U.S. patent application number 12/813516 was filed with the patent office on 2011-06-16 for method and system for prophesying behavior of online gamer, and computer program product thereof.
This patent application is currently assigned to NATIONAL TAIWAN UNIVERSITY. Invention is credited to Sheng-Wei Chen, Polly Huang, Pin-Yun Tarng.
Application Number | 20110143829 12/813516 |
Document ID | / |
Family ID | 44143560 |
Filed Date | 2011-06-16 |
United States Patent
Application |
20110143829 |
Kind Code |
A1 |
Huang; Polly ; et
al. |
June 16, 2011 |
METHOD AND SYSTEM FOR PROPHESYING BEHAVIOR OF ONLINE GAMER, AND
COMPUTER PROGRAM PRODUCT THEREOF
Abstract
A method and a system for prophesying a behavior of an online
gamer, and a computer program product thereof are provided. In the
present method, a gamer descriptor of a specific gamer of an online
game accumulated before a time point is obtained firstly. Then, the
gamer descriptor is divided into a plurality of sub-traces
according to the time. Thereafter, at least one feature is derived
from each of the sub-traces, and all features derived from the
sub-traces are used for determining a possibility that the specific
gamer quits the online game within a specific time period in the
future.
Inventors: |
Huang; Polly; (Taipei City,
TW) ; Chen; Sheng-Wei; (Taipei, TW) ; Tarng;
Pin-Yun; (Kaohsiung City, TW) |
Assignee: |
NATIONAL TAIWAN UNIVERSITY
Taipei
TW
ACADEMIA SINICA
Taipei
TW
|
Family ID: |
44143560 |
Appl. No.: |
12/813516 |
Filed: |
June 11, 2010 |
Current U.S.
Class: |
463/1 ; 463/42;
463/43 |
Current CPC
Class: |
A63F 13/79 20140902;
A63F 2300/535 20130101; A63F 2300/5593 20130101; A63F 13/67
20140902; A63F 2300/6027 20130101; A63F 2300/5546 20130101 |
Class at
Publication: |
463/1 ; 463/42;
463/43 |
International
Class: |
A63F 9/24 20060101
A63F009/24 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 11, 2009 |
TW |
98142520 |
Claims
1. A method for prophesying behavior of an online gamer, suitable
for prophesying a behavior of a specific gamer of an online game at
a time point, comprising: obtaining a gamer descriptor of the
specific gamer accumulated before the time point; dividing the
gamer descriptor into a plurality of sub-traces according to time;
respectively deriving at least one feature value from each of the
sub-traces; and determining a possibility that the specific gamer
quits the online game within a specific time period in the future
according to the at least one feature value derived from each of
the sub-traces.
2. The method for prophesying the behavior of the online gamer as
claimed in claim 1, wherein the gamer descriptor at least comprises
one of a game related record of the specific gamer and a life
related record of the specific gamer.
3. The method for prophesying the behavior of the online gamer as
claimed in claim 2, wherein the game related record at least
comprises one of an online/offline record, a character profile
information, and a character behavior information.
4. The method for prophesying the behavior of the online gamer as
claimed in claim 3, wherein when the gamer descriptor comprises the
online/offline record, the step of respectively deriving the at
least one feature value from each of the sub-traces comprises:
respectively deducing at least one of an average daily playtime, a
playing density, an average session time point, an average of each
session time, and a variation of daily playtime corresponding to
each of the sub-traces according to each of the sub-traces, so as
to serve as the at least one feature value.
5. The method for prophesying the behavior of the online gamer as
claimed in claim 2, wherein the life related record at least
comprises one of a gamer profile information and a gamer behavior
information.
6. The method for prophesying the behavior of the online gamer as
claimed in claim 1, wherein the step of determining the possibility
that the specific gamer quits the online game within the specific
time period in the future according to the at least one feature
value derived from each of the sub-traces comprises: using a
machine learning mechanism to process the at least one feature
value derived from each of the sub-traces, so as to calculate the
possibility that the specific gamer quits the online game within
the specific time period in the future.
7. The method for prophesying the behavior of the online gamer as
claimed in claim 6, wherein the machine learning mechanism
comprises one of a supervised learning classification and a
non-supervised learning classification.
8. The method for prophesying the behavior of the online gamer as
claimed in claim 7, wherein the supervised learning classification
comprises a support vector machine (SVM).
