DETAILED ACTION
Applicant’s response filed on 07/29/2026 has been entered and made of record.
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 Status
No claim is amended.
No new claim is/are added.
Claims 1 and 3-19 are pending for examination.
Applicant Argument
Applicant’s response has been fully considered. Below are applicant’s main arguments and examiner’s response to those arguments:
Applicant’s arguments: (remark pages 8-9), filed on 07/29/2026, with respect to claim 1, ‘1. Connection Probabilities The Applicant respectfully disagrees. Gauthier does not teach or suggest obtaining connection probabilities of a mobile terminal on a coverage area of a base station in which the mobile terminal interacted with the base station during timestamped network events, these connection probabilities enabling to obtain a first and second probability densities of the variable representative of a movement of the mobile termina l … cell-specific physical and radiation characteristics are ignored; overlapping zones between cells are not taken into account; no a priori location assumptions are used)’.
Examiner’s response: Examiner respectfully disagrees. First, see MPEP 2111 [Claim Interpretation; Broadest Reasonable Interpretation]: “The court explained that “reading a claim in light of the specification, to thereby interpret limitations explicitly recited in the claim, is a quite different thing from ‘reading limitations of the specification into a claim,’ to thereby narrow the scope of the claim by implicitly adding disclosed limitations which have no express basis in the claim.” The court found that applicant was advocating the latter, i.e., the impermissible importation of subject matter from the specification into the claim.). See also In re Morris, 127 F.3d 1048, 1054-55, 44 USPQ2d 1023, 1027-28 (Fed. Cir. 1997)".
Gauthier teaches obtain a value of a variable (distance) (Gauthier: [FIG.8], [Page 4]) and a first probability density obtained from two connection probabilities of coverage area associated with two base stations based on a sequence of timestamped network events involving the terminal (Gauthier: [FIG.6A], [FIG.6B], [FIG.7]; [FIG.8], [FIG.9], [Page 10], [Page 4], [Page 8], [Page 9], [Page 12]), which teaches the subject matters as claimed. See the detailed Office Action bellow under 35 U.S.C. § 103 section.
Applicant’s arguments: (remark pages 9-10), filed on 07/29/2026, with respect to claim 1, ‘2. Timestamped Network Events … Specifically, Gauthier does not teach or suggest obtaining a second probability density of the movement variable obtained by using connection probabilities within a third coverage area of a third base station for a third and fourth timestamped network event … The subject matter of claim 1 is therefore not anticipated by Gauthier’.
Examiner’s response: Examiner respectfully disagrees. Gauthier teaches the scenario that the terminal is stationary with a same base station and all the sequence of timestamped network events involving the terminal are associated with the same base station, in another word, the probability density of the variable (distance) representative of a movement of the mobile terminal is obtained by using a connection probabilities of the terminal within a coverage area of one same base station with which the mobile terminal interacted during multiple network events involving the mobile terminal (Gauthier: [FIG.6A], [Page 9], [Page 10]), which teaches the subject matters as claimed. See the detailed Office Action bellow under 35 U.S.C. § 103 section.
Applicant’s arguments: (remark pages 10-12), filed on 07/29/2026, with respect to claim 1, ‘C. Liang 1. Not Analogous Art … The skilled person would not be prompted to extract a pixel-matching similarity coefficient from video frame as taught by Liang and apply it to the Gauthier's telecommunication Voronoi-based signaling data to determine whether a cell phone has physically moved or not …3. Finally, none of Gauthier and Liang discloses or suggest obtaining a value of a variable representative of a movement of a mobile terminal in the following way … Because these limitations are entirely absent from both references, the combination of Gauthier with Liang fails to render the subject matter of claim 1 obvious under 35 U.S.C. § 103’.
