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 .
Response to Amendment
The amendment filed January 28, 2026 has been entered. Claims 1-12, 17, and 20 pending in the application.
Response to Arguments
Rejection of claims 3 and 10 under 35 U.S.C. 112(b) and rejection of claim 20 under 35 U.S.C. 101 are withdrawn in view of Applicant’s amendments.
Applicant’s arguments with respect to claims 1-12, 17, and 20 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.
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.
Claims 1–6, 10–12, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bo et al. (China Publication No. 106211194 A) in view of Thiel et al. (U.S. Patent No. 9,584,966) and further in view of Goyal (U.S. Publication No. 2015/0087264).
Regarding claim 1, Bo teaches “[a] method for estimating a ratio of indoor-to-outdoor traffic or user equipment distribution, the method being performed by a network node” (see Abstract, p. 1, lines 28-30, and p. 2, lines 11-13; the invention discloses a MR (Measurement Report) data indoor and outdoor separation method based on a statistic model; the method comprises carrying out separation of a mixed Gaussian distribution and probability calculation; for a certain statistical interval value, the indoor probability ratio PA and the outdoor probability ratio PB are further calculated by the corresponding indoor probability ratio A and the outdoor probability ratio B, respectively; MR (Measurement Report) data is a real measurement result of user communication collected and collected by a network communication device (i.e., a network node); thus, a method for estimating a ratio of indoor-to-outdoor traffic or user equipment distribution, and performed by a network node, is described),
Bo further teaches “the method comprising: obtaining, for a set of user equipment, radio signal measurements, the radio signal measurements having a probability distribution function” (see p. 1, lines 28 – 31, p. 2, lines 11-13, and p. 3, lines 13 – 15, and FIG. 1; MR (Measurement Report) data includes RSRP (Reference Signal Receiving Power), RSRQ (Reference Signal Receiving Quality), and LTE reference signal reception quality (i.e., radio signal measurements); MR data is a real measurement result of user communication (i.e., data from user equipment) collected, and collected (i.e., obtaining) by a network communication device; the statistical value of the sampled values (i.e., of the MR data) distributed at the level value (e.g., RSRP) is the vertical axis, and the histogram (i.e., a probability distribution function) is drawn. Therefore, the distributed sampled values have a characteristic to form a probability distribution function. Thus, radio signal measurements having a probability distribution function are obtained);
Bo further teaches “separating the probability distribution function into a set of clusters, each cluster having its own individual probability distribution function” (see p. 3, lines 15, 16, 20, 21, 49, and 51 – 55; it will be observed that its (i.e., MR data/ the radio signal measurements) distribution (i.e., the probability distribution of the radio signal measurements) is formed by the superposition of two normal distributions (i.e., separate probability distribution functions) of indoor signals and outdoor signals (i.e., a set of clusters); the distribution formed by the superposition of multiple normal distributions is called the mixed Gaussian distribution (GMM); then the separation and probability calculation of the mixed Gaussian distribution is performed, and after the separation is completed, according to the separated sampling points, the parameters (mean and variance) of the two distributions (i.e., distributions of the separated clusters, where each has its own normal distributions (probability distribution functions) are obtained);
Bo further teaches “estimating the ratio of indoor-to-outdoor traffic or user equipment distribution by predicting, from the individual probability distribution functions of the clusters, which, if any, of the clusters represent indoor user equipment and which, if any, of the clusters represent outdoor user equipment” (see p. 4, lines 2 – 6; superposition the separate curves (i.e., the individual probability distribution functions of the clusters), and determine the probability (i.e., predicting) that each statistical interval is indoor or outdoor (i.e., indoor or outdoor communication, and when the communication is predicted as indoor, that part of the curve represents user equipment being indoor/indoor user equipment being used for that communication and when the communication is predicted as outdoor, that part of the curve represents user equipment being outdoor/outdoor user equipment being used for that communication); where the two fitting curves overlap up and down, for a certain statistical interval value, the ratio of indoor and outdoor ratios can be respectively taken by the two curves, and the ratio is the indoor probability ratio A and the outdoor probability ratio B of the interval value (i.e., estimating the ratio of indoor-to-outdoor traffic). Therefore, the disclosed method of predicting whether the user communication is indoor user communication or outdoor user communication, which in turn, represents communication from equipment being indoor user equipment or outdoor user equipment, estimates indoor ratio and outdoor ratio of traffic).
