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 .
Application Status
This Final action is in response to applicant’s amendments of 12/17/2025. Claims 1-20 are examined and pending. Claims 1, 8-9,18, and 20 are currently amended.
Response to Arguments
Applicant’s amendments, with respect to the claim objection as set forth in the Office Action have been fully considered and are persuasive. As such, the objection has been withdrawn.
Applicant’s arguments with respect to the rejection under 35 U.S.C. § 103 have been fully considered but are moot because the new ground of rejection does not rely on any reference(s) applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s amendments/arguments with respect to the rejection under 35 USC 101 as being directed to an abstract idea without significantly more have been carefully considered and are not persuasive.
Applicant specifically argues the following:
Step 2A: The claims are not directed to an abstract idea
In the first prong, examiners evaluate whether a claim recites a judicial exception, and in particular an abstract idea, by (a) identifying the specific limitation(s) in the claim under examination (individually or in combination) that the examiner believes recites the abstract idea; and (b) determining whether the identified limitation(s) falls within the subject matter groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity, and mental processes). MPEP § 2106.04(a).
The Office Action alleges, at Page 4, that the claims fall within the "Mental Process" grouping of abstract ideas. Applicant respectfully disagrees. As stated in the August 4, 2025 USPTO memorandum (Kim, Charles. "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101." August 4, 2025), "Examiners should be careful to distinguish claims that recite an exception (which require further eligibility analysis) from claims that merely involve an exception (which are eligible and do not require further eligibility analysis)." (Emphasis added.) Applicant respectfully submits that, even if the current claim limitations were considered to involve an exception, these claim limitations do not recite the exception at least because the claims recite activity that cannot be practically performed in the human mind.
For example, amended Claim 1 recites a processor configured to "generate, based upon the trip request using an artificial intelligence (AI) model, a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation, wherein the Al model is trained using historical trip records including historical trip data associated with historical trips, the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips." These recitations do not fall within any of the enumerated categories of abstract ideas.
The examiner has considered the arguments for step 2A prong 1 and respectfully disagree. The independent claims recite generating, based upon the trip request a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation. These limitation(s), as drafted, is (are) a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
That is, other than reciting “an artificial intelligence (AI) model and at least one processor” nothing in the claim(s) limitation(s) preclude the steps form practically being performed in the mind. For example, the claim(s) limitations encompass a person looking at data such as destination location(s), plurality of routes and types of transportation for each segment of a route could generate/determine, based upon the trip request a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation. The mere nominal recitation of the “an artificial intelligence (AI) model and at least one processor” to perform the abstract idea does not take the claim limitation(s) out of the mental process grouping. Thus, the claim recites a mental process. (Step 2A – Prong 1: Judicial Exception Recited: Yes).
Secondly, applicant argues Step 2A, Prong 2 as follows:
Nevertheless, in any event, even assuming arguendo that the independent claims recite an enumerated judicial exception, the present claims are subject-matter eligible under the second prong of Step 2A. In the second prong, the Office evaluates whether a claim as a whole integrates the judicial exception into a practical application of the exception. Examiners evaluate integration into a practical application by: (a) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (b) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application. Importantly, "Step 2A specifically excludes consideration of whether the additional elements represent well-understood, routine, conventional activity. Accordingly, in Step 2A Prong Two, examiners should ensure that they give weight to all additional elements, whether or not they are conventional, when evaluating whether a judicial exception has been integrated into a practical application." MPEP § 2106.04(d)(I).
One path to establishing a practical application is to show an improvement to another technology. See MPEP § 2106.04(d)(1). In this case, Claim 1 provides a technical improvement to machine learning by enabling a machine learning model to generate a route with different types of transportation selected based in part on route safety. This is accomplished by training the machine learning model using historical drip data that defines a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips.
Accordingly, in this case, "the specification sets forth an improvement in technology ... [and] the claim includes the components or steps of the invention that provide the improvement described in the specification," which is sufficient to establish a practical application. See MPEP § 2104.04(d)(1). Independent Claims 18 and 20, although differing in scope, include similar recitations.
