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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 12/18/2025 and 03/11/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
This action is in response to amendments and remarks filed on 12/18/2025. Claims 1, 3-4, 6-8, 12, and 34-47 are considered in this office action. Claims 1, 3, 6-8, 12, 34, 35, 37-42, and 44-46 are amended. Claims 1, 3-4, 6-8, 12, and 34-47 are pending examination. The objection to the specification has been withdrawn in view of the applicants amendment to paragraph [0148] of the specification. Additionally, the applicant arguments have been considered and are convincing. Thus, the 101 rejection has been withdrawn. Applicant's new claim limitations necessitated new grounds of rejection therefore it was found that claims 1, 3-4, 6-8, 12, and 34-47 are rejected as necessitated by amendments.
Response to Arguments
Applicant presents the following arguments regarding the previous office action:
The 35 U.S.C. § 102 prior art rejection of claims 1, 34, and 41 do not disclose the amended features of at least Accessing sparse location data comprising a sparse set of location points collected by a user device during a workout, or the concept of sparse data points as a whole. Consequentially, claims 1, 34, and 41 and their dependent claims, should be allowed.
Applicant’s argument A, with respect to the independent claims has been fully considered and are moot in light of the new grounds for rejection below.
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, 3-4, 6, 12, 34-37, and 40-44 are all rejected under 35 U.S.C. 103 as being unpatentable over Pesyna et al. (US20220390238A1), in view of Case et al. (US20060136173A1).
Regarding claim 1, Regarding claim 1, Pesyna discloses, a computer-implemented method, comprising: accessing location data comprising a set of location points collected by a user device during a workout (Abstract, periodically obtaining position information data from a receiver of a navigation system during a movement event), and defining an initial route, the set of location points comprising an initial location point and a final location point (0047, in one or more implementations, the path may be estimated interpolating successive positions of the device based on the position information data obtained from the receiver), wherein the user device collects the location data during the workout using a sparse location data collection mode of a plurality of location data collection modes (0047, the position information is periodically received, the position information received at successive time points is interpolated to estimate the path traversed by the user. In some implementations, the path estimated in real-time as the receiver receives the position information data. Any algorithm known in the art for interpolating successive position points may be used), accessing map data comprising a plurality of paths within a geographic region (0072, determining, by the processor, previously mapped features within a proximity of the estimated path on a map; matching, by the processor, the estimated path to a pre-mapped feature on the map based on the previously mapped features to obtain a map-matched path); for a plurality of location point pairs of the set of location points, determining a route segment between the pair of location points that follows a path segment of the plurality of paths (0047, any algorithm known in the art for interpolating successive position points may be used. In one or more implementations, the path may then be estimated by joining together the interpolated segments over the duration of the movement event); combining route segments for each location point pair of the plurality of location point pairs to define a reconstructed route that begins with a first location route pair based on the initial location point and ends with a second location route pair based on the final location point (0014, the track log can be processed to obtain a path traversed by the user during the event, e.g., by joining together the track points. The path obtained by processing the track log may or may not represent the actual route (also referred to herein as the actual path) traversed by the user depending on how the track log is processed to obtain the path); generating a graphical representation of the reconstructed route; and providing the graphical representation of the reconstructed route for presentation at the user device (0057, a display device may be caused (310) to render the map-matched path on an image of the map. FIG. 6 shows an example of a smoothed path using a map-matched smoothing process).
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However, Pesyna does not explicitly disclose the amended limitation of , accessing sparse location data comprising a sparse set of location points collected by a user device during a workout, a sparse set of location points comprising an initial location point and a final location point, the sparse location data during the workout using a sparse location data mode of a plurality of location data collection modes, a plurality of sparse location point pairs of the sparse set of location points, accessing map data comprising a plurality of paths within a geographic region, and determining a route segment between sparse location point pairs, and combining route segments for each sparse location point pair of the plurality of sparse location point pairs.