Examiner’s response: Examiner respectfully disagrees. First, Gauthier’s teaching of high spatial noise of cellular observations (Gauthier: [Page 11]) and the filtering, such as Kalman filter, to eliminate erroneous estimation (Gauthier: [Page 9]) would implicitly teach to consider both a first probability density associated with movement between two base stations and a second probability associated with a given base station, which would implicitly teach the subject matters as claimed.
Moreover, the issue in concern is to compare the similarity of two given probability density of a variable for a given area (region) to determine confidence level of a given value of the variable. Liang addresses the same issue and teaches Bhattacharyya coefficient to calculate the similarity of the probability density of features with the probability density of a candidate target region to get a confidence level (Liang: [Abstract], [0115]), and also see Gauther’s teaching as response above. Therefore, combination of Gauthier and Liang teaches to obtain the value of a variable (distance) representative of a movement of a mobile terminal based on similarity of a first probability density of the variable (distance) and a second probability density of the variable (distance), which teaches the subject matters as claimed and is in light of specification (specification PGPUB paragraph [0129]-[0131]). See the detailed Office Action bellow under 35 U.S.C. § 103 section.
Applicant’s arguments (remark pages 7-13), filed on 07/29/2026, with respect to claims 1 and 3-19 have been considered but are not convincing. The claim rejections under 35 USC § 103 are not withdrawn.
This Office Action is 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 (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.
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.
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, 3-9 and 11-19 are rejected under 35 U.S.C. 103 as being unpatentable over Gauthier et al. (FR 3046006 A1), hereinafter “Gauthier”, in view of Liang et al. (US 20150206004 A1), hereinafter “Liang”.
Per claim 1 and 11:
Regarding to claim 11, Gauthier teaches ‘A device’ (Gauthier: [FIG.2]: device; [Page 10]: “A server”); ‘capable of determining a movement of a mobile terminal’ (Gauthier: [Page 10]: “A server can implement the method according to the invention. By "server", it is necessary to understand a computer system, for example hosted at the provider of the trajectory estimation service”); ‘said device comprising: at least one processor adapted to’ (Gauthier: existence of processor for the device (server) is implied);
‘obtain a value of a variable representative of a movement of a mobile terminal from’ (Gauthier: [FIG.8]: (a): distance vs probability density; [Page 4]: “FIGS. 6A and 6B illustrate an example of calculation of the probability of emission of the Statistical model”; [Page 9]: “d (vm, v ") the geodesic distance of the arc connecting the positions m and n … dtj the Euclidean distance between the observation ot and the position vj”);
‘a first probability density of said variable representative of a movement of the mobile terminal according to’ (Gauthier: [Page 9]: “The statistical model is preferably a hidden Markov model, where the hidden states advantageously correspond to the stations of the rail network and / or to the intersections of the road network. In this case, the positions of the base stations advantageously correspond to the hidden states of the hidden Markov model. The transition probability Tr (vh vj) between two states v, and y ,, corresponding to two base station positions … The conditional probability of emission Pr (Oi | y, ·) of an observation ot, corresponding to the position of a base station”; [Page 12]: “The probability density of the length of trajectories estimated in kilometers is shown in Figure 17. The probability density of the trajectories estimated in minutes is shown in Figure 18”);
‘first connection probabilities of said terminal on a first coverage area of a first base station with which the mobile terminal interacted during a first timestamped network event involving said mobile terminal’ (Gauthier: [FIG.6A]; [FIG.6B]; [FIG.7]; [FIG.8]; [FIG.9]; [Page 10]: “during the user's movement, to capture a plurality of signaling data representative of the successive connection of the mobile device to different network stations, and - knowing the position of the base stations in relation to the transport axes of the area, using a statistical model, this signaling data to map data, to estimate the trajectory of the user”; [Page 11]: “probabilities of the statistical model are used to estimate the trajectory of the user, in particular the probability of emission? R (oi \ vj) of an observation ot, corresponding to the position of a base station, since the statistical model is at position y, as shown in FIGS. 6A and 6B, and the transition probability Tr (v, vj) between two base station positions vh vh as shown in FIG. 7”; [Page 8]: “The captured signaling data advantageously lead to a sequence of observations (0i, ..., On), where On is the triplet On = {x "y> n, tn} defined by the position {xn, yn) of the base station to which the mobile device is connected at time tn”, timestamped signaling data (network event); [Page 8]: “PDP context data … switching from 2G to 3G or when switching from the connection in standby mode to the active mode, or during a change of base station ... the signaling data come from the voice channels”, multiple kinds of signaling data; each base station has a mapped connection probabilities from a plurality of timestamped signaling data (network event). A first probability density obtained from two connection probabilities of coverage area associated with two base stations based on a sequence of timestamped network events involving the terminal);
