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 .
DETAILED ACTION
Status of Claims
Claims 1-8, 11-18 are subject to examination.
Claims 9, 10, 19, 20 are withdrawn.
Specification
Amendment to the specification dated 1/13/26 is acknowledged.
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 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 of this title, 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.
Claim(s) 1, 3, 6, 7, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun et al., 20200372624 in view of Zhu et al., 9,756,460, ROM et al., 20200250966 and Lee et al., KR 102164800 B1.
Referring to claim(s) 1, Braun substantially discloses a method for collecting geospatial information, the method comprising: determining a domain region for an original location of a terminal;
[0006] operating a geospatial data assessment system for quantifying the quality of a geospatial data set. The systems and methods described herein allow for consistent and accurate assessment of a geospatial data by identifying a subset of the data for review, generating statistically valid and repeatable quality measurements, and providing tools for comparison of the measurements against intended uses of the data set. The systems and methods allow a user to review significantly less data while still accurately estimating the overall error rate of a geospatial data set.
[0010] identifying geospatial data for quality review is provided, the method comprising: receiving a geospatial data set representing a geographic area, wherein the geospatial data set comprises data representing a plurality of map features, and wherein the plurality of map features is associated with one or more feature classes; determining a value for map features in the geospatial data set; and selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value.
[0077] A geospatial data set may be generated based on any type of suitable image. For example, a geospatial data set may be generated based on an image, such as captured by a camera or other sensor, which may be mounted on a drone, satellite, aircraft, vehicle, stationary structures, or other location.
defining a reference probability for sampling in the domain region according to a relationship between a reference region in the domain region and the location for the original location; determining a region parameter of the reference region so as to minimize the expected value
[0117] local score estimate corresponding to the average raw score among adjacent regions
[0120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region.
[0121] A minimum sample size may be determined based on the size of the data set, an allowable error rate indicated by one or more target quality scores, and/or confidence interval. To determine whether selected regions comprise a minimum sample size, an amount of features in a selected region corresponding to the selected sampling parameters may be determined. If the amount of features in all selected regions corresponding to the selected sampling parameters for the selected regions is greater than or equal to a threshold value, then sampling may be complete. If the number of features corresponding to the selected sampling parameters for the selected regions is less than a threshold value, an additional region may be selected. Additional regions may be selected until the amount of features corresponding to the selected sampling parameters for the selected regions is greater than or equal to a threshold value.
[0124] At step 806, a determination may be made whether the quantity of features corresponding to the sampling parameters in all selected regions is less than a threshold value. If the number of features is less than a threshold value, then the method may return to step 802, and an additional region may be selected. If the number of features is greater than or equal to a threshold value, then no additional regions may be selected. After regions have been selected, information corresponding to the selected regions may be generated and stored, such as in storage 140, for future reference. For example, information indicating which regions were selected, the probability score of selected regions, the number and type of features in selected regions, and/or other information may be stored.
[0137] In some embodiments, a quality score may be determined based on a lot tolerance percent defective (LTPD) statistical sampling technique. For example, the lot size may correspond to the total amount of features in the data set corresponding to a sampling parameter. For example, for a sampling parameter based on the length of all linear features in the data set, the lot size may correspond to the length of all linear features in the data set. For a sampling parameter based on the number of features corresponding to a feature class, the lot size may be the number of features in the data set corresponding to the feature class. The sample size may correspond to an amount of features in the regions selected for review corresponding to the sampling parameters, and the number of errors may be the number of calls dropped in the selected regions during review
selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value, claim 1
Braun does not specifically mention about, which is well-known in the art, which ROM discloses,
sampling a location, deriving an expected value of an error between the original location and the location based on the reference probability (
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A difference between the predicted location and an actual location sensed by a location of the vehicle is calculated (602). A service for installation on or near the road segment is recommended (603) based on the difference. The installation of lights is recommended as the service. The difference is correlated with respective location of mobile urban service. The sampling rate for location sensor of the vehicle is adjusted based on dynamic location of the mobile urban service.abstract
calculating difference between predicted location and actual location sensed by location of vehicle (602) para 108
[0003] According to one embodiment, a method comprises calculating an estimated time of arrival at an end node of a road segment from a beginning node of the road segment based on historical traversal time data for the road segment. The method also comprises determining a sampling rate for a location sensor of a vehicle traveling the road segment based on the estimated time of arrival. The method further comprises configuring the location sensor to collect location data using the sampling rate.
[0007] According to one embodiment, a method comprises initiating a capture of a current location of a vehicle on a road segment by a location sensor based on a keep-alive sampling rate. The location sensor, for instance, is configured to operate at a sampling rate that is reduced from a default sampling rate in addition to the keep-alive sampling rate. The method also comprises determining a predicted location of the vehicle based on an estimated time of arrival of the vehicle at an end node of the road segment. The estimated time of arrival is based on historical traversal time data for the road segment. The method further comprises reconfiguring the location sensor to operate at the default sampling frequency based on determining that the predicted location differs from the current location by more than a threshold distance.
[0036] FIG. 6 is a flowchart of a process for providing map-based dynamic location sampling
[0065] For example, in areas where there is consistent disagreement between predicted locations and sampled locations above a threshold distance, the system 100 can mark those areas as low-predictable areas and recommend options to increase accuracy at that location or road segment (e.g., recommend installation of external positioning sensors, beacons, etc.). Conversely, in areas where there is consistent agreement within a threshold distance of the predicted locations and sampled locations, the system 100 can mark those areas as high-predictable areas and use location offsets determined from those areas to correct location readings for other vehicles in the area on road segment. In other embodiments, detected differences between the predicted or expected location and the sampled location can indicate the need for installation of other services such as but not limited to urban lights, emergency services, travel services, etc. In yet other embodiments, the detected differences can be used to infer real-time traffic conditions or incidents to update, for instance, a real-time data layer 119 of the geographic database 113.
