DETAILED ACTION
Applicant's submission filed on November 18, 2025 has been entered.
Response to Amendment
Applicant’s arguments with respect to claims 1-20 have been considered but are moot in view of new ground of rejection necessitated due to claim amendments.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7-15, 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly (US Patent Application Publication No. 2024/0049352), and further in view of Bivens (US Patent Application Publication No. 2012/0218102).
Regarding claim 1, Kelly teaches an emergency response assistant system (Figs. 1, 4-5), comprising:
memory storing instructions; and one or more processors coupled to the memory and operable to execute the instructions to perform one or more operations, the one or more operations (Paragraphs 0069-0070, 0075) comprising:
provide an emergency management application operable by an emergency communications center (ECC) computing system to display an emergency management user interface (UI) at one of a plurality of ECCs (Paragraphs 0071, 0075 application 222);
store, in one or more data structures, standard operating procedure (SOP) data related to emergency response procedures (Paragraphs 0047-0048, 0054-0056, 0062);
receive call data for a 911 call directed to the one of the plurality of ECCs, wherein the call data includes live call audio data for the 911 call (Paragraph 0079) and includes location data representative of a device-based location of a user device that imitates the 911 call (Paragraphs 0037, 0077, 0112, 0117);
characterize a call nature of the 911 call based on an analysis of content of the live call audio data (Paragraph 0079);
provide the response to the emergency management application to display the relevant portion of the SOP data on the ECC computing system with the emergency management UI at the one of the plurality of ECCs (Paragraphs 0024, 0059, 0082 provide “an electronic suggestion to dispatch the primary public-safety responder type 130”, 0085, 0097-0103, 0121-0133 render corresponding subset of responder types) (Paragraphs 0013-0140 for complete details).
Kelly teaches retrieving extra data related to the call data (Paragraphs 0079 extra call data, 0047 data from external database, 0086-0088, 0093, 0105, 0108 extra data of availability/ unavailability of responder and/or equipment) and Kelly also teaches an artificial intelligence (Al) model (Paragraph 0072-0075 PSAP executes application 222 which acts as an AI model) to analyze the call nature (Paragraph 0093 using incident type) and the external data (Figs. 1, 4 item 124) to search the SOP data to generate a response to the prompt, wherein the response includes a relevant portion of the SOP data; and receive the response from the Al model (Paragraphs 0054, 0072-0073, 0082 provide “an electronic suggestion to dispatch the primary public-safety responder type 130”, 0092-0094, 0097-0103, 0121-0133 corresponding subset of responder types), and Kelly teaches distributed cloud computing devices (Paragraphs 0013, 0031, 0036, 0060) thus PSAP device obviously prompting an external device and receiving a response to select the corresponding public-safety responders to display, but Kelly does not explicitly teach to retrieve external data, related to the call data, from one or more external data sources; and search the SOP data to generate a response to the prompt.
However, in the similar field, Bivens teaches to retrieve external data, related to the call data, from one or more external data sources; and artificial intelligence (Al) model using the external data to analyze the call data and to search the SOP data to generate a response to the prompt (Paragraphs 0040-0056, 0059, 0071 ERM retrieving other sensor data and using machine-based analysis techniques to determine responders and response actions, 0014 emergency response system as cloud computing environment, thus obviously prompting different functional modules with query and collecting the response).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Kelly to include retrieving external data, related to the call data, from one or more external data sources; and artificial intelligence (Al) model using the external data to analyze the extra data and to search the SOP data to generate a response to the prompt as taught by Bivens in order to “determine the type of emergency, the severity of the emergency, the medical condition of the victims, traffic conditions around the emergency area, and other related information based on the information gathered” and “use this information in conjunction with the information in a responder profile to select civilians to respond to the emergency” (Bivens, Paragraph 0044).
Regarding claim 2, Bivens teaches wherein the one or more external data sources include a video feed from video camera located in proximity to the device-based location of the user device, wherein the external data includes video data from the video camera (Paragraphs 0017, 0022, 0029, 0043).
