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 .
Status of Claims
This Office Action is in response to the application filed on July 08, 2026. Claims 1, 2, 4, 6, 7, 10-13, 18, and 21 have been amended. Claims 5, 22, and 25 have been canceled, claims 14, 15, 17, 19, and 20 were previously canceled. Claims 26-28 are newly added. Claims 1-4, 6-13, 16, 18, 21, 23, 24, and 26-28 are presently pending and are presented for examination.
Response to Amendments
In response to Applicant's Amendments dated July 08, 2026, Examiner withdraws the rejection under 35 U.S.C. § 101 and the previous prior art rejections.
Response to Arguments
Applicant's arguments filed on July 08, 2026 have been fully considered, but they are moot in view of the new ground(s) of rejections.
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 is incorrect, any correction of the statutory basis 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
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, 6, 7, 9-11, 13, 16, 18, 21, 23, 24, 27, and 28 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2019/0206009 (hereinafter, "Gibson"; previously of record), in view of U.S. Pub. No. 2010/0036599 (hereinafter, "Froeberg"; previously of record), and in further view of U.S. Pub. No. 2015/0276419 (hereinafter, "Hashem"; newly of record).
Regarding claim 1, Gibson discloses a computer-implemented method for generating a user-specific transportation recommendation based on location information and transportation option availability, the method comprising:
receiving, by a processor (Fig. 10, # 1002), and from a mobile computing device (“A user client device includes a mobile device, such as a laptop, smartphone, or tablet associated with a user” (para 0039)), an indication of a first location of an end point of a trip (“receive requests from persons who use a mobile application to request transport from a work location to an entertainment venue, sporting venue, or other destination.” (para 0001));
receiving, by the processor (Fig. 10, # 1002), geolocation data indicating a current location of the mobile computing device (“receive sensory data from the provider client devices 110a-110n and/or the user client devices 114a-114n, respectively, to determine location coordinates for each device (e.g., longitudinal and latitudinal degrees)” (para 0036));
However, Gibson does not explicitly teach
identifying, by the processor, and based on the first location and the current location, a plurality of transportation options for travel between the current location and the first location;
accessing, by the processor, a transportation profile associated with the mobile computing device, wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device;
determining, by the processor, and based on the transportation profile, weighting factors corresponding to the plurality of transportation modes, wherein a respective weighting factor of a particular transportation mode of the plurality of transportation modes indicates frequency of use of the particular transportation mode;
determining, by the processor, and based on comparing the weighting factors to a threshold, a subset of the plurality of transportation options, each transportation option of the subset comprising one or more transportation modes of the plurality of transportation modes;
determining, by the processor, a transportation risk level corresponding to each transportation option of the subset;
providing, by the processor, and via an application running on the mobile computing device, the subset of the plurality of transportation options ordered based on the transportation risk level;
receiving, by the processor, via the application, and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options; and
updating, by the processor, and based on the selection, the transportation profile associated with the user.
Hashem, in the same field of endeavor, teaches
identifying, by the processor (“the travel planner device 1201 includes… a processor 1203 coupled with the bus 1202 for processing the information” (para 0019)), and based on the first location and the current location, a plurality of transportation options for travel between the current location and the first location (“the travel planner device 1201 can list travel options of how you can get to the locations from where the person is” (para 0051) and “Based on location-specific information associated with the user's current location, the intelligent calculator unit 514 can assess what modes of transportation there are” (para 0066));
accessing, by the processor, a transportation profile associated with the mobile computing device (“the calculator 512 performs calculations using the user profile and all the elements related to the users current location to obtain plan suggestions” (para 0068) and “The travel planner device 1201 may implement a number of methods for obtaining information about the user… by obtaining user information via social network profiles and auto-profile information options” (para 0082)), wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing devicе (“the travel planner device 1201 may determine whether it is usual for the user to be walking or running at this time or on this particular day. By generating and storing a historical log of the user's movements, the travel planner device 1201 can learn what the user's habits are and introduce incremental changes to their routines and methods of their travel” (para 0034));
determining, by the processor, and based on the transportation profile (“The device updates the user profile to include the preference rankings” (para 0009)), weighting factors corresponding to the plurality of transportation modes (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009)), wherein a respective weighting factor of a particular transportation mode of the plurality of transportation modes indicates frequency of use of the particular transportation mode (“analyzing information included in user profile and generating a score for each category (e.g., a preference percentage)” (para 0042) and “the intelligent calculator unit may receive a historical travel log of the user's travel… to enable the intelligent calculator unit 512 to formulate predictions of future travel modes” (para 0065));
determining, by the processor, and based on comparing the weighting factors to a threshold (“The score may then be compared to predetermined threshold values corresponding to various levels of interest in a given category” (para 0042)), a subset of the plurality of transportation options (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009)), each transportation option of the subset comprising one or more transportation modes of the plurality of transportation modes (“deciding method of transport to take (e.g., whether a person is trying to decide whether to take public transport or private transport or a personal vehicle or a rented one)” (para 0008));
receiving, by the processor, via the application, and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options (Fig. 2, #204-#206);
updating, by the processor, and based on the selection, the transportation profile associated with the user (“calculates to be the most important attributes to the user. It is achieved by matching the best generated algorithm to the user profile algorithm, there are many over laying considerations to the user profile that can change the results of the generated search, such as selecting to have a preferred mode of transport 104 or selecting a preferred set of variables 114” (para 0041) and “the user's decision is then logged in the memory 1204, where it can be later applied to help build the user's profile 504 for future recommendations”) (para 0075)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to build a profile of a user that can predict the type of transportation the traveler may want to take based on their personality; see Hashem at least at [0009].