sampling a location, deriving an expected value of an error between the original location and the location based on the reference probability (
adjusted based on dynamic location of the mobile urban service, claim 3
[0081] FIG. 10 is diagram illustrating an example of performing path recovery when using map-based dynamic location sampling, according to one embodiment. In this example, a vehicle 105 is traveling from a beginning node 1001a to an end node 1001b connected by a directed edge 1003 corresponding to a road segment that is 1 mile long. The ETA from the beginning node 1001a to the end node 1001b is 10 mins based on historical data. The location sensor associated with the vehicle 105 is configured to take a keep-alive location reading at 5 mins. The predicted location of the vehicle using a time-based extrapolation would then be approximately 0.5 miles from the beginning node (e.g., 0.5 miles=1 mile×(5 mins/10 mins) based the extrapolation equation described above) as indicated by location 1005. However, the keep-alive location reading indicates that vehicle 105 is actually 0.25 miles from the beginning node 1001a as indicated by location 1007. The difference between the predicted location 1007 and the current location 1007 is then 0.25 miles. In this example, the distance threshold is 0.1 miles. Accordingly, because the difference (0.25 miles) is greater than the distance threshold (0.1 miles), the location sensor of the vehicle is reconfigured with a default or high-frequency sampling rate to improve accuracy until the vehicle reaches the end node 1001b of the directed edge 1003 (i.e., path recovery)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing sampling a location and deriving an expected value of an error. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it would enable representing the regions, para 7.
Braun and ROM does not specifically mention about, which is well-known in the art, which Zhu discloses, perturbed location, based upon a perturbed location generated by the terminal for the original location (
(19) As described herein, a location measurement 104 or a series of location measurements can be processed (e.g., perturbed) by the mobile device 105 and/or the ALPM 106. The perturbed location measurement or the perturbed series of location measurements can be sent over a channel col., 4, lines 1-20
(8) Furthermore, users of LBSs may prefer personalized privacy protection in tracing of their location trajectories, in which a profile (e.g., a personalized privacy preference profile) can be adaptively adjusted for individual segments in a trajectory (e.g., areas, regions, etc., in a trajectory potentially transited by the mobile device and, thus, the user) in order to achieve a balance between privacy and quality of service (QoS) within the trajectory. As described herein, mobile device can, for example, be a portable telephone, such as a cellular phone, a smartphone, a personal computer, a personal digital assistant, a tablet, a notebook, a computing system in a vehicle, etc., col., 2, lines 50-65
adaptive location perturbation, comprising: selecting, by a processing resource of a mobile device executing instructions stored on a non-transitory medium, between a plurality of localization technologies to perturb a plurality of locations measured by the mobile device; smoothen a directional transition of the mobile communication device by smoothening at least one of the perturbed locations to create a smoothened perturbed trajectory; and sending the smoothened perturbed trajectory and the perturbed location measurements of the mobile device, claim 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing perturbed location. One of ordinary skilled in the art would readily know that perturbed location can be generated by a portable telephone, such as a cellular phone, a smartphone, a personal computer, a personal digital assistant, a tablet, a notebook, a computing system in a vehicle, etc. A plurality of localization technologies available (well-known) to perturb a plurality of locations measured by the device would assist in smoothen a directional transition of the communication device by smoothening at least one of the perturbed locations to create a smoothened perturbed trajectory; and sending the smoothened perturbed trajectory and the perturbed location measurements of the mobile device for further use, col., 2, lines 50-65, claim 1.
Braun, Zhu and ROM does not specifically mention about, which is well-known in the art, which Lee discloses, a center of a reference region and defining a relation equation for setting the center of the reference region (5th para, page 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing setting the center of the reference region. One of ordinary skilled in the art would readily know that what the center of the reference region is.
The center of the reference region is a conceptual point used for describing a region’s location. It is not physical but a center location within the region that serves as a reference for entities such as a vehicle.
The center of the reference region of interest 411 for the vehicle would enable locating of the vehicle in a plurality of preset directions, 5th para, page 6.
Referring to claim(s) 3, Braun discloses wherein the reference probability is a probability for the location, and includes a first reference probability for a case where the location is within the reference region and a second reference probability for a case where the location is within the domain region out of the reference region (
[0117] local score estimate corresponding to the average raw score among adjacent regions
[0120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region.
selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value, claim 1
Zhu discloses, perturbed location (
(19) As described herein, a location measurement 104 or a series of location measurements can be processed (e.g., perturbed) by the mobile device 105 and/or the ALPM 106. The perturbed location measurement or the perturbed series of location measurements can be sent over a channel col., 4, lines 1-20
(8) Furthermore, users of LBSs may prefer personalized privacy protection in tracing of their location trajectories, in which a profile (e.g., a personalized privacy preference profile) can be adaptively adjusted for individual segments in a trajectory (e.g., areas, regions, etc., in a trajectory potentially transited by the mobile device and, thus, the user) in order to achieve a balance between privacy and quality of service (QoS) within the trajectory. As described herein, mobile device can, for example, be a portable telephone, such as a cellular phone, a smartphone, a personal computer, a personal digital assistant, a tablet, a notebook, a computing system in a vehicle, etc., col., 2, lines 50-65
adaptive location perturbation, comprising: selecting, by a processing resource of a mobile device executing instructions stored on a non-transitory medium, between a plurality of localization technologies to perturb a plurality of locations measured by the mobile device; smoothen a directional transition of the mobile communication device by smoothening at least one of the perturbed locations to create a smoothened perturbed trajectory; and sending the smoothened perturbed trajectory and the perturbed location measurements of the mobile device, claim 1)
Referring to claim(s) 6, Braun discloses wherein the first reference probability is defined to sample the location with a higher probability than the second reference probability (
[120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region
[0010] identifying geospatial data for quality review is provided, the method comprising: receiving a geospatial data set representing a geographic area, wherein the geospatial data set comprises data representing a plurality of map features, and wherein the plurality of map features is associated with one or more feature classes; determining a value for map features in the geospatial data set; and selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value.
Zhu discloses, perturbed location, col., 4, lines 1-20, claim 1.
Referring to claim(s) 7, Braun discloses wherein each of the original location and the location includes a latitude and a longitude, and is mapped on the domain region (Last para, page 4, 1st para, page 4). Zhu discloses, perturbed location, col., 4, lines 1-20, claim 1.
