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 .
Introduction
The following is a non-final Office action in response to Applicant’s RCE submission filed on 3/26/2026. Currently claims 1-20 are pending and claims 1, 14, and 19 are independent. Claims 1, 14, and 19 have been amended from the previous claim set dated 10/31/2025. No claims have been added or cancelled.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/26/2026 has been entered.
Response to Amendments
Applicant' s amendments are acknowledged and necessitated the new grounds of rejection in this Office Action.
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.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mcdougall et al. (US 20210258756 A1) in view of Moore (US 20070225993 A1) further in view of Locke et al. (US 11164269 B1) further in view of Pal et al. (US 11175152 B2)
Regarding claim 1 (Amended), Mcdougall discloses a method for travel assistance (Mcdougall ¶24 - The alert processing system, therefore, puts the data sharing control in the hands of citizens by providing them with instant access to vital information that can assist the citizens in containing, treating, and mitigating risks as soon as they are identified), the method comprising: receiving, at a travel assistance system and from a mobile device, a request for a travel assistance response from the travel assistance system, the request comprising a geographic location of the mobile device (Mcdougall ¶54 - FIG. 9 shows a mobile check-in user interface 900 generated in accordance with examples disclosed herein. The calculation of a risk score for a specific user request is illustrated in this example); obtaining, from one or more databases, secondary data associated with the geographic location of the mobile device and demographic data of a user of the mobile device (Mcdougall ¶25 - In some examples, the risk assessment situations can include emergencies such as extreme weather conditions including storms, heat waves, etc., natural disasters such as earthquakes, landslides, health-based emergencies such as epidemics, or law and order-based emergencies such as riots, active-shooter situations, etc., or man-made disasters such as bombings, missile strikes, etc. - Mcdougall ¶26 -The risk processing platform 110 can be connected to a plurality of data sources 150a, . . . , 150n (where ‘n’ is a natural number and ‘n’ is greater than or equal to 1) such as government data sources {i.e. demographic data}, news sources, social media, or even the plurality of client devices 130a, . . . , 130m.); calculating, based on the secondary data, a risk assessment score (Mcdougall ABS - When information regarding a risk assessment situation is received, the location and time attributes of the risk assessment situation are extracted, a risk score is calculated and a risk alert is generated to include the attributes and the risk score upon validating the received information for risk) based on at least the demographic data of the user obtained from the one or more databases (Mcdougall ¶26 -The risk processing platform 110 can be connected to a plurality of data sources 150a, . . . , 150n (where ‘n’ is a natural number and ‘n’ is greater than or equal to 1) such as government data sources {i.e. demographic data}, news sources, social media, or even the plurality of client devices 130a, . . . , 130m.), the demographic data comprising at least an age of the user and the one or more associated parties (Mcdougall Fig. 10); executing a machine-learning algorithm to generate a travel assistance response model, the travel assistance response model configured to determine the travel assistance response (Mcdougall ¶21 - the risk score can be determined by a selected one or more of a plurality of machine learning (ML) based risk assessment models that are trained in generating the risk scores for different risk assessment situations); and transmitting one or more instructions to a computing device in communication with the travel assistance system and corresponding to the type of the travel assistance response, the one or more instructions causing the computing device to execute the type of the travel assistance response (Mcdougall ¶30 - Furthermore, the alert generator 114 can access pre-configured risk-handling instructions to be included in the risk alert 190. For example, the alert generator 114 can extract the instructions from an instructions library 116 which may be stored in a data store 170 communicatively coupled to the risk processing platform 110. The instructions library 116 can include steps that the user can take to maximize protection and minimize damage in case of a specific emergency. The emergency instructions can also further include emergency contact numbers that the user can call or help/response centers that the user can visit for assistance during the emergency. The risk alert 190 thus generated to include the location attributes can be transmitted to the client devices 130a, . . . , 130m to be displayed on a user interface (UI) associated with a risk processing ‘app’ installed on the client devices 130a, . . . , 130m) and wherein the machine-learning travel assistance response model utilized is determined by applying an algorithm to an input dataset comprising the secondary data to correlate a portion of the input dataset with one or more generated response models, wherein the machine-learning travel assistance model is selected based on an accuracy score generated using feedback data from previously executed travel assistance responses, and wherein determining the machine-learning assistance response model includes choosing from the one or more generated response models based on a particular accuracy of a chosen generated response model to the input dataset, wherein the particular accuracy is determined by obtaining an error value below a threshold value (Mcdougall ¶72 - The machine learning model may compare the performance scores for each machine learning model and may select the machine learning model with the best (e.g., highest accuracy, lowest error, closest to a desired threshold, and/or the like) performance score as the trained machine learning model 1145).
