CN113572903A - Call center man-machine coupling coordination method, device, equipment and storage medium - Google Patents
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Abstract
The application discloses a call center man-machine coupling cooperation method, a call center man-machine coupling cooperation device, call center man-machine coupling cooperation equipment and a storage medium. A man-machine coupling coordination method for a call center comprises the following steps: acquiring the voice content of the intelligent agent and the client in chatting; judging whether a switching condition is met or not according to the voice content, and if so, switching the intelligent seat to an artificial seat; acquiring the voice content of the chat between the manual agent and the client; and judging whether a switching condition is met or not according to the voice content of the chatting between the artificial seat and the customer, and if so, switching the artificial seat into an intelligent seat so as to enable the intelligent seat to provide service for the customer. According to the method, the artificial seat and the intelligent seat can be deeply coupled and intelligently switched, so that the experience degree of a user is improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a man-machine coupling cooperation method, a man-machine coupling cooperation device, man-machine coupling cooperation equipment and a storage medium for a call center.
Background
The call center is provided with an artificial seat and an intelligent seat. Two forms of agents provide services to users. The two agents have no clear switching mechanism, the common method is that a screen is monitored in real time by manual customer service, random cut-in is subjectively determined, the subjectivity is high, and good service experience is difficult to provide for users.
Disclosure of Invention
The present application mainly aims to provide a call center man-machine coupling coordination method, device, equipment and storage medium to solve the above problems.
In order to achieve the above object, according to an aspect of the present application, there is provided a call center man-machine coupling coordination method, including:
acquiring the voice content of the intelligent agent and the client in chatting;
judging whether a switching condition is met or not according to the voice content, and if so, switching the intelligent seat to an artificial seat;
acquiring the voice content of the chat between the manual agent and the client;
and judging whether a switching condition is met or not according to the voice content of the chatting between the artificial seat and the customer, and if so, switching the artificial seat into an intelligent seat so as to enable the intelligent seat to provide service for the customer.
In one embodiment, before the obtaining the voice content of the intelligent agent chatting with the client, the method further comprises: and starting the artificial seat to enable the artificial seat to monitor the plurality of intelligent seats.
In one embodiment, the determining whether a switching condition is reached according to the voice content includes:
calculating a score according to a preset scoring rule according to the voice content;
determining to switch if the score reaches a predetermined score threshold.
In one embodiment, calculating the score according to a preset scoring rule according to the voice content comprises:
calculating a score according to a preset scoring rule according to the voice content, wherein the score comprises the following steps:
recognizing keywords in the voice content;
for any one keyword, the method comprises:
determining the grade of the keyword;
determining the score of the keyword according to the corresponding relation between the grade and a preset grade score;
and accumulating the scores of all the keywords to obtain a total score.
In one embodiment, if there are a plurality of intelligent agents to be switched, switching the intelligent agents to be switched to artificial agents comprises:
acquiring conversation content of each intelligent agent needing to be switched;
determining the switching priority of each intelligent agent needing to be switched according to the conversation content;
the intelligent agents needing to be switched are subjected to priority sequencing according to the sequence of the switching priority from high to low;
and sequentially switching the intelligent seats to be switched into the artificial seats according to the priority sequence.
In one embodiment, determining the switching priority of each intelligent agent needing to be switched according to the conversation content comprises the following steps:
determining the emotional degree of the client according to the conversation content of each intelligent agent;
determining the switching priority of each intelligent agent according to the emotional degree of the client; or,
determining identity attribute parameters of the user according to the conversation content;
and determining the switching priority of each intelligent seat according to the identity attribute parameters of the user.
In one embodiment, prompt information for requesting switching is sent to the display screen of the intelligent agent, so that the display screen of the intelligent agent displays a prompt box for requesting switching.
In order to achieve the above object, according to a second aspect of the present application, there is provided a call center human-machine coupling coordination apparatus; the device includes:
the acquisition module is used for acquiring the voice content of the intelligent agent and the client in chatting;
the judging module is used for judging whether a switching condition is met or not according to the voice content;
the switching module is used for switching the intelligent seat to the artificial seat if the judging module determines that the switching condition is met;
the acquisition module is also used for acquiring the voice content of the manual agent chatting with the client;
the judging module is also used for judging whether a switching condition is reached according to the voice content of the manual seat chatting with the client;
the switching module is also used for switching the artificial seat into the intelligent seat if the judging module determines that the switching condition is met, so that the intelligent seat provides service for the customer.