Froeberg, in the same field of endeavor, teaches
determining, by the processor, a transportation risk level corresponding to each transportation option of the subset (“determining a candidate risk value for each of the candidate routes, wherein the one candidate route having the at least one portion traversable by the at least two different modes of transportation has a different candidate risk value corresponding to each of the at least two different modes of transportation” (claim 58)); and
providing, by the processor, and via an application running on the mobile computing device (Fig. 1A, #102 and #105), the subset of the plurality of transportation options ordered based on the transportation risk level (“the user may prioritize optimizing route safety with respect to other available route optimizations, such as optimizing for a shortest time of travel, a least distance, an avoidance of highways, and the like” (para 0026)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Froeberg in order to determine the safest route; see Froeberg at least at [0026].
Regarding claim 2, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses wherein the transportation profile associated with the user indicates one or more of:
typical modes of transportation used, vehicles owned by the user, membership of ride-share or vehicle-share networks, a fitness tracker account information, or a transit system account information (“A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location” (para 0226)), and
However, Gibson does not explicitly teach
the one or more transportation modes includes one of:
a train, a ride-share vehicle, a taxi. a self-driving vehicle, a bicycle, or a walking route.
Froeberg, in the same field of endeavor, teaches
the one or more transportation modes includes one of:
a train, a ride-share vehicle, a taxi. a self-driving vehicle, a bicycle, or a walking route (“a transportation mode may include a mode of pedestrian ambulation, such as walking, running, or swimming. A transportation mode may be a motor vehicle classifiable by governmental agency, such as a personal car, a truck, a farm vehicle, a motorcycle, a bus, an airplane, and the like. A transportation mode may be a personal transportation device with or without a motor, such as skates, a skateboard, a bicycle, a unicycle, a canoe, a windsurf board, a Segway©, to name a few. A transportation mode may include public transportation or vehicles that operate in and/or on the water. A mode of transportation may be public or private, commercial or non-commercial.” (para 0034)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Froeberg in order for the user to specify mode of transportation; see Froeberg at least at [0093].
Regarding claim 3, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses further comprising:
receiving, by the processor, via a geolocation unit of the mobile computing device, indications of a plurality of locations of the mobile computing device at a corresponding plurality of times (“the user client device 114a repeatedly sends location coordinates to the transportation matching system 102” (para 0066)); and
determining, by the processor, and based at least in part on the plurality of locations and the corresponding plurality of times, the transportation profile associated with the user (“Transportation matching system 1102 may generate, store, receive, and send data, such as, for example, user-profile data, concept-profile data, text data, transportation request data, GPS location data, provider data, requestor data, vehicle data, or other suitable data related to the transportation matching network” (para 0221), “A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location” (para 0226).
Regarding claim 4, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses wherein a transportation option of the plurality of transportation options comprises:
a route between the current location and the first location (“the transportation matching system can modify a pickup location and/or station based on a travel time (or route) to a user's ultimate destination” (para 0028));
a transportation environment indicating one or more of: time of day, type of geographic area, weather condition, duration of transportation, distance of transportation, or physical condition of the user (“the transportation matching system 102 receives notification of the weather event 428 from a software application, RSS feed, or website of a mass-transit system, weather organization, or regional transportation department” (para 0145) and “ the transportation matching system 102 determines the estimated transit time 510 based on an average user traveling speed (e.g., 3.1 miles per hour for walking) and a location within the mass-transit station 514 (e.g., by multiplying the average user traveling speed by the distance from the location within the mass-transit station 514 to the pickup location)” (para 0156)).
