Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to Application No. 18/585692, filed on 23-FEB-2024. Claims 1-20 are currently pending and have been examined. Claims 1-20 have been rejected as follows.
Claim Rejections - 35 USC § 101
Claims 1-20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claims 1-8 are directed method for navigation based on popular routes (i.e., a process). Claims 9-16 are directed to a system for navigation based on popular routes (i.e., a machine). Claims 17-20 are directed to a machine-storage medium for navigation based on popular routes (i.e., a manufacture). Therefore, claims 1-20 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong
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: capturing trip data of a plurality of users traversing routes by monitoring user devices of the plurality of users
analyzing the trip data between an origin/destination (O/D) pair to determine candidate popular trips between the O/D pair comprising detours
determining top-ranking waypoints of segments of the candidate popular trips
storing the top-ranking waypoints of the O/D pair in a waypoint data storage
in response to receiving a request for a transportation service between the O/D pair from a user, reconstructing a popular route using the top-ranking waypoints
causing presentation of on a user device of the user of a plurality of route options for selection by the user, the plurality of route options including the reconstructed popular route
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process”, because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “analyzing…”, “determining…”, “storing…”, and “reconstructing” in the context of this claim encompasses a person assessing trip data, determining and memorizing the most popular parts of the trips, and determining a new route based on these parts. Accordingly, the claim recites at least 4 abstract ideas.
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 idea into a 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, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
A method comprising: capturing trip data of a plurality of users traversing routes by monitoring user devices of the plurality of users
analyzing the trip data between an origin/destination (O/D) pair to determine candidate popular trips between the O/D pair comprising detours
determining top-ranking waypoints of segments of the candidate popular trips
storing the top-ranking waypoints of the O/D pair in a waypoint data storage
in response to receiving a request for a transportation service between the O/D pair from a user, reconstructing a popular route using the top-ranking waypoints
causing presentation of on a user device of the user of a plurality of route options for selection by the user, the plurality of route options including the reconstructed popular route
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “capturing…”, “in response to… receiving …” and “causing…”, the examiner submits that these limitations are insignificant extra-solution activities as they are broad enough to include the pre-solution activity gathering data and post-solution activity of displaying data. In particular, the “capturing…”, “in response to… receiving …” and “causing…” steps are recited at a high level of generality (i.e. as a general capturing trip data, receiving a user request, and presenting routes respectively), and amounts to mere data gathering and displaying which is a form of insignificant extra-solution activity. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, 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, the additional limitations of “capturing…”, “in response to… receiving …” and “causing…” the examiner submits that these limitations are insignificant extra-solution activities. Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well- understood, routine, conventional activity in the field. The additional limitations “capturing…”, “in response to… receiving …” and “causing…” are well-understood, routine, and conventional activities as is merely the collection and display of data. 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. 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. Hence, the claim is not patent eligible.
Dependent claims 2-8 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of dependent claims are 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 as none of the dependent claims narrow the scope to not encompass performance of the limitations in the human mind. It is noted that claim 5 is directed to a mathematical concept, specifically a mathematical calculation, via the explicit claiming of D/C hexagons. Therefore, dependent claims 2-8 are not patent eligible under the same rationale as provided for in the rejection of claim 1. Similarly, claim 9 and claim 17 are rejected under the same rationale provided for the rejection of claim 1, and dependent claims 10-16, and 19-20 are not patent eligible.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a) the invention was known or used by others in this country, or patented or described in a printed publication in this or a foreign country, before the invention thereof by the applicant for a patent.
Claims 1-4, 7, 9-12, 15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lewis et al. (US 2025/0369758 A1).
