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 .
Status of Claims
This communication is a First Office Action Non-Final on Merits. Claims 1-34 are currently pending and have been considered below.
Priority
The present application, filed on 12/09/2025, is a CIP to application 18/912,415. The application claims priority to Provisional Application 63/544,026, filed on 10/13/2023.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 02/11/2026 and 05/11/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claims 9 and 26 are objected to because of the following informality: Claims 9 and 26 recite “wherein the wherein the” and it should be – wherein the --. Appropriate correction is required. Claims 12 and 29 are objected to because of the following informality: Claims 12 and 29 recite “drone has a camera a live feed” and it should be – drone has a camera and a live feed --. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 13, 14, 17, 30, 31 and 34 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AlA 35 U.S.C. 112, the applicant), regards as the invention. Claims 13 and 30 recite the acronym “GPS”. However, the Applicant has failed to explicitly define what the acronym refers to. Further clarification is needed. Claims 14 and 31 recite the acronym “API”. However, the Applicant has failed to explicitly define what the acronym refers to. Further clarification is needed. Claims 17 and 34 recite the acronym “QR”. However, the Applicant has failed to explicitly define what the acronym refers to. Further clarification is needed. Dependent claims inherit the deficiencies of the parent claims and thus dependent claims are rejected on the same basis as indicated above for the respective parent claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 - 34 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-17 are directed to a system (i.e. a machine). Claims 18-34 are directed to a method (i.e. a process). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-34 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Representative independent claim 1, analogous to independent claim 18 recites: A threat detection system comprising: located proximate a coverage zone, capture environmental data relating to the coverage zone and generate data; and analyze the data and detect at least one of a threat subject and/or threat context in the coverage zone based on the data and generate a threat signal based on detecting the threat subject or threat context, wherein the threat signal includes at least one of the threat signal and the threat context, identify and generate alerts for potential threats based on visual recognition of firearms; receive the threat, analyze one or more threat signals to determine if a valid threat exists, and generate an alarm signal when a valid threat is determined to exist; receive the alarm signal from the threat, generate a threat notification based on the alarm signal and transmit the threat notification to at least one notification endpoint. The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including advertising, marketing or sales activities or behaviors; business relations (managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). As the claims discuss a threat detection system including analyzing data and detecting at least one of a threat subject, identify and generate alerts for potential threats based on visual recognition of firearms; determine if a valid threat exists, and generate an alarm signal when a valid threat is determined, which is one of certain methods of organizing human activity.
Mental Processes (concepts performed in the human mind (including an observation, evaluation, judgement, opinion (independent claim 1, analogous to independent claims 18 recites for example, “capture environmental data relating to the coverage zone and generate data”; “analyze the data and detect at least one of a threat subject and/or threat context in the coverage zone based on the data”; “generate a threat signal based on detecting the threat subject or threat context, wherein the threat signal includes at least one of the threat signal and the threat context”; “identify and generate alerts for potential threats based on visual recognition of firearms”; “receive the threat, analyze one or more threat signals to determine if a valid threat exists”, “generate an alarm signal when a valid threat is determined to exist”; “receive the alarm signal from the threat”, “generate a threat notification based on the alarm signal and transmit the threat notification”.) Concepts performed in the human mind as mental processes because the steps of obtaining, extracting, comparing, screening, transmitting and analyzing data mimic human thought processes of observation, evaluation, judgement and opinion, perhaps with paper and pencil, where data interpretation is perceptible in the human mind. See In re TLI Commc’ns LLCPatentLitig., 823 F.3d 607, 611 (Fed. Cir. 2016); FairWarning IP, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016)).