McDougall lacks data of one or more associated parties and corresponding to the type of response, dispatch one or more travel assistance vehicles.
Moore, from the same field of endeavor, teaches data of one or more associated parties (Moore ¶103 - This information may also indicate whether any space is presently available to accommodate non-subscribers who might wish to accompany the authorized beneficiary (such as visiting relatives, children's friends, or the like)) and corresponding to the type of response, dispatch one or more travel assistance vehicles (Moore ¶98 - Providing arrangements 102 for at least the transport service also include arranging 204 or developing a pick-up schedule to be provided to the authorized beneficiaries (and other non-subscribers as is deemed appropriate)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the disaster transportation techniques of Moore because Moore discloses “the transport service is provided to evacuate the authorized beneficiaries and bring them to safety (Moore ¶53)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional disaster transportation techniques that Moore discloses because it would augment the assistance by considering/including bringing people to safety.
Mcdougall further fails to clearly disclose demographic data of a user and one or more associated parties of the user of the mobile device, the demographic data comprising at least an age of the user and the one or more associated parties; calculating, at the travel assistance system and based on the secondary data, a risk assessment score based on at least the demographic data of the user obtained from the one or more databases.
Locke, from the same field of endeavor, teaches demographic data of a user of the user of the mobile device, the demographic data comprising at least an age of the user (Locke COL 18 ROW 43 - Furthermore, an age of a user associated with the user device 210 and/or the presence of preexisting conditions can be considered by the itinerary generator 202 in generating the itinerary 206); calculating, at the travel assistance system and based on the secondary data, a risk assessment score based on at least the demographic data of the user obtained from the one or more databases (Locke COL 21 ROW 21- Furthermore, in addition to, or instead of generating the travel risk score based on the destination risk data, the travel application 124 can generate the travel risk score based on personal risk data associated with the user. The personal risk data can indicate a risk associated with the user of the user device 210. The personal risk data can indicate the age of the user, the body mass index (BMI) of the user, medical conditions of the user (e.g., asthma, heart related problems), and/or activities performed by the user (e.g., whether the user frequently exercises, smokes, adheres to a vegan or vegetarian diet, consumes alcohol, etc.). In this regard, the travel risk score may be higher for a user that has asthma and is older than a particular age. In comparison, an infection travel risk scores of a user that exercises frequently and is younger than the particular age may have a lower risk score).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the dynamic travel planning techniques of Locke because Locke discloses “The system can evaluate risk levels associated with the travel of the user while the user is traveling. This can result in an evolving indication of risk that is easily understandable by a user. Furthermore, because the system can cause user interfaces to display indicators such as the evolving risk and the suggested updates to the travel itinerary, a user can easily understand what actions the user can take to reduce the infection risk that the user is exposed to during their trip (Locke COL 12 ROW 43)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional dynamic travel planning techniques that Locke discloses because it would augment the assistance provided by Mcdougall by considering/including infection risks that the user should consider/plan for.
Mcdougall further lacks determining, based on the risk assessment score and utilizing a machine-learning travel assistance response model of the travel assistance system, a type of the travel assistance response, the machine-learning travel assistance response model outputting the travel assistance response based on at least the risk assessment score.
