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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 1 December 2022 is being considered by the examiner.
Claim Interpretation
In claims 1, 8, and 15, the limitation “at the destination via a user communication interface on the one or more autonomous vehicles” is being interpreted by the examiner as information entered at the user communication interface located on an autonomous vehicle which is at the destination.
In claims 1, 8, and 15, the limitation “the one or more autonomous vehicles” is being interpreted by the examiner as the two or more autonomous vehicles.
In claims 1, 8, and 15, the limitation “determining a level of emergency and a disease condition at the destination via a user communication interface” is going to interpreted by the examiner as a method where information is entered on the interface on the first autonomous vehicle. Then the method, not the computer of the first autonomous vehicle, will determine a level of emergency and a disease condition based on the information entered.
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 1-20 are 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 pre-AIA the applicant regards as the invention.
The limitation "determining a level of emergency and a disease condition at the destination via a user communication interface on the one or more autonomous vehicles" in claims 1, 8, and 15 is indefinite. The portion of the limitation “via a user communication interface on the one or more autonomous vehicles” is being interpreted by the examiner as information entered at the user communication interface located on an autonomous vehicle. This is back by the applicant’s Figure 2 where the user interface 232 is located on autonomous vehicle 230. If the information is entered “at the destination via a user communication interface” then one autonomous vehicle is at the destination. It would be unusual for a method to get information from first autonomous vehicle located at the destination and then dispatch that first autonomous vehicle to deliver medical supplies to the destination. For the method to work, the first vehicle would need to leave the medical emergency at the destination, drive to the location of medical supplies, and then return to the destination with the medical supplies. The examiner is going to interpret “the one or more autonomous vehicles” as the two or more autonomous vehicles. The method claims “determining a level of emergency and a disease condition at the destination via a user communication interface” which is claiming the autonomous vehicle at the destination is determining the level of emergency. Applicant’s Figure 2 has the determining module 222 on the host server 210. The examiner is going to interpret claims 1, 8, and 15 to be a method where information is entered on the interface on the first autonomous vehicle. Then the method, not the computer of the first autonomous vehicle, will determine a level of emergency and a disease condition based on the information entered. The examiner suggest the applicant amend claims 1, 8, and 15 to clarify the invention.
Because claims 1, 8, and 15 are rejected under 112, all dependent claims, claims 2-7, 9-14, and 16-20, are therefore rejected based on their dependency to claims 1, 8, and 15.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
[Broadest Reasonable Interpretation] The claim's recited simulating can be performed mentally because it corresponds with a person envisioning how they might drive. Similarly, one might then envision driving, but at a higher speed.
[Step 1] Representative claim 1 teaches a method for efficiently dispatching one or more autonomous vehicles to deliver medical supplies to a destination. This falls under “process”, which is a statutory invention category.
[Step 2A: Prong 1] This is a mathematical relationships. But for the computer, the one or more autonomous vehicles, and the non-transitory computer-readable medium required to carry out the steps which are not explicitly recited in the claims, claim 1 is merely drawn to a series of steps:
determining a level of emergency and a disease condition at the destination via a user communication interface on the one or more autonomous vehicles
prioritizing dispatching the one or more autonomous vehicles based on the level of emergency and the disease condition at the destination
optimizing a route of the one or more autonomous vehicles to the destination
integrating a plurality of available data sources across multiple locations
comparing the plurality of available data sources across the multiple locations
building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations
The steps are, essentially, a process of predicting need and dispatching vehicles from a fleet. It is similar to a dispatcher forecasting and dispatching cabs to a determined location, such as a baseball stadium as a baseball game ends. This is an abstract idea or ideas characterized under mathematical relationships.
[Step 2A: Prong 2] This judicial exception is not integrated into a practical application. Other than the above-cited abstract idea, claim 1 doesn’t explicitly claim a specific type of autonomous vehicle, non-transitory computer-readable medium and computer that would be necessary to carry out the steps. There are no special hardware features of the process recited in the claims as presented. The hardware of one or more autonomous vehicles, a non-transitory computer-readable medium, and a computer is recited in the specification at a high-level of generality, (see [Specification Para. 21, 24, 40]) such that it amounts to no more than mere instructions of the judicial exception linked to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. These limitations essentially linking the use of a judicial exception to a particular technological environment or field of use (MPEP2106.05(h), MPEP2106.04(d)). This claim is directed to an abstract idea.