Further, dependent claims add additional limitations, for example: (claims 2 and 19) presents and SOS panic button integrated to allow users to manually initiate emergency alerts for threats; (claims 3 and 20) thereby provide situational awareness and remote monitoring capabilities; (claims 4 and 21) comprising at least three tiers of users: Owners, Admins, and Viewers, where each tier has distinct permissions for accessing and managing alerts, sensors, and emergency responses; (claims 5 and 22)
send alerts to authorized users and members of the public upon detection of a shooting threat by initiated by activation of the SOS panic button; (claims 6 and 23) provides location-based directions to help users navigate to secure areas or away from active threat locations, based on threat data; (claims 7 and 24)
displays threat alerts, panic notifications, and live video streams, allowing for efficient navigation and management of emergency situations; (claims 8 and 25) minimize false positives while maintaining high accuracy in detecting threats; (claims 9 and 26) notify both first responders and members of the public, who have opted to receive alerts, providing immediate situational awareness to a broad audience; (claims 10 and 27) dynamically updates the navigation routes based on changes in threat conditions or new information from active alerts; (claims 11 and 28) providing various perspectives of the threat location to improve situational awareness for emergency responders; (claims 12 and 29) location data included in the threat context; (claims 13 and 30) the location data includes GPS coordinates or a sensor-reported location; (claims 14 and 31) receives the alert; (claims 15 and 32) allow users to manually initiate emergency alerts for threats not detected, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of certain methods of organizing human activity and mental processes, but for the recitation of generic computing components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (claim 1) sensing module; sensor(s); threat processor; artificial intelligence (AI) agent(s) trained; threat notification module; communications network; threat server; notification server; (claim 2 and 19) mobile application; mobile user device; user interface (UI); (claims 3 and 20) bodycam; real-time video streaming; (claims 4 and 21) membership module; (claims 5 and 22) locked screen notifications module; (claims 6 and 23) navigation module; (claims 7 and 24) unified threat management interface; (claims 8 and 25) computer vision and machine learning algorithm(s); (claims 12 and 29) drone system; (claims 15 and 32) hardware-based SOS panic button; (claims 16 and 33) hardware tag; actuator; (claims 17 and 34) QR code; link. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-34 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 8, 18 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Smith et al. (US 2008/0258880 A1), hereinafter “Smith”, over Wright (US 2021/0021592 A1), hereinafter “Wright”.
Regarding Claim 1, Smith teaches A threat detection system comprising: (See at least Smith, Abstract; Figure 10; Smith, para 0037-0038, teaches an example for providing a security network is provided that utilizes road traffic controllers as one component. The method includes embedding threat detection software in a network of roadway traffic controllers. At least one threat detection sensor, such as chemical, biological, radiological, nuclear and/or explosive detection sensors... FIG. 1 is a schematic of an example of a multi-tier security network);
a sensing module located proximate a coverage zone, the sensing module including: at least one sensor configured to capture environmental data relating to the coverage zone and generate sensor data; and (See at least Smith, Fig. 1 and 2; Fig. 3 shows a local sub-network 16 that includes sensors; Figure 8; para 0045, security network is an overarching network that coordinates multiple tiers of networks that span a particular coverage area of interest, e.g., a nationwide coverage; para 0054, communicates with others of the stations over a communications grid; para 0063 and 0052 each sensor node is connected to one or more sensors, through which it gathers data);
a threat processor (Smith, processor within local command center 23; Fig. 9) configured to analyze the sensor data and detect at least one of a threat subject and/or threat context in the coverage zone (Smith, Fig. 1 & 2) based on the sensor data and generate a threat signal based on detecting the threat subject or threat context, (Smith, Fig. 1 & 2; para 0010, method for using a security network described in this document includes embedding threat detection software in a plurality of roadway traffic signal controllers; coupling at least one threat detection sensor to a corresponding roadway traffic signal controller; and monitoring output of the at least one threat detection sensor) wherein the threat signal includes at least one of the threat signal and the threat context, wherein the threat processor includes at least one artificial intelligence ... trained to identify and generate alerts for potential threats based on visual recognition of firearms; and (Smith, para 0040, sensors to provide identification of weapons; para 0151, artificial intelligence methodologies to present complicated information in a way that humans can rapidly interpret, understand and act upon with high levels of situational awareness; Smith, para 0039, para 0041, The network 10 can be configured to be context driven so that use of sensors is prioritized and communication bandwidth is designated for particular functions upon threat detection; para 0065, sensors can include biomechanical sensors (breath signature, heart rate, etc.) to determine threat related manifestations; biometrics to identify the individual of interest (facial recognition, gait analysis, vein pattern, iris/retinal scans, fingerprinting, etc.) by comparison to a known database; and behavioral assessment techniques as provided by baseline optical or other sensor data and subsequent machine interpretation defining behavior of interest);
