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 .
DETAILED ACTION
Response to Amendment
. Applicant’s amendment, filed on 10/6/2025, has been fully considered and entered. Claims 6 and 19 are amended, and claims 1-20 are currently pending.
Priority
This application is a continuation of International Application No. PCT/EP2020/087721, filed on December 22, 2020.
Information Disclosure Statement
The information disclosure statements (IDS) is submitted on 9/22/2024 was filed in compliance with the provisions of 37 CFR 1.97. According, the information disclosure statement has been considered by the examiner.
Election/Restrictions
Applicant's election with traverse of Group I (Claims 1-11 and 14-20) in the reply filed on 10/6/2025 is acknowledged. The traversal is on the ground(s) that the office action does not provide adequate reason for examples supporting the conclusion that examining all of the claims is burdensome to the Office. This is not found persuasive because Group I is drawn to process compressed channel measurements to determine refined location, which is classified in H04W 4/029, while Group II is drawn to mobile device implement a neural network using the received weights, which is classified in H04W 16/225. Searching in different areas of classification base on two inventive concepts would be a serious search and/or examination burden.
The requirement is still deemed proper and is therefore made FINAL.
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 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 of this title, 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1, 5-7, 10, 11, 14 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wu et al. (“A Deep Neural Network-Based Indoor Positioning Method using Channel State Information”, 2018 International Conference on Computing, Networking and Communications (CNC), XP033361045, 5 March 2018, and Wu hereinafter), in view of Fan et al. (CN 111464220 B and Fan hereinafter).
Regarding claim 1, Wu teaches an apparatus for estimating a refined location (Figure 1 and Page 290 Col 1 Abstract: estimate the receiver position) in dependence on a plurality of measurements of one or more communication channels (page 292 Col 2 CSI measurements at N RPs), the apparatus comprising:
a memory configured to store instructions (Figure 1 and Page 291; mobile device, thus comprising a memory storing instructions); and
one or more processors coupled to the memory and configured to execute the instructions to cause the apparatus (Figure 1 and Page 291; mobile device, thus comprising a memory storing instructions and executed by a processor) to:
process the channel measurements using a neural network to form a plurality of intermediate location estimates (Figure 1 and Page 290 in Abstract; we present a deep neural network DNN based indoor positioning FP system using CSI, which is termed DNNFI. Page 293 Col 1; the output of the DNN Yo is the probability of the receiver being on each RP); and
process the intermediate location estimates to form the refined location (Figure 1 and Page 293 Col 1; we select out the indices of RPs with the first K largest wil and drop out the rest).
Wu does not explicitly teach compress each channel measurement. In an analogous art, Fan teaches compress each channel measurement (Page 3; in step S5, the channel state information reconstruction network based on deep learning comprises a pre-training model and fine tune model). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Wu and Fan because it would help obtain accurate CSI and also reduce the calculation complexity (Fan, Page 2).
Regarding claim 14, claim 14 recites similar features as claim 1, therefore is rejected for at least the same reason as discussed above regarding claim 1.
Regarding claims 5 and 18, the combination of Wu and Fan teaches all of the limitations of claims 1 and 14, as described above. Further, Wu teaches wherein each channel measurement is indicative of an estimate of channel state information for one or more radio frequency channels and on one or more antennas (Page 290 Abstract and Col 2; channel state information (CSI) measurement, which characterize the multipath channel between the transmitter and receiver in a wireless network).
Regarding claims 6 and 19, the combination of Wu and Fan teaches all of the limitations of claims 1 and 14, as described above. Further, Wu teaches wherein the one or more processors are configured to digitally pre-process each channel measurement (Figure 1 and Page 292 Col 2; DL pre-processing; normalized CSI).
Regarding claims 7 and 20, the combination of Wu and Fan teaches all of the limitations of claims 1 and 14, as described above. Further, Fan teaches wherein the one or more processors are configured to delete each channel measurement once each channel measurement has been compressed (Page 6; obtain the measurement value under different compression ratio. converting the measured value of each compression ratio into a two-dimensional matrix with size of size1; then cutting the two-dimensional matrix into smaller size of the two-dimensional matrix of size2). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Wu and Fan because it would help obtain accurate CSI and also reduce the calculation complexity (Fan, Page 2).
Regarding claim 10, the combination of Wu and Fan teaches all of the limitations of claim 1, as described above. Further, Wu teaches wherein the refined location is an estimate of a location of the apparatus (Figure 1 and Page 290 Col 1 and Abstract; estimate the indoor position of the devices).