Regarding claim 6, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses further comprising:
updating, by the processor, the subset of the plurality of transportation options to exclude transportation options including the unwanted transportation scenario ("the transportation matching system 102 determines a number of transportation vehicles available to transport users from the station. Upon determining that the number of available transportation vehicles falls below (or satisfies) a threshold number of available transportation vehicles” (para 0081) and “the transportation matching system 102 excludes transportation vehicles that—if selected—would wait more than the threshold wait time” (para 0163));
updating, by the processor, and based on the input, the transportation profile associated with the user of the mobile computing device (“The transportation matching system 102 may further consider additional factors when selecting transportation vehicles, such as provider rating or vehicle type. In the example shown in FIG. 2B, the transportation matching system 102 selects the transportation vehicle corresponding to the provider client device 110a” (para 0080) and “based on receiving an updated destination in an updated transportation request from the first user client device 308 a, the transportation matching system 102 determines that a transportation vehicle transporting the first user” (para 0116));
However, Gibson does not explicitly teach
receiving, by the processor and via the application running on the mobile computing device, input indicating an unwanted transportation scenario comprising a particular transportation mode or a particular transportation environment.
Hashem, in the same field of endeavor, teaches
receiving, by the processor and via the application running on the mobile computing device, input indicating an unwanted transportation scenario comprising a particular transportation mode or a particular transportation environment (“The device generates a preference ranking for the following environmental, scenic, weather and mode of transport for the user. The device updates the user profile to include the preference rankings’ (para 0009) and “analyzing information included in user profile and generating a score for each category (e.g., a preference percentage)” (para 0042)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to generate useful recommendations for methods of transport based on preference ranking; see Hashem at least at [0009].
Regarding claim 7, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. However, Gibson does not explicitly teach wherein the
transportation risk level corresponding to the transportation option is indicative of a risk of at least one of:
harm to the user. or damage to property of the user, and the method further comprising:
accessing, by the processor, data related to an accident history associated with the user; and
determining, by the processor and based at least in part on the data, a personal risk associated with the user,
wherein the transportation risk level is further based on the personal risk.
Froeberg, in the same field of endeavor, teaches
wherein the transportation risk level corresponding to the transportation option is indicative of a risk of at least one of:
harm to the user. or damage to property of the user (“Another safety criterion or safety factor on which the risk value 350 may depend is a potential risky maneuver 310 associated with a traversal of the route” (para 0087) and “Personal safety preferences 340 may be selectable, may be prioritized with respect to importance, and may include one or more attributes such as … area crime statistics” (para 0097)), and the method further comprising:
accessing, by the processor, data related to an accident history associated with the user (“uses statistical data 322, one or more appropriate databases may be accessed, for example, accident statistics” (para 0110) and “the traveler profile 312 may include parameters such as traveler age, experience in operating a vehicle to be used. For example, an inexperienced driver may be more likely to be at risk” (para 0088)); and
determining, by the processor and based at least in part on the data, a personal risk associated with the user (“the risk value 350 may depend may be a traveler profile 312” (para 0088)),
wherein the transportation risk level is further based on the personal risk (“the risk value 350 may depend may be a traveler profile 312. The traveler profile 312 may include parameters such as traveler age, experience in operating a vehicle to be used on the route (such as operating, for instance, a car, a truck, a boat or other vehicle), familiarity in using a mode of transportation to be used on the route (such as, for example, using a subway, a bus or a train route), attributes of the traveler (e.g., uses a wheelchair or pulls rolling luggage, is visually impaired, is hearing impaired, etc.), and/or other parameters that may profile or describe attributes of the traveler)” (para 0088)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Froeberg in order to determine the safest route; see Froeberg at least at [0026].
Regarding claim 9, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses further comprising:
receiving, by the processor, real-time data indicating an availability of the plurality of transportation options (“receive a transportation-request notification based on availability (e.g., by selecting a transportation vehicle closest to a pickup location for the user 118a or selecting a transportation vehicle dispatched to a location near a destination for the user 118a)” (para 0081)),
wherein the subset of the plurality of transportation options is determined based at least in part on the real-time data (“the transportation matching system 102, via the server(s) 104, performs the act 206 of receiving scheduling information from a mass-transit system. For instance, the scheduling information may be a static schedule or updated scheduling information (e.g., real-time updates)” (para 0064)).