Bo does not explicitly disclose using “user equipment battery status” and “performing a network related action in accordance with the ratio of indoor-to- outdoor traffic or user equipment distribution” of claim 1. However, the foregoing limitations were well known in the art prior to the effective filing date of the claimed invention. For example, Thiel teaches “performing a network related action in accordance with the ratio of indoor-to- outdoor traffic or user equipment distribution, the network related action pertaininq to one or more of adaptation of mobile network resources, network deployment, and user behaviour contextualization” (see col. 13, lines 49-53; a network element/system causes (i.e., performing network related action) the mobile device that placed the call to increase or decrease (i.e., adaptation of) a transmit power level (i.e., mobile network resources) based on the indoor/outdoor classification of the calls). Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo to incorporate the teachings of Thiel to perform a network related action in accordance with the ratio of indoor/outdoor traffic. The suggestion to do so would have been to use the classification date to improve performance of the network (see col. 3, lines 13-14 of Thiel).
The combination of Bo and Thiel does not explicitly disclose using “user equipment battery status” of claim 1. However, the foregoing limitations were well known in the art prior to the effective filing date of the claimed invention. For example, Goyal teaches “the ratio of indoor-to-outdoor traffic or user equipment distribution is further estimated based on user equipment battery status” (see ¶¶ [0080] and [0081]; the device would determine its macro-location context; the device may be in a At Destination State or a In Transit State; if in a At Destination State, the device may be in a variety of places, including but not limited to Home, Work, Gym, Restaurant, Theater, etc. (i.e., indoors); if in a In Transit State, the device may be in a variety of places, including but not limited to with a user or without a user; if the device is with a user, that user could be walking, running, bicycling, inside a motorized vehicle, etc (i.e., outdoors); the device could also be in a motorized vehicle without a person (i.e., outdoors); furthermore device may also determine contextual state based on the device power state, where the device may be charging or the device may not be charging (i.e., battery status). In either scenario, the device can determine its battery state (i.e., battery status), which would include but not be limited to low battery, full battery, and sufficient battery states; thus, Goyal teaches using battery status to determine whether user equipment is indoor or outdoor). Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo in view of Thiel to incorporate the teachings of Goyal to user equipment battery status to determine the ratio of indoor/outdoor traffic or user equipment distribution. The suggestion to do so would have been to improve the ability to determine whether the user equipment is indoors or outdoors (see ¶¶ [0004] and [0005] of Goyal).
Regarding claim 2, the combination of Bo, Thiel, and Goyal teaches the method of claim 1, and further teaches “estimating whether an individual user equipment in the set of user equipment is indoor or outdoor using the prediction using the prediction of which if any, of the clusters represent indoor user equipment and which, if any, of the clusters represent outdoor user equipment; and performing a network related action for the individual user equipment in accordance with whether the individual user equipment is estimated to be indoor or outdoor” (see p. 4, lines 2 – 6 of Bo; and see col. 8, lines 48 – 50; col. 8, line 60 – col. 9, line 4; col. 10, lines 30 – 33; and col. 13, lines 47-53 of Thiel; obtaining metric information of one or more calls; metric information include transmit power level of the mobile device, RSRQ of the mobile device, etc.; determining an indoor/outdoor classification (i.e., predicting) for the one or more calls based on the metric information; determine configuration activities to perform with regard to the mobile device (i.e., the mobile device that placed the call) based on the indoor/outdoor classification (i.e., the prediction); To determine configuration activities for a mobile device, that mobile device must have been identified or known. Therefore, the network node/ system of Thiel is also determining/estimating whether the mobile device (individual user equipment) that placed the call is indoor or outdoor when the indoor/outdoor classification (the prediction) of the call is performed. Accordingly, Thiel teaches estimating whether an individual user equipment in the set of user equipment is indoor or outdoor using the prediction. Thiel further teaches cause the mobile device that placed the call (i.e., performing a network related action for the individual user equipment) to increase or decrease a transmit power level based on the indoor/outdoor classification (i.e., in accordance with whether the individual user equipment is estimated to be indoor or outdoor)). Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo to incorporate the teachings of Thiel to estimate whether a UE is indoor UE or outdoor UE using a prediction based on radio signal measurements and perform a network related action in accordance with the UE being indoor or outdoor. The suggestion to do so would have been to use the classification data to improve performance of the network (see col. 3, lines 13-14 of Thiel).
