DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a Non-Final Action on the Merits. Claims 1-20 are currently pending and are addressed below.
Response to Amendments
The amendment filed on May 21st, 2026 has been considered and entered. Accordingly, claim 12 has been amended.
Election/Restrictions
Applicant's election with traverse of Group I (Claims 1-11) in the reply filed on May 21st, 2026 in response to the Office Action dated on March 26th, 2026 is acknowledged. The traversal is on the ground(s) that the “Applicant respectfully disagrees with the Examiner’s characterization of the alleged features of the pending claims. This is not found persuasive because independent claim 1 requires a generation of a risk score based on historic transportation characteristics associated with a user and travel data, whereas amended independent claim 12 requires generating a risk score based on historic transportation characteristics associated with a first user, historic transportation characteristics associated with a second user, and geographic data and independent claim 17 requires a generation of a risk score based on historic transportation characteristics associated with a first user and historic transportation characteristics associated with a second user .
The requirement is still deemed proper and is therefore made FINAL.
Applicant’s election without traverse of Species i in Group A (claim 3) in the phone call with Attorney Chris Beglinger on June 24th, 2026 is acknowledged. Claim 4 has been withdrawn from consideration and claim 3 is currently examined below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on September 9th, 2025 has been considered and entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3 and 5-11 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
In sum, claims 1-3 and 5-11 are rejected under 35 U.S.C. §101 because the claimed invention is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and do not include an inventive concept that is something “significantly more” than the judicial exception under the January 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process. Therefore, we proceed to step 2A, Prong 1.
Revised Guidance Step 2A – Prong 1
Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Here, with respect to independent claim 1, the claim recites the abstract idea of determining risk scores for a plurality of routes, and mentally determine “determining, using the first geographic location and the second geographic location, a plurality of routes between the first geographic location and the second geographic location … generating, using historic transportation characteristics associated with the user and the travel data, a risk score for each of the plurality of routes, the risk score indicating an estimated risk of traveling along the route”, where these claims fall within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, specifically, a mental process, that can be performed in the human mind since each of the above steps could alternatively be performed in the human mind or with the aid of pen and paper. This conclusion follows from CyberSource Corp. v. Retail Decisions, Inc., where our reviewing court held that section 101 did not embrace a process defined simply as using a computer to perform a series of mental steps that people, aware of each step, can and regularly do perform in their heads. 654 F.3d 1366, 1373 (Fed. Cir. 2011); see also In re Grams, 888 F.2d 835, 840–41 (Fed. Cir. 1989); In re Meyer, 688 F.2d 789, 794–95 (CCPA 1982); Elec. Power Group, LLC v. Alstom S.A., 830 F. 3d 1350, 1354–1354 (Fed. Cir. 2016) (“we have treated analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, as essentially mental processes within the abstract-idea category”).
Additionally, mental processes remain unpatentable even when automated to reduce the burden on the user of what once could have been done with pen and paper. See CyberSource, 654 F.3d at 1375 (“That purely mental processes can be unpatentable, even when performed by a computer, was precisely the holding of the Supreme Court in Gottschalk v. Benson.”). These limitations, as drafted, are a simple process that under their broadest reasonable interpretation, covers the performance of the limitations of the mind. For example, the claim limitation encompasses mentally determining risk scores for a plurality of routes based off of the information provided by the car’s sensors while traveling, or alternatively, mentally determining risk scores for a plurality of routes based on observations by a human.
For example, a human could mentally and with the aid of pen and paper determine risk scores for a plurality of routes.
Revised Guidance Step 2A – Prong 2
Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which the claim is directed does not include limitations that integrate the abstract idea into a practical application, since the additional elements of a vehicle, a processor, and memory are merely generic components used as a tool (“apply it”) to implement the abstract idea. (See, e.g., MPEP §2106.05(f)). See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”)
In addition, the limitation “receiving a transportation request associated with a user; identifying, using the transportation request, a first geographic location and a second geographic location associated with the transportation request … receiving travel data associated with each of the plurality of routes, the travel data indicating at least one of a current or predicted future travel condition along each of the plurality of routes” constitutes insignificant presolution activity that merely gathers data and, therefore, do not integrate the exception into a practical application. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff' d on other grounds, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity); see also CyberSource, 654 F.3d at 1371–72 (noting that even if some physical steps are required to obtain information from a database (e.g., entering a query via a keyboard, clicking a mouse), such data-gathering steps cannot alone confer patentability); OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering). Accord Guidance, 84 Fed. Reg. at 55 (citing MPEP § 2106.05(g)).
