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 Amendment
This action is responsive to RCE and amendments/remarks received 06/26/2026. Claims 1, 3-4, 9, 12, 14-15, and 18-19 amended. Claims 2, 13 and 20 previously cancelled. Claims 1, 3-12 and 14-19 are pending.
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, 3-8, 11-12, 14-16 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (US Patent No. 20210134143), in view of Bodbyl (US Patent No. 20230021850 A1).
In re claim 1, Subramanian teaches A method for operating a security system of a facility (Abstract: “A system for preventing a false alarm that occurs at a building, the system includes a processing circuit configured to receive, via a communications interface, building data including events for the building devices.”), wherein the facility includes a plurality of secure regions (Para [0093]: “FIG. 22 is a block diagram of zones of the building of FIG. 1 and the expansion module for servicing additional zones, according to an exemplary embodiment.”) each having security cameras therein (Para [0139]: “Security subsystem 238 can include occupancy sensors, video surveillance cameras, digital video recorders, video processing servers, intrusion detection devices, access control devices and servers, or other security-related devices.”), the method comprising:
receiving alarms issued by the security system of the facility, wherein each alarm includes an alarm type, an alarm time stamp, and an alarm location in the facility (Para [0173]: “The signals generated by building systems (e.g., from sensors the building subsystems 228 e.g., intrusion, fire, or HVAC systems) may be discrete events or continuous signals generated in response to certain actions performed either by human beings or based on based on sensor data (e.g., detecting an intrusion event, detecting motion in a zone, etc.). In some embodiments, the events are marked by site, system, date, event type, zone, alarm, and/or can include a comment. An example is Table 1 below…”; note table 1 includes ‘Date and Time’.);
logging the received alarms in an alarm log (Para [0165]: “The security system 306 may implement an interface system 308, an alarm analysis system 310, and a database storing historical security data 312, security system data collected from the security systems 302a-302d.”);
identifying alarms in the alarm log that have an alarm time stamp that fall within a predetermined time window of interest (Para [0012]: “In some embodiments, performing the parameter search to group the events includes at least one of searching the events based on a time window by identifying events that are associated with a time that is within the time window or performing the parameter search to group the events by searching the events based on a spatial distance window by identifying events that are associated with a location that is within the spatial distance window. In some embodiments, performing the parameter search based on the spatial distance window groups events that occur in a predefined area.”);
based at least in part on the identified alarms, predicting that a first one of the plurality of secure regions of the facility will have at least a first threshold number of alarms during a first predicted future time period (Para [0013]: “In some embodiments, the processing circuit is configured to receive first building data including first events associated with a first period of time, determine a number of times that the false alarm rule triggered during the first period of time based on the first building data, predict a number of times that the alarm rule will trigger in the future during a second period of time after the first period of time based on the number of times that the false alarm rule triggered during the first period of time…”); and
outputting a first indication that the first one of the plurality of secure regions of the facility is predicted to have at least the first threshold number of alarms during the first predicted future time period (Para [0013]: “…and generate an insight based on the predicted number of times the alarm rule will trigger in the future during the second period of time and provide the insight to a user via a user device.”).
