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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 February 19, 2026 has been entered.
Status of Claims
This Office Action is in response to the application filed on February 19, 2026. Claims 1, 6, 10, 13, 18, and 21 have been amended. Claims 14-15 are canceled, and claims 17, 19-20 were previously canceled. Claims 24-25 are newly added. Claims 1-13, 16, 18, and 21-25 are presently pending and are presented for examination.
Response to Amendments
In response to Applicant's Amendments dated February 19, 2026, Examiner withdraws the previous prior art rejections.
Response to Arguments
Applicant's arguments filed on February 19, 2026 have been fully considered, but they are moot in view of the new ground(s) of rejections.
Claim Rejections - 35 USC 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefore, subject to the conditions and requirements of this title.
Claims 1-13, 16, 18, and 21-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Independent claims 1, 13, and 18 are directed toward a computer-implemented method and claim 13 also recites a system. Therefore, independent claims 1, 13, and 18 along with the corresponding dependent claims 2-12, 16, and 21-25 are directed to a statutory category of invention under Step 1.
Under Step 2A and Step 2B, the independent claims are also directed to an abstract idea without significantly more. Specifically, the claims, under their broadest reasonable interpretation, cover certain mental processes and methods of organizing human activity. The language of independent claim 1 is used for illustration:
A method of generating a user-specific transportation recommendation, the method comprising:
Receiving, from a mobile computing device, an indication of a first location of an end point of a trip (a person may note a destination and current location);
Accessing a transportation profile associated with the mobile computing device, the profile indicating prior transportation modes used by the user (a person may recall prior transportation choices);
Determining weighting factors based on the transportation profile (a person may mentally assign preferences or likelihoods to transportation modes);
Identifying a subset of transportation options based on the weighting factors (a person may mentally select preferred travel options); and
Providing the subset ordered by transportation risk level (a person may rank options based on risk considerations).
These steps describe data collection, evaluation, and recommendation, all of which can be performed mentally or with pen and paper.
Under Step 2A, Prong One, independent claims 1, 13, and 18 recite a method and/or system. Other than generic computer components, such as a "processor" or "mobile computing device," nothing in the claims precludes the steps from being directed toward mental processes and organizational methods. Therefore, independent claims 1, 13, and 18 recite a judicial exception of an abstract idea.
Under Step 2A, Prong Two, the abstract idea is not integrated into a practical application. For example, the claims recite using a processor or mobile computing device to perform conventional functions (receiving, determining, and providing data). Merely executing the abstract idea on a generic computer does not provide a technical improvement or meaningful limitation to the abstract idea.
Under Step 2B, the claims do not include additional elements sufficient to provide "significantly more" than the abstract idea. Limiting the abstract idea to a specific environment or using generic computer functions does not supply an inventive concept. Therefore, independent claims 1, 13, and 18 are not patent eligible.
Dependent claims 2-12, 16, and 21-25 have been analyzed individually and in combination with their respective independent claims. These claims do not recite additional limitations that integrate the abstract idea into a practical application or amount to significantly more than the abstract idea. Accordingly, dependent claims 2-12, 16, and 21-25 are also patent ineligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to ATA 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 1-7, 9-11, 13, 16, 18, and 21-25 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. Pat. No. 11,015,952 (hereinafter, "Lyle"; 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 devicе;
determining, by the processor, and based on the transportation profile, weighting factors corresponding to the plurality of transportation modes,
wherein a weighting factor of the weighting factors is indicative of a respective frequency of use of a particular transportation mode of the plurality of transportation modes;
determining, by the processor, and based on the weighting factors, a subset of the plurality of transportation options, each transportation option of the subset being characterized by 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; and
providing, by the processor, and on a display associated with the mobile computing device, the subset of the plurality of transportation options ordered based on the transportation risk level corresponding to each transportation option of the subset.