In order to achieve the above object, according to a third aspect of the present application, there is provided an electronic apparatus; comprising at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform any of the above steps.
According to a fourth aspect of the present application, there is provided a computer readable storage medium having one or more program instructions embodied therein for performing the steps of any of the above.
According to the technical scheme, the voice content of chatting between the intelligent seat and the client is obtained; judging whether a switching condition is met or not according to the voice content, and if so, switching the intelligent seat to an artificial seat; acquiring the voice content of the chat between the manual agent and the client; and judging whether a switching condition is met or not according to the voice content of the chatting between the artificial seat and the customer, and if so, switching the artificial seat into an intelligent seat so that the intelligent seat provides service for the customer, thereby realizing the deep coupling of man and machine and improving the switching efficiency of artificial customer service.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it. In the drawings:
fig. 1 is a schematic view of an application scenario of a call center according to an embodiment of the present application;
FIG. 2 is a flowchart of a call center human-machine coupling coordination method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an artificial agent monitoring intelligent agent according to an embodiment of the application;
FIG. 4 is a schematic structural diagram of a call center human-machine coupling coordination apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the application described herein may be used. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The call center receives telephone consultation of a user every day, referring to an application scene schematic diagram of the call center shown in fig. 1, both an artificial seat and an intelligent seat can provide services for the user. In the prior art, manual agents are switched randomly according to observed conditions, and intelligent agents are switched to manual agents for service. However, due to manual factors including carelessness, poor opening, incomplete monitoring, etc., the quality of service needs to be improved.
Based on this, the present application proposes a call center man-machine coupling cooperation method, refer to a flow chart of a call center man-machine coupling cooperation method shown in fig. 2; the method comprises the following steps:
step S102, obtaining the voice content of the intelligent agent and the client chat;
specifically, conversation content of the intelligent agent and the client in a chat is converted from voice to text in real time, the text is stored, and the text is displayed on a display screen of the manual agent. Before the step S102, the call center starts an artificial agent, so that the artificial agent monitors a plurality of intelligent agents.
Preferably, a single human agent can monitor 4-8 intelligent agents. Referring to FIG. 3, a schematic diagram of an artificial agent monitoring intelligent agent is shown; on the manual display screen, four chat dialog boxes of the intelligent agent and the customer are displayed simultaneously. The customer service can be manually switched according to the chat content displayed on the display screen, or prompt information can be automatically popped up on the screen according to the chat content to prompt the customer service to switch. The server of the call center monitors the chatting voice content of the intelligent seat and the client in real time, and particularly the voice content answered by the client. And extracting the key words in the chat content by adopting a regular expression. And classifying the key words according to the emotional color degree.
Step S104, judging whether a switching condition is reached according to the voice content, if so, executing step S106;
step S106, switching the intelligent seat to an artificial seat;
specifically, a message requesting switching may be popped up on the dialog box, so that the customer service notices the message and manually switches. Voice prompt messages can also be sent, such as 'an intelligent agent 1 requests manual switching', and customer service manually switches; and the prompt message can be automatically switched to manual work when being carried out, and the customer service can serve the customer.
Illustratively, the customer service of the human agent simultaneously monitors the chat content of 4 intelligent agent channels on a large display screen. And if the chat content between the intelligent agent 1 and the client meets the switching condition, popping up a dialog box requesting switching, requesting switching to an artificial agent, and manually switching after the customer service notices the dialog box.
Or the customer service does not need to be manually switched, but is directly switched to a manual seat, and the customer service directly serves. In this method, it is necessary to first determine whether the human seat is busy, and if the human seat is busy, the following two embodiments are adopted,
in the first mode, the switching is performed after waiting until the manual customer service is idle.
And the second mode is that the channel needing to be switched is compared with the priority of the currently busy line, if the priority of the currently busy line is higher, the channel is waited, and if the priority of the currently busy line is lower, the channel is directly switched without waiting, and the channel of the manual customer service is occupied.
Step S108, acquiring the voice content of the manual agent chatting with the client;
step S110, judging whether a switching condition is reached according to the voice content of the manual agent chatting with the client, if so, executing step S112;
and step S112, switching the artificial seat into an intelligent seat so that the intelligent seat provides service for the customer.
