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 the Claims
The following office action is in response to the application filed 10/24/2024. Claims 1-20 are currently pending.
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 non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis -Step 1 Claim 1 is directed to a topology map based route guiding apparatus, and claim 11 is directed to a topology map based route guiding method. Therefore, claims 1, 11 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong 1
Regarding prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claims 1, and 11 include limitations that recite an abstract idea (emphasized below). The claims include identical limitations in the form of apparatus and method so claim 1 will be used as a representation for the rejection.
Claim 1 recites:
A topology map based route guiding apparatus comprising:
a topology map generated from a guide map image based on an optical character recognition (OCR) character recognition and an image processing;
a destination determiner configured to determine, as a destination, a location selected by a user in the topology map;
an origin determiner configured to determine, as an origin, a current location of the user detected by comparing a character recognized from a surrounding image provided by the user and location information of the topology map; and
a route generator configured to generate a final route from the origin to the destination based on a congestion degree and a preference.
The examiner submits that the foregoing bolded limitations constitute a “mental process” because under its broadest reasonable interpretation, the claim covers the performance of the limitation in the human mind. For example, “a topology map generated…“, “ a destination determiner configured to determine…”, an origin determiner configured to determine…” and “a route generator configured to generate…” in the context of these claims 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 2
Regarding prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, there are no additional limitations beyond the above-noted abstract ideas
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitations as an ordered combination or as a whole, the limitations 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 limitations do 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 2019 PEG, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above during the claim 1 analysis, with respect to integration of the abstract idea into a practical application, the additional elements of generating a topology map, determining a destination selected by a user, determining a current location of the user, and generating a final route amount to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, there are no additional limitations.
Further, a conclusion that an additional element is an insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well-understood, routine, conventional activity in the field. The additional limitations of “a topology map generated…“, “ a destination determiner configured to determine…”, an origin determiner configured to determine…” and “a route generator configured to generate…” are well-understood routine / conventional activities in the field because the specification does not provide any indication that the activities are completed by anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 2-10, and 12-20 do not recite any further limitations that cause the claims to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and well-understood, routine, and conventional additional elements that do not integrate the judicial exception into a practical application. Therefore, dependent claims 2-10, and 12-20 are not patent eligible under the same rationale as provided for in the rejection of independent claims 1, and 11.
Therefore, claims 1-20 are ineligible under 35 U.S.C 101.
Claim Rejections - 35 USC § 103
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 1-4, 5, 9, 11-14, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over CN 107883956 A hereinafter Wang in view of JP 2019007811 A hereinafter Suzuki .
Regarding claim 1, Wang teaches a topology map-based route guiding apparatus, comprising:
a topology map generated (And S3, carrying out Dijkstra algorithm by using the weight corresponding to the comprehensive weight to replace the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight. The step is completely consistent with the Dijkstra algorithm, and only the path distance weight in the Dijkstra algorithm is replaced by the comprehensive weight correspondingly. The invention adopts the following indoor path network topological graph to carry out specific example simulation test, and the path network topological graph is shown as the following figure 2. The node 0 in the road network is set as an initial node, and three values marked on a road section between two nodes respectively represent a path distance, a congestion degree and a user preference degree weight in sequence. Detailed description.)