a threat notification module including remotely coupled to the sensing module over a communications network and including: (See at least Smith, Fig. 2 & 9 and Figure 1, shows communications network between stations [nodes]; para 0061, sensor node 26 can be implemented in various configurations. A sensor node 26 can include a node module that includes a microprocessor that run a set of node module software tools for various node operations, an interface with one or more sensors, and a communication module for communicating with other nodes; para 0077 and Figure 10
an overarching communications system that links command center and the higher level command
centers; the local command centers can provide alerts based upon analysis of data from the sensors);
a threat server configured to receive the threat signal from the sensing module, analyze one or more threat signals to determine if a valid threat exists, and generate an alarm signal when a valid threat is determined to exist; and (See at least Smith, Fig. 2 & 9; [0061], 'A sensor ... interface with one or more sensors, and a communication module for communicating with other nodes, a local command center or a higher tier node in the network.'; [0077], an overarching communications system that links command center and the higher level command centers, such as regional command center and national command center and distributes information published by other command centers to stations within its control.'; [0157], data server; Further alerts are taught throughout Smith, see at least para 0138, Alert: a system flag triggered by detection of a threat, or a human user in response to a threat or other condition);
a notification server configured to receive the alarm signal from the threat server, generate a threat notification based on the alarm signal and transmit the threat notification to at least one notification endpoint (See at least Smith, Fig. 2 & 9; para 0061 interface with one or more sensors, and a communication module; [0077] alerts, to an overarching communications system that links command center, such as regional command center and national command center and distributes information). While Smith teaches artificial intelligence, Smith does not appear to explicitly teach and in the same field of endeavor Wright teaches (AI) agent (See at least Wright, para 0026, an agent (e.g., software and/or hardware driven, such as an Ethical Agent powered by AI) can be employed into an IoT environment to scan devices and/or data traffic. The agents can scan for threats; Further, see at least Wright, para 0041). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Smith with (AI) agent as taught by Wright with the motivation for security systems and methods to continuously monitor for known threats and proactively pursue information on emerging or unknown threats on devices and data (Wright, Abstract). The Smith invention now incorporating the Wright invention, has all the limitations of claim 1.
Regarding Claim 8, Smith, now incorporating Wright, teaches The threat detection system of claim 1, wherein the at least one AI agent utilizes computer vision and machine learning algorithms to minimize false positives while maintaining high accuracy in detecting threats (Smith teaches cameras and video throughout; Further see at least Wright, para 0039, teaches using cameras in detecting threats; Wright, Abstract, an algorithm scanning engine protects against algorithmic biases with respect to race, gender, economic status, etc.)
Regarding Claim 18 the claim is an obvious variant to claim 1 above, and is therefore rejected on the same premise. Claim 18 recites “A threat detection method”. See at least Smith, para 0010, teaches a method for using a security network.
Regarding claim 25, the claim recites analogous limitations to claim 8 above, and is therefore rejected on the same premise.
Claims 2-7, 9-17, 19-24 and 26-34 are rejected under 35 U.S.C. 103 as being unpatentable over Smith and Wright and over Kerning et al. (US 2018/0261025 A1), hereinafter “Kerning”.
Regarding Claim 2, Smith, now incorporating Wright, teaches the threat detection system of claim 1, further comprising a mobile application executing on mobile user device and including. Yet Smith and Wright do not appear to explicitly teach and in the same field of endeavor Kerning teaches a user interface (UI) that presents and SOS panic button integrated to allow users to manually initiate emergency alerts for threats not detected by the at least one autonomous AI agent (See at least Kerning, Figures 8 and 9a, teaches a “Panic” button on a mobile application on a mobile user device). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Smith and Wright with a user interface (UI) that presents and SOS panic button integrated to allow users to manually initiate emergency alerts for threats not detected by the at least one autonomous AI agent as taught by Kerning with the motivation for improvements in security and public safety systems, and more particularly to a platform of mobile solutions that are devised to improve an individual's ability to be warned/informed and to warn others of hazards or events (Kerning, para 0002). The Smith and Wright invention now incorporating the Kerning invention, has all the limitations of claim 2.
Regarding Claim 3, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 2, wherein the mobile application includes a bodycam feature that enables real-time video streaming from a user device to thereby provide situational awareness and remote monitoring capabilities (Analyzing video data is taught throughout Smith, see at least para 0072, teaches cell phones; para 0076, teaches collecting and analyzing any type of desired data including, but not limited to video data; Further see Kerning, Figure 12).
