DETAILED ACTION
This communication is a Non-Final Office Action on the Merits. Claims 1-19 as originally filed are pending and have been considered as follows.
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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is: “storage module configured to store” in Claim 1.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-19 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Huang (WO 2021/218131 A1; citations to US Pub. No. 2023/0154327).
As per Claim 1, Huang discloses an apparatus (101, 103, 104; Fig. 7) for searching for a route (as per S to F in Fig. 5) using estimated time of arrival prediction (as per “Arrived before” in Fig. 5) based on a graph neural network (as per “GCN” in ¶44, 48-50, 54, 57-59), the apparatus (101, 103, 104; Fig. 7) comprising:
a storage module (104; 702) configured to store digital map data (as per “the server 104 may collect and maintain, in advance, user trajectory data uploaded by the terminal devices … and these data my constitute traffic flow feature data of a road network” in ¶33; as per “the storage data region may store data created according to the use of the electronic device” in ¶147) (Figs. 1, 7; ¶30-35, 143-147); and
a processor (104; 701) configured to perform a route search (as per “the apparatus for route planning is disposed in and runs in the server 104” in ¶33; as per “The processor 701 … implements the method for route planning” in ¶146) based on an ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141), according to a route exploration request (as per “When the user inputs a starting position and a finishing position to perform route planning” in ¶87) (Figs. 1-2, 5, 7; ¶30-41, 86-87, 111-116, 143-146),
wherein the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) is generated based on a graph neural network (as per “GCN” in ¶44, 48-50, 54, 57-59) formed by converting (as per “To fit an association relationship between different road segments, the association relationship may be built using the GCN, and vector representations obtained from the encoding and a road network link relationship are input to the GCN. The vector representations output by the GCN may be concatenated with the road attribute feature data and the environment feature data in the second historical time slice and then input to the fully-connected layer. The fully-connected layer obtains the predicted passage duration on the road segment in the second historical time slice” in ¶48) road information (as per “road segments” in ¶48) into a graph (as per “Graph Convolutional Network” in ¶44; as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) (Figs. 1-3, 5; ¶30-50, 54, 57-59, 86-87, 111-116, 141).
As per Claim 2, Huang further discloses wherein the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) is generated (as per “At 203, route planning is performed” in ¶86, as per “if the planned route recommended to the user includes a road segment with a congested state change risk, a second estimated arrival time of the route may be determined using a predicted passage duration of the road segment including the congested state change risk; an interval of the estimated arrival time of the road segment is displayed using a first estimated arrival time determined when the congested state change risk is not considered, and the second estimated arrival time” in ¶114) by converting a link of road information (as per “road segments” in ¶48) into a node (as per “Nodes in the topological diagram are crossings” in ¶87) of a graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) and converting connectivity between links into edges (as per “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87).
As per Claim 3, Huang further discloses wherein the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) is generated based on a graph neural network (as per “GCN” in ¶44, 48-50, 54, 57-59) including features (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33) of the route (as per S to F in Fig. 5) as global attributes (as per “The obtained traffic flow feature data” in ¶39).
As per Claim 4, Huang further discloses wherein a feature of the node (as per “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes information (as per “The obtained traffic flow feature data may include one of or any combination of traffic flow statistics data, speed data and times of sudden deceleration on the road segments” in ¶39) on a past speed or a passage time of a link (as per “road segments” in ¶48) corresponding to a node (as per “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87).
As per Claim 5, Huang further discloses wherein a weight (as per “weighting coefficients” in ¶84) configured to predict a value (as per weighted risk coefficients in ¶82-84) that is greater (as per “manually-set empirical value or experimental value” in ¶84 that is revised upward) than a predicted value that is smaller (as per “manually-set empirical value or experimental value” in ¶84 before being revised upward) than a correct answer (as per updated weight in ¶90-94), and
wherein the weight (as per “weighting coefficients” in ¶84) is configured to be applied (as per “route planning is performed using the state change risk information of the route segments” in ¶86 and “if the planned route recommended to the user includes a road segment with a congested state change risk, a second estimated arrival time of the route may be determined using a predicted passage duration of the road segment including the congested state change risk; an interval of the estimated arrival time of the road segment is displayed using a first estimated arrival time determined when the congested state change risk is not considered, and the second estimated arrival time” in ¶114) to the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141).
As per Claim 6, Huang further discloses wherein a weight (as per “weighting coefficients” in ¶84) is configured to increase an influence of a preset time zone (as per effect of weight on each risk coefficient of each segment in ¶78-84) on an output value of the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141), and
wherein the weight (as per “weighting coefficients” in ¶84) is configured to be applied (as per “route planning is performed using the state change risk information of the route segments” in ¶86 and “if the planned route recommended to the user includes a road segment with a congested state change risk, a second estimated arrival time of the route may be determined using a predicted passage duration of the road segment including the congested state change risk; an interval of the estimated arrival time of the road segment is displayed using a first estimated arrival time determined when the congested state change risk is not considered, and the second estimated arrival time” in ¶114) to the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141).
