A METHOD AND SYSTEM FOR SHARING KNOWLEDGE
Field of the Invention
The present invention relates to the field of knowledge bases. More
particularly, the invention relates to a method and system for capturing
and sharing knowledge between the qualified personnel of a company and
users or between users and qualified personnel associated with different
companies.
Background of the Invention
Troubleshooting and problem resolution pose a similar challenge:
compressing large amounts of documentation, experience and information
into an organized, coherent repository of knowledge and expertise. The
same challenge exists in other types,of knowledge-intensive processes, such
as design, advice, consulting, guidance, and decision-making. In the
following, the term 'problem resolution1 is mean to include all such
processes.
In troubleshooting and problem resolution, if expertise is not shared, the
same problem that an expert solves immediately might ultimately have to
be solved by a less-experienced person, after much wasted effort, time and
resources.
Since experts are difficult to find, the goal has always been to let the
expert's capabilities be shared by the less experienced engineers through
methods such as formal and on-the-job training, mentor sponsorships, etc.,
and involving the expert in the production of the troubleshooting manuals.
All of these methods place a demand on the . expert's time. Furthermore,
not always the most outstanding engineer is also the best instructor of new
service persons or any other qualified personnel.
A more direct approach involves equipping each qualified engineer with
"packaged knowledge" in the form of problem resolution software. By
replicating the knowledge, it is possible to supply each engineer with the
organization's combined troubleshooting expertise.
With this approach, the question becomes how to find the knowledge, and
how to introduce it into the problem resolution software. After all, the
essential problem is that a few available experts are overloaded by the
need to support the whole organization. It would be impractical to attempt
to solve the dilemma by asking the experts to spend months computerizing
their knowledge, also because problems are solved as they present
themselves, and may be difficult to remember and characterize at a
distance in time.
Two prevalent methods of eliciting and organizing diagnostics expertise are
the model-based method - which asks the expert to specify the components,
functions, possible symptoms and available repair for the unit being
serviced, and the case-based method, which relies on collecting cases
(wherein each case includes the original symptom, the fact-finding or
repair actions undertaken by the service engineer, and the problem
resolution). By looking closely into the requirements of problem resolution
software, we find that both methods play important roles. We also find that
both methods share the problem discussed here: getting knowledge into the
expert system.
Just as no human becomes an expert effortlessly, neither can a software
expert system acquire all knowledge immediately. Information has to be
input before we can get anything out. Therefore, a knowledge-based system
might be a suitable solution.
The Prior Art
Available commercial problem-solving software for the above-mentioned
purposes include:
- The EXP Company offers a knowledge-based service to the public on
www.exp.com Web site, on a number of subjects. The experts usually
are paid by the users, and are also rated according to two categories:
the number of questions they have been asked, and the users'
recommendations.
- The ClickSoftware company offers ClickFix, a Web-based solution that
supports the complete call life cycle. It enable the customers of a firm to
perform simple troubleshooting procedures themselves, 24 hours a day.
The expert technical team provides' service for "real" service calls, and
then providing them with easy access to the database and case history
file. It guides engineers and technicians through each and every repair
and installation with ease and by providing intuitive diagrams and
instructions.
- http:/ / www.clicksoftware.com/
- http://www.serviceware.com/
- http://www.acknosoft.com/
- http://www.primus.com/
- http://www.keen.com/
- and many other.
Combining model-based and case-based knowledge
Fig. 5 schematically illustrates the overall approach, according to the prior
art. It combines the pre-packaged Universal Domain Knowledge (511) with
Initial Service Knowledge (512) for the specific equipment type and with
accumulated case data (513). The emphasis on "Initial Service Knowledge"
is central to this approach: It specifies tapping whatever knowledge is
easily available for automatic import, and gaining additional knowledge
(by implementing algorithms 517: Analytic 515 and Analogical 516) from
analyzing (514) case data, using the initial knowledge to structure and
refine the cases (avoiding the laborious filtering and restructuring required
when relying only on case-based reasoning).
References:
• Ben-Bassat M., Berriaminy L, Joseph D., Different Approaches to Fault
Isolation Support Software, Proceedings of the 1998 IEEE International
Workshop on System Test and Diagnosis.
• Ben-Bassat M., Beniaminy I., Can Model-Based and Case-Based Expert
Systems Operate Together? in "System Test and Diagnosis: Recent
Trends and Case Studies", edited by John W. Sheppard and Wilham R.
Simpson.
The problem resolution model: central knowledge repository
Fig. 6 schematically illustrates a possible method of defining the different
parts of the problem resolution model and their interaction, according to
the prior art.
The Element Hierarchy 600 contains:
- Subsystem A (601), which comprises Board Al (602) and Board A2
(603);
- Subsystem B (604), which comprises Board Bl (605) and Board B2
(606);
The Symptom/Test Hierarchy 650 contains:
- Symptoml (651) and Symptom2 (652);
- Test Accl (656) and Test Acc2 (657);
- Test Diagl (658);
- Test Diag2 (659);
- Symptom Section (653);
- Acceptance Section (654);
- Diagnostic Section (655);
One part of this model may be read as follows: "Board A2 (of subsystem A,
603) may fail in three different malfunction modes (MF). If mode MF1
occurs, there is a high (H) probability that symptom 1 (651) will be present,
a very high (VH) probability that test Accl (656) will fail, and a medium
(M) probability that test Diagl (658) will fail". Such information is readily
available from several sources - input from system and service engineers,
as well as automatic analysis of failure patterns in recorded cases. Once in
the problem resolution knowledge base, all sources contribute to the model
stored in the central knowledge repository.
