DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claims 9 and 11-12 are objected to because of the following informalities:
Claims 11-12 recites limitations in quotation marks. The quotation marks are not needed and they should be deleted.
Claim 9 recites the limitation using “if”. The limitations using “if” are not positively recited and seem to be unclear. The limitations should be reviewed and corrected.
Appropriate corrections are required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-4, 6-8 and 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over US 20190223138 A1 (Shai Ronen, hereinafter Shai) in view of US 10492023 B1 (Michael Gurin, hereinafter Gurin).
Regarding claim 1, Shai discloses a method comprising:
receiving, from a cellular network, measurements performed by user devices connected to the cellular network (par. [0081], “If for example an indoor area is covered with Grid Points (GP) every 1-5 meters, a comprehensive measurement per a point will include the vector (or group) of values of each and every received signal (e.g. WiFi, GPS/A-GPS, GSM etc.)”; par. [0115], “multisensory data vector that is measured by crowd of users, transmitted and stored in DB.”);
identifying, among the measurements, a plurality of measurement sequences each one associated with a respective user device, each measurement sequence comprising the measurements performed by the associated user device in chronological order (par. [0101], “…navigation over time. Positioning from another source (usually GPS for outdoors) is measured continuously and injected…”; par. [0125], “…Longer static measurement produces valuable data as for the changes or fluctuations over time…”; par. [0279], “Device's built-in Gyroscopes readings are also used to evaluate the actual change in orientation from previous time measures.”; par. [0215], “Push message may be used to ask for user help in building the database. Once identified that movement in x, y (corresponds to Northing and Easting for example) is below threshold and no steps are sensed (via the accelerometer and Dead Reckoning related sensor) while Z (up) is increasing and GSM signal is down an ‘Is that an Elevator’ message may pop.”);
for each measurement sequence, determining position estimates of positions taken by the associated user device when performing the measurements of that measurement sequence (par. [0080], “(a specific location) a measurement was performed...”; par. [0123], “absolute location based on gathered data from crowd and updated measurement as for the data vector of their location fingerprinting.”);
for each measurement sequence, determining an [average] speed estimate of the associated user device based on the respective position estimates (par. [0273], “the mathematical procedure associated with extracting change in velocity based on measured accelerations, and changes in position based on velocity computed changes—is involved.”);
determining, among the plurality of measurement sequences, first measurement sequences, the first measurement sequences comprising the measurement sequences, among the plurality of measurement sequences, for which the respective average speed estimates of the associated user devices are below a threshold speed (par. [0125], “In case that the user stopped for a few seconds (no steps monitored and velocity in x, y and z below minimal threshold logic) a longer sample is taken and such grid point accuracy rank for location fingerprinting measurement (as oppose for absolute accuracy) is increased.”);
determining, among the first measurement sequences, second measurement sequences each one comprising at least one indoor measurement, the at least one indoor measurement of each second measurement sequence comprising at least one measurement, among the measurements of that second measurement sequence, that is associated with no positioning information or with an invalid positioning information (par. [0234], “The first man to enter an indoor place of interest with his mobile device (in pocket, hand, or every other place) for the first time (assuming he had downloaded the application and launched it) will have a very good accuracy upon first losing the GPS signal.”; par. [0238] “He enriched the database with up to date measurements from indoor location.”; par. [0123], “FIG. 4… at a certain stage the accuracy of the indoor positioning for the first user that entered the indoor area will drop under acceptable accuracy for good user experience in indoor positioning.”; par. [0237], “after a period of time, accuracies drops to a point in which indoor positioning is of limited benefit to him.”),
for each second measurement sequence, determining an indoor location associated with the respective at least one indoor measurement, said determining an indoor location comprising refining the position estimate associated with the at least one indoor measurement (par. [0304],”The method may include receiving (296) multiple multisensory data vectors acquired by a mobile device at a plurality of indoor locations before receiving a multisensory data vector at a certain location in which the granularity of the GPS based navigation is below the threshold, and increasing an accuracy level of the location estimates of the plurality of indoor locations…”. Note: the limitation, “determining an indoor location comprising refining the position estimate associated with the at least one indoor measurement”, provides only a result to be achieved without providing details about how the result is going to be achieved).