Regarding claim 3, Kelly teaches wherein the one or more operations further comprise: provide an Al agent integrated with the emergency management application, wherein the Al agent operates based on Al agent logic that is at least partially stored in the instructions, wherein the call nature is received (Paragraphs 0072-0075, 0093-0094) or generated by the Al agent (Paragraphs 0072-0075, 0079, 0094); wherein the response is provided with the Al agent; and store chat history between the user and the Al agent, wherein the prompt to the Al model includes a request to generate the response to the prompt at least partially based on the chat history (Paragraphs 0072-0073, 0094, 0079 using chat history).
Regarding claim 4, Kelly teaches wherein the response includes at least one of dispatch suggestions (suggested responders), dispatch determinant codes (URLs), emergency response suggestions use of (rescue equipment, medical equipment), suggested actions, or emergency response notifications (Paragraphs 0059, 0082, 0086, 0093, 0095, 0108).
Bivens teaches wherein the response includes at least one of dispatch suggestions, dispatch determinant codes (registered responders suited to emergency), emergency response suggestions (direction to emergency location for responder), suggested actions (steps to perform), or emergency response notifications (response description) (Paragraphs 0045, 0053-0060).
Regarding claim 5, Kelly teaches wherein the external data includes at least one of: sensor data, location data, building data, telematics data, floor plan data, geofence data, ambient conditions data, public records data, traffic data, weather data, news feed data, medical data, arrest records, residential addresses, personal property records, or public record data (Paragraphs 0013, 0037, 0053, 0055-0056, 0079, 0092-0093, 0098, 0102, 0123).
Bivens teaches wherein the external data includes at least one of: sensor data, location data, building data, telematics data, floor plan data, geofence data, ambient conditions data, public records data, traffic data, weather data, news feed data, medical data, arrest records, residential addresses, personal property records, or public record data (Paragraphs 0017-0018, 0024, 0027, 0029, 0040, 0042, 0054, 0058).
Regarding claim 7, Kelly teaches wherein the one or more data structures include a vector database, wherein the SOP data is stored in the vector database as vector data (Paragraphs 0028, 0048-0050, 0072, 0100, 0123, 0125-0126, 0129 multi-dimensional data associated with corresponding responder types).
Bivens teaches wherein the one or more data structures include a vector database, wherein the SOP data is stored in the vector database as vector data (Fig. 2, Paragraphs 0033-0038).
Regarding claim 8, Kelly teaches wherein the Al model is operable to search the SOP data using a vector search of the vector data in the vector database (Paragraphs 0072-0073, 0094, 0112-0132 searching database with vector information).
Regarding claim 9, Kelly teaches wherein the SOP data includes at least one of automotive manuals, appliance manuals, first aid procedures, poison control information, evacuation routes, application programming interface (API) calls, public safety SOPs, electric vehicle emergency protocols, response plans for specific buildings, floor plans, building staff schedules, or incident response plans (Paragraphs 0013-0014, 0097-0098, 0100-0101, 0102, 01206-0108, 0113-0132).
Bivens teaches wherein the SOP data includes at least one of automotive manuals, appliance manuals, first aid procedures, poison control information, evacuation routes, application programming interface (API) calls, public safety SOPs, electric vehicle emergency protocols, response plans for specific buildings, floor plans, building staff schedules, or incident response plans (Paragraphs 0030, 0035, 0050, 0056, 0059-0060).
Regarding claim 10, Kelly teaches to transcribe the live call audio data to provide 911 call transcripts to the AI model as context to characterize the call nature from the content of the live call audio data (Paragraph 0079 automated call-taker using transcript to determine incident type, 0072-0075 PSAP executes application 222 which act as an AI model).