By using the terms 'technical personnel', 'qualified personnel' or 'qualified
crew', it is meant to include any personnel whose roles require acquiring,
preserving and delivering knowledge relevant to the organization's tasks.
By using the term 'technical documents', it is meant to include any
document containing such knowledge.
All the methods described above have not yet provided satisfactory
solutions to the problem of capturing and sharing knowledge of technical
issues among the technical personnel of an organization, and between it
and the public.
It is an object of the present invention to provide a method and system for
sharing knowledge among the personnel of a company.
It is another object of the present invention to provide a method, and
system for sharing knowledge between the personnel of a company and the
users.
It is a further object of the present invention to provide a method and
system for contribution of knowledge by the technical personnel of a
company.
It is a still further object of the present invention to provide a method and
system for contribution of knowledge by the customers of a company.
It is a still further object of the invention to provide a method and system
for sharing knowledge inside a qualified crew regardless of where they
work (large corporation, small business, independent).
It is a still further object of the invention to provide a method and system
for sharing knowledge between qualified crew and users.
It is a still further object of the invention to provide a method and system
for sharing knowledge among users of equipment.
It is a still further object of the invention to provide an incentive for
qualified personnel to share their knowledge with the company and its
personnel and customers, and to enrich the knowledge base.
It is a still further object of the invention to provide a handling case(s) in
which technical personnel from one company can share their knowledge
with skilled technicians from another company.
It is a still further object of the invention to provide a handling case(s)
wherein technical personnel from one company can share their knowledge
with users associated with another company.
Other objects and advantages of the invention will become apparent as the
description proceeds.
Summary of the Invention
In one aspect, the invention is directed to a method for sharing knowledge
among a plurality of individuals, comprising:
a) providing a dynamically updated knowledge base that can be accessed
over a network;
b)- allowing an individual encountering a problem the solution of which is
not known, to query the knowledge base to determine whether the
problem and its solution are found in it;
c) if the solution to the problem is not available in the knowledge base,
allowing the individual to prepare a query including the details of the
problem;
d) broadcasting the query described in step (c) over the network;
e) allowing the experts who "listen in" the network to answer the query by
filling-in their reply to the query form, and if desired, also by additional
person-to-person communication;
f) conveying the answer to the query via the network to the individual
who queried the system;
g) optionally, once the problem has been solved, or when the suggestions
have produced no positive result, reporting by the individual the
outcome of the query to the system, thereby to complete the dialogue
between the individual and the system;
h) adding to the knowledge base, and making part of it, the dialogue, the
problem, and information on the suggestion that led to the solution of
the problem;
i) reusing the knowledge contributed in steps (a) to (g) when similar
problems subsequently arise, so that they are completely solved at step
(b); and
j) keeping track of the knowledge contributed by each expert, and
subsequent cases solved by such knowledge, as a tool for performance
monitoring and incentive generation.
Optionally, the individual querying the system is a service person.
Optionally, the individual querying the system is a user.
Optionally, broadcasting is effected through a communication channel
selected from a computer network, a telephone network, e-mail, and
satellite.
Optionally, the query is broadcast solely, or preferentially to those experts
that, according to the knowledge base, are knowledgeable in the relevant
field.
Optionally, the query is broadcast to human experts, as described in claim
1 step (c), at the same time that the individual is interacting with the
knowledge base as described in claim 1 step (b).
Optionally, different priorities are assigned to the queries.
Optionally, the priority is a function of the problem severity and/or the
service guaranteed to the individual and/or the type of equipment
regarding the query.
Optionally, the process of adding the problem information, dialogue and
solution to the knowledge base, as described in claim 1, step (h), goes'
through a quality control process wherein each such new information is
tagged for review by appropriately- authorized experts.
Optionally, the experts are notified of new and as-yet-unreviewed additions
to the knowledge base, and are asked to approve, reject or edit and then
approve each new addition of the information.
Optionally, if more than one expert answers the query, the system relays
one or more of the answers, according to a predetermined criterion.
Optionally, the criterion is the seniority of the expert.
Optionally, the criterion is a "first to answer" criterion.
Optionally, the criterion is the level of expertise of the answering expert in
the specific problem context, as defined by management and or as updated
by reviewing the performance of the expert in an earlier, similar context,
and/or other strategies defined by management.
Optionally, if more than one expert and/or knowledge base mechanism
answers the query, then all the answers are ranked according to the source
rehability and to the ranking reported by the source itself, and relays the
top-ranked answers.
The invention is also directed to a method for sharing knowledge via a
knowledge base, among expert(s), company personnel and user(s), wherein
the expert(s), the personnel and the user(s) are equipped with a
computer/terminal connected to a network, the knowledge base being
accessible via the network, comprising:
a) launching a query from the user(s) or member(s) or the personnel to the
knowledge base;
b) receiving answer(s) from the knowledge base as a result of the query;
c) directing the query to the expert(s), in case of unsatisfactory answer(s);
and
d) registering the answer (s) in the knowledge base.
Optionally, the personnel is in charge of the maintenance / service /
technical issues / help desk of the company.
Optionally, the expert(s) are members of the personnel.
Optionally, the information stored in the knowledge base is controlled,
maintained, edited and removed from the knowledge base by the experts(s)
and/or by the personnel and/or by the user(s).
Optionally, the query is composed of keyword(s) and optionally Boolean
relations between the keywords.
Optionally, the query is free-text sentence(s).
Optionally, the knowledge base is model-based oriented (MBR).
Optionally, the knowledge base is case-based oriented (CBR).
The method may further comprise creating incentive and rewarding
experts for contributing knowledge into the knowledge base.
Optionally, the contribution is based on an off-line basis and/or based on a
rating policy of the the experts' contributions.