Furthermore, the limitation “generating a user interface providing one or more indicators associated with the risk scores of one or more of the plurality of routes” is insignificant post-solution activity. The Supreme Court guides that the “prohibition against patenting abstract ideas ‘cannot be circumvented by attempting to limit the use of the formula to a particular technological environment' or [by] adding ‘insignificant postsolution activity.' ” Bilski, 561 U.S. at 610–11 (quoting Diehr, 450 U.S. at 191–92).
Generating a user interface to provide information of a plurality of routes is mere insignificant extra-solution activity, as supported by the MPEP 2106.05(g), see printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
In addition, merely “[u]sing a computer to accelerate an ineligible mental process does not make that process patent-eligible.” Bancorp Servs., L.L.C. v. Sun Life Assur. Co. of Canada (U.S.), 687 F.3d 1266, 1279 (Fed. Cir. 2012); see also CLS Bank Int’l v. Alice Corp. Pty. Ltd., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) (“simply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.”), aff’d, 573 U.S. 208 (2014). Accordingly, the additional element of a processor does not transform the abstract idea into a practical application of the abstract idea.
Revised Guidance Step 2B
Under the 2019 PEG step 2B analysis, the additional elements are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the additional elements, such as: a processor and a memory does not amount to an innovative concept since, as stated above in the step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming. (See, e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality to simply implement the abstract idea and are not themselves being technologically improved. (See, e.g., MPEP §2106.05 I.A.). See Alice, 573 U.S. at 223 (“[T]he mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). Thus, these elements, taken individually or together, do not amount to “significantly more” than the abstract ideas themselves.
The additional elements of the dependent claims 2-3 and 5-11 merely refine and further limit the abstract idea of the independent claims and do not add any feature that is an “inventive concept” which cures the deficiencies of their respective parent claim under the 2019 PEG analysis. None of the dependent claims considered individually, including their respective limitations, include an “inventive concept” of some additional element or combination of elements sufficient to ensure that the claims in practice amount to something “significantly more” than patent-ineligible subject matter to which the claims are directed.
The elements of the instant claimed invention, when taken in combination do not offer substantially more than the sum of the functions of the elements when each is taken alone. The claims as a whole, do not amount to significantly more than the abstract idea itself because the claims do not effect an improvement to another technology or technical field; the claims do not amount to an improvement to the functioning of an electronic device itself which implements the abstract idea (e.g., the general purpose computer and/or the computer system which implements the process are not made more efficient or technologically improved); the claims do not perform a transformation or reduction of a particular article to a different state or thing (i.e., the claims do not use the abstract idea in the claimed process to bring about a physical change. See, e.g., Diamond v. Diehr, 450 U.S. 175 (1981), where a physical change, and thus patentability, was imparted by the claimed process; contrast, Parker v. Flook, 437 U.S. 584 (1978), where a physical change, and thus patentability, was not imparted by the claimed process); and the claims do not move beyond a general link of the use of the abstract idea to a particular technological environment (e.g., “for generating a transportation recommendation. . . processors” claim 1).
Accordingly, claims 1-3 and 5-11 are rejected under 35 USC 101 as being drawn to an abstract idea without significantly more, and thus are ineligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-3, 6, 5-8, and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Kislovskiy (US 20180340790 A1) (“Kislovskiy”) in view of Ramirez (US 20230306457 A1) (“Ramirez”).