Subramanian fails to teach wherein outputting the first indication comprises displaying, during the first predicted future time period, from a first security camera located in
However, Bodbyl teaches wherein outputting the first indication comprises displaying, during the first predicted future time period, from a first security camera located in Paras [0113]-[0114]: “Conversely, a forecast risk score may represent a quantified risk evaluation for some time in the future based on predicted or forecast conditions. In some embodiments, the forecast risk evaluation process 1206 may include processing historical premises data that has been stored at data store 454. By processing historical premises data, the forecast risk evaluation process 1206 may identify trends or other patterns that can be utilized to predict the risk at a premises at some time in the future. In some embodiments, the forecast risk evaluation process 1206 may include processing real-time premises data in addition to, or instead of, historical premises data.” “At step 1210 a forecast risk score, or information indicative of the forecast risk score, is presented to a user, for example, via an interface 204. The manner in which a forecast risk score, or associated information, is presented will vary in different embodiments. In some embodiments, an end user, such as a premises owner or security manager, may access interface 204 to view a dashboard that includes various information associated with the premises 404. This information can include current conditions based on premises sensor data, live streams from monitoring devices 420, such as security cameras 422, as well as forecast risk information that the end user can use to perform security analytics associated with their premises. For example, using forecast risk information, an end user can plan and evaluate security needs at the premises 404, plan security upgrades, evaluate insurance needs, etc.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Subramanian to incorporate the teachings of Bodbyl to provide wherein outputting the first indication comprises displaying, during the first predicted future time period, from a first security camera located in Doing so enables an end user to plan and evaluate security needs at the premises 404, plan security upgrades, evaluate insurance needs, etc, as recognized by Bodbyl (Para [0114]).
Security system claim 12 and non-transitory computer readable medium claim 18 are rejected for the same reasons as method claim 1 for having similar limitations and being similar in scope; examiner notes ‘one or more alarm generating devices’ are taught in Subramanian in para [0030] in the form of motion sensors.
In re claim 3, Subramanian and Bodbyl teach all of the limitations of claim 1 stated above where Subramanian further teaches further comprising:
based at least in part on the identified alarms, predicting a second one of the plurality of secure regions of the facility will have at least a second threshold number of alarms during a second predicted future time period (SEE para [0049] and para [0271]: “In step 1804, the alarm analysis system 310 can generate a battery life probability distribution identifying the probability of times between the AC power failure event and the LB event. It may be desirable that the battery be replaced before the LB event following the AC power failure event. In some embodiments, the distribution is a prediction performed with a machine learning technique e.g., Bayesian modeling, Metropolis Hastings Algorithm, etc. In some embodiments, step 1804 is performed in response to the step 1802 being performed. In some embodiments, the step 1804 is performed prior to the step 1802 occurring such that machine learning can be performed prior to the AC power failure event occurring since the machine learning used to generate the distribution 1700 may require a predefined amount of time to occur.”); and
outputting a second indication that the second one of the plurality of secure regions of the facility is predicted to have at least the second threshold number of alarms during the second predicted future time period (Para [0177]: “The alarm analysis system 310 can further be configured to gather additional event data and apply a second round of analytics on the rules relative to the additional data. This may allow the false alarm rules to be updated or modified based on data that was not considered when the rule was generated. The false alarm rules, and the data used to generate the false alarm rules, may span multiple sites or one particular site. Furthermore, the false alarm rules and the data used to generate the false alarm rules may be associated with particular sites in a vertical. For example, a certain square footage may define a vertical so that similarly sized building sites can be analyzed together. By analyzing data for a particular group of building sites, recommendations can be generated for the building sites of the group based on a large data set. Another example of a vertical may be a market vertical (e.g., law firm buildings may form one vertical, grocery stores may form a vertical, schools may form another vertical, etc.)”), [wherein outputting the second indication includes displaying] [on the operator console of the security system] at the second predicted future time period (Para [0013]: “…and generate an insight based on the predicted number of times the alarm rule will trigger in the future during the second period of time and provide the insight to a user via a user device.”).
