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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Step 1 - Statutory Category: Step 1 of the 2019 Guidance requires the examiner to determine if the claims are to one of the statutory categories of invention. Applied to the present application, claims 1-26 are directed to a process (method) and a machine (device/system) and a non-transitory computer readable storage medium (manufacture). Accordingly, claims 1-26 fall within at least one of the four statutory categories of invention (process, machine, manufacture, or composition of matter) under 35 U.S.C. 101.
Claim 1 is reproduced below with the abstract idea underlined.
Claim 1: A method for building a magnetic map of an area, wherein the area comprises a plurality of positions and wherein the magnetic map comprises magnetic field values for the positions, the method comprising: a) for each of a plurality of platforms traversing at least a portion of the area, obtaining magnetic field measurements from at least one magnetometer associated with each platform, available absolute navigational information, and motion sensor data from a sensor assembly associated with each platform; b) employing the absolute navigational information and motion sensor data to determine a first set of poses for each platform; c) obtaining information from the magnetic map for any existing magnetic field values for the first set of poses; d) determining magnetic constraints on poses of the platform based at least in part on the obtained magnetic map information and the obtained magnetic field measurements; e) determining a second set of poses for each platform based at least in part on the determined magnetic constraints, the obtained magnetic field measurements, absolute navigational information and motion sensor data; and f) updating the magnetic field values of the magnetic map based at least in part on the second set of poses for each platform.
Under Step 2A, Prong 1, Claim 1’s underlined limitation recites mathematical concepts and/or mental processes in the form of determining first and second sets of poses using magnetic field measurements, navigational information, motion sensor data and magnetic constraints, and updating magnetic field values based on the determined poses. These limitations describe mathematical estimation and optimization operations that evaluate and process measurement data to generate updated positional and map information.
Accordingly claim 1 recites abstract idea in form of mathematical concepts and/or mental processes.
Step 2A, Prong 2: The examiner needs to determine if the claim(s) recite additional elements
that integrate the exception into a practical application of the exception. The additional elements in the
claim 1 have been left in normal font. The claim integrates the recited mathematical concepts into a practical application. The mathematical concepts are applied to sensor measurements collected from physical platforms to determine platform poses and updating a magnetic map of a physical environment, thereby improving magnetic mapping and localization technology.
Accordingly, claim 1 integrates the judicial exception into a practical application.
The analysis of claim 1 applies equally to claims 19 and 25 because they recite substantially similar limitations in different statutory classes.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-10, 15, 17-22, 24-26 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Etienne Le Grand (US20160025498A1) hereinafter Le Grand.
Regarding claim 1, Le Grand discloses a method for building a magnetic map of an area (Le Grand describes a multi-step approach for map generation, Abstract. It identifies maps of a magnetic field as one of the types of maps that can be generated and used for localization,¶ [96]), Le Grand further teaches wherein the area comprises a plurality of positions (plurality of locations, ¶ [4]) and wherein the magnetic map comprises magnetic field values for the positions (magnetic field signal strength for a given location, ¶ [80]), It discloses that the method comprising:
a) for each of a plurality of platforms (data or traces of data are received from a number of devices ¶ [19] also data received from many devices over time ¶ [31]. It teaches the client device 102 may be any type of computing device or transmitter including a laptop computer, a mobile telephone, or tablet computing device, ¶ 34]) traversing at least a portion of the area (as the devices traverse a space collecting the data, ¶ [19]), obtaining magnetic field measurements (signal strength ¶ [19]) from at least one magnetometer (magnetometers ¶ [19]) associated with each platform (outputs of sensors of devices indicating various parameters of where a user walked and a duration in time, for example, ¶[21]), available absolute navigational information (GPS,¶ [19]), and motion sensor data from a sensor assembly (accelerometers, gyroscopes ¶ [19]) associated with each platform (outputs of sensors of devices indicating various parameters of where a user walked and a duration in time, for example, ¶[21]);