Accordingly, Applicant respectfully requests that the § 101 rejection be withdrawn because the claims are not "directed to" an abstract idea. In the instant Application, the pending claims clearly recite more than well-understood, routine, or conventional functionality vehicle systems, at least with respect to the at least one processor configured to "generate, based upon the trip request using an artificial intelligence (AI) model, a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation, wherein the Al model is trained using historical trip records including historical trip data associated with historical trips, the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips."
The Office Action provides no indication that these recitations of the pending claims (alone or as an ordered combination) are well understood, routine, or conventional in computer systems. As the Federal Circuit confirmed in Berkheimer, even if the Office Action asserts that the pending claims are rendered obvious by virtue of disparate publications, this does not amount to evidence that the recitations are well understood, routine, or conventional under this second step. See Berkheimer, 881 F.3d at 1369 ("Whether a particular technology is well-understood, routine, and conventional goes beyond what was simply known in the prior art. The merefact that something is disclosed in a piece of prior art, for example, does not mean that it was well-understood, routine and conventional.") (emphasis added); see also MPEP §2106.05(d) ("An additional element (or combination of additional elements) that is known in the art can still be unconventional or non- routine. The question of whether a particular claimed invention is novel or obvious is 'fully apart' from the question of whether it is eligible."). The fact that the pending claims overcome the cited art for the reasons described below with respect to the traversal of the Section 102 rejection strengthens the conclusion that these steps are not well understood, routine, and conventional.
The examiner has considered the arguments for step 2A prong 2 and respectfully disagree. The independent claim(s) recite(s) the additional limitations of receive, a trip request including a destination location for a trip; generate a user interface, the user interface including instructions associated with the generated route, the instructions indicating the respective type of transportation to be used for each route segment; and cause the user device to display the generated user interface; wherein the Al model is trained using historical trip records including historical trip data associated with historical trips, the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips; a computing device for generating multi-mode routes using telematics data and artificial intelligence (AI) tools; at least one processor in communication with at least one memory device and with a user device corresponding to a user; and at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon. The receiving step is recited at a high level of generality (i.e., as a general means of gathering data destination location(s), such as plurality of routes data and types of transportation data for each segment of a route, and amount to mere data gathering, which is a form of insignificant extra-solution activity. The generating step is recited at a high level of generality (i.e., as a general action or change being taken based on the results of the determining/planning step(s)) and amount to mere post solution actions, which is a form of insignificant extra-solution activity. The recited additional limitation(s) of a computing device for generating multi-mode routes using telematics data and artificial intelligence (AI) tools; at least one processor in communication with at least one memory device and with a user device corresponding to a user; and at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon are recited at a high level of generality and merely function to automate the generating steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim(s) is/are directed to the abstract idea (Step 2A—Prong 2: Practical Application?: No).
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving, generating, and devices/model (e.g. one or more processor and AI model) elements/steps were considered to be extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification does not provide any indication that these elements/steps are performed by anything other than conventional components performing the conventional activity (steps) of the claim. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible (Step 2B: Inventive Concept?: No).
Thus, the claims as presented are directed to an abstract idea without significantly more. As such, the rejection under USC 101 is maintained herein.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is not directed to patent eligible subject matter.
101 Analysis
Based upon consideration of all of the relevant factors with respect to the claim as a whole, the claim is determined to be directed to an abstract idea. The rationale for this determination is explained below:
When considering subject matter eligibility under 35 U.S.C. § 101 under the 2019 Revised Patent Subject Matter Eligibility Guidance, the Office is charged with determining whether the scope of the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1).
If the claim falls within one of the statutory categories (Step 1), the Office must then determine the two-prong inquiry for Step 2A whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, or abstract idea), and if so, whether the claim is integrated into a practical application of the exception.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claim invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1: Statutory Category
The independent claims are rejected under 35 USC §101 because the claimed invention is directed to a process and machine respectively, which are statutory categories of invention (Step 1: Yes).
