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 .
Response to Amendment
This action is in response to amendments and remarks filed on 02/09/2026. The examiner notes the following adjustments to the claims by the applicant:
Claims 1, 18 and 20 are amended.
Therefore, Claims 1-20 are pending examination, in which Claims 1, 18 and 20 are independent claims.
In light of the instant amendments and arguments:
Further examination resulted in a new rejection of Claims 1-20 under 35 U.S.C. § 103, as detailed below.
THIS ACTION IS MADE FINAL. Necessitated by amendment.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
To overcome the 35 U.S.C. § 103 rejection, the applicant has amended each independent claim to include the additional underlined limitations: "wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded from the direct sunlight between the initial position and the destination along the plurality of edges";
“With reference to "the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded from the direct sunlight between the initial position and the destination along the plurality of edges," as recited in claim 1, it is submitted that the cited references, alone or in combination, fail to suggest such features. The Office indicates that Galoogahi and Oostveen do not explicitly teach all the features of claim 1 and cites the Abstract and FIG. 3 of Han as disclosing the features missing in Galoogahi and Oostveen.”;
“Han's teaching of the navigation processing scheme configured to determine the shading and protection sub-path in the navigation path, and determine the route selection auxiliary mark according to the weather warning sub-path, or that sections or segments of a route are identified on a map to indicate favorable or unfavorable weather conditions is unrelated to "the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded from the direct sunlight between the initial position and the destination along the plurality of edges," as recited in claim 1. Thus, Han does not disclose the features missing in Galoogahi and Oostveen.”.
Applicant's arguments A., B. and C. appear to be directed to the instantly amended subject matter. Accordingly, they have been addressed in the rejections below.
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 1-20 are rejected under 35 U.S.C. §103 as being unpatentable over the combination of Galoogahi et al. (US 11,967,106 B2, henceforth Galoogahi), Duan et al. (CN 110110896 A, henceforth Duan), and Han (CN 114252085 A).
Regarding Claim 1, Galoogahi explicitly recites the limitations: a method comprising: determining, by a navigation system {Autonomous vehicle system 120, Fig. 1; Autonomous navigation system 1308, Fig. 8; “autonomous navigation system 1308 includes a control circuit that is responsible for directing navigation of the autonomous vehicle.”, Col. 19, Lns. 29-31}, a route to a destination from an initial position of the navigation system {path 1012, Fig. 10}, the route comprising a plurality of edges of a graph representing a map of navigable paths, wherein each edge represents a path through an environment {Edges 1010 in Fig. 10, such as 1010a-c, as described in Col. 15, Lns. 52-57, including numerical weighting for each edge, Col. 16, Lns. 10-13}, the path connecting a first vertex and a second vertex, and each vertex represents at least one of: an endpoint of a path or a junction of two or more paths {“The nodes 1006a-d are connected by edges 1010a-c. If two nodes 1006a-b are connected by an edge 1010a, it is possible for an AV 100 to travel between one node 1006a and the other node 1006b, e.g., without having to travel to an intermediate node before arriving at the other node 1006b.”, Col. 15, Lns. 52-57}, wherein determining the route comprises: determining an environmental complexity parameter of the path {effort or cost to deal with factors imposed upon a vehicle/moving object by the environment: “An edge 1010a-c has an associated cost 1014a-b. The cost 1014a-b is a value that represents the resources that will be expended if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014 a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 7-20} in view of a time-varying environmental characteristic of the path at a current time {real-time weather conditions data: “the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions”, Col. 7, Lns. 38-40; “Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 18-20}; determining a weight of the edge in view of the environmental complexity parameter {edge weights/costs are represented in Fig. 10 as 1’s and 2’s, and are positioned atop the edge connecting lines; they are a result of the cost considerations in Col. 16, Lns. 7-20, see above}; wherein the environmental complexity parameter includes environmental characteristics that are assigned a higher priority than other path characteristics {a rule-based planning module wherein the rules can establish priority: “the planning module 404 includes database data 914 (e.g., from the database module 410 shown in FIG. 4)…the database data 914 includes rules used in planning...In any given situation encountered by the AV 100, at least some of the rules will apply to the situation. A rule applies to a given situation if the rule has conditions that are met based on information available to the AV 100, e.g., information about the surrounding environment. Rules can have priority. For example, a rule that says, “if the road is a freeway, move to the leftmost lane” can have a lower priority than “if the exit is approaching within a mile, move to the rightmost lane.”, Col. 14, Ln. 60 to Col. 15, Ln. 10; and, further, the surrounding environment includes consideration of a wide range of factors, including traffic, driving performance and weather conditions, see Col. 7, Lns. 31-42}; and assigning the weight to the edge {edge weights/costs are represented in Fig. 10 as 1’s and 2’s, and are positioned atop the edge connecting lines}, wherein the route is determined in view of the weight of the edge {heavy line path 1012 in Fig. 10 is determined by planning module 404 to be the least costly: “FIG. 10 shows a directed graph 1000 used in path planning, e.g., by the planning module 404 (FIG. 4)”, Col. 15, Lns. 11-12, and “the planning module 404 uses the directed graph 1000 to identify a path 1012 made up of nodes and edges between the start point 1002 and end point 1004. An edge 1010a-c has an associated cost 1014a-b.”, Col. 16, Lns. 3-7, and “When the planning module 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning module 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.”, Col. 16, Lns. 21-25}; and directing a vehicle associated with the navigation system along the route {“Computing devices 146 located on the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.”, Col. 8, Lns. 27-30}.
