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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 8/20/2024 was filed in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claim(s) 1-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0274460 A1 by Chen et al. (hereafter referred to as Chen).
Regarding claim 1, Chen teaches A processor implemented method, comprising:
receiving, via one or more hardware processors, Channel State Information (CSI) from a plurality of access points inside a region, wherein the CSI pertains to one or more mobile devices (see at least Fig. 3 and ¶ [0062]; “Step S300: configuring a wireless device to collect WI-FI® fingerprint data at a current position where the wireless device located in a target area. For example, the wireless device can be used as an object to be measured, or be set at the same location as the object to be measured, to obtain the WI-FI® fingerprint data of the current position of the object under test. For example, received multiple wireless signals provided by multiple WI-FI® access points, and multiple corresponding signal strengths at the current position.” and at least [0063]; “Step S301: configuring a communication module of the computing device to receive collected WI-FI® fingerprint data from the wireless device.”).
transmitting, via the one or more hardware processors, the CSI to a plurality of Location Estimation Models (LEMs);
obtaining, via the one or more hardware processors, a position estimate computed by each of the plurality of LEMs based on the CSI from the plurality of access points;
identifying, via the one or more hardware processors, a candidate sub-region of the one or more user devices by each of the plurality of LEMs based on the position estimate to obtain a plurality of candidate sub-regions (see at least ¶ [0065]; “Step S303: estimating an estimated global coordinate of the current position according to the collected WI-FI® fingerprint data through the global positioning model. For example, reference is made to FIG. 9, which is a schematic diagram showing an estimated global coordinate and corresponding primary sub-regions according to an embodiment of the present disclosure. The global coordinate Pg estimated by the global positioning model is located at the overlap of the primary sub-regions PSR1 and PSR2 as shown in FIG. 9.” and at least ¶ [0066]; “Step S304: obtaining the corresponding coarse positioning model according to the primary sub-region corresponding to the estimated global coordinate.”);
identifying, via the one or more hardware processors, at least one LEM amongst the plurality of LEMs based on the candidate sub-region and an accuracy map; and
determining, via the one or more hardware processors, an actual location of the one or more user devices based on the identified at least one LEM (see at least ¶ [0013]; “The global positioning model is used for estimating a global coordinate in the target area based on input WI-FI® fingerprint data. The plurality of coarse positioning models respectively correspond to the primary sub-regions, and each of the coarse positioning models is used for estimating a coarse coordinate in the corresponding primary sub-region based on the input WI-FI® fingerprint data. The plurality of fine positioning models respectively corresponding to the plurality of secondary sub-regions, and each of the plurality of fine positioning models is used for estimating a fine coordinate in the secondary sub-region based on the input WI-FI® fingerprint data.”).
Regarding claim 2, Chen teaches the processor implemented method of claim 1. In addition, Chen teaches wherein the accuracy map is obtained by:
receiving an input data comprising a plurality of CSI measurements from a plurality of positions associated with the region, wherein the plurality of CSI measurements from the plurality of positions are received with respect to a plurality of actual position coordinates (see at least ¶ [0013]; “The global positioning model is used for estimating a global coordinate in the target area based on input WI-FI® fingerprint data. The plurality of coarse positioning models respectively correspond to the primary sub-regions, and each of the coarse positioning models is used for estimating a coarse coordinate in the corresponding primary sub-region based on the input WI-FI® fingerprint data. The plurality of fine positioning models respectively corresponding to the plurality of secondary sub-regions, and each of the plurality of fine positioning models is used for estimating a fine coordinate in the secondary sub-region based on the input WI-FI® fingerprint data.”); and
training the plurality of LEMs using the input data and the plurality of actual position coordinates, wherein during the training the plurality of LEMs, the region is partitioned into a plurality of sub-regions, wherein each sub-region from the plurality of sub-regions has one or more specific signal properties, and wherein the accuracy map is created, the accuracy map further comprising a mapping of each sub-region from the plurality of sub-regions to a specific accurate LEM (see at least ¶ [0013]; “In another aspect, the present disclosure provides a positioning method based on neural network models, the positioning method includes: configuring a communication module included in a computing device to receive collected WI-FI® fingerprint data from the wireless device, wherein the computing device further includes a processor and a database, the database is configured to store positioning map data of the target area and a group of neural network models. The positioning map data includes a plurality of records of WI-FI® fingerprint data corresponding to a plurality of collection points in the target area and a group of neural network models, the target area is divided into a plurality of primary sub-regions, and each of the primary sub-regions further includes a plurality of secondary sub-regions, wherein the group of neural network models is generated by performing a training process according to the positioning map data and a model definition file and includes a global positioning model, a plurality of coarse positioning models and a plurality of fine positioning models.”).
Regarding claim 3, Chen teaches the processor implemented method of claim 1. In addition, Chen teaches wherein the step of identifying at least one LEM amongst the plurality of LEMs is further based on number of LEMs identifying the candidate sub-region or a probable sub-region that is in proximity of the candidate sub-region (see at least ¶ [0013]; “The global positioning model is used for estimating a global coordinate in the target area based on input WI-FI® fingerprint data. The plurality of coarse positioning models respectively correspond to the primary sub-regions, and each of the coarse positioning models is used for estimating a coarse coordinate in the corresponding primary sub-region based on the input WI-FI® fingerprint data. The plurality of fine positioning models respectively corresponding to the plurality of secondary sub-regions, and each of the plurality of fine positioning models is used for estimating a fine coordinate in the secondary sub-region based on the input WI-FI® fingerprint data.”).
Regarding claim 4, Chen teaches the processor implemented method of claim 3. In addition, Chen teaches further comprising communicating, a probable change in one or more sub-regions, to a network administrator for collecting a set of CSI measurements based on the number of LEMs identifying the candidate sub-region or the probable sub-region that is in proximity of the candidate sub-region (see at least Figs. 2, 3, and ¶ [0040]; “Reference can be further made to FIGS. 2 and 3, FIG. 2 is a schematic diagram of a database according to an embodiment of the present disclosure, and FIG. 3 is a schematic diagram of a target area and multiple collection points according to an embodiment of the present disclosure. As shown in FIG. 2, the database 102 stores positioning map data 1020 of a target area and a group of neural network models 1022, where the positioning map data 1020 includes a plurality of records of WI-FI® fingerprint data corresponding to a plurality of collection points in the target area. The database 102 can be, for example, a memory system, which can include non-volatile memory (such as flash memory) and system memory (such as DRAM), and related supporting database management software can be included in the database 102.”).
Regarding claim 5, Chen teaches the processor implemented method of claim 2. In addition, Chen teaches wherein the one or more specific signal properties are associated with each sub-region based on one or more obstacles present therein (see at least ¶ [0041], [0049], [0075]; building floors).
As to claims 6-10 see rejection of claims 1-5, except this is a claim to a system with the same limitations as claims 1-5.
As to claims 11-15 see rejection of claims 1-5, except this is a claim to a non-transitory with the same limitations as claims 1-5.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NATASHA W COSME whose telephone number is (571)270-7225. The examiner can normally be reached M-F 7:30-4.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ayman Abaza can be reached at 571-270-0422. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NATASHA W COSME/Primary Examiner, Art Unit 2465
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