a destination determiner configured to determine, as a destination, a location selected by a user in the topology map; (The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user. A comprehensive optimal path which is more in line with the individual requirements of users and is from the initial node to the target node is planned. Set 0 as the source node. The traditional Dijkstra algorithm path planning model is adopted to solve the planned path from the starting node 0 to each target node in the path network topological graph. DETAILED DESCRIPTION)
an origin determiner configured to determine, as an origin, a current location of the user (The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user. A comprehensive optimal path which is more in line with the individual requirements of users and is from the initial node to the target node is planned. Set 0 as the source node. The traditional Dijkstra algorithm path planning model is adopted to solve the planned path from the starting node 0 to each target node in the path network topological graph. DETAILED DESCRIPTION)
a route generator configured to generate a final route from the origin to the destination based on a congestion degree and a preference. (The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user. The congestion degree is a conceptual numerical value comprehensively reflecting the smoothness or congestion of the road network, and the congestion degree weight is combined to optimize the path, so that congested road sections can be effectively avoided, and the travel time is saved. An urban road traffic operation evaluation index system is published in Beijing City in 2011 month 4 and formally implemented with August in the same year, traffic operation indexes are used for comprehensively reflecting traffic jam conditions of a road network, the urban road traffic operation evaluation index system qualitatively divides the traffic jam degree into five levels, the numerical value range is 0 to 10, every two levels are divided into one level respectively corresponding to 'unblocked', 'basically unblocked', 'slightly jammed', 'moderately jammed', 'severely jammed', 'seriously jammed', the smaller the numerical value is, the more unblocked traffic is indicated, and the more serious the numerical value is, the more serious the traffic jam condition is indicated. By referring to and referring to a definition scheme of the congestion degree in a traffic evaluation index system in outdoor navigation, a specific definition rule of the indoor road congestion degree for indoor navigation path planning can be determined. In the field of outdoor navigation, the traffic congestion degree mainly reflects the real-time traffic congestion condition through the traffic flow, and similarly, in the indoor path navigation, the indoor congestion degree is reflected through the traffic flow. In practical application, people flow data of a certain building can be actually measured, a large amount of data is subjected to statistical analysis to obtain an indoor road congestion degree index, and the characteristics of the congestion degree of the indoor building are further reflected. Referring to the definition rule of the traffic congestion degree of the urban road, the indoor congestion degree evaluation index is divided into five levels according to the embodiment, and the evaluation index is as follows: "clear", "substantially clear", "light congestion", "moderate congestion", "severe congestion". The value range of the congestion degree index is [0, 10], two numbers are divided into a grade at intervals, and the value of the congestion degree index can be directly used as the weight of the influence factor. A congestion degree value closer to 0 indicates a more clear link, and a value closer to 10 indicates a more congested link. DETAILED DESCRIPTION)
Wang does not teach a topology map generated from a guide map image based on an optical character recognition (OCR) character recognition and an image processing;
detected by comparing a character recognized from a surrounding image provided by the user and location information of the topology map;
However, Suzuki teaches a topology map generated from a guide map image based on an optical character recognition (OCR) character recognition and an image processing; (Similar to the image recognition unit 232 of the first embodiment, the image recognition unit 232A reads the probe data stored in the travel DB 300A by the probe control unit 220, and performs image processing on the image data included in the read probe data. It is determined whether or not the image includes a signboard representing a facility or the like or a guide sign (hereinafter referred to as a signboard or the like), but recognition of characters or the like is not performed. The character recognition unit 232B performs character recognition processing on a signboard or the like whose image has been recognized by the image processing of the image recognition unit 232A, thereby recognizing characters or the like included in the image. Characters recognized by the character recognition unit 232B become signboard data. Although character recognition can be performed by various methods, here, as an example, OCR (Optical Character Recognition / Reader) is performed. Embodiment 2)
detected by comparing a character recognized from a surrounding image provided by the user and location information of the topology map; (The character recognition unit 232B performs character recognition processing on a signboard or the like whose image has been recognized by the image processing of the image recognition unit 232A, thereby recognizing characters or the like included in the image. Characters recognized by the character recognition unit 232B become signboard data. Although character recognition can be performed by various methods, here, as an example, OCR (Optical Character Recognition / Reader) is performed. Embodiment 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topology map generator and origin determiner disclosed by Wang to include the use of optical character recognition of Suzuki. One of ordinary skill in the art would have been motivated to make this modification because it would enable the generated map and origin determiner to accurately locate an individual and provide the correct information based on their current location as suggested by Suzuki in embodiment 2.