[Step 2B] This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into one or more autonomous vehicles, a non-transitory computer-readable medium, and a computer amount to no more than mere instructions of the judicial exception linked to a particular technological environment. There is no inventive concept in the autonomous vehicle, the non-transitory computer-readable medium, and the computer. Mere linking the judicial exception to a particular technological environment cannot provide an integrated inventive concept. This claim is not patent eligible.
The dependent claims 2-7 have been rejected on the same grounds and recite substantially similar abstract ideas to the cited independent claim 1.
2. The computer-implemented method of claim 1, wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles; and storing knowledge of the one or more autonomous vehicles in a centralized region.
3. The computer-implemented method of claim 2, further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition; and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition.
4. The computer-implemented method of claim 1, further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination.
5. The computer-implemented method of claim 1, wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model.
6. The computer-implemented method of claim 1, wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations, and a level of severity of other disease conditions or accidents.
7. The computer-implemented method of claim 1, further comprising: scheduling an arrival of medical staff in conjunction with the delivery of the medical supplies to the destination via the dispatched one or more autonomous vehicles.
The 101 analysis for claim 1 would apply similarly to the dependent claims above. Therefore, dependent claims 2-7 are also rejected under 35 U.S.C. 101.
[Step 1] Representative claim 8 teaches a computer program product for efficiently dispatching one or more autonomous vehicles to deliver medical supplies to a destination. This falls under “machine”, which is a statutory invention category.
[Step 2A: Prong 1] This is a mathematical relationships. But for the computer, the one or more autonomous vehicles, and the non-transitory computer-readable medium required to carry out the steps which are not explicitly recited in the claims, claim 8 is merely drawn to a series of steps:
determining a level of emergency and a disease condition at the destination via a user communication interface on the one or more autonomous vehicles
prioritizing dispatching the one or more autonomous vehicles based on the level of emergency and the disease condition at the destination
optimizing a route of the one or more autonomous vehicles to the destination
integrating a plurality of available data sources across multiple locations
comparing the plurality of available data sources across the multiple locations
building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations
The steps are, essentially, a process of predicting need and dispatching vehicles from a fleet. It is similar to a dispatcher forecasting and dispatching cabs to a determined location, such as a baseball stadium as a baseball game ends. This is an abstract idea or ideas characterized under mathematical relationships.
[Step 2A: Prong 2] This judicial exception is not integrated into a practical application. Other than the above-cited abstract idea, claim 8 doesn’t explicitly claim a specific type of autonomous vehicle, non-transitory computer-readable medium and computer that would be necessary to carry out the steps. There are no special hardware features of the process recited in the claims as presented. The hardware of one or more autonomous vehicles, a non-transitory computer-readable medium, and a computer is recited in the specification at a high-level of generality, (see [Specification Para. 21, 24, 40]) such that it amounts to no more than mere instructions of the judicial exception linked to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. These limitations essentially linking the use of a judicial exception to a particular technological environment or field of use (MPEP2106.05(h), MPEP2106.04(d)). This claim is directed to an abstract idea.
[Step 2B] This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into one or more autonomous vehicles, a non-transitory computer-readable medium, and a computer amount to no more than mere instructions of the judicial exception linked to a particular technological environment. There is no inventive concept in the autonomous vehicle, the non-transitory computer-readable medium, and the computer. Mere linking the judicial exception to a particular technological environment cannot provide an integrated inventive concept. This claim is not patent eligible.
The dependent claims 9-14 have been rejected on the same grounds and recite substantially similar abstract ideas to the cited independent claim 8.
9. The computer program product of claim 8, wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles; and storing knowledge of the one or more autonomous vehicles in a centralized region.
10. The computer program product of claim 9, further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition; and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition.
11. The computer program product of claim 8, further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination.
12. The computer program product of claim 8, wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model.
13. The computer program product of claim 8, wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations, and a level of severity of other disease conditions or accidents.
14. The computer program product of claim 8, further comprising: scheduling an arrival of medical staff in conjunction with the delivery of the medical supplies to the destination via the dispatched one or more autonomous vehicles.
The 101 analysis for claim 8 would apply similarly to the dependent claims above. Therefore, dependent claims 9-14 are also rejected under 35 U.S.C. 101.
[Step 1] Representative claim 15 teaches a computer system for implementing a program. This falls under “machine”, which is a statutory invention category.
