Application Serial No.: 09/770,838
`Document: "Response to Oflice Action Dated May 17. 2002
`
`142, a confidence score is determined for areas within the location center service area. More particularly,
`if a firnction, “f”, is a function of the confidence(s) of location hypotheses, and f is a monotonic function
`
`, cf") = CSA for confidences cf; of location hypotheses H;
`in its parameters and f(cfi, cfz, cf3,
`i=1,2,...,N, with CSA contained in the area' estimate for H3, then “f” is denoted a confidence score function.
`
`[ ]Accordingly, there are many embodiments for a confidence score function f that may be utilized in
`computing confidence scores with the present invention; e.g.,
`
`(a) flcfi, cfz.
`(b) f(cf., cfz,
`
`, cfn) = [3]; cf; = CSA;
`, cf") = [5]; cf,“ = csA, n = 1, 3, 5,
`
`(c) f(cfl, cfz,
`
`, cf") = [S]; (K.- " cfi) = CSA , wherein K,, i = 1, 2,
`
`are positive system (tunable)
`
`constants (possibly dependent on environmental characteristics such as topography, time, date, traffic,
`weather, and/or the type of base station(s) 1.22 from which location signatures with the target MS 140 are
`being generated, etc.).
`
`The paragraph beginning on page 43, line 27 and ending on page 44, line 23 has been replaced with
`the following paragraph:
`
`In one embodiment of a method and system for determining such (transmission) area type
`approximations, a partition (denoted hereinafier as P0) is imposed upon the radio coverage area 120 for
`partitioning for radio coverage area into subareas, wherein each subarea is an estimate of an area having
`included MS 140 locations that are likely to have is at least a minimal amount of similarity in their
`wireless signaling characteristics. To obtain the partition P0 of the radio coverage area 120, the following
`steps are performed:
`
`(23.8.4.1) Partition the radio coverage area .120 into subareas, wherein in each subarea is:
`
`(a) connected, (b) variations in the lengths of chords sectioning the subarea
`
`through the centroid of the subarea are below a predetermined threshold, (c)
`
`the subarea has an area below a predetermined value, and (d) for most locations
`
`(e.g., within a first or second standard deviation) within the subarea whose
`
`wireless signaling characteristics have been verified, it is likely (e.g., within a
`
`first or second stande deviation ) that an MS 140 at one of these locations
`will detect (forward transmission path) and/or will be detected (reverse
`
`transmission path) by a same collection of base stations 122. For example, in a
`
`CDMA context, a first such collection may be (for the forward transmission
`
`20 of 100
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`Application Serial Na.: 09/770,838
`Document: “Response to Oflice Action Dated May I 7. 2002
`
`path) the active set of base stations 122, or, the union of the active and
`
`candidate sets, or, the union of the active, candidate and/or remaining sets of
`
`base stations 122 detected by “most” M55 140 in the subarea. Additionally (or
`
`alternatively), a second such collection may be the base stations 122 that are
`
`expected to detect M35 140 at locations within the subarea. Of course, the
`
`union or intersection of the first and second collections is also within the scope
`
`of the present invention for partitioning the radio coverage area 120 according
`
`to (d) above. It is worth noting that it is believed that base station 122 power
`
`, levels will be substantially constant. However, even if this is not the case, one
`
`or more collections for (d) above may be determined empirically and/or by
`
`computationally simulating the power output of each base station 122 at a
`
`predeterrnined level. Moreover, it is also worth mentioning that this step is
`relatively straightforward to implement using the data stored in the location
`
`signature data base 1320 (i.e., the verified location signature clusters discussed
`
`in detail hereinbelow). Denote the resulting partition here as P].
`
`(23.8.4.2) Partition the radio coverage area 120 into subareas, wherein each subarea
`
`appears to have substantially homogeneous‘terrain characteristics. Note, this
`
`may be performed periodically substantially automatically by scanning radio
`
`coverage area images obtained from aerial or satellite imaging. For example,
`
`EarthWatch Inc. of Longmont, CO can provide geographic with 3 meter
`resolution from satellite imaging data.- Denote the resulting partition here as
`P2.
