Detailed Action
Claims 1-2, 6-22 are pending in this application. Claims 3-5 were cancelled. Claims 21-22 were newly added. This is a response to the Amendments/Remarks filed on 7/15/26. This is a Final Rejection.
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 1-2, 6-22 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recites
1. A method for interacting with a user to monitor and/or control one or more security devices of a facility, the method comprising:
receiving a natural language query to monitor and/or control one or more security devices;
processing the natural language query to:
identify one or more descriptors of one or more security system devices that are a subject of the natural language query;
identify a desired result of the natural language query;
based at least in part on the identified one more descriptors, assembling and submitting one or more security commands to identify one or more specific security system devices that correspond to the natural language query, wherein the one or more security commands submitted to identify the one or more specific security system devices comprise one or more device-identification queries submitted to obtain information identifying the one or more specific security devices;
based at least in part on the desired result and information identifying the one or more specific security devices, assembling and submitting one or more security commands to each of the specific security system devices that are tailored to achieve the desired result, wherein the one or more security commands are configured to change or determine an operational state of the corresponding specific security system device, and in response to submitting the one or more security system commands, receiving one or more return message, each return message corresponding to a respective security system command submitted to a respective specific security system device;
based at least in part on the one or more return messages, determining whether the desired result was achieved; and
report whether the natural language query was carried out successfully, and if not, report one or more problems that were encountered.
2. The method of claim 1, wherein the natural language query is processed that identifies the one or more descriptors of one or more security devices that are the subject of the natural language query and identify the desired result of the natural language query.
5. The method of claim 4, wherein determines whether the desired result was achieved.
6. The method of claim 4, wherein, reports whether the natural language query was carried out successfully, and if not, reports one or more problems that were encountered.
7. The method of claim 1, wherein the one or more descriptors identify one or more of: a type of a security device; a location of a security device in the facility; a region of a security device in the facility; and a current operational status of a security device.
8. The method of claim 1, comprising: determining access rights of the user; and determining whether the user has access rights to each of the specific security devices, and if not, not submitting the one or more security system commands to the specific security devices that the user lacks access rights.
9. The method of claim 1, comprising: storing, for each of at least some of a plurality of historical natural language queries received, information indicative of one or more descriptors of one or more security system devices and a desired result identified by processing the corresponding historical natural language query; and suggesting a natural language query to the user based at least in part on the stored information indicative of the one or more descriptors and the desired results for the plurality of historical natural language queries.
10. The method of claim 9, comprising: processing the plurality of historical natural language queries received over time.
11. The method of claim 1, wherein when the identity of the one more descriptors and/or the desired result is ambiguous, prompting the user to resolve the ambiguity.
12. A system for monitoring and/or controlling a security of a facility comprising:
a security system including a plurality of security system devices;
receiving a natural language query from a user;
provide the natural language query received and configured to identify and return:
one or more descriptors of one or more security devices that are a subject of the natural language query;
a desired result of the natural language query;
assemble and submit one or more security commands to identify one or more specific security devices of the security system that correspond to the natural language query based at least in part on the identified one more descriptors;
assemble and submit one or more security commands to each of the specific security devices of the security system that are tailored to achieve the desired result based at least in part on the desired result, and in response, submitting the one or more security commands, receive one or more return message, each return message corresponding to a respective security command submitted to a respective specific security device;
determine whether the desired result was achieved based at least in part on the one or more return messages; and provide whether the natural language query was carried out successfully.
14. The system of claim 13, wherein the security system control n includes a floorplan that displays for each of one or more of the plurality of security devices:
a location of the corresponding security device on the floor plan;
an identifier of the corresponding security device; and
a current status of the corresponding security device.
15. The system of claim 14, wherein the natural language query includes a reference to one or more of the security device identifiers displayed on the floorplan.
16. The system of claim 12,
determine access rights of the user; and
determine whether the user has access rights to each of the specific security devices, and if not, not submitting the one or more security system commands to the specific security system devices that the user lacks access rights.
17. A method of interacting with a user to monitor and/or control one or more security devices of a facility, the method comprising:
receiving a natural language query;
identifying specific security devices that correspond to the natural language query, including submitting one or more queries to identify one or more of the specific security devices that correspond to the natural language query;
processing the natural language query to identify a desired result to be achieved;
determining and sending one or more security system commands to the specific security devices that are intended to achieve the desired result;
determining whether the desired result was successfully achieved based on return messaged received from each of the specific security devices in response to each security system commands; and reporting whether the natural language query was successfully achieved.
18. The method of claim 17, comprising: when the natural language query was not successfully achieved, reporting one or more reasons.
19. The method of claim 17, wherein submitting one or more queries to identify specific security devices that correspond to the natural language query comprises one or more of:
submitting a query to identify one or more security system devices that have a specified device type;
submitting a query to identify one or more security system devices that are located at a specified location in the facility;
submitting a query to identify one or more security system devices that are located at a specified region of the facility; and
submitting a query to identify one or more security system devices that have a specified current operational status.
20. The method of claim 17, wherein identifying the desired result to be achieved comprises processing the natural language query.
21. The method of claim 1, wherein the one or more specific security system devices comprise an access-control device associated with a door of the facility, wherein the one or more security system commands submitted to the access-control device comprise a lock command or an unlock command.
22. The method of claim 21, wherein the one or more return messages comprise a status message indicating whether a lock status of the door changed.
The claims are directed towards using a chatbot and LLM for processing queries for a security system and sending commands to the security system which reduces load on security system operators, applicant’s spec. para.3.
Therefore the claims and the specification is drawn to certain methods of organizing human activity(in particular managing personal behavior or relationships or interactions between people(ie following rules or instructions)) and/or mental processes(the steps of receiving, processing the natural language query, identifying, submitting commands, determine whether result was achieved, and reporting can be performed mentally and/or with the aid of pen and paper).
