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 .
Status of Claims
Claims 1-20 of U.S. Application No. 18/953,562 filed on 11/20/2024 have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. The claimed invention is directed to the concept of searching and predicting navigation routes for a vehicle. This judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception and do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
The Examiner will further explain in view of the Revised Patent Subject Matter Eligibility Guidance:
Claims 1 is directed to a system of controlling a vehicle (i.e., an apparatus). 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, 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 claims 1, 10 and 14 include limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection.
Claims 1, 10 and 14 recites:
An apparatus for searching for a route using geospatial embedding based on dynamic resolution, the apparatus comprising:
a storage module configured to store digital map data;
and a processor configured to perform route search based on an estimated time of arrival (ETA) prediction model in response to a route search request,
wherein the ETA prediction model is configured to use a road network split into a plurality of tiles.
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, “search , perform , predict …” in the context of this claim encompasses a person looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted, 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”):
An apparatus for searching for a route using geospatial embedding based on dynamic resolution, the apparatus comprising:
a storage module configured to store digital map data;
and a processor configured to perform route search based on an estimated time of arrival (ETA) prediction model in response to a route search request,
wherein the ETA prediction model is configured to use a road network split into a plurality of tiles.
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 “storage, Processor” the examiner submits that these limitations are an attempt to generally link additional elements to a technological environment. In particular, the search, perform, predict by a processor is recited at a high level of generality and merely automates the determining steps, therefore acting as a generic computer to perform the abstract idea. The processor is claimed generically and is operating in its ordinary capacity and does not use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using a computer processor.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, 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, the additional element of “storage, processor” amounts to nothing more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Hence, the claim is not patent eligible.
Dependent claims 2-9, 11-13 and 15-20 do not recite any further limitations that cause the claim(s) 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. Therefore, dependent claims 22-9, 11-13 and 15-20 are not patent eligible under the same rationale as provided for in the rejection of Claims 1, 10 and 14.
Therefore, claims 1-20 are ineligible under 35 USC §101.
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.
Claims 1-9 and 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Jing et al. (US20210403032A1) in view of Wang (US20180364062A1) and in further view of Snyder (US10902655B1), hereinafter referred to as Jing, Wang and Snyder respectively.
Regarding claims 1 and 14, Jing discloses an apparatus for searching for a route (“The disclosed two-level optimal path planning includes a first level offline optimal path library generator and online path reader, and a second level online optimal path planner that starts a path search from the first level results.” [0029] using geospatial embedding based on dynamic resolution (“discretizes a driving or position space into a grid node map…[0055] The map of an area may be overlaid by a grid map…[0084]” and offline/online planning with path evaluation (¶ [0030]).
a storage module configured to store digital map data (“The system includes a data storage device for storing a path library, wherein the path library comprises a plurality of paths each path starting at one of a plurality of starting locations and ending at one of a plurality of endling locations, wherein the plurality of starting locations and the plurality of endling locations correspond to points on a map.” [0008]);
Jing does not explicitly teach a processor configured to perform route search based on an estimated time of arrival (ETA) prediction model in response to a route search request.
However, Wang does teach a processor configured to perform route search based on an estimated time of arrival (ETA) prediction model in response to a route search request (“method for determining and providing an individualized estimated time for arrival (ETA) comprising establishing a current route of a driver to a destination location, the current route being based on a set of possible routes between a current geographic location of the driver and the destination location, identifying (i) a total distance between the current geographic location of the driver and the destination location, (ii) an average speed of the driver along a first route segment of the current route, …….. and calculating an individualized ETA of the driver at the destination location based at least on the predicted speed of the driver along the one or more additional route segments of the current route.” [0019]). Both Jing and Wang teach methods for vehicle navigation planning. However, Wang explicitly teaches a processor configured to perform route search based on an estimated time of arrival (ETA) prediction model in response to a route search request.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include a processor configured to perform route search based on an estimated time of arrival (ETA) prediction model in response to a route search request, as taught by Wang, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Wang, 0019]).