[Step 2A: Prong 1] This is a mathematical relationships. But for the computer, the one or more autonomous vehicles, and the non-transitory computer-readable medium required to carry out the steps which are not explicitly recited in the claims, claim 15 is merely drawn to a series of steps:
determining a level of emergency and a disease condition at the destination via a user communication interface on the one or more autonomous vehicles
prioritizing dispatching the one or more autonomous vehicles based on the level of emergency and the disease condition at the destination
optimizing a route of the one or more autonomous vehicles to the destination
integrating a plurality of available data sources across multiple locations
comparing the plurality of available data sources across the multiple locations
building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations
The steps are, essentially, a process of predicting need and dispatching vehicles from a fleet. It is similar to a dispatcher forecasting and dispatching cabs to a determined location, such as a baseball stadium as a baseball game ends. This is an abstract idea or ideas characterized under mathematical relationships.
[Step 2A: Prong 2] This judicial exception is not integrated into a practical application. Other than the above-cited abstract idea, claim 15 doesn’t explicitly claim a specific type of autonomous vehicle, non-transitory computer-readable medium and computer that would be necessary to carry out the steps. There are no special hardware features of the process recited in the claims as presented. The hardware of one or more autonomous vehicles, a non-transitory computer-readable medium, and a computer is recited in the specification at a high-level of generality, (see [Specification Para. 21, 24, 40]) such that it amounts to no more than mere instructions of the judicial exception linked to a particular technological environment. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they not impose any meaningful limits on practicing the abstract idea. These limitations essentially linking the use of a judicial exception to a particular technological environment or field of use (MPEP2106.05(h), MPEP2106.04(d)). This claim is directed to an abstract idea.
[Step 2B] This claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into one or more autonomous vehicles, a non-transitory computer-readable medium, and a computer amount to no more than mere instructions of the judicial exception linked to a particular technological environment. There is no inventive concept in the autonomous vehicle, the non-transitory computer-readable medium, and the computer. Mere linking the judicial exception to a particular technological environment cannot provide an integrated inventive concept. This claim is not patent eligible.
The dependent claims 16-20 have been rejected on the same grounds and recite substantially similar abstract ideas to the cited independent claim 15.
16. The computer system of claim 15, wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles; and storing knowledge of the one or more autonomous vehicles in a centralized region.
17. The computer system of claim 16, further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition; and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition.
18. The computer system of claim 15, further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination.
19. The computer system of claim 15, wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model.
20. The computer system of claim 15, wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations, and a level of severity of other disease conditions or accidents.
The 101 analysis for claim 15 would apply similarly to the dependent claims above. Therefore, dependent claims 16-20 are also rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-4, 7-11, 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Peeters et al. (US Patent 8,948,935 B1) in view of Ashar et al. (US Publication 2023/0106673 A1), Kogan et al. (US Publication 2023/0005607 A1), and in further view of Zak et al. (US Publication 2025/0046436 A1).
Regarding claim 1, Peeters teaches a computer-implemented method for efficiently dispatching one or more autonomous vehicles to deliver medical supplies to a destination, comprising: (Peeters: Col. 3 Lines 12-16, Col. 5 Line 20; classify the particular type of medical situation that is occurring, to select the appropriate UAV from those that are available, and to dispatch the selected UAV to the scene of the medical situation; UAV may be autonomous) determining a level of emergency and a disease condition at the destination (Peeters: Col. 10 Lines 37-42; a request for medical support at the home of a person who appears to have suffered from cardiac arrest; select the closest available UAV to the person's home that is configured to provide medical support when a heart attack has occurred) via a user communication interface (Peeters: Col. 8 Lines 53-55; UI may allow an operator to specify certain details related to the medical situation to which the UAV is being dispatched) …….. ; prioritizing dispatching the one or more autonomous vehicles based on the level of emergency and the disease condition at the destination (Peeters: Col. 10 Lines 43-47; the central dispatch system may select an available UAV that is within a certain distance from the person's home (which may or may not be the closest), and which is configured to provide medical support when cardiac arrest has occurred); …….. integrating a plurality of available data sources across multiple locations (Peeters: Col. 21 Lines 42-58, Col. 22 Lines 8-18; the target location may be determined based on a various types of location information: location information that is provided by the remote device; the police also receive location information, such as GPS coordinates; obtain location information from image data that is captured by a remote device); comparing the plurality of available data sources across the multiple locations (Peeters: Col. 22 Lines 8-18; analyze such image data to detect, e.g., street signs and/or landmarks such as buildings or sculptures, which may help to identify the location of a medical situation).
Peeters doesn’t explicitly teach on the one or more autonomous vehicles.
