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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 October 9, 2025 has been entered.
Response to Amendment
Applicant submitted amendments and remarks on October 9, 2025. Therein, Applicant submitted substantive arguments. Claims 1, 17, and 19 have been amended. No claims were added or cancelled.
The submitted claims are considered below.
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, 17, and 19 are rejected under 35 U.S.C. 101 because the claimed invention is
directed to an abstract idea with significantly more.
Regarding claim 1,
101 Analysis – Step 1
Claim 1 is directed toward a method which involves processing vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations in the order of a first, second, and third stop location, determining a first route cost from a first stop location to a third stop location via the second stop location, determining a second route cost from the first stop location directly to the third stop location, determining a classification of the second stop location as a task stop or a rest stop based on comparing the first and second route costs and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location, providing the classification as an output, segmenting the vehicle location trace data into one or more vehicle journeys represented by the vehicle location trace data in which one or more journeys is a route segment starting from a first task stop and ending at a second task stop, and storing the journeys in a geographic database for use in training a machine learning model configured to predict estimated times of arrival for logistical vehicles (a method). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method comprising:
processing vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations, wherein the sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order;
determining a first route cost from the first stop location to the third stop location via the second stop location;
determining a second route cost from the first stop location directly to the third stop location;
determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location;
and providing the classification as an output;
segmenting the vehicle location trace data into one or more journeys of a vehicle represented in the vehicle location trace data, wherein the one or more journeys is a route segment starting from a first task stop and ending at a second task stop;
and storing the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles.
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining”, “providing”, and “segmenting” in the context of this claim encompasses a person (driver) looking at information collected and forming a simple judgment.
Accordingly, the claim recites at one abstract idea.
101 Analysis - Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” In the present case, there is the additional limitations of processing and storing; however, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional limitations that amount to significantly more.
Dependent claims 7-11 do not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim is directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 7 uses the limitation of “…dynamically determining the threshold value based on the second route cost”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 8 uses the limitation of “…querying a geographic database to determine one or more places within a threshold proximity of the second stop location, wherein the classification of the second stop location is further based on the one or more places”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 9 uses the limitation of “…processing the vehicle location trace data to determine a stop arrival time, a stop departure time, or a combination thereof for the second stop location, wherein the classification of the second stop location is further based on the stop arrival time, the stop departure time, or a combination thereof” , which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 10 uses the limitation of “…determining an inter-stop duration between the second stop location and another stop location, wherein the classification of the second stop location is further based on the inter-stop duration”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Claim 11 uses the limitation of “…determining a plurality of observations of the second stop location based on clustering the vehicle location trace data, wherein the classification of the second stop location is based on arbitration of a plurality of stop classifications respectively associated with the plurality of observations”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Regarding claim 17,
101 Analysis – Step 1
Claim 17 is directed toward an apparatus containing a processor and memory containing computer code which has the ability to process vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations in the order of a first, second, and third stop location, determine a first route cost from a first stop location to a third stop location via the second stop location, determine a second route cost from the first stop location directly to the third stop location, determine a classification of the second stop location as a task stop or a rest stop based on comparting the first and second route lengths and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location, and provide the classification as an output, segmenting the vehicle location trace data into one or more vehicle journeys represented by the vehicle location trace data in which one or more journeys is a route segment starting from a first task stop and ending at a second task stop, and storing the journeys in a geographic database for use in training a machine learning model configured to predict estimated times of arrival for logistical vehicles (a machine). Therefore, claim 17 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 17 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 17 recites:
An apparatus comprising:
at least one processor;
and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following:
process vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations, wherein the sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order;
determine a first route cost from the first stop location to the third stop location via the second stop location;
determine a second route cost from the first stop location directly to the third stop location;
determine a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location;
provide the classification as an output;
segment the vehicle location trace data into one or more journeys of a vehicle represented in the vehicle location trace data, wherein the one or more journeys is a route segment starting from a first task stop and ending at a second task stop;
and store the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles.
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determine”, and “segment”, in the context of this claim encompasses a person (driver) looking at information collected and forming a simple judgment.
Accordingly, the claim recites at one abstract idea.
101 Analysis - Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” In the present case, therefore since there are no additional limitation beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent claim 17 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional limitations that amount to significantly more.
Dependent claim 18 does not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim is directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 18 uses the limitation of “…query a geographic database to determine one or more places within a threshold proximity of the second stop location, wherein the classification of the second stop location is further based on the one or more places”, which amounts to data gathering and is a form of insignificant extra-solution activity.