‘second connection probabilities of said terminal on a second coverage area of a second base station with which the mobile terminal interacted during a second timestamped network event involving said mobile terminal’ (discussed in element above);
‘a second probability density of said variable representative of a movement of the mobile terminal, obtained by using third connection probabilities of said terminal within a third coverage area of a third base station with which the mobile terminal interacted during a third network event involving the mobile terminal, and a fourth network event involving the mobile terminal’ (Gauthier: [FIG.6A]: Base Station: “C1” – “C3”; [Page 9]: “The conditional probability of emission Pr (Oi | y, ·) of an observation ot, corresponding to the position of a base station … It is possible to eliminate from the estimated trajectory of the base stations that appear to be erroneous, as a function of at least the probability density of the distance between the base stations of the network, by filtering, in particular by low-pass filtering”; [Page 8]: “These signaling data flows inform the position of mobile devices as soon as their connection to the internet is established and as long as the connection is active. Today, many applications running on so-called smartphones, or "smartphones" in English, are constantly operating and connecting frequently to the mobile data network, including email applications, sending out notifications when a new email is received, or social network applications, sending notifications signaling a new action … By means of the signaling data, the frequency of updating the control of the connection of the mobile device to a base station is advantageously between 1 minute and 18 minutes, better between 5 minutes and 15 minutes”; [Page 10]: “It is preferably estimated the end of a trajectory when the mobile device remains connected to the same base station for a duration greater than a predefined threshold, in particular equal to 30 minutes”, the same base station would receive multiple signaling data from a smartphone during the 30 minutes, in this scenario, all the (O1…On) are connected to a same base station; a second probability density using mapped connection probabilities from multiple timestamped signaling data (network event) interacted with a same base station).
Although Gauthier’s teaching of high spatial noise of cellular observations (Gauthier: [Page 11]: “high spatial noise of cellular observations”) and the filtering, such as Kalman filter, to eliminate erroneous estimation (Gauthier: [Page 9]: “In one variant, the statistical model is based on conditional random fields, on a particulate filter, on a Kalman filter … It is possible to eliminate from the estimated trajectory of the base stations that appear to be erroneous, as a function of at least the probability density of the distance between the base stations of the network, by filtering, in particular by low-pass filtering. It is possible to carry out filtering, in particular high-pass filtering, to retain only trajectories having a radius of gyration rg (t) greater than a predefined threshold”) would implicitly teach to consider both a first probability density associated with movement between two base stations and a second probability associated with a given base station, Gauthier does not expressly teach obtain a value from both of them.
Nevertheless, Liang in the same field of endeavor teaches Bhattacharyya coefficient to calculate the similarity of the probability density of features with the probability density of a candidate target region to get a confidence level (Liang: [Abstract]: “acquiring a target template which is expressed by a probability density distribution of features; expressing, by a probability density distribution of features, a candidate target whose position moves in the candidate target region; calculating, based on a probability density expression of the target template and a probability density expression of the candidate target, a similarity between the target template and the candidate target, so as to get a confidence level”; [0115]: “the similarity of the two may be calculated on the basis of the Bhattacharyya coefficient of the two”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Liang’s teaching with that of Gauthier to obtain a value of a variable representative of a movement of a mobile terminal from both of a first probability density and a second probability density in order to get a confidence level by similarity (see reference quotes in element above).