9. A system for prophesying a behavior of an online gamer,
comprising: an input/output interface; a storage unit, configured
to store gamer descriptors of a plurality of gamers of an online
game; a feature deriving unit, coupled to the input/output
interface and the storage unit, wherein after the input/output
interface obtains a specific gamer and a time point, the feature
deriving unit is configured to obtain the gamer descriptor of the
specific gamer that is accumulated before the time point from the
storage unit, divide the gamer descriptor into a plurality of
sub-traces according to time, and respectively derive at least one
feature value from each of the sub-traces; and a prophesying unit,
coupled to the input/output interface and the feature deriving
unit, the prophesying unit is configured to determine a possibility
that the specific gamer quits the online game within a specific
time period in the future according to the at least one feature
value derived from each of the sub-traces, and output the
possibility through the input/output interface.
10. The system for prophesying the behavior of the online gamer as
claimed in claim 9, wherein the gamer descriptor at least comprises
one of a game related record of the specific gamer and a life
related record of the specific gamer.
11. The system for prophesying the behavior of the online gamer as
claimed in claim 10, wherein the game related record at least
comprises one of an online/offline record, a character profile
information, and a character behavior information.
12. The system for prophesying the behavior of the online gamer as
claimed in claim 11, wherein when the gamer descriptor comprises
the online/offline record, the feature deriving unit respectively
deduces at least one of an average daily playtime, a playing
density, an average session time point, an average of each session
time, and a variation of daily playtime corresponding to each of
the sub-traces according to each of the sub-traces, so as to serve
as the at least one feature value.
13. The system for prophesying the behavior of the online gamer as
claimed in claim 10, wherein the life related record at least
comprises one of a gamer profile information and a gamer behavior
information.
14. The system for prophesying the behavior of the online gamer as
claimed in claim 9, wherein the prophesying unit uses a machine
learning mechanism to process the at least one feature value
derived from each of the sub-traces, so as to calculate the
possibility that the specific gamer quits the online game within
the specific time period in the future.
15. The system for prophesying the behavior of the online gamer as
claimed in claim 14, wherein the machine learning mechanism
comprises one of a supervised learning classification and a
non-supervised learning classification.
16. The system for prophesying the behavior of the online gamer as
claimed in claim 15, wherein the supervised learning classification
comprises a support vector machine (SVM).
17. A computer program product comprising a plurality of program
instructions, the program instructions being loaded to a computer
system to execute following steps: obtaining a gamer descriptor of
a specific gamer of an online game that is accumulated before a
time point; dividing the gamer descriptor into a plurality of
sub-traces according to time; respectively deriving at least one
feature value from each of the sub-traces; and determining a
possibility that the specific gamer quits the online game within a
specific time period in the future according to the at least one
feature value derived from each of the sub-traces.
18. The computer program product as claimed in claim 17, wherein
the gamer descriptor at least comprises one of a game related
record of the specific gamer and a life related record of the
specific gamer.
19. The computer program product as claimed in claim 18, wherein
the game related record at least comprises one of an online/offline
record, a character profile information, and a character behavior
information.
20. The computer program product as claimed in claim 19, wherein
the program instructions respectively deduce at least one of an
average daily playtime, a playing density, an average session time
point, an average of each session time, and a variation of daily
playtime corresponding to each of the sub-traces according to each
of the sub-traces, so as to serve as the at least one feature value
if the gamer descriptor comprises the online/offline record.
21. The computer program product as claimed in claim 18, wherein
the life related record at least comprises one of a gamer profile
information and a gamer behavior information.
22. The computer program product as claimed in claim 17, wherein
the program instructions use a machine learning mechanism to
process the at least one feature value derived from each of the
sub-traces, so as to calculate the possibility that the specific
gamer quits the online game within the specific time period in the
future.
23. The computer program product as claimed in claim 22, wherein
the machine learning mechanism comprises one of a supervised
learning classification and a non-supervised learning
classification.
24. The computer program product as claimed in claim 23, wherein
the supervised learning classification comprises a support vector
machine (SVM).
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the priority benefit of Taiwan
application serial no. 98142520, filed on Dec. 11, 2009. The
entirety of the above-mentioned patent application is hereby
incorporated by reference herein and made a part of
specification.