However Ashar, in the same field of endeavor, teaches on the one or more autonomous vehicles (Asghar: Para. 57, 67; vehicle user interface process can render user interface elements based on, for example, a status from the vehicle and the occupant monitoring process; upon receiving an occupant monitoring event indicating a health emergency, the vehicle can engage an autonomous capability).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) with a reasonable expectation of success because upon receiving occupant data of a medical emergency the autonomous vehicle can safely stop the vehicle or reroute the autonomous vehicle to a hospital or clinic (Ashar: Para. 57, 67).
Peeters and Ashar don’t explicitly teach optimizing a route of the one or more autonomous vehicles to the destination.
However Kogan, in the same field of endeavor, teaches optimizing a route of the one or more autonomous vehicles to the destination (Kogan: Para. 68; AORPS optimizes travel to locations of the patients).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Peeters, Ashar, and Kogan don’t explicitly teach building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations.
However Zak, in the same field of endeavor, teaches building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations (Zak: Para. 10; scheduling model outputs one or more of automatic scheduling, tracking of epidemiological data for research, and resource/supply management; scheduling model outputs predicted medical consumable needs; deep reinforcement learning).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), and deep reinforcement learning scheduling model (Zak: Para. 10) with a reasonable expectation of success because dispatching autonomous vehicles by predicted medical consumable needs based on deep reinforcement learning scheduling model (Zak: Para. 5, 10, 17, 435).
Regarding claim 2, Peeters teaches the computer-implemented method of claim 1, wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: …….. ; and storing knowledge of the one or more autonomous vehicles in a centralized region (Peeters: Col. 10 Lines 16-21; central dispatch system may keep track of which UAVs are located at which local dispatch systems, which UAVs are currently available for deployment, and/or which medical situation or situations each of the UAVs is configured for).
Peeters, Ashar, and Kogan don’t explicitly teach dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles.
However Zak, in the same field of endeavor, teaches dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles (Zak: Para. 5, 17, 435; NLP can be used to interpret computer code, written text and spoken speech and then process the information, make comparisons and analytics; coordination of IoT with medical devices, allows one to determine effectiveness of medical devices against patient outcome and cost; continuous learning between interoperable ‘smart systems’, such as smart cities and autonomous dispatching, providing optimal traffic routes).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), and deep reinforcement learning scheduling model (Zak: Para. 10) with a reasonable expectation of success because dispatching autonomous vehicles by predicted medical consumable needs based on deep reinforcement learning scheduling model (Zak: Para. 5, 10, 17, 435).
Regarding claim 3, Peeters and Ashar don’t explicitly teach clustering cities, and streets within the cities, based on a distribution of the disease condition; and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition.
However Kogan, in the same field of endeavor, teaches clustering cities, and streets within the cities, based on a distribution of the disease condition (Kogan: Para. 15, 48, 112; clustering by similar age, conditions; geo-center of a neighborhood cluster; clustering patients similar in that regard, availability of the healthcare providers and the onsite care coordinators, driving distances to the locations of the patients to minimize driving); and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition (Kogan: Para. 58, 60, 110; calculates visit priorities based on a risk of an acute episode for a given patient; optimization is then a sum of home visit times weighted by visit priorities; Patient Clinical Priority (PCP), 0 to 1, which represents a risk stratification metric representing immediate health).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Regarding claim 4, Peeters and Ashar don’t explicitly teach enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination.
However Kogan, in the same field of endeavor, teaches enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination (Kogan: Para. 47; generates the appointment schedule with the travel routes dynamically based on optimization factors derived from the received client input, the collated patient data, the generated input matrix, the healthcare data, and the generated predictive model).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Regarding claim 7, Peeters teaches the computer-implemented method of claim 1, further comprising: scheduling an arrival of medical staff in conjunction with the delivery of the medical supplies to the destination via the dispatched one or more autonomous vehicles (Peeters: Col. 18 Lines 46-57, Col. 21 Lines 8-18; dive-accident package, the UAV may drop a flotation device to help the diver stay afloat until the diver can be reached by rescuers; once the diver has been rescued, the UAV may display visual instructions and/or play back auditory instructions for CPR, which may help to revive a drowning victim).