Lyle, in the same field of endeavor, teaches
identifying, by the processor (“A server 102 is a computing device comprising a processor” (Col. 5, lines 43-44)), and based on the first location and the current location (“The server 102 may receive a request from a user device 108 operated by a user for a recommended mode of a transportation to travel for one location to another location” (Col. 6, lines 25-27)), a plurality of transportation options for travel between the current location and the first location (“determining one or more modes of transportation for a user to travel from one location to another location” (Col. 14, lines 4-6));
accessing, by the processor, a transportation profile (“a system may include a database storing user profile” (Col. 3, lines 4-5) and “a profile data of the user retrieved from a database” (Col. 3, lines 14-15)) associated with the mobile computing device (a system may include a database storing user profile a server. The server is configured to receive a request for a mode of transportation from a user device (Col. 3, lines 5-7) and Fig. 1, #104a), wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing devicе (“the server 102 based on data gathered from the user's previous travel patterns can determine whether the user prefers bikes, personal vehicles, or uses taxi often, and accordingly recommend a mode of transportation to travel from destination A to destination B” (Col. 5, lines 23-27));
determining, by the processor (Fig. 1, #102), and based on the transportation profile (“determine the one or more modes of transportation available to the user to travel from the start location to the destination location based on data in the user profile, the user profile data of the user may include one or more preferable modes of transportation of the user” (Col. 15, lines 27-32)), weighting factors corresponding to the plurality of transportation modes (“the server 102 may rank each of the available modes of transportation to travel from the start location to the destination location based at least in part on the one or more attributes” (Col. 10, lines 3-6)),
wherein a weighting factor of the weighting factors is indicative of a respective frequency of use of a particular transportation mode of the plurality of transportation modes (“determine whether it is usual for the user to be biking, using private car, or public transportation to travel” (Col. 5, lines 31-34), “the server may use a predetermined weighting factors or the user's preferences to identify the modes of transportation to travel from the start location to the destination location” (Col. 17, lines 49-52) , “on one or more user preferences related to past, current, and future route requests by the user and/or the user device, user preferences may include types of routes, preferred vehicle type”(Col. 18, lines 33-37));
determining, by the processor, and based on the weighting factors, a subset of the plurality of transportation options (“the server 102 may rank each of the available modes of transportation to travel from the start location to the destination location based at least in part on the one or more attributes” (Col. 10, lines 3-6) and “the server may use a predetermined weighting factors or the user's preferences to identify the modes of transportation to travel from the start location to the destination location” (Col. 17, lines 49-52)), each transportation option of the subset being characterized by one or more transportation modes of the plurality of transportation modes (“A server may determine each route represented by the first indicator 302a, 302b, and 302c of the plurality of first indicators 302 based on processing of geographical and/or topographical data between the location A and the location B. A server may dynamically adjust each route represented by the plurality of first indicators 302 based on traffic data received in real time. Each route represented by the plurality of first indicators 302 is further associated with at least one mode of transportation” (Col. 20, lines 15-24) and “the server may filter all available modes of transportation, and then display on the graphical user interface the modes of transportation having an optimal energy efficiency and an optimal cost to travel from destination A to destination B” (Col. 1, lines 55-59));
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 Lyle in order to filter all available modes of transportation to travel based on user profile, pre-defined criteria, and user preferences; see Lyle at least at [Col. 5, lines 10-20].
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 on a display associated with the mobile computing device (“displaying safety factors for user selection that are to be used in determining a safest route” (para 0008) and “The browser 138 may reside on a computing device 140 other than the computer 102 (e.g., the user's computing device) such as a conventional computer, a PDA, a cell phone, a wireless device or any electronic device and may be in communication with the safest routing software program 112” (para 0045)), the subset of the plurality of transportation options ordered based on the transportation risk level corresponding to each transportation option of the subset (“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 Lyle 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)).
Regarding claim 3, Gibson discloses and the combination of Froeberg and Lyle 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) and “the transportation matching system 102 determines an estimated transit time to the station based on scheduling information from the mass-transit system 122, or historical travel information from one or more users (e.g., average time taken for a plurality of users to travel on a train to the station at a particular time of day)” (para 0069)).
Regarding claim 4, Gibson discloses and the combination of Froeberg and Lyle 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));
one or more transportation modes including at least one of: a train (“the transportation matching system can determine a transit time for a user on a mass-transit vehicle to a station (e.g., an airport or train station)” (para 0021)), a ride-share vehicle, a taxi, a self-driving vehicle, a bicycle, or a walking route (“transportation vehicles as automobiles, such as cars, mopeds, shuttles, or sport utility vehicles, but a vehicle subsystem may use other transportation vehicles, such as a boat” (para 0050)); and
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 5, Gibson discloses and the combination of Froeberg and Lyle teaches the computer-implemented method of claim 4. 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
based at least on the one or more transportation modes and the transportation environment associated with the transportation option.
