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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/3/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claims 1, 8, 19 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant amended the independent claims to include a new limitation. Examiner is using Ye et al. (US 20210029500 A1) to address the new limitation.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4, 19, 22, 33 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20150341753 A1; which has been provided in the International Search Report) in view of Ye et al. (US 20210029500 A1).
Regarding claim 1, Chen discloses
a method performed by a user equipment, UE, for handling positioning of the UE in a wireless communication network (Fig. 3 shows wireless network; UE 12; [0011]; [0030]; Fig. 4 shows UE receiving signal from 310 and determining indoor/outdoor in block 412), the method comprising:
and - initiating a process for determining whether the UE is indoors or outdoors (Fig. 4, block 412; [0034] discloses “… the mobile device 12 can perform indoor/outdoor detection (classification) using ambient radio broadcast signals”; [0036] – [0037] discloses method for determining indoors/outdoors; Fig. 11, block 1110; [0071]).
Chen does not disclose (1) - measuring a channel impulse response, CIR, of a signal from a radio network node; (2) a machine learning, ML, model that distinguishes between indoor and outdoor multipath propagation patterns in the measured CIR, wherein the ML model is trained on CIR measurements from indoor environments and CIR measurements from outdoor environments.
In the same field of endeavor, however, Ye discloses:
(1) - measuring a channel impulse response, CIR, of a signal from a radio network node (
[0073] discloses “In one embodiment, the collected RF channel characteristics and the calculated device or object location based on instantaneous measurements can form a labeled data sample …..In another embodiment, multiple RF characteristics can form the data sample, such as utilizing both RSS and CIR for each of the locations“;
[0068] discloses “For any unique location within the area, there is a vector of values representing the RF characteristics (or combination of different RF characteristics such as Receiving Signal Strength (RSS) and Channel Impulse Response (CIR) of the channel between the object and the particular anchors”);
(2) a machine learning, ML, model that distinguishes between indoor and outdoor multipath propagation patterns in the measured CIR, wherein the ML model is trained on CIR measurements from indoor environments and CIR measurements from outdoor environments ([0068] discloses:
“RF fingerprint(s) (e.g., RF signature(s)) can be generated that describes, or is otherwise indicative of, one or more RF channel characteristics for positions or zones within a particular area (e.g., factory, storage facility, outdoor venue, indoor venue, ……
The RF fingerprint can be an RF fingerprint model generated from machine learning according to observed RF characteristics of RF channels for various mobile devices or objects at various positions (or various zones) within the particular area …..
For any unique location within the area, there is a vector of values representing the RF characteristics (or combination of different RF characteristics such as Receiving Signal Strength (RSS) and Channel Impulse Response (CIR) of the channel between the object and the particular anchors …..
RF characteristic (e.g., RSS or CIR) is being collected”).
[0074] discloses:
“In one or more embodiments, when a sufficient number of labeled data samples are collected, they can form a training set to train a machine learning model which can learn the RF fingerprint or signature of the environment from the data. The trained RF fingerprint model can be utilized to identify a position or zone when RF characteristics for communications associated with a mobile device or object in the demarcated area are received.”
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by Ye, in the system of Chen because using a machine learning model would enable quick and accurate detection of whether the UE is indoors or outdoors.
Regarding claim 4, Chen does not disclose initiating the process comprises using the ML model with the CIR as input to determine whether the UE is indoors or outdoors.
However, this is disclosed by Ye as in claim 1 above (e.g. Ye [0074]; wherein CIR would be a RF characteristic).
Claim 19 is similarly analyzed as claim 1, with claim 19 reciting equivalent apparatus limitations.
Claim 22 is similarly analyzed as claim 4.
Regarding claim 33, Chen does not disclose the measured CIR comprises a time- series of CIR values.
In the same field of endeavor, however, Ye discloses the measured CIR comprises a time-series of CIR values ([0068] discloses “The RF fingerprint can also be based on other data for the various locations, such as instantaneous radio measurements”; wherein a time series is interpreted as radio measurements for various locations).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by Ye, in the system of Chen because using a time series of CIR ensures more measurements, hence possibly improving accuracy.
Claims 2 - 3, 5 - 6, 8 - 15, 20 - 21 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20150341753 A1; which has been provided in the International Search Report) in view of Ye et al. (US 20210029500 A1) and further in view of EP3751900A1.