Regarding Claim 4, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 2, wherein the mobile application includes a membership module comprising at least three tiers of users: Owners, Admins, and Viewers, where each tier has distinct permissions for accessing and managing alerts, sensors, and emergency responses (Smith, para 0072, teaches authorized or registered system users can access the system via the nodes via system portal browser interface installed on user computers. Authorized or registered system users can also access the system through nodes via mobile web interface via mobile communication devices such as cell phones, PDAs and mobile computers.)
Examiner note: While Smith discloses registered and authorized users, Smith does not explicitly disclose “Owners, Admins, and Viewers”. However, the Examiner asserts that the data identifying the user is simply a label for the data and adds little, if anything, to the claimed acts or steps and thus does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the specific type of information) which does not explicitly alter or impact the steps of the system or method does not patentably distinguish the claimed invention from the prior art in terms of patentability.
Regarding Claim 5, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 2, wherein the mobile application includes a locked screen notifications module configured to send real-time alerts to the locked screens of authorized users and members of the public upon detection of a shooting threat by the at least one AI agent or initiated by activation of the SOS panic button (See at least Kerning, Figures 8 and 9a, teaches a “Panic” button on a mobile application on a mobile user device; Kerning, para 0143, when a shooting incident occurs on campus or in a community, in proximity to the App users... In this case the APP may send a warning to the App user to direct them away from the shooting incident... To avoid the risk that a student would delay receiving such an alert, the App may be set up to use the speaker of the user's device to provide a loud (e.g., full volume) aural warning (e.g., “Warning—Danger, Shooting in Progress—exit campus immediately avoiding Building X”)).
Regarding Claim 6, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 2, wherein the mobile application includes a navigation module that provides location-based directions to help users navigate to secure areas or away from active threat locations, based on real-time threat data (Smith, para 0063, various different sensors may be deployed at the location and such sensors can operate in concert to provide sensing data to a sensor node; Smith para 0155, a Global Positioning System (GPS); Further, see Kerning, para 0143, the aural alert may continue at a reduced volume, until the person has traveled away from, proximity to the perceived danger zone (e.g., moves to be a threshold distance away, e.g., at least a quarter of a mile away, from GPS location of the shooting incident or the shooter's current location if on the move)).
Regarding Claim 7, Smith, now incorporating Wright and Kerning, teaches The threat detection The threat detection system of claim 2, wherein the UI of the mobile application includes a unified threat management interface that displays threat alerts, panic notifications, and live video streams, allowing for efficient navigation and management of emergency situations (See at least Kerning, Figures 8 and 9a, teaches a “Panic” button on a mobile application on a mobile user device; Kerning, para 0143, when a shooting incident occurs on campus or in a community, in proximity to the App users... In this case the APP may send a warning to the App user to direct them away from the shooting incident... To avoid the risk that a student would delay receiving such an alert, the App may be set up to use the speaker of the user's device to provide a loud (e.g., full volume) aural warning (e.g., “Warning—Danger, Shooting in Progress—exit campus immediately avoiding Building X”); See at least Kerning, para 0151, video architecture; The incident mobile app may stream live video of an incident directly to the application's system).
Regarding Claim 9, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 5, wherein the wherein the locked screen notifications module is configured to notify both first responders and members of the public, who have opted to receive alerts, providing immediate situational awareness to a broad audience (See at least Kerning, para 0143, when a shooting incident occurs in a community, in proximity to the App users... In this case the APP may send a warning to the App user to direct them away from the shooting incident... To avoid the risk that a student would delay receiving such an alert, the App may be set up to use the speaker of the user's device to provide a loud (e.g., full volume) aural warning (e.g., “Warning—Danger, Shooting in Progress—exit campus immediately avoiding Building X”)).
Regarding Claim 10, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 6, wherein the navigation module dynamically updates the navigation routes based on changes in threat conditions or new information from active alerts (Wright, para 0014, systems and methods secure data with tools that are built to protect in real-time; Smith, para 0063, various different sensors may be deployed at the location and such sensors can operate in concert to provide sensing data to a sensor node; Smith para 0155, a Global Positioning System (GPS); Further, see Kerning, para 0143, the aural alert may continue at a reduced volume, until the person has traveled away from, proximity to the perceived danger zone (e.g., moves to be a threshold distance away, e.g., at least a quarter of a mile away, from GPS location of the shooting incident or the shooter's current location if on the move)).