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 (“risk values were expressed using a casualty cost per unit distance measure, i.e., fatalities per million miles. A "casualty cost," as used herein, may be a cost generally indicating an amount of casualty to people and/or property, such as fatalities, injury, accident or loss” (para 0030)), and
based at least on the one or more transportation modes and the transportation environment associated with the transportation option (“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 6, Gibson discloses and the combination of Froeberg and Lyle teaches the computer-implemented method of claim 1. Additionally, Gibson discloses further comprising:
receiving, by the processor and from the mobile computing device, input indicating an unwanted transportation scenario comprising a particular transportation mode or a particular transportation environment (“ the transportation matching system 102 excludes transportation vehicles that—if selected—would wait more than the threshold wait time” (para 0163) and “providing a selectable option for requesting transport. As indicated by the arrow associated with the act 216, the transportation matching system 102, via the server(s) 104, provides the selectable option to the user client device 114 a. The user client device 114 a then performs the act 218 of presenting the selectable option for requesting transport” (para 0071));
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)); and
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)).
Regarding claim 7, Gibson discloses and the combination of Froeberg and Lyle teaches the computer-implemented method of claim 1. However, Gibson does not explicitly teach 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
accessing, by the processor, data related to an accident history associated with the user (“an accident risk factor based on at least one of map data and statistical data; one or more personal safety preferences of the user; one or more personal convenience preferences of the user; a profile of a traveler including at least one of a traveler age, a traveler experience with each of the at least two different modes of transportation, or an indication of an accessibility restriction of the traveler; or one or more legal regulations associated with the each at least one candidate route” (claim 61) and “Parameters of the traveler profile 312 may be obtained via a priori or real-time user input (e.g., via block 332 of FIG. 3), or default parameters for the traveler profile 312 may be provided” (para 0089)); 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 Lyle 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 Lyle teaches the computer-implemented method of claim 1. Additionally, Gibson discloses further comprising:
receiving, by the processor, and from the mobile computing device, a selection of a particular transportation option from the subset of the plurality of transportation options (“receiving an indication of the selection, the transportation matching system 102, via the server(s) 104, performs the act 222 of selecting one or more transportation vehicles. To select a transportation vehicle” (para 0079));
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 on the display; 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 Lyle teaches the computer-implemented method of claim 10. Additionally, Gibson discloses further comprising:
updating, by the processor, and based on the selection, the transportation profile associated with the user (“the transportation matching system relies on a user's travel history to identify a station at which the user previously used or exited a mass-transit vehicle” (para 0022), “ server(s) 104 can store a user's travel history within the transportation matching database 106” (para 0068) and “ analyzes one or more of (i) travel history, (ii) sensory data, or (iii) scheduling information from a mass-transit system to determine a probability that the user 118a utilizes a transportation vehicle departing from a station” (para 0074)); and
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 (“the transportation matching system relies on a user's travel history to identify a station at which the user previously used or exited a mass-transit vehicle” (para 0022), “ server(s) 104 can store a user's travel history within the transportation matching database 106” (para 0068) and “ analyzes one or more of (i) travel history, (ii) sensory data, or (iii) scheduling information from a mass-transit system to determine a probability that the user 118a utilizes a transportation vehicle departing from a station” (para 0074)).
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 weighting factor of the weighting factors is indicative of a respective frequency of use of a particular transportation mode of the plurality of transportation modes;
determine, based on the weighting factors, a subset of the plurality of transportation options, each transportation option off the subset being characterized by one or more transportation modes of the plurality of transportation modes;
determine a transportation risk level corresponding to each transportation option of the subset ; and
provide, on a display associated with the mobile computing device, the subset of the plurality of transportation options ordered based on the transportation risk level corresponding to each transportation option of the subset.