101 Analysis – Step 2A Prong 1: Judicial Exception Recited
The claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea). The abstract idea falls under “Mental Processes” Grouping. The independent claims recite generating, based upon the trip request a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation. These limitation(s), as drafted, is (are) a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind.
That is, other than reciting “an artificial intelligence (AI) model and at least one processor” nothing in the claim(s) limitation(s) preclude the steps form practically being performed in the mind. For example, the claim(s) limitations encompass a person looking at data such as destination location(s), plurality of routes and types of transportation for each segment of a route could generate/determine, based upon the trip request a route including a plurality of route segments, each of the plurality of route segments associated with a respective type of transportation. The mere nominal recitation of the “an artificial intelligence (AI) model and at least one processor” to perform the abstract idea does not take the claim limitation(s) out of the mental process grouping. Thus, the claim recites a mental process. (Step 2A – Prong 1: Judicial Exception Recited: Yes).
101 Analysis – Step 2A Prong 2: Practical Application
The independent claim(s) recite(s) the additional limitations of receive, a trip request including a destination location for a trip; generate a user interface, the user interface including instructions associated with the generated route, the instructions indicating the respective type of transportation to be used for each route segment; and cause the user device to display the generated user interface; wherein the Al model is trained using historical trip records including historical trip data associated with historical trips, the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips; a computing device for generating multi-mode routes using telematics data and artificial intelligence (AI) tools; at least one processor in communication with at least one memory device and with a user device corresponding to a user; and at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon.
The receiving step is recited at a high level of generality (i.e., as a general means of gathering data destination location(s), such as plurality of routes data and types of transportation data for each segment of a route, and amount to mere data gathering, which is a form of insignificant extra-solution activity. The generating step is recited at a high level of generality (i.e., as a general action or change being taken based on the results of the determining/planning step(s)) and amount to mere post solution actions, which is a form of insignificant extra-solution activity. The recited additional limitation(s) of a computing device for generating multi-mode routes using telematics data and artificial intelligence (AI) tools; at least one processor in communication with at least one memory device and with a user device corresponding to a user; and at least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon are recited at a high level of generality and merely function to automate the generating steps.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The claim(s) is/are directed to the abstract idea (Step 2A—Prong 2: Practical Application?: No).
101 Analysis – Step 2B: Inventive Concept
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than insignificant extra-solution activity.
Under the 2019 PEG, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B. Here, the receiving, generating, and devices/model (e.g. one or more processor and AI model) elements/steps were considered to be extra-solution activity in Step 2A, and thus they are re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The specification does not provide any indication that these elements/steps are performed by anything other than conventional components performing the conventional activity (steps) of the claim. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Further, the Federal Circuit in Trading Techs. Int’l v. IBG LLC, 921 F.3d 1084, 1093 (Fed. Cir. 2019), and Intellectual Ventures I LLC v. Erie Indemnity Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017), for example, indicated that the mere displaying of data is a well understood, routine, and conventional function. Accordingly, a conclusion that the collecting step is well-understood, routine, conventional activity is supported under Berkheimer. The claim is ineligible (Step 2B: Inventive Concept?: No).
Dependent claims 2-17 and 19 do not include any other additional elements that are sufficient to amount to significantly more than the judicial exception. Therefore, the Claims 1-20 are rejected under 35 U.S.C. §101 as being directed to non-statutory subject matter.
3Claim 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 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.
Claims 1-9, 13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kline et al (US 20210025721 A1) in view of Ghaddar et al (US 20180211337 A1).