Optionally, the rating policy is based on users' assessments of knowledge
provided by the expert, and/or the number of problems solved by the
expert, and/or the responsiveness of the expert, and/or number of items the
expert contributed to the knowledge base, and/or number of times that
knowledge contributed by the expert into the knowledge base was used in
successful problem resolution, and/or time spent on-line handling user
problems.
Optionally, the rating is used for financial bonus and/or. for publishing
periodic rating and "top contributors", and/or for accomplishing points and
"virtual money" for use in frequent-flier programs and other purchasing
plans, and/or for being used as a factor in periodic employee reviews.
Optionally, the suggestions include list(s) of required resources.
Optionally, the list(s) contains tools and/or parts and/or service(s).
Optionally, the list(s) is linked to an e-commerce system and/or an
inventory system and/or job management system.
Optionally, the individual may order item(s) from the list(s).
Optionally, the knowledge is presented by multimedia means.
Optionally, the interaction with the individual is carried out by multimedia
means.
Optionally, the individual may start a collaborative Web-session.
Optionally, the Web-session involves shared browsing and/or shared
apphcation(s) and/or shared whiteboard and/or conversations via
voice-over-IP and/or voice conversation(s).
Optionally, the database information is edited by direct authoring or
import of CBR and MBR content, and/or by direct authoring or import of
troubleshooting guides, and/or by importing of raw data, and/or by direct
dialog where either expert or software system highlight a specific step in
the problem resolution process and inspect the reasons for the step,
resulting in future knowledge base refinement.
Optionally, the raw data comprises cases and or hst(s) of symptoms and/or
list(s) of replaceable part(s) and/or list(s) of relevant observation(s) and
measurement(s) and/or list(s) of repair action(s) and/or engineering data
and/or directed dialog wherein either expert or software system highlight a
specific step in the problem resolution process and inspect the reasons for
the step, resulting in future knowledge base refinement.
Optionally, the engineering data comprise block diagram(s) and/or
simulation data and/or reliability estimates.
Optionally, the expert(s) are notified of new query(s)
Optionally, the notification is initiated due to a comparison between the
query(s) to stored notification profile(s).
Optionally, the notification consists record(s), each of which specifying
notification circumstances specified by field(s) of knowledge and/or
geographical region and or asker's profile and/or the time passed since the
query was first reported and/or expert's reliability and expertise ranking.
Optionally, the record(s) describes the notification method to be used if the
condition(s) specified by the record are met.
Optionally, the method is carried out by a telephone means and/or by
e-mail and or by instant messaging and/or by fax and/or by pager.
Optionally, the notification profile(s) are set and edited by the expert(s)
and/or by software means.
Optionally, the knowledge base is integrated with any combination of other
types of knowledge bases and knowledge retrieval.
Optionally, the types of knowledge base is fault trees and/or hst(s) of
common problem (s) and/or solution(s) to the problem (s) and/or
troubleshooting procedure(s) and/or categorized or non-categorized
searchable technical documents regarding problems.
The knowledge base may comprise information related to commerce, and/or
technical support, and/or entertainment, and or travel-related subjects,
and or software support, and/or finance consultation, and/or insurance
consultation, and/or medical assistance, and/or telecommunication
services.
In another aspect, the invention is directed to a system for sharing
knowledge among expert(s), and or personnel of a company and/or user(s),
wherein the expert(s), the personnel and the user(s) are equipped with a
communication terminal connected to a network, comprising:
- a knowledge base system for storing and retrieving knowledge;
- a software component for interacting with the user(s);
- a software component for presenting query results; and
- a software component for controlling, maintaining, editing and
removing information from the knowledge base system.
The system may further comprise a software component for setting up and
editing notification profile(s).
Optionally, the network is a Wide Area Network (WAN).
Optionally, the network is a Local Area Network (LAN).
Optionally, the network is the organization's Internet.
Optionally, the network is the organization's Intranet.
Optionally, wherein the network is a mobile and/or wireless network.
Optionally, the network is a one-directional or two-directional paging.
Optionally, the network is a circuit-switched or packet-switched.
Brief Description of the Drawings
The above and other characteristics and advantages of the invention will
be better understood through the following illustrative and non-limitative
detailed description of preferred embodiments thereof, with reference to
the appended drawings, wherein:
- Fig. 1 schematically illustrates a knowledge base system, according to
the prior art;
- Fig. 2 schematically illustrates a system for sharing knowledge,
according to a preferred embodiment of the invention;
- Fig. 3 schematically illustrates a Web site, according to a preferred
embodiment of the invention:
- Fig. 4 schematically illustrates the activities of a user on a Web site
that comprises a knowledge base, according to a preferred embodiment
of the invention;
- Fig. 5 schematically illustrates the overall approach, according to the
prior art;
- Fig. 6 schematically illustrates a possible method of defining the
different parts of the problem resolution model and their interaction,
according to the prior art; and
- Fig. 7 to 14 illustrates a scenario regarding the implementation of the
invention over the Internet, according to a preferred embodiment of the
' invention.
Detailed Description of Preferred Embodiments
The term "Knowledge Base System" (KBS) usually applies to a database,
which contains textual information, generally with a common denominator.
The information entities are categorized by keywords. The platform of the
stored information may be text-content documents, structured files,
multimedia files, etc.
In order to retrieve such information, knowledge bases usually contain a
subsystem that enables finding and retrieving information by the
keywords that categorize the stored information.
Fig. 1 schematically illustrates a knowledge base system, according to the
prior art. It contains text-content files 1, structured files 2, multimedia
files 3, and a table 4, which connects the mentioned components.