Claim(s) 2, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, Zhu, Lee and HOU et al., CN 109257385 A
Referring to claim(s) 2, Braun discloses wherein the reference probability is defined based on a sampling parameter defined based on the domain region, the reference region (
[0094] FIG. 5 illustrates a method for selecting subsets of a geospatial data set for review, according to some embodiments. The method provides for quantifying the features of the data set, such as determining a number of features in the data set and determining a number of features associated with each layer, theme, and/or attribute represented in the data set. The method further provides for determining sampling parameters. In some embodiments, the method may select regions to be statistically representative of one or more layers, themes, or attribute classes. In other embodiments, the method may select regions to be statistically representative of all features in the data set. Sampling parameters may correspond to the type or types of feature for which the system may select a statistically representative number of regions for review.
[0095] The data set may be divided into regions and a selection probability may be determined for each region. Regions may then be selected for review. Regions may be selected based on the selection probabilities. Regions may be selected until the sampling parameters of features in the selected region are large enough as a proportion of the sampling parameters for all features in the data set may be statistically representative of the whole data set within a confidence interval.
Braun, ROM and Zhu does not specifically mention about, privacy parameter, which HOU discloses it, abstract, claim 1.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known privacy parameter. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it would enable keeping private information not disclosed to public by using the private parameter.
Claim(s) 4, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, Zhu, Lee and Liu et al., 10139820.
Referring to claim(s) 4, Braun, ROM, Zhu discloses, transmitting the region parameter of the determined reference region, Braun, para 3, abstract. BRAUN, ROM, Zhu do not disclose transmitting the region parameter to a terminal which is a target of location collection, which Liu discloses it, col., 70, lines 24-34.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known region parameter for the collection of information. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it the entity would collect data from the provided region, which would enable implementing sampling of the information for the associated overall regions, col., 70, lines 24-34.
Claim(s) 5, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, Zhu, Lee and ZHANG et al., CN 105933357 B.
Referring to claim(s) 5, Braun, ROM, Zhu discloses, wherein the reference region may be a region , and the region parameter of the reference region, Braun, para 3, abstract. BRAUN, ROM. Zhu do not disclose square region includes a square side length, which ZHANG discloses it, claim 1.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known square region for the collection of information. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it the entity would collect data from the provided square region, which would enable implementing sampling of the information for the associated overall regions, claim 1.
Claim(s) 8, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, Zhu, Lee and BENNATI et al., 20210374280.
Referring to claim(s) 8, Braun, ROM, Zhu, Lee discloses, perturbed location. Zhu discloses, perturbed location, col., 4, lines 1-20, claim 1. BRAUN, ROM, Zhu do not disclose collecting the corresponding to the original location of each terminal from at least one terminal; and providing the collected location to a service provider, which BENNATI discloses,
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[0026] FIG. 1 is a diagram of a system for providing device-side trajectory anonymization based on negative gapping, according to one embodiment. As discussed above, many location-based service providers and companies collect location data to be used in its services and applications. In one embodiment, location data can be collected as a trajectory representing a sequence of data entries per individual moving entity (e.g., individual probe devices 101), where each entry (e.g., a probe point) consists of location (latitude, longitude), time stamp, a pseudonym (e.g., to indicate which the entries belong to the same entity), and possibly various additional information about the entity at the time (vehicle sensor data, speed, heading etc.). Examples of probe devices 101 include but are not limited to vehicles 103a-103n (also collectively referred to as vehicles 103), user equipment (UE) devices 105a-105m (also collectively referred to as UEs 105), and/or equivalent devices equipped with location sensors (e.g., Global Navigation Satellite System (GNSS) receivers) capable of generating location data (e.g., trajectory data 109).
[0028] In other words, the technical challenges facing location-based service providers and companies is as follows: given a dataset containing mobility traces (e.g., also referred to as trajectories or probe trajectories comprising the trajectory data 109) of multiple individuals (e.g., associated with individual probe devices 101), service providers and companies would like to transform this trajectory data 109 into anonymized trajectory data that preserves most of the potentially useful information of the initial trajectory data 109
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known service provider to collect location information for a particular region. The service provider would store the collected information and would provide it to the users for locating position within the associated region, claim 1.
Claim(s) 11, 13, 16, 17, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun et al., 20200372624 in view of ROM 20200250966, Lee and KWAK et al., EP 2822325 A1.
Referring to claim(s) 11, Braun substantially discloses a server for collecting geospatial information, the server comprising: one or more hardware processors a method for collecting geospatial information, the method comprising: determining a domain region for an original location of a terminal;
[0006] operating a geospatial data assessment system for quantifying the quality of a geospatial data set. The systems and methods described herein allow for consistent and accurate assessment of a geospatial data by identifying a subset of the data for review, generating statistically valid and repeatable quality measurements, and providing tools for comparison of the measurements against intended uses of the data set. The systems and methods allow a user to review significantly less data while still accurately estimating the overall error rate of a geospatial data set.
[0010] identifying geospatial data for quality review is provided, the method comprising: receiving a geospatial data set representing a geographic area, wherein the geospatial data set comprises data representing a plurality of map features, and wherein the plurality of map features is associated with one or more feature classes; determining a value for map features in the geospatial data set; and selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value.
[0077] A geospatial data set may be generated based on any type of suitable image. For example, a geospatial data set may be generated based on an image, such as captured by a camera or other sensor, which may be mounted on a drone, satellite, aircraft, vehicle, stationary structures, or other location.
defining a reference probability for sampling in the domain region according to a relationship between a reference region in the domain region and the location for the original location; determining a region parameter of the reference region so as to minimize the expected value
[0117] local score estimate corresponding to the average raw score among adjacent regions
[0120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region.
[0121] A minimum sample size may be determined based on the size of the data set, an allowable error rate indicated by one or more target quality scores, and/or confidence interval. To determine whether selected regions comprise a minimum sample size, an amount of features in a selected region corresponding to the selected sampling parameters may be determined. If the amount of features in all selected regions corresponding to the selected sampling parameters for the selected regions is greater than or equal to a threshold value, then sampling may be complete. If the number of features corresponding to the selected sampling parameters for the selected regions is less than a threshold value, an additional region may be selected. Additional regions may be selected until the amount of features corresponding to the selected sampling parameters for the selected regions is greater than or equal to a threshold value.
