DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is response to application filed 12/30/2024.
Status of the claims
Claims 1-22 are currently pending for examination.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-21 are rejected under 35 U.S.C. 103 as being unpatentable over Graham et al. (US 20240022343, hereafter Graham) in view of Mensch et al. (US 20240412856, hereafter Mensch).
Regarding claim 1, Graham discloses: A method comprising: receiving, by a cloud server, unstructured computer-aided-dispatch (CAD) incident data from a CAD system corresponding to a CAD incident record for an emergency call received at an emergency communication center (ECC) (Graham [0059; 0083] discloses: the system 400 responses through the data fusion module 410, the data fusion module 410 is activated by receiving the incident information 471. As aforementioned, the incident information 471 may come from a plurality of sources, and it can be an incident notice from 911 call center dispatcher, an incident notice from a current CAD system (which may be in turn triggered by 911 call center dispatcher), and an incident notice manually initiated by an authorized department or a dispatcher. In practice, an incident notice manually input from a department officer, or a dispatcher is usually a test or response drill. In one embodiment, the emergency response system 400 may include an API that is compatible with existing systems at 911 call center or current CAD systems to receive incident notice from the corresponding systems; [0049] discloses: The emergency response system and method may also be implemented by a cloud computing system);
Graham didn’t disclose, but Mensch discloses: performing entity extraction on the unstructured CAD incident data to generate output data (Mensch [0227] discloses: The machine learning analysis engine 638, for example, may be trained with historic question and answer script patterns and outcomes to determine a shortest path to obtaining the necessary information from the caller 602 for determining the nature of the medical emergency (e.g., corresponding dispatch codes 644) ; [0229] discloses: large language neural network models (LLMs) may be employed to interpret the language and context of a conversation and infer dispatch acuity with domain training specific to dispatch).
Graham and Mensch are analogous art because they are in the same field of endeavor a cloud-based computer-aided dispatch (CAD) system for emergency medical services (EMS). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Graham, to include the teaching of Mensch, in order to respond to requests in an accurate, consistent, and systematic manner. The suggestion to combine is to process medical transfer and response requests in an accurate, consistent, and systematic manner.
In addition, Graham discloses: sending the output data from the cloud server, to an emergency responder mobile device terminal to provide information related to the CAD incident record for the emergency call (Graham [0108] discloses the data fusion module 1231 may intelligently send the notification to relevant user's devices based on location and type of the incident. For example, if the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters. If the incident notice reports a violent incident, the notification will be pushed to police station and police officers that is close to the scene in addition to first-aid personnel).
Regarding claim 2, Graham as modified discloses: The method of claim 1, further comprising: formatting at least a portion of the output data to generate formatted data (Mensch [0010] discloses: convert EMS trip data from a first format or dispatch workflow associated with the first EMS agency to a second format or dispatch workflow associated with the second EMS agency); and
sending the formatted data from the cloud server to the emergency responder mobile device terminal to provide information related to the CAD incident record for the emergency call (Mensch [0010; 0070] discloses: convert EMS trip data from a first format or dispatch workflow associated with the first EMS agency to a second format or dispatch workflow associated with the second EMS agency).
Regarding claim 3, Graham as modified discloses: The method of claim 1, further comprising: generating a fire incident report using the output data (Graham [0108] discloses: the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters).
Regarding claim 4, Graham as modified discloses: The method of claim 1, further comprising: generating a patient care report using the output data (Mensch 0142] discloses: provides an electronic patient care report (ePCR)).