Nevertheless, Case who is in the same field of endeavor of monitoring of athletic performance discloses, accessing sparse location data comprising a sparse set of location points collected by a user device during a workout (0063, the GPS monitor may operate periodically, in the background, e.g., logging position and altitude tracking point data in a memory), a sparse set of location points comprising an initial location point and a final location point (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), the sparse location data during the workout using a sparse location data mode (0070, Additionally, if desired, the GPS system could be used sparingly or periodically, to save battery power, and the speedometer system could be used constantly (or at least more frequently) to provide the athlete with real time speed and distance information), accessing map data comprising a plurality of paths within a geographic region (0037, to prepare the candidate projection points, the map-matching module obtains data from GPS logs 408(1), 408(2), . . . , 408(N) and the road network database 312); a plurality of sparse location point pairs of the sparse set of location points (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), and determining a route segment between sparse location point pairs (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), combining route segments for each sparse location point pair of the plurality of sparse location point pairs (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
One of ordinary skill in the art prior to the effective filing date of the given invention
would have been motivated to combine Pesyna with Case. One of ordinary skill in the art would recognize it is a reasonable and advantageous design choice to combine known sparse data point algorithms disclosed by Case with Pesyna’s disclosure. This combination would generate movements independent of mapped paths and improve continuity with sparse points and preserve power, as was already stated in Case.
Justification for combining Pesyna, Lou, and Case disclosures not only come from the state of the art but from Case (0159, those skilled in the art will appreciate that there are numerous variations and permutations of the above described systems and methods).
Regarding claim 3, Pesyna and Case disclose the computer-implemented method of claim 1, as discussed supra. Additionally, Pesyna discloses, accessing the location data, accessing the map data, determining the route segment, and combining the route segment define a route reconstruction operation (0014, generate a track log corresponding to movement of the electronic device over a period of time, such as during an event (e.g., an exercise session). A track log may be a collection of track points along the course of a route of movements of the device. A track log can be created using data, such as GPS data obtained from an on-board navigation system, such as while the device moves along a route. Further, a track log can be saved in a track log file that lists one or more sequential coordinates in, for example, two-dimensional (2-D) or three-dimensional (3-D) space, such as latitude, longitude and elevation. The track log can be processed to obtain a path traversed by the user during the event) … (0014, the track log can be processed to obtain a path traversed by the user during the event, e.g., by joining together the track points).
Additionally, Case discloses, accessing the sparse location data, (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 4, Pesyna and Case disclose the computer-implemented method of claim 3, as discussed supra. Additionally, Pesyna discloses, the route reconstruction operation is performed in response to at least one of detecting a conclusion of the workout or collecting the final location (0058, the matching of the estimated path to the pre-mapped feature may be performed after the movement event has ended, and/or when the user wishes to render the map-matched path on the map).
Regarding claim 6, Pesyna and Case disclose the computer-implemented method of claim 1, as discussed supra. Additionally, Pesyna discloses collecting the location data during the workout (0058, some data processing may be performed during the movement event while other data processing may be performed following the movement event. For example, the estimation of the path traversed by the user based on raw position information data may be performed real-time as the data is received), and wherein accessing the location data, accessing the map data, determining the route segment, combining the route segment, generating the graphical representation, and providing the graphical representation are performed after conclusion of the workout (0058, on the other hand, because obtaining the map-matched path may be performed on an estimated path, the matching of the estimated path to the pre-mapped feature may be performed after the movement event has ended, and/or when the user wishes to render the map-matched path on the map).
Additionally, Case discloses, collecting the sparse location data during the workout, and accessing the sparse location data (0063, it may be desirable to provide athletic performance monitoring systems and methods that derive all “real time” speed and distance information (e.g., the information displayed to the athlete on the portable display device during the performance) from the accelerometers or pedometers, and not using the GPS to provide the real time information. In such systems, the GPS monitor may operate periodically, in the background, e.g., logging position and altitude tracking point data in a memory).