It further teaches b) employing the absolute navigational information (GPS data¶ [19]) and motion sensor data (accelerometers, gyroscopes ¶ [19]) to determine a first set of poses (It teaches using a non-linear optimization solver to adjust an estimate of state parameters (device positions) to match sensor observations ¶ [22 & 30 & 105]. Le Grand determines these location estimates by combining position with dead reckoning, which includes calculating the direction of travel (heading) and angle of turn using a compass or gyroscope, ¶ [91] ) for each platform (teaches determining first location of the device using logs of data ¶ [7])(It also describes a model builder that can utilize GPS data to determine locations and utilize dead reckoning (based on accelerometer and gyroscope outputs) to project a path, and optimize the path by jointly combining each, ¶ [60] );
It teaches c) obtaining information from the magnetic map (it teaches utilizing available known signal strength maps of corresponding data ¶ [72]) for any existing magnetic field values (measurements can be compared to a map of magnetic field signal strength ¶ [80]) for the first set of poses (after determining a position estimate, landmarks can be extracted from a map of the environment for the new position of the device, ¶ [103]. It also describes using a raster of all possible positions to select a most likely positions based on corresponding maps, ¶ [83]);
It further teaches d) determining magnetic constraints (a measurement probability map in which a given magnetic field signal strength corresponds to a signal strength of the magnetic field signal can be determined and used as the constraint, ¶ [80]) on poses of the platform (determining a constraint for locations of the device based on signal data, ¶ [99] and identifying likely positions of the device based on constraints, ¶ [76] ) based at least in part on the obtained magnetic map information (measurements can be compared to a map of magnetic field signal strength ¶ [80]) and the obtained magnetic field measurements (teaches that magnetic field data collected by the device is received in data log ¶ [71]. It describes a process where these measurements can be compared to a map to determine the constraint, ¶ [80]);
It teaches e) determining a second set of poses (second location estimates or a refined trajectory ¶ [89]. It also describes this step as performing a second SLAM optimization, Abstract) for each platform based at least in part on the determined magnetic constraints (the second location estimates are determined using first location estimates, ¶ [4 & 89]. These first estimates are themselves based on a constraint ¶ [5] derived form a comparison of data available in the logs of data with available known signal strength maps of corresponding data (including magnetic fields)), the obtained magnetic field measurements ( the system receives and processes logs of data or traces, ¶ [19]. These logs include data from magnetometers and magnetic field signal strength measurements¶ [19 & 70]), absolute navigational information (the system utilizes GPS data ¶ [19] that can be utilized to determine absolute location ¶ [2]. It also teaches using GPS data to localize the device as part of the trajectory optimization, ¶ [22]) and motion sensor data (teaches using dead reckoning based on outputs from accelerometers and gyroscopes to determine the refined path, ¶ [60]); and
It further teaches f) updating the magnetic field values of the magnetic map (maps (including maps of a magnetic field) can also be generated and further populated using simultaneous localization and mapping (SLAM) techniques on crowd-sourced data, ¶ 20]) based at least in part on the second set of poses for each platform (generating maps of areas based on the output location estimate of the device ¶ [96]. The output location estimate is the result of second SLAM optimization,¶ [94]).
Regarding claims 19 and 25, Le Grand teaches the corresponding limitations as claims 19 and 25 recite the same limitations as claim 1 in system and computer-readable medium forms, respectively. Therefore, claims 19 and 25 are rejected for the same reasons set forth with respect to rejection of claim 1.