This is akin to a security guard being asked to check whether all doors in a building are locked or unlocked and if unlocked, then a command is given to lock the door(s), the security guard reports back whether he/she successfully locked the door(s) or not.
If the claim under broadest reasonable interpretation covers limitation that is drawn to certain methods of organizing human activity and mental processes but for recitation of a generic computer and/or generic computer components described at a high level of generality or linking the use of the judicial exception to a particular technological environment or field of use, then it falls within the grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A, prong 1).
This judicial exception is not integrated into a practical application. In particular, the claims recites additional elements such as
1. via a chatbot, of a security system; to the security system from the security system, by an orchestrator operatively coupled to the chatbot
2. The method of claim 1, by a Large Language Model (LLM)
3. The method of claim 1, wherein an orchestrator.
4. The method of claim 3, wherein the orchestrator assembles and submits.
5. The method of claim 4, wherein the orchestrator determines
6. The method of claim 4, wherein a Large Language Model (LLM), operatively coupled to the orchestrator, reports via the chatbot.
10. The method of claim 9, comprising: using a trained Artificial Intelligence (AI) model, wherein the suggested natural language query is based at least in part on the trained AI model.
11. The method of claim 1, via the chatbot
12. a security system of a facility comprising:
a chatbot operatively coupled to the security system, the chatbot including:
a chatbot user interface for receiving ;
a large language model interface for interfacing with a Large Language Model (LLM);
an orchestrator operatively coupled to the chatbot user interface, the large language model interface and the security system;
the orchestrator is configured to:
via the chatbot user interface to the LLM via the large language model interface, wherein the LLM is configured to identify and return:
13. The system of claim 12, wherein the security system comprises a security system control application that includes a plurality of menus that can be navigated to monitor and control each of the plurality of security devices of the facility, wherein the chatbot user interface is integrated into the security system control application.
16. The system of claim 12, wherein the orchestrator is configured to:
17. via a chatbot to monitor
of the security system
via the security system
18. via the chatbot.
19. The method of claim 17, to the security system
20. The method of claim 17, via a Large Language Model (LLM).
The claim does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations of via a chatbot, of a security system; by an orchestrator operatively coupled to the chatbot, to the security system, from the security system, by a Large Language Model (LLM), wherein an orchestrator, wherein the orchestrator assembles and submits, wherein the orchestrator determines, wherein a Large Language Model (LLM), operatively coupled to the orchestrator, reports via the chatbot, using a trained Artificial Intelligence (AI) model, wherein the suggested natural language query is based at least in part on the trained AI model, wherein the security system comprises a security system control application that includes a plurality of menus that can be navigated to monitor and control each of the plurality of security devices of the facility, wherein the chatbot user interface is integrated into the security system control application are generic computer components described at a high level of generality and limitations amounts to mere instructions to implement the abstract idea on a computer and/or adding the words “apply it”(or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea. MPEP 2106.05(f).
The claim does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recites chatbot and LLM/AI, which is generally linking the use of the judicial exception to a particular technological environment or field of use, in this case to chat/instant messaging environment with LLM/AI. Such limitation are not enough to add significantly more to the claimed method and/or is an attempt to limit the use of the abstract idea to a particular technological environment for which to apply the underlying abstract concept, which does not add significantly more. The claims is directed to an abstract idea and merely links the judicial exception to a particular technological environment or field of use, chat/Instant Messaging,(MPEP 2106.05(h))
In further, the step of receiving a natural language query and reporting whether the natural language query was successfully and if not report problems can also be interpreted as insignificant extra-solution activity (pre and post-solution activity) to the judicial exception. The steps for receiving the query is merely the pre-solution activity and reporting is a post solution activity. Accordingly, the additional limitation/elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even considering all the additional element in combination, they are just providing a computerized system to perform the invention, but doesn’t improve the computing technology as the additional elements do not integrate the invention into a practical application, rather the claims and the additional limitations is drawn to an abstract idea that uses a computer as a tool with the addition of insignificant extra-solution activity, which does not integrate the judicial exception into a practical application. The claims is directed to an abstract idea and with the addition of insignificant extra-solution activity, which is not patent eligible and directed to an abstract idea. (MPEP 2106.05(g)).
Therefore the additional limitation/elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even considering all the additional element in combination, they are just providing a computerized system to perform the invention, but doesn’t improve the computing technology as the additional elements do not integrate the invention into a practical application. The claims is directed to an abstract idea and merely reciting generic computer components described at a high level of generality and limitations amounts to mere instructions to implement the abstract idea on a computer and/or adding the words “apply it”(or an equivalent) with the judicial exception, or merely uses a computer as a tool to perform an abstract idea MPEP 2106.05(f) and links the judicial exception to a particular technological environment or field of use, chat/Instant Messaging,(MPEP 2106.05(h)) with insignificant extra-solution activity (pre and post-solution activity) to the judicial exception. MPEP 2106.05(g). Therefore the claims are not patent eligible. (Step 2A, prong2).
The claim does not include additional elements/limitations that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements/limitations is drawn to limitations that use a computer as a tool, and includes well-understood, routine, and conventional activities(ie. receiving or transmitting data over a network MPEP 2106.05(d)(II)) that amount to no more than implementing the abstract idea with a computerized system. The claim is not patent eligible(Step 2B).
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 9-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 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-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 9, the term “at least some of a plurality of historical natural language queries received” is a relative term which renders the claim indefinite. The term “some” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The applicant provided para.43,47 to support the amendment, however these paragraphs does not provide define what is considered to be “some of a plurality of historical natural language queries”. Does “some” mean 2, 20, 200, 2000, or 20k?
All dependent claims rejected for the same reasons set forth above.
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 17-19 rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0355240 issued to Razak et al.(Razak) in view of US 2022/0076283 issued to Oliveira et al.(Oliveira).