Regarding claim 11, the combination of Wu and Fan teaches all of the limitations of claim 1, as described above. Further, Wu teaches wherein the neural network is configured to operate as a multi-class classifier, wherein a class estimate corresponds to a location on a discretized space (Page 290 Col 2; machine learning-based methods, which rely on a large amount of offline data being collected at known RPs, can be utilized to learn the classification rule. The classification rule is learned based on the similarity of the measured data between the measured object and the RP. A series of work adopt the concept of deep learning to classify the online measurements, and determine its position with the RPs of the most similar signatures).
Claims 2-4 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Wu in view of Fan, as applied to claims above, further in view of Courbariaux et al. (“Binarized Neural Netowrks: Training Neural Networks with Weights and Activations Constrained to +1 or -1”, arXiv:1602/02830v3 [cs.LG], XP055405835, March 17, 2016, and Courbariaux hereinafter).
Regarding claims 2 and 15, the combination of Wu and Fan teaches all of the limitations of claims 1 and 14, as described above. Further, Wu teaches wherein each channel measurement, and the neural network (Figure 1 and Page 290 in Abstract; we present a deep neural network DNN based indoor positioning FP system using CSI, which is termed DNNFI. Page 293 Col 1; the output of the DNN Yo is the probability of the receiver being on each RP).
The combination of Wu and Fan does not explicitly teach wherein each data is compressed to a binary form, the neural network is a binary neural network configured to operate in accordance with a neural network model defined by a set of weights, and all the weights are binary digits. In an analogous art, Courbariaux teaches wherein each data is compressed to a binary form, the neural network is a binary neural network configured to operate in accordance with a neural network model defined by a set of weights, and all the weights are binary digits (Page 1 Col 2; train Binarized-Neural Networks (BNNs), neural networks with binary weights and activations, at run-time, and when computing the parameters gradients at train-time). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Wu, Fan and Courbariaux because it would execute faster without suffering any loss in classification accuracy (Courbariaux, Page 1 Col 1).
Regarding claims 3 and 16, the combination of Wu/Fan/Courbariaux teaches all of the limitations of claims 2 and 15, as described above. Further, Courbariaux teaches wherein the one or more processors are configured to implement the neural network model using bitwise operations (Page 1 Col 2; train Binarized-Neural Networks (BNNs), neural networks with binary weights and activations, at run-time, and when computing the parameters gradients at train-time. Page 7 Col 1; 32-times speed-up on bitwise operations (e.g., XNOR)). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Wu, Fan and Courbariaux because it would execute faster without suffering any loss in classification accuracy (Courbariaux, Page 1 Col 1).
Regarding claims 4 and 17, the combination of Wu and Fan teaches all of the limitations of claims 1 and 14, as described above. Further, Wu teaches wherein the one or more processors are configured to: process the channel measurements using the neural network (Figure 1 and Page 290 in Abstract; we present a deep neural network DNN based indoor positioning FP system using CSI, which is termed DNNFI. Page 293 Col 1; the output of the DNN Yo is the probability of the receiver being on each RP) to form a respective measure of confidence for each intermediate location estimates (Page 291 Col 1; probabilities of the measured position being at RPs); and estimate the refined location in dependence on the measures of confidence (Figure 1 and Page 293 Col 1; we select out the indices of RPs with the first K largest wil and drop out the rest).
The combination of Wu and Fan does not explicitly teach process the binary forms of the data using the neural network. In an analogous art, Courbariaux teaches process the binary forms of the data using the neural network (Page 1 Col 2; train Binarized-Neural Networks (BNNs), neural networks with binary weights and activations, at run-time, and when computing the parameters gradients at train-time). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of Wu, Fan and Courbariaux because it would execute faster without suffering any loss in classification accuracy (Courbariaux, Page 1 Col 1).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choi et al. (US 20190362237 A1) discloses machine learning techniques for precise position determination based on channel state information.
Allowable Subject Matter
Claims 8 and 9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is an examiner’s statement of reasons for allowance:
Applicant's invention is drawn to determine refined location based on intermediate location estimates using channel measurements using machine learning.
The prior arts of record, Wu, Fan, Courbariaux, Choi, and a thorough search discloses various aspects and features of applicant's claimed invention but fail to explicitly or implicitly teach or disclose wherein each channel measurement is represented by a complex value comprising a real part and an imaginary part, and the one or more processors are configured to process each channel measurement by selecting a refined representation which comprises an amplitude of the complex value and the real part, as disclosed in claim 8.
These functions, in combination of remaining functions are neither taught nor disclosed by the prior art. Accordingly, claim 8 would be allowed. Claim 9 would be allowable based by virtue of its dependency from objected claim 8.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jing Gao whose telephone number is (571)270-7226. The examiner can normally be reached on 9am - 6pm M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Alison Slater can be reached on (571) 270-0375. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jing Gao/
Examiner
jing.gao@uspto.gov
Art Unit 2647
Prosecution Insights