Telephonic communications – Diagnostic testing – malfunction indication – or electrical... – Of centralized switching system
Reexamination Certificate
2002-03-21
2003-08-26
Nguyen, Duc (Department: 2643)
Telephonic communications
Diagnostic testing, malfunction indication, or electrical...
Of centralized switching system
C379S022030, C379S029080, C379S029090, C324S539000
Reexamination Certificate
active
06611579
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This application relates to use of a computer system for trouble diagnosis and performance forecasting, and more particularly to techniques executed in a computer system for performance fitting, diagnosing, and forecasting of telephone connections based on curve fitting of data points involving telephone cable characteristics.
2. Description of Related Art
Numerous types of procedures involve many steps which are associated with particular components of the procedures. Each component in turn may have particular characteristics which, along with other factors, dictate the performance of each component. Having information related to component performances is advantageous for making diagnoses of trouble, minimizing costs, making repairs, and suggesting new, more efficient alternatives. Sometimes, however, knowing the performance of each component is neither easy nor even desirable, even if all the performances could be known, because this amount of information would be too unwieldy. Instead, the performances of the components of a realization may be mapped to an overall performance of the realization. The overall performance gives an indication of the global success of the procedure.
A telephone cable connection, as well as other multi-component processes, is an example where performance or trouble incidence is generally recorded for the entire connection, rather than the individual components. Common analysis of trouble involves an aggregate categorization of trouble type for each connection. However, because, in general, each connection may have a different configuration, these common approaches to trouble analysis preclude a diagnosis of its components. Thus, an intuitive and anecdotal examination of these aggregate-trouble frequencies are sometimes used to subjectively assess component repair and replacement policies.
More objective approaches, such as the classical technique of using logistic regression to model cable trouble rates, do exist. However, although superficially relating holistic cable trouble and aggregate segment characteristics, these approaches do not account for the probabilistic structure relating cable trouble to component trouble, and do not allow an appropriate mathematical form for component trouble.
These traditional aggregate techniques do not objectively inform managers of the effects of their policies and tactics for individual components. The characteristics of the components cannot be related to trouble types and rates. The logistic regression solution does not appropriately exploit the relationship in trouble probabilities among the cable components. Although these techniques might be augmented by using more sophisticated nonlinear regression analysis, the high data storage and computational power requirements may be prohibitive.
SUMMARY OF THE INVENTION
According to the present invention, statistical relationships between aggregate troubles and process (e.g., cable) components are constructed in a manner that facilitates the fitting of the overall performance to field data with a relatively small number of free parameters. Such fitting can aid in the diagnosing and forecasting of performance problems that can be used to make policy decisions pertaining to what apparatus and technology to use. These decisions may then be evaluated by proper bench marking of the apparatus. The present method has sufficiently low data storage and computational demands to permit its implementation on virtually any microcomputer.
These desirable attributes of the present method are achieved by a judicious choice of a trial function, which relates component characteristics and component performances, to be used for data fitting. A good choice for such a function would lead to a number of useful functions of the characteristics instead of all the characteristics themselves. Because the number of such functions of the characteristics is considerably less than the total number of characteristics across a connection, a reduction in the data required to perform the curve fitting may be possible.
Specifically, a method executed in a computer system for diagnosing, and predicting trouble causes in a process involving realizations of performance is presented. The ith realization has n, steps and the performance is a function of a c-component vector of characteristics, the performance and vector having a value of &thgr;
i
[j, x
i
(j)] and x
i
(j), respectively, at the jth step of the ith realization. (The component of a vector should not be confused with the component of a connection; the former refers to each of the numbers in an ordered set defining a column or row vector, the latter is associated with each of the nj steps, of connection i.) The method comprises a) expressing an overall performance, &psgr;
i
as a function, &phgr;
i
, of the performance at each of the n
i
steps, &psgr;
i
=&PHgr;
i
{&thgr;
i
−[1, x
i
(
1
)], &thgr;
i
−
[2, x
i
(
2
)], . . . , &thgr;
i
[n
i
, x
i
(n
i
)]}; b) choosing a function, f, of a d-component vector of parameters, &bgr;, and the c-component vector of characteristics with which to approximate the performances, &thgr;
i
[j, x
i
(j)]≈f[&bgr;, x
i
(j)]. The function f is chosen such that the overall performance, &psgr;
i
, can be written as a function, &ggr;
i
, of the d-component vector of parameters and r c-component vectors, S
i,1
, s
i,2
, . . . , and s
i,r
, that depend on the n
i
, characteristic vectors x
i
(
1
), x
i
(
2
), . . . , and x
i
(n
i
),
ψ
i
=
φ
i
{
f
[
β
,
x
i
(
1
)
]
,
f
[
β
,
x
i
(
2
)
]
,
…
,
f
[
β
,
x
i
(
n
i
)
]
}
≡
γ
1
{
β
,
s
i
,
1
[
x
i
(
1
)
,
x
i
(
2
)
,
…
,
x
i
(
n
i
)
]
,
s
i
,
2
[
x
i
(
1
)
,
x
i
(
2
)
,
…
,
x
i
(
n
i
)
]
,
…
,
s
i
,
r
[
x
i
(
1
)
,
x
i
(
2
)
,
…
,
x
i
(
n
i
)
]
}
where r<n
i
; c) finding a best vector of parameters that results in a best fit of the function &ggr;,(&bgr;, s
i,1
, s
i,2
, . . . s
i,r
) to data points corresponding to the overall performance as a function of the r c-component vectors. s
i,1
, s
i,2
, and s
i,r
.
In a specific embodiment, the method further comprises predicting the overall performance of a realization from r c-component vectors that correspond to s
i,1
, s
i,2
, . . . , and s
i,r
by utilizing the best vector of parameters.
In another embodiment, the step of choosing a function, f, of a d-component vector of parameters, &bgr;, and the c-component vector of characteristics, includes choosing a function, f, of a c-component vector of parameters, &bgr;, and the c-component vector of characteristics.
In a specific embodiment, the method includes expressing the overall performance, &psgr;
i
, as &psgr;
i
=&thgr;
i
[
1
, x
i
(
1
)]&thgr;
i
[
2
, x
i
(
2
)]. . . &thgr;
i
[n
i
, x
i
(n
i
)], and choosing the function f=exp[&bgr;·x
i
(j)], where &bgr;·x
i
(j) denotes an inner product of &bgr; and x
i
(j).
In an embodiment of the invention, fitting the function &ggr;
i
(&bgr;, s
i,1
, s
i,2
, . . . , s
i,r
) proceeds by utilizing a microprocessor, and a regression algorithm, which may employ the Levenberg-Marquardt method, to estimate the vector of parameters, &bgr;, that approximately maximizes agreement between the function &ggr;
i
(&bgr;, s
i,1
, s
i,2
, . . . , s
i,r
) and the data points. The Levenberg-Marquardt method may utilize a figure-of-merit function that measures this agreement.
In an other embodiment of the present invention, a method executed in a computer system for predicting trouble in a telephone connection is presented comprising a) characterizing a jth component of a sequence of n
i
components of an ith telephone connection by a c-component vector of characteristics x
i
(
Betz, III Andrew Louis
Drew James Howard
Nguyen Duc
Suchyta Leonard Charles
Verizon Laboratories Inc.
Weixel James K.
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