Text-content files are usually documents that have been produced by a
word processor, HTML files, etc.
Structured files are usually composed of records with the same structure.
Due to their nature, they usually contain information such as part-lists,
erro -lists, etc.
Multimedia files usually contain visual and/or audio information, such as
drawings, illustrations, etc.
The keywords of a text-content file can be obtained by an automatic scan of
the file, and the same is true of structured files. However, in multimedia
files, the keywords are often provided manually.
In order to link the stored information and keywords describing it,
knowledge bases use a table 4. Such a table contains file descriptors (name
and location of a file) and a list of keywords which characterizes the files.
For instance, if a file contains a description of how to handle a certain
defect in an electronic system, the related keywords should describe the
defect, and preferably contain the keyword "repair", as well.
In order to locate the files containing a list of keywords, a software
component should scan the table 4. In matter of fact, the structure of the
system is usually more complicated than described above, due to the fact
that search speed is crucial for such a system, as is known to any database
expert. The above-simplified description was therefore made only for the
sake of brevity.
By employing a Knowledge Base System (KBS) in the company's service
layout or help desk, some benefits may be achieved. The service / help desk
personnel can add relevant information to the knowledge base. Such
information may describe flaws and their correction, instructions, etc. In
this way, information regarding product maintenance is shared among the
service / help desk personnel.
Additional benefits can be gained by making the knowledge base accessible
to the users. From a potential buyer's point of view, one of the issues
effecting the purchase decision is the service accessibility. If a product is
not supported by a repair service, the potential buyer may prefer a
competitor's product. However, there are users that prefer to perform
maintenance by themselves, usually in order to reduce costs. Such users
may prefer the product of a company supporting such repairs, and a
knowledge base open to the public, at least to some extent, may be helpful
in convincing potential customers of the advantages of a product.
The KBS should of course be accessible by the personnel of the company
and, as explained above, in some cases by the users. The users may try to
find a solution to problems that arise with the products, at least up to a
certain minimal level, before appealing to the company's qualified
personnel.
Fig. 2 schematically illustrates a system for sharing knowledge, according
to a preferred embodiment of the invention. Such a system comprises:
- A Knowledge Base System (KBS) 10, which contains information
relevant to the maintenance of products or technical issues belonging to
the enterprise that owns the KBS;
- Users 20, the customers of the company;
- Service personnel 30, employed by this company, or connected by other
agreement with this company;
- Experts 40, employed by this company, or connected to the company by
another agreement. Said experts may also be employed by another
company, self-employed, etc.
- A software component 11, for managing the interaction and directing
queries and responses between users 20, personnel 30 and experts 40.
According to a preferred embodiment of the invention, the software
component (hereinafter briefly called "MEM" - Multi- Engine Manager)
is a part of the KBS. According to another embodiment of the invention,
the software component is a part of the Web-site mechanism. According
to another embodiment of the invention, the software component is a
client-software, which resides on the users', the personnel and the
experts' computer device. According to another embodiment of the
invention, the ' MEM is a combination of KBS and/or Web-site
mechanism and/or client software.
The connection between the users 20, service personnel 30, experts 40 and
the KBS 10, is carried out using the network 50, which can be, for instance,
the Internet or other WAN. If the use of the system is limited to the
company personnel, the network can be an Intranet of the company.
The basic need of such systems is to provide advice to the user of such a
system (qualified persons, as well as customers) as fast as possible, and
sufficiently accurate to allow for timely problem resolution. This calls for
an effective combination of all the diagnostic mechanisms described above,
along with rapid creation of a quality knowledge base.
The advice to the qualified person will be a combination of the outputs of
all the following mechanisms:
• From a human expert
• From CBR (Case-Base Reasoning)
• From MBR (Model-Based Reasoning)
• Additional mechanisms (e.g. fault charts)
It is, therefore, usually suggested to utilize all three advice-providers'
together, using a knowledge base that might have been at least partially
created before but is also built on line.
Obviously, the quality of each 'member's' advice will vary based on the
status of the knowledge base. If there is no model yet, the chat mechanism
will be the only possible venue. The chat mechanism will help to create a
case repository, which will be used by the CBR. At the same time, the
model will be gradually created. The CBR will probably be the second
component to start providing effective advice, while the model-based
component will be the third.
Each component not only enhances the knowledge base to be subsequently
used by the others - the diagnostic process itself is dynamically influenced.
This means that advice provided by a human expert in the chat layer
actually adds an element to the current goal set (the list of probable faulty
elements or fault causes) to be immediately used by the MBR component
for its next step analysis. This same information is also used by the CBR
component to refine the retrieval matching cases. Moreover, the advice
provided by the CBR layer also adds elements to the goal set that is used
by the MBR. Hence, there is cross-pollination between the layers affecting
the diagnostics in real time.
All three answers will then be combined into a single set of
recommendations to the user, with the weight allocated to each layer's
answer reflecting its quality at that point.
Most knowledge bases used for problem resolution are more sophisticated,
and designed to carry out a question-and-answer session with the user. In
such systems, a case is defined as a collection of reported symptoms,
suggested actions (together with their results) and, if available, the actual
cause of the fault. A case might be generated as a result of the discussion
taking place in the chat room (see above), records stored as part of the
business's service database (for example, customer complaints and problem
resolutions), or specifically created as input for the CBR mechanism.
When a qualified person reports a problem, the application searches the
stored cases repository to find the closest matching case and provides a
suggestion accordingly. The search might result in a 'closest matching
case', one that yields a probable cause for the fault, thereby concluding the
diagnostic process. Or, it finds a similar case from which to retrieve the
next test to be suggested to the qualified person. In the latter case, the
process continues until a probable fault cause can be suggested. This
search is done according to proximity algorithms (such as Nearest
Neighbor), which can match cases even when the input to be matched is
incomplete.