Galoogahi does not appear to explicitly recites the limitations: wherein the weight of the edge is based on desirability of the environmental characteristics within the current time, wherein the environmental characteristics comprise light, wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges; wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded from the direct sunlight between the initial position and the destination along the plurality of edges.
However, Duan explicitly recites limitations: wherein the weight of the edge is based on desirability of the environmental characteristics within the current time, wherein the environmental characteristics comprise light {“a solution for a personalized route recommendation system for sun-shading, rain shelter, wind selection, and viewing. For the sunshade sunscreen model, first use the data sensor and data collector to capture the various data needed to build the model through the situational awareness (the size of the building, the intensity of the sunlight and the direction of the illumination”, ¶[0007]}, wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along the path {“Module 1: For the weather with high sun and strong ultraviolet rays, the user wants to choose a route with appropriate light intensity or shading as much as possible”, ¶[0011]}; wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded {from the direct sunlight between the initial position {above Galoogahi taught of assigning weighted values to edges, and here Duan teaches of protection from the sun, in which case sunlight and shade are opposing quantities, ¶[0003 & 0006]} and the destination along the plurality of edges {with regard to equation in ¶[0025]: “Module 1: For weather with high sun and strong ultraviolet light, users want to choose the route with proper light intensity or as much as possible”}.
The Galoogahi and Duan are analogous art because they both deal with navigating and vehicle route planning, and take into account external factors like weather conditions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Galoogahi and Duan before them, to modify the teachings of Galoogahi to include the teachings of Duan to allow a traveler to avoid direct sunlight, as much as possible {user’s preferences described in ¶[0006]: “An optimal path that satisfies people's needs, has a high degree of sun protection throughout the journey, or satisfies users' needs for drying and reasonable distances”}.
The combination of Galoogahi and Duan does not appear to explicitly disclose limitation: wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges.
However, Han explicitly recites the limitation: wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges {sections or segments of a route (i.e., sub-paths) are identified and demarcated on a map (e.g., dark line within dotted-lined box, Fig. 3 below) to indicate favorable or unfavorable weather conditions: “acquiring adverse weather information corresponding to the navigation path, and determining a weather warning sub-path in the navigation path according to the adverse weather information; acquiring terrain information of the navigation path , and according to the terrain information, determine the shading and protection sub-path in the navigation path; according to the weather warning sub-path and the shading and protection sub-path, determine the route selection auxiliary mark”, Abstract}.
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The combination of references Galoogahi and Duan along with Han are analogous art because they deal with navigating and vehicle route planning, and take into account external factors like weather conditions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Galoogahi, Duan and Han before them, to modify the teachings of the combination of Galoogahi and Duan to include the teachings of Han to identify and label sections of a navigation route with weather condition information of importance to the user {“after the weather warning sub-path and the shading protection sub-path are determined”, Pg. 13, Lns. 15-16}.
Regarding Claim 2, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 1, as discussed supra. In addition, Galoogahi explicitly recites the limitation: wherein the current time comprises a time at which a current position of the navigation system corresponds to a position associated with the path {starting point 1002 in Fig. 10: “When the planning module 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning module 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.”, Col. 16, Lns. 21-25}.