Regarding claim 1, Lewis et al. teaches: A method comprising: capturing trip data of a plurality of users traversing routes by monitoring user devices of the plurality of users (Figure 9A; element Trip Data; Paragraph [78], "trip data of one or more trips obtained by one or more telematic devices and the GPS records associated with each trip"); analyzing the trip data between an origin/destination (O/D) pair (Paragraph [90]) to determine candidate popular trips between the O/D pair (Paragraph [77], "The route usage and/or popularity may be employed to show sequences of road traversals between origin and destination regions or routes ranked in order of popularity for infrastructure evaluation, planning, and change management") comprising detours (Paragraph [92], "For example, deviations from a route that are within a threshold deviation distance but greater than a minimum deviation distance may be identified as a stop"); determining top-ranking waypoints of segments of the candidate popular trips (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); storing the top-ranking waypoints of the O/D pair in a waypoint data storage (Paragraph [45], "The telematics device 104a of FIG. 1 may include suitable hardware and/or software configured to collect, sense, receive, process, store, and/or transmit any appropriate telematics data associated with a vehicle"); in response to receiving a request for a transportation service between the O/D pair from a user (Paragraph [78], "The method may also include receiving an origin region and a destination region (e.g., from a user as user input, for example, using a graphical user interface)"), reconstructing a popular route using the top-ranking waypoints (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); and causing presentation of on a user device of the user of a plurality of route options for selection by the user, the plurality of route options including the reconstructed popular route (Figure 9B; element 903)
Regarding claim 2, Lewis et al. teaches: The method of claim 1, wherein analyzing the trip data comprises generating segment detour popularity scores for segments of the O/D pair whereby the segments were traversed by not suggested for a trip (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed. A method of providing vehicle trip information includes counting the number of telematic device traversals of the stop per hash")
Regarding claim 3, Lewis et al. teaches: The method of claim 2, wherein analyzing the trip data comprises generating a trip detour popularity score based on the segment detour popularity scores by applying a distance weighted average over the segments (Paragraph [82], "According to exemplary embodiments described herein, once routes are converted into an ordered sequences of edges (e.g., subsections of streets from one node to another where a node is an intersection), the ordered sequence of edges may be hashed")
Regarding claim 4, Lewis et al. teaches: The method of claim 1, wherein analyzing the trip data comprises joining matching detours to compute aggregated metrics (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop"), a segment being a matching detour when a threshold percentage of the segment matches a continuous stretch of a trajectory of a trip (Paragraph [80], "In some embodiments, the one or more criteria include shared edge distance greater than a threshold edge distance. In some embodiments, a threshold edge distance may be greater than or equal to 75% of the total trip distance, 80% of the total trip distance, 85% of the total trip distance, 90% of the total trip distance, or another suitable distance. According to such embodiments, routes with substantial overlap may be grouped together")
Regarding claim 7, Lewis et al. teaches: The method of claim 1, wherein reconstructing the popular route comprises: determining a primary route based on the request (Paragraph [83], "main route"); identifying a candidate set of segments for detours that occur on the primary route (Paragraph [92], "For example, deviations from a route that are within a threshold deviation distance but greater than a minimum deviation distance may be identified as a stop"); accessing waypoints associated with the candidate set of segments (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.).") from the waypoint data storage (Paragraph [45], "The telematics device 104a of FIG. 1 may include suitable hardware and/or software configured to collect, sense, receive, process, store, and/or transmit any appropriate telematics data associated with a vehicle"); and ranking the accessed waypoints based on corresponding popularity scores to identify the top-ranking waypoints (Paragraph [89], "benefits of a method of providing vehicle trip information showing the popularity and/or usage of various stops (e.g., driveways, fueling stations, commercial buildings such as restaurants, etc.) between two geographical regions (e.g., points). The stop usage and/or popularity may be employed to show stops ranked in order of popularity for infrastructure evaluation, planning, and change management")