Regarding claim 10, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses further comprising:
determining, by the processor, an availability of the particular transportation option (“the transportation matching system 102 determines a number of transportation vehicles available to transport users from the station, the transportation matching system 102 selects one or more transportation vehicles to receive a transportation-request notification based on availability” (para 0081)); and
based on determining the availability, providing, by the processor and via a graphical user
interface (GUI) of the application (“graphical user interfaces of a user client device presenting selectable options for requesting transport” (para 0031)):
a reservation option that enables the user to reserve access to the particular transportation option (“the transportation matching system 102 selects one or more transportation vehicles to receive a transportation-request notification based on availability (e.g., by selecting a transportation vehicle closest to a pickup location for the user 118a or selecting a transportation vehicle dispatched to a location near a destination for the user 118a” (para 0081)), or
a purchase option associated with the particular transportation option that enables the user to purchase access to the particular transportation option (“the transportation matching system 102 communicates with a mass-transit system to purchase (or arrange purchase of) a transportation pass for the user to the destination via the mass-transit vehicle” (para 0178)).
Regarding claim 11, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 10. Additionally, Gibson discloses further comprising:
updating, by the processor, and based on the selection, an additional transportation profile associated with an additional user with transportation profile characteristics similar to the user (“optionally creates user groups based on the destination indicated by each of the transportation requests from the user client devices 408a-408e. Accordingly, in some embodiments, the transportation matching system 102 creates user groups of users who have indicated destinations in a similar direction relative to destinations indicated by other users within the mass-transit vehicle 402. Additionally, or alternatively, the transportation matching system 102 uses destinations indicated by travel histories for one or more of the users 407a-407e” (para 0126)).
Regarding claim 13, Gibson discloses a system for generating a user-specific transportation recommendation based on location information and transportation option availability, comprising:
a processor (Fig. 10, # 1002); and
computer-readable media storing instructions which, when executed by the processor (“ a non-transitory computer readable storage medium can comprise instructions that, when executed by one or more processors, cause a computing device to perform the acts” (para 0180)), causes the processor to:
receive, from a mobile computing device, an indication of a first location of an end point of a trip (“receive requests from persons who use a mobile application to request transport from a work location to an entertainment venue, sporting venue, or other destination.” (para 0001));
receive geolocation data indicating a current location of the mobile computing device (“receive sensory data from the provider client devices 110a-110n and/or the user client devices 114a-114n, respectively, to determine location coordinates for each device (e.g., longitudinal and latitudinal degrees)” (para 0036));
However, Gibson does not explicitly teach
identify, based on the first location and the current location, a plurality of transportation options for travel between the current location and the first location;
access a transportation profile associated with the mobile computing device, wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device;
determine, based on the transportation profile, weighting factors corresponding to the plurality of transportation modes, wherein a respective weighting factor of a particular transportation mode of the plurality of transportation modes indicates frequency of use of the particular transportation mode;
determine, based on comparing the weighting factors to a threshold, a subset of the plurality of transportation options, each transportation option of the subset including one or more transportation modes of the plurality of transportation modes;
determine a transportation risk level corresponding to each transportation option of the subset;
provide, via an application running on the mobile computing device, the subset of the plurality of transportation options ordered based on the transportation risk level;
receive, via the application and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options; and
update the transportation profile associated with the user based on the selection.
Hashem, in the same field of endeavor, teaches
identify, based on the first location and the current location, a plurality of transportation options for travel between the current location and the first location (“the travel planner device 1201 can list travel options of how you can get to the locations from where the person is” (para 0051) and “Based on location-specific information associated with the user's current location, the intelligent calculator unit 514 can assess what modes of transportation there are” (para 0066));
access a transportation profile associated with the mobile computing device (“the calculator 512 performs calculations using the user profile and all the elements related to the users current location to obtain plan suggestions” (para 0068) and “The travel planner device 1201 may implement a number of methods for obtaining information about the user… by obtaining user information via social network profiles and auto-profile information options” (para 0082)), wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device (“the travel planner device 1201 may determine whether it is usual for the user to be walking or running at this time or on this particular day. By generating and storing a historical log of the user's movements, the travel planner device 1201 can learn what the user's habits are and introduce incremental changes to their routines and methods of their travel” (para 0034));
determine, based on the transportation profile (“The device updates the user profile to include the preference rankings” (para 0009)), weighting factors corresponding to the plurality of transportation modes (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009)), wherein a respective weighting factor of a particular transportation mode of the plurality of transportation modes indicates frequency of use of the particular transportation mode (“analyzing information included in user profile and generating a score for each category (e.g., a preference percentage)” (para 0042) and “the intelligent calculator unit may receive a historical travel log of the user's travel… to enable the intelligent calculator unit 512 to formulate predictions of future travel modes” (para 0065));
determine, based on comparing the weighting factors to a threshold (“The score may then be compared to predetermined threshold values corresponding to various levels of interest in a given category” (para 0042)), a subset of the plurality of transportation options, each transportation option of the subset including one or more transportation modes of the plurality of transportation modes (“deciding method of transport to take (e.g., whether a person is trying to decide whether to take public transport or private transport or a personal vehicle or a rented one)” (para 0008));
receive, via the application and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options (Fig. 2, #204-#206); and
update the transportation profile associated with the user based on the selection (“calculates to be the most important attributes to the user. It is achieved by matching the best generated algorithm to the user profile algorithm, there are many over laying considerations to the user profile that can change the results of the generated search, such as selecting to have a preferred mode of transport 104 or selecting a preferred set of variables 114” (para 0041) and “the user's decision is then logged in the memory 1204, where it can be later applied to help build the user's profile 504 for future recommendations”) (para 0075)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to build a profile of a user that can predict the type of transportation the traveler may want to take based on their personality; see Hashem at least at [0009].