With respect to claim 1, Kline discloses a computing device for generating multi-mode routes using telematics data, the computing device comprising at least one processor in communication with at least one memory device and with a user device corresponding to a user (see at least [abstract], [0012-0015], and [0047-0049]), the at least one processor configured to: receive, from the user device, a trip request including a destination location for a trip (see at least [0016] and [0024], “…the travel itinerary specifies an origin location and a destination location…”); generate, based upon the trip request using an (AI) model, a route including a plurality of route segments (see at least [0025], [0029-0035], and [0043-0045], “multi-mode transportation program 112 generates a route that includes two modes of transportation between an origin location and a destination location, where the two modes of transportation include a public bus and a public train…”, “Multi-mode transportation program 112 utilizes historical travel data and machine learning to generate routes while considering previous trip information, along with any modifications that multi-mode transportation program 112 performs during a trip to ensure a confidence level for the user is maintained…”), each of the plurality of route segments associated with a respective type of transportation (see at least [0025], [0029-0035], and [0043-0045], “multi-mode transportation program 112 instructs (i.e., displays guidance) the traveling user to proceed from origin location A towards point 302 and walk (i.e., first mode of transportation) on an initial portion of the route towards point 304 along the generated route. As the user approaches point 304, multi-mode transportation program 112 instructs the user to alter the mode of transportation at point 304 to a personal vehicle and continue on the remaining portion of the route towards destination location B in the personal vehicle.”), wherein the AI model is trained using historical trip records including historical trip data associated with historical trips (see at least [0025], [0029-0035], and [0043-0045], “Multi-mode transportation program 112 utilizes historical travel data and machine learning to generate routes while considering previous trip information, along with any modifications that multi-mode transportation program 112 performs during a trip to ensure a confidence level for the user is maintained…”); generate a user interface, the user interface including instructions associated with the generated route (see at least [0014], [0016], [0030-0034], and [0042-0045]), the instructions indicating the respective type of transportation to be used for each route segment; and cause the user device to display the generated user interface (see at least [0014], [0016], [0030-0034], and [0042-0045]).
However, Kline do not specifically disclose generating multi-mode routes using artificial intelligence (AI) tools, the computing device; and wherein the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips.
Ghaddar teaches generating multi-mode routes using artificial intelligence (AI) tools, the computing device (see at least [0065]); the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips (see at least [0016], [0063], [0065], [0070], [0074], [0078], and [0085]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline, with a reasonable expectation of success to incorporate the teachings of Ghaddar of generating multi-mode routes using artificial intelligence (AI) tools, the computing device; and wherein the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips. This would be done for improving travel mobility as a service (MaaS) by providing customized multi-modal solutions based on user preference (see Ghaddar para 0001).
With respect to claim 2, Kline teaches wherein the at least one processor is further configured to, in response to receiving the trip request, retrieve one or more of geographic data, contextual data, and/or user profile data (see at least [0018], [0021-0022], [0024-0025], [0029-0035], and [0043-0045]), wherein the route is generated based at least in part upon the one or more of the geographic data, the contextual data, and/or the user profile data (see at least [0018], [0021-0022], [0024-0025], [0029-0035], and [0043-0045]).
With respect to claim 3, Kline discloses wherein the at least one processor is further configured to select the respective type of transportation for each of the plurality of route segments based at least in part upon the one or more of the geographic data, the contextual data, and/or the user profile data (see at least [0018], [0021-0022], [0024-0025], [0029-0035], and [0043-0045]).
With respect to claim 4, Kline discloses wherein the at least one processor is further configured to generate the route to minimize one or more of a distance, a duration, a number of route segments, and/or a cost associated with the trip (see at least [0022] and [0029-0031]).
With respect to claim 5, Kline discloses wherein the historical trip data includes one or more of historical destinations, historical routes, historical types of transportation used, historical costs, user feedback, and/or historical events associated with historical trips (see at least [0018], [0025], and [0028]).
With respect to claim 6, Kline discloses wherein the at least one processor is further configured to automatically purchase transportation services associated with at least one of the plurality of route segments (see at least [0040-0042]).
With respect to claim 7, Kline discloses wherein the at least one processor is further configured to cause the user device to display credentials associated with the purchased transportation services (see at least [0030], “…multi-mode transportation program 112 can display a generated route with the personal vehicle, a monetary cost of travel with the personal vehicle, and an expected time of arrival.”).