Regarding claim 8, Peeters teaches a computer program product for implementing a program that manages a device, comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processor of a computer to perform a method, the method comprising: (Peeters: Col. 1 Line 63- Col. 2 Line 8; a non-transitory computer readable medium may have stored therein instructions that are executable to cause a computing system to perform functions) determining a level of emergency and a disease condition at the destination (Peeters: Col. 10 Lines 37-42; a request for medical support at the home of a person who appears to have suffered from cardiac arrest; select the closest available UAV to the person's home that is configured to provide medical support when a heart attack has occurred) via a user communication interface (Peeters: Col. 8 Lines 53-55; UI may allow an operator to specify certain details related to the medical situation to which the UAV is being dispatched) ………… ; prioritizing dispatching the one or more autonomous vehicles based on the level of emergency and the disease condition at the destination (Peeters: Col. 10 Lines 43-47; the central dispatch system may select an available UAV that is within a certain distance from the person's home (which may or may not be the closest), and which is configured to provide medical support when cardiac arrest has occurred); ……… ; integrating a plurality of available data sources across multiple locations (Peeters: Col. 21 Lines 42-58, Col. 22 Lines 8-18; the target location may be determined based on a various types of location information: location information that is provided by the remote device; the police also receive location information, such as GPS coordinates; obtain location information from image data that is captured by a remote device); comparing the plurality of available data sources across the multiple locations (Peeters: Col. 22 Lines 8-18; analyze such image data to detect, e.g., street signs and/or landmarks such as buildings or sculptures, which may help to identify the location of a medical situation).
Peeters doesn’t explicitly teach on the one or more autonomous vehicles.
However Ashar, in the same field of endeavor, teaches on the one or more autonomous vehicles (Asghar: Para. 57, 67; vehicle user interface process can render user interface elements based on, for example, a status from the vehicle and the occupant monitoring process; upon receiving an occupant monitoring event indicating a health emergency, the vehicle can engage an autonomous capability).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) with a reasonable expectation of success because upon receiving occupant data of a medical emergency the autonomous vehicle can safely stop the vehicle or reroute the autonomous vehicle to a hospital or clinic (Ashar: Para. 57, 67).
Peeters and Ashar don’t explicitly teach optimizing a route of the one or more autonomous vehicles to the destination.
However Kogan, in the same field of endeavor, teaches optimizing a route of the one or more autonomous vehicles to the destination (Kogan: Para. 68; AORPS optimizes travel to locations of the patients).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Peeters, Ashar, and Kogan don’t explicitly teach building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations.
However Zak, in the same field of endeavor, teaches building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations (Zak: Para. 10; scheduling model outputs one or more of automatic scheduling, tracking of epidemiological data for research, and resource/supply management; scheduling model outputs predicted medical consumable needs; deep reinforcement learning).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), and deep reinforcement learning scheduling model (Zak: Para. 10) with a reasonable expectation of success because dispatching autonomous vehicles by predicted medical consumable needs based on deep reinforcement learning scheduling model (Zak: Para. 5, 10, 17, 435).
Regarding claim 9, Peeters teaches the computer program product of claim 8, ……… ; and storing knowledge of the one or more autonomous vehicles in a centralized region (Peeters: Col. 10 Lines 16-21; central dispatch system may keep track of which UAVs are located at which local dispatch systems, which UAVs are currently available for deployment, and/or which medical situation or situations each of the UAVs is configured for).
Peeters, Ashar, and Kogan don’t explicitly teach wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles.
However Zak, in the same field of endeavor, teaches wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles (Zak: Para. 5, 17, 435; NLP can be used to interpret computer code, written text and spoken speech and then process the information, make comparisons and analytics; coordination of IoT with medical devices, allows one to determine effectiveness of medical devices against patient outcome and cost; continuous learning between interoperable ‘smart systems’, such as smart cities and autonomous dispatching, providing optimal traffic routes).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), and deep reinforcement learning scheduling model (Zak: Para. 10) with a reasonable expectation of success because dispatching autonomous vehicles by predicted medical consumable needs based on deep reinforcement learning scheduling model (Zak: Para. 5, 10, 17, 435).
Regarding claim 10, Peeters and Ashar don’t explicitly teach further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition; and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition.
However Kogan, in the same field of endeavor, teaches further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition (Kogan: Para. 15, 48, 112; clustering by similar age, conditions; geo-center of a neighborhood cluster; clustering patients similar in that regard, availability of the healthcare providers and the onsite care coordinators, driving distances to the locations of the patients to minimize driving); and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition (Kogan: Para. 58, 60, 110; calculates visit priorities based on a risk of an acute episode for a given patient; optimization is then a sum of home visit times weighted by visit priorities; Patient Clinical Priority (PCP), 0 to 1, which represents a risk stratification metric representing immediate health).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Regarding claim 11, Peeters and Ashar don’t explicitly teach further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination.