Regarding claim 19,
101 Analysis – Step 1
Claim 19 is directed toward a non-transitory computer-readable storage medium carrying a sequence of instructions which processes vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations in the order of a first, second, and third stop location, determines a first route cost from a first stop location to a third stop location via the second stop location, determine a second route cost from the first stop location directly to the third stop location, determines a classification of the second stop location as a task stop or a rest stop based on comparting the first and second route lengths and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location, and provides the classification as an output, segmenting the vehicle location trace data into one or more vehicle journeys represented by the vehicle location trace data in which one or more journeys is a route segment starting from a first task stop and ending at a second task stop, and storing the journeys in a geographic database for use in training a machine learning model configured to predict estimated times of arrival for logistical vehicles (a machine). Therefore, claim 19 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 19 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 19 recites:
A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform the following steps:
processing vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations, wherein the sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order;
determining a first route cost from the first stop location to the third stop location via the second stop location;
determining a second route cost from the first stop location directly to the third stop location;
determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location;
providing the classification as an output;
segmenting the vehicle location trace data into one or more journeys of a vehicle represented in the vehicle location trace data, wherein the one or more journeys is a route segment starting from a first task stop and ending at a second task stop;
and storing the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles.
The examiner submits that the foregoing bolded limitation constitutes a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “determining”, “providing”, and “segmenting”, in the context of this claim encompasses a person (driver) looking at information collected and forming a simple judgment.
Accordingly, the claim recites at one abstract idea.
101 Analysis - Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into the practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” In the present case, therefore since there are no additional limitation beyond the above-noted abstract idea above, there is no integration into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as noted above, representative independent claim 19 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, there are no additional limitations that amount to significantly more.
Dependent claim 20 does not recite any further limitations that cause the claim to be patent eligible. Rather, the limitations of the dependent claim is directed toward additional aspects of the judicial exception and/or well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 20 uses the limitation of “…querying a geographic database to determine one or more places within a threshold proximity of the second stop location, wherein the classification of the second stop location is further based on the one or more places”, which amounts to data gathering and is a form of insignificant extra-solution activity.
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.
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 1-2 and 4-20 are rejected under 35 U.S.C. 103 as being unpatentable over Lear, et al. (U.S. Patent No. 11402222) in view of Weinstein, et al. (U.S. Patent Application Publication No. 20210095979).
Regarding claim 1, Lear, et al. teaches: A method comprising: processing vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations, wherein the sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order; (Col. 16, lines 6-12: "…input component (350) [processing vehicle information] can include a sensor for sensing information (e.g., a global positioning system (GPS) component [GPS samples], an accelerometer [accelerator position], a gyroscope, and/or an actuator)." ; Fig. 1D, Step (130), Col. 7, line 63 to Col. 8, lines 1-3: "…FIG. 1D, and by reference number (130), the route management platform can determine an initial route through the set of stops that includes the set of waypoints. For example, the route management platform can perform one or more path-finding techniques [processing vehicle location trace data] that determine a best-fit path from the origin location [first stop location] to a destination location (shown as initial stop (2) [third stop location]), where the initial route passes through each stop in the set of stops [second stop location].")
determining a first route cost from the first stop location to the third stop location via the second stop location; (Fig. 1C, Step (120), Col. 7, lines 15-21: "…the route management platform can identify a set of legs of an initial route and can compare leg origin areas [first stop location] and leg destination areas [third stop location] to the route restriction origin area and the route restriction destination area to determine whether the rule is satisfied (which would require the route to pass through waypoint 1 and waypoint 2) [second stop location].")
providing the classification as an output (Block (460), Fig. 4B, Col. 18, lines 59-67: "…process (400) can include providing, to the device, the final route to permit the vehicle to be navigated based on the final route (block (460)). For example, the route management platform (e.g., using computing resource (235), processor (320), output component (360) [output]")
segmenting the vehicle location trace data into one or more journeys of a vehicle represented in the vehicle location trace data, wherein the one or more journeys is a route segment starting from a first task stop and ending at a second task stop (Fig. 1E, Step (140), Col. 8, lines 35-40: "…reference number (140), the route management platform can identify a subset of fuel stops that are to be considered as potential stops to add to the set of stops of a final route. For example, the route management platform can identify a subset of fuel stops that are within a threshold distance of the initial route [segmenting vehicle location trace data into multiple journeys - first and second stops].").
Lear, et al. does not teach determining a second route cost from the first stop location directly to the third stop location; determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route cost and the second route cost and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location.
In a similar field of endeavor (selection of stop locations of route options), Weinstein, et al. teaches: determining a second route cost from the first stop location directly to the third stop location; (Paragraph [0081]: "…coalescing controller system (102) can offset costs/inefficiencies of the provider (701) and/or requestor (713) by determining a direct driving route option proceeding from the pick-up location of the requestor (703) to the coalesced drop-off location for the requestors (703) and (713) without intermediate pick-ups or drop-offs [second route cost - directly from first to third stop location].")
determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route cost and the second route cost and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location (Paragraph [0024]: "…set of candidate stop locations for each requestor in a given combination of requestors who is eligible to walk, the coalescing controller system can generate corresponding route scores. In turn, the coalescing controller system can select a first driving route option with an optimal route score for the given combination of requestors that includes one of the candidate stop locations [task stop or rest stop determination]. In the first driving route option, the coalescing controller system may not coalesce one or more stop locations. Rather, the coalescing controller system can use the first driving route option as a baseline for comparison to a second driving route option that coalesces one or more stop locations for the given combination of requestors [comparison of first and second route costs]." ; Paragraph [0025]: "Conversely, if the route score for the first driving route option is higher than the route score for the second driving route option, then the coalescing controller system can determine to coalesce one or more stop locations in accordance with the second driving route option. Based on the selected driving route option, the coalescing controller system can then send instructions to one or both of a transportation provider device associated with a provider and a requestor device associated with a requestor [data associated with transport function at second stop location]." ; Paragraph [0115]: "The transportation matching system (1202) may generate, store, receive, and send data, such as, for example, [...] vehicle data, or other suitable data related to the ride share transportation network [vehicle operational data]").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Lear, et al. to include the teaching of Weinstein, et al. based on a reasonable expectation of success and motivation to improve the selection of stop locations within selected routes in a dynamic transportation system (Weinstein, et al. Paragraph [0021]).
The combination of Lear, et al. and Weinstein, et al. does not teach and storing the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles.
In a similar field of endeavor (determining estimating time of arrival), Wang, et al. teaches: and storing the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles (Steps (510) – (520), Col. 12, lines 1-8: "At (510), the processor (300) (e.g., the acquisition unit ((310)) may obtain information related to a target trip. The target trip may be a trip between one or more locations. In some embodiments, the target trip may be an order initiated by a user (e.g., a passenger) via the passenger terminal (130). The order may relate to a starting location and a destination. The target trip may be a trip between the starting location and the destination [contents of stored geographic database information (e.g. journeys)]." ; Col. 12, lines 41-45: "…the information and/or data relating to the target trip may be stored in the data storage (150). The information and/or data relating to the target trip may further be accessed by the server (110) via the network (120) [stored in geographic database]." ; Col. 12, lines 46-54: "At (520), the processor (300) may obtain a prediction model for estimating time of arrival [predict estimates times of arrival for logistical vehicles]. […] Alternatively or additionally, the prediction model may be trained and/or updated in real time. The prediction model may be trained using one or more machine learning techniques [machine learning model]").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify the combination of Lear, et al. and Weinstein, et al. to include the teaching of Wang, et al. based on a reasonable expectation of success and motivation to improve the process of determining an estimated time of arrival relating to a designated trip (Wang, et al. Col. 5, lines 38-51).
Regarding claim 2, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, wherein the first route cost, the second route cost, or a combination thereof is based a route length, a travel time, a fuel consumption, a preference for a road that supports commercial vehicle traffic, or a combination thereof (Lear, et al. Fig. 1E, Step (140), Col. 8, lines 46-52: "…route management platform can determine a distance between each fuel stop, of the set of fuel stops, and the initial route. In this case, the route management platform can determine a location within the initial route that is a closest available location to a fuel stop and can calculate a distance between the location and the fuel stop [cost calculation based on route length].").
Regarding claim 4, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, Lear, et al. teaches: wherein the first route cost is determined using a routing engine (Lear, et al. Fig. 1F, Step (155), Col. 9, lines 25-30: "As shown by reference number (155), the route management platform can select a fuel stop to include in the set of stops. For example, the route management platform can select a fuel stop, of the subset of identified fuel stops, using one or more fuel stop selection techniques (e.g., a data model trained using machine learning, a rules engine [first cost is determined using routing engine]").
Lear, et al. does not teach a second route cost established through the use of a routing engine.
In a similar field of endeavor (selection of stop locations of route options), Weinstein, et al. teaches: second route cost (Paragraph [0050]: "…generated candidate stop locations from the stop location generator (204), the coalescing controller system (102) can then generate various driving route options and, in turn, generate corresponding scores for the driving route options via a route scoring engine (206). In particular, the route scoring engine (206) can generate route scores for up to each driving route option of one or more combinations of requestors (108a) - (108n). For example, the route scoring engine (206) can generate route scores based on a driving score and/or a walking score for each given driving route option [second cost determined using routing engine].").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Lear, et al. to include the teaching of Weinstein, et al. based on a reasonable expectation of success and motivation to improve the selection of stop locations within selected routes in a dynamic transportation system (Weinstein, et al. Paragraph [0021]).
Regarding claim 5, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, wherein the first route cost is a first route length and the second route cost is a second route length, and wherein the first route length and the second route length are determined based on a geometric distance (Weinstein, et al. Fig. 4B, Paragraph [0064]: "…coalescing controller system (102) generates for analysis one or more driving route options as shown in FIG. 4B, arranged in accordance with one or more embodiments. In particular, the coalescing controller system (102) can consider driving route options of, for example: (D-A.sub.0-B-A′-B′); (D-A.sub.1-B-A′-B′); and so forth to (D-A.sub.b-B-A′-B′) [different geometric route lengths]. In these example driving route options, A.sub.0 represents a candidate stop location associated with a position of the requestor (402) (e.g., a request location where the requestor (402) sends a transportation request to the dynamic transportation matching system 103). In addition, A.sub.1 represents another candidate stop location positioned within a threshold distance and/or threshold ETA (e.g., relative to A.sub.0) [example - different locations on route - distance/length value]].").