Regarding Claim 3, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 1, as discussed supra. In addition, Galoogahi explicitly recites the limitations: determining a path length in view of a distance between the endpoint or junction represented by the first vertex and the endpoint or junction represented by the second vertex {circles for nodes and straight lines for edges in Fig. 10}, wherein the weight of the edge is further determined in view of the path length {“if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014a of the first edge 1010a may be 1014b of the second edge 1010b.”, Col. 16, Lns. 10-13, this is evident in the bottom, right-hand portion of Fig. 10 where a shorter edge is followed by two longer edges, and the corresponding weights/costs are 1, 2, 2, respectively}.
Regarding Claim 4, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 3, as discussed supra. In addition, Galoogahi explicitly recites the limitation: determining a path traversal time in view of the path length and a speed of travel associated with the edge, wherein the weight of the edge is further determined in view of the path traversal time {the effort or cost to deal with factors imposed upon a vehicle/moving object by the environment, such as travel time and weather conditions: “An edge 1010a-c has an associated cost 1014a-b. The cost 1014a-b is a value that represents the resources that will be expended if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014 a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 7-20}.
Regarding Claim 5, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 1, as discussed supra. Galoogahi does not appear to explicitly recite the limitations: wherein the environmental characteristics include sunlight, and wherein the environmental complexity parameter comprises a sunlight parameter determined in view of a length of a portion of the path that is shaded from direct sunlight at the current time, wherein the sunlight parameter corresponds to a negative amount of complexity that reduces the weight of the edge.
However, Duan explicitly recites the limitations: wherein the environmental characteristics include sunlight {“An optimal path that satisfies people's needs, has a high degree of sun protection throughout the journey, or satisfies users' needs for drying and reasonable distances;”, ¶[0006]; “a personalized route recommendation system for sun-shading, rain shelter, wind selection, and viewing”, ¶[0007]}, and wherein the environmental complexity parameter {the combination of Shadow, Comfort, Scenery and Need algorithms in ¶[0010], and an example of how these factors can be combined in provided in ¶[0011] under Module No. 1} comprises a sunlight parameter determined in view of a length of a portion of the path that is shaded from direct sunlight at the current time {the amount of shade versus the amount of sunlight exposure are two sides of the same coin, here we have the amount of shade being focused on: “Shadow indicates the area where a building or a tree or other object effectively produces shadows”, ¶[0007], and the Shadow algorithm in ¶[0010] includes factors for “size of the sunlit area of the building”, “the size of the sunlit area of the tree”, current time, and the intensity and angle of the sun's rays}, wherein the sunlight parameter corresponds to a negative amount of complexity that reduces the weight of the edge {The shadow algorithm in ¶[0010] represents the degree to which “the area where a building or a tree or other object effectively produces shadows.”, which can be considered the positive aspect and, conversely, the amount of sun exposer a negative}.
Regarding Claim 6, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 5, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the environmental complexity parameter is determined in view of a difference between a length of the path and a length of a portion of the path that is shaded from direct sunlight by a physical object at the current time.
However, Duan explicitly recites the limitation: wherein the environmental complexity parameter {the combination of Shadow, Comfort, Scenery and Need algorithms in ¶[0010]} is determined in view of a difference between a length of the path and a length of a portion of the path that is shaded {“An optimal path that satisfies people's needs, has a high degree of sun protection throughout the journey, or satisfies users' needs for drying and reasonable distances”, ¶[0006]} from direct sunlight by a physical object at the current time {The shadow algorithm in ¶[0010] represents the degree to which “the area where a building or a tree or other object effectively produces shadows”, and includes factors for “size of the sunlit area of the building”, “the size of the sunlit area of the tree”, current time, and the intensity and angle of the sun's rays}.
Regarding Claim 7, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 6, as discussed supra. Galoogahi does not appear to explicitly recite the limitations: identifying, using map data, the physical object, wherein the physical object is within a threshold distance of the path and has a size sufficient to block a portion of the path from direct sunlight at the current time, and wherein the environmental complexity parameter is determined based on a length of the portion of the path that is blocked from direct sunlight.
However, Duan explicitly recites the limitations: identifying, using map data {Fig. 3}, the physical object, wherein the physical object is within a threshold distance of the path and has a size sufficient to block a portion of the path from direct sunlight at the current time, and wherein the environmental complexity parameter {the combination of Shadow, Comfort, Scenery and Need algorithms in ¶[0010]} is determined based on a length of the portion of the path that is blocked from direct sunlight {The shadow algorithm in ¶[0010] represents the degree to which “the area where a building or a tree or other object effectively produces shadows”, and includes factors for “size of the sunlit area of the building”, “the size of the sunlit area of the tree”, current time, and the intensity and angle of the sun's rays}.