[0124] At step 806, a determination may be made whether the quantity of features corresponding to the sampling parameters in all selected regions is less than a threshold value. If the number of features is less than a threshold value, then the method may return to step 802, and an additional region may be selected. If the number of features is greater than or equal to a threshold value, then no additional regions may be selected. After regions have been selected, information corresponding to the selected regions may be generated and stored, such as in storage 140, for future reference. For example, information indicating which regions were selected, the probability score of selected regions, the number and type of features in selected regions, and/or other information may be stored.
[0137] In some embodiments, a quality score may be determined based on a lot tolerance percent defective (LTPD) statistical sampling technique. For example, the lot size may correspond to the total amount of features in the data set corresponding to a sampling parameter. For example, for a sampling parameter based on the length of all linear features in the data set, the lot size may correspond to the length of all linear features in the data set. For a sampling parameter based on the number of features corresponding to a feature class, the lot size may be the number of features in the data set corresponding to the feature class. The sample size may correspond to an amount of features in the regions selected for review corresponding to the sampling parameters, and the number of errors may be the number of calls dropped in the selected regions during review
selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value, claim 1
Braun does not specifically mention about, which is well-known in the art, which ROM discloses,
sampling a location, deriving an expected value of an error between the original location and the location based on the reference probability (
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A difference between the predicted location and an actual location sensed by a location of the vehicle is calculated (602). A service for installation on or near the road segment is recommended (603) based on the difference. The installation of lights is recommended as the service. The difference is correlated with respective location of mobile urban service. The sampling rate for location sensor of the vehicle is adjusted based on dynamic location of the mobile urban service.abstract
calculating difference between predicted location and actual location sensed by location of vehicle (602) para 108
[0003] According to one embodiment, a method comprises calculating an estimated time of arrival at an end node of a road segment from a beginning node of the road segment based on historical traversal time data for the road segment. The method also comprises determining a sampling rate for a location sensor of a vehicle traveling the road segment based on the estimated time of arrival. The method further comprises configuring the location sensor to collect location data using the sampling rate.
[0007] According to one embodiment, a method comprises initiating a capture of a current location of a vehicle on a road segment by a location sensor based on a keep-alive sampling rate. The location sensor, for instance, is configured to operate at a sampling rate that is reduced from a default sampling rate in addition to the keep-alive sampling rate. The method also comprises determining a predicted location of the vehicle based on an estimated time of arrival of the vehicle at an end node of the road segment. The estimated time of arrival is based on historical traversal time data for the road segment. The method further comprises reconfiguring the location sensor to operate at the default sampling frequency based on determining that the predicted location differs from the current location by more than a threshold distance.
[0036] FIG. 6 is a flowchart of a process for providing map-based dynamic location sampling
[0065] For example, in areas where there is consistent disagreement between predicted locations and sampled locations above a threshold distance, the system 100 can mark those areas as low-predictable areas and recommend options to increase accuracy at that location or road segment (e.g., recommend installation of external positioning sensors, beacons, etc.). Conversely, in areas where there is consistent agreement within a threshold distance of the predicted locations and sampled locations, the system 100 can mark those areas as high-predictable areas and use location offsets determined from those areas to correct location readings for other vehicles in the area on road segment. In other embodiments, detected differences between the predicted or expected location and the sampled location can indicate the need for installation of other services such as but not limited to urban lights, emergency services, travel services, etc. In yet other embodiments, the detected differences can be used to infer real-time traffic conditions or incidents to update, for instance, a real-time data layer 119 of the geographic database 113.
sampling a location, deriving an expected value of an error between the original location and the location based on the reference probability (
adjusted based on dynamic location of the mobile urban service, claim 3
[0081] FIG. 10 is diagram illustrating an example of performing path recovery when using map-based dynamic location sampling, according to one embodiment. In this example, a vehicle 105 is traveling from a beginning node 1001a to an end node 1001b connected by a directed edge 1003 corresponding to a road segment that is 1 mile long. The ETA from the beginning node 1001a to the end node 1001b is 10 mins based on historical data. The location sensor associated with the vehicle 105 is configured to take a keep-alive location reading at 5 mins. The predicted location of the vehicle using a time-based extrapolation would then be approximately 0.5 miles from the beginning node (e.g., 0.5 miles=1 mile×(5 mins/10 mins) based the extrapolation equation described above) as indicated by location 1005. However, the keep-alive location reading indicates that vehicle 105 is actually 0.25 miles from the beginning node 1001a as indicated by location 1007. The difference between the predicted location 1007 and the current location 1007 is then 0.25 miles. In this example, the distance threshold is 0.1 miles. Accordingly, because the difference (0.25 miles) is greater than the distance threshold (0.1 miles), the location sensor of the vehicle is reconfigured with a default or high-frequency sampling rate to improve accuracy until the vehicle reaches the end node 1001b of the directed edge 1003 (i.e., path recovery)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing sampling a location and deriving an expected value of an error. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it would enable representing the regions, para 7.