Regarding claim 12, Pesyna and Case disclose the computer-implemented method of claim 1 as discussed supra. Additionally, Case discloses selecting the sparse location data collection mode (0063, the GPS data may be sampled less frequently, thereby saving power, while still providing continuous and sufficiently accurate speed and distance information to the athlete in real time from the accelerometer or other pedometer based system) from a plurality of data collection modes; (0059, as another option or alternative, if desired, an athlete could press a button on the portable portion of the monitoring system or otherwise command the system to collect calibration or correction data); and switching from a first data collection mode of the plurality of data collection modes to the sparse location data collection mode based at least in part on the selection (0070, Additionally, if desired, the GPS system could be used sparingly or periodically, to save battery power, and the speedometer system could be used constantly (or at least more frequently) to provide the athlete with real time speed and distance information), wherein the sparse location data collection mode is configured for collecting fewer location points than the first data collection mode (0063, the GPS data may be sampled less frequently, thereby saving power, while still providing continuous and sufficiently accurate speed and distance information to the athlete in real time from the accelerometer or other pedometer based system).
Regarding claim 34 Pesyna discloses a computing system, comprising: one or more processors (0029, processing subsystem 202 can be implemented as one or more integrated circuits, e.g., one or more single-core or multi-core microprocessors or microcontrollers, examples of which are known in the art. In operation, processing subsystem 202 can control the operation of electronic device 200); and one or more computer-readable media having stored thereon a sequence of instructions (0066, the electronic system 700 may include various types of computer readable media and interfaces for various other types of computer readable media), that, when executed, cause the one or more processors to: access location data comprising a set of location points collected by a user device during a workout and defining an initial route, (0047, in one or more implementations, the path may be estimated interpolating successive positions of the device based on the position information data obtained from the receiver), the set of location points comprising an initial location point and a final location point (0047, in one or more implementations, the path may be estimated interpolating successive positions of the device based on the position information data obtained from the receiver), wherein the user device collects the location data during the workout using a sparse location data collection mode of a plurality of location data collection modes (0047, the position information is periodically received, the position information received at successive time points is interpolated to estimate the path traversed by the user. In some implementations, the path estimated in real-time as the receiver receives the position information data. Any algorithm known in the art for interpolating successive position points may be used), access map data comprising a plurality of paths within a geographic region (0072, determining, by the processor, previously mapped features within a proximity of the estimated path on a map; matching, by the processor, the estimated path to a pre-mapped feature on the map based on the previously mapped features to obtain a map-matched path); for a plurality of location point pairs of the set of location points, determining a route segment between the pair of location points that follows a path segment of the plurality of paths (0047, any algorithm known in the art for interpolating successive position points may be used. In one or more implementations, the path may then be estimated by joining together the interpolated segments over the duration of the movement event); combine route segments for each location point pair of the plurality of location point pairs to define a reconstructed route that begins with a first location route pair based on the initial location point and ends with a second location route pair based on the final location point; (0014, the track log can be processed to obtain a path traversed by the user during the event, e.g., by joining together the track points. The path obtained by processing the track log may or may not represent the actual route (also referred to herein as the actual path) traversed by the user depending on how the track log is processed to obtain the path); generate a graphical representation of the reconstructed route (0057, a display device may be caused (310) to render the map-matched path on an image of the map. FIG. 6 shows an example of a smoothed path using a map-matched smoothing process), and provide the graphical representation of the reconstructed route for presentation at the user device (0057, a display device may be caused (310) to render the map-matched path on an image of the map. FIG. 6 shows an example of a smoothed path using a map-matched smoothing process).
Nevertheless, Case discloses, accessing sparse location data comprising a sparse set of location points collected by a user device during a workout (0063, the GPS monitor may operate periodically, in the background, e.g., logging position and altitude tracking point data in a memory), a sparse set of location points comprising an initial location point and a final location point (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), the sparse location data during the workout using a sparse location data mode (0070, Additionally, if desired, the GPS system could be used sparingly or periodically, to save battery power, and the speedometer system could be used constantly (or at least more frequently) to provide the athlete with real time speed and distance information), accessing map data comprising a plurality of paths within a geographic region (0037, to prepare the candidate projection points, the map-matching module obtains data from GPS logs 408(1), 408(2), . . . , 408(N) and the road network database 312); a plurality of sparse location point pairs of the sparse set of location points (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), and determining a route segment between sparse location point pairs (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), combining route segments for each sparse location point pair of the plurality of sparse location point pairs (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 35 Pesyna and Case disclose the computing system of claim 34, as discussed supra. Additionally, Pesyna discloses, accessing the location data, accessing the map data, determining the route segment, and combining the route segment define a route reconstruction operation (0014, generate a track log corresponding to movement of the electronic device over a period of time, such as during an event (e.g., an exercise session). A track log may be a collection of track points along the course of a route of movements of the device. A track log can be created using data, such as GPS data obtained from an on-board navigation system, such as while the device moves along a route. Further, a track log can be saved in a track log file that lists one or more sequential coordinates in, for example, two-dimensional (2-D) or three-dimensional (3-D) space, such as latitude, longitude and elevation. The track log can be processed to obtain a path traversed by the user during the event) … (0014, the track log can be processed to obtain a path traversed by the user during the event, e.g., by joining together the track points).