As per claim 17, Razak teaches a method of interacting with a user via a chatbot to monitor and/or control one or more security devices of a security system of a facility, the method comprising: receiving a natural language query via the chatbot(Fig.6, [0053] Each of building subsystems 428 can include any number of devices, controllers, and connections for completing its individual functions and control activities.…Security subsystem 438 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. [0073]; [0105] Referring to FIG. 6, a flowchart of a process flow 500 for implementing a virtual maintenance manager which can be used to monitor building 10 is shown, according to some embodiments. Process flow 500 is shown to include inputting a user query (step 502). In some embodiments, a user can query the virtual maintenance manager through a chatbot (e.g., Slack). Alternatively, the user can provide a query through a voice-recognition application. Process flow 500 is further shown to include processing the query using natural language processing (NLP) software (step 504). The NLP software can interpret the meaning of the user's query. In some embodiments, the NLP software is the same as and/or similar to natural language processing 484. As shown, process flow 500 includes classifying the query (step 506). In some embodiments, the processed query can be classified by identifying intent and entity parameters. An intent parameter can provide an indication of purpose. An entity parameter can provide a specific term or object that provides context for the intent parameter. As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.);
identifying specific security devices of the security system that correspond to the natural language query, including submitting one or more queries to the security system to identify one or more of the specific security devices that correspond to the natural language query([0053] …. Security subsystem 438 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. [0105] Referring to FIG. 6, a flowchart of a process flow 500 for implementing a virtual maintenance manager which can be used to monitor building 10 is shown, according to some embodiments. Process flow 500 is shown to include inputting a user query (step 502). In some embodiments, a user can query the virtual maintenance manager through a chatbot (e.g., Slack). Alternatively, the user can provide a query through a voice-recognition application. Process flow 500 is further shown to include processing the query using natural language processing (NLP) software (step 504). The NLP software can interpret the meaning of the user's query. In some embodiments, the NLP software is the same as and/or similar to natural language processing 484. As shown, process flow 500 includes classifying the query (step 506). In some embodiments, the processed query can be classified by identifying intent and entity parameters. An intent parameter can provide an indication of purpose. An entity parameter can provide a specific term or object that provides context for the intent parameter. As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query. [0106] Still referring to FIG. 6, process flow 500 is shown to include processing the query (step 508). In some embodiments, this can include using the intent and entity parameters to obtain an appropriate response and/or output. In some embodiments, answers can be obtained from an analysis database (step 510). The analysis database can include rules (step 512), recommendations (514), and/or system test indicator(s) (step 516). System test indicators can be used to identify when a test of the system (e.g., HVAC system 100, security subsystem 438) has been performed. In some embodiments, the analysis database of step 510 can be updated based on new alarm events (step 518). The update can occur in real-time, or at specified intervals of time. In some embodiments, the rules and recommendations are the same as alarm rules 462 and recommendations 464 discussed above. In some embodiments, processing 488 processes the user query. Additionally or alternatively, processing 488 may interact with one or more elements of false alarm reduction system 450 (e.g., alarm analysis module 460, recommendation generator module 470, report generator module 494, etc.) to process the user query.)
processing the natural language query to identify a desired result to be achieved(Fig.7A-E, [0105] … The NLP software can interpret the meaning of the user's query. In some embodiments, the NLP software is the same as and/or similar to natural language processing 484. As shown, process flow 500 includes classifying the query (step 506). In some embodiments, the processed query can be classified by identifying intent and entity parameters...);
determining whether the desired result was successfully achieved based on return messaged received from each of the specific security devices in response to each security system commands([0075] System 451 may receive alarm events from security subsystem 438, classify the alarm events according to one or more rules and determine if the alarm events represent false alarms. If the alarm events represent false alarms, the system 451 may determine a root cause of the false alarm and provide one or more recommendations to a user to prevent future false alarms. In some embodiments, system 451 may provide an indication that an alarm event represents a false alarm thereby preventing the false alarm altogether. Additionally or alternatively, system 451 may receive request from a user for information and/or data related to BMS 400 and/or building subsystems 428 (e.g., security subsystem 438) and provide the requested information and/or data. In some embodiments, system 451 dynamically responds to user queries (input) via a virtual maintenance, as described in detail below.);
reporting via the chatbot whether the natural language query was successfully achieved(Fig.7A-E, [0108] As shown, once a user selects an output format, process flow 500 includes formatting the output (step 526). In some embodiments, the formatting can include preparing graphs or images, and/or using text-to-speech conversion. For example, report generator module 494 may generate a histogram of alarm activity. Process flow 500 is further shown to include displaying the result (step 528). For example, the result may be displayed via a web application on user device 496. In some embodiments, an audio output can be provided to the user, as opposed to a visual display. The display output can include text, values, and/or visual representations of data)..
Razak does not explicitly teach determining and sending via the security system one or more security system commands to the specific security devices that are intended to achieve the desired result.
Oliveira teaches determining and sending via the security system one or more security system commands to the specific security devices that are intended to achieve the desired result([0037] Moreover, chatbot manager 218 may automatically perform a set of action steps based on the extracted relevant information and generated insights. The set of action steps may include, for example, chatbot manager 218 automatically connecting to and controlling a set of one or more other or external systems of the corporation using a set of defined application programming interfaces. For example, chatbot manager 218 may automatically control a heating, ventilation, and air conditioning system of a building corresponding to the corporation to automatically adjust a temperature in the building or in one or more specific rooms of the building in response to identifying a temperature issue in one or more employee responses complaining about the current temperature of their work environment. As another example, chatbot manager 218 may automatically control a security system of the building to automatically lock doors to the building or one or more specific doors in the building in response to identifying a security issue in one or more employee responses indicating fear due to a possible threat in their work environment. Further, chatbot manager 218 may automatically control a communication system of the building to automatically contact corporate security and/or the police department in response to identifying the security issue. As a further example, chatbot manager 218 may automatically control a fire suppression system of the building to automatically start fire suppression in the building or in one or more specific rooms of the building in response to identifying a fire issue in one or more employee responses indicating intense heat levels, heavy amounts of smoke, and/or fire in their work environment. Furthermore, chatbot manager 218 may automatically control the communication system to automatically contact the fire department in response to identifying the fire issue. Moreover, chatbot manager 218 may use the communication system to automatically contact medical professionals in response to identifying a medical issue in one or more employee responses indicating injury, shortness of breath, or the like.).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Razak’s teaching of building management for control, monitor, and manage equipment in or around building or building area to apply the teachings of Oliveira’s teaching of chatbot connecting and controlling different system(s) of a building such as locking doors, heating, cooling, etc in order to provide the predictable result of chatbot management for control, monitor, and manage equipment in a building.