Subramanian fails to teach wherein outputting the second indication includes displaying, during the second predicted future time period, from a second security camera located in
However, Bodbyl teaches wherein outputting the second indication includes displaying, during the second predicted future time period, from a second security camera located in Paras [0113]-[0114]: “Conversely, a forecast risk score may represent a quantified risk evaluation for some time in the future based on predicted or forecast conditions. In some embodiments, the forecast risk evaluation process 1206 may include processing historical premises data that has been stored at data store 454. By processing historical premises data, the forecast risk evaluation process 1206 may identify trends or other patterns that can be utilized to predict the risk at a premises at some time in the future. In some embodiments, the forecast risk evaluation process 1206 may include processing real-time premises data in addition to, or instead of, historical premises data.” “At step 1210 a forecast risk score, or information indicative of the forecast risk score, is presented to a user, for example, via an interface 204. The manner in which a forecast risk score, or associated information, is presented will vary in different embodiments. In some embodiments, an end user, such as a premises owner or security manager, may access interface 204 to view a dashboard that includes various information associated with the premises 404. This information can include current conditions based on premises sensor data, live streams from monitoring devices 420, such as security cameras 422, as well as forecast risk information that the end user can use to perform security analytics associated with their premises. For example, using forecast risk information, an end user can plan and evaluate security needs at the premises 404, plan security upgrades, evaluate insurance needs, etc.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Subramanian and Bodbyl to further incorporate the teachings of Bodbyl to provide wherein outputting the second indication includes displaying, during the second predicted future time period, from a second security camera located in Doing so enables an end user to plan and evaluate security needs at the premises 404, plan security upgrades, evaluate insurance needs, etc, as recognized by Bodbyl (Para [0114]).
Security system claim 14 is rejected for the same reasons as method claim 3 for having similar limitations and being similar in scope.
In re claim 4, Subramanian and Bodbyl teach all of the limitations of claim 3 stated above where Bodbyl further teaches wherein the operator console has a plurality of displays including a dedicated hotspot display (SEE FIGS. 10 and 11 depicting plurality of displays, and Para [0037]: “FIG. 2 shows a diagram of an example network environment 200 in which the introduced technique can be performed. As shown in FIG. 2 , the network environment includes a security platform 202. Individuals can interface with the security platform 202 via an interface 204. For example, administrators may access the interface 204 to develop and/or train risk models or to configure alarm procedures. Monitoring center operators may access interface 204 to review generated alarms and trigger deterrent and/or enforcement measures. Premises client may access interface 204 to view situational dashboards and/or to review generated alarms.” and para [0039]: “The interface 204 may be accessible via a web browser, desktop application, mobile application, or over-the-top (OTT) application. Accordingly, the interface 204 may be viewed on a personal computer, tablet computer, mobile workstation, personal digital assistant (PDA), mobile phone, game console, music player, wearable electronic device (e.g., a watch or fitness accessory), network-connected (“smart”) electronic device, (e.g., a television or home assistant device), virtual/augmented reality system (e.g., a head-mounted display), or some other electronic device.”), and wherein outputting the first indication comprises displaying the first camera feed second camera feed Paras [0113]-[0114]: “Conversely, a forecast risk score may represent a quantified risk evaluation for some time in the future based on predicted or forecast conditions. In some embodiments, the forecast risk evaluation process 1206 may include processing historical premises data that has been stored at data store 454. By processing historical premises data, the forecast risk evaluation process 1206 may identify trends or other patterns that can be utilized to predict the risk at a premises at some time in the future. In some embodiments, the forecast risk evaluation process 1206 may include processing real-time premises data in addition to, or instead of, historical premises data.” “At step 1210 a forecast risk score, or information indicative of the forecast risk score, is presented to a user, for example, via an interface 204. The manner in which a forecast risk score, or associated information, is presented will vary in different embodiments. In some embodiments, an end user, such as a premises owner or security manager, may access interface 204 to view a dashboard that includes various information associated with the premises 404. This information can include current conditions based on premises sensor data, live streams from monitoring devices 420, such as security cameras 422, as well as forecast risk information that the end user can use to perform security analytics associated with their premises. For example, using forecast risk information, an end user can plan and evaluate security needs at the premises 404, plan security upgrades, evaluate insurance needs, etc.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Subramanian and Bodbyl to further incorporate the teachings of Bodbyl to provide wherein the operator console has a plurality of displays including a dedicated hotspot display and wherein outputting the first indication comprises displaying the first camera feed second camera feed Doing so enables an end user to plan and evaluate security needs at the premises 404, plan security upgrades, evaluate insurance needs, etc, as recognized by Bodbyl (Para [0114]).