Braun and ROM does not specifically mention about, which is well-known in the art, which KWAK discloses, perturbed location (
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The method estimating said route between an origin location and a destination location of a cell phone user requesting said route by using a computer device receiving as inputs location data from a base station tower, said computer device: creating a plurality of perturbations of at least one of said computed origin or said destination location within said base station tower coverage; for each perturbation created querying a route calculation engine, based on geospatial data, to calculate a route between said perturbations or between said perturbations and said origin or destination and obtaining a list of routes, and choosing among said list of routes an optimal route by maximizing an utility function that assesses the efficacy of each route of said list of routes by using user's location data being performed within certain distance of said calculated routes, abstract
For each user who contact to a certain base station, their location is expressed as longitude and latitude of the base station. The real location of the user can be anywhere within the coverage of the base station tower. To consider these characteristics, the invention intentionally adds those perturbations P, i.e. noises, to the base station location of the origin H and the destination W presented in Fig. 1. The grey area represents the coverage of the base station tower, and the black cross sign denotes where the user makes calls. According to other embodiments, the perturbations can be applied to either of two locations, H and W, as is represented in Figs. 2A and 2B. This is for the less computation of perturbations P. Instead of picking randomly generated N perturbations from home H and work W each, making perturbations P only for home H or only for work W requires a half of computation costs (time and computer power). Last para, page 4
Within a circular area whose centre is the latitude and longitude of a base station and a radius represents the coverage of the tower, the invention creates N pairs of perturbed locations from the original location. N could be any number, and it is directly related with the complexity of the geography in that region because N reflects the possibility of locations / routes of users. For instance, in case there is only one big highway that passes from one to the other location, the invention wouldn't need to make a large number of perturbations P because that user has no other choices except taking that highway. On the other hand, if there are many streets in that cell region, the invention would take higher N because it will consider all possibilities with those streets. For instance, with CDR data collected from two well-developed cities, it is sufficient that N > 20. Then, for each pair of perturbed home H and work W locations, the route between both locations is queried to the route calculation engine, in this particular case being based on web mapping, and it is obtained the routes represented by the solid lines. 1st para, page 4
For each perturbation created, according to a preferred embodiment, it is added at least one intermediate point in order to improve the choosing of the optimal route, wherein said at least one intermediate point comprises a place where the user makes many calls and/or a popular or common location concerning the user and/or a highway where the user travels. In order to avoid overfitting, the addition of the intermediate points will be limited to a defined number. Preferably, the intermediate point is identified by means of computing an intermediate point score, 4th para, page 3
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing perturbed location. One of ordinary skilled in the art would readily know that perturbation refers to a small change or deviation in a system, object, or process from its normal or expected state or path, often caused by an external influence. In essence, it's the location associated with the perturbation would enable sampling associated the regions using collections of geospatial information, last para, page 4.
Braun, Zhu, KWAK and ROM does not specifically mention about, which is well-known in the art, which Lee discloses, a center of a reference region and defining a relation equation for setting the center of the reference region (5th para, page 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing setting the center of the reference region. One of ordinary skilled in the art would readily know that what the center of the reference region is.
The center of the reference region is a conceptual point used for describing a region’s location. It is not physical but a center location within the region that serves as a reference for entities such as a vehicle.
The center of the reference region of interest 411 for the vehicle would enable locating of the vehicle in a plurality of preset directions, 5th para, page 6.
Referring to claim(s) 3, 13, Braun discloses wherein the reference probability is a probability for the location, and includes a first reference probability for a case where the location is within the reference region and a second reference probability for a case where the location is within the domain region out of the reference region (
[0117] local score estimate corresponding to the average raw score among adjacent regions
[0120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region.
selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value, claim 1
KWAK discloses, perturbed location (
The method estimating said route between an origin location and a destination location of a cell phone user requesting said route by using a computer device receiving as inputs location data from a base station tower, said computer device: creating a plurality of perturbations of at least one of said computed origin or said destination location within said base station tower coverage; for each perturbation created querying a route calculation engine, based on geospatial data, to calculate a route between said perturbations or between said perturbations and said origin or destination and obtaining a list of routes, and choosing among said list of routes an optimal route by maximizing an utility function that assesses the efficacy of each route of said list of routes by using user's location data being performed within certain distance of said calculated routes, abstract
For each user who contact to a certain base station, their location is expressed as longitude and latitude of the base station. The real location of the user can be anywhere within the coverage of the base station tower. To consider these characteristics, the invention intentionally adds those perturbations P, i.e. noises, to the base station location of the origin H and the destination W presented in Fig. 1. The grey area represents the coverage of the base station tower, and the black cross sign denotes where the user makes calls. According to other embodiments, the perturbations can be applied to either of two locations, H and W, as is represented in Figs. 2A and 2B. This is for the less computation of perturbations P. Instead of picking randomly generated N perturbations from home H and work W each, making perturbations P only for home H or only for work W requires a half of computation costs (time and computer power). Last para, page 4
Within a circular area whose centre is the latitude and longitude of a base station and a radius represents the coverage of the tower, the invention creates N pairs of perturbed locations from the original location. N could be any number, and it is directly related with the complexity of the geography in that region because N reflects the possibility of locations / routes of users. For instance, in case there is only one big highway that passes from one to the other location, the invention wouldn't need to make a large number of perturbations P because that user has no other choices except taking that highway. On the other hand, if there are many streets in that cell region, the invention would take higher N because it will consider all possibilities with those streets. For instance, with CDR data collected from two well-developed cities, it is sufficient that N > 20. Then, for each pair of perturbed home H and work W locations, the route between both locations is queried to the route calculation engine, in this particular case being based on web mapping, and it is obtained the routes represented by the solid lines. 1st para, page 4
For each perturbation created, according to a preferred embodiment, it is added at least one intermediate point in order to improve the choosing of the optimal route, wherein said at least one intermediate point comprises a place where the user makes many calls and/or a popular or common location concerning the user and/or a highway where the user travels. In order to avoid overfitting, the addition of the intermediate points will be limited to a defined number. 4th para, page 3
Referring to claim(s) 6, 16, Braun discloses wherein the first reference probability is defined to sample the location with a higher probability than the second reference probability (
[120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region
[0010] identifying geospatial data for quality review is provided, the method comprising: receiving a geospatial data set representing a geographic area, wherein the geospatial data set comprises data representing a plurality of map features, and wherein the plurality of map features is associated with one or more feature classes; determining a value for map features in the geospatial data set; and selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value.
KWAK discloses, perturbed location, Last para, page 4
Referring to claim(s) 17, Braun discloses wherein each of the original location and the location includes a latitude and a longitude, and is mapped on the domain region (Last para, page 4, 1st para, page 4). KWAK discloses, perturbed location, Last para, page 4
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, KWAK, Lee and Liu et al., CN 105069371 A.