As per Claim 7, Huang further discloses wherein the processor (104; 701) is further configured to:
compute a plurality of candidate routes (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) according to a route search request (as per “When the user inputs a starting position and a finishing position to perform route planning” in ¶87); and
compute an ETA (as per “sorted according to any one of or any combination of a plurality of dimensions such as passage duration” in ¶87) of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) through the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141).
As per Claim 8, Huang further discloses wherein the processor (104; 701) is further configured to compute a cost of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) based on the computed ETA (as per “sorted according to any one of or any combination of a plurality of dimensions such as passage duration” in ¶87) of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94).
As per Claim 9, Huang discloses a method (Fig. 2) of generating an estimated time of arrival prediction model (as per determination of “estimated arrival time” in ¶114-115, 141), the method comprising:
converting, by a processor (104; 701), links of road information (as per “road segments” in ¶48) into nodes (as per “Nodes in the topological diagram are crossings” in ¶87) and converting connectivity between the links into edges (as per “edges are road segments” in ¶87) of a graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) to convert the road information (as per “road segments” in ¶48) into the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87);
inserting, by the processor (104; 701), related data into a global attribute (as per “The obtained traffic flow feature data” in ¶39) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87), a node attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87), and an edge attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87); and
learning (as per “training data is obtained” in ¶45), by the processor (104; 701), an ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) by using the global attribute (as per “The obtained traffic flow feature data” in ¶39) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87), the node attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87), and the edge attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) as input values (Figs. 2-3, 5; ¶36-51, 86-105, 111-116).
As per Claim 10, Huang further discloses wherein:
the global attribute (as per “The obtained traffic flow feature data” in ¶39) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes past ETA information (as per historical information of the road segments in the road network” in ¶45) for a route (as per “user trajectory data uploaded by the terminal devices … during use of the map-like application” in ¶33);
the node attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes information (as per “The obtained traffic flow feature data may include one of or any combination of traffic flow statistics data, speed data and times of sudden deceleration on the road segments” in ¶39) on a past speed or a passage time of a link (as per “road segments” in ¶48) corresponding to a node (as per “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87); and
the edge attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes information on connectivity (as per “factors for making the road segment impassible” in ¶61) between the nodes (as per “Nodes in the topological diagram are crossings” in ¶87) and information on whether a road type is changed (as per “The road attribute feature data may include information such as a length and a road class of the road segment” in ¶56).
As per Claim 11, Huang further discloses outputting, by the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141), a link passage time (as per “Less than 40 minutes” in Fig. 5) as an output value (Fig. 5).
As per Claim 12, Huang further discloses outputting, by the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141), ETA values (as per “Arrived before” in Fig. 5) for a route (Fig. 5).
As per Claim 13, Huang discloses a method (Fig. 2) of searching for a route (as per S to F in Fig. 5) using estimated time of arrival prediction (as per “Arrived before” in Fig. 5) based on a graph neural network (as per “GCN” in ¶44, 48-50, 54, 57-59), the method (Fig. 2) comprising:
receiving, by a processor (104; 701), a route search request (as per “When the user inputs a starting position and a finishing position to perform route planning” in ¶87) (Figs. 1-2, 5, 7; ¶30-41, 86-87, 111-116, 143-146);
performing, by the processor (104; 701), route search (as per “the apparatus for route planning is disposed in and runs in the server 104” in ¶33; as per “The processor 701 … implements the method for route planning” in ¶146) based on an ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) (Figs. 1-2, 5, 7; ¶30-41, 86-87, 111-116, 143-146); and
providing, the processor (104; 701), a route search result (as per 204; as per Fig. 5) (Figs. 1-2, 5, 7; ¶30-41, 86-87, 106-116, 143-146),
wherein the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) is generated based on a graph neural network (as per “GCN” in ¶44, 48-50, 54, 57-59) formed by converting (as per “To fit an association relationship between different road segments, the association relationship may be built using the GCN, and vector representations obtained from the encoding and a road network link relationship are input to the GCN. The vector representations output by the GCN may be concatenated with the road attribute feature data and the environment feature data in the second historical time slice and then input to the fully-connected layer. The fully-connected layer obtains the predicted passage duration on the road segment in the second historical time slice” in ¶48) road information (as per “road segments” in ¶48) into a graph (as per “Graph Convolutional Network” in ¶44; as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) (Figs. 1-3, 5; ¶30-50, 54, 57-59, 86-87, 111-116, 141).
As per Claim 14, Huang further discloses wherein performing the route search (as per “the apparatus for route planning is disposed in and runs in the server 104” in ¶33; as per “The processor 701 … implements the method for route planning” in ¶146) includes:
computing, by the processor (104; 701), a plurality of candidate routes (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) in response to the route search request (as per “When the user inputs a starting position and a finishing position to perform route planning” in ¶87); and
computing, by the processor (104; 701), an ETA (as per “sorted according to any one of or any combination of a plurality of dimensions such as passage duration” in ¶87) of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) through the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141).