Lyle, in the same field of endeavor, teaches
identify, based on the first location and the current location (“The server 102 may receive a request from a user device 108 operated by a user for a recommended mode of a transportation to travel for one location to another location” (Col. 6, lines 25-27)), a plurality of transportation options for travel between the current location and the first location (“determining one or more modes of transportation for a user to travel from one location to another location” (Col. 14, lines 4-6));
access a transportation profile (“a system may include a database storing user profile” (Col. 3, lines 4-5) and “a profile data of the user retrieved from a database” (Col. 3, lines 14-15)) associated with the mobile computing device (a system may include a database storing user profile a server. The server is configured to receive a request for a mode of transportation from a user device (Col. 3, lines 5-7) and Fig. 1, #104a), wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device (“the server 102 based on data gathered from the user's previous travel patterns can determine whether the user prefers bikes, personal vehicles, or uses taxi often, and accordingly recommend a mode of transportation to travel from destination A to destination B” (Col. 5, lines 23-27));
determine, based on the transportation profile (“determine the one or more modes of transportation available to the user to travel from the start location to the destination location based on data in the user profile, the user profile data of the user may include one or more preferable modes of transportation of the user” (Col. 15, lines 27-32)), weighting factors corresponding to the plurality of transportation modes (“the server 102 may rank each of the available modes of transportation to travel from the start location to the destination location based at least in part on the one or more attributes” (Col. 10, lines 3-6)),
wherein a weighting factor of the weighting factors is indicative of a respective frequency of use of a particular transportation mode of the plurality of transportation modes (“determine whether it is usual for the user to be biking, using private car, or public transportation to travel” (Col. 5, lines 31-34), “the server may use a predetermined weighting factors or the user's preferences to identify the modes of transportation to travel from the start location to the destination location” (Col. 17, lines 49-52) , “on one or more user preferences related to past, current, and future route requests by the user and/or the user device, user preferences may include types of routes, preferred vehicle type”(Col. 18, lines 33-37));
determine, based on the weighting factors, a subset of the plurality of transportation options (“the server 102 may rank each of the available modes of transportation to travel from the start location to the destination location based at least in part on the one or more attributes” (Col. 10, lines 3-6) and “the server may use a predetermined weighting factors or the user's preferences to identify the modes of transportation to travel from the start location to the destination location” (Col. 17, lines 49-52)), each transportation option off the subset being characterized by one or more transportation modes of the plurality of transportation modes (“A server may determine each route represented by the first indicator 302a, 302b, and 302c of the plurality of first indicators 302 based on processing of geographical and/or topographical data between the location A and the location B. A server may dynamically adjust each route represented by the plurality of first indicators 302 based on traffic data received in real time. Each route represented by the plurality of first indicators 302 is further associated with at least one mode of transportation” (Col. 20, lines 15-24) and “the server may filter all available modes of transportation, and then display on the graphical user interface the modes of transportation having an optimal energy efficiency and an optimal cost to travel from destination A to destination B” (Col. 1, lines 55-59));
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 Lyle in order to filter all available modes of transportation to travel based on user profile, pre-defined criteria, and user preferences; see Lyle at least at [Col. 5, lines 10-20].
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)); and
provide, on a display associated with the mobile computing device (“displaying safety factors for user selection that are to be used in determining a safest route” (para 0008) and “The browser 138 may reside on a computing device 140 other than the computer 102 (e.g., the user's computing device) such as a conventional computer, a PDA, a cell phone, a wireless device or any electronic device and may be in communication with the safest routing software program 112” (para 0045)), the subset of the plurality of transportation options ordered based on the transportation risk level corresponding to each transportation option of the subset (“ 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 Lyle 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));
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 transportation matching system 102 optionally changes a selection of a transportation vehicle or a pickup location based on additional inputs from a user client device. For example, in some embodiments, the user client device 308a sends updated sensory data to the transportation matching system 102 indicating that the first user 307a has changed locations within the mass-transit vehicle 302. Additionally, or alternatively, the first user client device 308a sends a destination (or an updated destination) to the transportation matching system 102 as part of a transportation request (or an updated transportation request)” (para 0114));
However, Gibson does not explicitly teach
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 weighting factor of the weighting factors is indicative of a respective frequency of use of a particular transportation mode of the plurality of transportation modes;
determine, based on the weighting factors, a subset of the plurality of transportation options, each transportation option of the subset being characterized by one or more transportation modes of the plurality of transportation modes;
determine a transportation risk level corresponding to each transportation option of the subset; and
provide, on a display associated with the mobile computing device, the subset of the plurality of transportation options ordered based on the transportation risk level corresponding to each transportation option of the subset.
