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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on June 14, 2024 has been considered by the Examiner and made of record in the application file.
Claim Rejections - 35 USC § 102
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.
Claims 1-4, 10-15 and 21 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lin et al. (US 2013/0023282, “Lin”).
Regarding claim 1, Lin teaches a system (110) (FIG. 2B) for predicting user location from radio data of a telecommunication network (106), said system (110) comprising: one or more processors (202) (FIG. 2B – processor 204), said one or more processors (202) operatively coupled to one or more first computing devices (104) associated with one or more users (102) (FIG. 2B observing computing devices 210), wherein the one or more first computing devices (104) are communicatively coupled to one or network elements (cells) of the telecommunication network (106) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202 (and possibly other devices as well)”),
wherein the one or more processors (202) executes a set of executable instructions that are stored in a memory (204), upon execution of which, the one or more processors (202) causes the system (110) to: receive a set of data packets, said set of data packets pertaining to interactions between the one or more first computing devices (104) and the one or more cells, wherein the set of data packets are received for a predefined period of time ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)” [0036] “To determine location, the mobile device takes measurements of signals from nearby mobile communications base stations (a.k.a., "cell towers") and reports time/distance readings back to the communication network which are then used to triangulate an approximate location of the handset”);
extract a first set of attributes from the received set of data packets, the first set of attributes pertaining to Global Positioning System (GPS) data of the one or more first computing devices (104) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)”);
extract a second set of attributes from the received set of data packets, the second set of attributes pertaining to Radio Frequency (RF) data of the one or more first computing devices (104) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)”);
based on the extracted first and second set of attributes, collect, using an artificial intelligence (AI) engine (216) associated with the one or more processors (202), a training data to generate a distance prediction model ([0089] “the training and test datasets consisting of observations (e.g., RSS measurements) are constructed at 712.” [0090] “At 716, a model is created that comprises training data set and possible RSS weighting function for each tile. At 718, the list of observations from the training dataset that will be used to compute the weighting function is filtered, for example, to include all the observations in the training dataset or a subset depending on the characteristics of the tile and the computational complexity of the weighting function.” [0091] “In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”);
train, using the AI engine (216), the distance prediction model using the training data to predict a distance of the one or more users (102) from a cell tower associated with the one or more cells ([0091] “the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data. In an implementation, the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data… In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”).
Regarding claim 2, Lin teaches claim 2 and further teaches wherein when a first computing device (104) of the one or more first computing devices (104) is latched to a cell, the system (110) is configured to observe, via the first computing device (104), one or more neighbour cells associated with the first computing device (104) ([0028] “Such techniques then determine the location of the device by computing location by cell identification and the signal strengths of the home and neighboring cells (i.e., base stations) which is continuously sent to the carrier network.” Note: home cell teaches “computing devices is latched to a cell”).
Regarding claim 3, Lin teaches claim 2 and further teaches wherein the distance prediction model predicts a distance of a user associated with the first computing device (104) from each of the one or more neighbour cells ([0028] “Such techniques then determine the location of the device by computing location by cell identification and the signal strengths of the home and neighboring cells (i.e., base stations) which is continuously sent to the carrier network.” [0091] “the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data. In an implementation, the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data. The optimal weighting function may also incorporate additional factors that may not be available during an inference request, like GPS quality or the speed of the device while traveling in a vehicle, for example. In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively).” [0043] “in many RSS-based methods, the signal levels detected from a Wi-Fi device may be found using multiple access points as in triangulation which attempts to determine a distance from each access point to the detecting device” It is understood distances are associated with the RSS which is the signal strengths of the home and neighboring cells).
Regarding claim 4, Lin teaches claim 1 and further teaches wherein the system (110) is configured to identify one or more grids in a coverage area of each of the one or more cells from the collected training data ([0089] “the training and test datasets consisting of observations (e.g., RSS measurements) are constructed at 712. At 714, these observations are partitioned per a mapping tile system” [0090] “At 716, a model is created that comprises training data set and possible RSS weighting function for each tile” [0091] “At 720, for each tile, the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data … In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”).