Regarding claim 6, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, wherein the second stop location is classified as the task stop based on determining that a difference between the first route cost and the second route cost is greater than a threshold value (Weinstein, et al. Paragraph [0051]: "…not within a threshold distance and/or threshold ETA from one or more different pick-up/drop-off locations of one or more walking-eligible requestors [condition - greater than threshold value], the threshold distance/ETA controller (208) can determine that coalescing stop locations is not available for a given driving route option [determining classification of second stop location].").
Regarding claim 7, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 6, and in a further embodiment, teach: The method of claim 6, further comprising: dynamically determining the threshold value based on the second route cost (Weinstein, et al. Paragraph [0061]: "…coalescing controller system (102) may implement varying threshold sizes (e.g., varying threshold distances or varying threshold ETAs) based on a threshold distances or varying threshold ETAs) based on a drop-off of requestors [dynamic control of threshold value based on second route cost calculations].").
Regarding claim 8, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, further comprising: querying the geographic database to determine one or more places within a threshold proximity of the second stop location, wherein the classification of the second stop location is further based on the one or more places (Lear, et al. Fig. 1E, Step (140), Col. 8, lines 52-58: "…route management platform can compare the distance to the threshold distance to determine whether the fuel stop is within the threshold distance of the initial route [querying geographic database - determine if location is within threshold proximity]. If the distance satisfies the threshold distance, the route management platform can identify the fuel stop as a fuel stop that is to be considered as a stop in a final route, as described further herein [classification of second stop location is further based on one or more places].").
Regarding claim 9, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, further comprising: processing the vehicle location trace data to determine a stop arrival time, a stop departure time, or a combination thereof for the second stop location, (Weinstein, et al. Paragraph [0101]: "…stop locations for a subset of transportation requests of the set of transportation requests within the batching window are eligible for coalescing based […] a threshold estimated time to arrival [stop arrival time] between the transportation requests of the subset of transportation requests [processing vehicle location trace data];")
wherein the classification of the second stop location is further based on the stop arrival time, the stop departure time, or a combination thereof (Weinstein, et al. Paragraph [0099]: "…first stop location and the second stop location are within a threshold distance or a threshold estimated time to arrival [stop arrival time]; and in response to the determining, generating the first route score and the second route score to determine whether to coalesce the first stop location and the second stop location [classification of second stop location - determination of further distance].").
Regarding claim 10, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, further comprising: determining an inter-stop duration between the second stop location and another stop location, wherein the classification of the second stop location is further based on the inter-stop duration (Weinstein, et al. Paragraph [0049]: "…the stop location generator (204) may filter out stop locations that initially include medians, alleys, side streets, highways, venues, clustered stop locations, dangerous areas, illegal areas, and other possibly unsuitable areas for picking up and/or dropping off the requestors (108a)-(108n) [classification of inter-stop duration]. Additional reasons for filtering out stop locations can include a walking time greater than some amount of time (e.g., five minutes) [example - specific duration]").
Regarding claim 11, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, further comprising: determining a plurality of observations of the second stop location based on clustering the vehicle location trace data, (Lear, et al. Fig. 1E, Step (140), Col. 8, lines 40-45: "…route management platform can be configured with a rule to identify fuel stops that are within a threshold distance of the initial route, within a threshold distance of a specified location within the initial route, within a threshold distance of a leg of the initial route, and/or the like [plurality of observations of second stop location - clustering rule].")
wherein the classification of the second stop location is based on arbitration of a plurality of stop classifications respectively associated with the plurality of observations (Lear, et al. Fig. 1E, Step (140), Col. 8, lines 59-64: "…the set of fuel stops can include (800) fuel stops within geographic region A (e.g., a state, a portion of a country, etc.). In this example, the route management platform can identify fuel stop (1), fuel stop (2), fuel stop (3), and fuel stop (4), as being part of a subset of fuel stops that are within a threshold distance of the route [second stop location is based on classification parameter with respect to plurality of observations].").
Regarding claim 12, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, wherein the classification is performed using a machine learning classifier (Lear, et al. Fig. 1F, Step (155), Col. 9, lines 25-31: "…the route management platform can select a fuel stop, of the subset of identified fuel stops, using one or more fuel stop selection techniques (e.g., a data model trained using machine learning [classification is performed using machine learning classifier]").