Regarding claim 3, the combination of Bo, Thiel, and Goyal teaches the method of claim 1, and further teaches “wherein the probability distribution function is separated into the set of clusters by Gaussian mixture modelling or Log-normal mixture modelling, of the probability distribution function” (see p. 3, lines 20, 21, 49, and 51 – 55 of Bo; the distribution (i.e., the probability distribution function of the radio signal measurements is formed by the superposition of two normal distributions of indoor signals and outdoor signals (i.e., the set of clusters); the superposition of multiple normal distributions is called the mixed Gaussian distribution (i.e., represented using GMM (Gaussian mixture modeling); separation of the mixed Gaussian distribution is realized using a mathematical model, such as EM algorithm (i.e., applied in GMM), into the two distributions (i.e., the set of clusters). Thus, the probability distribution function is separated into clusters by GMM).
Regarding claim 4, the combination of Bo, Thiel, and Goyal teaches the method of claim 3, and further teaches “wherein each of the clusters has a mixing proportion, wherein the mixing proportions of all the clusters sums to 1, and wherein the ratio of indoor-to-outdoor traffic or user equipment distribution is given by a ratio of a sum of all the mixing proportions of any of the clusters representing indoor user equipment and a sum of all the mixing proportions of any of the clusters representing outdoor user equipment” (p. 4, lines 3 – 14 of Bo; the indoor probability PA and the outdoor probability PB can be calculated, PA=A/(A+B)×100%, and PB=B/(A+B)×100% (i.e., each of the indoor and outdoor (the clusters) has a mixing proportion based on indoor and outdoor probability); for example, in FIG. 1, when the level intensity is 16, according to the interval statistical ratio result, the indoor level sampling point is 0.5%, the outdoor level is 0%, and the comparison level is 16 sampling point, the probability of indoor is 0.5% / (0.5% + 0%) = 100%, the probability of outdoor is 0% / (0.5% + 0%) = 0% (i.e., the indoor and outdoor probability (the mixing proportions of all the clusters) sums to 1); the ratio of indoor and outdoor ratios can be respectively taken by the two curves, and the ratio is the indoor probability ratio A and the outdoor probability ratio B of the interval value (i.e., the ratio of indoor-to-outdoor traffic is a ratio of a sum of all the mixing proportions of any of the clusters representing indoor user equipment and a sum of all the mixing proportions of any of the clusters representing outdoor user equipment)).
Regarding claim 5, the combination of Bo, Thiel, and Goyal teaches the method of claim 3, and further teaches “wherein each of the individual probability distribution functions of the clusters has a statistical measure, and wherein whether a given cluster of the clusters represents indoor user equipment or outdoor user equipment depends on whether the statistical measure for said given cluster satisfy a criterion or not” (see p. 4 lines 3 – 5, and 22 – 25 of Bo; the ratio of indoor and outdoor ratios can be respectively taken by the two curves (i.e., individual probability distribution functions of the clusters), and the ratio is the indoor probability ratio A and the outdoor probability ratio B (i.e., statistical measure) of the interval value; the sampling point is classified as indoor (i.e., a given cluster represents indoor user equipment) if the probability (i.e., statistical measure) that of the sampling point is indoor is greater than (i.e., satisfy) a certain threshold (i.e., a criterion); thus, whether a given cluster of the clusters represents indoor user equipment or outdoor user equipment depends on whether the statistical measure for said given cluster satisfy a criterion or not ).
Regarding claim 6, the combination of Bo, Thiel, and Goyal teaches the method of claim 5, and further teaches “wherein the statistical measure is a mean or a median value, and wherein whether said given cluster of the clusters represents indoor user equipment or outdoor user equipment depends on whether the mean or median value for said given cluster is above or below a threshold value” (see p. 4, lines 5 – 6, and 22 – 25 of Bo; the indoor probability PA (i.e., statistical measure) and the outdoor probability PB (i.e., statistical measure) can be calculated, PA=A/(A+B)×100%, and PB=B/(A+B)×100% (i.e., each probability (each statistical measure) is an average/mean of the two calculated probabilities); the sampling point is classified as indoor (i.e., a given cluster of clusters represents indoor user equipment) if the probability (i.e., statistical measure) that of the sampling point is indoor is greater than a certain threshold (i.e., above a threshold value)).
Regarding claim 10, the combination of Bo, Thiel, and Goyal teaches the method of claim 1, and further teaches “wherein each of the radio signal measurements is a signal strength measurement, including one or more of an RSRP value and a pathloss value” (see p. 1, lines 29 – 30 of Bo; MR (Measurement Report) data includes RSRP (Reference Signal Receiving Power) (i.e., radio signal measurements is a signal strength measurement)).