Regarding claim 10, Lin teaches claim 1 and further teaches wherein the system (110) is further configured to meticulously acquire the collected training data and deposit in a cloud-based data lake for further processing (FIG. 2B – computing device/cloud service 202 includes training dataset 106 and test data set 108).
Regarding claim 11, Lin teaches a user equipment (UE) (108) (FIG. 2B -the observing computing devices 210) in a telecommunication network (106), said user equipment (108) comprising: a processor (222) and a receiver (FIG. 8 – processing unit 802, Communication Connections 812), said processor (222) operatively coupled to one or more first computing devices (104) associated with one or more users (102) ([0107] “Computing device 800 may contain communications connection(s) 812 that allow the device to communicate with other devices”), wherein the one or more first computing devices (104) are communicatively coupled to one or more network elements (cells) of the telecommunication network (106) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202 (and possibly other devices as well)”), wherein the processor (222) executes a set of executable instructions that are stored in a memory (224) ([0103]), wherein the processor (222) is communicatively coupled to one or more processors (202) in a system (110) (FIG. 2B – processor 204), and wherein the one or more processors (202) are configured to:
receive a set of data packets, said set of data packets pertaining to interactions between the one or more first computing devices (104) and the one or more cells, wherein the set of data packets are received for a predefined period of time ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)” [0036] “To determine location, the mobile device takes measurements of signals from nearby mobile communications base stations (a.k.a., "cell towers") and reports time/distance readings back to the communication network which are then used to triangulate an approximate location of the handset”);
extract a first set of attributes from the received set of data packets, the first set of attributes pertaining to Global Positioning System (GPS) data of the one or more first computing devices (104) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)”);
extract a second set of attributes from the received set of data packets, the second set of attributes pertaining to Radio Frequency (RF) data of the one or more first computing devices (104) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)”);
based on the extracted first and second set of attributes, collect, using an artificial intelligence (AI) engine (216), a training data to generate a distance prediction model ([0089] “the training and test datasets consisting of observations (e.g., RSS measurements) are constructed at 712.” [0090] “At 716, a model is created that comprises training data set and possible RSS weighting function for each tile. At 718, the list of observations from the training dataset that will be used to compute the weighting function is filtered, for example, to include all the observations in the training dataset or a subset depending on the characteristics of the tile and the computational complexity of the weighting function.” [0091] “In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”);
train, using the AI engine (216), the distance prediction model using the training data to predict a distance of the one or more users (102) from a cell tower associated with the one or more cells ([0091] “the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data. In an implementation, the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data… In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”).
Regarding claim 12, Lin teaches a method for predicting user location (abstract) from radio data of a telecommunication network (106), said method comprising:
receiving, by one or more processors (202) (FIG. 2B – processor 204), a set of data packets, said set of data packets pertaining to interactions between one or more first computing devices (104) and one or more cells, wherein the set of data packets are received for a predefined period of time ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)” [0036] “To determine location, the mobile device takes measurements of signals from nearby mobile communications base stations (a.k.a., "cell towers") and reports time/distance readings back to the communication network which are then used to triangulate an approximate location of the handset”), wherein said one or more processors (202) are operatively coupled to the one or more first computing devices (104) associated with one or more users (102) (FIG. 2B – observing computing devices 210. [0065] “observing computing device (e.g., a mobile computing device)” it is understood a mobile computing device is associated with a user ), wherein the one or more first computing devices (104) are communicatively coupled to the one or more cells of the telecommunication network (106) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100), wherein the one or more processors (202) executes a set of executable instructions that are stored in a memory (204) ([0052]);
extracting, by the one or more processors (202), a first set of attributes from the received set of data packets, the first set of attributes pertaining to Global Positioning System (GPS) data of the one or more first computing devices (104) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)”);
extracting, by the one or more processors (202), a second set of attributes from the received set of data packets, the second set of attributes pertaining to Radio Frequency (RF) data of the one or more first computing devices (104) ([0054] “the observing computing devices 210 transmit, via a network, the location observations 102 (and the non-RF related factors 100) to the beacon store for access by the computing device 202” [0063] “the location observations 102 (as well as the non-RF related factors 100 in certain implementations) may be divided based on one or more of the following: geographic area, type of observing computing device, location data quality, mobility of observing computing device, received signal strength availability, and scan time difference (e.g., between the ends of Wi-Fi and GPS scans)”);
based on the extracted first and second set of attributes, collecting, using an artificial intelligence (AI) engine (216) associated with the one or more processors (202), a training data to generate a distance prediction model ([0089] “the training and test datasets consisting of observations (e.g., RSS measurements) are constructed at 712.” [0090] “At 716, a model is created that comprises training data set and possible RSS weighting function for each tile. At 718, the list of observations from the training dataset that will be used to compute the weighting function is filtered, for example, to include all the observations in the training dataset or a subset depending on the characteristics of the tile and the computational complexity of the weighting function.” [0091] “In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”); and
training, using the AI engine (216), the distance prediction model using the training data to predict a distance of the one or more users (102) from a cell tower associated with the one or more cells ([0091] “the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data. In an implementation, the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data… In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”).
