EXHIBIT A 

Declaration of Richard M. Chen in Support of Microsoft Corporation’s Motion for Summary 

Judgment of Patent Ineligibility

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I 1111111111111111 11111 1111111111 111111111111111 1111111111 111111111111111111 

02) United States Patent 

Marilly et al. 

(54) METHOD OF DETERMINING SERVICE 

TRENDS 

(75) Inventors: Emmanuel Marilly, Antony (FR); 

Stephane Betge-Brezetz, Paris (FR); 

Olivier Martinut, Draveil (FR) 

(73) Assignee: Alcatel, Paris (FR) 

( *) Notice: Subject to any disclaimer, the term ofthis 

patent is extended or adjusted under 35 

U.S.C. 154(b) by 1044 days. 

(21) Appl. No.: 10/307,461 

(22) Filed: Dec. 2, 2002 

(65) Prior Publication Data 

US 2003/0108049 Al Jun. 12, 2003 

(30) Foreign Application Priority Data 

Dec. 3, 2001 (FR) ................................... 01 15584 

(51) Int. Cl. 

H04L 12166 (2006.01) 

(52) U.S. Cl. .................. 370/352; 370/395.21; 709/226 

(58) Field of Classification Search ................ 370/352, 

370/230, 235, 395.2; 709/219, 232, 224, 

709/228; 379/9.03, 112.01 

See application file for complete search history. 

(56) References Cited 

U.S. PATENT DOCUMENTS 

5,909,750 A * 6/1999 Sanders ....................... 119/11 

START 

US007366160B2 

(IO) Patent No.: US 7,366,160 B2 

(45) Date of Patent: Apr. 29, 2008 

WO 

6,246,325 Bl * 

6,272,110 Bl* 

6,721,750 Bl* 

6/2001 Chittipeddi ................. 340/540 

8/2001 Tunnicliffe et al. ......... 370/232 

4/2004 Jones et al. ................. 707 /100 

FOREIGN PATENT DOCUMENTS 

WO 0180492 A2 10/2001 

OTHER PUBLICATIONS 

T. Volochine et al, "Network Management and Traffic Analysis for 

CICNET", IEEE Network, IEEE Inc., New York, US, vol. 5, No. 5 

Sep 1, 1991, pp. 41-50, XP000248472. 

* cited by examiner 

Primary Examiner-Melur Ramakrishnaiah 

(74) Attorney, Agent, or Firm-Sughrue Mion, PLLC 

(57) ABSTRACT 

The invention proposes a method of determining communications network service trends. The invention therefore 

selects two or more parameters of a network representative 

of a network service and variable in time, measures at two 

or more times values of the network parameters, determines 

at two or more times the value of a service indicator as a 

function of said measured parameter values, and determines 

a trend of the indicator as a function of said determined 

indicator values. The method is used in particular to determine a time remaining before a service violation or the 

crossing of a threshold. 

12 Claims, 4 Drawing Sheets 

SELECT A PLURALITY OF 

NETWORK PARAMETERS 

REPRESENTATIVE OF A SERVICE 

MEASURE PARAMETERS 

AT ONETIME 

MEASURE PARAMETERS 

AT ANOTHER TIME 

DETERMINE A SERVICE TREND 

END 

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U .s. Patent Apr. 29, 2008 Sheet 1 of 4 US 7,366,160 B2 

START 

SELECT A PLURALITY OF 

NETWORK PARAMETERS 

REPRESENTATIVE OF A SERVICE 

MEASURE PARAMETERS 

AT ONE TIME 

MEASURE PARAMETERS 

AT ANOTHER TIME 

DETERMINE A SERVICE TREND 

END 

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U.S. Patent 

Pa 

V 

':1 

Apr. 29, 2008 Sheet 2 of 4 

2 

1 

US 7,366,160 B2 

3 

4 

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U.S. Patent Apr. 29, 2008 Sheet 3 of 4 US 7,366,160 B2 

START 

MEASURE NETWORK PARAMETERS 

PO, P1, P2 AT SEVERAL TIMES 

DETERMINE TRENDS TPO, TP1 

OF PARAMETERS PO, P1 

YES 

TS1 DETERMINED 

NO 

YES 

TS2 DETERMIN~D 

YES 

VIOLATION 

AT t?;, 

YES 

VIOLATION 

AT t4 

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U.S. Patent Apr. 29, 2008 Sheet 4 of 4 US 7,366,160 B2 

6 

9 

S )_, 11 

~ . ~ 

8 

~ \ 

12 

10 

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1 

METHOD OF DETERMINING SERVICE 

TRENDS 

2 

The invention relates to network monitoring methods, and 

more particularly to methods of determining service trends 5 

in a telecommunications network. 