Regarding claim 9, Lewis et al. teaches: A system comprising: one or more hardware processors; and memory storing instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations (Paragraph [122]) comprising: capturing trip data of a plurality of users traversing routes by monitoring user devices of the plurality of users (Figure 9A; element Trip Data; Paragraph [78], "trip data of one or more trips obtained by one or more telematic devices and the GPS records associated with each trip"); analyzing the trip data between an origin/destination (O/D) pair (Paragraph [90]) to determine candidate popular trips between the O/D pair (Paragraph [77], "The route usage and/or popularity may be employed to show sequences of road traversals between origin and destination regions or routes ranked in order of popularity for infrastructure evaluation, planning, and change management") comprising detours (Paragraph [92], "For example, deviations from a route that are within a threshold deviation distance but greater than a minimum deviation distance may be identified as a stop"); determining top-ranking waypoints of segments of the candidate popular trips (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); storing the top-ranking waypoints of the O/D pair in a waypoint data storage (Paragraph [45], "The telematics device 104a of FIG. 1 may include suitable hardware and/or software configured to collect, sense, receive, process, store, and/or transmit any appropriate telematics data associated with a vehicle"); in response to receiving a request for a transportation service between the O/D pair from a user (Paragraph [78], "The method may also include receiving an origin region and a destination region (e.g., from a user as user input, for example, using a graphical user interface)"), reconstructing a popular route using the top-ranking waypoints (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); and causing presentation of on a user device of the user of a plurality of route options for selection by the user, the plurality of route options including the reconstructed popular route (Figure 9B; element 903)
Regarding claim 10, Lewis et al. teaches: The system of claim 9, wherein analyzing the trip data comprises generating segment detour popularity scores for segments of the O/D pair whereby the segments were traversed by not suggested for a trip (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed. A method of providing vehicle trip information includes counting the number of telematic device traversals of the stop per hash")
Regarding claim 11, Lewis et al. teaches: The system of claim 10, wherein analyzing the trip data comprises generating a trip detour popularity score based on the segment detour popularity scores by applying a distance weighted average over the segments (Paragraph [82], "According to exemplary embodiments described herein, once routes are converted into an ordered sequences of edges (e.g., subsections of streets from one node to another where a node is an intersection), the ordered sequence of edges may be hashed")
Regarding claim 12, Lewis et al. teaches: The system of claim 9, wherein analyzing the trip data comprises joining matching detours to compute aggregated metrics (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop"), a segment being a matching detour when a threshold percentage of the segment matches a continuous stretch of a trajectory of a trip (Paragraph [80], "In some embodiments, the one or more criteria include shared edge distance greater than a threshold edge distance. In some embodiments, a threshold edge distance may be greater than or equal to 75% of the total trip distance, 80% of the total trip distance, 85% of the total trip distance, 90% of the total trip distance, or another suitable distance. According to such embodiments, routes with substantial overlap may be grouped together")
Regarding claim 15, Lewis et al. teaches: The system of claim 9, wherein reconstructing the popular route comprises: determining a primary route based on the request (Paragraph [83], "main route"); identifying a candidate set of segments for detours that occur on the primary route (Paragraph [92], "For example, deviations from a route that are within a threshold deviation distance but greater than a minimum deviation distance may be identified as a stop"); accessing waypoints associated with the candidate set of segments (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.).") from the waypoint data storage (Paragraph [45], "The telematics device 104a of FIG. 1 may include suitable hardware and/or software configured to collect, sense, receive, process, store, and/or transmit any appropriate telematics data associated with a vehicle"); and ranking the accessed waypoints based on corresponding popularity scores to identify the top-ranking waypoints (Paragraph [89], "benefits of a method of providing vehicle trip information showing the popularity and/or usage of various stops (e.g., driveways, fueling stations, commercial buildings such as restaurants, etc.) between two geographical regions (e.g., points). The stop usage and/or popularity may be employed to show stops ranked in order of popularity for infrastructure evaluation, planning, and change management")
Regarding claim 17, Lewis et al. teaches: A machine-storage medium storing instructions that, when executed by one or more hardware processors of a machine, cause the machine to perform operations comprising (Paragraph [122]): capturing trip data of a plurality of users traversing routes by monitoring user devices of the plurality of users (Figure 9A; element Trip Data; Paragraph [78], "trip data of one or more trips obtained by one or more telematic devices and the GPS records associated with each trip"); analyzing the trip data between an origin/destination (O/D) pair (Paragraph [90]) to determine candidate popular trips between the O/D pair (Paragraph [77], "The route usage and/or popularity may be employed to show sequences of road traversals between origin and destination regions or routes ranked in order of popularity for infrastructure evaluation, planning, and change management") comprising detours (Paragraph [92], "For example, deviations from a route that are within a threshold deviation distance but greater than a minimum deviation distance may be identified as a stop"); determining top-ranking waypoints of segments of