In model-based systems, the knowledge base consists of an actual
representation (topological, functional, etc.) of the system to be diagnosed.
This presentation or model describes what functional parts the system
consists of, which observations can be made, which possible actions taken,
how these relate to the functional entities, and so on.
The model is created using a generic terminology and a set of entities that
allow modeling of practically any type of system to be determined by the
same generic algorithms.
The model allows the computer to 'understand' how the modeled system
behaves (or fails) under various conditions. When the qualified person
reports an observed symptom, the algorithm can use its 'understanding' of
the system to suggest probable causes for the symptom, and the
appropriate actions to be taken to better isolate the exact cause. The more
complete the model, the better the 'understanding' of the system, which
leads to more accurate and efficient diagnostics. Unlike CBR, which
matches the input symptom to its stored set of previously encountered
cases, the model-based approach can handle faults that have never
previously occurred but that can be explained by a good understanding of
the system's behavior. Thus, instead of using previous experience (which
might not always be available), the diagnostics use actual knowledge of the
diagnosed system to tackle ad-hoc occurrences, even extremely rare ones.
The process of the invention operates as follows:
Step 1: A user (customer or a qualified person), encountering a problem
the solution of which he does not know, queries the knowledge base to
determine whether the problem and its solution are known. Preparing the
query is software-assisted by automatically displaying the most relevant
terms (such as equipment types, equipment functions and more), known
problems, etc., while the user is constructing the query (by picking from
lists and/or typing free text).
For example:
• Software support:
- How can I print in columns?
- Program X crashes each time I run program Y. What shall I do?
- What add-on products exist for product Z?
• Finance:
- Are there any limitations on foreign nationals in investment of type
X?
- How long does it take to get a mortgage?
• Insurance (including medical):
- What earthquake cover do you offer?
- How do I report a theft?
• Telecommunications services:
- What weekend rates do you offer?
- How do I get e-mail on my cell phone?
- How do I make a conference call?
- I cannot hear any ringing when people call my cell phone - what do I
do?
• Internet service providers:
- How do I get higher-speed connections?
- Why don't I get my e-mail?
• Foods:
- How many calories are in product X?
- Please suggest recipes for cooking with Y.
• Consumer product:
- Where can I buy X?
- What accessories do you recommend for Y?
- How do I remove food stains" (question to either washing-machine
vendor, detergent vendor or fashion vendor)
This list can be extended to just about any type of commercial enterprise -
manufacturing as well as services - whose activities include answering
questions asked by internal staff, customers, partners, suppliers,
entertainment, travel, etc.
Some of these questions are not specific enough and would require some
dialog before giving the requested answer. For example, the question "How
long does it take to get a mortgage?" need to ask how much is needed, what
securities the asker has.
Step 2: The software component, which manages the interaction, 'queries'
the KBS and returns suggestions to the user. This is done in any suitable
format that is compatible with the knowledge base and its input.
Step 3: The query prepared in Step 1 is broadcast over the network 50. In
the context of the present invention broadcasting can be effected by any
suitable means, e.g., through a computer network, a telephone network,
e-mail, satellite, cellular, wireless, pager, etc. The query may be broadcast
over the network (to secure human advice) if:
(a) MEM finds that the KBS suggestions are below a specified quality (as
ranked by the KBS); and/or
(b) At least one expert has set a "notification profile" so that it is triggered
by the interaction thus far gathered in the case; and/or
(c) The user who originated the current issue explicitly requests direct
expert advice (other causes may also apply).
The system may broadcast the query solely, or preferentially, to those
experts that, according to the knowledge base, are highly experienced in
the specific relevant field. For instance, if a company services aircrafts and
coffee machines, the coffee machines experts will preferentially not receive
queries having to do with aircraft engines.
Step 4: The experts who "listen in" the network (as said, by any suitable
communication means) may answer the query by filling-in their reply to
the query form, and if they, desire, also by additional person-to-person
communication (e.g., via a voice conversation on Voice-Over-IP, landline
phone, or cellular phone). If more than one expert answers the query, the
system may decide to take only one of the answers (whether according to
seniority of the technician, or on a "first to answer" basis, or on the basis of
the technician's expertise and past performance on similar problems, or
other management-defined criteria), or to combine some or all answers to
the query, ranked according to the aforementioned criteria.
Step 5: The answer to the query (which may be combined from several
sources such as the CBR or the MBR component of the KBS and the
human expert contributions) is then conveyed (again, via the network) to
the qualified person (or user), who tries to fix the problem according to the
suggestions received.
Step 6: Once the problem has been solved, or when the suggestions have
produced no positive result, the qualified person (or user) reports the
outcome to the system, again, in the same way, and this completes the
dialogue between the qualified person (or user) and the system. The
dialogue, the problem, and' information on the suggestion that led to the
solution of the problem, are then added to the knowledge base and become
part of it, so that the next query on the same problem will automatically
produce the suggestions as the first approach to a solution.
Steps 2 to 5 may be iterated any number of times, with the result of
qualified person (or user) actions performed in step 5 used as input for the
next iteration.
In another embodiment of the invention, the information gathered in the
case (e.g., problem, dialogue, and solution, if any) is added to the KBS but
flagged as not-yet-approved. Such flagged information goes through a
quality control process wherein each such new information is tagged for
review by appropriately- authorized experts. These experts are notified of
new and as-yet-unreviewed additions to the knowledge base, and are asked
to approve, reject, or edit (and then approve) each new addition. This
process may use e-mail, database management, version control, and/or
workflow principles. The system may be set to never retrieve
as-yet-unapproved knowledge except when the approved knowledge has no
answer.