Regarding claim 13, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, wherein the vehicle location trace data is collected from one or more transport vehicles, (Lear, et al. Fig. 1A, Step (105), Col. 5, lines 37-40: "…first data storage device can store other related data. The other related data can include vehicle data for a fleet of vehicles [vehicle location trace data is collected from transport vehicles], driver data for a group of drivers, and/or the like.")
wherein the task stop is a stop location at which the one or more transport vehicles perform a transport function, (Lear, et al. Fig. 1H, Step (180), Col. 11, 55-61: "…route management platform can periodically provide the user device (or another device) with a new route. For example, while the vehicle is traveling to the set of stops of the final route (e.g., to perform a set of deliveries) [transport vehicle is performing transport function at stop]")
and wherein the rest stop is any other stop location that is not a task stop (Lear, et al. Fig. 1H, Step (180), Col. 11, 59-61: "the route management platform can receive new fuel selection data that is to be used to identify an updated set of fuel stops [rest stop is fuel stop].")).
Regarding claim 14, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, wherein the first stop location, the second stop location, and the third stop location are consecutive stop locations in the sequence of vehicle stop locations (Lear, et al. Fig. 1F, Step (160), Col. 10, lines 36-41: "…reference number (160), the route management platform can update the set of stops to include the selected fuel stop. For example, as shown, the stops data that identifies the set of stops can be updated to include waypoint (1) as a first stop, initial stop (1) as a second stop, waypoint (2) as a third stop [consecutive stop locations]").
Regarding claim 15, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, herein the sequence of vehicle stop locations is processed using a three-stop location sliding window to determine a respective stop classification for each stop location in the sequence (Lear, et al. Fig. 1F, Step (160), Col. 10, lines 36-42: "…reference number (160), the route management platform can update the set of stops to include the selected fuel stop. For example, as shown, the stops data that identifies the set of stops can be updated to include waypoint (1) as a first stop, initial stop (1) as a second stop, waypoint (2) as a third stop, fuel stop (4) as a fourth stop, and initial stop (2) as a fifth stop [can shift stops using three-stop location sliding window for stop classification].").
Regarding claim 16, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 1, and in a further embodiment, teach: The method of claim 1, further comprising: storing the classification as an attribute of the second stop location in the geographic database (Lear, et al. Fig. 1A, Step (105), Col. 4, lines 33-39: "…a first data storage device (shown as Data Storage Device 1) can store route restriction data and/or other related data. For example, the first data storage device can store route restriction data and/or other related data in a manner that is accessible to the route management platform [storing information in geographic database]" ; Lear, et al. Fig. 1D, Step (130), Col. 7, line 63 to Col. 8, lines 1-3: "…route management platform can perform one or more path-finding techniques [processing vehicle location trace data] that determine a best-fit path from the origin location to a destination location (shown as initial stop (2)), where the initial route passes through each stop in the set of stops [second stop location].").
Regarding claim 17, Lear, et al. teaches: An apparatus comprising: at least one processor; and (Fig. 3, Col. 15, lines 40-44: “…processor (320) [processor]”)
at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following: (Col. 16, lines 26-30: "…Device (300) can perform these processes based on processor (320) [processor] executing software instructions [computer program code] stored by a non-transitory computer-readable medium, such as memory (330) [memory]")
process vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position determine a sequence of vehicle stop locations, wherein the sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order; (Col. 16, lines 6-12: "…input component (350) [processing vehicle information] can include a sensor for sensing information (e.g., a global positioning system (GPS) component [GPS samples], an accelerometer [accelerator position], a gyroscope, and/or an actuator)." ; Col. 7, line 63 to Col. 8, lines 1-3: "the route management platform can determine an initial route through the set of stops that includes the set of waypoints. For example, the route management platform can perform one or more path-finding techniques [processing vehicle location trace data] that determine a best-fit path from the origin location [first stop location] to a destination location (shown as initial stop (2) [third stop location]), where the initial route passes through each stop in the set of stops [second stop location].")
determine a first route cost from the first stop location to the third stop location via the second stop location; (Col. 7, lines 15-21: "…the route management platform can identify a set of legs of an initial route and can compare leg origin areas [first stop location] and leg destination areas [third stop location] to the route restriction origin area and the route restriction destination area to determine whether the rule is satisfied (which would require the route to pass through waypoint 1 and waypoint 2) [second stop location].")
and provide the classification as an output; (Col. 18, lines 60-67: "the final route to permit the vehicle to be navigated based on the final route (block (460)). For example, the route management platform (e.g., using computing resource (235), processor (320), output component (360) [output]")
segment the vehicle location trace data into one or more journeys of a vehicle represented in the vehicle location trace data, wherein the one or more journeys is a route segment starting from a first task stop and ending at a second task stop; (Col. 8, lines 35-40: "…reference number (140), the route management platform can identify a subset of fuel stops that are to be considered as potential stops to add to the set of stops of a final route. For example, the route management platform can identify a subset of fuel stops that are within a threshold distance of the initial route [segmenting vehicle location trace data into multiple journeys - first and second stops].").