According to the method, the artificial seat and the intelligent seat can be deeply coupled and intelligently switched, so that the user experience is improved. When complex problems are encountered, such as emotional excitement of a user and manual detailed explanation is needed, switching to a manual seat can be performed, and the manual seat performs detailed explanation. When the chat content is not important and general program items are carried out, such as simple questions of age, occupation, name and the like, the intelligent agent is switched to.
In one implementation mode, when judging whether a switching condition is reached according to the voice content, calculating a score according to a preset scoring rule according to the voice content; determining to switch if the score reaches a predetermined score threshold.
In one embodiment, when calculating the score according to the preset scoring rule according to the voice content, the following steps are taken:
step S201, identifying keywords in the voice content;
step S202, for any keyword, determining the grade of the keyword;
specifically, the keywords are classified in advance and divided into a plurality of different category grades; specifically, the classification is performed according to the emotional intensity of each keyword. The keywords are generally classified into general keywords and strong keywords according to emotional levels. The specific classification is positive and negative.
For the positive category, it specifically includes: it is understood that, knowing that interest is very desirable to buy, the emotional level increases in turn.
For the negative category, the following is specifically included: not interested, not wanted, complained.
Illustratively, "ask how to handle" is a high level ranking and "want to know" is a low level ranking.
Step S203, determining the weight of the keyword according to the corresponding relation between the grade and a preset grade weight;
step S204, accumulating the weight values of all the keywords to obtain a total score.
Specifically, the chat content between the intelligent agent and the client is monitored and obtained in real time, and keywords are identified, wherein the keywords can be multiple or single. Determining the emotional colors of the words; determining the grade of the word according to the emotional color; determining a corresponding weight value according to the grade; and performing accumulated calculation and summation on the plurality of words according to the weight values to obtain a total score.
For example, the scoring rules are shown in table 1:
TABLE 1
In table 1, the threshold value of the total score G is 75 points, but it is needless to say that the threshold value may be flexibly set as needed, and the application is not limited.
In table 1, the speech contents according to the dialog are divided into two main categories, the first main category, and four sub-categories,
K1a score value representing a first subclass; the weight of the first subclass is 10, but may be set to other values as needed, and the present application is not limited thereto. Key to the first subclassWords include, but are not limited to: "understand, introduce, can, be".
K2 denotes the score value of the second subclass; the weight of the second subclass is 15, but of course, other values may be set as necessary, and the present application is not limited thereto. Keywords of the second subclass include, but are not limited to: "better, more satisfactory, etc.
K3A score value representing a third subclass; the weight of the third subclass is 20, but may be set to other values as needed, and the present application is not limited thereto. Keywords of the third subclass include, but are not limited to: "satisfied, good, what activities are handled now, limited lifespan of the activities".
K4 denotes the score value of the fourth subclass; the weight of the fourth subclass is 25, but it is needless to say that other values may be set as necessary, and the present application is not limited thereto. Keywords of the fourth subclass include, but are not limited to: how to handle, through what channel, where to handle.
From the first subclass to the fourth subclass, the emotional level of the customers is sequentially enhanced. The weight of the first subclass < the weight of the second subclass < the weight of the third subclass < the weight of the fourth subclass. More subclasses can be set, each subclass is provided with a corresponding weight, and the weights are sequentially increased to perform more detailed division.
In the second category, there are four sub-categories, T, based on speech content1A score value representing a fifth subclass; the weight of the fifth subclass is 10, but may be set to other values as needed, and the present application is not limited thereto. Keywords of the fifth subclass include, but are not limited to: "saying, not remembering, not knowing, unclear".
T2A score value representing a sixth subclass; the weight of the sixth subclass is 15, but of course, other values may be set as necessary, and the present application is not limited thereto. Keywords of the sixth subclass include, but are not limited to: "unknown, not understood, not spoken, not heard, not supported".
T3A score value representing a seventh subclass; right of the seventh subclassThe value is 20, but it is needless to say that other values may be set as necessary, and the present application is not limited thereto. Keywords of the seventh subclass include, but are not limited to: "unsatisfied, disliked, determined".
T4A score value representing a eighth subclass; the weight of the eighth subclass is 25, but may be set to other values as necessary, and the present application is not limited thereto. Keywords of the eighth subclass include, but are not limited to: "complain, do not want to communicate with you, find your leadership to communicate".