Froeberg, in the same field of endeavor, teaches
determine a transportation risk level corresponding to each transportation option of the subset (“determining a candidate risk value for each of the candidate routes, wherein the one candidate route having the at least one portion traversable by the at least two different modes of transportation has a different candidate risk value corresponding to each of the at least two different modes of transportation” (claim 58));
provide, via an application running on the mobile computing device (Fig. 1A, #102 and #105), the subset of the plurality of transportation options ordered based on the transportation risk level (“the user may prioritize optimizing route safety with respect to other available route optimizations, such as optimizing for a shortest time of travel, a least distance, an avoidance of highways, and the like” (para 0026)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Froeberg in order to determine the safest route; see Froeberg at least at [0026].
Regarding claim 16, Gibson discloses and the combination of Froeberg and Hashem teaches the system of claim 13. Additionally, Gibson discloses wherein the instructions, when executed, further cause the processor to:
acquire current environmental data associated with the subset of the plurality of transportation options (“the transportation matching system 102 receives notification of the weather event 428 from a software application, RSS feed, or website of a mass-transit system, weather organization, or regional transportation department” (para 0145) and “ the transportation matching system 102 determines the estimated transit time 510 based on an average user traveling speed (e.g., 3.1 miles per hour for walking) and a location within the mass-transit station 514 (e.g., by multiplying the average user traveling speed by the distance from the location within the mass-transit station 514 to the pickup location)” (para 0156)),
However, Gibson does not explicitly teach
wherein the transportation risk level corresponding to each of the plurality of transportation options is determined based in part on the current environmental data.
Froeberg, in the same field of endeavor, teaches
wherein the transportation risk level corresponding to each of the plurality of transportation options is determined based in part on the current environmental data (“The risk value may be determined based upon one or more safety criteria or safety factors, including physical route attributes, personal safety preferences, personal convenience factors, and other types of risk factors)” (para 0008)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Froeberg in order to determine the safest route; see Froeberg at least at [0026].
Regarding claim 18, Gibson discloses a non-transitory computer-readable medium storing instructions for generating a user-specific transportation recommendation based on location information and transportation option availability which, when executed by a processor, causes the processor to:
receive, from a mobile computing device, an indication of a first location of an end point of a trip (“receive requests from persons who use a mobile application to request transport from a work location to an entertainment venue, sporting venue, or other destination.” (para 0001));
receive geolocation data indicating a current location of the mobile computing device (“receive sensory data from the provider client devices 110a-110n and/or the user client devices 114a-114n, respectively, to determine location coordinates for each device (e.g., longitudinal and latitudinal degrees)” (para 0036));
However, Gibson does not explicitly teach
identify, based on the first location and the current location, a plurality of transportation options for travel between the current location and the first location;
access a transportation profile associated with the mobile computing device, wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device;
determine, based on the transportation profile, weighting factors corresponding to the plurality of transportation modes, wherein a respective weighting factor of a particular transportation mode of the plurality of transportation modes indicates frequency of use of the particular transportation mode;
determine, based on comparing the weighting factors to a threshold, a subset of the plurality of transportation options, each transportation option of the subset including one or more transportation modes of the plurality of transportation modes;
determine a transportation risk level corresponding to each transportation option of the subset;
provide, via an application running on the mobile computing device, the subset of the plurality of transportation options ordered based on the transportation risk level;
receive, via the application and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options; and
update the transportation profile associated with the user based on the selection.