Regarding claim 2, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein the magnetic map (when a magnetic field signal is collected, it is compared to a map of magnetic field signal strength to determine a measurement probability map that is used to constraint for device’s location ¶ [80]) further comprises covariances of the magnetic field values (it utilizes GraphSLAM and non-linear least squares to build maps ¶ [21]. It further teaches that the uncertainty of an estimate is derived by fitting a distribution function, specifically naming a multivariate Gaussian distribution, for which a mean and standard covariance are adjusted to represent the state of the map and the device, ¶ [72] ), further comprising updating the covariances of magnetic field values of the magnetic map ( describes a multi-step approach where maps are updated and refined as more crowdsourced data is received ¶ [96]. It describes an iterative process where the resulting estimate of the state (which include covariance ¶ [26]) is used as the initialization to use for the next iteration ¶ [111]) based at least in part on the second set of poses for each platform (teaches that the map generation and updates are based on the output location estimates (the refined trajectory) resulting from the SLAM optimization ¶ [94 & 96]. It further teaches that the trajectory of many devices and a map of an environment of the devices are simultaneously estimated ¶ [21]).
Claims 24 and 26 are rejected for the same reasons as claims 19, 25 and 2 as set forth in rejection of these claims.
Regarding claim 3, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein determining the second set comprises matching the obtained magnetic field measurements (teaches a model builder that can optimize the path to match [sensor] data to the reference … maps to align a path that most likely resembles a path that the device traversed through the environment ¶ [60]. It further describes that respective data (raw sensor logs) is used to indicate further refined trajectory, ¶ [74 & 94]) with the obtained magnetic map information (optimization involves a comparison of data available in the logs of data with available known signal strength map of corresponding data, ¶ [72]. It further teaches using maps of magnetic field for this comparison and localization, ¶ [96]) using the determined magnetic constraints (it teaches a second stage of refinement that involves using the synthetic convex constraints [determined in first step] in a second step to reach final refined trajectory (output location estimates) ¶ [94]).
Regarding claim 4, Le Grand teaches the method of claim 3 as set forth with respect to rejection of claim 3.
It further teaches wherein the matching includes employing consecutive (It defines a trace or sensor log as a collection of data output from sensors on the device over some time period and collected over a number of locations ¶ [62]. It describes matching these traces to maps to align a path that the device traversed ¶ [60]) magnetic field measurements (It includes magnetometers and magnetic field data as the sensor types collected within the logs/traces ¶ [19]. It teaches that these magnetic field maps are used to localize the device ¶ 64]).
Regarding claim 5, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein determining the second set comprises employing the obtained magnetic field measurements (teaches receiving and processing data logs that include measurements from magnetometers and magnetic field data ¶ [19 & 70-71]) to estimate a magnetic heading for at least some of the poses (It teaches utilizing a magnetometer as a compass heading provider to determine a platform’s direction of travel, ¶ [91 & 92]. Moreover, the trace 700 and path 714 lines in fig. 7 visually represent the sequence of orientations (heading) the device took as it moved from one likely position to the next. This trace is a collection of data representing the position and heading of a device and /or user ¶ [91]) using the determined magnetic constraints (It teaches a second stage of optimization that is performed using the synthetic convex constraint determined in first step ¶ [23 & 94]. These constraint are derived from comparing sensor signals (specifically magnetic field signals) with existing maps ¶ [7 & 80]).
Regarding Claim 6, Le Grand teaches the method of claim 5 as set forth with respect to rejection of claim 5.
Le Grand further teaches wherein estimating the magnetic heading (It teaches utilizing a magnetometer as a compass heading provider to determine a platform’s direction of travel, ¶ [91 & 92]. Moreover, the trace 700 and path 714 lines in fig. 7 visually represent the sequence of orientations (heading) the device took as it moved from one likely position to the next. This trace is a collection of data representing the position and heading of a device and /or user ¶ [91]) is also based on the obtained magnetic map information ( It describes a comparison of data available in the logs … with available known signal strength maps including maps of a magnetic field to determine the device’s trajectory ¶ [96 & 99]. Also, considering fig. 7 path 714 being aligned based on constraints from the measurements and the map. Because the magnetometer is the compass heading provider for this path the alignment shown in fig. 7 visually represents the estimation of heading based on map-derived constraints).