Regarding claim 11, Kelly teaches a computer-implemented method of providing digital emergency response assistance (Paragraphs 0069-0070, 0075), comprising:
providing an emergency management application operable by an emergency communications center (ECC) computing system to display an emergency management user interface (UI) at one of a plurality of ECCs (Paragraphs 0071, 0075 application 222);
storing, in one or more data structures, standard operating procedure (SOP) data related to emergency response procedures (Paragraphs 0047-0048, 0054-0056, 0062);
receiving call data for a live 911 call directed to the one of the plurality of ECCs, wherein the call data includes live call audio data for the 911 call (Paragraph 0079) and includes location data representative of a location of a device used to initiate the live 911 call (Paragraphs 0037, 0077, 0112, 0117);
using an artificial intelligence (AI) model to determine a call nature of the 911 call based on content of the live call audio data (Paragraph 0079 automated call-taker using transcript to determine incident type, 0072-0075 PSAP executes application 222 which acts as an AI model);
using the Al model to use the call nature of the 911 call data as context for the Al model to search the SOP data and identify portions of the emergency response procedures that are relevant to the 911 call (Paragraphs 0072-0073, 0094 using AI model, 0057, 0080, 0093, 0113, 0118 query database for emergency type and corresponding public safety responder type);
receiving the portions of the emergency response procedures that are relevant to the 911 call from the Al model; and displaying the portions of the emergency response procedures that are relevant to the 911 call with the emergency management UI at the one of the plurality of ECCs to facilitate dispatch of an emergency event (Paragraphs 0024, 0059, 0082 provide “an electronic suggestion to dispatch the primary public-safety responder type 130”, 0085, 0097-0103, 0121-0133 receive and display subset of corresponding suggested responder types) (Paragraphs 0013-0140 for complete details).
Kelly teaches retrieving extra data related to the call data (Paragraphs 0079 extra call data, 0047 data from external database, 0086-0088, 0093, 0105, 0108 extra data of availability/ unavailability of responder and/or equipment) and Kelly also teaches an artificial intelligence (Al) model (Paragraph 0072-0075 PSAP executes application 222 which acts as an AI model) to analyze the call nature (Paragraph 0093 using incident type) and the external data (Figs. 1, 4 item 124) to search the SOP data to generate a response to the prompt, wherein the response includes a relevant portion of the SOP data; and receive the response from the Al model (Paragraphs 0054, 0072-0073, 0082 provide “an electronic suggestion to dispatch the primary public-safety responder type 130”, 0092-0094, 0097-0103, 0121-0133 corresponding subset of responder types), and Kelly teaches distributed cloud computing devices (Paragraphs 0013, 0031, 0036, 0060) thus PSAP device obviously prompting an external device and receiving a response to select the corresponding public-safety responders to display, but Kelly does not explicitly teach to retrieve external data, related to the call data, from one or more external data sources; and search the SOP data to generate a response to the prompt.
However, in the similar field, Bivens teaches to retrieve external data, related to the call data, from one or more external data sources; and artificial intelligence (Al) model using the external data to analyze the call data and to search the SOP data to generate a response to the prompt (Paragraphs 0040-0056, 0059, 0071 ERM retrieving other sensor data and using machine-based analysis techniques to determine responders and response actions, 0014 emergency response system as cloud computing environment, thus obviously prompting different functional modules with query and collecting the response).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Kelly to include retrieving external data, related to the call data, from one or more external data sources; and artificial intelligence (Al) model using the external data to analyze the extra data and to search the SOP data to generate a response to the prompt as taught by Bivens in order to “determine the type of emergency, the severity of the emergency, the medical condition of the victims, traffic conditions around the emergency area, and other related information based on the information gathered” and “use this information in conjunction with the information in a responder profile to select civilians to respond to the emergency” (Bivens, Paragraph 0044).
Regarding claim 12, Bivens teaches wherein the one or more external data sources include a video feed from video camera located in proximity to the device, wherein the external data includes video data from the video camera (Paragraphs 0017, 0022, 0029, 0043).
Regarding claim 13, Kelly teaches providing an Al agent integrated with the emergency management application; providing a conversational query interface to a user with the Al agent that is displayed in the emergency management UI; storing a chat history between the user and the Al agent; and providing the chat history to the Al model as additional context for searches of the SOP data (Paragraphs 0072-0073, 0094, 0079 using chat history).
Regarding claim 14, Kelly teaches wherein the portions of the emergency response procedures that are relevant to the 911 call include at least one of dispatch suggestions (suggested responders), dispatch determinant codes (URLs), emergency response suggestions (rescue equipment, medical equipment), suggested actions, or emergency response notifications (Paragraphs 0059, 0082, 0086, 0093, 0095, 0108).
Bivens teaches wherein the portions of the emergency response procedures that are relevant to the 911 call include at least one of dispatch suggestions, dispatch determinant codes (registered responders suited to emergency), emergency response suggestions (direction to emergency location for responder), suggested actions (steps to perform), or emergency response notifications (response description) (Paragraphs 0045, 0053-0060).