Hashem, in the same field of endeavor, teaches
identify, based on the first location and the current location, a plurality of transportation options for travel between the current location and the first location (“the travel planner device 1201 can list travel options of how you can get to the locations from where the person is” (para 0051) and “Based on location-specific information associated with the user's current location, the intelligent calculator unit 514 can assess what modes of transportation there are” (para 0066));
access a transportation profile associated with the mobile computing device (“the calculator 512 performs calculations using the user profile and all the elements related to the users current location to obtain plan suggestions” (para 0068) and “The travel planner device 1201 may implement a number of methods for obtaining information about the user… by obtaining user information via social network profiles and auto-profile information options” (para 0082)), wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device (“the travel planner device 1201 may determine whether it is usual for the user to be walking or running at this time or on this particular day. By generating and storing a historical log of the user's movements, the travel planner device 1201 can learn what the user's habits are and introduce incremental changes to their routines and methods of their travel” (para 0034));
determine, based on the transportation profile (“The device updates the user profile to include the preference rankings” (para 0009)), weighting factors corresponding to the plurality of transportation modes (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009)), wherein a respective weighting factor of a particular transportation mode of the plurality of transportation modes indicates frequency of use of the particular transportation mode (“analyzing information included in user profile and generating a score for each category (e.g., a preference percentage)” (para 0042) and “the intelligent calculator unit may receive a historical travel log of the user's travel… to enable the intelligent calculator unit 512 to formulate predictions of future travel modes” (para 0065));
determine, based on comparing the weighting factors to a threshold (“The score may then be compared to predetermined threshold d values corresponding to various levels of interest in a given category” (para 0042)), a subset of the plurality of transportation options (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009)), each transportation option of the subset including one or more transportation modes of the plurality of transportation modes (“deciding method of transport to take (e.g., whether a person is trying to decide whether to take public transport or private transport or a personal vehicle or a rented one)” (para 0008));
receive, via the application and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options (Fig. 2, #204-#206); and
update the transportation profile associated with the user based on the selection (“calculates to be the most important attributes to the user. It is achieved by matching the best generated algorithm to the user profile algorithm, there are many over laying considerations to the user profile that can change the results of the generated search, such as selecting to have a preferred mode of transport 104 or selecting a preferred set of variables 114” (para 0041) and “the user's decision is then logged in the memory 1204, where it can be later applied to help build the user's profile 504 for future recommendations”) (para 0075)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to build a profile of a user that can predict the type of transportation the traveler may want to take based on their personality; see Hashem at least at [0009];
Froeberg, in the same field of endeavor, teaches
determine a transportation risk level corresponding to each transportation option of the subset (“determining a candidate risk value for each of the candidate routes, wherein the one candidate route having the at least one portion traversable by the at least two different modes of transportation has a different candidate risk value corresponding to each of the at least two different modes of transportation” (claim 58));
provide, via an application running on the mobile computing device (Fig. 1A, #102 and #105), the subset of the plurality of transportation options ordered based on the transportation risk level (“the user may prioritize optimizing route safety with respect to other available route optimizations, such as optimizing for a shortest time of travel, a least distance, an avoidance of highways, and the like” (para 0026)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Froeberg in order to determine the safest route; see Froeberg at least at [0026].
Regarding claim 21, Gibson discloses and the combination of Froeberg and Hashem teaches the system of claim 13. Additionally, Gibson discloses wherein the instructions, when executed, further cause the processor to:
receive, via a geolocation unit of the mobile computing device, indications of a plurality of locations of the mobile computing device at a corresponding plurality of times (“the transportation matching system 102, via the server(s) 104, communicates with the provider client devices 110a-110n and the user client devices 114a-114n via the network 124 to determine locations of the provider client devices 110a-110n and the user client devices 114a-114n, respectively” (para 0036) and “the transportation matching system 102 identifies a location corresponding to a task based on travel history (e.g., by identifying that the first user 307a travels to a particular location at a time corresponding to past calendar events of a same or similar calendar event)” (para 0096));
However, Gibson does not explicitly teach
acquire, based on the plurality of locations and the plurality of times, environmental data associated with the plurality of locations; and
determine, based at least in part on the plurality of locations, the environmental data, and the plurality of times, the transportation profile associated with the user.