In a further implementation the training step comprises 

inserting into the neural network a training base providing 

service trend values associated with service parameter values. 

In a still further implementation the neural network is a 

multilayer network. 

BACKGROUND OF THE INVENTION 

The method may further comprise a step of calculating a 

mathematical expectation of financial loss as a function of 

the network service trend determined. 

BMC Software's "Patrol Dashboard" product uses a net- 10 

work monitoring method which measures the bandwidth of 

In one implementation the method further comprises a 

step of determining a capacity to provide a network service 

a network at difterent times and determines a bandwidth at a given time. 

trend as a function of the measurements. 

Patent application WO 01/80492 discloses a service qualThe invention further provides a network and/or service 

management system using the above method. 

ity indicator monitoring technique for use in telecommuni- 15 

cations networks. However, monitoring is based on a subjective indicator and not on a trend calculation. 

Consequently, the variation over time of the monitored 

indicator cannot be determined. 

Thus the invention indicates a trend of the measured 

indicator, unlike the above-mentioned patent application 

WO 01/80492, for example. The trend can show whether the 

indicator is likely to cross a defined threshold or even 

determine when it will cross the threshold. In one impleIn service level agreements (SLAs) between a network 

service provider and a user, the service provider undertakes 

to provide a network service with a given failure level. The 

network service may consist in real time transfer of sound or 

pictures, for example. At present there are no tools for 

forecasting accurately the failure of a network service. 

OBJECTS AND SUMMARY OF THE 

INVENTION 

Thus there is a need for a method that solves this problem. 

The invention therefore proposes a method of determining 

communications network service trends, the method comprising the steps of: 

selecting two or more parameters of a network representative of a network service and variable in time; 

measuring and/or calculating at two or more times values 

of the network parameters; 

determining at two or more times the value of a service 

indicator as a function of said measured and/or calculated parameter values; and 

determining a trend of the indicator as a function of said 

determined indicator values. 

20 mcntation of the invention, a reliability criterion may also be 

determined for the estimated threshold crossing time. 

25 

BRIEF DESCRIPTION OF THE DRAWINGS 

Other features and advantages of the invention become 

apparent on reading the following description of implementations of the invention, given by way of example and with 

reference to the drawings, in which: 

FIG. 1 shows an algorithm of a simplified example of a 

30 method in accordance with the invention for determining 

network service trends; 

PIG. 2 shows a first implementation of a method of 

determining network service trends; 

FIG. 3 shows a simplified algorithm of a second imple35 mentation of the invention; and 

40 

FIG. 4 is a diagram of a neural network used in a third 

implementation of the invention. 

MORE DETAILED DESCRIPTION 

Tn one implementation, the method further comprises a 

subsequent step of determining as a function of the trend of 

the indicator a time of the service indicator crossing a 45 

defined threshold. 

The invention proposes a method of determining service 

trends. To this end various network parameters representative of a service are selected, after which a network service 

trend is determined as a function of those parameters. 

FIG. 1 shows a simplified algorithm of one example of a 

method of the invention for determining service trends. In a 

first step of the method, a plurality of network transmission 

parameters that are representative of a network service is 

selected. In a second step, the values of the selected paramIn another implementation the network service is selected 

from the group comprising: voice; videophone; telephone; 

multimedia; video on demand; private virtual network; real 

time data; interactive data; and data stream services. 

In a further implementation the method further comprises 

a step of determining an indicator plane by linear regression 

of the measured and/or calculated network parameters. 

In a still further implementation determining the service 

trend includes comparing parameter values with predetermined thresholds. 

Determining the service trend may further comprise: 

determining a network paran1eter trend; and 

50 eters at a given time are measured or calculated. In a third 

step, the values of the parameters at another time are 

measured or calculated. In a fourth step, a service trend at 

the later time is determined from the measured parameters. 