One ordinary skill in the art would have been motivated to combine the teachings in order to provide a customized work environment for employees(Oliveira, para.1-4).
As per claim 18, Razak in view of Oliveira teaches the method of claim 17, comprising: when the natural language query was not successfully achieved, reporting one or more reasons via the chatbot(Razak, Fig.7A-E, [0108] As shown, once a user selects an output format, process flow 500 includes formatting the output (step 526). In some embodiments, the formatting can include preparing graphs or images, and/or using text-to-speech conversion. For example, report generator module 494 may generate a histogram of alarm activity. Process flow 500 is further shown to include displaying the result (step 528). For example, the result may be displayed via a web application on user device 496. In some embodiments, an audio output can be provided to the user, as opposed to a visual display. The display output can include text, values, and/or visual representations of data).
As per claim 19, Razak in view of Oliveira teaches the method of claim 17, wherein submitting one or more queries to the security system to identify specific security devices that correspond to the natural language query comprises one or more of: submitting a query to identify one or more security system devices that have a specified device type([0105],[106] [0053] …. security subsystem includes an 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); submitting a query to identify one or more security system devices that are located at a specified location in the facility (Razak, [0105] … As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.); submitting a query to identify one or more security system devices that are located at a specified region of the facility; and submitting a query to identify one or more security system devices that have a specified current operational status.
Claims 12, 20 rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0355240 issued to Razak et al.(Razak) in view of US 2022/0076283 issued to Oliveira et al.(Oliveira) in view of 2025/0112878 issued to Bayless et al.(Bayless).
As per claim 12, Razak teaches a system for monitoring and/or controlling a security system of a facility comprising: a security system including a plurality of security system devices((Fig.6, [0053] …Security subsystem 438 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); a chatbot operatively coupled to the security system, the chatbot including: a chatbot user interface for receiving a natural language query from a user([0105] Referring to FIG. 6, a flowchart of a process flow 500 for implementing a virtual maintenance manager which can be used to monitor building 10 is shown, according to some embodiments. Process flow 500 is shown to include inputting a user query (step 502). In some embodiments, a user can query the virtual maintenance manager through a chatbot (e.g., Slack). Alternatively, the user can provide a query through a voice-recognition application. Process flow 500 is further shown to include processing the query using natural language processing (NLP) software (step 504). The NLP software can interpret the meaning of the user's query. In some embodiments, the NLP software is the same as and/or similar to natural language processing 484. As shown, process flow 500 includes classifying the query (step 506). In some embodiments, the processed query can be classified by identifying intent and entity parameters. An intent parameter can provide an indication of purpose. An entity parameter can provide a specific term or object that provides context for the intent parameter. As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.); the chatbot user interface, and the security system(Fig. 4,5);
provide the natural language query received via the chatbot user interface is configured to identify and return(Fig.7A-E, [0105] … The NLP software can interpret the meaning of the user's query. In some embodiments, the NLP software is the same as and/or similar to natural language processing 484. As shown, process flow 500 includes classifying the query (step 506). In some embodiments, the processed query can be classified by identifying intent and entity parameters...):
one or more descriptors of one or more security system devices of the security system that are a subject of the natural language query([0105] … As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.);
a desired result of the natural language query(Fig.7A-E, [0105] ……. In some embodiments, the processed query can be classified by identifying intent and entity parameters. An intent parameter can provide an indication of purpose. An entity parameter can provide a specific term or object that provides context for the intent parameter. As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.);
assemble and submit one or more security system commands to the security system to identify one or more specific security system devices of the security system that correspond to the natural language query based at least in part on the identified one more descriptors([0106] Still referring to FIG. 6, process flow 500 is shown to include processing the query (step 508). In some embodiments, this can include using the intent and entity parameters to obtain an appropriate response and/or output. In some embodiments, answers can be obtained from an analysis database (step 510). The analysis database can include rules (step 512), recommendations (514), and/or system test indicator(s) (step 516). System test indicators can be used to identify when a test of the system (e.g., HVAC system 100, security subsystem 438) has been performed. In some embodiments, the analysis database of step 510 can be updated based on new alarm events (step 518). The update can occur in real-time, or at specified intervals of time. In some embodiments, the rules and recommendations are the same as alarm rules 462 and recommendations 464 discussed above. In some embodiments, processing 488 processes the user query. Additionally or alternatively, processing 488 may interact with one or more elements of false alarm reduction system 450 (e.g., alarm analysis module 460, recommendation generator module 470, report generator module 494, etc.) to process the user query.);
in response to submitting the one or more security system commands, receive one or more return messages from the security system, each return message corresponding to a respective security system command submitted to a respective specific security system device ([0075] System 451 may receive alarm events from security subsystem 438, classify the alarm events according to one or more rules and determine if the alarm events represent false alarms. If the alarm events represent false alarms, the system 451 may determine a root cause of the false alarm and provide one or more recommendations to a user to prevent future false alarms. In some embodiments, system 451 may provide an indication that an alarm event represents a false alarm thereby preventing the false alarm altogether. Additionally or alternatively, system 451 may receive request from a user for information and/or data related to BMS 400 and/or building subsystems 428 (e.g., security subsystem 438) and provide the requested information and/or data. In some embodiments, system 451 dynamically responds to user queries (input) via a virtual maintenance, as described in detail below.);