`
`(23.8.4.3) Overlay both of the above partitions of the radio coverage area 120 to obtain
`new subareas that are intersections of the subareas from each of the above
`
`partitions. This new partition is Po (i.e., P0 = P1 intersect P2), and the subareas
`
`of it are denoted as “Po subareas”.
`
`The paragraph beginning on page 47, line 4 and ending on page 47, line 22 has been replaced with
`the following paragraph:
`’
`
`There are four fundamental entity types (or object classes in an object oriented programming
`
`paradigm) utilized in the location signature data base 1320. Briefly, these data entities are described in the
`
`items (24. I) through (24.4) that follow:
`
`RI anOQ
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`Application Serial Na: 09/770,838
`Document: "Response 10 Oflice Action Dated May I 7, 2002
`
`(verified) location signatures: Each such (verified) location signature describes the wireless signal
`(24.1)
`characteristic measurements between a given base station (e.g., BS 122 or LBS 152) and an MS 140 at a
`
`(verified or known) location associated with the (verified) location signature. _'I'hat is, a verified location
`signature con'esponds to a location whose coordinates such as latitude-longitude coordinates are known,
`
`while simply a location signature may have a known or unknown location corresponding with it. Note that
`
`the term (verified) location signature is also denoted by the abbreviation, “(verified) loc Sig” hereinbelow;
`
`(24.2)
`
`(verified) location signature clusters: Each such (verified) location signature cluster includes a
`
`collection of (verified) location signatures corresponding to all the location signatures between a target MS
`140 at a (possibly verified) presumed substantially stationary location and each BS (e.g., 122 or 152) from
`
`which the'target MS 140 can detect the BS’s pilot channel [gardlesslregardless of the classification of the BS
`
`in the target MS (i.e., for CDMA, regardless of whether a BS is in the MS’s active, candidate or remaining
`
`base station sets, as one skilled in the art will understand). Note that for simplicity here, it is presumed that
`
`each location signature cluster has a single fixed primary base station to which the target MS 140
`
`synchronizes or obtains its timing;
`
`(24.3)
`
`“composite location objects (or entities)”: Each such entity is a more general entity than the verified
`
`location signature cluster. An object of this type is a collection of (verified) location signatures that are
`
`'
`
`associated with the same MS 140 at substantially the same location at the same time and each such loc sig is
`associated with a different base station. However, [ lthere is'no requirement that a loc sig from each BS 122
`
`for which the MS 140 can detect the 88’s pilot channel is included in the “composite location ‘object (or
`entity)”; and
`
`(24.4) MS location estimation data that includes MS location estimates output by one or more MS location
`
`estimating fust order models 1224, such MS location estimate data is described in detail hereinbelow.
`
`The paragraph beginning on page 47, line 30 has been replaced with the following paragraph:
`
`In particular, for each (verified) loc sig includes the following:
`
`(25.1) MS_type: the make and model of the target MS 140 associated with a location signature
`
`instantiation; note that the type of MS 140 can also be derived from this entry; e.g., whether
`
`MS 140 is a handset MS, car-set MS, or an MS for location only. Note as an aside, for at least
`
`CDMA, the type of MS 140 provides information as to the number of fingers that may be
`
`measured by the MS[.], as one skilled in the will appreciate.
`
`The paragraph beginning on page 48, line 24 has been replaced with the following paragraph:
`
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`Application Serial No: 09/770, 838
`Document: "Response to Oflice Action Dated May I 7, 2002
`
`(25.7) signal topography characteristics: In one embodiment, the signal topography characteristics
`
`retained can be represented as characteristics of at lem a two-dimensional generated surface.