Lear, et al. does not teach determine a second route cost from the first stop location directly to the third stop location; determine a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location.
In a similar field of endeavor (selection of stop locations of route options), Weinstein, et al. teaches: determine a second route cost from the first stop location directly to the third stop location; (Paragraph [0081]: "…coalescing controller system (102) can offset costs/inefficiencies of the provider (701) and/or requestor (713) by determining a direct driving route option proceeding from the pick-up location of the requestor (703) to the coalesced drop-off location for the requestors (703) and (713) without intermediate pick-ups or drop-offs [second route cost - directly from first to third stop location].")
determine a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location (Paragraph [0024]: "…set of candidate stop locations for each requestor in a given combination of requestors who is eligible to walk, the coalescing controller system can generate corresponding route scores. In turn, the coalescing controller system can select a first driving route option with an optimal route score for the given combination of requestors that includes one of the candidate stop locations [task stop or rest stop determination]. In the first driving route option, the coalescing controller system may not coalesce one or more stop locations. Rather, the coalescing controller system can use the first driving route option as a baseline for comparison to a second driving route option that coalesces one or more stop locations for the given combination of requestors [comparison of first and second route costs]." ; Paragraph [0025]: "Conversely, if the route score for the first driving route option is higher than the route score for the second driving route option, then the coalescing controller system can determine to coalesce one or more stop locations in accordance with the second driving route option. Based on the selected driving route option, the coalescing controller system can then send instructions to one or both of a transportation provider device associated with a provider and a requestor device associated with a requestor [data associated with transport function at second stop location]." ; Paragraph [0115]: "The transportation matching system (1202) may generate, store, receive, and send data, such as, for example, [...] vehicle data, or other suitable data related to the ride share transportation network [vehicle operational data]").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Lear, et al. to include the teaching of Weinstein, et al. based on a reasonable expectation of success and motivation to improve the selection of stop locations within selected routes in a dynamic transportation system (Weinstein, et al. Paragraph [0021]).
The combination of Lear, et al. and Weinstein, et al. does not teach and store the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles.
In a similar field of endeavor (determining estimating time of arrival), Wang, et al. teaches: and store the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles (Col. 12, lines 1-8: "…the processor (300) (e.g., the acquisition unit ((310)) may obtain information related to a target trip. The target trip may be a trip between one or more locations. In some embodiments, the target trip may be an order initiated by a user (e.g., a passenger) via the passenger terminal (130). The order may relate to a starting location and a destination. The target trip may be a trip between the starting location and the destination [contents of stored geographic database information (e.g. journeys)]." ; Col. 12, lines 41-45: "…the information and/or data relating to the target trip may be stored in the data storage (150). The information and/or data relating to the target trip may further be accessed by the server (110) via the network (120) [stored in geographic database]." ; Col. 12, lines 46-54: "…the processor (300) may obtain a prediction model for estimating time of arrival [predict estimates times of arrival for logistical vehicles]. […] Alternatively or additionally, the prediction model may be trained and/or updated in real time. The prediction model may be trained using one or more machine learning techniques [machine learning model]").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify the combination of Lear, et al. and Weinstein, et al. to include the teaching of Wang, et al. based on a reasonable expectation of success and motivation to improve the process of determining an estimated time of arrival relating to a designated trip (Wang, et al. Col. 5, lines 38-51).
Regarding claim 18, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 17, and in a further embodiment, teach: The apparatus of claim 17, wherein the apparatus is further caused to: query the geographic database to determine one or more places within a threshold proximity of the second stop location, wherein the classification of the second stop location is further based on the one or more places (Lear, et al. Col. 8, lines 52-58: "…route management platform can compare the distance to the threshold distance to determine whether the fuel stop is within the threshold distance of the initial route [querying geographic database - determine if location is within threshold proximity]. If the distance satisfies the threshold distance, the route management platform can identify the fuel stop as a fuel stop that is to be considered as a stop in a final route, as described further herein [classification of second stop location is further based on one or more places].").
Regarding claim 19, Lear, et al. teaches: A non-transitory computer-readable storage medium carrying one or more sequences of one or more instructions which, when executed by one or more processors, cause an apparatus to at least perform the following steps: (Col. 16, lines 26-30: "…Device (300) can perform these processes based on processor (320) [processor] executing software instructions [instructions] stored by a non-transitory computer-readable medium [non-transitory computer storage medium], such as memory (330)")
processing vehicle location trace data comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position to determine a sequence of vehicle stop locations, wherein the sequence of vehicle stop locations comprises a first stop location, a second stop location, and a third stop location in chronological order; (Col. 16, lines 6-12: "… input component (350) [processing vehicle information] can include a sensor for sensing information (e.g., a global positioning system (GPS) component [GPS samples], an accelerometer [accelerator position], a gyroscope, and/or an actuator)." ; Col. 7, line 63 to Col. 8, lines 1-3: "the route management platform can determine an initial route through the set of stops that includes the set of waypoints. For example, the route management platform can perform one or more path-finding techniques [processing vehicle location trace data] that determine a best-fit path from the origin location [first stop location] to a destination location (shown as initial stop (2) [third stop location]), where the initial route passes through each stop in the set of stops [second stop location].")