Lyle, in the same field of endeavor, teaches
access a transportation profile (“a system may include a database storing user profile” (Col. 3, lines 4-5) and “a profile data of the user retrieved from a database” (Col. 3, lines 14-15)) associated with the mobile computing device (a system may include a database storing user profile a server. The server is configured to receive a request for a mode of transportation from a user device (Col. 3, lines 5-7) and Fig. 1, #104a), wherein the transportation profile indicates a plurality of transportation modes previously used by a user of the mobile computing device (“the server 102 based on data gathered from the user's previous travel patterns can determine whether the user prefers bikes, personal vehicles, or uses taxi often, and accordingly recommend a mode of transportation to travel from destination A to destination B” (Col. 5, lines 23-27));
determine, based on the transportation profile (“determine the one or more modes of transportation available to the user to travel from the start location to the destination location based on data in the user profile, the user profile data of the user may include one or more preferable modes of transportation of the user” (Col. 15, lines 27-32)), weighting factors corresponding to the plurality of transportation modes (“the server 102 may rank each of the available modes of transportation to travel from the start location to the destination location based at least in part on the one or more attributes” (Col. 10, lines 3-6));
wherein a weighting factor of the weighting factors is indicative of a respective frequency of use of a particular transportation mode of the plurality of transportation modes (“determine whether it is usual for the user to be biking, using private car, or public transportation to travel” (Col. 5, lines 31-34), “the server may use a predetermined weighting factors or the user's preferences to identify the modes of transportation to travel from the start location to the destination location” (Col. 17, lines 49-52) , “on one or more user preferences related to past, current, and future route requests by the user and/or the user device, user preferences may include types of routes, preferred vehicle type”(Col. 18, lines 33-37));
determine, based on the weighting factors, a subset of the plurality of transportation options (“the server 102 may rank each of the available modes of transportation to travel from the start location to the destination location based at least in part on the one or more attributes” (Col. 10, lines 3-6) and “the server may use a predetermined weighting factors or the user's preferences to identify the modes of transportation to travel from the start location to the destination location” (Col. 17, lines 49-52)), each transportation option of the subset being characterized by one or more transportation modes of the plurality of transportation modes (“A server may determine each route represented by the first indicator 302a, 302b, and 302c of the plurality of first indicators 302 based on processing of geographical and/or topographical data between the location A and the location B. A server may dynamically adjust each route represented by the plurality of first indicators 302 based on traffic data received in real time. Each route represented by the plurality of first indicators 302 is further associated with at least one mode of transportation” (Col. 20, lines 15-24) and “the server may filter all available modes of transportation, and then display on the graphical user interface the modes of transportation having an optimal energy efficiency and an optimal cost to travel from destination A to destination B” (Col. 1, lines 55-59));
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 Lyle in order to filter all available modes of transportation to travel based on user profile, pre-defined criteria, and user preferences; see Lyle at least at [Col. 5, lines 10-20].
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)); and
provide, on a display associated with the mobile computing device (“ displaying safety factors for user selection that are to be used in determining a safest route are disclosed in detail” (para 0008)), the subset of the plurality of transportation options ordered based on the transportation risk level corresponding to each transportation option of the subset (“ 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 Lyle 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
determining, 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.
Lyle, 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 server 102 receive the weather data from external devices and data sources” (Col. 8, lines 42-44) and “The server may also use one or more attributes including, but not limited to, weather condition of the start location of the user, weather condition of the destination location, and weather condition of locations between the start location and the destination location” (Col. 17, lines 6-10)); and
determining, based at least in part on the plurality of locations (“The server may also use one or more attributes including, but not limited to, weather condition of the start location of the user, weather condition of the destination location, and weather condition of locations between the start location and the destination location” (Col. 17, lines 6-10), the environmental data, and the plurality of times, the transportation profile associated with the user (“The server 102 may store the weather data in the database” (Col. 8, lines 52-53) and “a user may not desire to walk or ride a bicycle when the temperature is below a predetermined threshold or when chance of precipitation is higher than a predetermined threshold. Therefore, when one or more of these thresholds are met, the server may no longer display walking or riding a bicycle as modes of transportation” (Col. 18, lines 1-7)).