BACKGROUND OF THE INVENTION
[0002] 1. Field of the Invention
[0003] The present invention relates to a method for prophesying a
behavior. More particularly, the present invention relates to a
method and a system for prophesying a behavior of an online gamer,
and a computer program product thereof.
[0004] 2. Description of Related Art
[0005] With widespread of the Internet, people are gradually used
to obtain information from the Internet or contact others through
the Internet, and more and more people take Internet surfing as a
major leisure activity, which leads to a growth of online game
industry. Different to a conventional personal computer (PC) game,
an online game can achieve interactivities of the gamers, and the
gamers can setup their own guilds to perform group activities and
compete with each other, so that a variation degree and a sense of
freshness of the game are greatly increased. According to
statistics, in recent years, a market of the online game is still
increasingly growing.
[0006] Generally, a fee-charging model of the online game includes
a monthly fee model, an hour-based fee model, and a virtual
treasure sale model. Regardless of the fee-charging model, the
amount of players directly influences a profit of a game company,
especially those hard-core players who will spend a lot of time and
money on the game can greatly influence the profit of the game
company. Therefore, how to trace a perception and a loyalty of each
player to the game is undoubtedly an important issue for the game
company who wants to enhance the output value.
[0007] However, present studies of player behaviors of the online
game mostly take the behaviors of a group of players as an
analysing unit. For example, the studies are mainly focused on
number variations of the players in a same server, or the number of
the players influenced by stability of the network, etc. These
studies are mostly used to deduce increase or decrease of the
number of the players in the future, and cannot be used to prophesy
the players who will quit the game for a long time, so that the
information provided to the online game company is
insufficient.
SUMMARY OF THE INVENTION
[0008] Accordingly, the present invention is directed to a method
for prophesying a behavior of an online gamer, by which one gamer
is taken as a unit to determine whether the gamer will quit the
online game for a long time within a period time in the future.
[0009] The present invention is directed to a system for
prophesying a behavior of an online gamer, which may prophesy a
behavior that a single gamer is going to quit the online game.
[0010] The present invention is directed to a computer program
product comprising a plurality of program instructions, which is
loaded to a computer system to prophesy a behavior of an online
gamer.
[0011] The present invention provides a method for prophesying a
behavior of an online gamer, which is used for prophesying a
behavior of a specific gamer of an online game at a certain time
point. In the present method, a gamer descriptor of the specific
gamer accumulated before the time point is obtained. The gamer
descriptor is divided into a plurality of sub-traces according to
the time. At least one feature value is derived from each of the
sub-traces, and a possibility that the specific gamer quits the
online game within a specific time period in the future is
determined according to the at least one feature value derived from
each of the sub-traces.
[0012] In an embodiment of the present invention, the gamer
descriptor at least includes one of a game related record of the
specific gamer and a life related record of the specific gamer.
[0013] In an embodiment of the present invention, the game related
record at least includes one of an online/offline record, a
character profile information, and a character behavior
information.
[0014] In an embodiment of the present invention, when the gamer
descriptor includes the online/offline record, the step of deriving
the at least one feature value from each of the sub-traces includes
respectively deducing at least one of an average daily playtime, a
playing density, an average session time point, an average of each
session time, and a variation of daily playtime corresponding to
each of the sub-traces according to each of the sub-traces, so as
to serve as the at least one feature value.
[0015] In an embodiment of the present invention, the life related
record at least includes one of a gamer profile information and a
gamer behavior information.
[0016] In an embodiment of the present invention, the step of
determining the possibility that the specific gamer quits the
online game within the specific time period in the future according
to the at least one feature value derived from each of the
sub-traces includes using a machine learning mechanism to process
the feature values derived from the sub-traces, so as to calculate
the possibility that the specific gamer quits the online game
within the specific time period in the future. Wherein, the machine
learning mechanism may be a supervised learning classification or a
non-supervised learning classification, and the supervised learning
classification includes a support vector machine (SVM).
[0017] The present invention provides a system for prophesying a
behavior of an online gamer. The system includes an input/output
interface, a storage unit, a feature deriving unit and a
prophesying unit. The storage unit is used for storing gamer
descriptors of a plurality of gamers of an online game. The feature
deriving unit is coupled to the input/output interface and the
storage unit, and after the input/output interface obtains a
specific gamer and a time point, the feature deriving unit is used
for obtaining the gamer descriptor of the specific gamer that is
accumulated before the time point from the storage unit, dividing
the gamer descriptor into a plurality of sub-traces according to
time, and respectively deriving at least one feature value from
each of the sub-traces. The prophesying unit is coupled to the
input/output interface and the feature deriving unit, and is used
for determining a possibility that the specific gamer quits the
online game within a specific time period in the future according
to the at least one feature value derived from each of the
sub-traces, and outputting the possibility through the input/output
interface.