Pal, from the same field of endeavor, teaches determining, based on the risk assessment score and utilizing a machine-learning travel assistance response model of the travel assistance system, a type of the travel assistance response, the machine-learning travel assistance response model outputting the travel assistance response based on at least the risk assessment score (Pal COL 21 ROW 61 - The method 100 may optionally include a step S150 of generating an output based on the set of risk scores, the output recommending a route to the driver, recommending an action regarding the route. (e.g., while the driver is driving a route), identifying and manifesting unsafe driving behavior, advising the driver on areas for improvement, recommending infrastructure changes, insurance companies and/or others. entity of the route and/or driver hazards, and/or perform other appropriate functions - Pal COL 17 ROW 67 - Additionally or alternatively, the model can function to enable the determination of the risk associated with an entire route and/or any other suitable outputs (e.g., as in S150)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the risk determination techniques of Pal because Pal discloses “the method and/or system confers the benefit of better informing human drivers on the risk associated with any or all of: routes they have taken (e.g., which routes commonly taken by the driver are the riskiest), routes they are currently taking (e.g., to dynamically adjust the route in a navigation application, to send a notification to the driver, etc.), and/or routes they may take (e.g., to enable the driver to select one from a set of multiple routes based on the risk score). Additionally or alternatively, any of these benefits can be applied to mitigate risk associated with operation of an autonomous vehicle (Pal COL 3 ROW 22)”. Additionally, Mcdougall further details “A location-based alert processing system that generates and processes risk alerts related to situations that warrant risk assessments is disclosed (Mcdougall ¶21)” so it would be obvious to consider including the additional risk determination techniques that Pal discloses because it would augment the risk assessments provided by Mcdougall by considering/including route risks.
Regarding claim 2, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses the secondary data obtained from the one or more databases comprises at least one of an indication of a weather condition at the geographic location, a history of crime-related events at the geographic location, a safety alert associated with the geographic location, or data of a local service provider (Mcdougall ¶25 - In some examples, the risk assessment situations can include emergencies such as extreme weather conditions including storms, heat waves, etc., natural disasters such as earthquakes, landslides, health-based emergencies such as epidemics, or law and order-based emergencies such as riots, active-shooter situations, etc., or man-made disasters such as bombings, missile strikes, etc.).
Regarding claim 3, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses obtaining additional secondary data from the mobile device, the additional secondary data comprising at least one of a health condition of a user of the mobile device, an identifier of the mobile device, or inputs provided to the mobile device (Mcdougall ¶31 - The risk processing app can include a feedback mechanism {i.e. inputs} coupled to the instructions for handling the emergency. The feedback mechanism can provide one or more user-selectable options that cause the risk processing platform 110 to execute further actions. For example, the selection of one of the options can cause transmission of certain user-specific data such as the current location of the client device to the risk processing platform 110).
Regarding claim 4 and 18, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses obtaining, based on a unique identifier associated with the mobile device, a user profile from a database of user profiles, the obtained user profile comprising at least one user preference for the type of the travel assistance response (Mcdougall ¶37 - The risk alert 190 can be displayed to the user either within a UI 260 of the risk processing app 200 or within the UI of another app. User preferences and the severity of the alert can be taken into consideration to display the risk alert 190. preferences and alert severity define the device response(s)).
Regarding claim 5 and 20, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses assigning a risk score to each of the secondary data associated with the geographic location and the user profile; and calculating an average of the assigned risk scores to each of the secondary data and the user profile (Mcdougall ¶28 - If more than one risk assessment model is selected then the risk score 192 can be an average or weighted average of the partial risk scores generated by each of the selected risk assessment models).
Regarding claim 10, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses the request for the travel assistance response comprises a user identifier, the method further comprising: authenticating the user identifier with a database of user identifiers of the travel assistance system (Mcdougall ¶52 - The alert processing system 100 includes the ability to seamlessly move from being unknown to being known and maintaining state throughout the treatment plan. An authenticated digital id can be the key to enabling a user to seamlessly interact digitally and physically and shift from unknown to known).
Regarding claim 11 and 17, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses the travel assistance response model configured to determine the travel assistance response based at least on an input of the secondary data associated with the geographic location of the mobile device (Mcdougall ¶21 - the risk score can be determined by a selected one or more of a plurality of machine learning (ML) based risk assessment models that are trained in generating the risk scores for different risk assessment situations).
Regarding claim 12, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses establishing a communication session with the mobile device, the communication session comprising transmitting at least one of routing information to a destination, instructions to a user of the mobile device, or a communication from a travel assistance personnel (Mcdougall ¶49 - users traveling soon may enable functions of the alert processing system 100, at-risk users associated with emergencies may enable additional functions of the alert processing system 100, such as calling for assistance, arranging for rescue operations, etc., by providing consent to share individual identities, current location data, current movement data, etc).
Regarding claim 6, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses a method for travel assistance (Mcdougall ¶24 - The alert processing system, therefore, puts the data sharing control in the hands of citizens by providing them with instant access to vital information that can assist the citizens in containing, treating, and mitigating risks as soon as they are identified).