Regarding claim 8, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein updating the magnetic field values of the magnetic map (teaches maps (including maps of a magnetic field) can also be generated and further populated using simultaneous localization and mapping (SLAM) techniques on crowd-sourced data, ¶ [20]. The system uses optimizer to jointly optimize paths and maps and provide output map, ¶ [108]) is performed recursively (it teaches that SLAM optimization is performed iteratively on growing subsets of states and constraints, ¶ [111]. It further teaches that as new crowd-sourced traces arrive, a resulting state estimate can be further refined with additional iteration ¶ [30 & 94]) based at least in part on a relation between covariances (It describes determining position estimates that include a mean and covariance which are adjusted to represent the state of the map ¶ [72]. It utilizes these mean and covariances as synthetic convex constraints in non-linear least square fit (GraphSLAM) to simultaneously estimate trajectories and the map, ¶ [85 & 100]) of the obtained magnetic map information (discloses utilizing available known signal strength maps of corresponding data, Abstract. These are defined to include prior known maps of … maps of a magnetic field, ¶ [20 & 83]) and the obtained magnetic field measurements (discloses receiving logs of data collected by the device, Abstract. These logs include magnetic field data or magnetic field signals that have been collected as the device traverses an area, ¶ [70 & 80]).
Regarding claim 9, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein updating the magnetic field values (teaches maps (including maps of a magnetic field) can also be generated and further populated using simultaneous localization and mapping (SLAM) techniques on crowd-sourced data, ¶ [20]. The system uses optimizer to jointly optimize paths and maps and provide output map, ¶ [108]) and covariances (It describes determining position estimates that include a mean and covariance which are adjusted to represent the state of the map ¶ [72]. It utilizes these mean and covariances as synthetic convex constraints in non-linear least square fit (GraphSLAM) to simultaneously estimate trajectories and the map, ¶ [85 & 100]) of the magnetic map ( discloses utilizing available known signal strength maps of corresponding data, Abstract. These are defined to include prior known maps of … maps of a magnetic field, ¶ [20 & 83]) is further based on estimated uncertainties (discloses that for collected data, the uncertainty associated with this estimate is derived by fitting a simple distribution function (multivariate Gaussian) to a probability map, ¶ [72]. These uncertainties act as convex constraints for map refinement, ¶ [96]) associated with the magnetic field measurements (discloses receiving logs of data collected by the device, Abstract. These logs include magnetic field data or magnetic field signals that have been collected as the device traverses an area, ¶ [70 & 80]) by applying a robust estimation procedure (discloses utilizing GraphSLAM with an iterative approach with a non-linear least squares fit to determine a state of maximum likelihood, ¶ [21]. It specifies using a non-linear optimization (such as CERES optimizer) to reduce error and align the map, ¶ [105]).
Regarding claim 10, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein absolute navigational information is unavailable (It teaches that GPS may not always be available, ¶ [97]. It further notes that if no GPS data is available, less reliable data is used to provide the initial state estimate, ¶ [22 & 65]) for at least one pose for at least one of the platforms (discloses that data can be received from many devices over time (platforms) and that the system is configured to process traces from devices with unreliable data where absolute fixes are missing ¶ [31 & 109]).
Regarding claim 15, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches that the method of claim 1 further comprising creating a map of wireless signals (it teaches generating maps of WiFi received signals strength indicators ¶ [96]. ) based on received wireless signals ( It teaches that its mapping process uses WiFi signal strength or WiFi scans received as part of the logs of data or sensor traces collected by devices¶ [19 & 59]) and at least in part on the second set of poses for each platform ( It uses its output location estimate or refined trajectory (the result of its second SLAM optimization) to further populate its environmental maps)¶ [94]), wherein at least one of a plurality of platforms further comprises a wireless receiver (specifies that the client device (platform) includes WiFi components or a wireless communication component configured to receive network signals ¶ [40 & 42]).
Claim 22 is rejected for the same reasons set forth with respect to rejection of claim 19 and claim 15.