Various types of parameter measured as a function of 

55 different network services are described below. Examples of 

trend determination are also described below. 

A level of service indicator can be defined on the basis of 

comparing the parameter trend with a predetermined 60 

threshold. 

measured and/or calculated data. The value of the indicator 

at different times can be used to determine the service trend. 

One or more trends extrapolated from measurements of a 

parameter can also be used in the definition of the indicator. 

In one implementation the trend of the indicator is determined by a neural network. 

In another implementation the method further comprises 

a training step in which the neural network determines rules 

of association between a service trend and service parameter 

values. 

At a given time the indicator can also be defined by the 

values of different parameters at different times. 

In this way a number of network parameters, also known 

65 as service level specifications (SLSs ), can be identified for 

preferential observation in order to determine the level of 

service. These parameters include in particular: packet 

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3 4 

losses; time-delays between packets; jitter or stability; bandwidth; bandwidth stability; and the directionality of the 

communication. Parameters that have been calculated or 

extrapolated from measurements can therefore be used. For 

example, bandwidth stability changes can be calculated and 5 

used in determining the network service indicator. Parameters of this kind enable the reliability of the network service 

-continued 

BandService category width 

Request-oriented use: 2 

Client/Server, etc. 

Transfer use: 3 

Packet 

loss 

Timedelay Jitter 

to be determined, for example. Extrapolated parameters can 

also be used, for example trends of the parameters previFTP, back-ups, telesales, long 

Internet transactions, etc. 

ously identified. 10 

The service level can relate to any type of network 

service. The following services may nevertheless be highlighted: voice transfer services; telephone services; videophone services; multimedia transfer services; video on 

demand services; whiteboard application services; virtual 15 

private network services; real time data transfer services; 

and interactive data transfer services. These service can in 

particular be used for transmission services conforming to 

In a first implementation, shown in FIG. 2, a level of 

service indicator is defined by a geometrical shape, in this 

example by a plane 1. The measured parameters are represented along three axes defining a three-dimensional space, 

for example an axis x representing time, an axis y for the 

parameter values, and an axis z representing the parameters 

P0 , P1 and P2 . 

the Internet Protocol (IP). 

A plurality of network parameters can be selected to 

define the level of service as a function of the service or 

group of services concerned. The parameters can be 

weighted as a function of their importance for a given level 

of service. Weighting gives dominant importance to the most 

sensitive parameters in determining a given network service 

trend. The table below sets out one example of association 

and weighting between a network service and network 

parameters. 

The values in the table indicate the weighting or sensitivity of each parameter, from O for the minimum weighting 

to 3 for the maximum weighting. 

Packet Time- Band- Directionloss delay Jitter width ality 

Voice 3 3 2 2 

Videophone 3 2 3 2 

Telephone 1 0 0 1 

Multimedia 2 3 0 3 1.5 

Video on demand 3 0 3 1 

Whiteboard 2 0 0 2 

Virtual private network 2 

Real time data 3 0 2 2 

Interactive data 2 0 0 1 

Data streams 0 0 

Services can also be grouped into categories of services. 

The 3GPP and Tequila projects define categories of services 

and of parameters that are to be accorded preference for the 

categories concerned. The invention proposes to associate 

categories of services with network parameters. The table 

below sets out one example of the weighting of network 

parameters associated with different categories. 

Band- Packet TimeService category width loss delay Jitter 

Interactive use: 2 3 

Telnet, database access, short 

Internet transactions, Xwindow, 

etc. 

Interactive multimedia use: 2 2 3 3 

Voice or video over IP, etc. 

Non-interactive multimedia use: 2 

Remote training, broadcasting, 

etc. 

20 

An indicator plane 1 can be defined from the values of the 

parameters in this space. For example, the indicator plane 

can be obtained by linear regression of the values of the 

parameters P 0 , P 1 and P 2 in a given time interval preceding 

the time at which the trend is to be determined. For example, 

the basic principles of spatial-temporal filtering methods that 

25 transform the detection of movement in two spatial dimensions into a problem of estimating the orientation of a plane 

can be used (see Adelson E. H., Bergen J. R., "Spatiotemporal energy models for the perception of motion", J. Optical 

Society of America, vol. A2, no.2, pp. 284-299, 1985 and 30 Laplante P.A., Stoyenko A. D., "Real time imaging theory, 

technique and application", IEEE Press 1996. The plane is 

in itself a service trend indicator. 