Referring to claim(s) 12, Braun discloses wherein the reference probability is defined based on a sampling parameter defined based on the domain region, the reference region (
[0094] FIG. 5 illustrates a method for selecting subsets of a geospatial data set for review, according to some embodiments. The method provides for quantifying the features of the data set, such as determining a number of features in the data set and determining a number of features associated with each layer, theme, and/or attribute represented in the data set. The method further provides for determining sampling parameters. In some embodiments, the method may select regions to be statistically representative of one or more layers, themes, or attribute classes. In other embodiments, the method may select regions to be statistically representative of all features in the data set. Sampling parameters may correspond to the type or types of feature for which the system may select a statistically representative number of regions for review.
[0095] The data set may be divided into regions and a selection probability may be determined for each region. Regions may then be selected for review. Regions may be selected based on the selection probabilities. Regions may be selected until the sampling parameters of features in the selected region are large enough as a proportion of the sampling parameters for all features in the data set may be statistically representative of the whole data set within a confidence interval.
Braun, ROM, Zhu and KWAK does not specifically mention about, privacy parameter, which Liu discloses it, 3rd and 4th para, page 2, claim 1.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known privacy parameter. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it would enable keeping private information not disclosed to public by using the private parameter, 3rd and 4th para, page 2.
Claim(s) 14, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, KWAK, Lee and Liu et al., 10139820.
Referring to claim(s) 14, Braun, ROM, KWAK discloses, transmitting the region parameter of the determined reference region, Braun, para 3, abstract. BRAUN, ROM, KWAK do not disclose transmitting the region parameter to a terminal which is a target of location collection, which Liu discloses it, col., 70, lines 24-34.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known region parameter for the collection of information. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it the entity would collect data from the provided region, which would enable implementing sampling of the information for the associated overall regions, col., 70, lines 24-34.
Claim(s) 15, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, KWAK, Lee and ZHANG et al., CN 105933357 B.
Referring to claim(s) 15, Braun, ROM, KWAK discloses, wherein the reference region may be a region , and the region parameter of the reference region, Braun, para 3, abstract. BRAUN, ROM, KWAK do not disclose square region includes a square side length, which ZHANG discloses it, claim 1.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known square region for the collection of information. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it the entity would collect data from the provided square region, which would enable implementing sampling of the information for the associated overall regions, claim 1.
Claim(s) 18, is/are rejected under 35 U.S.C. 103 as being unpatentable over Braun in view of ROM, KWAK, Lee, Lee and BENNATI et al., 20210374280.
Referring to claim(s) 18, Braun, ROM, KWAK discloses, perturbed location. KWAK, Last para, page 4. BRAUN, ROM, KWAK do not disclose collecting the corresponding to the original location of each terminal from at least one terminal; and providing the collected location to a service provider, which BENNATI discloses,
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[0026] FIG. 1 is a diagram of a system for providing device-side trajectory anonymization based on negative gapping, according to one embodiment. As discussed above, many location-based service providers and companies collect location data to be used in its services and applications. In one embodiment, location data can be collected as a trajectory representing a sequence of data entries per individual moving entity (e.g., individual probe devices 101), where each entry (e.g., a probe point) consists of location (latitude, longitude), time stamp, a pseudonym (e.g., to indicate which the entries belong to the same entity), and possibly various additional information about the entity at the time (vehicle sensor data, speed, heading etc.). Examples of probe devices 101 include but are not limited to vehicles 103a-103n (also collectively referred to as vehicles 103), user equipment (UE) devices 105a-105m (also collectively referred to as UEs 105), and/or equivalent devices equipped with location sensors (e.g., Global Navigation Satellite System (GNSS) receivers) capable of generating location data (e.g., trajectory data 109).
[0028] In other words, the technical challenges facing location-based service providers and companies is as follows: given a dataset containing mobility traces (e.g., also referred to as trajectories or probe trajectories comprising the trajectory data 109) of multiple individuals (e.g., associated with individual probe devices 101), service providers and companies would like to transform this trajectory data 109 into anonymized trajectory data that preserves most of the potentially useful information of the initial trajectory data 109
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing well-known service provider to collect location information for a particular region. The service provider would store the collected information and would provide it to the users for locating position within the associated region, claim 1.
Response to Arguments
Remarks/Arguments filed 1/13/26, pages 7-29 have been fully considered but they are not persuasive. Therefore, rejection of claims 1-8, 11-18 is maintained.
Regarding the remarks for the amended claim 1, the rejections are updated accordingly. Please refer to the updated rejections for the amended limitations.
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Regarding the limitations of the claimed subject matter that is not amended, below response is still applicable.
Regarding limitations,
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The remarks fail to consider that the First inquiry must be into exactly what the claims define. See In re Wilder, 166 USPQ 545, 548 (CCPA 1970). What is claimed is:
As claimed, “collecting geospatial information”, is not limited to any particular way of collecting the information, when the information is collected, how the information is collected, how the information is different, etc.
As claimed, “an original location” is not limited to any particular location. It is not limited to a location of the stored locations, live location, etc., For example, the terminal’s location a week ago, one month ago, etc. Hence, it a mere location where the terminal is/was located.
As claimed, “perturbed location” is not limited to a generated location two weeks ago or live or one year ago. Also, it is not limited to how, when or why the terminal generated the perturbed location.
The step “determining a domain region” is not limited to when the determination is made, whether a user did the determination or an administrator, remotely determined, etc.
The claimed “domain region” is not limited to any particular size, shape on the earth. For example, other than the sea, etc.,
The claimed, “reference probability” is not limited to any particular value, for example, 0%, 100%, etc.
The claimed, “sampling” is not limited to which sampling method is used for a single perturbed location.
The step “defining a reference probability” is not limited to when the defining is made, whether a user did the defining or an administrator, remotely defined, etc.
The claimed, “a relationship between a reference region in the domain region and the perturbed location for the original location” is not limited to any particular relationship. The claimed reference region is not limited to same as the domain region or not. The claimed “reference region” is not limited to any particular size, shape on the earth. For example, other than the sea, etc.,
Since, the claimed reference region (for example, when the terminal was one week ago), the calculated single perturbed location was today, etc), the relationship is not limited to same or different.
The step “deriving” is also not limited to when the deriving is made, whether a user did the deriving or an administrator, remotely deriving, etc. The claimed expected value of error remains same regardless of the claimed reference probability (for example, 0%).