Regarding claim 2, the combination of Wang and Suzuki teach the map based guiding apparatus of claim 1. Wang also teaches wherein the topology map comprises:
a plural of vertices disposed in a travel passage on the map and respectively storing the location information; and (The idea of the Dijkstra algorithm is as follows: and G = (V, E) is a weighted directed graph, a set V of all vertexes in the graph is divided into two groups, the first group is a set of vertexes with the shortest paths already obtained (indicated by S, only one source point in S is initially obtained, each shortest path is obtained later, the set is added into the set S until all vertexes are added into S, the algorithm is ended), the second group is a set of vertexes (indicated by U) with the remaining undetermined shortest paths, and the vertexes of the second group are added into S in sequence according to the ascending order of the lengths of the shortest paths. In the joining process, the shortest path length from the source point v to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U. In addition, each vertex corresponds to a distance, the distance of the vertex in S is the shortest path length from v to the vertex, and the distance of the vertex in U is the current shortest path length from v to the vertex, only including the vertex in S as the middle vertex. Background)
edges connecting the vertices. (initially, S only contains the source point, i.e. S = { v }, the distance of v is 0.U includes vertices other than v, i.e., U = { remaining vertices }, where if v has an edge with vertex U in U, then < U, v > has a weight not ∞, and if U is not an edge-out adjacency point of v, then the < U, v > weight is ∞. Background)
Regarding claim 3, the combination of Wang and Suzuki teach the map based guiding apparatus of claim 2. Wang does not teach wherein the origin determiner is configured to estimate, as the current location of the user, a specific vertex on the topology map having location information matching the recognized character in the surrounding image.
However, Suzuki teaches wherein the origin determiner is configured to estimate, as the current location of the user, a specific vertex on the topology map having location information matching the recognized character in the surrounding image. (The searched route data includes links (roads) and nodes (intersections and nodes) from the current position to the destination. The character recognition unit 232B performs character recognition processing on a signboard or the like whose image has been recognized by the image processing of the image recognition unit 232A, thereby recognizing characters or the like included in the image. Characters recognized by the character recognition unit 232B become signboard data. Although character recognition can be performed by various methods, here, as an example, OCR (Optical Character Recognition / Reader) is performed. The DB creation unit 233A stores the signboard data generated by the character recognition unit 232B in the signboard guidance DB 300B together with the signboard ID, position data, and link data. The signboard ID, the signboard data, the position data, and the link data construct the signboard guide data. The signboard ID is assigned by the DB creation unit 233A in the order of creation. Embodiment 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the origin determiner disclosed by Wang to include the use of optical character recognition to match a specific vertex on the topology map to the current location of the user of Suzuki. One of ordinary skill in the art would have been motivated to make this modification because it would enable the generated map and origin determiner to accurately locate an individual and provide the correct information based on their current location as suggested by Suzuki in embodiment 2.
Regarding claim 4, the combination of Wang and Suzuki teach the map based route guiding apparatus of claim 3, Wang also teaches wherein the route generator is configured to, when a route passing through the origin and the destination is detected from among pre-stored routes, use a detected route as the final route and stop route generation. (the route model acquisition module is used for acquiring an indoor route model, wherein the indoor route model comprises weights of all selection factors among all nodes, and the selection factors are at least two; and the comprehensive weight calculation module is used for calculating the comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and q is calculated. Examiner notes that the system stores multiple routes and then weighs each individually before picking the best one)
Regarding claim 5, the combination of Wang and Suzuki teach the map based route guiding apparatus of claim 3. Wang also teaches wherein the route generator is configured to calculate the congestion degree based on the number of persons obtained in real time obtained through a closed-circuit television (CCTV) of the travel passage and a passage area, and (in the indoor path navigation, the indoor congestion degree is reflected through the traffic flow. In practical application, people flow data of a certain building can be actually measured, a large amount of data is subjected to statistical analysis to obtain an indoor road congestion degree index, and the characteristics of the congestion degree of the indoor building are further reflected. Referring to the definition rule of the traffic congestion degree of the urban road, the indoor congestion degree evaluation index is divided into five levels according to the embodiment, and the evaluation index is as follows: "clear", "substantially clear", "light congestion", "moderate congestion", "severe congestion". The value range of the congestion degree index is [0, 10], two numbers are divided into a grade at intervals, and the value of the congestion degree index can be directly used as the weight of the influence factor. A congestion degree value closer to 0 indicates a more clear link, and a value closer to 10 indicates a more congested link. the congestion degree is calculated for each edge. DETAILED DESCRIPTION.)