the candidate popular trips (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); storing the top-ranking waypoints of the O/D pair in a waypoint data storage (Paragraph [45], "The telematics device 104a of FIG. 1 may include suitable hardware and/or software configured to collect, sense, receive, process, store, and/or transmit any appropriate telematics data associated with a vehicle"); in response to receiving a request for a transportation service between the O/D pair from a user (Paragraph [78], "The method may also include receiving an origin region and a destination region (e.g., from a user as user input, for example, using a graphical user interface)"), reconstructing a popular route using the top-ranking waypoints (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); and causing presentation of on a user device of the user of a plurality of route options for selection by the user, the plurality of route options including the reconstructed popular route (Figure 9B; element 903)
Regarding claim 18, Lewis et al. teaches: The machine-storage medium of claim 17, wherein reconstructing the popular route comprises: determining a primary route based on the request (Paragraph [83], "main route"); identifying a candidate set of segments for detours that occur on the primary route (Paragraph [92], "For example, deviations from a route that are within a threshold deviation distance but greater than a minimum deviation distance may be identified as a stop"); accessing waypoints associated with the candidate set of segments (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.).") from the waypoint data storage (Paragraph [45], "The telematics device 104a of FIG. 1 may include suitable hardware and/or software configured to collect, sense, receive, process, store, and/or transmit any appropriate telematics data associated with a vehicle"); and ranking the accessed waypoints based on corresponding popularity scores to identify the top-ranking waypoints (Paragraph [89], "benefits of a method of providing vehicle trip information showing the popularity and/or usage of various stops (e.g., driveways, fueling stations, commercial buildings such as restaurants, etc.) between two geographical regions (e.g., points). The stop usage and/or popularity may be employed to show stops ranked in order of popularity for infrastructure evaluation, planning, and change management")
Regarding claim 19, Lewis et al. teaches: The machine-storage medium of claim 17, wherein analyzing the trip data comprises: generating segment detour popularity scores for segments of the O/D pair whereby the segments were traversed by not suggested for a trip (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed. A method of providing vehicle trip information includes counting the number of telematic device traversals of the stop per hash") and generating a trip detour popularity score based on the segment detour popularity scores by applying a distance weighted average over the segments (Paragraph [82], "According to exemplary embodiments described herein, once routes are converted into an ordered sequences of edges (e.g., subsections of streets from one node to another where a node is an intersection), the ordered sequence of edges may be hashed")
Regarding claim 20, Lewis et al. teaches: The machine-storage medium of claim 17, wherein analyzing the trip data comprises joining matching detours to compute aggregated metrics (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop"), a segment being a matching detour when a threshold percentage of the segment matches a continuous stretch of a trajectory of a trip (Paragraph [80], "In some embodiments, the one or more criteria include shared edge distance greater than a threshold edge distance. In some embodiments, a threshold edge distance may be greater than or equal to 75% of the total trip distance, 80% of the total trip distance, 85% of the total trip distance, 90% of the total trip distance, or another suitable distance. According to such embodiments, routes with substantial overlap may be grouped together")
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.
Claim 5, 6, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis et al. (US 2025/0369758 A1) in view Murdoch (US 20250237506 A1).
Regarding claim 5, Lewis et al. teaches: The method of claim 1, wherein determining the top-ranking waypoints comprises: grouping different segments that represent the detours (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop"); aggregating metrics at a D/C level for each grouping of a detour (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed"); and selecting a predetermined number of D/C segments for each detour based on the aggregated metrics (Paragraph [7], "The method also includes generating trip information for the trip using the association between the GPS data and the ordered sequence of edges")
While Lewis et al. teaches the limitations as stated above, it does not expressly teach:
by divergence/convergence (D/C) hexagons
wherein the routes are converted to hexagons at a same level as the D/C hexagons
However, Murdoch teaches: by divergence/convergence (D/C) hexagons (Figure 8; Paragraph [163], "deviation from the intended path by a particle is indicated by the solid white arrows"), wherein the routes are converted to hexagons at a same level as the D/C hexagons (Figure 7)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the main route output using deviation hashing and ordering edges of Lewis et al., to include using hexagonal areas to divide the mapping area, as taught by Murdoch. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: outputting a main route by dividing a mapped area into hexagons and using deviation hashing and ordering edges.