The step “determining a region parameter” is also not limited to when the determining is made, whether a user did the determining or an administrator, remotely determining, etc.
As claimed “region parameter” is a mere parameter, which has nothing to do with the size or shape of the two claimed regions in the claim. Mere “determining a region parameter” cannot “minimize the expected value”. Also, when the expected value of an error is zero it cannot be minimized. As claimed, the expected value of an error remains the same and the reference probability never changes, and “collecting geospatial information” is not accomplished.
Regarding,
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Which clearly states, original locations of the plurality of “geospatial information providing terminals”. However, the claim does not claim “geospatial information providing terminals”. Further it contains “may be”. Also, it clearly contains “actual” location, which is not limited to “current” location, and hence, the actual location of something/terminal/user about one week ago is not same as one month ago.
In response to Remarks/Arguments that the references fail to show certain features of claimed invention, it is noted that the features upon which claim relies, “original locations of the plurality of geospatial information providing terminals 200A to 200N (200) may be an actual or current location of the relevant terminal, and may be an actual or current location of a user who possesses the relevant terminal.”, are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Regarding the concern for the motivation, the reason or motivation to modify the reference may often suggest what the inventor has done, but for a different purpose or to solve a different problem. It is not necessary that the prior art suggest the combination to achieve the same advantage or result discovered by applicant. In re Linter, 458 F.2d 1013, 173 USPQ 560 (CCPA 1972). There is no requirement that the prior art provide the same reason as the applicant to make the claimed invention. Ex parte Levengood, 28 USPQ2d 1300, 1302 (Bd. Pat. App. & Inter. 1993).
Also, the remarks relies on irrelevant disclosure of the cited references.
Referring to claim(s) 1, Braun substantially discloses a method for collecting geospatial information, the method comprising: determining a domain region for an original location of a terminal;
[0006] operating a geospatial data assessment system for quantifying the quality of a geospatial data set. The systems and methods described herein allow for consistent and accurate assessment of a geospatial data by identifying a subset of the data for review, generating statistically valid and repeatable quality measurements, and providing tools for comparison of the measurements against intended uses of the data set. The systems and methods allow a user to review significantly less data while still accurately estimating the overall error rate of a geospatial data set.
[0010] identifying geospatial data for quality review is provided, the method comprising: receiving a geospatial data set representing a geographic area, wherein the geospatial data set comprises data representing a plurality of map features, and wherein the plurality of map features is associated with one or more feature classes; determining a value for map features in the geospatial data set; and selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value.
[0077] A geospatial data set may be generated based on any type of suitable image. For example, a geospatial data set may be generated based on an image, such as captured by a camera or other sensor, which may be mounted on a drone, satellite, aircraft, vehicle, stationary structures, or other location.
defining a reference probability for sampling in the domain region according to a relationship between a reference region in the domain region and the location for the original location; determining a region parameter of the reference region so as to minimize the expected value
[0117] local score estimate corresponding to the average raw score among adjacent regions
[0120] regions may be selected for review based on probability scores. A region may be selected based on the probability scores of the regions. The probability score of a region may correspond to a probability of the region being selected. In some embodiments, a region may be selected based on a weighted random selection, where the weights correspond to the probability scores of each region.
[0121] A minimum sample size may be determined based on the size of the data set, an allowable error rate indicated by one or more target quality scores, and/or confidence interval. To determine whether selected regions comprise a minimum sample size, an amount of features in a selected region corresponding to the selected sampling parameters may be determined. If the amount of features in all selected regions corresponding to the selected sampling parameters for the selected regions is greater than or equal to a threshold value, then sampling may be complete. If the number of features corresponding to the selected sampling parameters for the selected regions is less than a threshold value, an additional region may be selected. Additional regions may be selected until the amount of features corresponding to the selected sampling parameters for the selected regions is greater than or equal to a threshold value.
[0124] At step 806, a determination may be made whether the quantity of features corresponding to the sampling parameters in all selected regions is less than a threshold value. If the number of features is less than a threshold value, then the method may return to step 802, and an additional region may be selected. If the number of features is greater than or equal to a threshold value, then no additional regions may be selected. After regions have been selected, information corresponding to the selected regions may be generated and stored, such as in storage 140, for future reference. For example, information indicating which regions were selected, the probability score of selected regions, the number and type of features in selected regions, and/or other information may be stored.
[0137] In some embodiments, a quality score may be determined based on a lot tolerance percent defective (LTPD) statistical sampling technique. For example, the lot size may correspond to the total amount of features in the data set corresponding to a sampling parameter. For example, for a sampling parameter based on the length of all linear features in the data set, the lot size may correspond to the length of all linear features in the data set. For a sampling parameter based on the number of features corresponding to a feature class, the lot size may be the number of features in the data set corresponding to the feature class. The sample size may correspond to an amount of features in the regions selected for review corresponding to the sampling parameters, and the number of errors may be the number of calls dropped in the selected regions during review
selecting one or more regions in the geospatial data set for review, wherein selecting one or more regions in the geospatial data set for review comprises: dividing the geospatial data set into a plurality of regions; determining a value for map features in each region; determining a probability score for each region based on the value for map features in the region and the value for features in the geospatial data set; and selecting one or more regions for review based on the probability scores, wherein a number of selected regions is based on a value for map features in the one or more selected regions reaching a threshold value, claim 1
Braun does not specifically mention about, which is well-known in the art, which ROM discloses,
sampling a location, deriving an expected value of an error between the original location and the location based on the reference probability (
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A difference between the predicted location and an actual location sensed by a location of the vehicle is calculated (602). A service for installation on or near the road segment is recommended (603) based on the difference. The installation of lights is recommended as the service. The difference is correlated with respective location of mobile urban service. The sampling rate for location sensor of the vehicle is adjusted based on dynamic location of the mobile urban service.abstract
calculating difference between predicted location and actual location sensed by location of vehicle (602) para 108
[0003] According to one embodiment, a method comprises calculating an estimated time of arrival at an end node of a road segment from a beginning node of the road segment based on historical traversal time data for the road segment. The method also comprises determining a sampling rate for a location sensor of a vehicle traveling the road segment based on the estimated time of arrival. The method further comprises configuring the location sensor to collect location data using the sampling rate.