In another embodiment of the invention, Step 6 is optional: even without
it, information from the completed case is added to the KBS. Of course,
performing Step 6 makes it possible to contribute more information to the
KBS. For this reason, we also have the option of completing the details
after the fact, by an expert (who holds the required authorization), and not
just by the initiator, regardless of whether this expert participated in the
actual interaction.
It should be appreciated that while the actual type of the knowledge base,
the queries and input formats, and the processing of the information, are
all important parameters in the functioning of the system, the invention is
by no means limited to any type of knowledge base, form of query, dialogue
or information processing, and any suitable system can be used for these
purposes, while retaining all the advantages of the invention.
According to one embodiment of the invention, the system does not
broadcast each query to all known experts, but selects which experts
should be notified of each query based on the level of match between the
query parameters (e.g. problem type, equipment type, urgency, etc.) and
the "Notification Profile" listed for each expert. Optionally, if too many
experts are matched, the system uses a randomization mechanism to select
a smaller number for notification. A notification profile may comprise the
following:
• Fields of knowledge - described as a combination of types of
equipment, types of problems (e.g. operation, installation, etc. with
hierarchical structure - e.g. operation can be divided into "washing" and
"drying" for a combined washer-drier), subsystems (e.g. in a photocopier
- electrical, optical, mechanical), and a collection of relevant keywords;
• Geographical region;
• Profile of client (e.g. residential, business, terms of Service Level
Agreement);
• Duration of time since first reported (e.g. a senior engineer might set
his notification profile to "every problem reported more than 4 hours
ago and not yet solved");
• Expert's reliability and expertise ranking in the specific problem
context (as defined by management and/or as updated by reviewing
performance of that expert in earlier similar contexts).
From this it is clear that the profile for one expert can include any number
of records. For each such record, the engineer can specify how to notify
him, notification methods including at least business phone, cell phone,
home phone, fax, e-mail, pager, and instant-messaging (as in ICQ - if he is
online, he will see a pop-up message). Obviously, the profile should include
relevant information (e.g. phone numbers, ICQ user-id, e-mail). For
example, a profile might include the following entries:
• "Notify me of any photocopier problem if I'm on-line";
• "Notify me by pager and by message to my home fax if there's a
page-stuck problem on a model X76 copier"; and
• "Notify me by cell phone of any problem reported by users whose
service-level-agreement specifies all problems solved within 4 hours or i less".
Notification-Profiles may be set by the expert and/or by the expert's
manager. Additionally, the MEM may track the expert's performance and
periodically compare it to stated fields of knowledge defined for that expert.
This comparison may discover the need to add, delete or modify these
definitions. These changes may be configured to be automatically
performed, or forwarded to the expert (or his manager) for approval.
Example
A scenario regarding the implementation, of the invention over the
Internet, according to a preferred embodiment of the invention, may look
as follows:
A technician repairing VCR (video cassette recorder) at laboratory. Could
not solve problem.
In the following discussion, the software implementing the invention will
be designated as ClickFix or CF for short.
1. Technician — Joe Brown - opens CF (ClickFix) page on his browser.
2. Technician clicks "New problem".
3. Technician identifies brand and model of video - by typing it in or by
selecting from list boxes or tree.
4. CF asks technician to describe the problem. At this point the screen
looks conceptually as shown in Fig. 7.
As shown in the figure, CF displays hsts (optionally hierarchical) of known
symptoms. Each of these symptoms may have documentation (text, video,
audio - e.g. what kind of noise is heard while rewinding) associated with it,
which can be accessed by clicking on a "doc available" icon next to the
symptom name.
5. Technician starts typing and has reached "cannot load cassette". While
he's typing, CF automatically retrieves and displays best matches, as
shown in Fig. 8.
6. Technician may submit typed symptom or click on any named symptom.
In this case, technician clicks on 'VCR rejects cassette".
7. CF accesses its knowledge base and retrieves several possible actions
that may be relevant. It creates a display for the technician, as shown in
Fig. 9.
The suggestions all came from CF's knowledge base and are ranked by
likelihood and by time it takes to perform the action (marked with "!") or
answer the question (marked with "?").
Again, each such item (including possible answers - it will be shown later
that some questions are more complex than simple Yes/No answers) may
be hnked to documentation, and Joe can access any such information by
clicking on the appropriate icon (not shown) next to the item.
8. Joe answers all the questions, as shown in Fig. 10.
9. CF changes the display, as shown in Fig. 11.
Of course Joe is not forced to perform each action and answer each
question. He has the option (not shown) to mark" questions and actions as
"Skip", thereby telling CF he rejects these suggestions.
10. CF examines its list of expert profiles and finds that Mary White, Dave
Black and Alice Green are currently on-line and are experts on this brand
of VCR. Both receive high-priority notifications on their screen that a new
problem requires their attention. Both may be handling several calls at the
same time. Mary reacts first by clicking on the notification. This causes CF
to lower the priority of the notification displayed on Alice's and Dave's
screen. They may still access the problem and participate in the resolution
process, but this won't be shown in this scenario.
11. At this point, Mary's screen may look as in Fig. 12:
As in can be seen, Mary is handling two problems: Joe's problem from this
scenario and another one - Karen's DVD problem. She is now viewing
details of Joe's problem. She has already made some suggestions to Karen.
CF will notify Mary when Karen acts on Mary's suggestions and reports
some results. In the mean time, Mary is free to work on the VCR problem.