`That is, such a surface is generated by the signal processing subsystem 1220 from signal
`characteristics accumulated over (a relatively short) time interval. For example, in the two-
`
`dimensional surface case, the dimensions for the generated surface may be, for example, signal
`
`strength and time delay. That is, the accumulations over a brief time interval of signal
`
`_
`
`characteristic measurements between the BS 122 and the MS 140 (associated with the loc sig)
`
`may be classified according to the two signal characteristic dimensions (e.g., signal strength and
`
`corresponding time delay). That is, by sampling the signal characteristics and classifying the
`samples according to a mesh of discrete cells or bins, wherein each cell [correspondi
`]con'esponds to a different range of signal strengths and time delays a tally of the number of
`
`.
`samples falling in the range of each cell can be maintained. Accordingly, for each cell, its
`corresponding tally may be interpreted as height ofthe cell, so that when the heights ofall cells
`are considered, an undulating or mountainous surface is provided; In particular, for a cell mesh
`
`of appropriate fineness, the “mountainous surface”, is believed to, under most circumstances,
`
`provide a contour that is substantially unique tothe location of the target MS 140. Note that in
`
`one embodiment, the signal samples are typically obtained, throughout a predetermined signal
`sampling time interval of 2-5 seconds [ ]as is discussed elsewhere in this specification. In
`particular, the signal topography characteristics retained for a loc sig include certain
`itopographical characteristics of such a generated mountainous surface. For example, each be
`sigmay include: for each local maximum (ofthe loc sig surface) above a predetermined noise
`ceiling threshold, the (signal strength, time delay) coordinates of the cell of the local maximum
`
`and the corresponding height of the local maximum. Additionally, certain gradients may also be
`included for characterizing the “steepness” of the surface mountains. Moreover, note that in
`
`some embodiments, a frequency may also be associated with each local maximum. Thus, the
`data retained for each selected local maximum can include a quadruple of signal strength, time
`
`delay, height and frequency. Further note that the data types here may [ Ivary. However; for
`
`simplicity, in parts of the description of loc sig processing related to the signal characteristics
`
`here, it is assumed that the signal characteristic topography data structure here is a vector;
`
`The paragraph beginning on page 49, line 19 has been replaced with the following paragraph:
`
`in nrmo.
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`Application Serial No.: 09/770.838
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`
`(25.13) repeatable: TRUE iffthe loc sig is “repeatable" (as described hereinafter), FALSE otherwise.
`
`Note that each verified loc sig is designated as either “repeatable” or “random”. A loc sig is
`
`repeatable if the (verified/known) location associated with the loc sig is such that signal
`
`characteristic measurements between the associated BS 122 and this MS can be either replaced
`
`at periodic time intervals, or updated substantially on demand by most recent signal
`
`characteristic measurements between the associated base station and the associated MS 140 (or
`
`a comparable MS) at the verified/known location. Repeatable loc sigs may be, for example,
`
`provided by stationary or fixed location M85 140 (e.g., fixed location transceivers) distributed
`
`within certain areas of a geographical region serviced by the location center 142 for providing
`
`MS location estimates. That is, it is an aspect of the present invention that each such stationary
`
`_
`MS 140 can be contacted by the location center 142 (via the base stations ofthe wireless
`infrastructure) at substantially any time for providing a new collection (i.e., cluster) of wireless
`
`signal characteristics to be associated with the verified location for the transceiver.
`
`Alternatively, repeatable loc sigs may be obtained by, for example, obtaining location signal
`
`measurements manually from workers who regularly traverse a predetermined route through
`
`some portion of the radio coverage area; i.e., postal workers[ (as will be described in more
`
`detail hereinbelow)].