Additionally, Case discloses, accessing the sparse location data, (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 36 Pesyna and Case disclose the computing system of claim 35, as discussed supra. Additionally, Pesyna discloses the route reconstruction operation is performed in response to at least one of detecting a conclusion of the workout or collecting the final location point (0058, the matching of the estimated path to the pre-mapped feature may be performed after the movement event has ended, and/or when the user wishes to render the map-matched path on the map)
Regarding claim 37 Pesyna and Case disclose the computing system of claim 34, as discussed supra. Additionally, Pesyna discloses the sequence of instructions that, when executed, further cause the one or more processors to: collect the location data during the workout (0058, some data processing may be performed during the movement event while other data processing may be performed following the movement event. For example, the estimation of the path traversed by the user based on raw position information data may be performed real-time as the data is received), and wherein accessing the location data, accessing the map data, determining the route segment, combining the route segment, generating the graphical representation, and providing the graphical representation are performed after conclusion of the workout (0058, on the other hand, because obtaining the map-matched path may be performed on an estimated path, the matching of the estimated path to the pre-mapped feature may be performed after the movement event has ended, and/or when the user wishes to render the map-matched path on the map).
Additionally, Case discloses, collecting the sparse location data during the workout, and accessing the sparse location data (0063, it may be desirable to provide athletic performance monitoring systems and methods that derive all “real time” speed and distance information (e.g., the information displayed to the athlete on the portable display device during the performance) from the accelerometers or pedometers, and not using the GPS to provide the real time information. In such systems, the GPS monitor may operate periodically, in the background, e.g., logging position and altitude tracking point data in a memory).
Regarding claim 40, Pesyna and Case disclose the computing system of claim 34, as discussed supra. Additionally, Case discloses selecting the sparse location data collection mode (0063, the GPS data may be sampled less frequently, thereby saving power, while still providing continuous and sufficiently accurate speed and distance information to the athlete in real time from the accelerometer or other pedometer based system) from a plurality of data collection modes; (0059, as another option or alternative, if desired, an athlete could press a button on the portable portion of the monitoring system or otherwise command the system to collect calibration or correction data); and switching from a first data collection mode of the plurality of data collection modes to the sparse location data collection mode based at least in part on the selection (0070, Additionally, if desired, the GPS system could be used sparingly or periodically, to save battery power, and the speedometer system could be used constantly (or at least more frequently) to provide the athlete with real time speed and distance information), wherein the sparse location data collection mode is configured for collecting fewer location points than the first data collection mode (0063, the GPS data may be sampled less frequently, thereby saving power, while still providing continuous and sufficiently accurate speed and distance information to the athlete in real time from the accelerometer or other pedometer based system).