Moore further teaches determining a type of travel assistance vehicle associated with the type of the travel assistance response; obtaining one or more specifications of travel assistance vehicles in a fleet of available travel assistance vehicles; and associating the one or more specifications of travel assistance vehicles with the type of the travel assistance response wherein the one or more instructions comprise a route to the geographic location of the mobile device to cause the travel assistance vehicle to be dispatched to the geographic location (Moore ¶90 - By other approaches, the best route based on the latest information is provided to the drivers, and the drivers are then expected to strictly adhere to the route unless special circumstances (unforeseen road blockages, bad weather that prevents air travel, and so forth) warrant any deviation. It will also be understood that deviations and changes to the route may occur while the vehicle is traveling to the pick-up locations).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the disaster transportation techniques of Moore because Moore discloses “the transport service is provided to evacuate the authorized beneficiaries and bring them to safety (Moore ¶53)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional disaster transportation techniques that Moore discloses because it would augment the assistance by considering/including bringing people to safety.
Regarding claim 7, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses determining a travel assistance area corresponding to the geographic location of the mobile device, the travel assistance area associated with the type of the travel assistance response (Mcdougall ¶21 - The risk score can indicate the likelihood of damage or risk to users who may be present in the geographic location associated with the risky scenario or who may be traveling to that geographic location. A risk alert including the location attribute corresponding to the specific location at which the risky situation is present and a temporal or time-related attribute associated with the risky situation along with the risk score is generated).
Regarding claim 8, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses determining a travel assistance area corresponding to the geographic location of the mobile device, the travel assistance area associated with the type of the travel assistance response (Mcdougall ¶21 - The risk score can indicate the likelihood of damage or risk to users who may be present in the geographic location associated with the risky scenario or who may be traveling to that geographic location. A risk alert including the location attribute corresponding to the specific location at which the risky situation is present and a temporal or time-related attribute associated with the risky situation along with the risk score is generated).
Moore further teaches determining a geographic location of one or more travel assistance vehicles corresponding to the type of travel assistance vehicle is within the travel assistance area; and ranking the one or more travel assistance vehicles (Moore ¶91 - When using the predetermined locations 301, however, in many cases it may be helpful to dispose such a predetermined location 301 relatively proximal to one or more authorized beneficiaries 307 and in turn the pick-up locations 303-305. Such a position may aid with facilitating the timely transport of such authorized beneficiaries 307 during a time of need. Such proximity may be measured, for example, by distance and/or by a period of time as may reasonably be required to traverse the distance between the predetermined location 301 and at least some of the pick-up locations 303-305. Sensitivities in this regard may vary in response to various influences including but not limited to subscriber (or authorized beneficiary) wishes, applicable relevant threat scenarios, and the like. In some cases, as when a number of pick-up locations exist in a significant population center 300, it may be useful to dispose a (or an additional) predetermined location 301 within the population center 300 itself).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the disaster transportation techniques of Moore because Moore discloses “the transport service is provided to evacuate the authorized beneficiaries and bring them to safety (Moore ¶53)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional disaster transportation techniques that Moore discloses because it would augment the assistance by considering/including bringing people to safety.
Regarding claim 9, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses determining a travel assistance area corresponding to the geographic location of the mobile device, the travel assistance area associated with the type of the travel assistance response (Mcdougall ¶21 - The risk score can indicate the likelihood of damage or risk to users who may be present in the geographic location associated with the risky scenario or who may be traveling to that geographic location. A risk alert including the location attribute corresponding to the specific location at which the risky situation is present and a temporal or time-related attribute associated with the risky situation along with the risk score is generated).
Moore further teaches ranking the one or more travel assistance vehicles is based on the secondary data associated with the geographic location, the type of the travel assistance response, and a user profile (Moore ¶72 - As but one very simple illustration in this regard, such subscription opportunities can differ from one another at least with respect to cost. This, in turn, provides subscriber choice with respect to selecting a particular subscription that best meets their specific needs and/or budget limitations. For example, one subscription can provide for accessing transport services that are economically selected (by excluding, for example, certain options such as medical services or amount or durability of armor or the like) while another subscription might provide for rescue services that are more costly and in turn reflect, for example, a wider variety of choices with respect to rescue modality, safety, accommodations, service options, creature comforts, and so forth. Other possibilities are contemplated).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the disaster transportation techniques of Moore because Moore discloses “the transport service is provided to evacuate the authorized beneficiaries and bring them to safety (Moore ¶53)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional disaster transportation techniques that Moore discloses because it would augment the assistance by considering/including bringing people to safety.