Regarding claim 2, Chen does not disclose initiating the process comprises reporting the measured CIR to the radio network node.
In the same field of endeavor, however, EP3751900A1 discloses initiating the process comprises reporting the measured CIR to the radio network node ([0069], lines 49 – 55 disclose “…generating, by the user equipment (UE), one or more measurement reports based on the channel impulse response (CIR) parameter ….. and transmitting, by the user equipment (UE), the one or more measurement reports to the first network node).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by EP3751900A1 in the system of Chen because this would allow the network node to know the UE position, which would be useful in communicating with the UE, e.g. performing beamforming etc. ([0060]).
Regarding claim 3, Chen does not disclose data indicating indoor position or outdoor position is included in the reporting.
Ye discloses obtaining position or zone (i.e. indoor/outdoor) ([0074] discloses “The trained RF fingerprint model can be utilized to identify a position or zone when RF characteristics for communications associated with a mobile device or object in the demarcated area are received….”).
Hence, reporting indoor or outdoor position is obvious to try or an obvious variation (Rationales for Obviousness (MPEP 2143, Rationales E & F) of what EP3751900A1 teaches (e.g. as in claim 2).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, for the UE to also report indoor/outdoor position because this would allow the network node to adjust transmit power based on that information.
Regarding claim 5, Chen does not disclose initiating the process further comprises transmitting a result of the ML model to the radio network node.
However, this is obvious to try or an obvious variation (Rationales for Obviousness (MPEP 2143, Rationales E & F) of what EP3751900A1 teaches (e.g. as in claim 2 or 3).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, for the UE to also transmit the ML result (i.e. position of UE) because this would allow the network node to know the UE position, which would be useful in communicating with the UE, e.g. performing beamforming etc. ([0060]).
Regarding claim 6, Chen does not disclose comprising receiving a configuration for measuring the CIR.
In the same field of endeavor, however, EP3751900A1 discloses receiving a configuration for measuring the CIR ([0069], lines 46+ disclose “…receiving, by a user equipment (UE), measurement configuration information from a first network node of a plurality of network nodes”).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by EP3751900A1 in the system of Chen because this would allow the UE to make the measurement requested by the network node.
Regarding claim 8, Chen discloses
a method performed by a user equipment, UE, in a wireless communication network for determining whether the UE is indoors or outdoors (Fig. 3 shows wireless network; UE 12; [0011]; [0030]; Fig. 4 shows UE receiving signal from 310 and determining indoor/outdoor in block 412; Fig. 4, block 412; [0034] discloses “… the mobile device 12 can perform indoor/outdoor detection (classification) using ambient radio broadcast signals”; [0036] – [0037] discloses method for determining indoors/outdoors; Fig. 11, block 1110; [0071]).
Chen does not disclose (1) obtaining a measurement of a channel impulse response, CIR, of a signal in the wireless communication network (2) using a machine learning, ML, model with the measured CIR as input wherein the ML model is trained on CIR measurements from indoor environments and CIR measurements from outdoor environments and (3) transmitting the CIR to the network node so that the method can be performed by the radio network node.
In the same field of endeavor, however, Ye discloses:
(1) obtaining a measurement of a channel impulse response, CIR, of a signal in the wireless communication network (
[0073] discloses “In one embodiment, the collected RF channel characteristics and the calculated device or object location based on instantaneous measurements can form a labeled data sample …..In another embodiment, multiple RF characteristics can form the data sample, such as utilizing both RSS and CIR for each of the locations“;
[0068] discloses “For any unique location within the area, there is a vector of values representing the RF characteristics (or combination of different RF characteristics such as Receiving Signal Strength (RSS) and Channel Impulse Response (CIR) of the channel between the object and the particular anchors”);
(2) using a machine learning, ML, model with the measured CIR as input wherein the ML model is trained on CIR measurements from indoor environments and CIR measurements from outdoor environments ([0068] discloses:
“RF fingerprint(s) (e.g., RF signature(s)) can be generated that describes, or is otherwise indicative of, one or more RF channel characteristics for positions or zones within a particular area (e.g., factory, storage facility, outdoor venue, indoor venue, ……
The RF fingerprint can be an RF fingerprint model generated from machine learning according to observed RF characteristics of RF channels for various mobile devices or objects at various positions (or various zones) within the particular area …..
For any unique location within the area, there is a vector of values representing the RF characteristics (or combination of different RF characteristics such as Receiving Signal Strength (RSS) and Channel Impulse Response (CIR) of the channel between the object and the particular anchors …..