the congestion degree is calculated for each edge. (The invention adopts the following indoor path network topological graph to carry out specific example simulation test, and the path network topological graph is shown as the following figure 2. The node 0 in the road network is set as an initial node, and three values marked on a road section between two nodes respectively represent a path distance, a congestion degree and a user preference degree weight in sequence. DETAILED DESCRIPTION)
Regarding claim 9, the combination of Wang and Suzuki teach the map based route guiding apparatus of claim 1. Wang also teaches wherein the route generator is configured to determine, as the final route, a route of a shortest distance between the origin and the destination, (And comparing and analyzing the test results of the optimized path obtained by the improved indoor navigation path planning model and the optimized path obtained by path planning of the traditional Dijkstra algorithm. Taking the starting node 0 to the destination node 4 as an example, the optimized path obtained under the improved model is from the starting node 0 to the destination node 4 through the node 3; the path obtained under the traditional Dijkstra algorithm model is from the starting node 0 to the destination node 4 through the node 1. It can be seen that, when the path distance is taken as the only consideration factor, 0-1-4 is the shortest path from the starting node 0 to the destination node 4, and this situation only considers the length of the path, but due to the complexity and diversity of the indoor environment, the user is the main body of the indoor activity, the personalized demand is more and more strong, and the road congestion degree and the user preference degree are important influence factors in the indoor path navigation problem and need to be taken into account. The optimized path planned by the improved indoor navigation planning model is 0-3-4, although the path is not shortest, the congestion degree and the user preference degree are small, namely, the road is smooth, and the user prefers, so that the path 0-3-4 in the road network is a comprehensive and optimal path from the starting node 0 to the destination node 4. In an indoor environment, a user selects a shortest path, important factors such as the congestion degree of a road and the personalized preference degree of the user can be considered on the basis of the path distance, the improved indoor navigation planning model avoids the problem that the traditional Dijkstra algorithm only considers the singleness of the factor of the path distance, comprehensively considers the influence of the path distance, the congestion degree and the user preference degree on indoor navigation path planning, has comprehensiveness, and can better meet the user requirements. DETAILED DESCRIPTION)
wherein, when a plurality of candidate routes having the same distance between the origin and the destination exists, the candidate route having a low congestion degree is low and a high preference among the plurality of candidate routes is determined as the final route. (And comparing and analyzing the test results of the optimized path obtained by the improved indoor navigation path planning model and the optimized path obtained by path planning of the traditional Dijkstra algorithm. Taking the starting node 0 to the destination node 4 as an example, the optimized path obtained under the improved model is from the starting node 0 to the destination node 4 through the node 3; the path obtained under the traditional Dijkstra algorithm model is from the starting node 0 to the destination node 4 through the node 1. It can be seen that, when the path distance is taken as the only consideration factor, 0-1-4 is the shortest path from the starting node 0 to the destination node 4, and this situation only considers the length of the path, but due to the complexity and diversity of the indoor environment, the user is the main body of the indoor activity, the personalized demand is more and more strong, and the road congestion degree and the user preference degree are important influence factors in the indoor path navigation problem and need to be taken into account. The optimized path planned by the improved indoor navigation planning model is 0-3-4, although the path is not shortest, the congestion degree and the user preference degree are small, namely, the road is smooth, and the user prefers, so that the path 0-3-4 in the road network is a comprehensive and optimal path from the starting node 0 to the destination node 4. In an indoor environment, a user selects a shortest path, important factors such as the congestion degree of a road and the personalized preference degree of the user can be considered on the basis of the path distance, the improved indoor navigation planning model avoids the problem that the traditional Dijkstra algorithm only considers the singleness of the factor of the path distance, comprehensively considers the influence of the path distance, the congestion degree and the user preference degree on indoor navigation path planning, has comprehensiveness, and can better meet the user requirements. DETAILED DESCRIPTION Examiner notes that the algorithm described in the prior reference takes into account congestion, preference, and route distance therefore when deciding between routes of the same distance, the one with the lowest congestion degree and highest preference will be chosen as the claim limitation describes.)
