DETAILED ACTION
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 Arguments
Applicant's arguments filed 08/19/2026 have been fully considered but they are not persuasive. Examiner has thoroughly reviewed applicant’s arguments but firmly believes the cited reference to reasonably and properly meet the claimed limitations i.e. that obtained by using first connection probabilities. Examiner respectfully direct the Application to page 4, lines 9-12, page 5, lines 26-27, page 8-page 9, lines 5-25, page 11, line 28-page 12 line 14, Gauthier et al where discloses that the transition probability Tr (vt, vj) between two states v, and y, ·, corresponding to two base station positions, can be expressed as follows: the average speed wm "can be expressed by py. _Iwî; if vi> vj are adjacent lJ t 0 otherwise. Figure 7 illustrates an example of calculation of the transition probability of the statistical model used for the implementation of the invention, - Figure 8 illustrates the distribution of stations. FIG. 9 represents an example of calculation of the radius of gyration of a trajectory, FIGS. 10 to 12 illustrate an example of estimation of a trajectory according to the invention, FIGS. FIGS. 13 to 15 illustrate different trajectories estimated according to the invention, FIG. 16 represents statistical performances obtained according to the invention, FIGS. 17 and 18 represent statistics on the trajectories obtained, and FIG. 19 represents an aggregate. trajectories estimated according to the invention and the transition probability (Tr (vt, v /)) between two states (yh νβ of the model, corresponding to two base station positions (4), is expressed as follows: with wm "the average velocity on the arcs of the graph of the transport axes of the zone to be studied, and d (vm, v") the geodesic distance of Tare connecting the positions m and n, and SPvi, vj the shortest path between v, and v, on the graph. 9. Method according to the preceding claim, wherein the speed,. ,. ... (wa if Vi, Vi are adjacent mean (wm ") s expressed by: Ιψ), · = j J J t 0 otherwise, the conditional transmission probability (Pr (of | y,)) of an observation (ot), corresponding to the position of a base station (4). , since the hidden Markov model is in position (vj), is expressed as follows: with r · ax the distance between the Voronoi cell center associated with the observation ot and the end of its border, dtj the Euclidean distance between the observation ot and the position vj, and τ a threshold corresponding to the maximum distance where a mobile device (2) can be reached by a cellular antenna, in which erroneous base stations (4) are suppressed from the estimated trajectory as a function of at least the probability density of the distance between the base stations of the network. by filtering.
Additionally, the examiner has given the claim language its broadest reasonable interpretation. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). Anticipatory reference need not duplicate, word for word, what is in claims; anticipation can occur when claimed limitation is “inherent” or otherwise implicit in relevant reference (Standard Havens products Incorporated v. Gencor Industries Incorporated, 21 USPQ2d 1321). Applicant always has the opportunity to amend the claims during prosecution, and broad interpreted by the examiner reduces the possibility that the claim, once issued, will be interpreted more broadly than is justified. In re Prater, 415 F.2d 1393, 1404-05, 162 USPQ 541, 550-51 (CCPA 1969).
Therefore, the previous rejection is maintained.
Examiner’s Note: The Examiner has pointed out particular references contained in the prior art of record within the body of this action for the convenience of the Applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply. "The use of patents as references is not limited to what the patentees describe as their own inventions or to the problems with which they are concerned. They are part of the literature of the art, relevant for all they contain." In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275,277 (CCPA 1968)). A reference may be relied upon for all that it would have reasonably suggested to one having ordinary skill the art, including nonpreferred embodiments (see MPEP 2123). Therefore, Applicant, in preparing the response, must fully consider the entire disclosure of the cited references as potentially teaching all or part of the claimed invention, including the context of the cited passages as taught by the prior art disclosed by the Examiner.
Claim Rejections - 35 USC § 102
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Gauthier et al (FR 3046006).