Hashem, in the same field of endeavor, teaches
acquire, based on the plurality of locations and the plurality of times, environmental data associated with the plurality of locations (“the intelligent calculator unit 512 may receive inputs from weather forecasting system feeds” (para 0064)); and
determine, based at least in part on the plurality of locations (“the calculator 512 performs calculations using the user profile and all the elements related to the users current location to obtain plan suggestions” (para 0068)), the environmental data, and the plurality of times (“The device generates a preference ranking for the following fitness, time, safety, cost, health, environmental, scenic, weather and mode of transport for the user. The device updates the user profile to include the preference rankings” (para 0009)), the transportation profile associated with the user (“The user preferences received at step 614 are organized and a review of a historical log data is conducted” (para 0073)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to build an intricate profile for the user; see Hashem at least at [para 0062]
Regarding claim 23, Gibson discloses and the combination of Froeberg and Hashem teaches the non-transitory computer-readable medium of claim 18. Additionally, Gibson discloses wherein the instructions further cause the processor to:
match the transportation profile with an additional transportation profile of an additional user;
identify a transportation option selected by the additional user (“transportation matching system 1102 may include one or more user-profile stores for storing user profiles. A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location” (para 0226) and “the transportation matching system 102 creates user groups of users who have indicated destinations in a similar direction relative to destinations indicated by other users within the mass-transit vehicle 402. Additionally, or alternatively, the transportation matching system 102 uses destinations indicated by travel histories for one or more of the users 407a-407e” (para 0126)); and
determine the subset of the plurality of transportation options based at least in part on the transportation option selected by the additional user (“provide the client device with a selectable option for requesting transport” (para 0005) and “the transportation matching system can create user groups based on one or more transit characteristics of the multiple users” (para 0026)).
Regarding claim 24, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. However, Gibson does not explicitly teach wherein the transportation profile further indicates environmental conditions corresponding to the plurality of transportation modes previously used by the user, the method further comprising:
determining, by the processor, current environmental condition associated with the first location and the current location; and
identifying, by the processor and from the transportation profile, a subset of the plurality of transportation modes associated with corresponding environmental conditions similar to the current environmental condition,
wherein the weighting factors are determined based on the subset of the plurality of transportation modes.
Hashem, in the same field of endeavor, teaches
determining, by the processor, current environmental condition associated with the first location and the current location (“the intelligent calculator unit 512 may receive inputs from weather forecasting system feeds” (para 0064)); and
identifying, by the processor and from the transportation profile, a subset of the plurality of transportation modes associated with corresponding environmental conditions similar to the current environmental condition (“the preferred mode of transport may be ordered based on secondary (or higher order) preferences such as scenic route preferences, points of interest along a route, weather” (para 0047)),
wherein the weighting factors are determined based on the subset of the plurality of transportation modes (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to build a profile of a user that can predict the type of transportation the traveler may want to take based on their personality; see Hashem at least at [0009].
Regarding claim 27, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. Additionally, Gibson discloses wherein each transportation mode of the one or more transportation modes is indicated by a respective unique icon on a graphical user interface (GUI) of the application (Fig. 6B, #616).
Regarding claim 28, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. However, Gibson does not explicitly teach wherein determining the subset of the plurality of transportation options comprises:
determining, by the processor, that a respective weighting factor of the one or more transportation modes of each transportation option of the subset meets or exceeds the threshold.
Hashem, in the same field of endeavor, teaches
wherein determining the subset of the plurality of transportation options comprises:
determining, by the processor, that a respective weighting factor of the one or more transportation modes of each transportation option of the subset meets or exceeds the threshold (“The device generates a preference ranking for the following … mode of transport for the user” (para 0009) and “The score may then be compared to predetermined threshold d values corresponding to various levels of interest in a given category” (para 0042)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson with the teachings of Hashem in order to generate a preference ranking for mode of transport for the user; see Hashem at least at [0009].
Claims 8 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2019/0206009 (hereinafter, "Gibson"; previously of record), in view of U.S. Pub. No. 2010/0036599 (hereinafter, "Froeberg"; previously of record), in view of U.S. Pub. No. 2015/0276419 (hereinafter, "Hashem"; newly of record) as applied to claim 1 above, and in further view of U.S. Pub. No. 2011/0213628 (hereinafter, "Peak"; previously of record).
Regarding claim 8, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. However, the combination of Gibson and Froeberg does not explicitly teach wherein the transportation risk level is based at least in part on loss data obtained from insurance claims submitted by customers of an insurance company.