Regarding Claim 8, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 5, as discussed supra. Galoogahi does not appear to explicitly recite the limitations: wherein the environmental characteristics further include temperature, the method further comprising: reducing the sunlight parameter if the temperature is less than a temperature threshold.
However, Duan explicitly recites the limitations: wherein the environmental characteristics {the combination of Shadow, Comfort, Scenery and Need algorithms in ¶[0010]} further include temperature, the method further comprising: reducing the sunlight parameter if the temperature is less than a temperature threshold {the “user's comfort index” algorithm in ¶[0010] includes temperature, humidity and a “seasonal coefficient”, the latter corresponding to weighting the Comfort algorithm to take into account the time of the year and the changing weather conditions, such that a reduction in the Season coefficient of the Comfort algorithm reduces the Comfort algorithm value, and changes the influence of the Shadow algorithm – and hence the effects of sunshine – in a combined equation such the Index1 equation in Module 1 of ¶[0011]}.
Regarding Claim 9, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 1, as discussed supra. Galoogahi does not appear to explicitly recite the limitations: wherein the environmental characteristics include wind, and wherein the environmental complexity parameter comprises a wind parameter determined in view of a comparison between a direction toward which the wind is blowing at the current time and a direction of travel along the path.
However, Duan explicitly recites the limitations: wherein the environmental characteristics include wind {“a personalized route recommendation system for sun-shading, rain shelter, wind selection, and viewing”, ¶[0007]}, and wherein the environmental complexity parameter {the combination of Shadow, Comfort, Scenery and Need algorithms in ¶[0010]} comprises a wind parameter determined in view of a comparison between a direction toward which the wind is blowing at the current time and a direction of travel along the path {In ¶[0010], a Comfort Index algorithm is defined and accounts for wind strength/amplitude and direction/cos(θ), which means the wind direction, from any angle, is taken into account: “calculating the recommendation index of the candidate path according to the length of the downwind section and/or the upwind section and the total length of the candidate path.”}.
Regarding Claim 10, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 9, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the wind parameter is determined in view of a speed and direction of the wind relative to the direction of travel along the path at the current time.
However, Duan explicitly recites the limitation: wherein the wind parameter is determined in view of a speed and direction of the wind relative to the direction of travel along the path at the current time {In ¶[0010], a portion of the Comfort Index algorithm is directly related to wind effects, and is writing in the standard form for a directional vector: amplitude times directionality – WIND*cos(θ) – or wind strength times directionality}.
Regarding Claim 11, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 9, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the wind parameter corresponds to a negative amount of complexity that reduces the weight of the edge if the direction toward which the wind is blowing at the current time is within a threshold number of degrees of a direction of travel along the path.
However, Duan explicitly recites the limitation: wherein the wind parameter corresponds to a negative amount of complexity that reduces the weight of the edge if the direction toward which the wind is blowing at the current time is within a threshold number of degrees of a direction of travel along the path {the direction portion, cos(θ), of the wind vector in the Comfort Index algorithm, ¶[0010], goes from +1, when θ=0°, and -1, when θ=180°, thus the wind can have positive or negative effects on the Comfort Index algorithm}.
Regarding Claim 12, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 9, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the wind parameter corresponds to a positive amount of complexity if the direction toward which the wind is blowing at the current time is within a threshold number of degrees of a direction opposite a direction of travel along the path.
However, Duan explicitly recites the limitation: wherein the wind parameter corresponds to a positive amount of complexity if the direction toward which the wind is blowing at the current time is within a threshold number of degrees of a direction opposite a direction of travel along the path {the direction portion, cos(θ), of the wind vector in the Comfort Index algorithm, ¶[0010], goes from +1, when θ=0°, and -1, when θ=180°, thus the wind can have positive or negative effects on the Comfort Index algorithm}.
Regarding Claim 13, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 9, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the wind parameter is zero if the direction toward which the wind is blowing at the current time is within a threshold number of degrees of being perpendicular to the direction of the path.