Regarding claim 1, Gauthier et al discloses, an obtaining method implemented by a device and comprising (abstract, page 4-page7, fig. 2-9):
obtaining a value of a variable representing a movement of a mobile terminal, from a probability density of said variable representative of a movement of mobile terminal according to a first position of the mobile terminal during a first timestamped network event (page 4, lines 9-12, page 5, lines 26-27, page 8-page 9, lines 5-25, page 11, line 28-page 12 line 14, the captured signaling data advantageously correspond to metadata exchanged between the active elements of the mobile data network and contain information on the base station associated with a mobile device at a given time. 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. In one case, trajectories with a lower radius of gyration at 5 km were filtered, as explained previously and FIG. 8 (a) shows the probability density of the distance between neighboring base stations, FIG. 8 (b) shows the distribution function of the distance between neighboring base stations, and FIG. 8 (c) represents the division into Voronoi cells of a mobile operator network and 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 I y,-) of an observation of, corresponding to the position of a base station"; see 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. Station: "C1" - "C3"; see Page 9: "The conditional probability of emission Pr (Oi I y, -) of an observation of, 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 (01...On) are connected to a same base station; a second probability density using mapped connection and Figure 7 illustrates an example of calculation of the transition probability of the statistical model used for the implementation of the invention, - Figure 8 illustrates the distribution of stations. FIG. 9 represents an example of calculation of the radius of gyration of a trajectory, FIGS. 10 to 12 illustrate an example of estimation of a trajectory according to the invention, FIGS. FIGS. 13 to 15 illustrate different trajectories estimated according to the invention, FIG. 16 represents statistical performances obtained according to the invention, FIGS. 17 and 18 represent statistics on the trajectories obtained, and FIG. 19 represents an aggregate. trajectories estimated according to the invention);
obtained by using first connection probabilities, on a first coverage area of a first base station with which the mobile terminal interacted during said first network event, from said mobile terminal to said first base station, and to a second position of the mobile terminal during a second timestamped network event (see above further, page 11, line 28-page12 line 14);
obtained by second connection probabilities, on a second coverage area of a second base station with which the mobile terminal interacted during said second network event, from said mobile terminal, to said second base station (see above further, page 11, line 28-page12 line 14, During the user's move, in a step 11, several signaling data representative of the successive connection of the mobile device 2 to different base stations 4 of the network are captured. Preferably, and as in the example described, the signaling data come from PDP context data of the mobile data network and estimate the trajectory of the user. More precisely, a sequence of nodes of the transport axes of the area to be studied is preferably generated from the mapping between the signaling data and the map data, as a function of at least one probability of the statistical model).
Regarding claims 2, 11, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, 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 (page 15, lines 7-15).
Regarding claims 3, 12, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, at least two iterations of obtaining said probability density of said variable representative of a movement of the mobile terminal and further comprising determining a combined probability density of said variable representative of a movement of the mobile terminal by combining the probability densities of said variable representative of a movement of the mobile terminal determined during each of said iterations, the value of said variable representative of a movement of the mobile terminal being thus obtained from said average probability density (page 9, lines 26-29).
Regarding claims 4, 13, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, wherein the first event and the second event are temporally spaced by at least one first duration (page 15, lines 7-15).
Regarding claims 5, 14 Gauthier et al discloses in claim 1, further, Gauthier et al discloses, wherein the variable representative of a movement of a mobile terminal is a movement speed of the mobile terminal (page 8, lines 11-18).
Regarding claims 6, 15, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, wherein the probability density of said variable representative of a movement of the mobile terminal is determined by using a density of a distance travelled by the mobile terminal, obtained according said first connection probabilities and to said second connection probabilities, and to a duration separating the first and second events (page 8, lines 11-18).
Regarding claims 7, 16, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, wherein the variable representative of a movement of a mobile terminal is a movement direction of the mobile terminal (page 10, lines 10-12).
Regarding claims 8, 17, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, wherein the probability density of said variable representative of a movement of the mobile terminal is determined by using a density of a value of an angle representative of a movement direction of the mobile terminal obtained according to said first connection probabilities and to said second connection probabilities (page 10, lines 10-12).