Regarding claim 13, Lin teaches claim 12 and further teaches wherein when a first computing device (104) of the one or more first computing devices (104) is latched to a cell, the method comprises observing, via the first computing device (104), one or more neighbour cells associated with the first computing device (104) ([0028] “Such techniques then determine the location of the device by computing location by cell identification and the signal strengths of the home and neighboring cells (i.e., base stations) which is continuously sent to the carrier network.” Note: home cell teaches “computing devices is latched to a cell”).
Regarding claim 14, Lin teaches claim 12 and further teaches wherein the distance prediction model predicts a distance of a user associated with the first computing device (104) from each of the one or more neighbour cells ([0028] “Such techniques then determine the location of the device by computing location by cell identification and the signal strengths of the home and neighboring cells (i.e., base stations) which is continuously sent to the carrier network.” [0091] “the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data. In an implementation, the error may be a function of the deltas between GPS positions of observations in the test dataset and predicted positions from the RSS weighted functions applied to test data. The optimal weighting function may also incorporate additional factors that may not be available during an inference request, like GPS quality or the speed of the device while traveling in a vehicle, for example. In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively).” [0043] “in many RSS-based methods, the signal levels detected from a Wi-Fi device may be found using multiple access points as in triangulation which attempts to determine a distance from each access point to the detecting device” It is understood distances are associated with the RSS which is the signal strengths of the home and neighboring cells).
Regarding claim 15, Lin teaches claim 12 and further teaches wherein the method further comprises identifying, by the one or more processors (202), one or more grids in a coverage area of each of the one or more cells from the collected training data ([0089] “the training and test datasets consisting of observations (e.g., RSS measurements) are constructed at 712. At 714, these observations are partitioned per a mapping tile system” [0090] “At 716, a model is created that comprises training data set and possible RSS weighting function for each tile” [0091] “At 720, for each tile, the optimal weighting function is determined from among the possible RSS weighting functions based on the training dataset that minimizes the error for the test data … In any event, an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)”).
Regarding claim 21, Lin teaches claim 12 and further teaches wherein the method further comprises meticulously acquiring, by the one or more processors (202), the collected training data and deposit in a cloud-based data lake for further processing (FIG. 2B – computing device/cloud service 202 includes training dataset 106 and test data set 108).
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.
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 nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5-8 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Lin in view of Lindquist et al. (US 2023/0075165, “Lindquist”).
Regarding claim 5, Lin teaches claim 4 and further teaches wherein the system (110) is configured to estimate a location of the one or more users (102) based on {the predicted distance from the one or more cells and} the identified one or more grids ([0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid. At 768, this centroid is used to identify the location of the requesting device, and may be returned to the requesting device.” [0097] “the RSS weighted centroid mentioned above--which is to be selected as the optimal weighting function for each tile given a training and a test dataset”).