Regarding claim 11, Wang teaches a topology map-based route guiding method, comprising:
providing a topology map generated (And S3, carrying out Dijkstra algorithm by using the weight corresponding to the comprehensive weight to replace the path distance in the Dijkstra algorithm, and selecting a path with the minimum total comprehensive weight. The step is completely consistent with the Dijkstra algorithm, and only the path distance weight in the Dijkstra algorithm is replaced by the comprehensive weight correspondingly. The invention adopts the following indoor path network topological graph to carry out specific example simulation test, and the path network topological graph is shown as the following figure 2. The node 0 in the road network is set as an initial node, and three values marked on a road section between two nodes respectively represent a path distance, a congestion degree and a user preference degree weight in sequence. Detailed description.)
determining, as a destination, a location selected by a user in the topology map; (The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user. A comprehensive optimal path which is more in line with the individual requirements of users and is from the initial node to the target node is planned. Set 0 as the source node. The traditional Dijkstra algorithm path planning model is adopted to solve the planned path from the starting node 0 to each target node in the path network topological graph. DETAILED DESCRIPTION)
determining, as an origin, a current location of the user (The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user. A comprehensive optimal path which is more in line with the individual requirements of users and is from the initial node to the target node is planned. Set 0 as the source node. The traditional Dijkstra algorithm path planning model is adopted to solve the planned path from the starting node 0 to each target node in the path network topological graph. DETAILED DESCRIPTION)
generating a final route from the origin to the destination based on a congestion degree and a preference. (The indoor navigation path planning aims at planning a proper route from a starting point to a target point for the user, so that the user can walk a shorter path, use less time and better fit different preference degrees of people to the path. In this embodiment, a weight of a path distance, a weight of a congestion degree of a path, and a weight of a preference degree of a path are selected as three weight indexes influencing indoor path planning to describe, so as to obtain a comprehensive optimized route more comprehensively and more meeting personalized requirements of indoor navigation of a user. The congestion degree is a conceptual numerical value comprehensively reflecting the smoothness or congestion of the road network, and the congestion degree weight is combined to optimize the path, so that congested road sections can be effectively avoided, and the travel time is saved. An urban road traffic operation evaluation index system is published in Beijing City in 2011 month 4 and formally implemented with August in the same year, traffic operation indexes are used for comprehensively reflecting traffic jam conditions of a road network, the urban road traffic operation evaluation index system qualitatively divides the traffic jam degree into five levels, the numerical value range is 0 to 10, every two levels are divided into one level respectively corresponding to 'unblocked', 'basically unblocked', 'slightly jammed', 'moderately jammed', 'severely jammed', 'seriously jammed', the smaller the numerical value is, the more unblocked traffic is indicated, and the more serious the numerical value is, the more serious the traffic jam condition is indicated. By referring to and referring to a definition scheme of the congestion degree in a traffic evaluation index system in outdoor navigation, a specific definition rule of the indoor road congestion degree for indoor navigation path planning can be determined. In the field of outdoor navigation, the traffic congestion degree mainly reflects the real-time traffic congestion condition through the traffic flow, and similarly, in the indoor path navigation, the indoor congestion degree is reflected through the traffic flow. In practical application, people flow data of a certain building can be actually measured, a large amount of data is subjected to statistical analysis to obtain an indoor road congestion degree index, and the characteristics of the congestion degree of the indoor building are further reflected. Referring to the definition rule of the traffic congestion degree of the urban road, the indoor congestion degree evaluation index is divided into five levels according to the embodiment, and the evaluation index is as follows: "clear", "substantially clear", "light congestion", "moderate congestion", "severe congestion". The value range of the congestion degree index is [0, 10], two numbers are divided into a grade at intervals, and the value of the congestion degree index can be directly used as the weight of the influence factor. A congestion degree value closer to 0 indicates a more clear link, and a value closer to 10 indicates a more congested link. DETAILED DESCRIPTION)