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 Lyle in order to filter all available modes of transportation to travel based on weather data; see Lyle at least at [Col. 18, lines 1-7].
Regarding claim 22, Gibson discloses and the combination of Froeberg and Lyle teaches the non-transitory computer-readable medium of claim 18. Additionally, Gibson discloses wherein the transportation profile is determined based at least in part on location data received from a geolocation unit of the mobile computing device at a plurality of times (“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 can use average user traveling speed with location data. For instance, in some such embodiments, the transportation matching system 102 tracks a location and a transit time of the first user 307a from a mass-transit vehicle to a pickup location. The transportation matching system 102 stores such transit data as part of the travel history of the first user 307a. Based on the location data and transit times” (para 0108)).
Regarding claim 23, Gibson discloses and the combination of Froeberg and Lyle 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 Lyle 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.
Lyle, in the same field of endeavor, teaches
determining, by the processor, current environmental condition associated with the first location and the current location (“The server 102 receive the weather data from external devices and data sources” (Col. 8, lines 42-44) and “The server may also use one or more attributes including, but not limited to, weather condition of the start location of the user, weather condition of the destination location, and weather condition of locations between the start location and the destination location” (Col. 17, lines 6-10)); and
identifying, by the processor and from the transportation profile (“determine the one or more modes of transportation available to the user to travel from the start location to the destination location based on data in the user profile, the user profile data of the user may include one or more preferable modes of transportation of the user” (Col. 15, lines 27-32)), a subset of the plurality of transportation modes associated with corresponding environmental conditions similar to the current environmental condition (“the server may filter all available modes of transportation, and then display on the graphical user interface the modes of transportation having an optimal energy efficiency and an optimal cost to travel from destination A to destination B” (Col. 1, lines 55-59) and “determine whether it is usual for the user to be biking, using private car, or public transportation to travel” (Col. 5, lines 31-34), and “a mode of transportation to travel from the start location to the destination location may depend on one or more other variables, such as weather condition (e.g., temperature, precipitation, and the like)” (Col. 16, lines 8-11)),
wherein the weighting factors are determined based on the subset of the plurality of transportation modes (“when the measured temperature satisfies a predetermined threshold, the server may assign a higher weight to the temperature” (Col. 16, lines 23-34) and “the server may filter all available modes of transportation, and then display on the graphical user interface the modes of transportation having an optimal energy efficiency and an optimal cost to travel from destination A to destination B” (Col. 1, lines 55-59)).
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 Lyle in order to filter all available modes of transportation to travel based on weather data; see Lyle at least at [Col. 18, lines 1-7].
Regarding claim 25, Gibson discloses and the combination of Froeberg and Lyle teaches the computer-implemented method of claim 18. However, Gibson does not explicitly teach wherein the instructions further cause the processor to:
determine a current environmental condition associated with the first location or the current location,
wherein the transportation risk level is further based on the current environmental condition.
Lyle, in the same field of endeavor, teaches
determine a current environmental condition associated with the first location or the current location (“The server 102 receive the weather data from external devices and data sources” (Col. 8, lines 42-44) and “The server may also use one or more attributes including, but not limited to, weather condition of the start location of the user, weather condition of the destination location, and weather condition of locations between the start location and the destination location” (Col. 17, lines 6-10)),
wherein the transportation risk level is further based on the current environmental condition (“The weather data determined by the server 102 may further provide an indication of natural disasters such as avalanches, earthquakes, floods, tsunamis, volcanic eruptions, landslides, and mudslides at each of the one or more routes between the start location and the destination location” (Col. 8, lines 44-52)).
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 Lyle in order to filter all available modes of transportation to travel based on weather data; see Lyle at least at [Col. 18, lines 1-7].
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. Pat. No. 11,015,952 (hereinafter, "Lyle"; 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 Lyle 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 Lyle 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 Lyle 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 on the display, 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 on the display, 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 Lyle with the teachings of Peak in order to insure and underwrite individuals, see Peak at least at [0031].
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