With respect to claim 8, Kline discloses wherein the at least one processor is further configured to automatically cause the user device to display credentials in response to the user device communicatively linking to a transportation device associated with the transportation service (see at least [0030-0031]).
With respect to claim 9, Kline discloses wherein the at least one processor is further configured to automatically cause the user device to display credentials in response to determining a location of the user device corresponds to starting location of a route segment associated with the at least one of the plurality of route segments (see at least [0030-0031]).
With respect to claim 13, Kline do not specifically disclose wherein the at least one processor is further configured to generate a risk score associated with the generated route using the AI model.
Ghaddar teaches wherein the at least one processor is further configured to generate a risk score associated with the generated route using the AI model (see at least [0016], [0063], [0065], [0070], [0074], [0078], and [0085]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline, with a reasonable expectation of success to incorporate the teachings of Ghaddar of generating multi-mode routes using artificial intelligence (AI) tools, the computing device; and wherein the historical trip data defining a correlation between (i) historical routes associated with the historical trips and (ii) historical telematics data collected during the historical trips defining safety outcomes associated with the historical trips. This would be done for improving travel mobility as a service (MaaS) by providing customized multi-modal solutions based on user preference (see Ghaddar para 0001).
With respect to claims 18-19, they are computer-implemented method claims that recite substantially the same limitations as the respective computing device claims 1 and 2. As such, claims 18-19 are rejected for substantially the same reasons given for the respective computing device claims 1 and 2 and are incorporated herein.
With respect to claim 20, it is non-transitory computer-readable storage media claim that recite substantially the same limitations as the respective computing device claim 1. As such, claim 20 is rejected for substantially the same reasons given for the respective computing device claim 1 and is incorporated herein.
Claims 10-12 are rejected under 35 U.S.C. 103 as being unpatentable over Kline et al (US 20210025721 A1) in view of Ghaddar et al (US 20180211337 A1) in view of Vaughn et al (US 20200408546 A1).
With respect to claim 10, Kline discloses wherein the at least one processor is further configured to: receive, from the user device during the trip, telematics data (see at least [0015-0016] and [0018]).
However, Kline as modified by Ghaddar do not specifically disclose further training the AI model based upon the telematics data.
Vaughn teaches further training the AI model based upon the telematics data (see at least [0078] and [0132]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline as modified by Ghaddar, with a reasonable expectation of success to incorporate the teachings of Vaughn of further training the AI model based upon the telematics data. This would be done to improve understanding by a user of a route and less confusion to a user regarding a route being traversed by the user (see Vaughn para 0109).
With respect to claim 11, Kline discloses wherein the telematics data is generated by at least one sensor of the user device (see at least [0015], “Client device 106 includes sensors 116 for collecting biometric data of a user associated with client device 106 and environmental data in an area surrounding the user associate with client device 106. A user of client device 106 has the ability to customize and limit the biometric data and environmental data collected by sensors 116. Sensors 116 can include an accelerometer, a gyroscope, a magnetometer…”.).
With respect to claim 12, Kline discloses wherein the user device is configured to communicatively pair with a transportation device (see at least [0015]).
However, Kline do not specifically disclose wherein the telematics data is generated by at least one sensor of the transportation device.
Vaughn teaches wherein the telematics data is generated by at least one sensor of the transportation device (see at least [0055-0056])).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline, with a reasonable expectation of success to incorporate the teachings of Vaughn wherein the telematics data is generated by at least one sensor of the transportation device. This would be done to improve understanding by a user of a route and less confusion to a user regarding a route being traversed by the user (see Vaughn para 0109).
Claims 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kline et al (US 20210025721 A1) in view of Ghaddar et al (US 20180211337 A1) in view of Bolless et al (US 20220397402 A1).
With respect to claim 14, Kline as modified by Ghaddar do not specifically disclose wherein the at least one processor is further configured to determine an insurance cost associated with the generated route based upon the generated risk score.