Regarding claim 17, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches wherein the motion sensor data (it teaches receiving logs of data from a number of devices (platforms) that include output of various sensors, ¶ [19]) for at least one of the platforms (client device such as a mobile phone or tablet, ¶ [34]) comprises inertial motion sensor information (It states that logs of data includes measurements from accelerometers, gyroscopes, and IMUs (Inertial Measurement Units), ¶ [19] It further describes these specifically as inertial sensors, ¶ [57]).
Regarding claim 18, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand further teaches that the method of claim1 further comprising at least partially initializing (teaches localization algorithms rely on prior knowledge about environments, which acts as the initialization for the SLAM process, ¶ [20]) the magnetic map (creation of other types of maps, such as magnetometer maps,¶ [20]) using at least one of values from a global geomagnetic model ( utilizes prior known maps of a magnetic field ¶ [112]. These maps represent the macroscopic magnetic state used as a baseline.) and results of a previous mapping operation (teaches using prior known maps with reliable data or maps from previous iterations of the algorithm to initialize the state, ¶ [24]).
Regarding claim 20, Le Grand teaches the system of claim 19 as set forth with respect to rejection of claim 19.
Le Grand further teaches wherein at least one of the first set of poses (first location estimate ¶ [89]), the second set of poses (second location estimates or a refined trajectory ¶ [89]. It also describes this step as performing a second SLAM optimization, Abstract) and the magnetic constraints (a measurement probability map in which a given magnetic field signal strength corresponds to a signal strength of the magnetic field signal can be determined and used as the constrain, ¶ [80]) are determined by at least one of the platforms (the client device 102 (platform) may be any type of computing device, ¶ [34]. Functions of the method 500 may be fully performed by a computing device ¶ [69]).
Regarding claim 21, Le Grand teaches the system of claim 19 as set forth with respect to rejection of claim 19.
Le Grand further teaches wherein at least one of the first set of poses (first location estimate ¶ [89]), the second set of poses (second location estimates or a refined trajectory ¶ [89]. It also describes this step as performing a second SLAM optimization, Abstract) and the magnetic constraints (a measurement probability map in which a given magnetic field signal strength corresponds to a signal strength of the magnetic field signal can be determined and used as the constrain, ¶ [80]) are determined by at least one remote processor (it teaches a system environment where a client device communicates with a server. The server may be any entity or computing device arranged to carry out the methods, specifically including a location module configured to process received sensor data to determine locations. ¶ [35]. It also teaches the method 500 includes determining, by the one or more processors, second location estimates of the device using the first location estimates and relative position estimates of the device based on dead reckoning, ¶ [89]).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Le Grand as applied to claim 1 above, and further in view of Medhat Omr et al.(WO2022036284A1) hereinafter Omr .
Regarding claim 7, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand teaches determining first location estimates and second location estimates (or output location estimates) for a device trajectory over a time period ¶ [4 & 100-101] (wherein at least one of the first set of platforms' poses and the second set of platforms' poses). Le Grand further teaches utilizing GPS data (absolute information) and dead reckoning (motion data) and being configured to optimize the path by jointly combining each ¶ [60]. It teaches a SLAM optimization that adjusts state parameters to better match observations of data (GPS, relative position estimates) simultaneously, ¶ [105] (is determined).
However, Le Grand does not explicitly teach tightly integrating the absolute navigational information.
Omr teaches generating an integrated navigation solution for the platform which results in the determination of the platform’s state (poses)(Abstract). Omr teaches a tightly-coupled integration background section ¶ [199]. It explicitly defines this process as using pseudoranges (raw ranges) and Doppler shift (pseudorange rates) directly from GNSS satellites ¶ [200]. (by tightly integrating the absolute navigational information)
It would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to perform tight integration in Le Grand’s framework, as taught by Omr, to transform GPS input from a black box coordinate into a transparent set of constraints, allowing the SLAM optimizer to better handle signal outages, reject environmental noise, and achieve the meter-level accuracy necessary for high definition crowdsourced maps.