Regarding claim 5, Graham as modified discloses: The method of claim 1, wherein performing entity extraction on the unstructured CAD incident data to generate output data, comprises: analyzing the unstructured CAD incident data using an artificial intelligence model (Graham [0108] discloses: the data fusion module 1231 may intelligently send the notification to relevant user's devices based on location and type of the incident. For example, if the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters. If the incident notice reports a violent incident, the notification will be pushed to police station and police officers that is close to the scene in addition to first-aid personnel); and
Graham didn’t disclose, but Mensch discloses: performing entity extraction on the unstructured CAD incident data by the artificial intelligence model (Mensch [0227] discloses: The machine learning analysis engine 638, for example, may be trained with historic question and answer script patterns and outcomes to determine a shortest path to obtaining the necessary information from the caller 602 for determining the nature of the medical emergency (e.g., corresponding dispatch codes 644) ; [0229] discloses: large language neural network models (LLMs) may be employed to interpret the language and context of a conversation and infer dispatch acuity with domain training specific to dispatch).
Regarding claim 6, Graham as modified discloses: A method comprising: receiving, by a cloud server, unstructured computer-aided-dispatch (CAD) incident data from a CAD system corresponding to a CAD incident record for an emergency call received at an emergency communication center (ECC) (Graham [0059; 0083] discloses: the system 400 responses through the data fusion module 410, the data fusion module 410 is activated by receiving the incident information 471. As aforementioned, the incident information 471 may come from a plurality of sources, and it can be an incident notice from 911 call center dispatcher, an incident notice from a current CAD system (which may be in turn triggered by 911 call center dispatcher), and an incident notice manually initiated by an authorized department or a dispatcher. In practice, an incident notice manually input from a department officer, or a dispatcher is usually a test or response drill. In one embodiment, the emergency response system 400 may include an API that is compatible with existing systems at 911 call center or current CAD systems to receive incident notice from the corresponding systems; [0049] discloses: The emergency response system and method may also be implemented by a cloud computing system);
Graham didn’t disclose, but Mensch discloses: output data by an artificial intelligence model based on analyzing the unstructured CAD incident data (Mensch [0227] discloses: The machine learning analysis engine 638, for example, may be trained with historic question and answer script patterns and outcomes to determine a shortest path to obtaining the necessary information from the caller 602 for determining the nature of the medical emergency (e.g., corresponding dispatch codes 644) ; [0229] discloses: large language neural network models (LLMs) may be employed to interpret the language and context of a conversation and infer dispatch acuity with domain training specific to dispatch).
Graham and Mensch are analogous art because they are in the same field of endeavor a cloud-based computer-aided dispatch (CAD) system for emergency medical services (EMS). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Graham, to include the teaching of Mensch, in order to response requests in an accurate, consistent, and systematic manner. The suggestion to combine is to process medical transfer and response requests in an accurate, consistent, and systematic manner.
sending the output data from the cloud server, to an emergency responder mobile device terminal to provide information related to the CAD incident record for the emergency call (Graham [0108] discloses the data fusion module 1231 may intelligently send the notification to relevant user's devices based on location and type of the incident. For example, if the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters. If the incident notice reports a violent incident, the notification will be pushed to police station and police officers that is close to the scene in addition to first-aid personnel).
Regarding claim 7, Graham as modified discloses: The method of claim 6, further comprising: formatting at least a portion of the output data to generate formatted data (Mensch [0010] discloses: convert EMS trip data from a first format or dispatch workflow associated with the first EMS agency to a second format or dispatch workflow associated with the second EMS agency); and
sending the formatted data from the cloud server to the emergency responder mobile device terminal to provide information related to the CAD incident record for the emergency call (Mensch [0010; 0070] discloses: convert EMS trip data from a first format or dispatch workflow associated with the first EMS agency to a second format or dispatch workflow associated with the second EMS agency).
Regarding claim 8, Graham as modified discloses: The method of claim 6, further comprising: generating, by the artificial intelligence model, a fire incident report using the output data (Graham [0108] discloses: the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters).
Regarding claim 9, Graham as modified discloses: The method of claim 6, further comprising: generating, by the artificial intelligence model, a patient care report using the output data (Mensch 0142] discloses: provides an electronic patient care report (ePCR)).