However, Duan explicitly recites the limitation: wherein the wind parameter is zero if the direction toward which the wind is blowing at the current time is within a threshold number of degrees of being perpendicular to the direction of the path {the direction portion, cos(θ), of the wind vector in the Comfort Index algorithm, ¶[0010], goes from +1, when θ=0°, to -1, when θ=180°, or can be zero when θ=±90°, thus the wind can have positive, negative or no effect on the Comfort Index algorithm}.
Regarding Claim 14, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 9, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the direction toward which wind is blowing at the current time is determined using weather data.
However, Duan explicitly recites the limitation: wherein the direction toward which wind is blowing at the current time is determined using weather data {real-time data acquisition provides temperature, humidity, sun intensity, wind strength and direction data for the Shadow and Comfort algorithms in ¶[0010]: “Using the situational awareness principle, real-time data acquisition and analysis processing, it is of great significance to give high-precision prediction results for real-time changes in the environment.”, ¶[0014]}.
Regarding Claim 15, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 9, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein the environmental characteristics further include temperature, the method further comprising: reducing the wind parameter if the temperature is less than a temperature threshold.
However, Duan explicitly recites the limitation: wherein the environmental characteristics {the combination of Shadow, Comfort, Scenery and Need algorithms in ¶[0010]} further include temperature {the “user's comfort index” algorithm in ¶[0010] includes temperature, humidity and a “seasonal coefficient”, the latter corresponding to weighting the Comfort portion, of the combined Shadow, Comfort, Scenery and Need factors, to the time of the year and weather variations}, the method further comprising: reducing the wind parameter if the temperature is less than a temperature threshold {the “user's comfort index” algorithm in ¶[0010] includes temperature, humidity and a “seasonal coefficient”, the latter corresponding to weighting the Comfort algorithm to take into account the time of the year and the changing weather conditions, such that a reduction in the Season coefficient of the Comfort algorithm, will directly reduce the impact of wind effects}.
Regarding Claim 16, the combination of Galoogahi, Duan and Han discloses all the limitations of Claim 1, as discussed supra. Galoogahi does not appear to explicitly recite the limitation: wherein each environmental characteristic has at least circadian variability.
However, Duan explicitly recites the limitation: wherein each environmental characteristic has at least circadian variability {sunlight levels: “An optimal path that satisfies people's needs, has a high degree of sun protection throughout the journey, or satisfies users' needs for drying and reasonable distances;”, ¶[0006]; “a personalized route recommendation system for sun-shading, rain shelter, wind selection, and viewing”, ¶[0007]}.
Regarding Claim 17, Galoogahi, Duan and Han discloses all the limitations of Claim 1, as discussed supra. In addition, Galoogahi explicitly recites the limitation: wherein the current time comprises a date {real-time data about traffic and weather: “the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions.”, Col. 7, Lns. 38-42}.
Regarding Claim 18, Galoogahi explicitly recites the limitations: a system {Autonomous vehicle system 120, Fig. 1; Autonomous navigation system 1308, Fig. 8; “autonomous navigation system 1308 includes a control circuit that is responsible for directing navigation of the autonomous vehicle.”, Col. 19, Lns. 29-31} comprising: a memory device comprising a group of memory units; and a processing device, operatively coupled to the memory device, to determine: {Autonomous vehicle system 120, Fig. 1: “the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121.”, Col. 7, Lns. 31-34, and “FIG. 4 shows an example architecture 400 for an autonomous vehicle, e.g., the AV 100 shown in FIG. 1…any of the modules 402, 404, 406, 408, and 410 is a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs]), hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these things)”, Col. 11, Ln. 51 to Col. 12, Ln. 2} comprising: a route to a destination from an initial position of the navigation system {path 1012, Fig. 10}, the route comprising a plurality of edges of a graph representing a map of navigable paths, wherein each edge represents a path through an environment {Edges 1010 in Fig. 10, such as 1010a-c, as described in Col. 15, Lns. 52-57, including numerical weighting for each edge, Col. 16, Lns. 10-13}, the path connecting a first vertex and a second vertex, and each vertex represents at least one of: an endpoint of a path or a junction of two or more paths {“The nodes 1006a-d are connected by edges 1010a-c. If two nodes 1006a-b are connected by an edge 1010a, it is possible for an AV 100 to travel between one node 1006a and the other node 1006b, e.g., without having to travel to an intermediate node before arriving at the other node 1006b.”, Col. 15, Lns. 52-57}, wherein determining the route comprises: determining an environmental complexity parameter of the path {effort or cost to deal with factors imposed upon a vehicle/moving object by the environment: “An edge 1010a-c has an associated cost 1014a-b. The cost 1014a-b is a value that represents the resources that will be expended