Claims 11, 12, 13, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Le Grand as applied to claim 1 above, and further in view of Tao Li et al. (WO2017112414A1) hereinafter Li .
Regarding claim 11, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand teaches that the method may be performed using data received from many devices over time ¶ [31] and it specifies that a trajectory of many devices and a map of environment of the devices are simultaneously estimated, ¶ [21]. It teaches a computing device or client device that is used for data collection, ¶ [34]. It specifies that this device includes a Global Position System (GPS), sensors and a processor, ¶ [40]. It lists magnetometer or compass among the sensors used to collect data logs, ¶ [19]. It lists Mobile telephone, tablet computing device and laptop computers as portable devices, ¶ [34].( wherein the at least one magnetometer and the sensor assembly associated with at least one of the plurality of platforms is integrated into a portable device). It also accounts for the carrying position of the computing device by the user, ¶ [92] (conveyed by the platform).
Le Grand teaches that its method can handle passively collected traces, ¶ [112] and unreliable data (traces with unreliable GPS, fig. 8), ¶ [109].
However, Le Grand does not explicitly disclose the method is operable when the portable device is constrained within the platform and when the portable device is unconstrained within the platform.
Li teaches a method for enhancing a navigation solution of a portable device and a platform using map information, wherein the mobility of the device is constrained or unconstrained within the platform and wherein the device may be tilted to any orientation, ¶ [12].( wherein the method is operable when the portable device is constrained within the platform and when the portable device is unconstrained within the platform)
It would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to incorporate Li’s method into Le Grand’s SLAM optimization to ensure that the constraints represent true platform motion rather than device-specific movement and resolve the misalignment between the device and the platform (user) by mathematically leveling the sensors.
Regarding claim 12, Le Grand in view of Li teaches the method of claim 11 as set forth with respect to rejection of claim 11.
Le Grand teaches collecting data based on where a user walked, ¶ [21] establishing the pedestrian as the primary platform for generating its magnetic and signal maps (wherein the platform conveying the portable device is at least one of a pedestrian).
Le Grand does not teach that the platform is a vehicle.
Li teaches that the platform may also be considered a vehicle or vessel that conveys the user and the portable device, ¶ [45] (and a vehicle).
It would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to include vehicle as one of its platform in Le Grand’s framework as taught by Li because vehicles provide the high-volume overlapping data required for SLAM, and are the primary consumers of the resulting maps.
Regarding claim 13, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand teaches a system where data is collected by a client device (platform) such as a mobile telephone or tablet, ¶ [34]( for at least one of the platforms). It discloses an iterative GraphSLAM process to jointly optimize paths and maps ¶ [94 & 108] and specifically to create magnetometer maps, ¶ [112] (updating the magnetic map information).
Le Grand lists a barometer as a sensor whose data is received in its logs, ¶ [19]. However, it does not detail its use for height-indexed mapping.
Le Grand does not teach determining a height of the magnetometer updating the map that includes the determined height.
Li teaches that portable devices (platforms) include a barometer and it teaches a method to obtain altitude via barometer, ¶ [53] or to calculate a height difference. It uses this height data to detect level change events such as using stairs or elevators, ¶ [137] (determining a height of the). It further teaches that the height of each level is used as a parameter to determine the appropriate map entity, ¶ [113] ( updating the map includes the determined height )
It would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to integrate altitude determination, as taught by Li, with Le Grand’s sensor logging to allow the system to associate each magnetic measurement with a specific vertical coordinate (height). The resulting magnetometer map is no longer a simple 2D grid but a height-aware model where magnetic anomalies are indexed by the specific elevation. This will prevent Le Grand from merging magnetic data from different floors into a single, 2D map.
Claim 23 is rejected for the same reasons set forth with respect to rejection of claims 19 and 13.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Le Grand as applied to claim 1 above, and further in view of Bolun Zhang et al.(US20220197286A1) hereinafter Zhang.