`
`'
`
`Please replace the paragraph beginning on page 50, line 17 with the following paragraph:
`
`I
`
`(26.1) A “normalization” method for normalizing 10c sig data according to the associated MS 140
`and/or BS 12 signal processing and generating characteristics. That is, the signal processing
`subsystem 1220, one embodiment being described in the PCT patent application
`
`PCT/U597/ 1 5933 titled, “Wireless Location Using A Plurality of Commercial Network
`
`lnfi‘astmct'ures,” by F. W. LeBlanc and the present inventors, filed September 8, 1992 (which
`has a U.S.“national filing that is now us. Patent No. 6,236,365, filed July 8, 1999, note, both
`
`PCT/US97/15933 and US. Patent No. 6,236,365 are incommted firlly byvreference herein)
`
`provides (methods for Ice sig objects) for “normalizing" each loc sig so that variations in
`
`signal characteristics resulting from variations in (for example) MS signal processing and
`
`generating characteristics of different types of MS’s may be reduced. In particular, since
`
`wireless network designers are typically designing networks for effective use of hand set
`
`MS’s 140 having a substantially common minimum set of performance characteristics, the
`
`normalization methods provided here transform the loc sig data so that it appears as though
`
`R4 of mo
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`Apph'catr'an Serial No.1 09/770.838
`Document: “Response to Oflice Action Dated May [7, 2002
`
`the loc sig was provided by a common hand set MS 140. However, other methods may also
`
`be provided to “normalize” a loc sig so that it may be compared with Ice sigs obtained from
`
`other types of MS’s as well. Note that such normalimtion techniques include, for example,
`
`interpolating and extrapolating according to power levels so that loc sigs may be normalized
`
`to the same power level for, e.g., comparison purposes.
`
`Normalization for the BS 122 associated with a loc sig is similar to the normalization for MS
`
`signal processing and generating characteristics. Just as with the MS normalization, the signal
`
`processing subsystem 1220 provides a loc sig method for “normalizing” loc sigs according to
`
`base station signal processing and generating characteristics.
`
`The paragraph beginning on page 52, line 10 has been replaced with the following paragraph:
`
`A first functional group of location engine 139 modules is for performing signal processing and
`
`filtering of MS location signal data received from a conventional wireless (e.g., CDMA) infrastructure, as
`discussed in the steps (23.1) and (23.2) above.- This group is denoted the signal processing subsystem 1220
`
`herein. One embodiment of such a subsystem is described in the PCT patent application titled, “Wireless
`
`Location Using A Plurality of Commercial Network infrastructures,” by F. W. LeBlanc and the present
`
`inventor§_.[(s). .]
`
`The paragraph beginning on page 52, line 15 has been replaced with the following paragraph:
`
`A second functional group of location engine 139 modules is for generating various target MS 140
`location initial estimates, as described in step (23.3 ). Accordingly, the modules here use input provided by
`
`the signal processing subsystem 1220. This second functional group includes one or more signal analysis
`
`modules or models, each hereinafter denoted as a first order model 1224 (FOM), for generating location
`
`hypotheses for a target MS 140 to be located. Note that it is intended that each such FOM 1224 use a
`
`different technique for determining a location area estimate for the target MS 140. A brief description of
`
`some types of first order models is provided immediately below. Note that [Fig.]flg; 8 illustrates another,
`
`more [detainm view of the location system for the present invention. In particular, this figure illustrates
`
`some of the FOMs 1224 contemplated by the present invention, and additionally illustrates the primary
`
`communications with other modules of the location system for the present invention. However, it is
`
`important to note that the present invention is not limited to the FOMs 1224 shown and discussed herein.
`
`That is, it is a primary aspect of the present invention to easily incorporate FOMs using other signal
`
`processing and/or computational location estimating techniques than those presented herein. Further, note
`
`85 oflO9
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`Application Serial Na. : 09/770. 838
`Document: “Response to Oflice Action Dated May I 7, 2002
`
`that each FOM type may have a plurality of its models incorporated into an embodiment of the present
`invention.
`
`The following paragraph as been inserted immediately before the paragraph beginning on page 53,
`line 10:
`'
`'
`
`In one embodiment, such a distance model may p_erform the following steps:
`Determines a minimum distance between the tagget MS and each BS using TOA,
`TDOA, signal strengt_h on both forward and reverse paths;
`Generates an estimated error,
`
`Ou uts a location h
`
`thesis for estimatin a location of a MS: each such
`
`(a)
`
`(b)
`
`e
`
`hyp_othesis having: (i) one or more (nested) location area estimates for the MS, each
`location estimate having a confidence value (e.g., provided using the estimated
`error) indicating a perceived accuracy, and (ii) a reason for both the location
`
`estimate (e.g., substantial multipath, etc) and the confidence.