Jing does not explicitly teach model is configured to use a road network split into a plurality of tiles.
However, Snyder does teach
model is configured to use a road network split into a plurality of tiles (Snyder teaches tiles that incorporate varying numbers of road links/segments: “map tile data may include boundary, location, and attribute data corresponding to the map elements” including “street network” as a “collection of street segments” with differing densities (e.g., major roads vs. back roads) (Snyder, col. 12, lines 26–43). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is configured to use a road network split into a plurality of tiles.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is configured to use a road network split into a plurality of tiles, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, col. 12, lines 26–43]).
Regarding claim 2,
Jing does not explicitly teach model is further configured to use the road network split into the plurality of tiles by incorporating a number of links of a road.
However, Snyder does teach model is further configured to use the road network split into the plurality of tiles by incorporating a number of links of a road (Snyder teaches tiles that incorporate varying numbers of road links/segments: “map tile data may include boundary, location, and attribute data corresponding to the map elements” including “street network” as a “collection of street segments” with differing densities (e.g., major roads vs. back roads) (Snyder, col. 12, lines 26–43). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is further configured to use the road network split into the plurality of tiles by incorporating a number of links of a road.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is further configured to use the road network split into the plurality of tiles by incorporating a number of links of a road, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, col. 12, lines 26–43]).
Regarding claim 3,
Jing does not explicitly teach model is further configured to use the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile.
However, Snyder does teach model is further configured to use the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile ( resolution-based limits in tile construction: cached tiles aggregate or reduce elements “associated with particular map element resolutions (e.g., a resolution of the minimum size map element included in the tile)” to manage data volume per tile (Snyder, col. 16, lines 47–67). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is further configured to use the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is further configured to use the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, col. 16, lines 47–67]).
Regarding claim 4,
Jing does not explicitly teach model is further configured to use a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when the number of links inserted into one tile is greater than the maximum number of links.
However, Snyder does teach model is further configured to use a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when the number of links inserted into one tile is greater than the maximum number of links (a hierarchical (tree-like) grid structure: source map data is divided into individual tiles (e.g., 16 tiles labeled A1–D4), then reduced/aggregated into larger cached tiles using a preset grid factor (e.g., 2×2): “The set of cached map tiles 606 represents the reduction of the set of source map data 602 to a grid of 2×2 map tiles… each of the cached map tiles… corresponds to a region…” (Snyder, col. 20, lines 7–19) and “a square cached map tile image may have a total resolution of 128×128 pixels, generated from a set of four smaller cached map tile images, each with a resolution of 128×128 pixels. Accordingly, generation of the cached map tile image may include compression or other techniques for reducing the image resolution from an aggregation of the source images (which, stitched together at their native resolution, would generate an image having a resolution of 256×256 pixels). In this manner, an image for a cached map tile may have a lower resolution than the aggregation of corresponding image files used to generate the cached map tile.” [Col. 18 lines 8 - 19). Inserting road links/segments into regions and splitting/aggregating by preset factors when density exceeds practical limits per tile is the obvious implementation. Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is further configured to use a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when the number of links inserted into one tile is greater than the maximum number of links.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is further configured to use a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when the number of links inserted into one tile is greater than the maximum number of links, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, 0010 and 0011]).
Regarding claim 5,
Jing does not explicitly teach model is further configured to use link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded.
However, Snyder does teach model is further configured to use link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded (at the finest (leaf-like) source level, full road segment/link information is stored: “street segment identifier… longitude and latitude points… speed limits,” (Col.13 line 5-11) and “multiple layers of abstraction may be employed, such that a cached map tiles serve as source map data for other cached map tiles, such as in a scenario where three or more layers of abstraction are employed (e.g., multiple zoom levels with different associated types or numbers of map elements).” while higher levels aggregate (Snyder, col. 2, lines 50–55). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is further configured to use link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is further configured to use link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, 0010 and 0011]).
Regarding claim 6,
Jing does not explicitly teach model is further configured to use ID information assigned to each of the plurality of tiles that have been finally split so that the tree is no longer expanded.