determining a first route cost from the first stop location to the third stop location via the second stop location; (Col. 7, lines 15-21: "…the route management platform can identify a set of legs of an initial route and can compare leg origin areas [first stop location] and leg destination areas [third stop location] to the route restriction origin area and the route restriction destination area to determine whether the rule is satisfied (which would require the route to pass through waypoint 1 and waypoint 2) [second stop location].")
providing the classification as an output; (Col. 18, lines 60-67: "the final route to permit the vehicle to be navigated based on the final route (block (460)). For example, the route management platform (e.g., using computing resource (235), processor (320), output component (360) [output]")
segmenting the vehicle location trace data into one or more journeys of a vehicle represented in the vehicle location trace data, wherein the one or more journeys is a route segment starting from a first task stop and ending at a second task stop (Col. 8, lines 35-40: "…reference number (140), the route management platform can identify a subset of fuel stops that are to be considered as potential stops to add to the set of stops of a final route. For example, the route management platform can identify a subset of fuel stops that are within a threshold distance of the initial route [segmenting vehicle location trace data into multiple journeys - first and second stops].").
Lear, et al. does not teach determining a second route cost from the first stop location directly to the third stop location; determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location.
In a similar field of endeavor (selection of stop locations of route options), Weinstein, et al. teaches: determining a second route cost from the first stop location directly to the third stop location; (Paragraph [0081]: "…coalescing controller system (102) can offset costs/inefficiencies of the provider (701) and/or requestor (713) by determining a direct driving route option proceeding from the pick-up location of the requestor (703) to the coalesced drop-off location for the requestors (703) and (713) without intermediate pick-ups or drop-offs [second route cost - directly from first to third stop location].")
determining a classification of the second stop location as either a task stop or a rest stop based, at least in part, on a comparison of the first route length and the second route length and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location; (Paragraph [0024]: "…set of candidate stop locations for each requestor in a given combination of requestors who is eligible to walk, the coalescing controller system can generate corresponding route scores. In turn, the coalescing controller system can select a first driving route option with an optimal route score for the given combination of requestors that includes one of the candidate stop locations [task stop or rest stop determination]. In the first driving route option, the coalescing controller system may not coalesce one or more stop locations. Rather, the coalescing controller system can use the first driving route option as a baseline for comparison to a second driving route option that coalesces one or more stop locations for the given combination of requestors [comparison of first and second route costs]." ; Paragraph [0025]: "Conversely, if the route score for the first driving route option is higher than the route score for the second driving route option, then the coalescing controller system can determine to coalesce one or more stop locations in accordance with the second driving route option. Based on the selected driving route option, the coalescing controller system can then send instructions to one or both of a transportation provider device associated with a provider and a requestor device associated with a requestor [data associated with transport function at second stop location]." ; Paragraph [0115]: "The transportation matching system (1202) may generate, store, receive, and send data, such as, for example, [...] vehicle data, or other suitable data related to the ride share transportation network [vehicle operational data]").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify Lear, et al. to include the teaching of Weinstein, et al. based on a reasonable expectation of success and motivation to improve the selection of stop locations within selected routes in a dynamic transportation system (Weinstein, et al. Paragraph [0021]).
The combination of Lear, et al. and Weinstein, et al. does not teach and storing the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles.
In a similar field of endeavor (determining estimating time of arrival), Wang, et al. teaches: and storing the one or more journeys in a geographic database for use in training a machine learning model configured to predict estimates times of arrival for logistical vehicles (Col. 12, lines 1-8: "…the processor (300) (e.g., the acquisition unit ((310)) may obtain information related to a target trip. The target trip may be a trip between one or more locations. In some embodiments, the target trip may be an order initiated by a user (e.g., a passenger) via the passenger terminal (130). The order may relate to a starting location and a destination. The target trip may be a trip between the starting location and the destination [contents of stored geographic database information (e.g. journeys)]." ; Col. 12, lines 41-45: "…the information and/or data relating to the target trip may be stored in the data storage (150). The information and/or data relating to the target trip may further be accessed by the server (110) via the network (120) [stored in geographic database]." ; Col. 12, lines 46-54: "…the processor (300) may obtain a prediction model for estimating time of arrival [predict estimates times of arrival for logistical vehicles]. […] Alternatively or additionally, the prediction model may be trained and/or updated in real time. The prediction model may be trained using one or more machine learning techniques [machine learning model]").