Regarding claim 41 Pesyna discloses one or more non-transitory computer-readable media having stored thereon a sequence of instructions that, when executed by one or more processors of a computing system, cause the computing system to: access location data comprising a set of location points collected by a user device during a workout and defining an initial route (0029, processing subsystem 202 can be implemented as one or more integrated circuits, e.g., one or more single-core or multi-core microprocessors or microcontrollers, examples of which are known in the art. In operation, processing subsystem 202 can control the operation of electronic device 200); the set of location points comprising an initial location point and a final location point (0047, in one or more implementations, the path may be estimated interpolating successive positions of the device based on the position information data obtained from the receiver), wherein the user device collects the location data during the workout using a sparse location data collection mode of a plurality of location data collection modes (0047, the position information is periodically received, the position information received at successive time points is interpolated to estimate the path traversed by the user. In some implementations, the path estimated in real-time as the receiver receives the position information data. Any algorithm known in the art for interpolating successive position points may be used), access map data comprising a plurality of paths within a geographic region (0072, determining, by the processor, previously mapped features within a proximity of the estimated path on a map; matching, by the processor, the estimated path to a pre-mapped feature on the map based on the previously mapped features to obtain a map-matched path); for a plurality of location point pairs of the set of location points determining a route segment between the pair of location points that follows a path segment of the plurality of paths (0047, any algorithm known in the art for interpolating successive position points may be used. In one or more implementations, the path may then be estimated by joining together the interpolated segments over the duration of the movement event); combine route segments for each location point pair of the plurality of location point pairs to define a reconstructed route that begins with a first location route pair based on the initial location point and ends with a second location route pair based on the final location point (0014, the track log can be processed to obtain a path traversed by the user during the event, e.g., by joining together the track points. The path obtained by processing the track log may or may not represent the actual route (also referred to herein as the actual path) traversed by the user depending on how the track log is processed to obtain the path); generate a graphical representation of the reconstructed route (0057, a display device may be caused (310) to render the map-matched path on an image of the map. FIG. 6 shows an example of a smoothed path using a map-matched smoothing process) and provide the graphical representation of the reconstructed route for presentation at the user device (0057, a display device may be caused (310) to render the map-matched path on an image of the map. FIG. 6 shows an example of a smoothed path using a map-matched smoothing process).
Additionally, Case discloses the missing amended limitations of, accessing sparse location data comprising a sparse set of location points collected by a user device during a workout (0063, the GPS monitor may operate periodically, in the background, e.g., logging position and altitude tracking point data in a memory), a sparse set of location points comprising an initial location point and a final location point (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), the sparse location data during the workout using a sparse location data mode (0070, Additionally, if desired, the GPS system could be used sparingly or periodically, to save battery power, and the speedometer system could be used constantly (or at least more frequently) to provide the athlete with real time speed and distance information), accessing map data comprising a plurality of paths within a geographic region (0037, to prepare the candidate projection points, the map-matching module obtains data from GPS logs 408(1), 408(2), . . . , 408(N) and the road network database 312); a plurality of sparse location point pairs of the sparse set of location points (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), and determining a route segment between sparse location point pairs (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period), combining route segments for each sparse location point pair of the plurality of sparse location point pairs (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 42 Pesyna and Case disclose the one or more non-transitory computer-readable media of claim 41, as discussed supra. Additionally, Pesyna discloses accessing the location data, accessing the map data, determining the route segment, and combining the route segment define a route reconstruction operation (0014, generate a track log corresponding to movement of the electronic device over a period of time, such as during an event (e.g., an exercise session). A track log may be a collection of track points along the course of a route of movements of the device. A track log can be created using data, such as GPS data obtained from an on-board navigation system, such as while the device moves along a route. Further, a track log can be saved in a track log file that lists one or more sequential coordinates in, for example, two-dimensional (2-D) or three-dimensional (3-D) space, such as latitude, longitude and elevation. The track log can be processed to obtain a path traversed by the user during the event) … (0014, the track log can be processed to obtain a path traversed by the user during the event, e.g., by joining together the track points).
Additionally, Case discloses, accessing the sparse location data, (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 43 Pesyna and Case disclose the one or more non-transitory computer-readable media of claim 42, as discussed supra. Additionally, Pesyna discloses the route reconstruction operation is performed in response to at least one of detecting a conclusion of the workout or collecting the final location point (0058, the matching of the estimated path to the pre-mapped feature may be performed after the movement event has ended, and/or when the user wishes to render the map-matched path on the map).