Peak, in the same field of endeavor, teaches
wherein the transportation risk level is based at least in part on loss data obtained from insurance claims submitted by customers of an insurance company (“a process 300 may be performed to generate loss risk scores (including the Loss Risk Scores, the Trip Risk Scores, and/or the Vehicle or Person Risk Scores described above) that may be used in insurance processing. The process 300 may be performed on an as needed basis to assign loss risk scores to geographical regions (e.g., such as ZIP code areas, ZIP+5 areas, or more granular areas based on latitude and longitude). Processing begins at 302 where historical loss data are received for processing. Historical loss data may be obtained from a data source such as historical loss database 106 of FIG. 1. In some embodiments, the historical loss data may be data associated with a single insurer. For example, in situations where the system 100 is operated by or on behalf of a particular insurer, the historical loss data may be loss data accumulated by that insurer. In some embodiments, a group, association or affiliation of insurers may aggregate historical loss data to provide a more accurate loss risk score” (para 0066)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson and the teachings of Froeberg and Hashem with the teachings of Peak in order to determine a relative risk score, see Peak at least at [0035].
Regarding claim 12, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. However, the combination of Gibson and Froeberg does not explicitly teach wherein the subset of the plurality of transportation options are associated with types of insurance coverage or levels of insurance coverage related to transportation, the method further comprising:
providing, by the processor and via the application, at least one insurance purchase option enabling a user to purchase an insurance policy for a transportation option of the subset of the plurality of transportation options.
Peak, in the same field of endeavor, teaches
wherein the subset of the plurality of transportation options are associated with types of insurance coverage or levels of insurance coverage related to transportation, the method further comprising:
providing, by the processor and via the application (“install a mobile application that collects data about the driver's driving patterns. The data is collected by the mobile device and wirelessly transmitted to an insurance processing system for analysis. The insurance processing system may use the information to determine a relative risk score associated with the driver's driving patterns” (para 0035)), at least one insurance purchase option enabling a user to purchase an insurance policy for a transportation option of the subset of the plurality of transportation options (“the system forwards an offer for insurance to the mobile device 1330 or employee/agent terminal 1305 (at 1620). (para 0146), ("the factors and criteria used in conjunction with any given insurer or product will be selected and used in a manner that is in conformance with any applicable laws and regulations” (para 0064), and “The loss risk factors in the storage device 2030 might include, for example, road segment information, weather information, traffic information, a time of day, a day of week, litigation information, crime information, topographical information, governmental response information, a transportation mode, a vehicle type, and/or population density” (para 0185)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson and the teachings of Froeberg and Hashem with the teachings of Peak in order to insure and underwrite individuals, see Peak at least at [0031].
Claim 26 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2019/0206009 (hereinafter, "Gibson"; previously of record), in view of U.S. Pub. No. 2010/0036599 (hereinafter, "Froeberg"; previously of record), in view of U.S. Pub. No. 2015/0276419 (hereinafter, "Hashem"; newly of record) as applied to claim 1 above, and in further view of U.S. Pub. No. 2017/0213273 (hereinafter, "Dietrich"; newly of record).
Regarding claim 26, Gibson discloses and the combination of Froeberg and Hashem teaches the computer-implemented method of claim 1. However, Gibson does not explicitly teach wherein providing the subset of the plurality of transportation options comprises:
causing, by the processor, the subset of the plurality of transportation options to be displayed, in ranked order according to the respective transportation risk level, on a graphical user interface (GUI) of the application,
wherein the GUI indicates the respective transportation risk level.
Dietrich, in the same field of endeavor, teaches
wherein providing the subset of the plurality of transportation options comprises:
causing, by the processor, the subset of the plurality of transportation options to be displayed, in ranked order according to the respective transportation risk level, on a graphical user interface (GUI) of the application (“The user interfaces 50 allow a user to select a weighting of risk factors for analysis of transportation options through the analyzer module 30” (para 0033) and “The travel itinerary as output by the analyzer module 30 can define the modes of transport for the route, the order of the modes of transport, a route for each mode, start and end points for each mode, start time and end time for each mode, and duration of travel for each mode. Plural travel itineraries may be determined upon by the analyzer module 30 to allow for user selection of a preferred course” (para 0042)),
wherein the GUI indicates the respective transportation risk level (“The user interface 50 further includes time, cost and risk indicators” (para 0034)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Gibson and the teachings of Froeberg and Hashem with the teachings of Dietrich in order to analyze a plurality of different transportation options available to a user, based on criteria related to risk factors, see Dietrich at least at [0005].
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM ALHARBI whose telephone number is (313)446-6621. The examiner can normally be reached M-F 10am-6:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ADAM M ALHARBI/Primary Examiner, Art Unit 3663
Prosecution Insights