With respect to claim 1, Kislovskiy teaches a transportation system for generating a transportation recommendation, the system comprising: one or more memory devices having instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving a transportation request associated with a user; identifying, using the transportation request, a first geographic location and a second geographic location associated with the transportation request; determining, using the first geographic location and the second geographic location, a plurality of routes between the first geographic location and the second geographic location (See at least Kislovskiy FIG. 8 and Paragraph 129 “According to various examples, the transport management system can receive transport requests from requesting users (805). The transport requests can include a pick-up location (807) and a destination (809). In certain implementations, the transport management system can determine one or more optimal routes for the transport request (810). For example, the transport management system can identify a set of routes, and select a route that has the lowest estimated time to destination based on such factors as current traffic conditions, projected traffic conditions, and distance. In variations, the transport management system can first determine the aggregate risk values for each route prior to selecting a most optimal route for the trip based partially on risk.”);
receiving travel data associated with each of the plurality of routes, the travel data indicating at least one of a current or predicted future travel condition along each of the plurality of routes (See at least Kislovskiy FIGS. 7-8 and Paragraph 130 “The transport management system can determine a risk quantity for each of the one or more optimal routes (815). According to various examples, the transport management system can determine the aggregate risk quantity through coordination with the risk regression system described with respect to FIG. 7, and represented by reference “A” in FIG. 8. Thus, the risk quantity determined at step (815) can be based on historical fractional harmful event data, a current set of conditions, and the aggregated fractional risk values as determined by the risk regression system. The transport management system may classify the trip based on the aggregated risk quantity and a set of risk thresholds (820). In classifying the trip, the transport management system ultimately determines which vehicle types (822) executing which software version (if any) are certified to service the transport request (824). Detailed description of the software version precertification and verification is provided below with respect to FIG. 9, and is represented by reference “B” in both FIGS. 8 and 9. In particular, each software version and vehicle type may be associated with a risk threshold. In further implementations, the use of an unverified software version can be attributed to two distinct risk thresholds—a first risk threshold for including the trip in its verification mileage set, and a second risk threshold for excluding the trip from its verification set.”);
generating, using historic transportation characteristics associated with the user and the travel data, a risk score for each of the plurality of routes, the risk score indicating an estimated risk of traveling along the route (See at least Kislovskiy FIGS. 7-8 and Paragraph 84 “For a driver, the risk regressor 330 can determine the individual risk value 333 for the driver based on, for example, how long the driver has been on-duty and the current and/or historical driving characteristics of the driver (e.g., aggressive, fast, slow, gentle, normal). In determining the current or historical driving characteristics of the driver, the on-demand transport management system 300 can receive accelerometer data or inertial measurement unit (IMU) data (e.g., gyroscope data, magnetometer data, and accelerometer data) from the driver's vehicle or the driver's computing device 385 (e.g., via access to the device 385 through the driver app 386). The accelerometer or IMU data can indicate hard braking, steering, and acceleration events that the risk regressor 330 can generalize into the driver's driving style and weigh into the driver's individual risk score 333. In addition or alternatively, the on-demand transport management system can further receive GPS data, image or video data, and/or audio data from a microphone of the driver device 385 or vehicle hardware to determine the individual risk value 333.” | Paragraph 122 “The risk regression system can further collect sensor data from human-driven vehicles (non-autonomous vehicles) (704). For example, the risk regression system can receive IMU data or accelerometer data from the driver's computing device 600. In certain implementations, the risk regression system can time and/or location correlate the log data and/or sensor data with a current set of conditions (705). For example, the data may be correlated to environmental conditions (706), path conditions (707), path geometry and/or complexity (708), vehicle hardware (709), and/or traffic conditions (710). Such correlations allow for the risk regression system to provide condition-dependent risk calculations for any given path segment of a given road network (e.g., an autonomy grid 105 on which AVs operate), which can be leveraged to assess current risk quantities for those path segments at any time and in any current set of conditions. In certain aspects, the risk regression system can further correlate the log data or sensor data to a static set of risk parameters corresponding to nominal environmental conditions and nominal path conditions (e.g., a dry road). In certain implementations, the risk regression system can further collect historical harmful event data from any number of third party resources (e.g., traffic accident or collision report data).” | Paragraph 130 “The transport management system can determine a risk quantity for each of the one or more optimal routes (815). According to various examples, the transport management system can determine the aggregate risk quantity through coordination with the risk regression system described with respect to FIG. 7, and represented by reference “A” in FIG. 8. Thus, the risk quantity determined at step (815) can be based on historical fractional harmful event data, a current set of conditions, and the aggregated fractional risk values as determined by the risk regression system. The transport management system may classify the trip based on the aggregated risk quantity and a set of risk thresholds (820). In classifying the trip, the transport management system ultimately determines which vehicle types (822) executing which software version (if any) are certified to service the transport request (824). Detailed description of the software version precertification and verification is provided below with respect to FIG. 9, and is represented by reference “B” in both FIGS. 8 and 9. In particular, each software version and vehicle type may be associated with a risk threshold. In further implementations, the use of an unverified software version can be attributed to two distinct risk thresholds—a first risk threshold for including the trip in its verification mileage set, and a second risk threshold for excluding the trip from its verification set.”);
Kislovskiy, however, fails to explicitly disclose generating a user interface providing one or more indicators associated with the risk scores of one or more of the plurality of routes.