Regarding claim 13, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses a method for travel assistance (Mcdougall ¶24 - The alert processing system, therefore, puts the data sharing control in the hands of citizens by providing them with instant access to vital information that can assist the citizens in containing, treating, and mitigating risks as soon as they are identified).
Moore further teaches establishing a communication session with a communication system of the travel assistance vehicle, the communication system of the travel assistance vehicle comprising at least one of a video communication system or an audio communication system (Moore ¶105 - The communication device may also be used to communicate with the vehicle 401 itself ).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the disaster transportation techniques of Moore because Moore discloses “the transport service is provided to evacuate the authorized beneficiaries and bring them to safety (Moore ¶53)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional disaster transportation techniques that Moore discloses because it would augment the assistance by considering/including bringing people to safety.
Regarding claims 14 and 19 (Amended), Mcdougall discloses a method for travel assistance (Mcdougall ¶24 - The alert processing system, therefore, puts the data sharing control in the hands of citizens by providing them with instant access to vital information that can assist the citizens in containing, treating, and mitigating risks as soon as they are identified), the method comprising: receiving, at a travel assistance system and from a mobile device, a request for a travel assistance response from the travel assistance system, the request comprising a geographic location of the mobile device (Mcdougall ¶54 - FIG. 9 shows a mobile check-in user interface 900 generated in accordance with examples disclosed herein. The calculation of a risk score for a specific user request is illustrated in this example); obtaining, from one or more databases, secondary data associated with the geographic location of the mobile device and demographic data of a user of the mobile device and demographic data of a user of the mobile device (Mcdougall ¶25 - In some examples, the risk assessment situations can include emergencies such as extreme weather conditions including storms, heat waves, etc., natural disasters such as earthquakes, landslides, health-based emergencies such as epidemics, or law and order-based emergencies such as riots, active-shooter situations, etc., or man-made disasters such as bombings, missile strikes, etc. - Mcdougall ¶26 -The risk processing platform 110 can be connected to a plurality of data sources 150a, . . . , 150n (where ‘n’ is a natural number and ‘n’ is greater than or equal to 1) such as government data sources {i.e. demographic data}, news sources, social media, or even the plurality of client devices 130a, . . . , 130m.), the demographic data comprising at least an age of the user and the one or more associated parties (Mcdougall Fig. 10); calculating, based on the secondary data, a risk assessment score (Mcdougall ABS - When information regarding a risk assessment situation is received, the location and time attributes of the risk assessment situation are extracted, a risk score is calculated and a risk alert is generated to include the attributes and the risk score upon validating the received information for risk) based on at least the demographic data of the user obtained from the one or more databases (Mcdougall ¶26 -The risk processing platform 110 can be connected to a plurality of data sources 150a, . . . , 150n (where ‘n’ is a natural number and ‘n’ is greater than or equal to 1) such as government data sources {i.e. demographic data}, news sources, social media, or even the plurality of client devices 130a, . . . , 130m.); executing a machine-learning algorithm to generate a travel assistance response model, the travel assistance response model configured to determine the travel assistance response (Mcdougall ¶21 - the risk score can be determined by a selected one or more of a plurality of machine learning (ML) based risk assessment models that are trained in generating the risk scores for different risk assessment situations); and transmitting one or more instructions to a computing device in communication with the travel assistance system and corresponding to the type of the travel assistance response, the one or more instructions causing the computing device to execute the type of the travel assistance response (Mcdougall ¶30 - Furthermore, the alert generator 114 can access pre-configured risk-handling instructions to be included in the risk alert 190. For example, the alert generator 114 can extract the instructions from an instructions library 116 which may be stored in a data store 170 communicatively coupled to the risk processing platform 110. The instructions library 116 can include steps that the user can take to maximize protection and minimize damage in case of a specific emergency. The emergency instructions can also further include emergency contact numbers that the user can call or help/response centers that the user can visit for assistance during the emergency. The risk alert 190 thus generated to include the location attributes can be transmitted to the client devices 130a, . . . , 130m to be displayed on a user interface (UI) associated with a risk processing ‘app’ installed on the client devices 130a, . . . , 130m) and wherein the machine-learning travel assistance response model utilized is determined by applying an algorithm to an input dataset comprising the secondary data to correlate a portion of the input dataset with one or more generated response models, wherein the machine-learning travel assistance response model is selected based on an accuracy score generated using feedback data from previously executed travel assistance responses, and wherein determining the machine-learning assistance response model includes choosing from the one or more generated response models based on a particular accuracy of a chosen generated response model to the input dataset, wherein the particular accuracy is determined by obtaining an error value below a threshold value (Mcdougall ¶72 - The machine learning model may compare the performance scores for each machine learning model and may select the machine learning model with the best (e.g., highest accuracy, lowest error, closest to a desired threshold, and/or the like) performance score as the trained machine learning model 1145).