if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014 a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 7-20} in view of a time-varying environmental characteristic of the path at a current time {real-time weather conditions data: “the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions”, Col. 7, Lns. 38-40; “Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 18-20}; determine an environmental complexity parameter of the path {effort or cost to deal with factors imposed upon a vehicle/moving object by the environment: “An edge 1010a-c has an associated cost 1014a-b. The cost 1014a-b is a value that represents the resources that will be expended if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 7-20} in view of a time-varying environmental characteristics of the path at a current time {real-time weather conditions data: “the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions”, Col. 7, Lns. 38-40; “Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 18-20}; determine, by the processing device, a weight of the edge in view of the environmental complexity parameter {edge weights/costs are represented in Fig. 10 as 1’s and 2’s, and are positioned atop the edge connecting lines; they are a result of the cost considerations in Col. 16, Lns. 7-20, see above}, wherein the environmental complexity parameter includes environmental characteristics {“the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121...the stored information includes maps, driving performance, traffic congestion updates or weather conditions.”, Col. 7, Lns. 31-39} that are assigned a higher priority than other path characteristics {a navigation planning module prioritizes decision-making/rules-implementation based on environment information: “the planning module 404 includes database data 914 (e.g., from the database module 410 shown in FIG. 4)…the database data 914 includes rules used in planning…In any given situation encountered by the AV 100, at least some of the rules will apply to the situation. A rule applies to a given situation if the rule has conditions that are met based on information available to the AV 100, e.g., information about the surrounding environment. Rules can have priority. For example, a rule that says, “if the road is a freeway, move to the leftmost lane” can have a lower priority than “if the exit is approaching within a mile, move to the rightmost lane.”, Col. 14, Ln. 60 to Col. 15, Ln. 10; and, further, the surrounding environment includes consideration of a wide range of factors, including traffic, driving performance and weather conditions, see Col. 7, Lns. 31-42}; and assigning the weight to the edge {edge weights/costs are represented in Fig. 10 as 1’s and 2’s, and are positioned atop the edge connecting lines}, wherein the route is determined in view of the weight of the edge {heavy line path 1012 in Fig. 10 is determined by planning module 404 to be the least costly: “FIG. 10 shows a directed graph 1000 used in path planning, e.g., by the planning module 404 (FIG. 4)”, Col. 15, Lns. 11-12, and “the planning module 404 uses the directed graph 1000 to identify a path 1012 made up of nodes and edges between the start point 1002 and end point 1004. An edge 1010a-c has an associated cost 1014a-b.”, Col. 16, Lns. 3-7, and “When the planning module 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning module 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.”, Col. 16, Lns. 21-25}; and directing a vehicle associated with the navigation system along the route {“Computing devices 146 located on the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.”, Col. 8, Lns. 27-30}.
Galoogahi does not appear to explicitly recites the limitations: wherein the weight of the edge is based on desirability of the environmental characteristics within the current time, wherein the environmental characteristics comprise light, wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges; wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded from the direct sunlight between the initial position and the destination along the plurality of edges.
However, Duan explicitly recites limitations: wherein the weight of the edge is based on desirability of the environmental characteristics within the current time, wherein the environmental characteristics comprise light {“a solution for a personalized route recommendation system for sun-shading, rain shelter, wind selection, and viewing. For the sunshade sunscreen model, first use the data sensor and data collector to capture the various data needed to build the model through the situational awareness (the size of the building, the intensity of the sunlight and the direction of the illumination”, ¶[0007]}, wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along the path {“Module 1: For the weather with high sun and strong ultraviolet rays, the user wants to choose a route with appropriate light intensity or shading as much as possible”, ¶[0011]}; wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded {from the direct sunlight between the initial position {above Galoogahi taught of assigning weighted values to edges, and here Duan teaches of protection from the sun, in which case sunlight and shade are opposing quantities, ¶[0003 & 0006]} and the destination along the plurality of edges {with regard to equation in ¶[0025]: “Module 1: For weather with high sun and strong ultraviolet light, users want to choose the route with proper light intensity or as much as possible”}.
The combination of Galoogahi and Duan does not appear to explicitly disclose limitation: wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges.