Shai discloses where the respective speed estimates of the associated user devices are bellow a threshold speed.
Shai does not specifically disclose where the respective average speed estimates of the associated user devices are below a threshold speed.
However, in related art concerning dynamic resource location coordination control system, Gurin discloses where the respective average speed estimates of the associated user devices are below a threshold speed (col. 15, lines 44-63).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Gurin’s teachings about using average speed estimates with a reference measurement with the location fingerprinting map disclosed by Shai because one of ordinary skill in the art would have recognized that it is well recognized that average speed estimates are frequency used in location determination algorithms because using average speed provides more accurate representation of the device/s speed over time, which aids in more precise location determination. Also, by using average speed, analysis and interpretation of results are easier to interpret in real-time monitoring which is particularly useful in indoor environments.
Regarding claim 13, Shai and Gurin disclose Location processing module (par. [0120], [0129], [0160], “GP1 10 and GP2 20 are recorded and sent over the web connection to the grid database on the remote server.”), the location processing module being configured to:
receive, from a cellular network, measurements performed by user devices connected to the cellular network (par. [0081], “If for example an indoor area is covered with Grid Points (GP) every 1-5 meters, a comprehensive measurement per a point will include the vector (or group) of values of each and every received signal (e.g. WiFi, GPS/A-GPS, GSM etc.)”; par. [0115], “multisensory data vector that is measured by crowd of users, transmitted and stored in DB.”);
identify, among the measurements, a plurality of measurement sequences each one associated with a respective user device, each measurement sequence comprising the measurements performed by the associated user device in chronological order (par. [0101], “…navigation over time. Positioning from another source (usually GPS for outdoors) is measured continuously and injected…”; par. [0125], “…Longer static measurement produces valuable data as for the changes or fluctuations over time…”; par. [0279], “Device's built-in Gyroscopes readings are also used to evaluate the actual change in orientation from previous time measures.”; par. [0215], “Push message may be used to ask for user help in building the database. Once identified that movement in x, y (corresponds to Northing and Easting for example) is below threshold and no steps are sensed (via the accelerometer and Dead Reckoning related sensor) while Z (up) is increasing and GSM signal is down an ‘Is that an Elevator’ message may pop.”);
for each measurement sequence, receive, from the cellular network, position estimates of positions taken by the associated user device when performing the measurements of that measurement sequence (par. [0080], “(a specific location) a measurement was performed...”; par. [0123], “absolute location based on gathered data from crowd and updated measurement as for the data vector of their location fingerprinting.”);
for each measurement sequence, determine an [average] speed estimate of the associated user device based on the respective position estimates (par. [0273], “the mathematical procedure associated with extracting change in velocity based on measured accelerations, and changes in position based on velocity computed changes—is involved.”);
determine, among the plurality of measurement sequences, first measurement sequences, the first measurement sequences comprising the measurement sequences, among the plurality of measurement sequences, for which the respective average speed estimates of the associated user devices are below a threshold speed (par. [0125], “In case that the user stopped for a few seconds (no steps monitored and velocity in x, y and z below minimal threshold logic) a longer sample is taken and such grid point accuracy rank for location fingerprinting measurement (as oppose for absolute accuracy) is increased.”);
determine, among the first measurement sequences, second measurement sequences each one comprising at least one indoor measurement, the at least one indoor measurement of each second measurement sequence comprising at least one measurement, among the measurements of that second measurement sequence, that is associated with no positioning information or with an invalid positioning information (par. [0234], “The first man to enter an indoor place of interest with his mobile device (in pocket, hand, or every other place) for the first time (assuming he had downloaded the application and launched it) will have a very good accuracy upon first losing the GPS signal.”; par. [0238] “He enriched the database with up to date measurements from indoor location.”; par. [0123], “FIG. 4… at a certain stage the accuracy of the indoor positioning for the first user that entered the indoor area will drop under acceptable accuracy for good user experience in indoor positioning.”; par. [0237], “after a period of time, accuracies drops to a point in which indoor positioning is of limited benefit to him.”),
for each second measurement sequence, determine an indoor location associated with the respective at least one indoor measurement, said determining an indoor location comprising refining the position estimate associated with the at least one indoor measurement (par. [0304],”The method may include receiving (296) multiple multisensory data vectors acquired by a mobile device at a plurality of indoor locations before receiving a multisensory data vector at a certain location in which the granularity of the GPS based navigation is below the threshold, and increasing an accuracy level of the location estimates of the plurality of indoor locations…”).