Regarding claim 9, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, an electronic device capable of obtaining a value of a variable representative of a movement of a mobile terminal (abstract, fig. 2-19), wherein said device comprises:
least one processor adapted to:
obtain a value of a variable representing a movement of a mobile terminal from a probability density of said variable representative of a movement of the mobile terminal according to a first position of the mobile terminal during a first timestamped network event (page 4, lines 9-12, page 5, lines 26-27, page 9, lines 5-25, page 11, line 28-page 12 line 14, the captured signaling data advantageously correspond to metadata exchanged between the active elements of the mobile data network and contain information on the base station associated with a mobile device at a given time; 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. In one case, trajectories with a lower radius of gyration at 5 km were filtered, as explained previously and FIG. 8 (a) shows the probability density of the distance between neighboring base stations, FIG. 8 (b) shows the distribution function of the distance between neighboring base stations, and FIG. 8 (c) represents the division into Voronoi cells of a mobile operator network), obtained by using first connection probabilities, on a first coverage area of a first base station with which the mobile terminal interacted during said first network event, from said mobile terminal to said first base station, and to a second position of the mobile terminal during a second timestamped network event (page 4, lines 9-12, page 5, lines 26-27, page 9, lines 5-25, page 11, line 28-page 12 line 14, the captured signaling data advantageously correspond to metadata exchanged between the active elements of the mobile data network and contain information on the base station associated with a mobile device at a given time; 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. In one case, trajectories with a lower radius of gyration at 5 km were filtered, as explained previously and FIG. 8 (a) shows the probability density of the distance between neighboring base stations, FIG. 8 (b) shows the distribution function of the distance between neighboring base stations, and FIG. 8 (c) represents the division into Voronoi cells of a mobile operator network);
obtained by using second connection probabilities, on a second coverage area of a second base station with which the mobile terminal interacted during said second network event, from said mobile terminal to said second base station (page 4, lines 9-12, page 5, lines 26-27, page 9, lines 5-25, page 11, line 28-page 12 line 14, the captured signaling data advantageously correspond to metadata exchanged between the active elements of the mobile data network and contain information on the base station associated with a mobile device at a given time; 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. In one case, trajectories with a lower radius of gyration at 5 km were filtered, as explained previously and FIG. 8 (a) shows the probability density of the distance between neighboring base stations, FIG. 8 (b) shows the distribution function of the distance between neighboring base stations, and FIG. 8 (c) represents the division into Voronoi cells of a mobile operator network).
Regarding claim 10, Gauthier et al discloses in claim 1, further, Gauthier et al discloses, a non-transitory computer readable medium comprising a computer program product stored thereon comprising program code instructions for implementing, when executed by a processor, a method for obtaining a value of a variable representative of a movement of a mobile terminal, said method comprising (abstract, fig. 2-19):
obtaining a value of a variable representative of a movement of a mobile terminal from determining a probability density of said variable representative of a movement of the mobile terminal according to a first position of the mobile terminal during a first timestamped network event, obtained by means of said using first connection probabilities, on a first coverage area of a first base station with which the mobile terminal interacted during said first network event, from said mobile terminal to said first base station, and to a second position of the mobile terminal during a second timestamped network event; obtained by using second station with which the mobile terminal interacted during said second network event, from said mobile terminal to said second base station (page 4, lines 9-12, page 5, lines 26-27, page 9, lines 5-25, page 11, line 28-page 12 line 14, the captured signaling data advantageously correspond to metadata exchanged between the active elements of the mobile data network and contain information on the base station associated with a mobile device at a given time. 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. In one case, trajectories with a lower radius of gyration at 5 km were filtered, as explained previously and FIG. 8 (a) shows the probability density of the distance between neighboring base stations, FIG. 8 (b) shows the distribution function of the distance between neighboring base stations, and FIG. 8 (c) represents the division into Voronoi cells of a mobile operator network).
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 KHAWAR IQBAL whose telephone number is (571)272-7909. The examiner can normally be reached M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jinsong Hu can be reached at 5712723965. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KHAWAR IQBAL/ Primary Examiner, Art Unit 2643
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