Mcdougall lacks information about one or more associated parties and determining a type of travel assistance vehicle associated with the type of the travel assistance response; obtaining one or more specifications of travel assistance vehicles in a fleet of available travel assistance vehicles; and associating the one or more specifications of travel assistance vehicles with the type of the travel assistance response wherein the one or more instructions comprise a route to the geographic location of the mobile device to cause the travel assistance vehicle to be dispatched to the geographic location.
Moore, from the same field of endeavor, teaches information about one or more associated parties (Moore ¶103 - This information may also indicate whether any space is presently available to accommodate non-subscribers who might wish to accompany the authorized beneficiary (such as visiting relatives, children's friends, or the like)) and determining a type of travel assistance vehicle associated with the type of the travel assistance response; obtaining one or more specifications of travel assistance vehicles in a fleet of available travel assistance vehicles; and associating the one or more specifications of travel assistance vehicles with the type of the travel assistance response wherein the one or more instructions comprise a route to the geographic location of the mobile device to cause the travel assistance vehicle to be dispatched to the geographic location (Moore ¶90 - By other approaches, the best route based on the latest information is provided to the drivers, and the drivers are then expected to strictly adhere to the route unless special circumstances (unforeseen road blockages, bad weather that prevents air travel, and so forth) warrant any deviation. It will also be understood that deviations and changes to the route may occur while the vehicle is traveling to the pick-up locations).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the disaster transportation techniques of Moore because Moore discloses “the transport service is provided to evacuate the authorized beneficiaries and bring them to safety (Moore ¶53)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional disaster transportation techniques that Moore discloses because it would augment the assistance by considering/including bringing people to safety.
Mcdougall further fails to clearly disclose demographic data of a user and one or more associated parties of the user of the mobile device, the demographic data comprising at least an age of the user and the one or more associated parties; calculating, at the travel assistance system and based on the secondary data, a risk assessment score based on at least the demographic data of the user obtained from the one or more databases.
Locke, from the same field of endeavor, teaches demographic data of a user of the user of the mobile device, the demographic data comprising at least an age of the user (Locke COL 18 ROW 43 - Furthermore, an age of a user associated with the user device 210 and/or the presence of preexisting conditions can be considered by the itinerary generator 202 in generating the itinerary 206); calculating, at the travel assistance system and based on the secondary data, a risk assessment score based on at least the demographic data of the user obtained from the one or more databases (Locke COL 21 ROW 21- Furthermore, in addition to, or instead of generating the travel risk score based on the destination risk data, the travel application 124 can generate the travel risk score based on personal risk data associated with the user. The personal risk data can indicate a risk associated with the user of the user device 210. The personal risk data can indicate the age of the user, the body mass index (BMI) of the user, medical conditions of the user (e.g., asthma, heart related problems), and/or activities performed by the user (e.g., whether the user frequently exercises, smokes, adheres to a vegan or vegetarian diet, consumes alcohol, etc.). In this regard, the travel risk score may be higher for a user that has asthma and is older than a particular age. In comparison, an infection travel risk scores of a user that exercises frequently and is younger than the particular age may have a lower risk score).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the dynamic travel planning techniques of Locke because Locke discloses “The system can evaluate risk levels associated with the travel of the user while the user is traveling. This can result in an evolving indication of risk that is easily understandable by a user. Furthermore, because the system can cause user interfaces to display indicators such as the evolving risk and the suggested updates to the travel itinerary, a user can easily understand what actions the user can take to reduce the infection risk that the user is exposed to during their trip (Locke COL 12 ROW 43)”. Additionally, Mcdougall further details that “the alert processing system enables providing targeted assistance based on personalized needs (Mcdougall ¶24)” so it would be obvious to consider including the additional dynamic travel planning techniques that Locke discloses because it would augment the assistance provided by Mcdougall by considering/including infection risks that the user should consider/plan for.
