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 § 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.
Claims 1-2, 5-6, 8-9, 12-13, 15-16, 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Chen et al. US 20210274496 A1 (hereinafter Chen).
Regarding claim 1, Chen discloses
A method comprising: selecting a subset of access points from among multiple candidate access points associated with a building;
("serving the calculated metadata as a filtering condition related to an identification rate, and extracting WI-FI® fingerprint data with the relatively high identification rate", [0013]; “More specifically, as shown in FIG. 3, a target area MAP1 can be an indoor place or building that is predetermined to perform positioning, the positioning map data 1020 can include one or more maps of each floor of the place or building mentioned above”, Chen[0034]; ” WI-FI® fingerprint data can be of multiple types, and any feature with unique location can be used as one record of WI-FI® fingerprint data. For example, multipath structure of the WI-FI® signal at a certain location, whether an access point (AP) or base station can be detected at a certain location, a received signal strength (RSS) from an access point can be detected at a certain location, and a round-trip time or delay of a signal when communicating via WI-FI® at a certain location, these can be used as one record of WI-FI® fingerprint data, or the above features can be combined as one record of WI-FI® fingerprint data. In other words, one record of the WI-FI® fingerprint data can be composed of a plurality of characteristic values of a plurality of WI-FI® access points”, [0035])
receiving Wi-Fi signal strength data (received signal strength indicator (RSSI)) from the subset of access points;
("an indoor positioning system based on WI-FI® and received signal strength indicator (RSSI) signals has a higher accuracy, the number of WI-FI® signals may be too large in the same field, resulting in high complexity and change rates. Therefore, it is difficult to establish an accurate positioning system purely based on WI-FI® signals and strengths. Thus, the present disclosure further extracts a data set with a high identification rate for spatial locations from a WI-FI® data set."[0036])
and using a machine learning (ML) localization algorithm to determine a proximity (current location) of a device (wireless device) within the building (the target area/building) based on the received Wi-Fi signal strength data.
(“a target area MAP1 can be an indoor place or building”, Chen[0034]; "Step S105: Establishing a machine learning model for estimating a relevant position in the target area based on the fingerprint data of the WI-FI® access points, and training the machine learning model with the extracted WI-FI® fingerprint data and corresponding spatial coordinates to generate a trained machine learning model."[0057] "Step S107: Configuring the trained machine learning model to estimate, according to the WI-FI® fingerprint data collected by the wireless device, the relevant position as the current location of the wireless device in the target area."[0065])
Regarding claim 2, limitations of parent claim 1 have been discussed above. Chen teaches
wherein the subset of access points is selected based on clustering (density-based spatial clustering) and similarity (analyze correlation).
(“an access point (AP) or base station can be detected at a certain location… these can be used as one record of WI-FI® fingerprint data”, [0035]; “plurality of records of collected data includes WI-FI® fingerprint data”, [0033]; “import program to read the original collected data and perform data integrity checking process”, [0038]; “Performing a clustering processing process to allocate the plurality of records of collected data ”, [0039]; " clustering algorithm can be, for example, a density-based spatial clustering of applications with noise (DBSCAN). By executing DBSCAN, the plurality of records of collected data are allocated to the plurality of reference groups in the target area according to the collection coordinates of the plurality of records of collected data in the target area, and each reference group includes a plurality of adjacent records of collected data. In this step, all collected data in the target area are divided into different groups based on spatial information, so as to analyze correlation between the WI-FI® fingerprint data in the plurality of records of collected data and the corresponding collection coordinates later. "[0040])
Regarding claim 5, limitations of parent claim 1 have been discussed above. Chen teaches
wherein the subset of access points is selected by calculating information theory-based metrics (information entropy).
("calculating, for the plurality of records of the fingerprint data in each of the plurality of reference groups in the plurality of records of collected data in the target, metadata of each of the WI-FI® access points. In detail, the metadata can include an attendance of the corresponding WI-FI® access points, information entropy of the characteristic value distribution, and a space-related information gain of the characteristic values of the corresponding WI-FI® access point."[0041])
Regarding claim 6, limitations of parent claim 1 have been discussed above. Chen teaches
wherein the information theory-based metrics (information entropy) comprise information gain metrics (information gain) or mutual information metrics.