determine whether the desired result was achieved based at least in part on the one or more return messages from the security system([0082] Speaking generally, alarm analysis module 460 may analyze and classify alarm events. In some embodiments, alarm analysis module 460 may identify conditions leading to false alarm events and fix them before they occur. Recommendation generator module 470 provides recommendations to a user to fix conditions leading to false alarms. In some embodiments, alarm analysis module 460 identifies conditions leading to false alarm events and recommendation generator module 470 determines a corresponding recommendation to address the false alarm event condition. User interface module 480 provides the recommendations from recommendation generator module 470 to a user. In some embodiments, user interface module 480 receives queries from a user for information and/or data relating to BMS 400 and/or building subsystems 428 (e.g., security subsystem 438) and responds to the queries. Root cause module 490 receives alarm events determined to be false alarm events from alarm analysis module 460 and determines a root cause of the false alarm events. Historical security database 492 stores alarm event data received from BMS 400 and/or building subsystems 428 (e.g., security subsystem 438). Additionally or alternatively, historical security database 492 may store results produced by alarm analysis module 460 and/or any classification data associated with received alarm events. Report generator module 494 receives requests to generate graphical displays of information and/or data associated with BMS 400 and/or building subsystems 428 (e.g., security subsystem 438) and generates the graphical displays accordingly.);; and
provide whether the natural language query was carried out successfully to the chatbot user interface(Fig.7A-E, [0108] As shown, once a user selects an output format, process flow 500 includes formatting the output (step 526). In some embodiments, the formatting can include preparing graphs or images, and/or using text-to-speech conversion. For example, report generator module 494 may generate a histogram of alarm activity. Process flow 500 is further shown to include displaying the result (step 528). For example, the result may be displayed via a web application on user device 496. In some embodiments, an audio output can be provided to the user, as opposed to a visual display. The display output can include text, values, and/or visual representations of data).
Razak does not explicitly teach the orchestrator coupled to the chatbot user interface; a large language model interface for interfacing with a Large Language Model (LLM); to the LLM via the large language model interface, wherein the LLM; the large language model interface; assemble and submit one or more security system commands to each of the specific security system devices of the security system that are tailored to achieve the desired result based at least in part on the desired result,
Oliveira teaches assemble and submit one or more security system commands to each of the specific security system devices of the security system that are tailored to achieve the desired result based at least in part on the desired result([0037] Moreover, chatbot manager 218 may automatically perform a set of action steps based on the extracted relevant information and generated insights. The set of action steps may include, for example, chatbot manager 218 automatically connecting to and controlling a set of one or more other or external systems of the corporation using a set of defined application programming interfaces. For example, chatbot manager 218 may automatically control a heating, ventilation, and air conditioning system of a building corresponding to the corporation to automatically adjust a temperature in the building or in one or more specific rooms of the building in response to identifying a temperature issue in one or more employee responses complaining about the current temperature of their work environment. As another example, chatbot manager 218 may automatically control a security system of the building to automatically lock doors to the building or one or more specific doors in the building in response to identifying a security issue in one or more employee responses indicating fear due to a possible threat in their work environment. Further, chatbot manager 218 may automatically control a communication system of the building to automatically contact corporate security and/or the police department in response to identifying the security issue. As a further example, chatbot manager 218 may automatically control a fire suppression system of the building to automatically start fire suppression in the building or in one or more specific rooms of the building in response to identifying a fire issue in one or more employee responses indicating intense heat levels, heavy amounts of smoke, and/or fire in their work environment. Furthermore, chatbot manager 218 may automatically control the communication system to automatically contact the fire department in response to identifying the fire issue. Moreover, chatbot manager 218 may use the communication system to automatically contact medical professionals in response to identifying a medical issue in one or more employee responses indicating injury, shortness of breath, or the like.).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Razak’s teaching of building management for control, monitor, and manage equipment in or around building or building area to apply the teachings of Oliveira’s teaching of chatbot connecting and controlling different system(s) of a building such as locking doors, heating, cooling, etc in order to provide the predictable result of chatbot management for control, monitor, and manage equipment in a building.
One ordinary skill in the art would have been motivated to combine the teachings in order to provide a customized work environment for employees(Oliveira, para.1-4)
Razak in view of Oliveira does not explicitly teach the orchestrator coupled to the chatbot user interface, a large language model interface for interfacing with a Large Language Model (LLM); to the LLM via the large language model interface, wherein the LLM; the large language model interface.
Bayless explicitly teaches the orchestrator coupled to the chatbot user interface(Fig.1, [0036] The chatbot system 110 can determine answers for queries of the users 106 using knowledge graphs and/or LLMs as described herein. The chatbot system 110 may include a chatbot component 112 that serves as an orchestrator for the chatbot system 110 and communicates with other components, as well as one or more chatbot interfaces 114 that provide mechanisms through which the users 106 can submit queries. The chatbot interfaces 114 may be a chat interface through which users 106 and/or programs are able to submit text (and other input) prompts. However, the chatbot interfaces 114 may be any type of interface through which software and/or users 106 can communicate with the chatbot system 110, such as CLIs, APIs, or any other type of software instructions.);
a large language model interface for interfacing with a Large Language Model (LLM); to the LLM via the large language model interface, wherein the LLM; the large language model interface([0334] In some embodiments, the chatbot 145 comprises an ML chatbot. The ML chatbot may provide advanced features as compared to a non-ML chatbot, which may include and/or derive functionality from a large language model (LLM). The ML chatbot may be trained on a server, such as server 140, using large training datasets of text which may provide sophisticated capability for natural-language tasks, such as answering questions and/or holding conversations. The ML chatbot may include a general-purpose pretrained LLM which, when provided with a starting set of words (prompt) as an input, may attempt to provide an output (response) of the most likely set of words that follow from the input. In one aspect, the prompt may be provided to, and/or the response received from, the ML chatbot and/or any other ML model, via a user interface of the server. This may include a user interface device operably connected to the server via an I/O module. Exemplary user interface devices may include a touchscreen, a keyboard, a mouse, a microphone, a speaker, a display, and/or any other suitable user interface devices).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Razak in view of Oliveira of chatbot management for control, monitor, and manage equipment in a building to apply the known art of Bayless of using LLM in order to provide the predictable result of chatbot management with LLM for control, monitor, and manage equipment in a building.