However Kogan, in the same field of endeavor, teaches further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination (Kogan: Para. 47; generates the appointment schedule with the travel routes dynamically based on optimization factors derived from the received client input, the collated patient data, the generated input matrix, the healthcare data, and the generated predictive model).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Regarding claim 14, Peeters teaches the computer program product of claim 8, further comprising: scheduling an arrival of medical staff in conjunction with the delivery of the medical supplies to the destination via the dispatched one or more autonomous vehicles (Peeters: Col. 18 Lines 46-57, Col. 21 Lines 8-18; dive-accident package, the UAV may drop a flotation device to help the diver stay afloat until the diver can be reached by rescuers; once the diver has been rescued, the UAV may display visual instructions and/or play back auditory instructions for CPR, which may help to revive a drowning victim).
Regarding claim 15, Peeters teaches a computer system for implementing a program that manages a device, comprising: one or more computer devices each having one or more processors and one or more tangible storage devices; and a program embodied on at least one of the one or more storage devices, the program having a plurality of program instructions for execution by the one or more processors, the program instructions comprising instructions (Peeters: Col. 1 Line 63- Col. 2 Line 8; a non-transitory computer readable medium may have stored therein instructions that are executable to cause a computing system to perform functions) for: determining a level of emergency and a disease condition at the destination (Peeters: Col. 10 Lines 37-42; a request for medical support at the home of a person who appears to have suffered from cardiac arrest; select the closest available UAV to the person's home that is configured to provide medical support when a heart attack has occurred) via a user communication interface (Peeters: Col. Col. 8 Lines 53-55; UI may allow an operator to specify certain details related to the medical situation to which the UAV is being dispatched) …….. ; prioritizing dispatching the one or more autonomous vehicles based on the level of emergency and the disease condition at the destination (Peeters: Col. 10 Lines 43-47; the central dispatch system may select an available UAV that is within a certain distance from the person's home (which may or may not be the closest), and which is configured to provide medical support when cardiac arrest has occurred); ……. integrating a plurality of available data sources across multiple locations (Peeters: Col. 21 Lines 42-58, Col. 22 Lines 8-18; the target location may be determined based on a various types of location information: location information that is provided by the remote device; the police also receive location information, such as GPS coordinates; obtain location information from image data that is captured by a remote device); comparing the plurality of available data sources across the multiple locations (Peeters: Col. 22 Lines 8-18; analyze such image data to detect, e.g., street signs and/or landmarks such as buildings or sculptures, which may help to identify the location of a medical situation).
Peeters doesn’t explicitly teach on the one or more autonomous vehicles.
However Ashar, in the same field of endeavor, teaches on the one or more autonomous vehicles (Asghar: Para. 57, 67; vehicle user interface process can render user interface elements based on, for example, a status from the vehicle and the occupant monitoring process; upon receiving an occupant monitoring event indicating a health emergency, the vehicle can engage an autonomous capability).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) with a reasonable expectation of success because upon receiving occupant data of a medical emergency the autonomous vehicle can safely stop the vehicle or reroute the autonomous vehicle to a hospital or clinic (Ashar: Para. 57, 67).
Peeters and Ashar don’t explicitly teach optimizing a route of the one or more autonomous vehicles to the destination.
However Kogan, in the same field of endeavor, teaches optimizing a route of the one or more autonomous vehicles to the destination (Kogan: Para. 68; AORPS optimizes travel to locations of the patients).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Peeters, Ashar, and Kogan don’t explicitly teach building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations.
However Zak, in the same field of endeavor, teaches building deep learning (DL) forecasting models to predict several different medicines required, corresponding to a distribution of the disease condition, across the multiple locations (Zak: Para. 10; scheduling model outputs one or more of automatic scheduling, tracking of epidemiological data for research, and resource/supply management; scheduling model outputs predicted medical consumable needs; deep reinforcement learning).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), and deep reinforcement learning scheduling model (Zak: Para. 10) with a reasonable expectation of success because dispatching autonomous vehicles by predicted medical consumable needs based on deep reinforcement learning scheduling model (Zak: Para. 5, 10, 17, 435).
Regarding claim 16, Peeters teaches the computer system of claim 15, ……. ; and storing knowledge of the one or more autonomous vehicles in a centralized region (Peeters: Col. 10 Lines 16-21; central dispatch system may keep track of which UAVs are located at which local dispatch systems, which UAVs are currently available for deployment, and/or which medical situation or situations each of the UAVs is configured for).
Peeters, Ashar, and Kogan don’t explicitly teach wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles.