Regarding claim 6, Lewis et al. teaches: The method of claim 5, associating the aggregated metrics for their respective D/C segment to each waypoint (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed"), the top-ranking waypoints being based on the associated metrics (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop")
While Lewis et al. teaches the limitations as stated above, it does not expressly teach:
wherein determining the top-ranking waypoints comprises: extracting three waypoints for each selected D/C segment
the three waypoints comprising a waypoint at a middle of the detour
a waypoint in a beginning of the detour
and a waypoint towards an end of the detour
However, Murdoch teaches: wherein determining the top-ranking waypoints comprises: extracting three waypoints for each selected D/C segment (Figure 8), the three waypoints comprising a waypoint at a middle of the detour (element H12), a waypoint in a beginning of the detour (element H10), and a waypoint towards an end of the detour (element H9)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify outputting a main route by dividing a mapped area into hexagons and using deviation hashing and ordering edges of Lewis et al. and Murdoch, to include the three points associated with a deviation, as taught by Murdoch. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: outputting a main route by dividing a mapped area into hexagons and using deviation hashing, wherein a deviation is three points from the intended path, and ordering edges.
Regarding claim 13, Lewis et al. teaches: The system of claim 9, wherein determining the top-ranking waypoints comprises: grouping different segments that represent the detours (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop"); aggregating metrics at a D/C level for each grouping of a detour (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed"); and selecting a predetermined number of D/C segments for each detour based on the aggregated metrics (Paragraph [7], "The method also includes generating trip information for the trip using the association between the GPS data and the ordered sequence of edges")
While Lewis et al. teaches the limitations as stated above, it does not expressly teach:
by divergence/convergence (D/C) hexagons
wherein the routes are converted to hexagons at a same level as the D/C hexagons
However, Murdoch teaches: by divergence/convergence (D/C) hexagons (Figure 8; Paragraph [163], "deviation from the intended path by a particle is indicated by the solid white arrows"), wherein the routes are converted to hexagons at a same level as the D/C hexagons (Figure 7)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the main route output using deviation hashing and ordering edges of Lewis et al., to include using hexagonal areas to divide the mapping area, as taught by Murdoch. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: outputting a main route by dividing a mapped area into hexagons and using deviation hashing and ordering edges.
Regarding claim 14, Lewis et al. teaches: The system of claim 13, associating the aggregated metrics for their respective D/C segment to each waypoint (Paragraph [94], "According to exemplary embodiments described herein, once stops are identified (e.g., as an edge), the stop may be hashed"), the top-ranking waypoints being based on the associated metrics (Paragraph [39], "The deviations may be counted where the same deviations occur in multiple trips between the origin and destination, such that the deviations with the greatest number of occurrences may be provided to a user as a popular stop")
While Lewis et al. teaches the limitations as stated above, it does not expressly teach:
wherein determining the top-ranking waypoints comprises: extracting three waypoints for each selected D/C segment
the three waypoints comprising a waypoint at a middle of the detour
a waypoint in a beginning of the detour
and a waypoint towards an end of the detour
However, Murdoch teaches: The method of claim 5, wherein determining the top-ranking waypoints comprises: extracting three waypoints for each selected D/C segment (Figure 8), the three waypoints comprising a waypoint at a middle of the detour (element H12), a waypoint in a beginning of the detour (element H10), and a waypoint towards an end of the detour (element H9)
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify outputting a main route by dividing a mapped area into hexagons and using deviation hashing and ordering edges of Lewis et al. and Murdoch, to include the three points associated with a deviation, as taught by Murdoch. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: outputting a main route by dividing a mapped area into hexagons and using deviation hashing, wherein a deviation is three points from the intended path, and ordering edges.