("for the plurality of records of the fingerprint data in each of the plurality of reference groups in the plurality of records of collected data in the target, metadata of each of the WI-FI® access points. In detail, the metadata can include an attendance of the corresponding WI-FI® access points, information entropy of the characteristic value distribution, and a space-related information gain of the characteristic values of the corresponding WI-FI® access point."[0041])
Regarding claim 8, Chan teaches
A device comprising: a transceiver; and a processor operably connected to the transceiver, the processor configured to:
(“the present disclosure provides a positioning method based on WI-FI® fingerprints, the positioning method implemented on at least one computing device, each of the computing devices includes at least one processor” [0008] “The wireless device 12 can include a wireless transceiver to receive and transmit signals, and the wireless device 12 can be, for example, a mobile device such as a tablet computer, a mobile phone, or a proprietary hardware platform” [0031])
select a subset of access points from among multiple candidate access points associated with a building;
("serving the calculated metadata as a filtering condition related to an identification rate, and extracting WI-FI® fingerprint data with the relatively high identification rate"[0013]; “a target area MAP1 can be an indoor place or building”, [0034]; ” WI-FI® fingerprint data can be of multiple types, and any feature with unique location can be used as one record of WI-FI® fingerprint data. For example, multipath structure of the WI-FI® signal at a certain location, whether an access point (AP) or base station can be detected… these can be used as one record of WI-FI® fingerprint data... one record of the WI-FI® fingerprint data can be composed of a plurality of characteristic values of a plurality of WI-FI® access points”, [0035])
receiving Wi-Fi signal strength data (received signal strength indicator (RSSI)) from the subset of access points;
("an indoor positioning system based on WI-FI® and received signal strength indicator (RSSI) signals has a higher accuracy, the number of WI-FI® signals may be too large in the same field, resulting in high complexity and change rates. Therefore, it is difficult to establish an accurate positioning system purely based on WI-FI® signals and strengths. Thus, the present disclosure further extracts a data set with a high identification rate for spatial locations from a WI-FI® data set."[0036])
and use a machine learning (ML) localization algorithm to determine a proximity of the device (wireless device) within the building (target area) based on the received Wi-Fi signal strength data.
(“a target area MAP1 can be an indoor place or building”, Chen[0034]; "Step S105: Establishing a machine learning model for estimating a relevant position in the target area based on the fingerprint data of the WI-FI® access points, and training the machine learning model with the extracted WI-FI® fingerprint data and corresponding spatial coordinates to generate a trained machine learning model."[0057] "Step S107: Configuring the trained machine learning model to estimate, according to the WI-FI® fingerprint data collected by the wireless device, the relevant position as the current location of the wireless device in the target area."[0065])
Regarding claim 9, limitations of parent claim 8 have been discussed above. Claim 9 reflects article of manufacture comprising computer executable instructions for implementing method in claim 2 and is rejected along the same rationale.
Regarding claim 12, Limitations of parent claim 8 have been discussed above. Claim 12 reflects article of manufacture comprising computer executable instructions for implementing method in claim 5 and is rejected along the same rationale.
Regarding claim 13, Limitations of parent claim 8 have been discussed above. Claim 13 reflects article of manufacture comprising computer executable instructions for implementing method in claim 6 and is rejected along the same rationale.
Regarding claim 15, claim 15 reflects article of manufacture comprising computer executable instructions for implementing method in claim 1 and is rejected along the same rationale.
Regarding claim 16, Limitations of parent claim 15 have been discussed above. Claim 16 reflects article of manufacture comprising computer executable instructions for implementing method in claim 2 and is rejected along the same rationale.
Regarding claim 19, Limitations of parent claim 15 have been discussed above. Claim 19 reflects article of manufacture comprising computer executable instructions for implementing method in claim 5 and is rejected along the same rationale.