[0007] According to one embodiment, a method comprises initiating a capture of a current location of a vehicle on a road segment by a location sensor based on a keep-alive sampling rate. The location sensor, for instance, is configured to operate at a sampling rate that is reduced from a default sampling rate in addition to the keep-alive sampling rate. The method also comprises determining a predicted location of the vehicle based on an estimated time of arrival of the vehicle at an end node of the road segment. The estimated time of arrival is based on historical traversal time data for the road segment. The method further comprises reconfiguring the location sensor to operate at the default sampling frequency based on determining that the predicted location differs from the current location by more than a threshold distance.
[0036] FIG. 6 is a flowchart of a process for providing map-based dynamic location sampling
[0065] For example, in areas where there is consistent disagreement between predicted locations and sampled locations above a threshold distance, the system 100 can mark those areas as low-predictable areas and recommend options to increase accuracy at that location or road segment (e.g., recommend installation of external positioning sensors, beacons, etc.). Conversely, in areas where there is consistent agreement within a threshold distance of the predicted locations and sampled locations, the system 100 can mark those areas as high-predictable areas and use location offsets determined from those areas to correct location readings for other vehicles in the area on road segment. In other embodiments, detected differences between the predicted or expected location and the sampled location can indicate the need for installation of other services such as but not limited to urban lights, emergency services, travel services, etc. In yet other embodiments, the detected differences can be used to infer real-time traffic conditions or incidents to update, for instance, a real-time data layer 119 of the geographic database 113.
sampling a location, deriving an expected value of an error between the original location and the location based on the reference probability (
adjusted based on dynamic location of the mobile urban service, claim 3
[0081] FIG. 10 is diagram illustrating an example of performing path recovery when using map-based dynamic location sampling, according to one embodiment. In this example, a vehicle 105 is traveling from a beginning node 1001a to an end node 1001b connected by a directed edge 1003 corresponding to a road segment that is 1 mile long. The ETA from the beginning node 1001a to the end node 1001b is 10 mins based on historical data. The location sensor associated with the vehicle 105 is configured to take a keep-alive location reading at 5 mins. The predicted location of the vehicle using a time-based extrapolation would then be approximately 0.5 miles from the beginning node (e.g., 0.5 miles=1 mile×(5 mins/10 mins) based the extrapolation equation described above) as indicated by location 1005. However, the keep-alive location reading indicates that vehicle 105 is actually 0.25 miles from the beginning node 1001a as indicated by location 1007. The difference between the predicted location 1007 and the current location 1007 is then 0.25 miles. In this example, the distance threshold is 0.1 miles. Accordingly, because the difference (0.25 miles) is greater than the distance threshold (0.1 miles), the location sensor of the vehicle is reconfigured with a default or high-frequency sampling rate to improve accuracy until the vehicle reaches the end node 1001b of the directed edge 1003 (i.e., path recovery)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing sampling a location and deriving an expected value of an error. One of ordinary skilled in the art would readily know that a sampling location refers to the specific points or areas where samples are collected for analysis, crucial in various fields for quality control, research, or process monitoring. In essence, it's the precise place where something or someone gathers a portion of a larger entity to represent the whole. Hence, it would enable representing the regions, para 7.
Braun and ROM does not specifically mention about, which is well-known in the art, which Zhu discloses, perturbed location, based upon a perturbed location generated by the terminal for the original location (
(19) As described herein, a location measurement 104 or a series of location measurements can be processed (e.g., perturbed) by the mobile device 105 and/or the ALPM 106. The perturbed location measurement or the perturbed series of location measurements can be sent over a channel col., 4, lines 1-20
(8) Furthermore, users of LBSs may prefer personalized privacy protection in tracing of their location trajectories, in which a profile (e.g., a personalized privacy preference profile) can be adaptively adjusted for individual segments in a trajectory (e.g., areas, regions, etc., in a trajectory potentially transited by the mobile device and, thus, the user) in order to achieve a balance between privacy and quality of service (QoS) within the trajectory. As described herein, mobile device can, for example, be a portable telephone, such as a cellular phone, a smartphone, a personal computer, a personal digital assistant, a tablet, a notebook, a computing system in a vehicle, etc., col., 2, lines 50-65
adaptive location perturbation, comprising: selecting, by a processing resource of a mobile device executing instructions stored on a non-transitory medium, between a plurality of localization technologies to perturb a plurality of locations measured by the mobile device; smoothen a directional transition of the mobile communication device by smoothening at least one of the perturbed locations to create a smoothened perturbed trajectory; and sending the smoothened perturbed trajectory and the perturbed location measurements of the mobile device, claim 1)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing perturbed location. One of ordinary skilled in the art would readily know that perturbed location can be generated by a portable telephone, such as a cellular phone, a smartphone, a personal computer, a personal digital assistant, a tablet, a notebook, a computing system in a vehicle, etc. A plurality of localization technologies available (well-known) to perturb a plurality of locations measured by the device would assist in smoothen a directional transition of the communication device by smoothening at least one of the perturbed locations to create a smoothened perturbed trajectory; and sending the smoothened perturbed trajectory and the perturbed location measurements of the mobile device for further use, col., 2, lines 50-65, claim 1.
Braun, Zhu and ROM does not specifically mention about, which is well-known in the art, which Lee discloses, a center of a reference region and defining a relation equation for setting the center of the reference region (5th para, page 6).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention disclosed by Braun to implement these limitations and also one of ordinary skill in the art would have been motivated to do so because it could provide utilizing setting the center of the reference region. One of ordinary skilled in the art would readily know that what the center of the reference region is.
The center of the reference region is a conceptual point used for describing a region’s location. It is not physical but a center location within the region that serves as a reference for entities such as a vehicle.
The center of the reference region of interest 411 for the vehicle would enable locating of the vehicle in a plurality of preset directions, 5th para, page 6.
Conclusion
Pertinent prior art:
HOU et al., CN 109257385 A, abstract.
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/HARESH N PATEL/Primary Examiner, Art Unit 2496
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