Regarding claim 10, Graham as modified discloses: The method of claim 6, wherein generating output data by an artificial intelligence model, comprises: performing entity extraction on the unstructured CAD incident data by the artificial intelligence model (Mensch [0227] discloses: The machine learning analysis engine 638, for example, may be trained with historic question and answer script patterns and outcomes to determine a shortest path to obtaining the necessary information from the caller 602 for determining the nature of the medical emergency (e.g., corresponding dispatch codes 644) ; [0229] discloses: large language neural network models (LLMs) may be employed to interpret the language and context of a conversation and infer dispatch acuity with domain training specific to dispatch).
Regarding claim 11, Graham as modified discloses: The method of claim 6, wherein generating output data by an artificial intelligence model, comprises: analyzing the unstructured CAD incident data by a large language model (Mensch [0229]).
Regarding claim 12, Graham as modified discloses: The method of claim 6, wherein generating output data by an artificial intelligence model, comprises: analyzing the unstructured CAD incident data by a generative pre-trained transformer (GPT) model (Mensch [0229]).
Regarding claim 13, Graham as modified discloses: An emergency responder communication system comprising: a cloud server, operative to: connect to a computer-aided-dispatch (CAD) system located at an emergency communication center (ECC) via a network connection (Graham [0059; 0083] discloses: the system 400 responses through the data fusion module 410, the data fusion module 410 is activated by receiving the incident information 471. As aforementioned, the incident information 471 may come from a plurality of sources, and it can be an incident notice from 911 call center dispatcher, an incident notice from a current CAD system (which may be in turn triggered by 911 call center dispatcher), and an incident notice manually initiated by an authorized department or a dispatcher. In practice, an incident notice manually input from a department officer, or a dispatcher is usually a test or response drill. In one embodiment, the emergency response system 400 may include an API that is compatible with existing systems at 911 call center or current CAD systems to receive incident notice from the corresponding systems; [0049] discloses: The emergency response system and method may also be implemented by a cloud computing system);
receive unstructured computer-aided-dispatch (CAD) incident data therefrom (Graham [0059; 0083] discloses: the system 400 responses through the data fusion module 410, the data fusion module 410 is activated by receiving the incident information 471. As aforementioned, the incident information 471 may come from a plurality of sources, and it can be an incident notice from 911 call center dispatcher, an incident notice from a current CAD system (which may be in turn triggered by 911 call center dispatcher), and an incident notice manually initiated by an authorized department or a dispatcher. In practice, an incident notice manually input from a department officer, or a dispatcher is usually a test or response drill. In one embodiment, the emergency response system 400 may include an API that is compatible with existing systems at 911 call center or current CAD systems to receive incident notice from the corresponding systems; [0049] discloses: The emergency response system and method may also be implemented by a cloud computing system);
send output data to an emergency responder mobile device terminal to provide information related to a CAD incident record for an emergency call received by the ECC and corresponding to the CAD incident data (Graham [0108] discloses the data fusion module 1231 may intelligently send the notification to relevant user's devices based on location and type of the incident. For example, if the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters. If the incident notice reports a violent incident, the notification will be pushed to police station and police officers that is close to the scene in addition to first-aid personnel).; and
Graham didn’t disclose, but Mensch discloses: an artificial intelligence module, operative to execute an artificial intelligence model, the artificial intelligence model operative to: generate the output data based on analyzing the unstructured CAD incident data (Mensch [0227] discloses: The machine learning analysis engine 638, for example, may be trained with historic question and answer script patterns and outcomes to determine a shortest path to obtaining the necessary information from the caller 602 for determining the nature of the medical emergency (e.g., corresponding dispatch codes 644) ; [0229] discloses: large language neural network models (LLMs) may be employed to interpret the language and context of a conversation and infer dispatch acuity with domain training specific to dispatch).
Graham and Mensch are analogous art because they are in the same field of endeavor a cloud-based computer-aided dispatch (CAD) system for emergency medical services (EMS). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify Graham, to include the teaching of Mensch, in order to response requests in an accurate, consistent, and systematic manner. The suggestion to combine is to process medical transfer and response requests in an accurate, consistent, and systematic manner.