Regarding claim 20, Limitations of parent claim 15 have been discussed above. Claim 20 reflects article of manufacture comprising computer executable instructions for implementing method in claim 6 and is rejected along the same rationale.
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 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Guan et al., CN 111461251 A (hereinafter Guan).
Regarding claim 3, limitations of parent claim 1 have been discussed above. Chen teaches
wherein selecting the subset of access points from among the multiple candidate access points comprises: calculating similarity measures between pairs (according to the collection coordinates/ adjacent records) of the candidate access points;
(" a density-based spatial clustering of applications with noise (DBSCAN).By executing DBSCAN, the plurality of records of collected data are allocated to the plurality of reference groups in the target area according to the collection coordinates of the plurality of records of collected data in the target area, and each reference group includes a plurality of adjacent records of collected data."[0040])
applying a clustering algorithm (clustering processing process) to the candidate access points to form clusters of access points (plurality of reference group) based on the similarity measures;
("Performing a clustering processing process to allocate the plurality of records of collected data into a plurality of reference groups in the target area according to the corresponding collection coordinates to generate a plurality of records of collected data corresponding to the plurality of reference groups."[0039])
and upon a determination that the randomly permuting of the Wi-Fi signal strength data for a particular cluster reduces an accuracy (identification rate/ metadata meet the predetermined condition) of the ML localization algorithm, adding the particular cluster to the subset of access points.
("Step S105: Establishing a machine learning model for estimating a relevant position in the target area based on the fingerprint data of the WI-FI® access points, and training the machine learning model with the extracted WI-FI® fingerprint data and corresponding spatial coordinates to generate a trained machine learning model."[0057]"Step S107: Configuring the trained machine learning model to estimate, according to the WI-FI® fingerprint data collected by the wireless device, the relevant position as the current location of the wireless device in the target area."[0065]" serving the calculated metadata as the filtering condition related to the identification rate further includes determining whether or not the metadata of the plurality of WI-FI® access points meet a predetermined condition, and if so, taking the characteristic values of the WI-FI® access points whose metadata meet the predetermined condition out from the plurality of records of fingerprint data to be reorganized as the WI-FI® fingerprint data with the relatively high identification rate."[0011])
Chen fails to expressly teach randomly permuting the Wi-Fi signal strength data corresponding to each cluster;
However, Guan teaches a system and method compromising:
randomly permuting (random sampling / perturbations)the Wi-Fi signal strength data corresponding to each cluster;
("selecting features by adopting a random forest algorithm: calculating an importance metric for each AP ...And eliminating APs below an importance measure threshold value min _ D, setting min _ D as a minimum importance measure, and determining whether the APs are in a normal state or not … Deleting signals of the AP less than min _ D from the training set, forming a training set S by a fingerprint database collected in advance, forming an input matrix for the training set S by the received signal strength of N wireless access points with M groups and each group, outputting Y as a target vector to represent the floor data predicted by the random forest algorithm,the random sampling put back from the training set S forms ntree new sample sets (Sk, k is 1,2, …, ntree), the characteristic number of each new sample set Sk is the same as that of the original training set, but the number of elements is only two thirds of that of the original sample set, the data not in the new sample set is called out-of-bag data, the data in the new sample set is called in-bag data, and the random sampling put back ensures that each new training set S The data in the bag are not identical, so the structure of each decision tree is different, and such a strategy is called Bagging, and samples S are adopted Modeling is carried out on each decision tree independently, each decision tree gives a result in a random forest, a majority voting method is finally adopted as a final output prediction value, and data outside a bag does not participate in the construction of the decision trees in the random forest, so that error and the importance of each feature can be predicted by using the data outside the bag as a test set, and the importance measurement… for each feature X To, for… Feature X in Making perturbations using T Classifying the disturbed data, and recording the accurate number of classification as … Representing the number of correctly classified samples on the kth decision tree after the jth characteristic value is disturbed"[page 7])
Accordingly, it would have been obvious to a person having of ordinary skill in the art before the effective filling date of the claimed invention to combine Guan’s permutation importance test with Chen’s DBSCAN clustering and similarity based grouping of Wi-Fi fingerprints. Guan and Chen both address the same core problem, selecting relevant access points from a larger set to improve indoor positioning accuracy while reducing computational burden. A person having ordinary skill in the art would have been motivated to make this combination for the benefit of yielding a ranked list of clusters by their importance to localization accuracy resulting in a predictable outcome that provides a clear basis for selecting a subset of Aps. A person having of ordinary skill in the art would expect the combination of a routine and predictable manner.