`
`The paragraph beginning on page 53, line 10 has been replaced withthe following paragraph:
`
`Another type of F0M 1224 is a statistically based first order model 1224, wherein a statistical
`
`technique, such as regression techniques (e.g., least squares, partial least squares, principle decomposition), or
`
`e.g., Bollenger Bands (e.g., for computing minimum and maximum base station offsets). In general, models
`ofthis type output location hypothesesmdetermined by performing one or more statistical techniques
`or comparisons between the verified location signatures in location signature data base 1320, and the wireless
`
`signal measurements from a target MS. Models ofthis type are also referred to hereinafter as [ ]a “stochastic
`signal (first order) model" or a “stochastic FOM” or a “statistical model."
`
`..u-‘In'n
`
`The following paragraph has been inserted immediately before the paragraph beginning on page
`53, line 16:
`"
`
`In one embodimentI such a stochastic sigpal model may output location hypotheses determined by
`
`one or more statistical comparisons with loc sigs in the Location Sigpature database I320 (e.g., comparing
`MS location signals with verified signal characteristics for predetermined geographical areas).
`
`86 nfl09
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`Application Serial No: 09/770,838
`Document: "Response to Oflice Action Dated May I 7. 2002
`
`The following paragraph has been inserted immediatelv before the paragraph beginning on page
`53, line 24:
`
`In one embodiment, an adaptive learning model such as a model based on an artificial neural network
`
`may determine an MS 140 location estimate using base station IDs, data on signal-tomoise, other signal data
`e.
`. anumber of si
`al characteristics includin
`e.
`. all CDMA fin ers. Moreover the ou ut from
`such a model may include: a latitude and longitude for a center ofa circle having radius R (R may be an
`input to such an artificial neural network), and is in the output format of the distance model(s).
`
`The paragraph beginning on page 53, line 24 has been replaced with the following paragraph:
`
`Yet another type ofFOM 1224 can be based on a collection ofdispersed low power, low cost fixed
`location wireless transceivers (also denoted “location base stations 152” hereinabove) that are provided for
`detecting a target MS 140 in areas where, e.g., there is insufiicient base station 122 infrastructure coverage
`for providing a desired level of MS 140 location accuracy. For example, it may uneconomical to provide
`high traffic wireless voice coverage ofa typical wireless base station 122 in a nature preserve or at a fair
`ground that is only populated a few days out of the year. However, if such low cost location base stations
`
`152 can be directed to activate and deactivate via the direction ofa FOM 1224 of the present type, then these
`location base stations can be used to both [locationflofl a target MS 140 and also provide indications of
`where the target MS is not. For example, ifthere are location base stations 152 populating an area where the
`target MS 140 is presumed to be, then by activating these location base stations 152, evidence may be
`obtained as to whether or net the target MS is actually in the area; e.g., ifthe target MS 140 is detected by a
`location base station 152, then a conesponding locationhypothesis having a location estimate corresponding
`to the coverage area ofthe location base station may have a very high confidence value. Alternatively, ifthe
`target MS 140 is not detected by a location base station 152, then a corresponding location hypothesis having
`a location estimate conesponding to the coverage area ofthe location base station may have a very low
`confidence value. Models ofthis type are referred to hereinafier as “location base station models.”
`
`The following paragraph has been inserted immediately before the paragraph beginning on page
`54, line 3:
`'
`
`In one embodiment, such a location base station model may perform the following steps:
`a
`If an in ut is received then the tar et MS 140 is detected b a
`location base station 152 (i.e.I 3 LBS being a unit having a reduced
`power BS and a MS ).
`
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`Application Serial No. : 09/770. 838
`Document: “Resporue to Oflice Action Dated May I7. 2002
`
`([2)
`
`(c)
`
`If an input is obtained, then the output is a hypothesis data
`structure having a small area of the highest confidence.
`If no input is received from a LBS then a hypothesis having an
`area with highest negative confidence is output.