Regarding claim 11, the combination of Bo, Thiel, and Goyal teaches the method of claim 1, and further teaches “wherein the ratio of indoor-to-outdoor traffic or user equipment distribution further is estimated based on at least one of: user equipment speed, throughput, positioning availability, location accuracy, timing advance measurements of the set of user equipment” (see p. 1, lines 28 – 31 of Bo; MR (Measurement Report) data is a real measurement result of user communication collected and collected by a network communication device, and includes RSRP (Reference Signal Receiving Power), RSRQ (Reference Signal Receiving Quality), and LTE reference signal reception quality (i.e., throughput) of the user device).
Regarding claim 12, the combination of Bo, Thiel, and Goyal teaches the method of claim 1, and further teaches “wherein all user equipment in the set of user equipment are served in one and the same cell” (see col. 3, lines 23 – 26 and FIG. 1 of Thiel; all mobile devices (i.e., all user equipment) in a particular area which includes a cell of a mobile network, may place calls via a base station of a mobile network (i.e., served in one and the same cell)). It would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo to incorporate the teachings of Thiel to have all UEs served by the same cell. The suggestion to do so would have been to use the classification data to improve performance of different devices in the network (see col. 3, lines 13-14 of Thiel).
Regarding claim 17, it is the apparatus claim corresponding to the method claim 1 that has been rejected above. Applicant’s attention is directed to the rejection of claim 1. Claim 17 is rejected under the same rationale as claim 1.
Regarding claim 20, it is the computer-readable storage medium claim corresponding to the method claim 1 that has been rejected above. Applicant’s attention is directed to the rejection of claim 1. Claim 20 is rejected under the same rationale as claim 1.
Claims 7 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Bo in view of Thiel, further in view of Goyal, and further in view of Fjelberg et al. (U.S. Publication No. 2016/0366565).
Regarding claim 7, the combination of Bo, Thiel, and Goyal teaches the method of claim 1, but does not explicitly discuss “wherein the probability distribution function is separated into the set of clusters by machine learning using an unsupervised clustering methodology of the probability distribution function” of claim 7. However, Fjelberg teaches the foregoing limitation of claim 7 (see ¶ [0120]; uses unsupervised machine learning for various clustering methods, which is based on dividing (i.e., separate[ing]) data (i.e., radio signal measurement data having probability distribution function) into subsets which share a common signature with respect to some features, and one of the clustering method is based on Gaussian Mixture Models (GMM)). Therefore, it would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo in view of Thiel to incorporate the teachings of Fjelberg to use unsupervised machine learning for separating the probability distribution function into clusters. The suggestion to do so would have been to improve statistical modeling for a cell (see ¶ [0044] of Fjelberg).
Regarding claim 8, the combination of Bo, Thiel, Goyal, and Fjelberg teaches method of claim 7, and further teaches “wherein the unsupervised clustering methodology is a Gaussian mixture model or a Log-normal mixture model” (see ¶ [0120] of Fjelberg; uses unsupervised machine learning for various clustering methods, and one of the clustering method is based on Gaussian Mixture Models (GMM)). It would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo in view of Thiel to incorporate the teachings of Fjelberg to use a unsupervised machine learning clustering method is Gaussian Mixture Models (GMM). The suggestion to do so would have been to improve statistical modeling for a cell (see ¶ [0044] of Fjelberg).
Regarding claim 9, the combination of Bo, Thiel, Goyal, and Fjelberg teaches method of claim 7, and further teaches “wherein whether a given cluster of the clusters represent indoor user equipment or outdoor user equipment depends on a majority vote of samples obtained from a dataset” (see ¶ [0182] of Fjelberg; the classification algorithm is based on random forest, and responsive variable in the algorithm is determined by the votes (positive or negative) of the majority of the trees. Therefore, whether data divided (i.e., a cluster) represents indoor or outdoor is based on (i.e., depends on) a majority of votes being positive or negative for the data). It would have been obvious to one of ordinary skill in the art, before effective filing date of the claimed invention, to have modified the invention of Bo in view of Thiel to incorporate the teachings of Fjelberg to use a majority of votes to determine whether a given cluster represents indoor user equipment or outdoor user equipment. The suggestion to do so would have been to improve statistical modeling for a cell (see ¶ [0044] of Fjelberg).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SRIHARSHA REDDY VANGAPATY whose telephone number is (571)272-7655. The examiner can normally be reached M-F 8-5 EST.
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/SRIHARSHA REDDY VANGAPATY/Examiner, Art Unit 2475
/KHALED M KASSIM/supervisory patent examiner, Art Unit 2475