As per Claim 15, Huang further discloses wherein performing the route search (as per “the apparatus for route planning is disposed in and runs in the server 104” in ¶33; as per “The processor 701 … implements the method for route planning” in ¶146) further includes computing, by the processor (104; 701), a cost of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) based on the computed ETA (as per “sorted according to any one of or any combination of a plurality of dimensions such as passage duration” in ¶87) of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94).
As per Claim 16, Huang further discloses wherein computing the ETA (as per “sorted according to any one of or any combination of a plurality of dimensions such as passage duration” in ¶87) of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) through the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) includes:
converting, by the processor (104; 701), each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) into a respective graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) by converting links (as per “road segments” in ¶48) of the candidate routes (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) into nodes (as per “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) and by converting connectivity between the links into edges (as per “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87); and
calculating, by the processor (104; 701), an ETA (as per “sorted according to any one of or any combination of a plurality of dimensions such as passage duration” in ¶87) of each candidate route (as per “The candidate routes obtained by looking up are sorted” in ¶87; as per “obtain at least one candidate route” in ¶94) through the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141) by inputting data related to a global attribute (as per “The obtained traffic flow feature data” in ¶39) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87), a node attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87), and an edge attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) as input values (Figs. 2-3, 5; ¶36-51, 86-105, 111-116).
As per Claim 17, Huang further discloses wherein:
the global attribute (as per “The obtained traffic flow feature data” in ¶39) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes past ETA information (as per historical information of the road segments in the road network” in ¶45) on the route (as per “user trajectory data uploaded by the terminal devices … during use of the map-like application” in ¶33);
the node attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes information (as per “The obtained traffic flow feature data may include one of or any combination of traffic flow statistics data, speed data and times of sudden deceleration on the road segments” in ¶39) on a past speed or a passage time of a link (as per “road segments” in ¶48) corresponding to a node (as per “Nodes in the topological diagram are crossings” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87); and
the edge attribute (as per “user data trajectory data uploaded by the terminal devices … during use of the map-like application and traffic flow data uploaded by various traffic sensors, and these data my constitute traffic flow feature data of a road network” in ¶33 for each “edges are road segments” in ¶87) of the graph (as per “a mesh-like topological diagram is built from all road segments on the road network” in ¶87) includes information on connectivity (as per “factors for making the road segment impassible” in ¶61) between the nodes (as per “Nodes in the topological diagram are crossings” in ¶87) and information on whether a road type is changed (as per “The road attribute feature data may include information such as a length and a road class of the road segment” in ¶56).
As per Claim 18, Huang further discloses predicting, by a weight (as per “weighting coefficients” in ¶84), a value (as per weighted risk coefficients in ¶82-84) that is greater (as per “manually-set empirical value or experimental value” in ¶84 that is revised upward ) than a predicted value that is smaller (as per “manually-set empirical value or experimental value” in ¶84 before being revised upward) than a correct answer (as per updated weight in ¶90-94); and
applying (as per “route planning is performed using the state change risk information of the route segments” in ¶86 and “if the planned route recommended to the user includes a road segment with a congested state change risk, a second estimated arrival time of the route may be determined using a predicted passage duration of the road segment including the congested state change risk; an interval of the estimated arrival time of the road segment is displayed using a first estimated arrival time determined when the congested state change risk is not considered, and the second estimated arrival time” in ¶114) the weight (as per “weighting coefficients” in ¶84) to the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141).
As per Claim 19, Huang further discloses increasing, by a weight (as per “weighting coefficients” in ¶84), an influence of a preset time zone (as per effect of weight on each risk coefficient of each segment in ¶78-84) on an output value of the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141); and
applying (as per “route planning is performed using the state change risk information of the route segments” in ¶86 and “if the planned route recommended to the user includes a road segment with a congested state change risk, a second estimated arrival time of the route may be determined using a predicted passage duration of the road segment including the congested state change risk; an interval of the estimated arrival time of the road segment is displayed using a first estimated arrival time determined when the congested state change risk is not considered, and the second estimated arrival time” in ¶114) the weight (as per “weighting coefficients” in ¶84) to the ETA prediction model (as per determination of “estimated arrival time” in ¶114-115, 141).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yoshizumi (US Pub. No. 2013/0238242) discloses a route selection system, method and program. Sykora (US Pub. No. 2021/0248460) discloses systems and methods for optimized multi-agent routing between nodes. Kim (US Pub. No. 2021/0302180) discloses an optimal route searching device and operation method thereof. Davies (US Pub. No. 2021/0326699) discloses travel speed prediction.
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/STEPHEN HOLWERDA/Primary Examiner, Art Unit 3656