Therefore, it would have been obvious to one of the ordinary skill of the art before the effective filing date of the claimed invention to modify the combination of Lear, et al. and Weinstein, et al. to include the teaching of Wang, et al. based on a reasonable expectation of success and motivation to improve the process of determining an estimated time of arrival relating to a designated trip (Wang, et al. Col. 5, lines 38-51).
Regarding claim 20, Lear, et al., Weinstein, et al., and Wang, et al. remain as applied to claim 19, and in a further embodiment, teach: The non-transitory computer-readable storage medium of claim 19, wherein the apparatus is caused to further perform: querying a geographic database to determine one or more places within a threshold proximity of the second stop location, wherein the classification of the second stop location is further based on the one or more places (Lear, et al. Col. 8, lines 52-58: "…route management platform can compare the distance to the threshold distance to determine whether the fuel stop is within the threshold distance of the initial route [querying geographic database - determine if location is within threshold proximity]. If the distance satisfies the threshold distance, the route management platform can identify the fuel stop as a fuel stop that is to be considered as a stop in a final route, as described further herein [classification of second stop location is further based on one or more places].").
Response to Arguments
Applicant's arguments filed on October 9, 2025 have been fully considered but they are not persuasive.
Applicant asserted that amended claims 1, 17, and 19 were patentable over Lear, et al. (U.S. Patent No. 11402222) in view of Weinstein, et al. (U.S. Patent Application Publication No. 20210095979) and further in view of Wang, et al. (U.S. Patent No. 10816352) because the references did not meet the claim limitation “comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position”. The examiner disagrees. In Lear, et al. the inputted vehicle operation information via input component (350) can be inclusive of “…a sensor for sensing information (e.g., a global positioning system (GPS) component” for collecting GPS samples, and “…an accelerometer” for measuring accelerator position” (Col. 16, lines 6-10). Subsequently, it would have been obvious to combine Lear, et al. with Weinstein, et al. and Wang, et al. because Weinstein, et al. teaches the process of determining a second route cost and determining the classification of a second stop location of a task stop or rest stop based on a cost comparison (Paragraphs [0081], [0024]) and Wang, et al. teaches the process of storing trip records in a geographic database for the purpose of training a machine learning model to predict estimated times of arrival (ETAs) for vehicles (Col. 12, lines 1-8, lines 41-45, and lines 46-54).
Applicant also asserted that amended claims 1, 17, and 19 were patentable over Lear, et al. (U.S. Patent No. 11402222) in view of Weinstein, et al. (U.S. Patent Application Publication No. 20210095979) and further in view of Wang, et al. (U.S. Patent No. 10816352) because the references did not meet the claim limitation “and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location”. The examiner disagrees. In Weinstein, et al., the process of indicating activity associated with a transport function at the second stop location is conducted by first determining “…if the route score for the first driving route option is higher than the route score for the second driving route option, then the coalescing controller system can determine to coalesce one or more stop locations in accordance with the second driving route option”, and then by sending this instructional data “to one or both of a transportation provider device associated with a provider and a requestor device associated with a requestor” (Paragraph [0025]). This data within the transportation matching system (1202) is inclusive of “…vehicle data, or other suitable data related to the ride share transportation network”, or vehicle operational data (Paragraph [0115]). Subsequently, it would have been obvious to combine Weinstein, et al. with Lear, et al. and Wang, et al. because Lear, et al. teaches the process of using vehicle location trace data to determine a sequence of vehicle stop locations and an appropriate cost with respect to transportation between the respective locations (Col. 7, line 63 to Col. 8, lines 1-3, Col. 7, lines 15-21) and Wang, et al. teaches the process of storing trip records in a geographic database for the purpose of training a machine learning model to predict estimated times of arrival (ETAs) for vehicles (Col. 12, lines 1-8, lines 41-45, and lines 46-54).
Therefore, it can be concluded that since the combination of Lear, et al., Weinstein, et al. and Wang, et al. reads on the claim limitations “comprising GPS samples and vehicle operational data including at least one of ignition state, braking state, steering angle, or accelerator position” and “and further based on the vehicle operational data indicating activity associated with a transport function at the second stop location”, as stated in amended claims 1, 17, and 19, the arguments presented by the Applicant are not persuasive, and the rejection is maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Morales (U.S. Patent Application Publication No. 20180172463) describes a device, server, and instructions that provide a geo-tracking based navigation of user progress from one point to another using waypoint distance measurements.
Applicant is considered to have implicit knowledge of the entire disclosure once a reference has been cited. Therefore, any previously cited figures, columns and lines should not be considered to limit the references in any way. The entire reference must be taken as a whole; accordingly, the Examiner contends that the art supports the rejection of the claims and the rejection is maintained.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TORRENCE S MARUNDA II whose telephone number is (571)272-5172. The examiner can normally be reached Monday-Friday 8:00-5:30.
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/TORRENCE S MARUNDA II/ Examiner, Art Unit 3663
/ANGELA Y ORTIZ/ Supervisory Patent Examiner, Art Unit 3663
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