However, Snyder does teach model is further configured to use ID information assigned to each of the plurality of tiles that have been finally split so that the tree is no longer expanded (“The set of cached map tiles 606 represents … labeled along axes A-B and 1-2” with final tiles identified by coordinate labels (Snyder, col. 20, lines 7–19), and “In some embodiments, the unique identifier may be coordinates associated with the updated map data, such as latitude and longitude coordinates, of each updated data element, or the like. A cached map tile generator 410 accesses the tile update log 408 and the dynamic map data 406 to manage the generation and/or modification of a set of cached map tile data 412.” (Snyder, col. 16, lines 9–20). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is further configured to use ID information assigned to each of the plurality of tiles that have been finally split so that the tree is no longer expanded.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is further configured to use ID information assigned to each of the plurality of tiles that have been finally split so that the tree is no longer expanded, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, col. 16, lines 9–20and col. 16, lines 9–20]).
Regarding claim 7,
Jing does not explicitly teach model is further configured to use, as an input value, ID information of a tile, among the plurality of the tiles, comprising a link according to the route search.
However, Snyder does teach model is further configured to use, as an input value, ID information of a tile, among the plurality of the tiles, comprising a link according to the route search (“a user computing entity 110 may store/record map data as a part of a digital map, e.g., as part of a feature layer, raster layer, service layer, icons/graphics layer, geoprocessing layer, basemap layer, satellite layer, street network layer, points of interest layer, service point layer, and/or the like. The term digital map is intended to include any map that can electronically display geographic areas. As will be described in greater detail below, the geographical maps and/or map information/data can be used to manage, create, modify, delete, add, and/or the like map elements to assigned areas, regions, routes, paths, and/or the like.” [Snyder, col. 12, lines 32–43]). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches model is further configured to use, as an input value, ID information of a tile, among the plurality of the tiles, comprising a link according to the route search.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include model is further configured to use, as an input value, ID information of a tile, among the plurality of the tiles, comprising a link according to the route search, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, col. 12, lines 32–43]).
Regarding claims 8 and 15,
Jing does not explicitly teach calculate a plurality of candidate routes in response to the route search request and calculate an ETA of each of the candidate routes through the ETA prediction model.
However, Wang does teach calculate a plurality of candidate routes in response to the route search request (“The LOF with the requested route, whether direct or indirect, will be calculated by comparing the requested route with the routes of service requests previously completed by the same driver by tracking how common all or any parts of the requested route are to the routes of previously completed trips or service requests.” [0041]);
and calculate an ETA of each of the candidate routes through the ETA prediction model (“the system may calculate the ETA by further identifying a plurality of possible routes between the current geographic location of the driver and the destination location,” [0067]). Both Jing and Wang teach methods for vehicle navigation planning. However, Wang explicitly teaches calculate a plurality of candidate routes in response to the route search request and calculate an ETA of each of the candidate routes through the ETA prediction model.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include calculate a plurality of candidate routes in response to the route search request and calculate an ETA of each of the candidate routes through the ETA prediction model, as taught by Wang, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Wang, 0041 and 0067]).
Regarding claims 9 and 16,
Jing does not explicitly teach wherein the processor is further configured to calculate a cost for each of the candidate routes based on the calculated ETA of each of the candidate routes.
However, Wang does teach wherein the processor is further configured to calculate a cost for each of the candidate routes based on the calculated ETA of each of the candidate routes (“For example, each driver D1-D4 may have an ETA to the pick-up location, a cost of the ride to the destination, an indication that the driver is a favorite driver (e.g., “FV”) or a preferred driver, etc” [0111]). Both Jing and Wang teach methods for vehicle navigation planning. However, Wang explicitly teaches wherein the processor is further configured to calculate a cost for each of the candidate routes based on the calculated ETA of each of the candidate routes.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include wherein the processor is further configured to calculate a cost for each of the candidate routes based on the calculated ETA of each of the candidate routes, as taught by Wang, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Wang, 0111]).