Lim does not specifically teach estimate a location of the one or more users (102) based on the predicted distance from the one or more cells
However, it should be noted Lin teaches location determination using weighted RSS (see [0089]) and training process for weighting RSS such that that minimizes the differences between actual distances and predicted distances. It is obvious Lin suggests the distances derived from weighted RSS are used to determine location information using the well-known method “trilateration” as disclosed in [0036]. Further, such process is also taught by Lindquist. Lindquist discloses “sensor data of a sample may comprise RSS data which indicates a distance between the measurement position associated with the sample and a radio transmitter. The measured signal strength of a radio transmitter may decay with increasing distance from the radio transmitter, e.g. decay exponentially. Thus, the distance between the radio transmitter and the measurement position where the RSS data was measured may be calculated from the received signal strength and the equation for the expected exponential decay” ([0019]). “[I]f a number of distance dependent measurements, such as RSS measurements have been collected at a measurement position, e.g. by a mobile phone contributing in a crowdsourcing scheme, the measurement position may be estimated by a trilateration calculations if estimates of the radio transmitter positions are available from a model of estimated radio transmitter positions” ([0039]). “For example, RSS measurements of the sample may indicate distances to a number of radio transmitters whose estimated positions can be found in the model. By trilateration, the estimated measurement position of the sample and the estimated accuracy may be found” ([0161]).
It would have been obvious before the effective filing date of the claimed invention for a person having ordinary skilled in the art to include the feature estimate a location of the one or more users (102) based on the predicted distance from the one or more cells, as taught by Lindquist in Lim to facilitate accurate indoor localization.
Regarding claim 6, Lin in view of Lindquist teaches claim 5 and further teaches
wherein the system (110) is configured to estimate the location of the one or more users (102) based on {a predicted distance from} one or more random cells that get associated with the one or more first computing devices (104) ([0094] “at 764, the beacons pertaining to a received inference request are obtained along with RSS data corresponding to each beacon” [0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid. At 768, this centroid is used to identify the location of the requesting device, and may be returned to the requesting device”).
Lim does not specifically teach to estimate the location of the one or more users (102) based on a predicted distances.
However, it should be noted Lin teaches location determination using weighted RSS (see [0089]) and training process for weighting RSS such that that minimizes the differences between actual distances and predicted distances. It is obvious Lin suggests the distances derived from weighted RSS are used to determine location information using the well-known method “trilateration” as disclosed in [0036]. Further, such process is also taught by Lindquist. Lindquist discloses “sensor data of a sample may comprise RSS data which indicates a distance between the measurement position associated with the sample and a radio transmitter. The measured signal strength of a radio transmitter may decay with increasing distance from the radio transmitter, e.g. decay exponentially. Thus, the distance between the radio transmitter and the measurement position where the RSS data was measured may be calculated from the received signal strength and the equation for the expected exponential decay” ([0019]). “[I]f a number of distance dependent measurements, such as RSS measurements have been collected at a measurement position, e.g. by a mobile phone contributing in a crowdsourcing scheme, the measurement position may be estimated by a trilateration calculations if estimates of the radio transmitter positions are available from a model of estimated radio transmitter positions” ([0039]). “For example, RSS measurements of the sample may indicate distances to a number of radio transmitters whose estimated positions can be found in the model. By trilateration, the estimated measurement position of the sample and the estimated accuracy may be found” ([0161]).
It would have been obvious before the effective filing date of the claimed invention for a person having ordinary skilled in the art to include the feature estimate the location of the one or more users (102) based on {a predicted distances, as taught by Lindquist in Lim to facilitate accurate indoor localization.
Regarding claim 7, Lin in view of Lindquist teaches claim 5 and further teaches wherein the system (110) is configured to estimate one or more correction factors in prediction of the distance, and wherein the one or more correction factors are associated with one or more aspects comprising at least one of the one or more cells, the one or more first computing devices (104), and one or more geohash values of the one or more cells ([0091] “an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)” [0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid” [0045] “Exemplary beacons 212 include cellular towers (or sectors if directional antennas are employed), base stations”).