[0018] In an embodiment of the present invention, the gamer
descriptor at least includes one of a game related record of the
specific gamer and a life related record of the specific gamer.
[0019] In an embodiment of the present invention, the game related
record at least includes one of an online/offline record, a
character profile information, and a character behavior
information.
[0020] In an embodiment of the present invention, when the gamer
descriptor includes the online/offline record, the feature deriving
unit respectively deduces at least one of a average daily playtime,
a playing density, an average session time point, an average of
each session time, and a variation of daily playtime corresponding
to each of the sub-traces according to each of the sub-traces, so
as to serve as the at least one feature value.
[0021] In an embodiment of the present invention, the life related
record at least includes one of a gamer profile information and a
gamer behavior information.
[0022] In an embodiment of the present invention, the prophesying
unit uses a machine learning mechanism to process the at least one
feature value derived from each of the sub-traces, so as to
calculate the possibility that the specific gamer quits the online
game within the specific time period in the future. Wherein, the
machine learning mechanism includes a supervised learning
classification or a non-supervised learning classification, and the
supervised learning classification includes a SVM.
[0023] The present invention provides a computer program product
including a plurality of program instructions, after the program
instructions are loaded to a computer system, the aforementioned
method for prophesying a behavior of an online gamer is
executed.
[0024] According to the above descriptions, in the present
invention, a gamer descriptor of a single specific gamer is
obtained, and a plurality of feature values are derived from the
gamer descriptor, so as to determine a possibility that the
specific gamer quits the online game for a long time within a
specific time period in the future according to the above feature
values. Therefore, before the gamer completely lose an enthusiasm
to the game, it has a chance to improve a playability of the game
to enhance a willingness of the gamer to continually play the
game.
[0025] In order to make the aforementioned and other features and
advantages of the present invention comprehensible, several
exemplary embodiments accompanied with figures are described in
detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] The accompanying drawings are included to provide a further
understanding of the invention, and are incorporated in and
constitute a part of this specification. The drawings illustrate
embodiments of the invention and, together with the description,
serve to explain the principles of the invention.
[0027] FIG. 1 is a block diagram illustrating a system for
prophesying a behavior of an online gamer according to an
embodiment of the present invention.
[0028] FIG. 2 is a flowchart illustrating a method for prophesying
a behavior of an online gamer according to an embodiment of the
present invention.
[0029] FIG. 3 is a flowchart illustrating a method for prophesying
a behavior of an online gamer according to another embodiment of
the present invention.
DESCRIPTION OF THE EMBODIMENTS
[0030] Since a perception of a gamer for an online game may be
reflected by a related record of the gamer, if a behavior of the
gamer can be deduced by analysing the related record, a game
content can be improved when a satisfactory of the gamer is
decreased, so as to maintain an enthusiasm of the gamer to the
game. Therefore, a method and a system for prophesying a behavior
of an online gamer, and a computer program product thereof are
provided according to the above concept. To fully convey the
concept of the present invention, embodiments are provided below
for describing the present invention in detail.
[0031] FIG. 1 is a block diagram illustrating a system for
prophesying a behavior of an online gamer according to an
embodiment of the present invention. Referring to FIG. 1, the
system 100 for prophesying a behavior of an online gamer includes
an input/output interface 110, a storage unit 120, a feature
deriving unit 130 and a prophesying unit 140. The system 100 is
used for prophesying a behavior of a single gamer of an online
game, especially for prophesying a behavior of the gamer intended
to quit the online game.
[0032] In the present embodiment, the input/output interface 110
is, for example, a combination of a keyboard and a screen, or a
touch screen, etc., which is used for receiving an input of a
prophesying target and outputting a prophesying result. The storage
unit 120 is, for example, any storage unit such as a memory, a
memory card or a hard disk, etc., which is not limited by the
present invention. In the present embodiment, the storage unit 120
stores a continuously accumulated gamer descriptor of each of the
gamers of the online game.