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jing in view of Wang and in further view of Snyder and in further view of Johnson et al. (US11448516B2),, hereinafter referred to as Jing, Wang, Snyder and Johnson respectively.
Regarding claim 17,
Jing does not explicitly teach training, by the processor.
However, Johnson does teach training, by the processor (“the method includes training a machine learning model using (i) map feature data for each of a plurality of road segments, and (ii) a scenic metric for each of the plurality of road segments indicative of a quality of a view from the road segment. The method further includes receiving a request for navigation directions from a starting location to a destination location, and identifying a set of candidate routes for navigating from the starting location to the destination location.” [Col.3 lines 36-55]). Both Jing and Johnson teach methods for vehicle navigation planning. However, Johnson explicitly teaches training, by the processor.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include training, by the processor, as taught by Johnson, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Johnson, Col.3 lines 36-55]).
Jing does not explicitly teach the ETA prediction model by using ID information of a tile, among the plurality of the tiles, comprising a link according to a route having an ETA predicted as an input value, before receiving the route search request.
However, Wang does teach the ETA prediction model by using ID information of a tile, among the plurality of the tiles, comprising a link according to a route having an ETA predicted as an input value, before receiving the route search request (“The application may be further configured to cause server 108 to establish the individualized ETA for the driver by calculating a predicted speed of the driver for one or more segments of the route in accordance with one or more predetermined rules, the predicted speed of the driver being computed at least by identifying the time-varying geographic location data of the driver, the location data being updated in real-time, by determining one or more characteristics (and/or assigning weights to) each of the one or more characteristics in accordance with the one or more predetermined rules.” [0066]). Both Jing and Wang teach methods for vehicle navigation planning. However, Wang explicitly teaches the ETA prediction model by using ID information of a tile, among the plurality of the tiles, comprising a link according to a route having an ETA predicted as an input value, before receiving the route search request.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include the ETA prediction model by using ID information of a tile, among the plurality of the tiles, comprising a link according to a route having an ETA predicted as an input value, before receiving the route search request, as taught by Wang, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Wang, 0066]).
Regarding claim 18,
Jing does not explicitly teach using, by the ETA prediction model, the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile.
However, Snyder does teach using, by the ETA prediction model, the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile (resolution-based limits in tile construction: cached tiles aggregate or reduce elements “associated with particular map element resolutions (e.g., a resolution of the minimum size map element included in the tile)” to manage data volume per tile (Snyder, col. 16, lines 47–67). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches using, by the ETA prediction model, the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include using, by the ETA prediction model, the road network split into the plurality of tiles based on a maximum number of links that are able to be included in one tile, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, col. 16, lines 47–67]).
Regarding claim 19,
Jing does not explicitly teach using, by the ETA prediction model, a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when a number of links inserted into one tile is greater than the maximum number of links
However, Snyder does teach using, by the ETA prediction model, a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when a number of links inserted into one tile is greater than the maximum number of links (a hierarchical (tree-like) grid structure: source map data is divided into individual tiles (e.g., 16 tiles labeled A1–D4), then reduced/aggregated into larger cached tiles using a preset grid factor (e.g., 2×2): “The set of cached map tiles 606 represents the reduction of the set of source map data 602 to a grid of 2×2 map tiles… each of the cached map tiles… corresponds to a region…” (Snyder, col. 20, lines 7–19) and “a square cached map tile image may have a total resolution of 128×128 pixels, generated from a set of four smaller cached map tile images, each with a resolution of 128×128 pixels. Accordingly, generation of the cached map tile image may include compression or other techniques for reducing the image resolution from an aggregation of the source images (which, stitched together at their native resolution, would generate an image having a resolution of 256×256 pixels). In this manner, an image for a cached map tile may have a lower resolution than the aggregation of corresponding image files used to generate the cached map tile.” [Col. 18 lines 8 - 19). Inserting road links/segments into regions and splitting/aggregating by preset factors when density exceeds practical limits per tile is the obvious implementation. Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches using, by the ETA prediction model, a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when a number of links inserted into one tile is greater than the maximum number of links.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include using, by the ETA prediction model, a tree expanded by inserting all of the links of the road network into the tile and splitting the tile by a preset number when a number of links inserted into one tile is greater than the maximum number of links, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, Col. 18 lines 8 - 19]).