35 

40 

45 

The parameters represented in the space can be transformed in various ways. Thus a multiplier coefficient can be 

applied to values of a parameter so that the parameter values 

have comparable amplitudes. A parameter whose trend is 

generally the opposite of that of the other parameters can be 

inverted. A weighting as defined previously can also be used 

to establish the equation of the indicator plane. For example, 

weighted regression methods with a priori weights or with 

weights that are a function of the residues can be used. 

Another trend indicator can also be extrapolated from the 

plane defined above. For example, a trend vector 4 can be 

defined having coordinates (x, y, o) in the indicator plane. 

The vector provides a simplified indicator representative of 

the service trend. Another trend indicator can be extrapolated from the plane 1. For example, this indicator can be the 

slope of a straight line segment defined by the intersection 

50 of the indicator plane and a reference plane with the equation 

Z=O. 

These trend indicators are used to define service evolution 

forecasts, for example. It is therefore possible to establish a 

time remaining up to a threshold crossing, a level of service 

55 after a predetermined time period, a service provision capacity before failure, or a mathematical expectation of loss as a 

function of the applicable service agreement. 

60 

65 

Provision can be made for generating a signal to warn of 

a predetermined trend being exceeded, for example a visual 

or an audible signal. A trend having a high slope may be 

synonymous with imminent failure of the network service, 

and this kind of signal can enable appropriate preventive 

measures to be implemented. This form of trend determination also enables the evolution requirements of the network in the short, medium and long term to be determined. 

Thus the duration of the measurements used to determine the 

trend as a function of the term to be forecast can be modified. 

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5 

An example of service level threshold crossing forecasting is given first. In FIG. 2, for example, a service threshold 

geometrical surface such as a plane 2 can be defined. A curve 

3 can then be detern1ined at the intersection between the 

indicator plane 1 and the threshold geometrical surface or 

plane 2. For parameters represented with values from Oto N 

along the z axis, a time remaining can then be determined 

from the portion of the intersection curve whose values of z 

are from Oto N. The average value of the time of the curve 

portion or one of its extreme values for z from Oto N can be 

used. The lower time value T 1 of the curve portion can thus 

be used to supply the least favorable threshold crossing 

hypothesis. 

Although both a reference geometrical element 2 and an 

indicator geometrical element 1 are described above as 

being in the form of planes, any appropriate geometrical 

shape can be used to extrapolate a trend. 

Service trend forecasting can also be used to calculate the 

mathematical expectation of financial loss. An SLA can 

specify that the network service provider must pay the user 

an indenmity. A loss at a given time can then be determined 

as a function of the terms and conditions of the SLA. 

Network service priorities can therefore be forecast as a 

function of loss expectations calculated for different SLA. 

Trend determination can also be used to determine the 

service provision capacity at a given date. If the level of 

service is forecast at a given level at a given time, a service 

provision capacity at that time can be determined. A capacity 

to enter into service level agreements before reaching a 

saturation service level can therefore be defined. 

These forms of service trend determination can also be 

used in conjunction with other methods of determining 

trends, a few examples of which are given below. 

A second implementation of the method of determining 

the service level trend, shown in FIG. 3, analyses specific 

instances of a plurality of network parameters to determine 

6 

layer 9 having 2N+l neurons and an output layer 11 having 

a number of outputs chosen hy the user are used, for 

example. 

Values of network parameters 6 and/or trends 7 of those 

5 parameters are applied to the input neurons 5. Threshold 

values 8 of the parameters SLS can also be applied to the 

input neurons 5. The values 6, 7 and 8 applied at the input 

are then processed in the hidden layer 9. The output layer 

then supplies a number of defined signals 12. The signals 12 

10 can indicate different crossings, for example a warning 

signal can therefore be provided, such as: a trend threshold 

crossing warning signal; a time remaining before a service 

violation threshold crossing warning signal; or any other 

appropriate warning signal to warn the network operator of 

15 how the service is changing. 

Use of a neural network can comprise a plurality of steps. 