Regarding Claim 11, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 3, wherein the live bodycam feature supports multiple simultaneous video streams from different users, providing various perspectives of the threat location to improve situational awareness for emergency responders (Analyzing video data is taught throughout Smith, see at least para 0072, teaches cell phones; para 0076, teaches collecting and analyzing any type of desired data including, but not limited to video data; Further see Kerning, Figure 12; Kerning, para 0151, video architecture; The incident mobile app may stream live video of an incident directly to the application's system; teaches dual video streaming).
Regarding Claim 12, Smith, now incorporating Wright, teaches the threat detection system of claim 1, further comprising.
Yet Smith and Wright do not appear to explicitly teach and in the same field of endeavor Kerning teaches a drone system for dispatching a drone to location data included in the threat context, wherein the drone has a camera a live feed transmission capability (Drones are taught throughout, see at least Kerning, para 0116, teaches cameras, if available, to scan the location of the user's, mobile device, and if warranted, sending a drone to also observe the area). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Smith and Wright with a drone system for dispatching a drone to location data included in the threat context, wherein the drone has a camera a live feed transmission capability as taught by Kerning with the motivation for improvements in security and public safety systems, and more particularly to a platform of mobile solutions that are devised to improve an individual's ability to be warned/informed and to warn others of hazards or events (Kerning, para 0002). The Smith and Wright invention now incorporating the Kerning invention, has all the limitations of claim 12.
Regarding Claim 13, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 12, wherein the location data includes GPS coordinates or a sensor-reported location (Smith, para 0063, various different sensors may be deployed at the location and such sensors can operate in concert to provide sensing data to a sensor node; Smith para 0155, a Global Positioning System (GPS)).
Regarding Claim 14, Smith, now incorporating Wright and Kerning, teaches The threat detection system of claim 12, wherein the drone is dispatched by a third-party server that receives the alert through the API (Drones are taught throughout Kerning, see at least para 0006, see at least para 0116, the person may be unable to respond to the second message due to the severity of the situation, the open escalate response of the system may include directing PTZ cameras, if available, to scan the location of the user's, mobile device, and if warranted, sending a drone).
Regarding Claim 15, Smith, now incorporating Wright, teaches The threat detection system of claim 1 further comprising a hardware-based. Yet Smith and Wright do not appear to explicitly teach and in the same field of endeavor Kerning teaches SOS panic button configured to allow users to manually initiate emergency alerts for threats not detected by the at least one autonomous AI agent (See at least Kerning, Figures 8 and 9a, teaches a “Panic” button on a mobile application on a mobile user device). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Smith and Wright with SOS panic button configured to allow users to manually initiate emergency alerts for threats not detected by the at least one autonomous AI agent as taught by Kerning with the motivation for improvements in security and public safety systems, and more particularly to a platform of mobile solutions that are devised to improve an individual's ability to be warned/informed and to warn others of hazards or events (Kerning, para 0002). The Smith and Wright invention now incorporating the Kerning invention has all the limitations of claim 15.
Regarding Claim 16, Smith, now incorporating Wright and Kerning, teaches Tthethreat detection system of claim 15, where the SOS panic button is a hardware tag with an actuator (See at least Kerning, Figures 8 and 9a teaching panic button; Wright, para 0016, IoT sensors in smart ecosystems monitor individuals and devices and their activities in real-time. Examiner notes hardware tags with actuators are a crucial role in a smart ecosystem; Kerning, para 0191, teaches Bluetooth enabled tag).
Regarding Claim 17, Smith, now incorporating Wright and Kerning, teaches The threat detection system, of claim 15, where the SOS panic button is a hardware tag with a QR code that allows a camera of a mobile phone to read the QR code and present a link to a SOS panic button (See at least Kerning, Figures 8 and 9a teaching panic button; Kerning, para 0191, teaches Bluetooth enabled tag; Wright, para 0016, IoT sensors in smart ecosystems monitor individuals and devices and their activities in real-time. Examiner notes hardware tags are a crucial role in a smart ecosystem; Kerning, para 0182, teaches QR code).
Regarding claim 19, the claim recites analogous limitations to claim 2 above and is therefore rejected on the same premise.
Regarding claim 20, the claim recites analogous limitations to claim 3 above and is therefore rejected on the same premise.
Regarding claim 21, the claim recites analogous limitations to claim 4 above and is therefore rejected on the same premise.
Regarding claim 22, the claim recites analogous limitations to claim 5 above, and is therefore rejected on the same premise.