Ramirez teaches generating a user interface providing one or more indicators associated with the risk scores of one or more of the plurality of routes (See at least Ramirez FIGS. 16-23 and Paragraph 191 “FIGS. 16-23 show illustrative user interfaces displaying information corresponding to a determined safest route between the first location and the second location system according to one or more aspects described herein. FIGS. 16 and 17 display illustrative user interface screens for bookmarking a route (e.g., FIG. 16 ), where the driver can store a calculated route or road segment for future travel. FIG. 17 displays an illustrative overview screen that displays information regarding the driver and that this saved route is a route upon which the driver travels to work (e.g., work route). FIG. 18 shows an illustrative textual representation of a route. Such a route may also be indicated visually on a map or output as an audio representation of the route. FIG. 19 shows recently traveled trips (e.g., between 11:31 and 11:33 of today. The overview screen also indicates a total reward time earned and an indication that a safer route is available. FIGS. 20-22 show graphical representations of one or more routes on a map including the current route and the route that had been indicated as being safer. FIG. 23 shows an illustrative user interface screen that displays reasoning as to why the route was determined to be safer. In some cases, the road segment safety rating system 1416, 1436 and/or 1452 may be configured to generate directions for a return route, as shown in FIG. 24 . The return route may have a different safety rating than the original route. FIGS. 25 and 26 show illustrative user interface screens for notifying a user that the user had traveled the indicated safest route.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kislovskiy to include generating a user interface providing one or more indicators associated with the risk scores of one or more of the plurality of routes, as taught by Ramirez as disclosed above, in order to ensure an optimal route is provided to a user (Ramirez Paragraph 30 “In accordance with aspects of the invention, a new set of pricing tiers are disclosed herein for enabling safe driving and lower rates for insurance policy customers.”).
With respect to claim 2, Kislovskiy in view of Ramirez teach that the historic transportation characteristics associated with the user includes at least one of an average transportation speed, an average number of stops during a travel event, or an average travel time (See at least Kislovskiy Paragraph 84 “For a driver, the risk regressor 330 can determine the individual risk value 333 for the driver based on, for example, how long the driver has been on-duty and the current and/or historical driving characteristics of the driver (e.g., aggressive, fast, slow, gentle, normal). In determining the current or historical driving characteristics of the driver, the on-demand transport management system 300 can receive accelerometer data or inertial measurement unit (IMU) data (e.g., gyroscope data, magnetometer data, and accelerometer data) from the driver's vehicle or the driver's computing device 385 (e.g., via access to the device 385 through the driver app 386). The accelerometer or IMU data can indicate hard braking, steering, and acceleration events that the risk regressor 330 can generalize into the driver's driving style and weigh into the driver's individual risk score 333. In addition or alternatively, the on-demand transport management system can further receive GPS data, image or video data, and/or audio data from a microphone of the driver device 385 or vehicle hardware to determine the individual risk value 333.”).
With respect to claim 3, Kislovskiy in view of Ramirez teach that the current or predicted future travel conditions indicate at least one of a road density score indicating a number of crossroads along a route, a construction rating indicating one or more construction events along a route, or an obstruction rating indicating one or more obstruction events along a route (See at least Kislovskiy Paragraph 126 “In various examples, the risk regression system can receive transport route data for an on-demand transport request (720). Executing concurrently with the on-demand transport management system 300, the risk regression system can further receive on-demand transport requests, and, for each transport request, the risk regression system can determine one or more optimal routes between a pick-up location and destination of the transport request—denoted as reference “A” in FIG. 7. These one or more optimal routes can correspond to the transport route data received by the risk regression system. Thus, for each transport request and each route, the risk regression system can determine current conditions across the a set of possible routes for the transport request (725). In further examples, the risk regression system can predict a set of condition over the course of the trip (e.g., in general or along each route) (725). In various examples, the current or predicted conditions can include environmental conditions, weather conditions, whether the route involves road construction, road surface conditions, traffic conditions, any predicted or scheduled events, time of day, day of the week, and the like. The risk regression system may then execute a risk regression method using the fractional harmful event data—or conditions-based fractional risk values described herein—to determine an aggregate risk value for the route (730).”).