Regarding claim 44 Pesyna and Case disclose the one or more non-transitory computer-readable media of claim 41, as discussed supra. Additionally, Pesyna discloses the sequence of instructions that, when executed, further cause the one or more processors to: collect the location data during the workout (0058, some data processing may be performed during the movement event while other data processing may be performed following the movement event. For example, the estimation of the path traversed by the user based on raw position information data may be performed real-time as the data is received), and wherein accessing the location data, accessing the map data, determining the route segment, combining the route segment, generating the graphical representation, and providing the graphical representation are performed after conclusion of the workout (0058, on the other hand, because obtaining the map-matched path may be performed on an estimated path, the matching of the estimated path to the pre-mapped feature may be performed after the movement event has ended, and/or when the user wishes to render the map-matched path on the map).
Additionally, Case discloses, collecting the sparse location data during the workout, and accessing the sparse location data (0063, it may be desirable to provide athletic performance monitoring systems and methods that derive all “real time” speed and distance information (e.g., the information displayed to the athlete on the portable display device during the performance) from the accelerometers or pedometers, and not using the GPS to provide the real time information. In such systems, the GPS monitor may operate periodically, in the background, e.g., logging position and altitude tracking point data in a memory).
Claims 7-8, 38-39, and 45-46 are all rejected under 35 U.S.C. 103 as being unpatentable over Pesyna et al. (US20220390238A1), in view of Case et al. (US20060136173A1), further in view of Lou et al. (Map-Matching for Low-Sampling-Rate GPS Trajectories).
Regarding claim 7, Pesyna and Case disclose the computer-implemented method of claim 1 as discussed supra. Additionally, Lou who is in the same field of endeavor of map matching discloses, determining a route segment for the plurality of location point pairs comprises determining whether a shortest route segment that connects the respective pair of location points can be found (4. System Overview, spatial analysis not only considers the distance between a single GPS point and the candidate road segments for this point, but also takes into account the topological information of the road network. To avoid roundabout paths, we employ shortest path to measure the similarity between each candidate path and the “true” path).
Furthermore, Case discloses, determining a route segment for the plurality of sparse location point pairs that connects a respective pair of sparse location points (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
One of ordinary skill in the art prior to the effective filing date of the given invention
would have been motivated to combine the combination of Pesyna and Case with Lou. One of ordinary skill in the art would recognize it is a reasonable and advantageous design choice to combine known sparse data point algorithms disclosed by Case with Lou’s disclosure of connecting the shortest respective route points with segments. This would improve route creation by streamlining the quickest route available for a user for the sake of convenience.
Justification for combining the combination of Pesyna, Case, and Lou disclosures not only comes from the state of the art but from Pesyna (0226, variations of those preferred examples may become apparent to those of ordinary skill in the art upon reading the foregoing description).
Regarding claim 8, Pesyna, Case, and Lou disclose the computer-implemented method of claim 7, as discussed supra. Additionally, Case discloses the event that the shortest route segment along at least one of the plurality of paths cannot be found, the computer-implemented method further comprises: accessing pedometer data associated with a time between when the pair of location points were collected (0074, when/if the GPS receiver loses its signal (or if the signal is not taken at some times, e.g., due to power saving reasons), the athletic performance monitoring system and method according to this example still can use the other sensors' output to determine the changes in the athlete's position to fill in the “holes” and provide actual athlete path data until the GPS signal is regained or otherwise again sampled); and using the pedometer data to generate a potential path segment between the pair of location points (0062, during the athletic performance when GPS output is not sampled, a relatively low cost pedometer may be relied upon to accurately fill in the missing speed and distance data), wherein the potential path segment avoids any of the plurality of paths (0062, accelerometer-based or other pedometer-based speed and distance monitors also can provide some information that is not available from a GPS-based system, such as step count).
Regarding claim 38, Pesyna and Case disclose the computing system of claim 34, as discussed supra. Additionally, Lou discloses, determining a route segment for the plurality of location point pairs comprises determining whether a shortest route segment that connects the respective pair of location points can be found (4. System Overview, spatial analysis not only considers the distance between a single GPS point and the candidate road segments for this point, but also takes into account the topological information of the road network. To avoid roundabout paths, we employ shortest path to measure the similarity between each candidate path and the “true” path).