Security system claim 15 is rejected for the same reasons as method claim 4 for having similar limitations and being similar in scope.
Non-transitory computer readable medium claim 19 is rejected for the same reasons as method 3 and method claim 4 for having similar limitations and being similar in scope.
In re claim 5, Subramanian and Bodbyl teach all of the limitations of claim 1 stated above where Subramanian further teaches wherein the predetermined time window of interest comprises the last seven days (Para [0285]: “In some embodiments, the alarm analysis system 310 determines whether the alarm rule sequence 2400 occurs within a particular time, e.g., an opening time of the building 10a. If the sequence 2400 occurs during the opening time (e.g., a time window between 8:50 A.M. and 9:10 A.M. on a weekday), this may be indicative of a sequence of events that can cause a false alarm. However, if the sequence of events occurs outside the opening time, the alarm analysis system 310 may determine that the sequence relates to a true alarm.”; examiner notes ‘on a weekday’ indicates any day of the week, which includes a span of 7 days.).
Security system claim 16 is rejected for the same reasons as method claim 1 for having similar limitations and being similar in scope.
In re claim 6, Subramanian and Bodbyl teach all of the limitations of claim 1 stated above where Subramanian further teaches further comprising:
based at least in part on the identified alarms, predicting a second one of the plurality of secure regions of the facility will have at least a second threshold number of alarms during a second predicted future time period (Para [0271]: “In step 1804, the alarm analysis system 310 can generate a battery life probability distribution identifying the probability of times between the AC power failure event and the LB event. It may be desirable that the battery be replaced before the LB event following the AC power failure event. In some embodiments, the distribution is a prediction performed with a machine learning technique e.g., Bayesian modeling, Metropolis Hastings Algorithm, etc. In some embodiments, step 1804 is performed in response to the step 1802 being performed. In some embodiments, the step 1804 is performed prior to the step 1802 occurring such that machine learning can be performed prior to the AC power failure event occurring since the machine learning used to generate the distribution 1700 may require a predefined amount of time to occur.”); and
outputting a second indication that the second one of the plurality of secure regions of the facility is predicted to have at least the second threshold number of alarms during the second predicted future time period (Para [0177]: “The alarm analysis system 310 can further be configured to gather additional event data and apply a second round of analytics on the rules relative to the additional data. This may allow the false alarm rules to be updated or modified based on data that was not considered when the rule was generated. The false alarm rules, and the data used to generate the false alarm rules, may span multiple sites or one particular site. Furthermore, the false alarm rules and the data used to generate the false alarm rules may be associated with particular sites in a vertical. For example, a certain square footage may define a vertical so that similarly sized building sites can be analyzed together. By analyzing data for a particular group of building sites, recommendations can be generated for the building sites of the group based on a large data set. Another example of a vertical may be a market vertical (e.g., law firm buildings may form one vertical, grocery stores may form a vertical, schools may form another vertical, etc.)”).
In re claim 7, Subramanian and Bodbyl teach all of the limitations of claim 1 stated above where Subramanian further teaches further comprising:
building a security guard tour plan for one or more security guards of the facility, wherein the security guard tour plan places one or more security guards at the first one of the plurality of secure regions during the first predicted future time period (Para [0166]: “In either case, the sensor data can be analyzed to determine if a condition exists at the premises that requires attention by a security professional. For example, if a motion sensor detects that someone has entered a building at a time that the intrusion system is armed or if an access control system detects that a door is being forced open, that information is transmitted to the local or remote monitoring center which can deploy security guards or call the police.”).
In re claim 8, Subramanian and Bodbyl teach all of the limitations of claim 1 stated above where Subramanian further teaches further comprising:
based at least in part on the identified alarms, determining one or more upgrades and/or one or more configuration setting changes to the security system; and
making one or more of the upgrades and/or one or more of the configuration setting changes to the security system (Para [0131]: “The systems and methods disclosed herein can assess and reduce false alarms by analyzing event patterns in data collected from a building to identify and resolve situations at a building that are causing false alarms. The systems and methods described herein can continuously monitor and detect event patterns indicative of situations that cause false alarms and help to prevent false alarms by generating recommendations based on the event patterns that indicate various building changes to make to prevent the false alarms from occurring in the future.”).