One ordinary skill in the art would have been motivated to combine the teachings in order to provide a customized work environment for employees(Oliveira, para.1-4) and to increase productivity and creativity by automating complex language-based tasks.
As per claim 20, Razak in view of Oliveira teaches the method of claim 17, wherein the natural language query that identifies the one or more descriptors of one or more security system devices that are the subject of the natural language query and identify the desired result of the natural language query(Razak, [0105] Referring to FIG. 6, a flowchart of a process flow 500 for implementing a virtual maintenance manager which can be used to monitor building 10 is shown, according to some embodiments. Process flow 500 is shown to include inputting a user query (step 502). In some embodiments, a user can query the virtual maintenance manager through a chatbot (e.g., Slack). Alternatively, the user can provide a query through a voice-recognition application. Process flow 500 is further shown to include processing the query using natural language processing (NLP) software (step 504). The NLP software can interpret the meaning of the user's query. In some embodiments, the NLP software is the same as and/or similar to natural language processing 484. As shown, process flow 500 includes classifying the query (step 506). In some embodiments, the processed query can be classified by identifying intent and entity parameters. An intent parameter can provide an indication of purpose. An entity parameter can provide a specific term or object that provides context for the intent parameter. As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.), however does not explicitly teach a Large Language Model (LLM), which is taught by Bayless Fig.1, [0002] More recently, there have been advances in artificial intelligence (AI) that have enabled chatbots and other AI systems to perform complex tasks that normally require human intelligence. Generative AI is a type of artificial intelligence where models are used to create (or “generate”) new content based on inputs, often in the form of prompts from users. One type of generative AI model is particularly effective at generating text, specifically, the large language model (LLM). LLMs are trained on large sets or corpuses of text data to perceive and infer context from user queries, understand a broader range of queries, and generate human-like textual responses to the queries. Chatbots that are backed by LLMs are becoming increasingly popular among users due to their ability to perform complex tasks on behalf of users, para.19.
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Razak in view of Oliveira of chatbot management for control, monitor, and manage equipment in a building to apply the known art of Bayless of using LLM in order to provide the predictable result of chatbot management with LLM for control, monitor, and manage equipment in a building.
One ordinary skill in the art would have been motivated to combine the teachings in order to provide a customized work environment for employees(Oliveira, para.1-4) and to increase productivity and creativity by automating complex language-based tasks.
Claims 13-15 rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0355240 issued to Razak et al.(Razak) in view of US 2022/0076283 issued to Oliveira et al.(Oliveira) in view of in view of 2025/0112878 issued to Bayless et al.(Bayless) in view of 2019/0324986 issued to Schwartz et al.(Schwartz).
As per claim 13, Razak in view of Oliveira in view of Bayless teaches the system of claim 12, wherein the security system comprises a security system control application, wherein the chatbot user interface is integrated into the security system control application(Razak, Fig.4, Oliveira, Fig.3), however does not explicitly teach a plurality of menus that can be navigated to monitor and control each of the plurality of security devices of the facility, which is taught by Schwartz, Fig.13, [0089] It should be noted that the option to show multiple cameras may also be tied to the context of other tiles shown on the canvas. For example, a contextual menu (e.g., the contextual menu 1308 illustrated in FIG. 13) may present view options that are tied to the context of the floorplan tile 1208 that shows two cameras 1210 and 1212 and their location within the floorplan. Accordingly, when selecting the 2-camera view option from contextual menu associated with element 1204, the video feeds presented may corresponding to cameras 1210 and 1212, for example by showing the first video feed 1214 and the second video feed 1206 which correspond to camera indicators 1210 and 1212, respectively. [0091] FIG. 13 shows an embodiment 1300 that extends the previous concept by illustrating how an internal view (i.e., the newly created camera view 1306 within the CCC tile 1302). Here, a user can select to change the display in a current or different tile by selecting and interacting with controls 1308 provided in a same or different tile. For instance, a user selects a control to present different camera views in a first view 1306 showing the “Lobby Cam” 0 and a list of different views to select are presented in a different view. Notably, the selection tree menu options provided to the user for selecting different display options (e.g., different camera views) will be based on the context of the events being managed and/or canvas entity role(s). For instance, if the canvas context is for a fire in a stairwell, the camera views that are presented for selection will be the camera views associated with cameras proximate that stairwell.)
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Razak in view of Oliveira in view of Bayless of chatbot management for control, monitor, and manage equipment in a building to apply the known teachings of Schwartz of monitoring and controlling security cameras using different menus in order to provide the predictable result of users GUI with menus for control, monitor, and management of equipment in a building.
One ordinary skill in the art would have been motivated to combine the teachings in order to easily control security devices.
As per claim 14, Razak in view of Oliveira in view of Bayless in view of Schwartz teaches the system of claim 13, wherein the security system control application includes a floorplan that displays for each of one or more of the plurality of security devices: a location of the corresponding security device on the floor plan(Schwartz, [0083] FIG. 10 illustrates an embodiment 1000 in which resources like cameras are identified in a tile (e.g., the floorplan tile). For example, in floorplan tile 1016, the locations of two security cameras 1002 and 1004 within the floorplan are illustrated. In some instances, the resources that are identified and displayed are selected from a plurality of available resources based on a detected context. Here, for example, a security threat or disaster context, such as the incident 1006 shown in the incident list tile 1008, that is based on sensor information received in a location proximate the cameras 1002 and 1004 will trigger a display of the cameras and/or other resources that are relevant and proximate the detected incident, for example in closed circuit camera (CCC) tile 1010.); an identifier of the corresponding security device; and a current status of the corresponding security device.). Motivation to combine set forth in claim 13.