Regarding claim 23, the claim recites analogous limitations to claim 6 above, and is therefore rejected on the same premise.
Regarding claim 24, the claim recites analogous limitations to claim 7 above, and is therefore rejected on the same premise.
Regarding claim 26, the claim recites analogous limitations to claim 9 above, and is therefore rejected on the same premise.
Regarding claim 27, the claim recites analogous limitations to claim 10 above, and is therefore rejected on the same premise.
Regarding claim 28, the claim recites analogous limitations to claim 11 above, and is therefore rejected on the same premise.
Regarding claim 29, the claim recites analogous limitations to claim 12 above, and is therefore rejected on the same premise.
Regarding claim 30, the claim recites analogous limitations to claim 13 above, and is therefore rejected on the same premise.
Regarding claim 31, the claim recites analogous limitations to claim 14 above, and is therefore rejected on the same premise.
Regarding claim 32, the claim recites analogous limitations to claim 15 above, and is therefore rejected on the same premise.
Regarding claim 33, the claim recites analogous limitations to claim 16 above, and is therefore rejected on the same premise.
Regarding claim 34, the claim recites analogous limitations to claim 17 above, and is therefore rejected on the same premise.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Deng US 11/293,712 - Systems and methods are provided for weapon monitoring and action tagging, including storing a defined weapon action, wherein the defined weapon action is defined based on an inertial measurement from a weapon, the inertial measurement data indicative of at least one of a movement of the weapon, an orientation of the weapon, or a direction of the weapon, receiving sensor information from a weapon, wherein the sensor information includes inertial measurement data, evaluating the sensor information to determine the presence of the defined weapon action, and displaying, in response to determining that the defined weapon action occurred, indicia communicating the occurrence of the defined weapon action to a viewer via a graphical interface.
Saigh et al. US 2013/0183924 - In described embodiments, a system establishes a perimeter around an area, and mobile devices within the established perimeter communicate with a server that provides and collects personal and asset safety information. The provided information might enable users associated with the mobile devices to plan actions or take routes based on a given criteria, such as a safest route, through display on the mobile device. The collected information from the mobile device might be location, emergency event, environmental factors, sensor information and the like, which might then be communicated to users and/or administrators of the system. Location information, such as through global positioning system (GPS), might provide tracking of mobile devices and users or assets associated with each mobile device. GPS functionality associates latitude, longitude and elevation (X-Y-Z coordinate axis) data with the collected and provided information.
WO 2022/035371 A2 - The most happening trend today in digital world is usage of Artificial Intelligence (AI) and Internet of Things (IoT) to solve many real world problems swiftly in various industries with minimal human intervention, with greater accuracy, in the shortest time span and in a seamless manner. The said industries are not limited to entertainment, real estate, retail and e-commerce, travel, banking and financial services, manufacturing, food tech, health care and logistics and transportation. In each of these industries the problems that AI platform is engaged to solve are numerous. AI engine needs training data and real time data to operate. Some of these said data are obtained from sensors interconnected to the internet with suitable internet protocol suite. Sensors interconnected using internet protocol (IP) is called the IoT. Some of the other data for AI training and real time evaluation is also obtained from Data Bases (DB) and interconnected third-party systems.
NPL – Linfeng Wu; Wei Ruan; Keda Sun; Liang Chen; Liu Yang; Tingting Ye, “Research and Evaluation of Intelligent Threat Detection Under Industrial Internet”, Published in: 2021 IEEE International Conference on Networking, Sensing and Control (ICNSC), https://ieeexplore.ieee.org/document/9702185 - At present, the security situation in the industrial internet is becoming more and more serious. Various threats such as network attacks, malicious code and vulnerability utilization are gradually increasing. Consequently, it is urgent to study industrial threat detection methods. In order to tackle typical network attacks, system vulnerabilities and malicious operations, a real-time intelligent industrial threat detection method is proposed by analyzing the network data in the industrial control system. Particularly, artificial intelligence technique, adversarial sample generation technique and deep learning model are used in the method. Besides, the proposed method is achieved in the real network, and the corresponding industrial threat detection platform is developed. The results show that the developed threat detection platform can detect a variety of typical network attacks, system vulnerabilities, malicious code, etc. At the same time, the platform has good throughput and compatibility and is suitable for the actual industrial environment.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Conclusion
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/R.R.N./Examiner, Art Unit 3629
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