Regarding claim 20,
Jing does not explicitly teach using, by the ETA prediction model, link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded.
However, Snyder does teach using, by the ETA prediction model, link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded (at the finest (leaf-like) source level, full road segment/link information is stored: “street segment identifier… longitude and latitude points… speed limits,” (Col.13 line 5-11) and “multiple layers of abstraction may be employed, such that a cached map tiles serve as source map data for other cached map tiles, such as in a scenario where three or more layers of abstraction are employed (e.g., multiple zoom levels with different associated types or numbers of map elements).” while higher levels aggregate (Snyder, col. 2, lines 50–55). Both Jing and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches using, by the ETA prediction model, link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Jing to also include using, by the ETA prediction model, link information split and stored in a leaf node when the tree becomes the leaf node that is no longer expanded, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, Col.13 line 5-11and col. 2, lines 50–55]).
Claims 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Wang in view of Snyder and in further view of Johnson et al. (US11448516B2), hereinafter referred to as Wang, Snyder and Johnson respectively.
Regarding claim 10, Wang discloses generating an estimated time of arrival (ETA) prediction model (“method for determining and providing an individualized estimated time for arrival (ETA) comprising identifying a preferred route of a driver to a destination location, the preferred route being based on a set of possible routes between a current geographic location of the driver and the destination location, setting the preferred route as a current route of the driver, identifying a total distance between the current geographic location of the driver and the destination location,” [0021] and “To utilize artificial intelligence (AI) to predict or otherwise anticipate the details of a customer's potential service request based on customer related details stored in the system's database to provide improved ETA;” [0016]),
an ETA prediction model by using the ID information of the tile comprising a link according to a route having an ETA predicted as an input value (“The application may be further configured to cause server 108 to establish the individualized ETA for the driver by calculating a predicted speed of the driver for one or more segments of the route in accordance with one or more predetermined rules, the predicted speed of the driver being computed at least by identifying the time-varying geographic location data of the driver, the location data being updated in real-time, by determining one or more characteristics (and/or assigning weights to) each of the one or more characteristics in accordance with the one or more predetermined rules.” [0066]).
Wang does not explicitly teach inserting, by a processor, a road link of a road network into a tree, expanding, by the processor, the tree by splitting a tile by a preset number when a number of links inserted into the tile of the tree is greater than a maximum number of links that are able to be included in one tile, assigning, by the processor, ID information to each of the tiles that have been finally split so that the tree is no longer expanded.
However, Snyder does teach inserting, by a processor, a road link of a road network into a tree (“ the geographical maps and/or map information/data can be used to manage, create, modify, delete, add, and/or the like map elements to assigned areas, regions, routes, paths, and/or the like.” []);
expanding, by the processor, the tree by splitting a tile by a preset number when a number of links inserted into the tile of the tree is greater than a maximum number of links that are able to be included in one tile (“The set of cached map tiles 606 represents the reduction of the set of source map data 602 to a grid of 2×2 map tiles… each of the cached map tiles… corresponds to a region…” (Snyder, col. 20, lines 7–19) and “a square cached map tile image may have a total resolution of 128×128 pixels, generated from a set of four smaller cached map tile images, each with a resolution of 128×128 pixels. Accordingly, generation of the cached map tile image may include compression or other techniques for reducing the image resolution from an aggregation of the source images (which, stitched together at their native resolution, would generate an image having a resolution of 256×256 pixels). In this manner, an image for a cached map tile may have a lower resolution than the aggregation of corresponding image files used to generate the cached map tile.” [Col. 18 lines 8 - 19). Inserting road links/segments into regions and splitting/aggregating by preset factors when density exceeds practical limits per tile is the obvious implementation;