In the satisfaction revisit scenario, see table 2:
TABLE 2
Active marketing scenario, see table 3:
TABLE 3
In one embodiment, if there are a plurality of intelligent agents to be switched, switching the intelligent agents to be switched to artificial agents comprises:
acquiring conversation content of each intelligent agent needing to be switched;
determining the switching priority of each intelligent agent needing to be switched according to the conversation content;
the intelligent agents needing to be switched are subjected to priority sequencing according to the sequence of the switching priority from high to low;
and sequentially switching the intelligent seats to be switched into the artificial seats according to the priority sequence.
In one embodiment, when determining the switching priority of each intelligent agent needing to be switched according to the conversation content, the following two embodiments are provided, namely, determining the emotional degree of a client according to the conversation content of each intelligent agent; determining the switching priority of each intelligent agent according to the emotional degree of the client;
illustratively, if it is recognized that the emotion of client a is more excited and the emotion of client B is stable, the switching priority of the smart customer service of client a is higher than the switching priority of the smart customer service of client B.
The other is to determine the switching priority according to the identity attribute parameters of the client, specifically, to determine the identity attribute parameters of the user according to the conversation content; and determining the switching priority of each intelligent seat according to the identity attribute parameters of the user.
For example, if the user is a VIP user, a gold card member, and the consumption amount is relatively high, the priority is first.
Another method for man-machine coupling cooperation in a call center is provided, and is shown in a schematic diagram in fig. 1;
1. the system firstly calls through the manual seat, and the manual seat waits for the communication of the user;
2. the intelligent seat calls the user to initially communicate and answer questions and know the intention of the user;
3. after the user is connected, the manual seat is reminded by popping a screen, the manual seat monitors multi-channel user conversation contents in real time, and intended users are screened by affirmatively answering or calling time;
4. aiming at the noninductive intervention of the manual seats of the intended users or users with manual requirements, the communication is taken over in real time, the problem is solved, and the service quality is improved;
5. after the manual seat service is finished, the non-sensing seat can be selected to be switched to the intelligent seat, and the communication is continued or finished.
The man-machine coupling cooperation method provided by the invention enables the manual customer service to simultaneously monitor the 4-8 split-screen man-machine conversation interface. The concurrent service efficiency of manual customer service is improved from 2-3 split screens in the traditional sense to 4-8 split screens for real-time monitoring, so that the working efficiency is improved by 2-4 times. The processing capacity of man-machine cooperation is improved, the concurrent processing capacity of manual customer service is improved, and the human resource cost is saved.
In a second aspect, the present invention further provides a call center human-machine coupling cooperation apparatus, referring to the schematic structural diagram of a call center human-machine coupling cooperation apparatus shown in fig. 4; the device includes:
an obtaining module 41, configured to obtain voice content of a chat between an intelligent agent and a client;
a judging module 42, configured to judge whether a switching condition is met according to the voice content;
a switching module 43, configured to switch the intelligent agent to an artificial agent if the determining module determines that the switching condition is met;
the obtaining module 41 is further configured to obtain voice content of a chat between the human agent and the client;
the judging module 42 is further configured to judge whether a switching condition is met according to the voice content of the manual agent chatting with the customer;
the switching module 43 is further configured to switch the artificial seat to the intelligent seat if the determining module determines that the switching condition is met, so that the intelligent seat provides service for the customer.
In one embodiment, before the obtaining the voice content of the intelligent agent chatting with the client, the method further comprises: and starting the artificial seat to enable the artificial seat to monitor the plurality of intelligent seats.
In one embodiment, the determining module 42 is further configured to calculate a score according to a preset scoring rule according to the voice content;
determining to switch if the score reaches a predetermined score threshold.
In one embodiment, the determining module 42 is further configured to identify keywords in the voice content;
for any keyword, determining the grade of the keyword;
determining the score of the keyword according to the corresponding relation between the grade and a preset grade score;
and accumulating the scores of all the keywords to obtain a total score.
In one embodiment, the switching module 43 is further configured to obtain the conversation content of each intelligent agent needing to be switched;
determining the switching priority of each intelligent agent needing to be switched according to the conversation content;
the intelligent agents needing to be switched are subjected to priority sequencing according to the sequence of the switching priority from high to low;
and sequentially switching the intelligent seats to be switched into the artificial seats according to the priority sequence.