`
`The paragraph beginning on page 54, line 3 has been replaced with the following paragraph:
`
`Yet another type of FOM 1224 can be based on input from a mobile base station 148, wherein
`
`location hypotheses may be generated from target MS 140 location data received from the mobile base station
`
`In one embodiment, such a mobile base station model may provide ouQut similar to the distance FOM
`148.
`1224 described hereinabove.
`
`The paragraph beginning on page 54, line 8 and ending on page 54, line 23 has been replaced with
`the following paragraphs. Note the commencement of two new paragraphs inserted at—
`Additionally, FOMS 1224— , and at —Moreover, other FOMs—.
`
`Note that the FOM types mentioned here as well as other FOM types are discussed in detail
`
`hereinbelow. Moreover, it is [ ]important to keep in mind that a novel aspect of the present invention is the
`
`simultaneous use or activation of a potentially large number of such first order models 1224, wherein such
`
`FOMs are not limited to those described herein. Thus, the present invention provides a framework for
`
`incorporating MS location estimators to be subsequently provided as new FOMs in a straightforward manner.
`For example, a FOM 1224 based on wireless signal time delay measurements from a distributed antenna
`
`system 168 for wireless communication may be incorporated into the present invention for locating a target
`MS 140 in an enclosed area serviced by the distributed antenna system (such a FOM is more fully described
`
`in the US. Patent 6,236,365 filed July 8, 1999 which is incogporated fiilly by reference herein). Accordingly,
`by using such adistributed antenna FOM 1224 (Eig. 8( 1 ii, the present invention may determine the floor ofa
`multi-story building from which a target MS is transmitting. Thus, M55 140 can be located in three
`
`dimensions using such a distributed antenna FOM 1224.
`
`'
`
`In one embodiment, such a distributed antenna model may perform the following steps:
`(a)
`Receives input only from a distributed antenna system.
`
`(b)
`
`If an input is received, then the output includes a lat-long and height of highest
`confidence.
`
`Additionally, FOMs 1224 for detecting certain registration changes within, for example, a public
`
`switched telephone network LZAcan also be used for locating a target MS 140. For example, for some M85
`
`140 there may be an associated or dedicated device for each such MS that allows the MS to fimction as a
`
`88 of109
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`Application Serial No.: 09/770,838
`Document: "Response to Oflice Action Dated May 17. 2002
`
`cordless phone to a line based telephone network when the device detects that the MS is within signaling
`
`range. In one use of such a device (also denoted herein as a “home base station”), the device registers with a
`
`home location register of the public switched telephone network mwhen there is a status change such as
`
`from not detecting the corresponding MS to detecting the MS, or visa versa, as one skilled in the art will
`
`understand. Accordingly, by providing a FOM 1224 denoted the “Home Base Station First Order Model” in
`
`Fig. 81 1 j) that accesses the MS status in the home location register, the location engine 139 can determine
`
`whether the MS is within signaling range of the home base station or not, and generate location hypotheses
`
`accordingly.
`
`I
`
`'
`
`In one embodiment, such a home base station model may perform the following steps:
`
`(a)
`
`(b)
`
`to)
`
`(d)
`
`Receives an input only fiom the Public Telephone Switching Network.
`
`If an input is received then the target MS 140 is detected by a home base station V
`
`associated with the target MS.
`
`If an input is obtained, then the output is a hypothesis data structure having a
`small area of the highest confidence.
`
`If no input and there is a home base station then a hypothesis having a negative
`area is of highest confidence is output.
`
`Moreover, other F0M5 based on, for example, chaos theory and/or fractal theory are also within the
`scope of the present invention.
`
`The paragraph beginning on page 54, line 24 has been replaced with the following paragraph:
`
`It is important to note the following aspects of the present invention relating to FOMS 1224:
`(28.1) Each such first order model 1224 may be relatively easily incorporated into and/or removed from the
`present invention. For example, assuming that the signal processing subsystem 1220 provides uniform input
`
`interface to the FOMs, and there is a uniform FOM output interface, it is believed that a large majority (if not
`
`substantially all) viable MS location estimation strategies may be accommodated. Thus, it is straightforward
`to add or delete such FOMS 1224.