Regarding claim 8, Lin in view of Lindquist teaches claim 5 and further teaches wherein the system (110) is configured to iteratively process the collected training data to estimate latitude and longitude of the one or more users (102) associated with the one or more first computing devices (104) ([0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid. At 768, this centroid is used to identify the location of the requesting device, and may be returned to the requesting device” [0097] “the RSS weighted centroid mentioned above--which is to be selected as the optimal weighting function for each tile given a training and a test dataset” [0029] “The objective of a location service is to infer the location of a client device at a given instance of time. Consequently, networks of land-based positioning transmitters (or "beacons") can enable specialized radio receivers to determine a two-dimensional position (longitude and latitude) on the surface of the Earth”).
Regarding claim 16, Lin teaches claim 15 and further teaches wherein the method further comprises estimating, by the one or more processors (202), a location of the one or more users (102) based on {the predicted distance from the one or more cells and the} the identified one or more grids ([0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid. At 768, this centroid is used to identify the location of the requesting device, and may be returned to the requesting device.” [0097] “the RSS weighted centroid mentioned above--which is to be selected as the optimal weighting function for each tile given a training and a test dataset-”).
Lim does not specifically teach estimating a location of the one or more users (102) based on the predicted distance from the one or more cells.
However, it should be noted Lin teaches location determination using weighted RSS (see [0089]) and training process for weighting RSS such that that minimizes the differences between actual distances and predicted distances. It is obvious Lin suggests the distances derived from weighted RSS are used to determine location information using the well-known method “trilateration” as disclosed in [0036]. Further, such process is also taught by Lindquist. Lindquist discloses “sensor data of a sample may comprise RSS data which indicates a distance between the measurement position associated with the sample and a radio transmitter. The measured signal strength of a radio transmitter may decay with increasing distance from the radio transmitter, e.g. decay exponentially. Thus, the distance between the radio transmitter and the measurement position where the RSS data was measured may be calculated from the received signal strength and the equation for the expected exponential decay” ([0019]). “[I]f a number of distance dependent measurements, such as RSS measurements have been collected at a measurement position, e.g. by a mobile phone contributing in a crowdsourcing scheme, the measurement position may be estimated by a trilateration calculations if estimates of the radio transmitter positions are available from a model of estimated radio transmitter positions” ([0039]). “For example, RSS measurements of the sample may indicate distances to a number of radio transmitters whose estimated positions can be found in the model. By trilateration, the estimated measurement position of the sample and the estimated accuracy may be found” ([0161]).
It would have been obvious before the effective filing date of the claimed invention for a person having ordinary skilled in the art to include the feature estimating a location of the one or more users (102) based on the predicted distance from the one or more cells, as taught by Lindquist in Lim to facilitate accurate indoor localization.
Regarding claim 17, Lin in view of Lindquist teaches claim 16 and further teaches
wherein the method further comprises estimating, by the one or more processors (202), the location of the one or more users (102) based on {a predicted distance from} one or more random cells that get associated with the one or more first computing devices (104) ([0094] “at 764, the beacons pertaining to a received inference request are obtained along with RSS data corresponding to each beacon” [0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid. At 768, this centroid is used to identify the location of the requesting device, and may be returned to the requesting device”).
Lim does not specifically teach to estimating the location of the one or more users (102) based on a predicted distances.
However, it should be noted Lin teaches location determination using weighted RSS (see [0089]) and training process for weighting RSS such that that minimizes the differences between actual distances and predicted distances. It is obvious Lin suggests the distances derived from weighted RSS are used to determine location information using the well-known method “trilateration” as disclosed in [0036]. Further, such process is also taught by Lindquist. Lindquist discloses “sensor data of a sample may comprise RSS data which indicates a distance between the measurement position associated with the sample and a radio transmitter. The measured signal strength of a radio transmitter may decay with increasing distance from the radio transmitter, e.g. decay exponentially. Thus, the distance between the radio transmitter and the measurement position where the RSS data was measured may be calculated from the received signal strength and the equation for the expected exponential decay” ([0019]). “[I]f a number of distance dependent measurements, such as RSS measurements have been collected at a measurement position, e.g. by a mobile phone contributing in a crowdsourcing scheme, the measurement position may be estimated by a trilateration calculations if estimates of the radio transmitter positions are available from a model of estimated radio transmitter positions” ([0039]). “For example, RSS measurements of the sample may indicate distances to a number of radio transmitters whose estimated positions can be found in the model. By trilateration, the estimated measurement position of the sample and the estimated accuracy may be found” ([0161]).