Shai discloses where the respective speed estimates of the associated user devices are bellow a threshold speed.
Shai does not specifically disclose where the respective average speed estimates of the associated user devices are below a threshold speed.
Gurin discloses where the respective average speed estimates of the associated user devices are below a threshold speed (col. 15, lines 44-63).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Gurin’s teachings about using average speed estimates with a reference measurement with the location fingerprinting map disclosed by Shai because one of ordinary skill in the art would have recognized that it is well recognized that average speed estimates are frequency used in location determination algorithms because using average speed provides more accurate representation of the device/s speed over time, which aids in more precise location determination. Also, by using average speed, analysis and interpretation of results are easier to interpret in real-time monitoring which is particularly useful in indoor environments.
Regarding claim 3, Shai and Gurin disclose all the limitations of claim 2. Shai further discloses wherein, for each second measurement sequence, the at least one indoor measurement comprises an indoor measurement associated with a serving network cell (par. [0123], and [0145], “Cellular wallet facilitating micro indoor positioning accuracies.” That uses a serving cell in the measurement process).
Regarding claim 4, Shai and Gurin disclose all the limitations of claim 3. Shai further discloses wherein, for each second measurement sequence, the at least one indoor measurement further comprises a set of indoor measurements each one associated with a respective adjacent network cell being adjacent to the serving network cell (par. [0116], “We will not limit the algorithm in using only specific (or a subset) of Cell towers of the chosen operator or the strongest ones but all. In addition, pico cells and femto cells (small local cellular antennas) that are installed in small places to handle few cellular devices can enhance cellular location and produce unique location fingerprinting. Both effects will be taken advantage of to improve indoor positioning”, where a group cell comprises primary and adjacent cells that can provide location measurements. Also, pico cells and femto cells are “adjacent” to serving cells in indoor locations or regions not reachable by the cellular cell).
Regarding claim 6, Shai and Gurin disclose all the limitations of claim 1. Shai further discloses wherein said determining an average speed estimate is based on a smoothing technique, such as a smoothing technique based on Kalman filtering, low-pass filtering or moving average (par. [0224], “Kalman Filter”).
Regarding claim 7, Shai and disclose all the limitations of claim 1. Shai further discloses wherein, for each measurement sequence, the threshold speed depends on an accuracy of the respective position estimates (par. [0125], “In case that the user stopped for a few seconds (no steps monitored and velocity in x, y and z below minimal threshold logic) a longer sample is taken and such grid point accuracy rank for location fingerprinting measurement (as oppose for absolute accuracy) is increased. Longer static measurement produces valuable data as for the changes or fluctuations over time of the location fingerprinting vector data.”), and/or on a duration of a communication session during which the respective measurements have been performed (only one of the limitations is required).
Regarding claim 8, Shai and Gurin disclose all the limitations of claim 1. Shai further discloses wherein said determining position estimates is based on Enhanced Cell ID positioning techniques (par. [0116], “enhance cellular location”).
Regarding claim 14, Shai and disclose a System comprising the location processing module (pars. [0120], [0129], [0160], “GP1 10 and GP2 20 are recorded and sent over the web connection to the grid database on the remote server.”) and the cellular network (par. [0116], cellular network used for enhanced cellular location) of claim 13.
Claims 2, 5 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Shai and Gurin, and further in view of US 10356562 B2 (Le Grand et al., hereinafter Le Grand).
Regarding claim 2, Shai and Gurin disclose all the limitations of claim 1.
Shai does not specifically disclose wherein the second measurement sequences comprise the measurement sequences, among the first measurement sequences, for which the at least one indoor measurement is in a predefined relationship with a reference measurement.