Regarding claim 10, Limitations of parent claim 8 have been discussed above. Claim 10 reflects article of manufacture comprising computer executable instructions for implementing method in claim 3 and is rejected along the same rationale.
Regarding claim 17, Limitations of parent claim 15 have been discussed above. Claim 17 reflects article of manufacture comprising computer executable instructions for implementing method in claim 3 and is rejected along the same rationale.
Claims 4, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Deng et al., CN 108712714 B (hereinafter Deng).
Regarding claim 4, limitations of parent claim 1 have been discussed above. Deng teaches a method and device
wherein each of the similarity measures is calculated using a Pearson correlation or a Kendall rank coefficient.
(Deng teaches “the calculating, according to the pearson correlation coefficient calculation formula and the obtained reference signal strength, the signal similarity between the APs, and determining the AP whose signal similarity satisfies a preset similarity condition to obtain the AP group” [page 3])
Accordingly, it would have been obvious to a person having of ordinary skill in the art before the effective filling date of the claimed invention to combine Deng’s teaching of calculating similarity between access points using Pearson correlation coefficient into Chen’s DBSCAN clustering and similarity based grouping of Wi-Fi fingerprints. Deng and Chen both address the same core problem, selecting relevant access points from a larger set to improve indoor positioning accuracy while reducing computational burden. A person having ordinary skill in the art would have been motivated to make this combination for the benefit of grouping APs with scores above a threshold yielding a predictable outcome of clusters of similarly behaved Aps that facilities the permutation importances testing. A person having of ordinary skill in the art would expect the combination to work as intend.
Regarding claim 11, Limitations of parent claim 8 have been discussed above. Claim 11 reflects article of manufacture comprising computer executable instructions for implementing method in claim 4 and is rejected along the same rationale.
Regarding claim 18, Limitations of parent claim 15 have been discussed above. Claim 18 reflects article of manufacture comprising computer executable instructions for implementing method in claim 4 and is rejected along the same rationale.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Chan in view of Zhu et al., CN 105338498 A (hereinafter Zhu).
Regarding claim 7, limitations of parent claim 1 have been discussed above.
Zhu teaches
further comprising: preprocessing the Wi-Fi signal strength data (AP signal strength and reference point data) using a Maxmean technique or a missing data filtering technique.
(“integrity detection carried out to each AP signal strength signal intensity and the reference point data being tested with missing data are filled up, decreasing work reference point being needed to multi collect, ensureing the integrality of fingerprint base” [page 3])
Accordingly, it would have been obvious to a person having of ordinary skill in the art before the effective filling date of the claimed invention to combine Zhu’s teaching of missing filtering-detecting reference points where AP data is missing and filling those missing values with Chen’s DBSCAN clustering and similarity based grouping of Wi-Fi fingerprints. A person having ordinary skill in the art would have been motivated to make this combination for the benefit of ensuring that the fingerprints database used for clustering, permutation importance testing, and similarity calculation is free from gaps caused by missing AP measurements, producing a complete fingerprinting database where every reference point has estimated signal values.
Regarding claim 14, Limitations of parent claim 8 have been discussed above. Claim 14 reflects article of manufacture comprising computer executable instructions for implementing method in claim 7 and is rejected along the same rationale.
Conclusion
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/F.O.M./Examiner, Art Unit 2646
/JEANETTE J PARKER/Supervisory Patent Examiner, Art Unit 2646
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