Bolless teaches wherein the at least one processor is further configured to determine an insurance cost associated with the generated route based upon the generated risk score (see at least [0444-0445]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline as modified by Ghaddar, with a reasonable expectation of success to incorporate the teachings of Bolless wherein the at least one processor is further configured to determine an insurance cost associated with the generated route based upon the generated risk score. This would be done to increase safety of a vehicle operating on a route. For example, receiving the safety score of a road segment from the system and take the safety score into account in making navigation decisions when driving along the road segment (e.g., reducing a speed when driving on a road segment having a lower safety score, changing a lane when one of multiple available lanes has a higher safety score, etc.) (see Bolless para 0350).
Claims 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Kline et al (US 20210025721 A1) in view of Ghaddar et al (US 20180211337 A1) in view of Cao (US 20160364678 A1).
With respect to claim 15, Kline as modified by Ghaddar do not specifically disclose wherein the at least one processor is further configured to: receive group data from the user device, the group data identifying one or more group members; and allocate a cost associated with the trip among the one or more group members.
Cao teaches wherein the at least one processor is further configured to: receive group data from the user device (see at least [0004-0007], [0014], and [0144-0154]), the group data identifying one or more group members (see at least [0004-0007], [0014], and [0144-0154]); and allocate a cost associated with the trip among the one or more group members (see at least [0004-0007], [0014], and [0144-0154]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline as modified by Ghaddar, with a reasonable expectation of success to incorporate the teachings of Cao wherein the at least one processor is further configured to: receive group data from the user device, the group data identifying one or more group members; and allocate a cost associated with the trip among the one or more group members. This would be done to increase user’s convenience when travelling in a group (see Cao para 0002).
With respect to claim 16, Kline as modified by Ghaddardo not specifically disclose wherein the at least one processor is further configured to cause the user device to capture an image of the one or more group members, and wherein the group data includes the captured image.
Cao teaches wherein the at least one processor is further configured to cause the user device to capture an image of the one or more group members (see at least [0091], “…when the fare was accepted, a picture of the driver, his rating etc.) may be communicated to a device of the rider. The driver may also be provided information about the rider (e.g. the picture of the rider, the rider's safety rating, the rider location or pickup location) …”)), and wherein the group data includes the captured image (see at least [0091], “…when the fare was accepted, a picture of the driver, his rating etc.) may be communicated to a device of the rider. The driver may also be provided information about the rider (e.g. the picture of the rider, the rider's safety rating, the rider location or pickup location) …”).).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline as modified by Ghaddar, with a reasonable expectation of success to incorporate the teachings of Cao wherein the at least one processor is further configured to cause the user device to capture an image of the one or more group members, and wherein the group data includes the captured image. This would be done to increase user’s convenience when travelling in a group (see Cao para 0002).
With respect to claim 17, Kline as modified by Ghaddar do not specifically disclose wherein the at least one processor is further configured to: identify an account associated with each of the one or more group members; and automatically transfer funds from the identified accounts based upon the allocation of the cost.
Cao teaches wherein the at least one processor is further configured to: identify an account associated with each of the one or more group members (see at least [0004-0007], [0014], and [0144-0154]); and automatically transfer funds from the identified accounts based upon the allocation of the cost (see at least [0004-0007], [0014], and [0144-0154]).
It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kline as modified by Ghaddar, with a reasonable expectation of success to incorporate the teachings of Cao wherein the at least one processor is further configured to: identify an account associated with each of the one or more group members; and automatically transfer funds from the identified accounts based upon the allocation of the cost. This would be done to increase user’s convenience when travelling in a group (see Cao para 0002).
Conclusion
Applicant’s amendment necessitated the new ground of rejection presented in the office action. Accordingly, THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
Inquiry
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ABDALLA A KHALED whose telephone number is (571)272-9174. The examiner can normally be reached on Monday-Thursday 8:00 Am-5:00, every other Friday 8:00A-5:00AM.
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/ABDALLA A KHALED/Examiner, Art Unit 3667