In one embodiment, the switching module 43 is further configured to determine the emotional level of the client according to the conversation content of each intelligent agent;
determining the switching priority of each intelligent agent according to the emotional degree of the client; or,
determining identity attribute parameters of the user according to the conversation content;
and determining the switching priority of each intelligent seat according to the identity attribute parameters of the user.
In one embodiment, prompt information for requesting switching is sent to the display screen of the intelligent agent, so that the display screen of the intelligent agent displays a prompt box for requesting switching.
According to a third aspect of the present application, there is provided an electronic device, see the schematic structural diagram of the electronic device shown in fig. 5; comprises at least one processor 51 and at least one memory 52; the memory 52 is used to store one or more program instructions; the processor 51 is configured to execute one or more program instructions to perform any one of the above methods.
In a fourth aspect, the present application also proposes a computer-readable storage medium having embodied therein one or more program instructions for executing the method of any one of the above.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), SLDRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A call center man-machine coupling coordination method is characterized by comprising the following steps:
acquiring the voice content of the intelligent agent and the client in chatting;
judging whether a switching condition is met or not according to the voice content, and if so, switching the intelligent seat to an artificial seat;
acquiring the voice content of the chat between the manual agent and the client;
and judging whether a switching condition is met or not according to the voice content of the chatting between the artificial seat and the customer, and if so, switching the artificial seat into an intelligent seat so as to enable the intelligent seat to provide service for the customer.
2. The call center human-machine coupling collaboration method of claim 1 further comprising, prior to said obtaining voice content of the intelligent agent and the customer chat: and starting the artificial seat to enable the artificial seat to monitor the plurality of intelligent seats.
3. The call center human-computer coupling cooperation method of claim 1, wherein judging whether a handover condition is reached according to the voice content comprises:
calculating a score according to a preset scoring rule according to the voice content;
determining to switch if the score reaches a predetermined score threshold.
4. The human-computer coupling collaboration method of claim 3, wherein calculating a score according to a preset scoring rule based on the voice content comprises:
calculating a score according to a preset scoring rule according to the voice content, wherein the score comprises the following steps:
recognizing keywords in the voice content;
for any one keyword, the method comprises:
determining the grade of the keyword;
determining the score of the keyword according to the corresponding relation between the grade and a preset grade score;
and accumulating the scores of all the keywords to obtain a total score.
5. The call center human-machine coupling cooperation method of claim 1, wherein if there are a plurality of intelligent agents to be switched, switching the intelligent agents to be switched to artificial agents comprises:
acquiring conversation content of each intelligent agent needing to be switched;
determining the switching priority of each intelligent agent needing to be switched according to the conversation content;
the intelligent agents needing to be switched are subjected to priority sequencing according to the sequence of the switching priority from high to low;
and sequentially switching the intelligent seats to be switched into the artificial seats according to the priority sequence.
6. The call center human-computer coupling cooperation method according to claim 5, wherein determining the switching priority of each intelligent agent needing to be switched according to the conversation content comprises:
determining the emotional degree of the client according to the conversation content of each intelligent agent;
determining the switching priority of each intelligent agent according to the emotional degree of the client; or,
determining identity attribute parameters of the user according to the conversation content;
and determining the switching priority of each intelligent seat according to the identity attribute parameters of the user.
7. The call center human-computer coupling cooperation method according to claim 1, wherein a prompt message requesting switching is sent to a display screen of the intelligent agent, so that the display screen of the intelligent agent displays a prompt box requesting switching.
8. A call center human-machine coupling coordination apparatus, comprising:
the acquisition module is used for acquiring the voice content of the intelligent agent and the client in chatting;
the judging module is used for judging whether a switching condition is met or not according to the voice content;
the switching module is used for switching the intelligent seat to the artificial seat if the judging module determines that the switching condition is met;
the acquisition module is also used for acquiring the voice content of the manual agent chatting with the client;
the judging module is also used for judging whether a switching condition is reached according to the voice content of the manual seat chatting with the client;
the switching module is also used for switching the artificial seat into the intelligent seat if the judging module determines that the switching condition is met, so that the intelligent seat provides service for the customer.
9. A call center human-machine coupled collaboration device, comprising: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor, configured to execute one or more program instructions to perform the method of any of claims 1-7.
10. A computer-readable storage medium having one or more program instructions embodied therein for performing the method of any one of claims 1-7.
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