In related art concerning systems and methods for graph-based localization and mapping, Le Grand discloses wherein the second measurement sequences comprise the measurement sequences, among the first measurement sequences, for which the at least one indoor measurement is in a predefined relationship with a reference measurement (column 11, lines 3-12, “…the SLAM engine 115 can determine the boundaries, or obtain previously determined boundaries, of indoor and outdoor regions and model propagation of magnetic signals and AP signals differently for the indoor and the outdoor regions.”, where the indoor measurements would be related to signal path attenuation models for indoor different to outdoor signal path attenuation models).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Le Grand’s teachings wherein the second measurement sequences comprise the measurement sequences, among the first measurement sequences, for which the at least one indoor measurement is in a predefined relationship with a reference measurement with the location fingerprinting map disclosed by Shai and Gurin because one of ordinary skill in the art would have recognized that by identifying indoor location, so that the system can “apply different signal path attenuation models for indoor and outdoor regions” (Le Grand, column 11, lines 3-12).
Regarding claim 5, Shai and disclose all the limitations of claim 2.
Shai does not specifically disclose wherein said reference measurement is based on at least one among:
- indoor expected powers, for example indoor expected powers determined based on theoretical models of electromagnetic propagation;
- outdoor average powers, for example outdoor average powers determined based on located measurements, among said measurements performed by the user devices, that are associated with valid positioning information;
- outdoor expected powers, for example outdoor expected powers determined based on electromagnetic simulations.
Le Grand discloses wherein said reference measurement is based on at least one among:
- indoor expected powers, for example indoor expected powers determined based on theoretical models of electromagnetic propagation (column 11, lines 3-12, “model propagation of magnetic signals and AP signals differently for the indoor and the outdoor regions.”);
- outdoor average powers, for example outdoor average powers determined based on located measurements, among said measurements performed by the user devices, that are associated with valid positioning information;
- outdoor expected powers, for example outdoor expected powers determined based on electromagnetic simulations (column 11, lines 3-12, “model propagation of magnetic signals and AP signals differently for the indoor and the outdoor regions.”, only one of the limitations is required from the choices provided by the applicant).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Le Grand’s teachings wherein wherein said reference measurement is based on at least one among:
- indoor expected powers, for example indoor expected powers determined based on theoretical models of electromagnetic propagation (column 11, lines 3-12, “model propagation of magnetic signals and AP signals differently for the indoor and the outdoor regions.”);
- outdoor average powers, for example outdoor average powers determined based on located measurements, among said measurements performed by the user devices, that are associated with valid positioning information;
- outdoor expected powers, for example outdoor expected powers determined based on electromagnetic simulations with a reference measurement with the location fingerprinting map disclosed by Shai and Gurin because one of ordinary skill in the art would have recognized that by using a simulation or theoretical models for indoor/outdoor signal propagation, the system can seamlessly switch to the best resources needed in an indoor environment compared to an outdoor environment and vice versa.
Regarding claim 10, Shai and disclose all the limitations of claim 1.
Shai does not specifically disclose wherein said determining an indoor location of the respective at least one indoor measurement is based on a last available valid positioning information before the at least one indoor measurement has been performed.
Le Grand discloses wherein said determining an indoor location of the respective at least one indoor measurement is based on a last available valid positioning information before the at least one indoor measurement has been performed (column 11, lines 3-12, “The SLAM engine 115 can use the external wall geometry 552 to constrain transitions in user's positions between indoor and outdoor regions, constrain the locations of APs to indoor regions (or both), and apply different signal path attenuation models for indoor and outdoor regions”, where the last readings/measurements made before the transition between outdoor/indoor detection can be considered as valid position information before the indoor measurements take place).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Le Grand’s teachings wherein said determining an indoor location of the respective at least one indoor measurement is based on a last available valid positioning information before the at least one indoor measurement has been performed with a reference measurement with the location fingerprinting map disclosed by Shai and Gurin because one of ordinary skill in the art would have recognized that the last measurement made outside, before transitioning indoors correspond to a valid measurements that can be used to compare to the new measurements and determine that a transition has occurred.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Shai and Gurin, and further in view of US 20200396710 A1 (Wigren et al., hereinafter Wigren).
Regarding claim 9, Shai and disclose all the limitations of claim 1.
Shai does not specifically disclose wherein said measurements comprise timing advance measurements, and wherein said determining an indoor location associated with the respective at least one indoor measurement is based:
if the at least one indoor measurement is associated with two or more timing advance measurements, on said two or more timing advance measurements, or
if the at least one indoor measurement is associated with a single timing advance measurement, on an average position estimate resulting from a smoothing of the respective position estimate.