Wang does not teach a topology map generated from a guide map image based on an optical character recognition (OCR) character recognition and an image processing;
detected by comparing a character recognized from a surrounding image provided by the user and location information of the topology map;
However, Suzuki teaches a topology map generated from a guide map image based on an optical character recognition (OCR) character recognition and an image processing; (Similar to the image recognition unit 232 of the first embodiment, the image recognition unit 232A reads the probe data stored in the travel DB 300A by the probe control unit 220, and performs image processing on the image data included in the read probe data. It is determined whether or not the image includes a signboard representing a facility or the like or a guide sign (hereinafter referred to as a signboard or the like), but recognition of characters or the like is not performed. The character recognition unit 232B performs character recognition processing on a signboard or the like whose image has been recognized by the image processing of the image recognition unit 232A, thereby recognizing characters or the like included in the image. Characters recognized by the character recognition unit 232B become signboard data. Although character recognition can be performed by various methods, here, as an example, OCR (Optical Character Recognition / Reader) is performed. Embodiment 2)
detected by comparing a character recognized from a surrounding image provided by the user and location information of the topology map; (The character recognition unit 232B performs character recognition processing on a signboard or the like whose image has been recognized by the image processing of the image recognition unit 232A, thereby recognizing characters or the like included in the image. Characters recognized by the character recognition unit 232B become signboard data. Although character recognition can be performed by various methods, here, as an example, OCR (Optical Character Recognition / Reader) is performed. Embodiment 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the topology map generator and origin determiner disclosed by Wang to include the use of optical character recognition of Suzuki. One of ordinary skill in the art would have been motivated to make this modification because it would enable the generated map and origin determiner to accurately locate an individual and provide the correct information based on their current location as suggested by Suzuki in embodiment 2.
Regarding claim 12, the combination of Wang and Suzuki teach the method according to claim 11. Wang also teaches wherein the topology map comprises:
a plurality of vertices disposed in a travel passage on the map and respectively storing the location information; and (The idea of the Dijkstra algorithm is as follows: and G = (V, E) is a weighted directed graph, a set V of all vertexes in the graph is divided into two groups, the first group is a set of vertexes with the shortest paths already obtained (indicated by S, only one source point in S is initially obtained, each shortest path is obtained later, the set is added into the set S until all vertexes are added into S, the algorithm is ended), the second group is a set of vertexes (indicated by U) with the remaining undetermined shortest paths, and the vertexes of the second group are added into S in sequence according to the ascending order of the lengths of the shortest paths. In the joining process, the shortest path length from the source point v to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U. In addition, each vertex corresponds to a distance, the distance of the vertex in S is the shortest path length from v to the vertex, and the distance of the vertex in U is the current shortest path length from v to the vertex, only including the vertex in S as the middle vertex. Background)
edges connecting the vertices. (initially, S only contains the source point, i.e. S = { v }, the distance of v is 0.U includes vertices other than v, i.e., U = { remaining vertices }, where if v has an edge with vertex U in U, then < U, v > has a weight not ∞, and if U is not an edge-out adjacency point of v, then the < U, v > weight is ∞. Background)
Regarding claim 13, the combination of Wang and Suzuki teach the method according to claim 12. Wang does not teach wherein determining the origin comprises estimating, as the current location of the user, a specific vertex on the topology map having location information matching the recognized character in the surrounding image.