However Zak, in the same field of endeavor, teaches wherein optimizing a route of the one or more autonomous vehicles to the destination further comprises: dynamically routing and re-routing the route of the one or more autonomous vehicles by utilizing natural language processing (NLP), Internet of Things (IoT), and analytics to efficiently dispatch the one or more autonomous vehicles (Zak: Para. 5, 17, 435; NLP can be used to interpret computer code, written text and spoken speech and then process the information, make comparisons and analytics; coordination of IoT with medical devices, allows one to determine effectiveness of medical devices against patient outcome and cost; continuous learning between interoperable ‘smart systems’, such as smart cities and autonomous dispatching, providing optimal traffic routes).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), and deep reinforcement learning scheduling model (Zak: Para. 10) with a reasonable expectation of success because dispatching autonomous vehicles by predicted medical consumable needs based on deep reinforcement learning scheduling model (Zak: Para. 5, 10, 17, 435).
Regarding claim 17, Peeters and Ashar don’t explicitly teach further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition; and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition.
However Kogan, in the same field of endeavor, teaches further comprising: clustering cities, and streets within the cities, based on a distribution of the disease condition (Kogan: Para. 15, 48, 112; clustering by similar age, conditions; geo-center of a neighborhood cluster; clustering patients similar in that regard, availability of the healthcare providers and the onsite care coordinators, driving distances to the locations of the patients to minimize driving); and prioritizing the dispatch of the one or more autonomous vehicles based on the distribution of the disease condition (Kogan: Para. 58, 60, 110; calculates visit priorities based on a risk of an acute episode for a given patient; optimization is then a sum of home visit times weighted by visit priorities; Patient Clinical Priority (PCP), 0 to 1, which represents a risk stratification metric representing immediate health).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Regarding claim 18, Peeters and Ashar don’t explicitly teach further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination.
However Kogan, in the same field of endeavor, teaches further comprising: enabling payload as a source for insights to reroute the one or more autonomous vehicles to save time required for the one or more autonomous vehicles to reach an urgently needed destination (Kogan: Para. 47; generates the appointment schedule with the travel routes dynamically based on optimization factors derived from the received client input, the collated patient data, the generated input matrix, the healthcare data, and the generated predictive model).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67) and an optimized route (Kogan: Para. 68) with a reasonable expectation of success because dynamically optimizing appointments and the route for dispatched vehicles based on live locational information, changing traffic, weather, patient data, and optimization factors to improve time utilization and quality of service (Ashar: Para. 10, 18, 68).
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Peeters et al. (US Patent 8,948,935 B1) in view of Ashar et al. (US Publication 2023/0106673 A1), Kogan et al. (US Publication 2023/0005607 A1), Zak et al. (US Publication 2025/0046436 A1), and in further view of Chien et al. (US Publication 2023/0137256 A1).
Regarding claim 5, Peeters, Ashar, Kogan, and Zak doesn’t explicitly teach wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model.
However Chien, in the same field of endeavor, teaches wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model (Chien: Para. 2, 35, 37; machine learning model (MLM) trained via a MLA may comprise a deep learning neural network; predicting future parking spot occupancy/vacancy may comprise a recurrent neural network (RNN), a long-short term memory (LSTM) neural network; MLA may incorporate an exponential smoothing algorithm; autonomous vehicles).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), deep reinforcement learning scheduling model (Zak: Para. 10), and long-short term memory and recurrent neural network (Chien: Para. 2, 35, 37) with a reasonable expectation of success because employing multiple machine learning algorithms to create a forecasting model improves search and rescue with autonomous vehicles (Chien: Para. 2, 35, 37).
Regarding claim 12, Peeters, Ashar, Kogan, and Zak doesn’t explicitly teach wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model.
However Chien, in the same field of endeavor, teaches wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model (Chien: Para. 2, 35, 37; machine learning model (MLM) trained via a MLA may comprise a deep learning neural network; predicting future parking spot occupancy/vacancy may comprise a recurrent neural network (RNN), a long-short term memory (LSTM) neural network; MLA may incorporate an exponential smoothing algorithm; autonomous vehicles).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), deep reinforcement learning scheduling model (Zak: Para. 10), and long-short term memory and recurrent neural network (Chien: Para. 2, 35, 37) with a reasonable expectation of success because employing multiple machine learning algorithms to create a forecasting model improves search and rescue with autonomous vehicles (Chien: Para. 2, 35, 37).
Regarding claim 19, Peeters, Ashar, Kogan, and Zak doesn’t explicitly teach wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model.