In related art concerning methods and devices for configuration of signals associated with multiple AOA positioning, Wigren disclose wherein said measurements comprise timing advance measurements, and wherein said determining an indoor location associated with the respective at least one indoor measurement is based:
if the at least one indoor measurement is associated with two or more timing advance measurements, on said two or more timing advance measurements, or
if the at least one indoor measurement is associated with a single timing advance measurement, on an average position estimate resulting from a smoothing of the respective position estimate (par. [0043], “The multiple AoA fingerprint positioning method thus provides for indoor Cartesian positioning using only AoA information derived from a single antenna. The fingerprinting method can build only on multiple AoA measurements associated with the same node, or can be applied in combination with, e.g. timing advance (TA) measurements in 3GPP 5G positioning nodes.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Wigren’s teachings wherein said measurements comprise timing advance measurements, and wherein said determining an indoor location associated with the respective at least one indoor measurement is based:
if the at least one indoor measurement is associated with two or more timing advance measurements, on said two or more timing advance measurements, or
if the at least one indoor measurement is associated with a single timing advance measurement, on an average position estimate resulting from a smoothing of the respective position estimate with the location fingerprinting map disclosed by Shai and Gurin because one of ordinary skill in the art would have recognized that timing advance (TA) measurements is one of many parameters available to the inventor for determining location, where TA provides more precise location by accounting for round-trip time of radio waves.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Shai and Gurin, and further in view of US 20210211839 A1 (Lee et al., hereinafter Lee).
Regarding claim 11, Shai and disclose all the limitations of claim 1.
Shai do not specifically disclose wherein said identifying, among the measurements, a plurality of measurement sequences is based on "International Mobile Subscriber Identity" numbers associated with the user devices, or on at least one between session identifiers and "Temporary Mobile Subscriber Identity" associated with the user devices.
In related art concerning electronic device detecting location, Lee discloses wherein said identifying, among the measurements, a plurality of measurement sequences is based on "International Mobile Subscriber Identity" numbers associated with the user devices, or on at least one between session identifiers and "Temporary Mobile Subscriber Identity" associated with the user devices (par. [0044], “The wireless communication module 192 may identify and authenticate the electronic device 101 in a communication network, such as the first network 198 or the second network 199, using subscriber information (e.g., international mobile subscriber identity (IMSI)) stored in the subscriber identification module 196.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Le Grand’s teachings wherein said identifying, among the measurements, a plurality of measurement sequences is based on "International Mobile Subscriber Identity" numbers associated with the user devices, or on at least one between session identifiers and "Temporary Mobile Subscriber Identity" associated with the user devices with the location fingerprinting map disclosed by Shai and Gurin because one of ordinary skill in the art would have recognized that the IMSI number uniquely identifies every mobile subscriber device, which is essential for authentication and accurate billing when operating in the service provider network/s as well as when roaming in external networks. Also, another application would be for tacking a specific devices.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Shai and Gurin, and further in view of US 20140256346 A1 (Venkatraman et al., hereinafter Venkatraman).
Regarding claim 12, Shai and disclose all the limitations of claim 1.
Shai does not specifically disclose wherein based on the determined indoor locations, providing quality-of-service maps for indoor environments within the geographic area, and/or setting one or more parameters of the cellular network by exploiting "Self-Organizing Network" functionalities of the cellular network.
In related art concerning adaptive assistance data for tiered service and effective indoor positioning, Venkatraman discloses wherein based on the determined indoor locations, providing quality-of-service maps for indoor environments within the geographic area, and/or setting one or more parameters of the cellular network by exploiting "Self-Organizing Network" functionalities of the cellular network (Par. [0023]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to use Venkatraman’s teachings wherein based on the determined indoor locations, providing quality-of-service maps for indoor environments within the geographic area with the user devices with the location fingerprinting map disclosed by Shai and Gurin because one of ordinary skill in the art would have recognized that a QoS map would provide clear and accurate navigation guidance withing complex indoor spaces, which reduces confusion and misdirection for users.
Note: the examiner has cited the written opinion of PCT/EP2022/069236 provided in the IDS dated 01/09/2024.
Conclusion
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/Angelica M. Perez/
Primary Examiner AU 2649