However, Suzuki teaches wherein determining the origin comprises estimating, as the current location of the user, a specific vertex on the topology map having location information matching the recognized character in the surrounding image. (The searched route data includes links (roads) and nodes (intersections and nodes) from the current position to the destination. The character recognition unit 232B performs character recognition processing on a signboard or the like whose image has been recognized by the image processing of the image recognition unit 232A, thereby recognizing characters or the like included in the image. Characters recognized by the character recognition unit 232B become signboard data. Although character recognition can be performed by various methods, here, as an example, OCR (Optical Character Recognition / Reader) is performed. The DB creation unit 233A stores the signboard data generated by the character recognition unit 232B in the signboard guidance DB 300B together with the signboard ID, position data, and link data. The signboard ID, the signboard data, the position data, and the link data construct the signboard guide data. The signboard ID is assigned by the DB creation unit 233A in the order of creation. Embodiment 2)
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the origin determiner disclosed by Wang to include the use of optical character recognition to match a specific vertex on the topology map to the current location of the user of Suzuki. One of ordinary skill in the art would have been motivated to make this modification because it would enable the generated map and origin determiner to accurately locate an individual and provide the correct information based on their current location as suggested by Suzuki in embodiment 2.
Regarding claim 14, the combination of Wang and Suzuki teach the method according to claim 13. Wang also teaches wherein generating the final route comprises, when a route passing through the origin and the destination is detected from among pre-stored routes, using a detected route as the final route and stopping route generation. (the route model acquisition module is used for acquiring an indoor route model, wherein the indoor route model comprises weights of all selection factors among all nodes, and the selection factors are at least two; and the comprehensive weight calculation module is used for calculating the comprehensive weight between each node of the indoor path model, wherein the comprehensive weight between any node p and q is calculated. Examiner notes that the system stores multiple routes and then weighs each individually before picking the best one)
Regarding claim 15, the combination of Wang and Suzuki teach the method according to claim 13. Wang also teaches wherein generating the final route comprises:
calculating the congestion degree based on the number of persons in real time obtained through a CCTV of the travel passage and a passage area; and (in the indoor path navigation, the indoor congestion degree is reflected through the traffic flow. In practical application, people flow data of a certain building can be actually measured, a large amount of data is subjected to statistical analysis to obtain an indoor road congestion degree index, and the characteristics of the congestion degree of the indoor building are further reflected. Referring to the definition rule of the traffic congestion degree of the urban road, the indoor congestion degree evaluation index is divided into five levels according to the embodiment, and the evaluation index is as follows: "clear", "substantially clear", "light congestion", "moderate congestion", "severe congestion". The value range of the congestion degree index is [0, 10], two numbers are divided into a grade at intervals, and the value of the congestion degree index can be directly used as the weight of the influence factor. A congestion degree value closer to 0 indicates a more clear link, and a value closer to 10 indicates a more congested link. the congestion degree is calculated for each edge. DETAILED DESCRIPTION.)
calculating the congestion degree for each edge. (The invention adopts the following indoor path network topological graph to carry out specific example simulation test, and the path network topological graph is shown as the following figure 2. The node 0 in the road network is set as an initial node, and three values marked on a road section between two nodes respectively represent a path distance, a congestion degree and a user preference degree weight in sequence. DETAILED DESCRIPTION)
Regarding claim 19, the combination of Wang and Suzuki teach the method according to claim 11. Wang also teaches wherein generating the final route comprises determining, as the final route, a route of a shortest distance between the origin and the destination is the shortest. (And comparing and analyzing the test results of the optimized path obtained by the improved indoor navigation path planning model and the optimized path obtained by path planning of the traditional Dijkstra algorithm. Taking the starting node 0 to the destination node 4 as an example, the optimized path obtained under the improved model is from the starting node 0 to the destination node 4 through the node 3; the path obtained under the traditional Dijkstra algorithm model is from the starting node 0 to the destination node 4 through the node 1. It can be seen that, when the path distance is taken as the only consideration factor, 0-1-4 is the shortest path from the starting node 0 to the destination node 4, and this situation only considers the length of the path, but due to the complexity and diversity of the indoor environment, the user is the main body of the indoor activity, the personalized