In re claim 11, Subramanian and Bodbyl teach all of the limitations of claim 8 stated above where Subramanian further teaches further comprising:
logging for each alarm in the alarm log an indication of how the respective alarm was resolved by a security operator; and
determining one or more of the configuration setting changes comprises tightening one or more security settings and/or relaxing one or more security settings based at least in part on the indication of how one or more of the alarms were resolved (Para [0130]: “Data intelligence e.g., data mining, machine learning, statistics, signal and network theories, can be used to derive actionable insights from raw data of a building to prevent false alarms. The data intelligence systems and methods described herein may start with the signal data, events, emanating from the sensors of intrusion, fire, or HVAC systems. The systems and methods can build upon the events by adding contextual information. The context can include spatial, time based, and/or neighbor based context that can be used to arrive at improved data representations that are robust and amenable for further processing by machine learning/data mining methods. Based on the enhanced data, the systems and methods described herein can employ an ensemble of techniques to derive actionable insights for reducing the number of false alarms that occur at a building.”).
Claims 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (US Patent No. 20210134143), in view of Bodbyl (US Patent No. 20230021850 A1) and further in view of Russo (US Patent No. 20230291870).
In re claim 9, Subramanian and Libal teach all of the limitations of claim 8 stated above but fails to teach wherein each of the security cameras provides a respective camera feed, and wherein one or more of the upgrades comprises adding one or more video analytics algorithms to process one or more camera feeds of the plurality of security cameras and/or adding one or more security cameras to the security system.
However, Russo teaches wherein each of the security cameras provides a respective camera feed, and wherein one or more of the upgrades comprises adding one or more video analytics algorithms to process one or more camera feeds of the plurality of security cameras and/or adding one or more security cameras to the security system (Para [0057]: “In such a case, video security performance may be evaluated over a period of time and it may be determined that, due to low optical zoom capabilities of the camera device 262, performance relative to one or more sub-areas of interest may be below a performance threshold. In such a case, the recommendation message may recommend that the camera device 262 be replaced by a different camera device with better optical zoom capabilities so that objects of interest within the waiting line-up area 288 may be recognized with greater confidence.” and para [0059]: “FIG. 3 is illustrative of a further “EXAMPLE TYPE III”. In particular, video analytics detection (and associated video analytics data) in relation to the smoke 360 may be assessed to determine and trigger a recommendation that the camera device 340 be replaced by a different camera device that is explosion protected in design. (It should be noted that recommending such a change is not necessarily tied to video analytics detection of fire or smoke. For instance, video analytics could detect and identify certain types of structures and structural detail that is within the security camera's FOV and associated with hazardous risk such as, for example, oil rig structure, propane storage tanks and containers, natural gas plant structure, and other similar types of structures and structural detail).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Subramanian and Bodbyl to further incorporate the teachings of Russo to provide wherein each of the security cameras provides a respective camera feed, and wherein one or more of the upgrades comprises adding one or more video analytics algorithms to process one or more camera feeds of the plurality of security cameras and/or adding one or more security cameras to the security system with the Building security system with false alarm reduction recommendations and automated self-healing for false alarm reduction of Subramanian as modified by Bodbyl. Doing so enables video analytics detection (and associated video analytics data) in relation to the smoke 360 may be assessed to determine and trigger a recommendation that the camera device 340 be replaced by a different camera device that is explosion protected in design, as recognized by Russo (para [0059]).