[0033] The feature deriving unit 130 is coupled to the input/output
interface 110 and the storage unit 120. After the input/output
interface 110 obtains a specific gamer serving as the prophesying
target, the feature deriving unit 130 obtains the related gamer
descriptor from the storage unit 120, and derives a plurality of
feature values from the gamer descriptor.
[0034] The prophesying unit 140 is coupled to the input/output
interface 110 and the feature deriving unit 130, which is used for
performing a prophesying operation according to the feature values
derived by the feature deriving unit 130, and displaying a
prophesying result on the input/output interface 110.
[0035] Another embodiment is provided below to further describe an
operation flow of the system 100 for prophesying the behavior of
the online gamer. FIG. 2 is a flowchart illustrating a method for
prophesying a behavior of an online gamer according to an
embodiment of the present invention.
[0036] Referring to FIG. 1 and FIG. 2, after a specific gamer
serving as the prophesying target and a time point corresponding to
the prophesying operation are obtained through the input/output
interface 110, in step 210, the feature deriving unit 130 obtains a
gamer descriptor of the specific gamer that is accumulated before
the time point from the storage unit 120. In the present
embodiment, the gamer descriptor of the specific gamer at least
includes one of a game related record and a life related record of
the specific gamer, though the present invention is not limited
thereto. In detail, the game related record at least includes one
of an online/offline record, a character profile information, and a
character behavior information. The life related record at least
includes one of a gamer profile information and a gamer behavior
information.
[0037] Next, in step 220, the feature deriving unit 130 divides the
gamer descriptor into a plurality of sub-traces according to time.
For example, the feature deriving unit 130 may equally divide the
gamer descriptor into K sub-traces according to the time, wherein K
may be 10 or other positive integers, which is not limited by the
present invention.
[0038] Next, in step 230, the feature deriving unit 130
respectively derives one or a plurality of feature values from each
of the sub-traces. It should be noticed that types of the feature
values derived by the feature deriving unit 130 may be different as
contents of the gamer descriptors are different. For example, when
the gamer descriptor includes the online/offline record of the
specific gamer, the feature deriving unit 130 may deduce at least
one of a average daily playtime, a playing density, an average
session time point, an average of each session time, and a
variation of daily playtime according to the content of each of the
sub-traces, so as to serve as the feature values corresponding to
each of the sub-traces.
[0039] When the gamer descriptor includes the character profile
information, the feature deriving unit 130 may take information
such as a race, a level, a level-up speed or equipments, etc. of
the character played by the specific gamer in the online game as
the feature values. When the gamer descriptor includes the
character behavior information, the feature deriving unit 130 may
obtain the feature values according to a consumption status of the
character played by the specific gamer in the game, or interactive
relationships with other characters in the game. Moreover, when the
gamer descriptor includes the gamer profile information, the
feature deriving unit 130 takes the information (for example, sex,
education, occupation, residence, or income, etc.) of the gamer as
the feature values. When the gamer descriptor includes the gamer
behavior information, the feature deriving unit 130 may analyses
actual behaviors (for example, a payment approach or whether there
is a delay in payment, etc.) of the gamer in the real life to
obtain the feature values.
[0040] It should be noticed that the gamer descriptor and the types
of the feature values are only examples of the present invention,
and when the system 100 performs the prophesying, determination may
be performed not only based on the aforementioned gamer descriptor
and the feature values. In detail, any static or dynamic
information related to the gamer may serve as the gamer descriptor,
and the feature deriving unit 130 may derive different feature
values according to different gamer descriptors.
[0041] Finally, in step 240, the prophesying unit 140 determines a
possibility that the specific gamer quits the online game within a
specific time period in the future (for example, within 10 days in
the future, or with in one month in the future, etc.) according to
the feature values derived from each of the sub-traces. In the
present embodiment, the prophesying unit 140 uses a machine
learning mechanism to process the feature values derived from the
sub-traces, so as to calculate the possibility that the specific
gamer quits the online game within the specific time period in the
future. Wherein, the machine learning mechanism may be a supervised
learning classification or a non-supervised learning
classification, and the supervised learning classification is, for
example, a support vector machine (SVM), which is used for
classifying the feature values to obtain the prophesying
result.
[0042] In the present embodiment, the prophesying unit 140
determines whether the specific gamer will quit the online game
within a specific time period in the future, or calculates a
possibility that the specific gamer quits the online game within
the specific time period in the future. The prophesying result is
outputted through the input/output interface 110.