However, Han explicitly recites the limitation: wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges {sections or segments of a route (i.e., sub-paths) are identified and demarcated on a map (e.g., dark line within dotted-lined box, Fig. 3 below) to indicate favorable or unfavorable weather conditions: “acquiring adverse weather information corresponding to the navigation path, and determining a weather warning sub-path in the navigation path according to the adverse weather information; acquiring terrain information of the navigation path , and according to the terrain information, determine the shading and protection sub-path in the navigation path; according to the weather warning sub-path and the shading and protection sub-path, determine the route selection auxiliary mark”, Abstract}.
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Regarding Claim 19, Galoogahi, Duan and Han discloses all the limitations of Claim 18, as discussed supra. In addition, Galoogahi explicitly recites the limitation: wherein the current time comprises a time at which a current position of the navigation system corresponds to a position associated with the path {starting point 1002 in Fig. 10: “When the planning module 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning module 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.”, Col. 16, Lns. 21-25}.
Regarding Claim 20, Galoogahi explicitly recites the limitations: a non-transitory machine-readable storage medium storing instructions that cause a processing device to: determining, by a navigation system {Autonomous vehicle system 120, Fig. 1: “the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121.”, Col. 7, Lns. 31-34, and “FIG. 4 shows an example architecture 400 for an autonomous vehicle, e.g., the AV 100 shown in FIG. 1…any of the modules 402, 404, 406, 408, and 410 is a combination of computer software (e.g., executable code stored on a computer-readable medium) and computer hardware (e.g., one or more microprocessors, microcontrollers, application-specific integrated circuits [ASICs]), hardware memory devices, other types of integrated circuits, other types of computer hardware, or a combination of any or all of these things).”, Col. 11, Ln. 51 - Col. 12, Ln. 2}, a route to a destination from an initial position of the navigation system {path 1012, Fig. 10}, the route comprising a plurality of edges of a graph representing a map of navigable paths, wherein each edge represents a path through an environment {Edges 1010 in Fig. 10, such as 1010a-c, as described in Col. 15, Lns. 52-57, including numerical weighting for each edge, Col. 16, Lns. 10-13}, the path connecting a first vertex and a second vertex, and each vertex represents at least one of: an endpoint of a path or a junction of two or more paths {“The nodes 1006a-d are connected by edges 1010a-c. If two nodes 1006a-b are connected by an edge 1010a, it is possible for an AV 100 to travel between one node 1006a and the other node 1006b, e.g., without having to travel to an intermediate node before arriving at the other node 1006b.”, Col. 15, Lns. 52-57}, wherein to determine the route, the processing device is to: determine an environmental complexity parameter of the path {effort or cost to deal with factors imposed upon a vehicle/moving object by the environment: “An edge 1010a-c has an associated cost 1014a-b. The cost 1014a-b is a value that represents the resources that will be expended if the AV 100 chooses that edge. A typical resource is time. For example, if one edge 1010a represents a physical distance that is twice that as another edge 1010b, then the associated cost 1014a of the first edge 1010a may be twice the associated cost 1014b of the second edge 1010b. Other factors that affect time include expected traffic, number of intersections, speed limit, etc. Another typical resource is fuel economy. Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 7-20} in view of a time-varying environmental characteristics of the path at a current time {real-time weather conditions data: “the data storage unit 142 and memory 144 store historical, real-time, and/or predictive information about the environment 190. In an embodiment, the stored information includes maps, driving performance, traffic congestion updates or weather conditions”, Col. 7, Lns. 38-40; “Two edges 1010a-b may represent the same physical distance, but one edge 1010a may require more fuel than another edge 1010b, e.g., because of road conditions, expected weather, etc.”, Col. 16, Lns. 18-20}; determine, by the processing device, a weight of the edge in view of the environmental complexity parameter {edge weights/costs are represented in Fig. 10 as 1’s and 2’s, and are positioned atop the edge connecting lines; they are a result of the cost considerations in Col. 16, Lns. 7-20, see above}, wherein the environmental complexity parameter includes environmental characteristics {“the AV system 120 includes a data storage unit 142 and memory 144 for storing machine instructions associated with computer processors 146 or data collected by sensors 121...the stored information includes maps, driving performance, traffic congestion updates or weather conditions.”, Col. 7, Lns. 31-39} that are assigned a higher priority than other path characteristics {a navigation planning module prioritizes decision-making/rules-implementation based on environment information: “the planning module 404 includes database data 914 (e.g., from the database module 410 shown in FIG. 4)…the database data 914 includes rules used in planning…In