Regarding claim 15, Kelly teaches wherein the external data include at least one of: sensor data, location data, building data, telematics data, floor plan data, geofence data, ambient conditions data, public records data, traffic data, weather data, news feed data, medical data, arrest records, residential addresses, personal property records, or public record data (Paragraphs 0013, 0037, 0053, 0055-0056, 0079, 0092-0093, 0098, 0102, 0123).
Bivens teaches wherein the external data include at least one of: sensor data, location data, building data, telematics data, floor plan data, geofence data, ambient conditions data, public records data, traffic data, weather data, news feed data, medical data, arrest records, residential addresses, personal property records, or public record data (Paragraphs 0017-0018, 0024, 0027, 0029, 0040, 0042, 0054, 0058).
Regarding claim 17, Kelly teaches wherein the one or more data structures include a vector database, wherein the SOP data is at least partially stored in the vector database as vector data (Paragraphs 0028, 0048-0050, 0072, 0100, 0123, 0125-0126, 0129 multi-dimensional data associated with corresponding responder types).
Bivens teaches wherein the one or more data structures include a vector database, wherein the SOP data is at least partially stored in the vector database as vector data (Fig. 2, Paragraphs 0033-0038).
Regarding claim 18, Kelly teaches wherein the Al model is operable to search the SOP data using a vector search of the vector data in the vector database (Paragraphs 0072-0073, 0094, 0112-0132 searching database with vector information).
Regarding claim 19, Kelly teaches wherein the SOP data includes at least one of automotive manuals, appliance manuals, first aid procedures, poison control information, evacuation routes, application programming interface (API) calls, public safety SOPs, electric vehicle emergency protocols, response plans for specific buildings, floor plans, building staff schedules, or incident response plans (Paragraphs 0013-0014, 0097-0098, 0100-0101, 0102, 01206-0108, 0113-0132).
Bivens teaches wherein the SOP data includes at least one of automotive manuals, appliance manuals, first aid procedures, poison control information, evacuation routes, application programming interface (API) calls, public safety SOPs, electric vehicle emergency protocols, response plans for specific buildings, floor plans, building staff schedules, or incident response plans (Paragraphs 0030, 0035, 0050, 0056, 0059-0060).
Regarding claim 20, Kelly teaches transcribing the live call audio data for the 911 call to provide 911 call transcripts to AI (Paragraph 0079 automated call-taker using transcript to determine incident type, 0072-0075 PSAP executes application 222 which act as an AI model).
Claims 6, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Kelly and Bivens as applied to claims 1, 11 above, and further in view of Ellis (US Patent No. 12,299,557).
Regarding claim 6, Kelly teaches different types of artificial intelligence models (Paragraph 0073), but Kelly and Bivens do not specifically teach the Al model is a large language model (LLM).
However, in the similar field, Ellis teaches the Al model is a large language model (LLM) (col. 2 ll. 49-58, col. 3 ll. 20-47, col. 4 ll. 9-35, col. 17 ll. 9-14, col. 41 ll. 32-39).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Kelly and Bivens to include the Al model as a large language model (LLM) as taught by Ellis in order to enable Action
Artificial-Intelligence Model based on “a large language model based on a site plan data, a policy data, a procedure data, a historical fire incident response data, and/or an emergency operation plan data associated with a jurisdictional entity” (Ellis, col. 3 ll. 23-27).
Regarding claim 16, Kelly teaches different types of artificial intelligence models (Paragraph 0073), but Kelly and Bivens do not specifically teach the Al model is a large language model (LLM).
However, in the similar field, Ellis teaches the Al model is a large language model (LLM) (col. 2 ll. 49-58, col. 3 ll. 20-47, col. 4 ll. 9-35, col. 17 ll. 9-14, col. 41 ll. 32-39).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the present invention to modify Kelly and Bivens to include the Al model as a large language model (LLM) as taught by Ellis in order to enable Action Artificial-Intelligence Model based on “a large language model based on a site plan data, a policy data, a procedure data, a historical fire incident response data, and/or an emergency operation plan data associated with a jurisdictional entity” (Ellis, col. 3 ll. 23-27).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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HEMANT PATEL
Primary Examiner
Art Unit 2694
/HEMANT S PATEL/ Primary Examiner, Art Unit 2694