Claim 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis et al. (US 2025/0369758 A1) in view Gu et al. (US 20220101208 A1).
Regarding claim 8, Lewis et al. teaches: popular segments within the candidate popular trips (Paragraph [80], "The method may also include counting the number of times each edge appears in the one or more trips"), or the top-ranking waypoints (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); receiving new trip data (Paragraph [84], "In some embodiments, the trip data may be obtained as an output of a method of associating trip data with a map, as discussed above, and may include trip information described herein. In some embodiments the base map data may be a current (e.g., within 1 month) whereas the trip data may include historical trips older than the current base ma")
While Lewis et al. teaches the limitations as stated above, it does not expressly teach:
training one or more machine learning models based on training data derived from the trip data
the one or more machine learning models used to analyze the trip data and determine one or more of the candidate popular trips
retraining the one or more machine learning models using the new trip data
However, Gu et al. teaches: The method of claim 1, further comprising: training one or more machine learning models based on training data derived from the trip data (Paragraph [79]), the one or more machine learning models used to analyze the trip data and determine one or more of the candidate popular trips (Paragraph [79], "Additionally or alternatively, the transportation sharing system 106 can identify common trips and times utilizing a machine-learning approach"); and retraining the one or more machine learning models using the new trip data (Paragraph [79], "For example, the transportation sharing system 106 can train a trip identifying model utilizing ground truth historical transportation data across a use history of previous requests and transportations associated with the new requestor")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the main route output using deviation hashing and ordering edges of Lewis et al., to include a machine-learning model that is trained for common trips, as taught by Gu et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: outputting a main route by using deviation hashing, ordering edges, and identifying common routes using a machine learning model.
Regarding claim 16, Lewis et al. teaches: The system of claim 9, further comprising: popular segments within the candidate popular trips (Paragraph [80], "The method may also include counting the number of times each edge appears in the one or more trips"), or the top-ranking waypoints (Paragraph [92], "In some such embodiments, the number of deviations for a given main route may be employed to output the most often used deviations, which in some cases may correspond to stops (e.g., gas stations, rest areas, off ramps, restaurants, hotels, etc.). In some embodiments, deviations may be counted across multiple vehicle trips. In some embodiment, one or more deviations having a greatest number of occurrences in the two or more trips may be output to a user (e.g., at a graphical user interface)"); receiving new trip data (Paragraph [84], "In some embodiments, the trip data may be obtained as an output of a method of associating trip data with a map, as discussed above, and may include trip information described herein. In some embodiments the base map data may be a current (e.g., within 1 month) whereas the trip data may include historical trips older than the current base ma")
While Lewis et al. teaches the limitations as stated above, it does not expressly teach:
training one or more machine learning models based on training data derived from the trip data
the one or more machine learning models used to analyze the trip data and determine one or more of the candidate popular trips
retraining the one or more machine learning models using the new trip data
However, Gu et al. teaches: training one or more machine learning models based on training data derived from the trip data (Paragraph [79]), the one or more machine learning models used to analyze the trip data and determine one or more of the candidate popular trips (Paragraph [79], "Additionally or alternatively, the transportation sharing system 106 can identify common trips and times utilizing a machine-learning approach"); and retraining the one or more machine learning models using the new trip data (Paragraph [79], "For example, the transportation sharing system 106 can train a trip identifying model utilizing ground truth historical transportation data across a use history of previous requests and transportations associated with the new requestor")
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the main route output using deviation hashing and ordering edges of Lewis et al., to include a machine-learning model that is trained for common trips, as taught by Gu et al. Such modification would have been obvious because such application would have been well within the level of skill of the person having ordinary skill in the art and would have yielded predictable results. The predictable results including: outputting a main route by using deviation hashing, ordering edges, and identifying common routes using a machine learning model.
Conclusion
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/A.T.T./Examiner, Art Unit 3656 /KHOI H TRAN/Supervisory Patent Examiner, Art Unit 3656