assigning, by the processor, ID information to each of the tiles that have been finally split so that the tree is no longer expanded (at the finest (leaf-like) source level, full road segment/link information is stored: “street segment identifier… longitude and latitude points… speed limits,” (Col.13 line 5-11) and “multiple layers of abstraction may be employed, such that a cached map tiles serve as source map data for other cached map tiles, such as in a scenario where three or more layers of abstraction are employed (e.g., multiple zoom levels with different associated types or numbers of map elements).” while higher levels aggregate (Snyder, col. 2, lines 50–55). Both Wang and Snyder teach methods for vehicle navigation planning. However, Snyder explicitly teaches inserting, by a processor, a road link of a road network into a tree, expanding, by the processor, the tree by splitting a tile by a preset number when a number of links inserted into the tile of the tree is greater than a maximum number of links that are able to be included in one tile, assigning, by the processor, ID information to each of the tiles that have been finally split so that the tree is no longer expanded.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Wang to also include inserting, by a processor, a road link of a road network into a tree, expanding, by the processor, the tree by splitting a tile by a preset number when a number of links inserted into the tile of the tree is greater than a maximum number of links that are able to be included in one tile, assigning, by the processor, ID information to each of the tiles that have been finally split so that the tree is no longer expanded, as taught by Snyder, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Snyder, Col. 18 lines 8 – 19, Col.13 line 5-11 and col. 2, lines 50–55]).
Wang does not explicitly teach training, by the processor.
However, Johnson does teach training, by the processor (“the method includes training a machine learning model using (i) map feature data for each of a plurality of road segments, and (ii) a scenic metric for each of the plurality of road segments indicative of a quality of a view from the road segment. The method further includes receiving a request for navigation directions from a starting location to a destination location, and identifying a set of candidate routes for navigating from the starting location to the destination location.” [Col.3 lines 36-55]). Both Wang and Johnson teach methods for vehicle navigation planning. However, Johnson explicitly teaches training, by the processor.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Wang to also include training, by the processor, as taught by Johnson, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Johnson, Col.3 lines 36-55]).
Regarding claims 11, Wang discloses
the ETA prediction model by further using, as the input value, dynamic features comprising time information and traffic features comprising passage speed information of the link WANG (“method for determining and providing an individualized estimated time for arrival (ETA) comprising establishing a current route of a driver to a destination location, the current route being based on a set of possible routes between a current geographic location of the driver and the destination location, identifying (i) a total distance between the current geographic location of the driver and the destination location, (ii) an average speed of the driver along a first route segment of the current route, …….. and calculating an individualized ETA of the driver at the destination location based at least on the predicted speed of the driver along the one or more additional route segments of the current route.” [0019]).
Wang does not explicitly teach training, by the processor.
However, Johnson does teach training, by the processor (“the method includes training a machine learning model using (i) map feature data for each of a plurality of road segments, and (ii) a scenic metric for each of the plurality of road segments indicative of a quality of a view from the road segment. The method further includes receiving a request for navigation directions from a starting location to a destination location, and identifying a set of candidate routes for navigating from the starting location to the destination location.” [Col.3 lines 36-55]). Both Wang and Johnson teach methods for vehicle navigation planning. However, Johnson explicitly teaches training, by the processor.
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to modify the vehicle navigation planning method of Wang to also include training, by the processor, as taught by Johnson, with a reasonable expectation of success. Doing so improves vehicle route searching and optimization (With regard to this reasoning, see at least [Johnson, Col.3 lines 36-55]).
Regarding claim 12, Wang discloses further comprising:
outputting, by the ETA prediction model, a link passage time as an output value US 062 “identify relevant data for generating and transmitting a notification (e.g., containing ETA information) to a customer” [0055]).
Regarding claim 13, Wang discloses further comprising:
outputting, by the ETA prediction model, an ETA of all of routes as an output value US 062 “identify relevant data for generating and transmitting a notification (e.g., containing ETA information) to a customer” [0055]).
Conclusion
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/AA/Examiner, Art Unit 3668
/Fadey S. Jabr/Supervisory Patent Examiner, Art Unit 3668