In re claim 10, Subramanian, Bodbyl and Russo teach all of the limitations of claim 9 stated above where Russo further teaches wherein adding one or more video analytics algorithms comprises adding a video analytics algorithm that provides video summarization and reporting of one or more camera feeds of the plurality of security cameras to an operator console of the security system (Para [0059]: “FIG. 3 is illustrative of a further “EXAMPLE TYPE III”. In particular, video analytics detection (and associated video analytics data) in relation to the smoke 360 may be assessed to determine and trigger a recommendation that the camera device 340 be replaced by a different camera device that is explosion protected in design. (It should be noted that recommending such a change is not necessarily tied to video analytics detection of fire or smoke. For instance, video analytics could detect and identify certain types of structures and structural detail that is within the security camera's FOV and associated with hazardous risk such as, for example, oil rig structure, propane storage tanks and containers, natural gas plant structure, and other similar types of structures and structural detail).”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Subramanian, Bodbyl and Russo to further incorporate the teachings of Russo to provide wherein adding one or more video analytics algorithms comprises adding a video analytics algorithm that provides video summarization and reporting of one or more camera feeds of the plurality of security cameras to an operator console of the security system with the Building security system with false alarm reduction recommendations and automated self-healing for false alarm reduction of Subramanian as modified by Bodbyl and Russo. Doing so enables video analytics could detect and identify certain types of structures and structural detail that is within the security camera's FOV and associated with hazardous risk, as recognized by Russo (Para [0059]).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Subramanian (US Patent No. 20210134143), in view of Bodbyl (US Patent No. 20230021850 A1) and further in view of Vincent (US Patent No. 10796554 B2).
In re claim 17, Subramanian and Bodbyl teach all of the limitations of claim 12 stated above but fails to teach wherein each alarm includes one or more of a device type of a device that generated the respective alarm, a device ID of the device that generated the respective alarm, an indication of how the respective alarm was resolved by a security operator, and an indication of which security operator resolved the respective alarm.
However, Vincent teaches wherein each alarm includes one or more of a device type of a device that generated the respective alarm, a device ID of the device that generated the respective alarm, an indication of how the respective alarm was resolved by a security operator, and an indication of which security operator resolved the respective alarm (SEE FIG 19 and col 27, lines 38-49: “Referring particularly to FIG. 19, interface 1900 is shown as an alarm details interface. Interface 1900 may be generated as a result of an alarm and is shown to include details 1902 describing properties of the alarm. Details 1902 may include, for example, the alarm type (e.g., “Duress”), alarm ID (i.e., “1711-PLMNK”), building location (e.g., address, city, state, country, campus, region, etc.), time zone (e.g., “GMT-8”), local time (e.g., “9:12 Tue, Nov. 28.sup.th”), and risk score (e.g., “98”). Interface 1900 may include other alarm specific information such as a list of cameras associated with the alarm event and a list of points of contact (POC) 1903 for the building.”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Subramanian and Bodbyl to further incorporate the teachings of Vincent to provide wherein each alarm includes one or more of a device type of a device that generated the respective alarm, a device ID of the device that generated the respective alarm, an indication of how the respective alarm was resolved by a security operator, and an indication of which security operator resolved the respective alarm with the Building security system with false alarm reduction recommendations and automated self-healing for false alarm reduction of Subramanian as modified by Bodbyl. Doing so enables showing as an alarm details interface, as recognized by Vincent (col 27, lines 38-49).
Response to Arguments
Applicant arguments filed 06/26/2026 have been fully considered but are moot in view of the new grounds of rejection, as necessitated by amendment.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 10514837 B1 teaches improved security services. In one aspect, a method is provided for controlling an autonomous data machine situated near a monitored environment. The method comprises: obtaining security data from a plurality of data sources; analyzing the security data to generate an analysis result; determining, based on the analysis result, an action to be performed by the autonomous data machine; and transmitting a command to the autonomous data machine causing it to perform the action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES EDWARD MUNION whose telephone number is (571)270-0437. The examiner can normally be reached Monday-Friday 7:30-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Steven Lim can be reached at 571-270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JAMES E MUNION/Examiner, Art Unit 2688 07/08/2026
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