`
`The paragraph beginning on page 56, line 1 has been replaced with the following paragraph:
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`(30.2) it enhances the accuracy of an initial location hypothesis generated by [an IgFOM by using the
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`initial location hypothesis as, essentially, a query or index into the location signature data base 1320 for
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`obtaining a corresponding enhanced location hypothesis, wherein the enhanced location hypothesis has
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`Application Serial No.: 09/770.838
`Document: "Response to Oflice Action Dated May I7. 2002
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`both an adjusted target MS location area estimate and an adjusted confidence based on past performance
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`of the FOM in the location service surrounding the target MS location estimate of the initial location
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`hypothesis;
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`The paragraph beginning on page 61,‘ line 8 and ending‘on page 61, line 24 has been replaced with
`the following paragraph:
`'
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`A fourth functional group of location engine 139 modules is the control and output gating modules
`which includes the location center control subsystem 1350, and the output gateway 1356. The location
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`' control subsystem 1350 provides the highest level ofcontrol and monitoring of the data processing performed
`by the location center 142. In particular, this subsystem performs the following functions:
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`(a) controls and monitors location estimating processing for each target MS 140. Note that this
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`includes high level exception or error handling functions;
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`(b) receives and routes external information as necessary. For instance, this subsystem may
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`receive (via, e.g., the public telephone switching network 12_4and Internet [1362]5_6_8) such
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`environmental information as increased signal noise in a particular service [aflm due to
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`increase traffic, a change in weather conditions, a base station 122 (or other infi'astructure
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`provisioning), change in operation status (e.g., operational to inactive);
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`g (:2) receives and directs location processing requests from other location centers 142 (via, e.g., ~
`the InternetAg);
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`(d) performs accounting and billing procedures;
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`(e)interacts with location center operators by, for example, receiving operator commands and
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`providing output indicative of processing resources being utilized and malfunctions;
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`(t) provides access to‘output requirements for various applications requesting location
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`estimates. For example, an Internet fllocation request fi-om a tnrcking company in Los
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`Angeles to a locationcenter 142 in Denver may only want to know if a particular truck or
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`driver is within the Denver area. Alternatively, a local medical rescue unit is likely to
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`request a precise a location estimate as possible.
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`The paragraph beginning on page 61, line 25 has been replaced with the following paragraph:
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`Note that in Fig. 6, (a) - (d) above are, at least at a high level, performed by utilizing the operator
`interface 1374[ 1.
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`The paragraph beginning on page 61, line 26 has been replaced with the following paragraph:
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`Referring now to the output gateway 1356, this module routes target MS I40 location estimates to
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`the appropriate location application(s). For instance, upon receiving a location estimate from the most
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`likelihood estimator 1344, the output gateway 1356 may determine that the location estimate is for an
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`automobile being tracked by the police and therefore must be provided [must be provided ]according to [the];
`particular protocol.
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`The paragraph-beginning on page .63, line 8 has been replaced with the following paragraph:
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`Taking a_CDMA or TDMA base station network as an example, each base station (BS) 122 is
`required to emit a constant signal-strength pilot channel pseudo-noise (PN) sequence on the forward link
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`channel identified uniquely in the network by a pilot sequence offset and frequency assignment.
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`It is
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`possible to use the pilot channels of the active, candidate, neighboring and remaining sets, maintained in
`the target MS, for obtaining signal characteristic measurements (e.g. TOA and/or TDOA measurements)
`between the target MS 140 and the base stations in one or more of these sets.
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`The paragraph beginning on page 63, line 26 has been replaced with the following paragraph:
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`Accordingly, some embodiments of distance FOMs may attempt to mitigate such ambiguity or
`inaccuracies by, e.g., identifying discrepancies (or consistencies) between arrival time measurements and
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`other measurements (e.g., signal strength), these discrepancies (or consistencies) may be used to filter out
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`at least those signal measurements and/or generated location estimates that appear less accurate.
`In
`particular, such identifying [may]J filtering can be performed by, for example, an expert system residing
`in the dist