Regarding to claim 1, claim 1 recites the method implemented by the device according to claim 11 (see rejection of claim 11 above).
Per claim 3 and 13:
Regarding to claim 13, combination of Gauthier and Liang teaches the device according to claim 11 (discussed above).
Gauthier teaches ‘wherein said first, second, third and/or fourth network event is associated respectively with a first, second, third and/or fourth set of signalling data comprising, among others, respectively, an item of timestamp data of said first, second, third and/or fourth network event’ (Gauthier: [Page 8]: “The captured signaling data advantageously lead to a sequence of observations (0i, ..., On), where On is the triplet On = {x "y> n, tn} defined by the position {xn, yn) of the base station to which the mobile device is connected at time tn”; [Page 7]: “signaling events”).
Regarding to claim 3, claim 3 recites the method implemented by the device according to claim 13 (see rejection of claim 13 above).
Per claim 4 and 14:
Regarding to claim 14, combination of Gauthier and Liang teaches the device according to claim 11 (discussed above).
Gauthier does not expressly teach ‘wherein obtaining said value representative of a variable representative of a movement of the mobile terminal comprises a comparison between the first probability density of said variable representative of a movement of the mobile terminal and the second probability density of said variable representative of a movement of the mobile terminal’.
Liang teaches Bhattacharyya coefficient to compare the similarity between two probability density (Liang: [0014]-[0015]: “the calculation part 160 calculates, on the basis of the probability density expression of the target template and the probability density expression of the candidate target, the similarity between the target template and the candidate target … the similarity of the two may be calculated on the basis of the Bhattacharyya coefficient of the two”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Liang’s teaching with that of Gauthier for the obtaining said value representative of a variable representative of a movement of the mobile terminal comprises a comparison between the first probability density of said variable representative of a movement of the mobile terminal and the second probability density of said variable representative of a movement of the mobile terminal in order to get a confidence level by similarity (Liang: [0024]: “a similarity between the target template and the candidate target, so as to get a confidence level”).
Regarding to claim 4, claim 4 recites the method implemented by the device according to claim 14 (see rejection of claim 14 above).
Per claim 5 and 15:
Regarding to claim 15, combination of Gauthier and Liang teaches the device according to claim 14 (discussed above).
Gauthier does not expressly teach ‘wherein the comparison between the first probability density of said variable representative of a movement of the mobile terminal and the second probability density of said variable representative of a movement of the mobile terminal comprises determining an overlap rate between the first probability density of a variable representative of a movement of the mobile terminal and the second probability density of said variable representative of a movement of the mobile terminal’
Liang teaches Bhattacharyya coefficient, which is to determine a similarity (overlap rate) between two probability density (Liang: [0015]: “the similarity of the two may be calculated on the basis of the Bhattacharyya coefficient of the two”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Liang’s teaching with that of Gauthier for the comparison between the first probability density of said variable representative of a movement of the mobile terminal and the second probability density of said variable representative of a movement of the mobile terminal comprises determining an overlap rate between the first probability density of a variable representative of a movement of the mobile terminal and the second probability density of said variable representative of a movement of the mobile terminal in order to get a confidence level by similarity (Liang: [0024]: “a similarity between the target template and the candidate target, so as to get a confidence level”).
Regarding to claim 5, claim 5 recites the method implemented by the device according to claim 15 (see rejection of claim 15 above).
Per claim 6 and 16:
Regarding to claim 16, combination of Gauthier and Liang teaches the device according to claim 11 (discussed above).
