLE/ Primary Examiner, Art Unit 2681