As per claim 15, Razak in view of Oliveira in view of Bayless in view of Schwartz teaches the system of claim 14, wherein the natural language query includes a reference to one or more of the security device (Razak, [0105] … As one example, if a user query includes “show me alarm data for building 3,” the intent parameter can be “show me,” and the entity parameters can be “alarm data” and “building 3.” In some embodiments, classification 486 classifies the user query.) identifiers displayed on the floorplan(Schwartz, Fig.9-10, , [0083] FIG. 10 illustrates an embodiment 1000 in which resources like cameras are identified in a tile (e.g., the floorplan tile). For example, in floorplan tile 1016, the locations of two security cameras 1002 and 1004 within the floorplan are illustrated. In some instances, the resources that are identified and displayed are selected from a plurality of available resources based on a detected context. Here, for example, a security threat or disaster context, such as the incident 1006 shown in the incident list tile 1008, that is based on sensor information received in a location proximate the cameras 1002 and 1004 will trigger a display of the cameras and/or other resources that are relevant and proximate the detected incident, for example in closed circuit camera (CCC) tile 1010.); an identifier of the corresponding security device; and a current status of the corresponding security device ). Motivation to combine set forth in claim 13.
Claim 16 rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0355240 issued to Razak et al.(Razak) in view of US 2022/0076283 issued to Oliveira et al.(Oliveira) in view of 2025/0112878 issued to Bayless et al.(Bayless) in view of US 2024/0428028 issued to Iglesias.
As per claim 16, Razak in view of Oliveira in view of Bayless teaches the system of claim 12, wherein the orchestrator (Bayless, Fig.1 [0036], however does not explicitly teach determine access rights of the user; and determine whether the user has access rights to each of the specific security system devices of the security system, and if not, not submitting the one or more security system commands to the specific security system devices of the security system that the user lacks access rights.
Iglesias teaches determine access rights of the user; and determine whether the user has access rights to each of the specific security system devices of the security system, and if not, not submitting the one or more security system commands to the specific security system devices of the security system that the user lacks access rights([0012] ….. Further, after the alarm of a security tag is activated, the security tag is configured to receive one or more user IDs from one or more ID tags via one or more communication protocols. After receiving a user ID when the alarm is activated, the security tag determines whether the user identified by the user ID has access rights to deactivate an alarm of a security tag. For example, the security tag determines whether the user credentials stored on the security tag indicate the user identified in the user ID is allowed to deactivate the alarm of the security tag. If the access rights of the user identified in the user ID indicate the user is allowed to deactivate the alarm, the security tag deactivates the alarm. In this way, only users with certain access rights are able to deactivate an alarm of a security tag, further helping ensure that sensitive devices do not leave certain environments.; obvious to one ordinary skill in the art that if the user doesn’t have access rights then the user would not be able to control the security device).
Therefore it would have been obvious to one ordinary skill in the art before the effective filing date of the claimed invention to modify Razak in view of Oliveira in view of Bayless of chatbot management for control, monitor, and manage equipment in a building to apply the known teachings of Igesias of access rights of a user to control devices in order to provide the predictable result of users with certain access rights can sends queries to chatbot management for control, monitor, and manage of equipment in a building.
One ordinary skill in the art would have been motivated to combine the teachings in order to provide security for devices or information(Igelsias, para.12).
Allowable Subject Matter
Claims 1-2, 6-11, 21-22 allowed over prior art.
Response to Arguments
The applicant amendments overcomes the claim objection and 112(b) rejection, therefore those objections/rejections are withdrawn.
Applicant's arguments filed 7/15/26 have been fully considered but they are not persuasive. The applicant argues in substance,
a) the applicant amended claim 1 to overcome the 101 rejection,pg.9-10,
1. A computer-implemented method comprising:
receiving at least one service case recommendation associated with an asset;
applying the at least one service case recommendation to an intelligence machine learning model, wherein the intelligence machine learning model is configured to determine a data value indicating a likelihood that each service case recommendation of the at least one service case recommendation is remotely performable, the data value being generated using weighting values maintained for the service case recommendation and based at least in part on natural-language-processing-derived features extracted from text of the service case recommendation_ wherein the data value indicates whether the service case recommendation is capable of being performed remotely without requiring physical interaction with the asset;
determining, via the data value output via the intelligence machine learning model for the at least one service case recommendation, at least one remotely performable maintenance suggestion from the at least one service case recommendation including; comparing the data value to at least one stored threshold indicating that the service case recommendation is remotely performable;
segregating the at least one service case recommendation into remotely performable service case recommendations and non-remotely performable service case recommendations based on the comparison of the data value to the at least one stored threshold; and
selecting the at least one remotely performable maintenance suggestion from the remotely performable service case recommendations;
outputting at least one notification associated with the at least one remotely performable maintenance suggestion to a user associated with remote access of the asset; and
automatically initiating at least a portion of a remotely-performable maintenance action associated with the remotely-performable maintenance suggestion by transmitting remote-execution commands to the asset via a communications network, wherein the remote-execution commands cause the asset to undergo an operational state change, and wherein the automatically initiating is performed in response to determining that the service case recommendation is remotely performable.