It would have been obvious before the effective filing date of the claimed invention for a person having ordinary skilled in the art to include the feature estimating the location of the one or more users (102) based on {a predicted distances, as taught by Lindquist in Lim to facilitate accurate indoor localization.
Regarding claim 18, Lin in view of Lindquist teaches claim 16 and further teaches wherein the method further comprises estimating, by the one or more processors (202), one or more correction factors in prediction of the distance, and wherein the one or more correction factors are associated with one or more aspects comprising at least one of the one or more cells, the one or more first computing devices (104), and one or more geohash values of the one or more cells ([0091] “an objective is to find the optimal weighting function that minimizes the differences between actual distances and predicted distances (based on the training and testing data respectively)” [0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid” [0045] “Exemplary beacons 212 include cellular towers (or sectors if directional antennas are employed), base stations”).
Regarding claim 19, Lin in view of Lindquist teaches claim 16 and further teaches wherein the method further comprises iteratively processing, by the one or more processors (202), the collected training data to estimate latitude and longitude of the one or more users (102) associated with the one or more first computing devices (104) ([0095] “At 766, the weight of each beacon is calculated as a function of the RSS measurements wherein the inferred location is the weighted centroid. At 768, this centroid is used to identify the location of the requesting device, and may be returned to the requesting device” [0097] “the RSS weighted centroid mentioned above--which is to be selected as the optimal weighting function for each tile given a training and a test dataset” [0029] “The objective of a location service is to infer the location of a client device at a given instance of time. Consequently, networks of land-based positioning transmitters (or "beacons") can enable specialized radio receivers to determine a two-dimensional position (longitude and latitude) on the surface of the Earth”).
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lin
Regarding claim 9, Lin teaches claim 1 and further teaches wherein the system (110) is configured to be remotely monitored and ensure that the collected training data, a set of execution steps for implementation of the collected training data, and the system (110) (FIG. 2B – computing device/cloud service 202 includes training dataset 106 and Test data set 108) {is secured}.
Lin does not teach ensure data… and system 110 is secured.
However, the examiner takes official notice securing cloud services and their data is a well-known practice to protect user privacy and proprietary work.
It would have been obvious before the effective filing date of the claimed invention for a person having ordinary kill in the art to include the feature ensure data… and system 110 is secured, to protect user privacy and proprietary work.
Regarding claim 20, Lin teaches claim 12 and further teaches wherein the method further comprises remotely monitoring and ensuring, by the one or more processors (202), that the collected training data, a set of execution steps for implementation of the collected training data, and the method (FIG. 2B – computing device/cloud service 202 includes training dataset 106 and Test data set 108) {is secured}.
Lin does not teach ensuring data… and method is secured.
However, the examiner takes official notice securing cloud services and their data is a well-known practice to protect user privacy and proprietary work.
It would have been obvious before the effective filing date of the claimed invention for a person having ordinary kill in the art to include the feature ensuring data… and method is secured, to protect user privacy and proprietary work.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Zec (US 2023/0037992) discloses “computing node then trains a machine learning (ML) model for estimating UE geographic locations based on the plurality of complete MR” reported by Geolocating Minimization of Drive Test (MDT) (abstract).
Tripathi et al. (US 2021/0099942) teach reverse engineering propagation mode that obtains measurements. “The reverse engineered propagation model 730 can be trained offline using a supervised learning of a neural network. The identified UE pseudo location 740 can be in cartesian or polar coordinates relative to the gNB” ([0110]).
Butt et al. (“RF Fingerprinting and Deep Learning Assisted UE positioning in 5G”, IEEE Xplore, 2020, 7 pages) teaches training a ML model using RSRP for positioning an UE in a 5G system.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QUOC THAI NGOC VU whose telephone number is (571)270-5901. The examiner can normally be reached M-F, 9:30AM-6:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Rafael Perez-Gutierrez can be reached at 571-272-7915. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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.
/QUOC THAI N VU/Primary Examiner, Art Unit 2642
Prosecution Insights