Gauthier teaches ‘wherein the first event and the second event are selected from a plurality of network events involving the mobile terminal, occurred during a first time window’ (Gauthier: [Page 8]: “PDP context data … when switching from 2G to 3G, or when switching from the connection in standby mode to the active mode, or during a change of base station … By means of the signaling data, the frequency of updating the control of the connection of the mobile device to a base station is advantageously between 1 minute and 18 minutes, better between 5 minutes and 15 minutes”).
Regarding to claim 6, claim 6 recites the method implemented by the device according to claim 16 (see rejection of claim 16 above).
Per claim 7 and 17:
Regarding to claim 17, combination of Gauthier and Liang teaches the device according to claim 11 (discussed above).
Gauthier teaches ‘at least two iterations of the obtaining’ (Gauthier: [Page 8]: “The captured signaling data advantageously lead to a sequence of observations (0i, ..., On), where On is the triplet On = {x "y> n, tn} defined by the position {xn, yn) of the base station to which the mobile device is connected at time tn”);
‘determining an average value of said variable representative of a movement of the mobile terminal by combining the first probability densities of said variable representative of a movement of the mobile terminal determined during each of said iterations’ (Gauthier: [Page 9]: “The transition probability Tr (vh vj) between two states v, and y ,, corresponding to two base station positions … with wmn the average speed on the arcs of the graph of the transport axes of the zone to be studied, and d (vm, v ") the geodesic distance of the arc connecting the positions m and n, and SPvlt VJ the shortest path between v, and v, on the graph”; [Page 36]: “
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”, average of n(t); [Page 12]: “Figure 16 are the average precision and recall rate of the results for the trajectories”).
Regarding to claim 7, claim 7 recites the method implemented by the device according to claim 17 (see rejection of claim 17 above).
Per claim 8 and 18:
Regarding to claim 18, combination of Gauthier and Liang teaches the device according to claim 11 (discussed above).
Gauthier teaches ‘wherein the first event and the second event are temporally spaced by at least one first duration’ (Gauthier: [Page 8]: “By means of the signaling data, the frequency of updating the control of the connection of the mobile device to a base station is advantageously between 1 minute and 18 minutes, better between 5 minutes and 15 minutes”).
Regarding to claim 8, claim 8 recites the method implemented by the device according to claim 18 (see rejection of claim 18 above).
Per claim 9 and 19:
Regarding to claim 19, combination of Gauthier and Liang teaches the device according to claim 11 (discussed above).
Gauthier teaches ‘wherein said variable representative of a movement of a mobile terminal is a movement distance of the mobile terminal’ (Gauthier: [Page 13: “the geodesic distance of the arc connecting the positions m and n”; [Page 14]: “the distance between the Voronoi cell center associated with the observation o, and the end of its border, by the Euclidean distance between the observation ot and the position vj … the estimated trajectory as a function of at least the probability density of the distance between the base stations of the network”).
Regarding to claim 9, claim 9 recites the method implemented by the device according to claim 19 (see rejection of claim 19 above).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over combination of Gauthier and Liang as applied to claim 1 above, further in view of Kim et al. (US 20090047970 A1), hereinafter “Kim”.
Regarding to claim 10, combination of Gauthier and Liang teaches the method according to claim 1 (discussed above).
Combination of Gauthier and Liang does not expressly teach ‘wherein the variable representative of a movement of a mobile terminal is a movement direction of the mobile terminal’.
Kim in the same field of endeavor teaches probability density for a movement direction (Kim: [0017]: “calculating an expected moving direction of the mobile terminal, based on a speed-based moving probability model represented by a probability density function for a movable direction”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim’s teaching with that of combination of Gauthier and Liang for the variable representative of a movement of a mobile terminal is a movement direction of the mobile terminal in order to minimize unnecessary network resource reservation by predicting an moving direction of a terminal (Kim: [0014]: “for minimizing unnecessary network resource reservation by predicting an moving direction of a terminal”).
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.
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/G.F./
Examiner, Art Unit 2462
/YEMANE MESFIN/Supervisory Patent Examiner, Art Unit 2462
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