Mcdougall further lacks determining, based on the risk assessment score and utilizing a machine-learning travel assistance response model of the travel assistance system, a type of the travel assistance response, the machine-learning travel assistance response model outputting the travel assistance response based on at least the risk assessment score.
Pal, from the same field of endeavor, teaches determining, based on the risk assessment score and utilizing a machine-learning travel assistance response model of the travel assistance system, a type of the travel assistance response, the machine-learning travel assistance response model outputting the travel assistance response based on at least the risk assessment score (Pal COL 21 ROW 61 - The method 100 may optionally include a step S150 of generating an output based on the set of risk scores, the output recommending a route to the driver, recommending an action regarding the route. (e.g., while the driver is driving a route), identifying and manifesting unsafe driving behavior, advising the driver on areas for improvement, recommending infrastructure changes, insurance companies and/or others. entity of the route and/or driver hazards, and/or perform other appropriate functions - Pal COL 17 ROW 67 - Additionally or alternatively, the model can function to enable the determination of the risk associated with an entire route and/or any other suitable outputs (e.g., as in S150)).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the travel assistance methodology/system of Mcdougall by including the risk determination techniques of Pal because Pal discloses “the method and/or system confers the benefit of better informing human drivers on the risk associated with any or all of: routes they have taken (e.g., which routes commonly taken by the driver are the riskiest), routes they are currently taking (e.g., to dynamically adjust the route in a navigation application, to send a notification to the driver, etc.), and/or routes they may take (e.g., to enable the driver to select one from a set of multiple routes based on the risk score). Additionally or alternatively, any of these benefits can be applied to mitigate risk associated with operation of an autonomous vehicle (Pal COL 3 ROW 22)”. Additionally, Mcdougall further details “A location-based alert processing system that generates and processes risk alerts related to situations that warrant risk assessments is disclosed (Mcdougall ¶21)” so it would be obvious to consider including the additional risk determination techniques that Pal discloses because it would augment the risk assessments provided by Mcdougall by considering/including route risks.
Regarding claim 15, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses the secondary data obtained from the one or more databases comprises at least one of an indication of a weather condition at the geographic location, a history of crime-related events at the geographic location, a safety alert associated with the geographic location, or data of a local service provider (Mcdougall ¶25 - In some examples, the risk assessment situations can include emergencies such as extreme weather conditions including storms, heat waves, etc., natural disasters such as earthquakes, landslides, health-based emergencies such as epidemics, or law and order-based emergencies such as riots, active-shooter situations, etc., or man-made disasters such as bombings, missile strikes, etc.).
Regarding claim 16, Mcdougall in view of Moore further in view of Locke further in view of Pal discloses obtaining additional secondary data from the mobile device, the additional secondary data comprising at least one of a health condition of a user of the mobile device, an identifier of the mobile device, or inputs provided to the mobile device (Mcdougall ¶31 - The risk processing app can include a feedback mechanism {i.e. inputs} coupled to the instructions for handling the emergency. The feedback mechanism can provide one or more user-selectable options that cause the risk processing platform 110 to execute further actions. For example, the selection of one of the options can cause transmission of certain user-specific data such as the current location of the client device to the risk processing platform 110).
Response to Arguments
Applicant's arguments filed 3/26/2026 have been fully considered but they are not persuasive and/or are moot in light of the new rejections addressed above.
Regarding the 35 USC § 103 rejections, Applicant argues that the claimed invention is different from the previously cited prior art due to the order in which processes occur, specifically, that the risk score is an input to the model, and not an output. Examiner found this argument persuasive, however, this necessitated updating the prior art search. As a result of this updated search and consideration, prior art was found that discloses the claimed order and is now cited (Pal as identified above). As such, Applicant' s arguments (with respect to the independent claims and their respective dependent claims) are now moot.
Conclusion
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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