Regarding claim 14, Graham as modified discloses: The emergency responder communication system of claim 13, wherein the artificial intelligence model is further operative to: format at least a portion of the output data to generate formatted data, wherein the formatted data is sent from the cloud server to the emergency responder mobile device terminal to provide information related to the CAD incident record for the emergency call (Mensch [0010; 0070] discloses: convert EMS trip data from a first format or dispatch workflow associated with the first EMS agency to a second format or dispatch workflow associated with the second EMS agency).
Regarding claim 15, Graham as modified discloses: The emergency responder communication system of claim 13, wherein the artificial intelligence model is further operative to: generate a fire incident report using the output data (Graham [0108] discloses: the incident notice reports a fire, the notification will be pushed to the user devices of related fire department or fire station that is close to the scene and to the mobile devices of related fire fighters).
Regarding claim 16, Graham as modified discloses: The emergency responder communication system of claim 13, wherein the artificial intelligence model is further operative to: generate a patient care report using the output data (Mensch 0142] discloses: provides an electronic patient care report (ePCR)).
Regarding claim 17, Graham as modified discloses: The emergency responder communication system of claim 13, wherein the artificial intelligence model is further operative to generate the output data by: performing entity extraction on the unstructured CAD incident data (Mensch [0227] discloses: The machine learning analysis engine 638, for example, may be trained with historic question and answer script patterns and outcomes to determine a shortest path to obtaining the necessary information from the caller 602 for determining the nature of the medical emergency (e.g., corresponding dispatch codes 644) ; [0229] discloses: large language neural network models (LLMs) may be employed to interpret the language and context of a conversation and infer dispatch acuity with domain training specific to dispatch).
Regarding claim 18, Graham as modified discloses: The emergency responder communication system of claim 13, wherein the artificial intelligence model is a large language model (Mensch [0229]).
Regarding claim 19, Graham as modified discloses: The emergency responder communication system of claim 13, wherein the artificial intelligence model is a generative pre-trained transformer (GPT) model (Mensch [0229]).
Regarding claim 20, Graham as modified discloses: The emergency responder communication system of claim 13, further comprising: a virtual private cloud, operatively coupled to the cloud server, wherein the artificial intelligence model is hosted within the virtual private cloud (Mensch [0141; 0276]).
Regarding claim 21, Graham as modified discloses: A method comprising: training an artificial intelligence module in a cloud-based emergency responder communication system using training data comprising computer-aided-dispatch (CAD) incident data from an emergency communication center (ECC) (Graham [0059; 0083] discloses: the system 400 responses through the data fusion module 410, the data fusion module 410 is activated by receiving the incident information 471. As aforementioned, the incident information 471 may come from a plurality of sources, and it can be an incident notice from 911 call center dispatcher, an incident notice from a current CAD system (which may be in turn triggered by 911 call center dispatcher), and an incident notice manually initiated by an authorized department or a dispatcher. In practice, an incident notice manually input from a department officer, or a dispatcher is usually a test or response drill. In one embodiment, the emergency response system 400 may include an API that is compatible with existing systems at 911 call center or current CAD systems to receive incident notice from the corresponding systems; [0049] discloses: The emergency response system and method may also be implemented by a cloud computing system);
Graham didn’t disclose, but Mensch discloses: generating a script, in response to the training data, by the artificial intelligence module, the script for converting CAD incident data into a format useable by an emergency responder mobile device terminal application (Mensch [0010; 0070] discloses: convert EMS trip data from a first format or dispatch workflow associated with the first EMS agency to a second format or dispatch workflow associated with the second EMS agency).; and
configuring a cloud-based processor using the script (Mensch [0037] discloses: cloud-based computer-aided dispatch configured for execution on a cloud-based distributed processing architecture’ [0128] discloses: uses intake script to obtain information regarding the medical request).
Allowable Subject Matter
Claim 22 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CINDY NGUYEN whose telephone number is (571)272-4025. The examiner can normally be reached M-F 8:00-4:30.
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/CINDY NGUYEN/Examiner, Art Unit 2156
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