In a first time period, a training base is formed, based on 

experimental results, for example. The training base comprises a multitude of output neuron states as a function of 

20 input neuron states. During a training step, the training base 

is integrated into the neural network. The training step 

consists of determining the weights of the neural connections of the neural network using training rules. Error 

gradient backpropagation rules can be used. This method is 

25 known in the art and consists in weighting the links between 

the neurons and recurrently correcting the weightings. Thus 

the result obtained with a test weighting is compared with a 

result fixed previously in the training base. The recurrent 

process is interrupted when the error in respect of the output 

30 neurons is below a predetermined threshold. Neural network 

software is available, for example the Stuttgart Neural 

Network Simulator. The neural network can also be implemented in the form of an integrated circuit. The person 

skilled in the art knows how to choose an appropriate 

35 number of hidden layers as a function of the complexity of 

the network service to be monitored. 

a trend. In this implementation, various network parameters 

are compared with predetermined threshold logic levels. 40 

Boolean criteria can be applied to the parameter values for 

this purpose. A predetermined service trend is then associated with a combination of parameter levels. This method 

can include a preliminary step of measuring network parameters and associating the parameters with network service 45 

trends. This implementation is particularly appropriate for 

detecting a trend as a function of practical situations noted 

Using a neural network is more rugged or resistant to error 

or uncertainty in respect of one or more parameters and also 

enables non-linear or cyclic behaviors to be observed. 

The implementations described by way of example herein 

are offered by way of illustrative and non-limiting example, 

and the invention is not limited to the details given here, but 

can be modified within the scope of the accompanying 

claims. 

What is claimed is: 

1. A method of determining collllllunications network 

service trends, the method comprising the steps of: 

selecting two or more parameters of a network representative of a network service and variable in time; 

measuring and/or calculating at two or more times values 

of the network parameters; 

by a network operator. In the FIG. 3 example, the trends TP 0 

and TP 1 of the parameters PO and P 1 are determined beforehand. These trends are then used to determine the network 50 

service trends TS1 or TS2 . Network parameter values are 

also used to determine the network service trend. The trends 

determined can be used for the forecasts previously 

described. Thus there can be a step of forecasting the 

network service violation time. To determine the service 55 

violation time for a particular service trend, account is taken 

of the time at which the service trend was calculated, for 

example. 

Another implementation is particularly suitable for determining network service trends in which the evolution of the 60 

measured or calculated parameters is non-linear. In this 

implementation, a neural network is used to determine the 

network service trend. FlG. 4 shows an example of a neural 

network used in the context of the invention. A multilayer 

neural network 10 is used, i.e. a network having one or more 65 

input layers 5, a hidden intermediate layer 9, and an output 

layer 11. An input layer 5 having N input neurons, a hidden 

determining at two or more times the value of a service 

indicator as a fi.mction of said measured and/or calculated parameter values; 

determining a trend of the indicator as a function of said 

determined indicator values, and 

determining as a function of the trend of the indicator a 

time of the service indicator crossing a defined threshold. 

2. The method of claim 1, wherein the network service is 

selected from the group comprising: voice; videophone; 

telephone; multimedia; video on demand; private virtual 

network; real time data; interactive data; and data stream 

services. 

3. The method of claim 1, further comprising a step of 

determining an indicator plane by linear regression of the 

measured and/or calculated network parameters. 

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7 

4. The method of claim 1, wherein the determining the 

service trend includes comparing parameter values to predetermined thresholds. 

5. The method of claim 4, wherein the determining the 

service trend further comprises: 

determining a network parameter trend, and 

comparing the parameter trend to a predetermined threshold. 

6. The method of claim 1, wherein the trend of the 10 

indicator is determined by a neural network. 

7. The method of claim 6, further comprising a training 

step in which the neural network determines rules of association between a service trend and service parameter values. 

8 

8. The method of claim 7, wherein the trmmng step 

comprises inserting into the neural network a training base 

providing service trend values associated with service 

parameter values. 

9. The method of claim 8, wherein the neural network is 

a multilayer network. 

10. The method of claim 1, further comprising a step of 

calculating a mathematical expectation of financial loss as a 

function of the network service trend determined. 

11. The method of claim L further comprising a step of 

determining a capacity to provide a network service at a 

given time. 

12. A network and/or service mmiagement system using a 

method according to claim 1. 

* * * * * 

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