any given situation encountered by the AV 100, at least some of the rules will apply to the situation. A rule applies to a given situation if the rule has conditions that are met based on information available to the AV 100, e.g., information about the surrounding environment. Rules can have priority. For example, a rule that says, “if the road is a freeway, move to the leftmost lane” can have a lower priority than “if the exit is approaching within a mile, move to the rightmost lane.”, Col. 14, Ln. 60 to Col. 15, Ln. 10; and, further, the surrounding environment includes consideration of a wide range of factors, including traffic, driving performance and weather conditions, see Col. 7, Lns. 31-42}; and assigning the weight to the edge {edge weights/costs are represented in Fig. 10 as 1’s and 2’s, and are positioned atop the edge connecting lines}, wherein the route is determined in view of the weight of the edge {heavy line path 1012 in Fig. 10 is determined by planning module 404 to be the least costly: “FIG. 10 shows a directed graph 1000 used in path planning, e.g., by the planning module 404 (FIG. 4)”, Col. 15, Lns. 11-12, and “the planning module 404 uses the directed graph 1000 to identify a path 1012 made up of nodes and edges between the start point 1002 and end point 1004. An edge 1010a-c has an associated cost 1014a-b.”, Col. 16, Lns. 3-7, and “When the planning module 404 identifies a path 1012 between the start point 1002 and end point 1004, the planning module 404 typically chooses a path optimized for cost, e.g., the path that has the least total cost when the individual costs of the edges are added together.”, Col. 16, Lns. 21-25}; and directing a vehicle associated with the navigation system along the route {“Computing devices 146 located on the AV 100 algorithmically generate control actions based on both real-time sensor data and prior information, allowing the AV system 120 to execute its autonomous driving capabilities.”, Col. 8, Lns. 27-30}.
Galoogahi does not appear to explicitly recites the limitations: wherein the weight of the edge is based on desirability of the environmental characteristics within the current time, wherein the environmental characteristics comprise light, wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges; wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded from the direct sunlight between the initial position and the destination along the plurality of edges.
However, Duan explicitly recites limitations: wherein the weight of the edge is based on desirability of the environmental characteristics within the current time, wherein the environmental characteristics comprise light {“a solution for a personalized route recommendation system for sun-shading, rain shelter, wind selection, and viewing. For the sunshade sunscreen model, first use the data sensor and data collector to capture the various data needed to build the model through the situational awareness (the size of the building, the intensity of the sunlight and the direction of the illumination”, ¶[0007]}, wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along the path {“Module 1: For the weather with high sun and strong ultraviolet rays, the user wants to choose a route with appropriate light intensity or shading as much as possible”, ¶[0011]}; wherein the environmental complexity parameter is based on a sum of a positive value for each length of the portion of the path exposed to direct sunlight and a negative value for each length of the portion of the path that is shaded {from the direct sunlight between the initial position {above Galoogahi taught of assigning weighted values to edges, and here Duan teaches of protection from the sun, in which case sunlight and shade are opposing quantities, ¶[0003 & 0006]} and the destination along the plurality of edges {with regard to equation in ¶[0025]: “Module 1: For weather with high sun and strong ultraviolet light, users want to choose the route with proper light intensity or as much as possible”}.
The combination of Galoogahi and Duan does not appear to explicitly disclose limitation: wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges.
However, Han explicitly recites the limitation: wherein the desirability of the environmental characteristics includes at least a determination of an amount of the light along each length of a portion of the path between a plurality of vertices linked by a plurality of edges {sections or segments of a route (i.e., sub-paths) are identified and demarcated on a map (e.g., dark line within dotted-lined box, Fig. 3 below) to indicate favorable or unfavorable weather conditions: “acquiring adverse weather information corresponding to the navigation path, and determining a weather warning sub-path in the navigation path according to the adverse weather information; acquiring terrain information of the navigation path , and according to the terrain information, determine the shading and protection sub-path in the navigation path; according to the weather warning sub-path and the shading and protection sub-path, determine the route selection auxiliary mark”, Abstract}.
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Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
EP 2037219 B1 - Navigation approach that assigns a numerical cost value to each road section, for a multiple road and environmental conditions including widely differing cost values for sunshine and shade.
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/R.E.G./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665