With respect to claim 5, Kislovskiy in view of Ramirez teach that the operations comprise: receiving travel data associated with the user, the travel data indicating a real-time travel characteristic associated with the user, the real-time travel characteristic including a current transportation speed of the user compared to an average transportation speed associated with the user; and wherein generating the risk scores for each of the plurality of routes includes generating the risk scores for each of the plurality of routes using the travel data associated with the user (See at least Ramirez Paragraph 51 “Likewise, the computing device 102 may also receive (in step 308) other information to enhance the accuracy of the risk value associated with a travel route. For example, the computing device 102 may receive (in step 306) the time of day when the driver is driving (or plans to drive) through a particular travel route. This information may improve the accuracy of the risk value retrieved (in step 310) for the travel route. For example, a particular segment of road through a wilderness area may have a higher rate of accidents involving deer during the night hours, but no accidents during the daylight hours. Therefore, the time of day may also be considered when retrieving the appropriate risk value (in step 310). In addition, the computing device may receive (in step 308) other information to improve the accuracy of the risk value retrieved (in step 310) for a travel route. Some examples of this other information include, but are not limited to, the vehicle's speed (e.g., a vehicle without a sport suspension attempting to take a dangerous curve at a high speed), vehicle's speed compared to the posted speed limit, vehicle's speed compared to typical or average speed, etc.”).
With respect to claim 6, Kislovskiy in view of Ramirez teach that the operations comprise: providing a recommended route of the plurality of routes, wherein the recommended route has a risk score indicating a lowest estimated risk of the estimated risks of traveling along each of the plurality of routes (See at least Kislovskiy Paragraphs 125-129 “In certain implementations, the risk regression system may also calculate a set of generalized fractional risk values for each path segment based on, for example, lane geometry, complexity (e.g., traffic signals and signs, intersecting lanes, bike lanes, crosswalks, blind turns, historical harmful events, etc.) (719). Accordingly, the risk regression system can also function to provide generalized aggregate risk quantities for any particular route given a current set of conditions. Such generalized aggregate risk quantities can be utilized to route AVs and HDVs along lower or lowest risk routes accordingly. In still further examples, the risk regression system can further determine the fractional risk quantity for each path segment based on off-vehicle replay of AV-logged data through new software, test track evaluation of the current system-under-test, actuarial statistics, and driving research publications … In some aspects, the risk regression system can transmit the aggregate risk value to the on-demand transport system to facilitation vehicle and/or route selection for the trip (735). In other aspects, the risk regression system can determine a most optimal route for based on the aggregate risk values, and determine whether to enable SDAVs and/or FAVs to service the transport request. For example, the risk regression system can execute concurrently with a trip classifier that enables SDAVs and FAVs to service any given transport request based on trip risk in accordance with a set of risk thresholds described herein. Once an SDAV, FAV, or HDV is selected to service the transport request, the risk regression system can actively monitor the trip to dynamically determine aggregate risk of a remainder of the trip, as described in detail below (740). In doing so, the risk regression system can monitor for changing environmental conditions (742) and changing traffic conditions (744) that may affect the fractional risk values … According to various examples, the transport management system can receive transport requests from requesting users (805). The transport requests can include a pick-up location (807) and a destination (809). In certain implementations, the transport management system can determine one or more optimal routes for the transport request (810). For example, the transport management system can identify a set of routes, and select a route that has the lowest estimated time to destination based on such factors as current traffic conditions, projected traffic conditions, and distance. In variations, the transport management system can first determine the aggregate risk values for each route prior to selecting a most optimal route for the trip based partially on risk.”).
With respect to claim 7, Kislovskiy in view of Ramirez teach that the operations further comprise: receiving user travel data including geolocation information of the user as the first travels between the first geographic location and the second geographic location; and comparing the user travel data with historical travel data associated with the plurality of routes to verify a recommended route (See at least Ramirez Paragraph 51 “Likewise, the computing device 102 may also receive (in step 308) other information to enhance the accuracy of the risk value associated with a travel route. For example, the computing device 102 may receive (in step 306) the time of day when the driver is driving (or plans to drive) through a particular travel route. This information may improve the accuracy of the risk value retrieved (in step 310) for the travel route. For example, a particular segment of road through a wilderness area may have a higher rate of accidents involving deer during the night hours, but no accidents during the daylight hours. Therefore, the time of day may also be considered when retrieving the appropriate risk value (in step 310). In addition, the computing device may receive (in step 308) other information to improve the accuracy of the risk value retrieved (in step 310) for a travel route. Some examples of this other information include, but are not limited to, the vehicle's speed (e.g., a vehicle without a sport suspension attempting to take a dangerous curve at a high speed), vehicle's speed compared to the posted speed limit, vehicle's speed compared to typical or average speed, etc.” | Paragraph 72 “In some embodiments, for rating purposes the route risk value may consider the driving information of the driver/vehicle. For example, the personal navigation device 110 (or other device) may record the route taken, as well as the time of day/month/year, weather conditions, traffic conditions, and the actual speed driven compared to the posted speed limit. The current weather and traffic conditions may be recorded from a data source 104, 106. Weather conditions and traffic conditions may be categorized to determine the risk type to apply. The posted speed limits may be included in the geographic information. For each segment of road with a different posted speed limit, the actual speed driven may be compared to the posted speed limit. The difference may be averaged over the entire distance of the route. In addition, various techniques may be used to handle the amount of time stopped in traffic, at traffic lights, etc. One illustrative technique may be to only count the amount of time spent driving over the speed limit and determine the average speed over the speed limit during that time. Another illustrative method may be to exclude from the total amount of time the portion where the vehicle is not moving. Then, upon completion of the trip, the route risk value may be calculated and stored in memory along with the other information related to the route risk score and mileage traveled. This information may later be transmitted to an insurance company's data store, as was described above.”).