RF characteristic (e.g., RSS or CIR) is being collected”).
[0074] discloses:
“In one or more embodiments, when a sufficient number of labeled data samples are collected, they can form a training set to train a machine learning model which can learn the RF fingerprint or signature of the environment from the data. The trained RF fingerprint model can be utilized to identify a position or zone when RF characteristics for communications associated with a mobile device or object in the demarcated area are received.”
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by Ye, in the system of Chen because using a machine learning model would enable quick and accurate detection of whether the UE is indoors or outdoors.
In the same field of endeavor, however, EP3751900A1 discloses reporting the measured CIR to the radio network node ([0069], lines 49 – 55 disclose “…generating, by the user equipment (UE), one or more measurement reports based on the channel impulse response (CIR) parameter ….. and transmitting, by the user equipment (UE), the one or more measurement reports to the first network node).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method, as taught by EP3751900A1 in the system of Chen because this would allow the network node to determine the UE position, which would be useful in communicating with the UE, e.g. performing beamforming etc. ([0060]).
Claim 9 is similarly analyzed as claim 2.
Regarding claim 10, Chen does not disclose obtaining the measurement of the CIR comprises measuring the CIR of a signal from the UE.
In the same field of endeavor, however, Ye discloses the UE obtaining a measurement of a channel impulse response, CIR, of a signal from the node ([0068]; [0073]).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by Ye, in the system of Chen because the CIR can be used to build the ML model for determining future positions of a UE.
Claim 11 is similarly analyzed as claim 3.
Regarding claim 12, Chen does not disclose the measurement of the CIR and the data is used to train the ML model.
In the same field of endeavor, however, Ye discloses the measurement of the CIR and the data is used to train the ML model ([0068]).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method, as taught by Ye in the system of Chen because machine learning models store lots of historical data, which can be used for future position estimation.
Regarding claim 13, Chen does not disclose selecting the ML model out of a number of ML models based on characteristics of the CIR and/or positioning data.
In the same field of endeavor, however, Ye discloses selecting the ML model out of a number of ML models based on characteristics of the CIR and/or positioning data ([0090] discloses “…machine learning model(s) …. Various machine learning algorithms can be utilized as described with respect to system 700 of FIG. 12. A single RF fingerprint model 904 for all mobile devices, one RF fingerprint model 904 for each type of mobile device, and/or a unique RF fingerprint model 904 for each mobile device can be maintained for the demarcated area.” i.e. selection made as above).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method as taught by Ye in the system of Chen because the number of models to be used will be selected based as above, which would help using the optimum model based on location etc
Claim 14 is similarly analyzed as claim 6.
Claim 15 is similarly analyzed as claim 5, wherein transmission to another node can be done similarly.
Claim 20 is similarly analyzed as claim 2.
Claim 21 is similarly analyzed as claim 3.
Claims 7, 16 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (US 20150341753 A1; which has been provided in the International Search Report) in view of Ye et al. (US 20210029500 A1) and further in view of CN114747246A.
Regarding claim 7, Chen does not disclose the ML model comprises a supervised classifier model.
In the same field of endeavor, however, CN114747246A discloses the ML model comprises a supervised classifier model (page 10, 2nd last paragraph discloses use of a supervised classifier model in ML).
Therefore, it would have been obvious to one having ordinary skill in the art, before the effective filing date of the invention, to use the method, as taught by CN114747246A in the system of Chen because this would enable classes to be formed based on, for example, approximate position, thereby making the ML search much faster.
Claim 16 is similarly analyzed as claim 7.
Other Prior Art Cited
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
The following patents/publications are cited to further show the state of the art with respect to indoor/outdoor position determining:
Kong et al. (US 20120169535 A1) Discloses Affecting Electronic Device Positioning Functions Based on Measured Communication Network Signal Parameters.
Bhattacharya et al. (US 20110156952 A1) discloses Positioning System and Positioning Method.
Soliman et al. (US 20070049295 A1) discloses Method and Apparatus for Classifying User Morphology for Efficient Use of Cell Phone System Resources.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADOLF DSOUZA whose telephone number is (571)272-1043. The examiner can normally be reached Mon - Fri 9 AM - 5 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chieh M Fan can be reached at 571-272-3042. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ADOLF DSOUZA/Primary Examiner, Art Unit 2632