demand is more and more strong, and the road congestion degree and the user preference degree are important influence factors in the indoor path navigation problem and need to be taken into account. The optimized path planned by the improved indoor navigation planning model is 0-3-4, although the path is not shortest, the congestion degree and the user preference degree are small, namely, the road is smooth, and the user prefers, so that the path 0-3-4 in the road network is a comprehensive and optimal path from the starting node 0 to the destination node 4. In an indoor environment, a user selects a shortest path, important factors such as the congestion degree of a road and the personalized preference degree of the user can be considered on the basis of the path distance, the improved indoor navigation planning model avoids the problem that the traditional Dijkstra algorithm only considers the singleness of the factor of the path distance, comprehensively considers the influence of the path distance, the congestion degree and the user preference degree on indoor navigation path planning, has comprehensiveness, and can better meet the user requirements. DETAILED DESCRIPTION)
wherein, when a plurality of candidate routes having the same distance between the origin and the destination exists, the candidate route having a low congestion degree and a high preference among the plurality of candidate routes is determined as the final route. (And comparing and analyzing the test results of the optimized path obtained by the improved indoor navigation path planning model and the optimized path obtained by path planning of the traditional Dijkstra algorithm. Taking the starting node 0 to the destination node 4 as an example, the optimized path obtained under the improved model is from the starting node 0 to the destination node 4 through the node 3; the path obtained under the traditional Dijkstra algorithm model is from the starting node 0 to the destination node 4 through the node 1. It can be seen that, when the path distance is taken as the only consideration factor, 0-1-4 is the shortest path from the starting node 0 to the destination node 4, and this situation only considers the length of the path, but due to the complexity and diversity of the indoor environment, the user is the main body of the indoor activity, the personalized demand is more and more strong, and the road congestion degree and the user preference degree are important influence factors in the indoor path navigation problem and need to be taken into account. The optimized path planned by the improved indoor navigation planning model is 0-3-4, although the path is not shortest, the congestion degree and the user preference degree are small, namely, the road is smooth, and the user prefers, so that the path 0-3-4 in the road network is a comprehensive and optimal path from the starting node 0 to the destination node 4. In an indoor environment, a user selects a shortest path, important factors such as the congestion degree of a road and the personalized preference degree of the user can be considered on the basis of the path distance, the improved indoor navigation planning model avoids the problem that the traditional Dijkstra algorithm only considers the singleness of the factor of the path distance, comprehensively considers the influence of the path distance, the congestion degree and the user preference degree on indoor navigation path planning, has comprehensiveness, and can better meet the user requirements. DETAILED DESCRIPTION Examiner notes that the algorithm described in the prior reference takes into account congestion, preference, and route distance therefore when deciding between routes of the same distance, the one with the lowest congestion degree and highest preference will be chosen as the claim limitation describes.)
Allowable Subject Matter
Claims 6-8, 10, 16-18 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims and if withstanding 35 U.S.C 101 rejections are overcome.
The following is a statement of reasons for the indication of allowable subject matter: Claims 6, 10, 16, and 20 include specific equations to calculate congestion degree and cost value weight which the examiner notes distinguishes itself over the prior art by reciting a specific combination of variables that factor into the determination of how congested an edge is. The closest prior art that was able to be found for the above noted limitations is JP 2023007183 A which appears in the pertinent art not relied upon section below. JP 20203007183 teaches a congestion calculation system that acquires detection information showing detection states of one or more photoelectric sensors for detecting a person existing at a prescribed place and position identification information of the place that they are going to. The system then estimates a degree of congestion at the prescribed place based on said estimate. However, the prior art fails to disclose where the congestion degree is calculated by adding a coefficient to the amount of people detected by CCTV divided by the optimal number of people in a specific passage where the optimal number of people is decided beforehand as claimed. Thus, even in combination, the prior art would not yield the invention as claimed to one of ordinary skill in the art.
Dependent claims 7-8, and 17-18 are allowable since they depend off of claims 6 and 16 which are deemed allowable for the above noted reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. JP 2023007183 teaches a congestion calculation system that uses photoelectric sensors to estimate congestion in an area.
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/JOSHUA JEFFREY PENKO/ Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
3/30/26