[0043] Accordingly, an online game company may use the system 100
for prophesying the behavior of the online gamer to trace each of
the gamers of the online game, so that at any time point, the
system 100 may prophesy whether a certain gamer will quit the
online game within a specific time period in the future according
to the gamer descriptor accumulated before the time point. Once it
is prophesied that the gamer will quit the online game for a long
time, an immediate investigation may be performed to positively
improve a content of the online game, so as to reduce a decreasing
rate of the gamers. Moreover, the online game company may further
analyse the gamers intended to quit the game according to the
prophesying results of the system 100, so as to determine whether
these gamers are all connected to a specific server, and if yes, it
may be deduced that a connection problem of the server that cause
the gamers losing their enthusiasm is probably occurred. Therefore,
the server is tested and inspected to enhance hardware or network
equipments thereof.
[0044] FIG. 3 is a flowchart illustrating a method for prophesying
a behavior of an online gamer according to another embodiment of
the present invention. In the following embodiment, the gamer
descriptor includes the online/offline record of the specific
gamer. Referring to FIG. 3, when a specific gamer of the online
game is prophesied at any time point, in step 310, the
online/offline record of the specific gamer that is accumulated
before the time point is obtained, wherein the online/offline
record includes a daily online/offline time record of the specific
gamer from a time point that the specific gamer start to play the
online game to the time point when the prophesying is
performed.
[0045] In step 320, the online/offline record of the specific gamer
is divided (for example, equally divided) into a plurality of
sub-traces according to the time. For example, if the specific
gamer has played the online game for 100 days when the prophesying
is performed, according to the step 320, a first to a 33rd days, a
34th to a 66th days, and a 67th to a 100th days are respectively
divided into a sub-trace.
[0046] Next, in step 330, an average daily playtime and a playing
density are respectively derived from each of the sub-traces to
serve as the feature values of each sub-trace. In detail, the
average daily playtime represents a ratio between a total session
time of the specific gamer and days covered by the sub-trace. The
playing density represents a ratio between days that the specific
gamer plays the online game and days covered by the sub-trace.
[0047] After the average daily playtime and the playing density
corresponding to each of the sub-traces are calculated, in a final
step 340, a possibility that the specific gamer quits the online
game within a specific time period in the future is determined
according to the average daily playtime and the playing density
corresponding to each of the sub-traces. In the present embodiment,
all of the calculated average daily playtimes and game playing
densities are, for example, input to the trained classifier for a
classification processing, so as to prophesy whether the specific
gamer will quit the online game within a specific time period in
the future.
[0048] In the present embodiment, to increase a prophesying
accuracy, the other gamer descriptors (for example, the gamer
profile information, the gamer behavior information, the character
profile information, or the character behavior information etc.) of
the specific gamer may also be added to derive more feature values.
Then, all of the derived feature values are input to the
prophesying unit to obtain the prophesying result.
[0049] The present invention provides a computer program product,
which is used for executing the method for prophesying a behavior
of an online gamer. The computer program product is basically
formed by a plurality of program instructions (for example, setting
program instruction or deployment program instruction, etc.). After
these program instructions are loaded to the computer system for
execution, the aforementioned steps for prophesying a behavior of
an online gamer are then implemented, so that the computer system
may prophesy a possibility that a gamer quits the online game
within a specific time period in the future by analysing a gamer
descriptor of the single gamer.
[0050] In summary, in the present invention, the method and the
system for prophesying a behavior of an online gamer, and the
computer program product thereof are used for prophesying a
behavior of a single gamer. Therefore, when the gamers intend to
quit the online game, the online game company is capable of finding
a reason why the gamers lose enthusiasm to the game through a
questionnaire survey, so as to suitably modify the game. Moreover,
regarding all of the gamers intended to quit the game, the online
game company is capable of analysing session status of these
gamers, so as to determine whether the relevant server operation is
abnormal, and accordingly enhance the related software and hardware
to achieve the purpose of consolidating the number of the
gamers.
[0051] It will be apparent to those skilled in the art that various
modifications and variations may be made to the structure of the
present invention without departing from the scope or spirit of the
invention. In view of the foregoing, it is intended that the
present invention cover modifications and variations of this
invention provided they fall within the scope of the following
claims and their equivalents.
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