However Chien, in the same field of endeavor, teaches wherein the DL forecasting models comprise a Long Short-Term Memory (LSTM) model and an Exponential Smoothing Model and Recurrent Neural Network (ESRNN) model (Chien: Para. 2, 35, 37; machine learning model (MLM) trained via a MLA may comprise a deep learning neural network; predicting future parking spot occupancy/vacancy may comprise a recurrent neural network (RNN), a long-short term memory (LSTM) neural network; MLA may incorporate an exponential smoothing algorithm; autonomous vehicles).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), deep reinforcement learning scheduling model (Zak: Para. 10), and long-short term memory and recurrent neural network (Chien: Para. 2, 35, 37) with a reasonable expectation of success because employing multiple machine learning algorithms to create a forecasting model improves search and rescue with autonomous vehicles (Chien: Para. 2, 35, 37).
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Peeters et al. (US Patent 8,948,935 B1) in view of Ashar et al. (US Publication 2023/0106673 A1), Kogan et al. (US Publication 2023/0005607 A1), Zak et al. (US Publication 2025/0046436 A1), and in further view of Pastore et al. (US Publication 2016/0140311 A1).
Regarding claim 6, Peeters teaches the computer-implemented method of claim 1, …….. ; and a level of severity of other disease conditions or accidents (Peeters: Col. 17 Lines 18-21, Col. 18 Lines 19-28 ; medical-support functionality; one or more items for medical support in the particular medical situation, and/or one or more medical-support modules that are designed to provide medical support in the particular medical situation).
Peeters, Ashar, Kogan, and Zak doesn’t explicitly teach wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations.
However Pastore, in the same field of endeavor, teaches wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations (Pastore: Para. 7; monitors disease outbreak from a database of spending records that includes SKU-level data, including temporal data, geographic data).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), deep reinforcement learning scheduling model (Zak: Para. 10), and SKU data (Pastore: Para. 7) with a reasonable expectation of success because correlating SKU-level, temporal, geographic, and purchase data with the indicia of infection can generate a geospatial outbreak detection model based on the probabilistic identification criteria and at least one simulation model of infection spread by geography (Pastore: Para. 8).
Regarding claim 13, Peeters teaches the computer program product of claim 8, ……… ; and a level of severity of other disease conditions or accidents (Peeters: Col. 17 Lines 18-21, Col. 18 Lines 19-28 ; medical-support functionality; one or more items for medical support in the particular medical situation, and/or one or more medical-support modules that are designed to provide medical support in the particular medical situation).
Peeters, Ashar, Kogan, and Zak doesn’t explicitly teach wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations.
However Pastore, in the same field of endeavor, teaches wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations (Pastore: Para. 7; monitors disease outbreak from a database of spending records that includes SKU-level data, including temporal data, geographic data).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), deep reinforcement learning scheduling model (Zak: Para. 10), and SKU data (Pastore: Para. 7) with a reasonable expectation of success because correlating SKU-level, temporal, geographic, and purchase data with the indicia of infection can generate a geospatial outbreak detection model based on the probabilistic identification criteria and at least one simulation model of infection spread by geography (Pastore: Para. 8).
Regarding claim 20, Peeters teaches the computer-implemented method of claim 1, …… ; and a level of severity of other disease conditions or accidents (Peeters: Col. 17 Lines 18-21, Col. 18 Lines 19-28 ; medical-support functionality; one or more items for medical support in the particular medical situation, and/or one or more medical-support modules that are designed to provide medical support in the particular medical situation).
Peeters, Ashar, Kogan, and Zak doesn’t explicitly teach wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations.
However Pastore, in the same field of endeavor, teaches wherein the plurality of available data sources across multiple locations comprises: a medical stock keeping unit (SKU) of various disease condition vaccinations (Pastore: Para. 7; monitors disease outbreak from a database of spending records that includes SKU-level data, including temporal data, geographic data).
It would have been obvious to one having ordinary skill in the art to modify the dispatching an appropriate UAV to the scene of a medical situation (Peeters: Col. 3 Lines 12-16) with the autonomous vehicle user interface (Ashar: Para. 57, 67), an optimized route (Kogan: Para. 68), deep reinforcement learning scheduling model (Zak: Para. 10), and SKU data (Pastore: Para. 7) with a reasonable expectation of success because correlating SKU-level, temporal, geographic, and purchase data with the indicia of infection can generate a geospatial outbreak detection model based on the probabilistic identification criteria and at least one simulation model of infection spread by geography (Pastore: Para. 8).
Conclusion
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/L.E.L./Examiner, Art Unit 3663
/ANGELA Y ORTIZ/Supervisory Patent Examiner, Art Unit 3663