We can also see Mary's queue, with a couple of other problems that CF has
determined she might be able to help with. The priority assigned by CF
depends on many parameters, including relevance of problem to Mary's
expertise; time that the asker is already waiting; number of other experts
who have relevant knowledge (if Mary is the only one, the priority will be
higher than if there are dozens); importance of customer who brought in
the VCR (e.g. existence and terms of service agreement); and more.
12. Mary composes one or more additional suggestions by using an
interface that in this example, is as shown in Fig. 13.
If Mary wants to suggest a question, she will need to specify whether it's a
Yes/No question or a question with several different possible answers -
single-choice such as "What is the voltage at TP5 - less than IV or 1-5V or
over 5V?" or multiple-choice such as "Does examining wheel W56 show
abrasion and or off-center movement and/or lack of oil?".
As in step 5, if Mary types text, CF will keep updating the hst of matches
that best fit the text typed so far. In this case, CF will also display the list
of possible answers defined for each retrieved question.
Mary may browse through available on-line documentation to find
appropriate web pages (or video, audio etc.) explaining her suggestions (e.g.
instructions on how to perform the suggested action), and link such pages
to her suggestions. Joe will see an "have doc" icon next to such items and
will be able to access the documentation marked by Mary. Mary and Joe
can also start a collaborative Web session, possibly involving shared
browsing, shared applications, shared whiteboard, and Voice-Over-IP or
"traditional" conversations.
Mary may indicate what resources are required for performing her
suggestions (e.g. voltmeter or the actual spare part for replacement action).
13. As Mary contributes her suggestions, they appear one-by-one on Joe's
screen. At this point, Joe and Mary (and any other expert — or maybe
technician — who has also accessed this problem) are actually collaborating
on the same view of the problem.
The suggestions are ranked by CF according to data supplied by Mary as
well as by data already stored in the knowledge base (e.g. time required for
each suggestion may already be known, as well as cost for wheel W56).
If Mary has contributed three suggestions. Joe's screen now looks as shown
in Fig. 14.
If a specific resource (e.g. Wheel W56) is required, Joe will see a "resource
icon" (not shown here) next to the suggestion requiring this resource. Joe
can click on that icon and either (a) state that he has it; (b) state that he
does not have it and does not expect to have it (in which case CF will
remove the suggestion, and others requiring the same resource, to the list
of skipped suggestions mentioned earlier, which may or may not be
displayed according to user preferences); or (c) request data on how to
obtain it (which may lead Joe to company-internal inventory management
screens or to e-commerce sites outside his company).
15. At this point, Mary's screen shows the suggestions she has made, and
shows the status of Joe's problem as "awaiting answer". She is now free to
work on another problem in her active-list or from her queue.
16. Once Joe performs one or more of the suggestions, this will be indicated
to Mary (and the status will change to "awaiting expert"), who will return
to the screen displaying details of Joe's problem and examine the new
"evidence".
17. CF's knowledge-base software continues monitoring the interaction,
and can add its own suggestions triggered by Mary's questions and Joe's
answers - it is quite possible that after a few more facts are known, CF
will find relevant knowledge in its knowledge base.
18. The process continues until Joe performs some action that solves the
problem. CF may be configured to ask Joe to verify the solution (guiding
him through final testing before notifying the customer that the VCR has
been fixed) and verify which action or actions were relevant to the solution.
Once this is finished, CF removes the problem from everybody's screens.
19. CF will "remember" the interaction and will reuse it or parts of it for
other problems, if they bear some similarities to this problem.
In some scenarios, each user may simultaneously play both roles, that of
the party seeking to solve some problems, and the role of expert on other
problems. For such cases, the invention would be embodied in a user
interface that combines elements of both types of user interfaces shown in
Fig. 7 to Fig. 14.
This example has of course been provided for the purpose of illustration
and is, therefore, simplified in nature.
In order to encourage usage of the knowledge base, a remuneration policy
can be implemented. Moreover, a rating policy can be used as well, so that
the experts will be motivated to do their best not only for financial gain,
but to improve their position within the organization. Implementation of
such a policy may comprise the following:
- Rating the experts according to the users' recommendations;
- Rating the experts according to the number of problems solved by them;
- Publishing the index of ratings, together with their names, in the
experts hst;
- Publishing the number of questions together with their names in the
experts hst;
- Financial bonus for every question they answer;
- Financial compensation for the time spent chatting with users.
It should be noted that incentive need not necessarily be represented by a
financial bonus. It might also be comprised of "points" to be used for many
different purposes, and the financial aspect is one of them. The incentive
may be also a factor in evaluating periodic employee ratings, the gaining of
frequent-fiier miles, "virtual dollars" to spend on entertainment and
services, etc.
The incentive may also depend on:
• How much of the knowledge currently stored in the KBS was
contributed ("knowledge equity");
• How many of the cases in the past period (e.g. month, quarter) were
resolved directly by the expert, or indirectly by the KBS, using
knowledge contributed by the expert.
The firm may also charge the users for use of the knowledge base, for
answers to their questions, etc. Thus, for instance, an independent service
person may gain access to the knowledge base of a large service company,
and benefit from the knowledge contained therein and by the contact with
problem-solving experts, in return for a payment on a usage (e.g., time or
number of queries) basis or on a periodic basis.
An independent service person may be asked by a large service company to
monitor reported problems which were not resolved quickly enough by the
company's personnel (such a person's notification profile will only notify
him regarding, or let him access, such cases). This person will be paid by
the service company for any case he resolves. This opens up business-model
possibilities in the "knowledge economy".
The knowledge base and the other services the Web site provides may be
used not only by the customers, but by the personnel of the company, as
well. However, since such information may be confidential, a suitable
system may enable different levels of access, each level with its own
attributes.