Furthermore, Case discloses, determining a route segment for the plurality of sparse location point pairs that connects a respective pair of sparse location points (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 39, Pesyna, Case, and Lou disclose the computing system of claim 38, as discussed supra. Additionally, Case discloses, in the event that the shortest route segment along at least one of the plurality of paths cannot be found, and wherein the sequence of instructions that, when executed, further cause the one or more processors to: access pedometer data associated with a time between when the pair of location points were collected (0074, when/if the GPS receiver loses its signal (or if the signal is not taken at some times, e.g., due to power saving reasons), the athletic performance monitoring system and method according to this example still can use the other sensors' output to determine the changes in the athlete's position to fill in the “holes” and provide actual athlete path data until the GPS signal is regained or otherwise again sampled); and use the pedometer data to generate a potential path segment between the pair of location points (0062, during the athletic performance when GPS output is not sampled, a relatively low cost pedometer may be relied upon to accurately fill in the missing speed and distance data), wherein the potential path segment avoids any of the plurality of paths (0062, accelerometer-based or other pedometer-based speed and distance monitors also can provide some information that is not available from a GPS-based system, such as step count).
Regarding claim 45, Pesyna and Case disclose the one or more non-transitory computer-readable media of claim 41, as discussed supra. Additionally, Lou discloses, determining a route segment for the plurality of location point pairs comprises determining whether a shortest route segment that connects the respective pair of location points can be found (4. System Overview, spatial analysis not only considers the distance between a single GPS point and the candidate road segments for this point, but also takes into account the topological information of the road network. To avoid roundabout paths, we employ shortest path to measure the similarity between each candidate path and the “true” path).
Furthermore, Case discloses, determining a route segment for the plurality of sparse location point pairs that connects a respective pair of sparse location points (0074, the data collected produces a straight line between the consecutively known GPS sampling points irrespective of the actual direction that the GPS receiver moved during this time period).
Regarding claim 46, Pesyna, Case, and Lou disclose the one or more non-transitory computer-readable media of claim 45, as discussed supra. Additionally, Case discloses, in the event that the shortest route segment along at least one of the plurality of paths cannot be found, and wherein the sequence of instructions that, when executed, further cause the one or more processors to: access pedometer data associated with a time between when the pair of location points were collected (0074, when/if the GPS receiver loses its signal (or if the signal is not taken at some times, e.g., due to power saving reasons), the athletic performance monitoring system and method according to this example still can use the other sensors' output to determine the changes in the athlete's position to fill in the “holes” and provide actual athlete path data until the GPS signal is regained or otherwise again sampled); and use the pedometer data to generate a potential path segment between the pair of location points (0062, during the athletic performance when GPS output is not sampled, a relatively low cost pedometer may be relied upon to accurately fill in the missing speed and distance data), wherein the potential path segment avoids any of the plurality of paths (0062, accelerometer-based or other pedometer-based speed and distance monitors also can provide some information that is not available from a GPS-based system, such as step count).
Claim 47 is rejected under 35 U.S.C. 103 as being unpatentable over Pesyna et al. (US20220390238A1), in view of Case et al. (US20060136173A1), further in view of Lu et al. (The Jigsaw Continuous Sensing Engine for Mobile Phone Applications).
Regarding claim 47, Pesyna and Case disclose the computer-implemented method of claim 41 as discussed supra. Additionally, Lu discloses, the sequence of instructions that, when executed, further cause the one or more processors to: determine to use the sparse location data collection mode based on contextual data (2.3. The GPS Pipeline, Jigsaw automatically adapts the GPS sampling schedule to minimize the expected localization error. The sampling schedule is adaptive to the mobility mode of the user by leveraging real-time classification of activity. Using movement detected by the accelerometer to switch the GPS sensor on/off has been proven to be effective).
One of ordinary skill in the art prior to the effective filing date of the given invention
would have been motivated to combine the combination of Pesyna and Case with Lu. This would preserve power during exercise while maintaining accuracy.
Further justification for combining Pesyna, Lou, and Case disclosures not only come from the state of the art but from Pesyna (0226, variations of those preferred examples may become apparent to those of ordinary skill in the art upon reading the foregoing description).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.E.D./Examiner, Art Unit 3665
/CHRISTIAN CHACE/Supervisory Patent Examiner, Art Unit 3665