With respect to claim 8, Kislovskiy in view of Ramirez teach providing, using the verification of the recommended route, the user interface including at least one insurance policy parameter associated with opting to travel along the recommended route (See at least Ramirez Paragraph 142 “In step 1104, one or more insurance incentives to offer to the user may be generated. Insurance incentives may include a lower premium, a discount on a premium, an amount of cash reward, or the like. In step 1106, the insurance incentive may be transmitted to the user. For instance, the generated one or more incentives may be transmitted to a computing device of a user and displayed therein.”).
With respect to claim 10, Kislovskiy in view of Ramirez teach that generating the user interface includes providing the risk scores for each of the plurality of routes (See at least Ramirez Paragraph 68 “The driver may be presented with an alternate route which is less risky than the initial route calculated, as will be discussed more fully herein. The personal navigation device 110 may display the difference in risk between the alternate routes and permit the driver to select the preferred route.”).
With respect to claim 11, Kislovskiy in view of Ramirez teach receiving a selection of a route from the plurality of routes; and providing, using the selected route, a recommended route via the user interface (See at least Ramirez Paragraph 138 “FIG. 10B illustrates example user interface 1050. Interface 1050 may be displayed upon determining that the road segment safety rating for the current road segment is below the predetermined threshold. Interface 1050 includes region 1052 in which the current road segment safety rating is provided to the user. In addition, interface 1050 further includes region 1054 in which one or more alternate routes or roads are provided to the user. The user may select one of the options provided and the system may automatically generate navigation directions (or turn-by-turn directions) to transition to the selected route.”).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Kislovskiy (US 20180340790 A1) (“Kislovskiy”) in view of Ramirez (US 20230306457 A1) (“Ramirez”) further in view of Wray (US 20220276653 A1) (“Wray”).
With respect to claim 9, Kislovskiy in view of Ramirez fails to explicitly disclose receiving a user preference indicating a relative preference between travel characteristics associated with traveling between the first geographic location and the second geographic location; and selecting a recommended route from the plurality of routes using the user preference.
Wray, however, teaches receiving a user preference indicating a relative preference between travel characteristics associated with traveling between the first geographic location and the second geographic location; and selecting a recommended route from the plurality of routes using the user preference (See at least Wray Paragraph 71 “To illustrate, and without loss of generality, the user may indicate a preference for slow lanes. As such, the route planner can integrate a “comfort” objective into its calculation of how to get to the destination. In another example, the user can additionally, or alternatively indicate a preference for lanes that minimize energy consumption. For example, if traffic on some lane segments is stop-and-go, which tend to require more energy consumption, then the route planner may prefer other road segments to them. As another example, a smooth road would be preferred by the route planner over roads with many ups and downs because such roads tend to result in more energy consumption. In another example, a road with many charging stations may be preferred over another road with sparse charging stations.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Kislovskiy in view of Ramirez to include receiving a user preference indicating a relative preference between travel characteristics associated with traveling between the first geographic location and the second geographic location; and selecting a recommended route from the plurality of routes using the user preference, as taught by Wray as disclosed above, in order to ensure optimal route selection (Wray Paragraph 28 “Learning with multi-objectives can be advantageous over single-objective learning at least because more concerns can be considered by the lane-level route planner, which can make automated driving more adaptable than single objective planners. When multiple objectives are possible, the lane-level route planner can learn (or can be taught by a user) about good (e.g., desirable, comfortable, etc.) routes, traffic patterns, user preferences, competence, and so on. For example, the user can directly encode a preference in an exact corresponding objective”).
Conclusion
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/IBRAHIM ABDOALATIF ALSOMAIRY/Examiner, Art Unit 3667
/KENNETH J MALKOWSKI/Primary Examiner, Art Unit 3667
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