Reusing captured knowledge
All the interactions, as described in the above example, are stored in the
KB as cases. When new problems are reported, ClickFix will use CBR
methods to retrieve these cases and suggest new solutions, so that - for
example - the suggestion composed by Mary in step 12 will be
automatically used by ClickFix, with no need for human-expert
involvement, in handling future cases.
Additionally, ClickFix's MBR component will use the cases generated
through such interactions in creating and or refining its Model-Base. The
MBR uses different learning and inference algorithms, and its cooperation
with CBR raises the quality of suggestions generated by ClickFix.
Other means of building the Knowledge Base
The description above showed how . the KB is generated as a result of
recording and reusing human-to-human interactions. Our invention
supports additional means of building and maintaining the KB:
A. The expert may create CBR and MBR content directly through software
knowledge authoring models;
B. The expert may import data (cases, hsts of symptoms, lists of
replaceable parts, hsts of applicable observations and measurements,
hsts of possible repair actions, engineering data such as block diagrams,
simulation data and reliability estimates etc.) into the KB;
C. After one or more cases have been handled, the expert may engage in a
dialog with the expert system concerning these cases. The expert may
highlight a suggestion made by the expert system and ask the software
why this suggestion was made, and if necessary supply specific
knowledge that will prevent this suggestion from being made again for
the same context. Similarly, the software may highlight a suggestion
made by the expert that contradicts the software's knowledge, and the
expert may then supply knowledge to support that suggestion and
contribute to the software's "understanding" of when it is applicable.
Additional features can be added to a system operating according to the
invention. For illustrative and non-limitative purposes, Fig. 3
schematically shows a Web site, according to a preferred embodiment of
the invention. A Web site may be a particularly suitable implementation of
the present invention, since the Web has become a common communication
channel.
In Fig. 3, the Web site 31 comprises:
- a knowledge base 32, that operates according to the system described
above with reference to Fig. 2, and in addition:
- a public board 33;
- an onhne service 34;
- a chat board 35; and
- expert boards 36 - 36®.
The public board 33 may be implemented in the fashion of the News
service of the Internet. The users may leave their questions, remarks, etc.
on the board, and other users or qualified persons may respond to the
questions. They may leave the answers on the board, or. directly respond to
the user by e-mail.
The online service 34 is a private chat room, wherein a user may carry on a
conversation (in the chat fashion) with a qualified person.
In the chat board 35, the users may carry out a group chat session. In such
a chat, the experts may participate, as well as the users, and therefore,
each participant may contribute his experience to the chat.
The expert boards 36 contain questions directed to individual experts, and
the answers provided by the experts. The users may prefer one expert or
the other according to the subjects he deals with, his experience and
expertise, etc.
Fig. 4 schematically illustrates the activities of a user on a Web site that
comprises a knowledge base, according to a preferred embodiment of the
invention, and which comprises additional options as detailed with
reference to Fig. 3.
- In Step 41, the process starts, i.e., with the display of the Web site
home-page;
- In Step 42, the user poses a query to the knowledge base. Such a query
comprises a list of keywords and the logical relation between them. For
instance: (Keywordl OR Keyword2) AND Keyword3. A more
sophisticated system may contain the ability to analyze free-text
queries, such as "My washing machine leaks".
- In Step 43, the user assesses the result of the query. If the answer is
satisfactory, then the process ends at step 51, otherwise, the user
selects one of the following paths:
- proceed to Step 44, directing the question to an individual expert;
- proceed to Step 45, attempting to get an answer from other users
engaged in a chat (who can be other qualified persons, when the
"user" is a qualified person and the network is an Intranet or other
private or semi-private WAN);
- proceed to Step 46, placing the question on the public board.
In Step 44, the user selects an expert from a hst. The particular expert
is selected by the user according to the subjects he deals with, his
expertise, his popularity, etc.;
- In Step 47, the user directs a question to the expert. The question may
be sent to the expert by e-mail, or by a public board which is edited by
the expert, etc.;
- In Step 45, the user selects a chat room. Usually a chat room is directed
to a subject or to an expert;
- In the next Step 48, the user carries out a chat with other users. In the
chat session, the user may ask for assistance;
- In Step 46, the user places a question on the public board. The public
boards have a general nature, i.e., are not dedicated to a specific
subject. In this way, the user may receive an answer to his question
from both experts and other users.
- In Step 49, the user assesses the answers he has received. Should the
answers prove unsatisfactory, the user may try again, by proceeding to
Step 42 or to Step 44, 45 or 46. He may refine his query by selecting
other keywords, by redefinition of the logical relations between the
keywords, etc. If the answer he received was satisfactory, he may quit
(Step 51).
The above options may be provided to users who are customers, or to
novice technicians, as "light options", i.e., to solve simple questions and
problems that do not require the intervention of experts as described with
reference to Fig. 2. Such options may prevent unnecessary demand on the
resources of the experts and the knowledge base, but they are of course
optional, and by no means form a necessary part of the invention. A
knowledge base that" is properly managed contains pointers to ' the
questions and answers dealt with.
If the above-mentioned "light options" have not provided a proper answer
to his question, the user may call the maintenance service or the help desk
personnel of the company, or, if his system allows it, even try to operate
the query system as described with reference to Fig. 2.
The above examples and description have of course been provided only for
the purpose of illustration, and are not intended to limit the invention in
any way. As will be appreciated by the skilled person, the invention can be
carried out in a great variety of ways and for a great variety of knowledge
domains such as commerce, entertainment, travel, telecommunication
services, insurance and financial services, employing more than one
technique from those described above, all without exceeding the scope of
the invention.