In reply to a); The claim 1 presented and argued in the Remarks appear to be different from the claims submitted, it is unclear to whether applicant was intending to amend the claims to the one cited in the Remarks, regardless the argument is moot as this is not the claims presented.
b) As per claim 1, the use of the orchestrator for identifying one or more specific security system devices and assemble and submit commands to each specific security system device where the commands are configured to change or determine operational state of the corresponding specific security system device is not merely link an abstract idea to a chat/instant messaging environment and not merely “apply” an abstract idea on a computer, Remarks, pg.10-11,
In reply to b); The Office disagrees, the specification does not describes what an “orchestrator” is, and broadest reasonable interpretation is that this merely a generic computer components described at a high level of generality, and to use the orchestrator for specific functions such as identification, assembling, and submitting is the use of the generic computer component to implement the judicial exception and/or “apply it”.
c) The claims recites “significantly more” because the amended claim 1 does not merely recite generic receiving, processing, and transmitting, instead recites an ordered combination (1) an orchestrator assembles and submits device-identification queries to the security system to obtain information identifying specific security system devices corresponding to a natural- language query; (2) the orchestrator uses the desired result and the obtained device- identifying information to assemble and submit commands to each specific security system device; (3) the commands are configured to change or determine an operational state of the corresponding specific security system device; (4) the security system returns messages corresponding to respective commands submitted to respective specific devices; and (5) the orchestrator determines whether the desired result was achieved based on those return messages and this ordered combination is not merely the use of a computer as a tool to perform a generic abstract idea, rather the amend claim recites a specific technical implementation of interacting with a security system via a chatbot and provides a remote asset maintenance, pg.11-12,
In reply to c); The Office disagrees, the steps of using an orchestrator to (1) assembles and submits device-identification queries to the security system to obtain information identifying specific security system devices corresponding to a natural- language query; (2) uses the desired result and the obtained device- identifying information to assemble and submit commands to each specific security system device; (3) the commands are configured to change or determine an operational state of the corresponding specific security system device; (4) the security system returns messages corresponding to respective commands submitted to respective specific devices; and (5) determines whether the desired result was achieved based on those return messages, is merely using the orchestrator which is generic computer component to perform the abstract idea and/or using a computer as a tool to perform the abstract idea.
Therefore the claims are not drawn to a technical improvement of a technology but rather the claims are drawn to help reduce the cognitive load on the security system operators by using a chatbot, which is using as a computer as tool as evident by the applicant specification, para.3.
[0003] Security systems, particularly in large facilities, can be complex and include a large number of security components such as doors, cameras, sensors and other devices. Security system operators may be required to navigate through intricate menus and perform multiple steps on an operator console in order to execute commands to the security system and/or to assess the status of various security components. This can pose difficulties for inexperienced operators, and can consume considerable time even for experienced operators. What would be desirable are improved processes for a security system operator to issue commands to the security system, and receive feedback that the commands were successfully executed. What would be desirable is an intelligent chatbot that can assist security system operators to more efficiently interact with the security system, which may reduce the cognitive load on the security system operators and allow the security system operators to direct their attention on more important tasks.
d) As per claim 17, the prior art does not teach submitting one or more queries to the security system to identify specific security devices that corresponds to the natural-language query because Razak does not identify specific devices, e.g. particular doors, cameras, sensors, badge readers, etc.
In reply to d); The Office disagrees, Razak,[0105],[106] teaches processing the query which identifies intent parameter and the entity parameters of HVAC and security subsystems. The entity parameters is interpreted as the specific security device because Razak, [0053] teaches the security subsystem includes an 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.
e) As per claim 19, the claims requires that one or more queries submitted to the security system identify specific security devices using criteria such as specified type, specified location, specified region, or specified current operation status because Razak does not teach a query to identify one or more security system devices having a specific security type or specified current operational status.
In reply e); The Office disagrees, claim 19 recites ,
wherein submitting one or more queries to the security system to identify specific security devices that correspond to the natural language query comprises one or more of:
submitting a query to identify one or more security system devices that have a specified device type;
submitting a query to identify one or more security system devices that are located at a specified location in the facility;
submitting a query to identify one or more security system devices that are located at a specified region of the facility; and
submitting a query to identify one or more security system devices that have a specified current operational status.
Therefore claim 19 does not require that the natural language query include all of specified type, specified location, specified region, and specified current operation status rather claim 19 requires one or more of specified type, specified location, specified region, and specified current operation. Razak,[0105],[106] teaches processing the query which identifies intent parameter and the entity parameters of HVAC and security subsystems. [0053] teaches the security subsystem includes an 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. Therefore Razak therefore teaches at least the identification of security devices having a specified device type(ie HVAC or security subsystems) or specified location (ie building 3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
US 2025/0202929 issued to Norrie, teaches a cybersecurity system includes a query module receiving a cybersecurity query from a user and identifying a user record corresponding to the user, a chatbot module, and a natural language module connected to the chatbot module and using natural language processing with reference to a plurality of cybersecurity information sources to determine a reply to the cybersecurity query in a natural language format. The user record includes a cybersecurity style associated with the user that is predetermined and pre-stored in the user record. The cybersecurity style represents a plurality of personality traits of the user that are indicative of a cybersecurity risk posed by the user. The chatbot module receives the cybersecurity query and the cybersecurity style of the user. The chatbot module adjusts the reply according to the cybersecurity style of the user and outputs the reply to the user.
US 8,528,101 issued to Miller et al., teaches a computing data center that contains a set of physically isolatable units of computing resources for which a physical security exception action plan is to be provided. Upon determining that a security event has occurred for one or more physically isolatable units, the computing data center implements physical security settings on potentially affected computing resources so that a physical security exception action plan can be met. The computing data center may, for example, remove data from the physically isolatable units and make the removed data available elsewhere.
US 2024/0338777 issued to Hurliman et al., teaches the following relates generally to a home ecosystem and home ecosystem app. In some embodiments, one or more processors: (1) receive data from a plurality of data sources, the plurality of data sources including: smart home devices, a weather database, an insurance company, a real estate & property data company, artificial intelligence (AI) company, an electrical data company, a security company, and/or a property risk data company; (2) determine, based upon the received data from the plurality of data sources, that an event has occurred that will damage or has damaged an insured asset; and/or (3) initiate an action based upon the determination that the event has occurred.
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A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BACKHEAN TIV/
Primary Examiner, Art Unit 2459
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