Regarding claim 14, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand teaches performing a global search on the position of the device based WiFi and BLE measurements (comprising integrating measurements from at least one of additional source of data) ()to determine a most likely position used as the first initialization (for determination at least one of the first) for its SLAM algorithm and it then refines the trajectory in a second SLAM optimization ¶ [5 &24] (and second sets of platforms poses). It utilizes a wireless network signal received from each AP ¶ [73] and it discloses client device 200 which includes a wireless communication component 204, ¶ [40].It teaches that the wireless communication component 204 may … measure an intensity of signals received from wireless access points, ¶ [43]. It also teaches that sensor 210 within the client device 200 include a camera, ¶ [47] (wherein at least one of a plurality of platforms further comprises at least one of a wireless receiver and camera from which the measurements from the additional source of data are acquired).
Le Grand does not teach a camera, a lidar, and a radar from which the measurements from the additional source of data are acquired.
Zhang teaches using camera, LIDAR and RADAR as the sensors located on autonomous vehicle used to trigger mapping and update the navigational map, Abstract and ¶ [21] (a camera, a lidar, and a radar, from which the measurements from the additional source of data are acquired.).
It would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to use camera, LIDAR and RADAR as additional source of data, as taught by Zhang, with the crowdsourced SLAM framework of Le Grand to perceive physical road feature to solve the local non-global maximum convergence problem ¶ [22] described in Le Grand. Lidar, radar, and cameras provide centimeter-level accuracy by detecting semantic road objects like lane marking and traffic signs. Integrating these high-precision measurements as the synthetic convex constraints for Le Grand’s first optimization stage ensures the platform trajectory is accurate enough to anchor magnetic field measurements to their correct real-world coordinates.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Le Grand as applied to claim 1 above, and further in view of Min-l James Chang (US6094163) hereinafter Chang .
Regarding claim 16, Le Grand teaches the method of claim 1 as set forth with respect to rejection of claim 1.
Le Grand teaches receiving logs of data or sensor traces from client devices (platform) that include output various sensors used to estimate movement, ¶ [19 & 21]. It teaches that the sensor 210 may include an accelerometer, gyroscope, pedometer, light sensors within the client device 200, ¶ [47] (wherein the motion sensor data for at least one of the platforms comprises information obtained from). It incorporates odometry measurements into its SLAM optimization, ¶ 103]. It include IMUs and pedometers, ¶ [19]( at least one of an odometry sensor) and It also teaches using a Barometer, ¶ [19]( and a pressure sensor).
Le Grand teaches using speed to determine current position, noting that estimated speed can … be received from a server, or derived or calculated from position determinations or other sensor data, ¶ [91]. It also mentions using on-board sensors such as a magnetometer or a compass to provide heading information ¶ [92].
However, Le Grand does not explicitly disclose using a velocity sensor.
Chang teaches using velocity measurement of Doppler velocity sensor to generate measurements for an alignment filter to align an inertial navigational system (Abstract)( velocity sensor).
It would have been obvious to one ordinary skill in the art before the effective filling date of the claimed invention to integrate the Doppler velocity sensor, as taught by Chang, with Le Grand sensor system to provide a direct, infrastructure-independent constraint on the platform’s velocity. This direct measurement effectively removes heading errors duo to gyro drift (Chang, Col. 2, ll. 46-48) and prevents the trajectory from weaving, ensuring that magnetic field measurements collected by Le Grand’s sensors are associated with the correct physical locations in the map.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gennadii Berkovich et. al. (US20200103477A1) discloses a method for generating accurate magnetic fingerprint maps by aggregating crowdsourced data from portable devices.
Conclusion
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Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SAEEDE NAFOOSHE/ Examiner, Art Unit 2857
/ANDREW SCHECHTER/ Supervisory Patent Examiner, Art Unit 2857
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