WO1996018260A1 - Appareil informatise muni d'un systeme d'entree base sur dialogue - Google Patents

Appareil informatise muni d'un systeme d'entree base sur dialogue Download PDF

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Publication number
WO1996018260A1
WO1996018260A1 PCT/GB1995/002887 GB9502887W WO9618260A1 WO 1996018260 A1 WO1996018260 A1 WO 1996018260A1 GB 9502887 W GB9502887 W GB 9502887W WO 9618260 A1 WO9618260 A1 WO 9618260A1
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WIPO (PCT)
Prior art keywords
user
linguistic
dialogue
response
objects
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PCT/GB1995/002887
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English (en)
Inventor
Kenneth Brownsey
Mary Zajicek
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Oxford Brookes University
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Priority to AU41825/96A priority Critical patent/AU4182596A/en
Publication of WO1996018260A1 publication Critical patent/WO1996018260A1/fr

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Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/02—Methods for producing synthetic speech; Speech synthesisers
    • G10L13/027—Concept to speech synthesisers; Generation of natural phrases from machine-based concepts
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/487—Arrangements for providing information services, e.g. recorded voice services or time announcements
    • H04M3/493—Interactive information services, e.g. directory enquiries ; Arrangements therefor, e.g. interactive voice response [IVR] systems or voice portals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M3/00—Automatic or semi-automatic exchanges
    • H04M3/42—Systems providing special services or facilities to subscribers
    • H04M3/50—Centralised arrangements for answering calls; Centralised arrangements for recording messages for absent or busy subscribers ; Centralised arrangements for recording messages
    • H04M3/527—Centralised call answering arrangements not requiring operator intervention
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
    • G10L2015/227—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of the speaker; Human-factor methodology
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M2201/00—Electronic components, circuits, software, systems or apparatus used in telephone systems
    • H04M2201/40—Electronic components, circuits, software, systems or apparatus used in telephone systems using speech recognition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M2203/00—Aspects of automatic or semi-automatic exchanges
    • H04M2203/35—Aspects of automatic or semi-automatic exchanges related to information services provided via a voice call
    • H04M2203/355—Interactive dialogue design tools, features or methods

Definitions

  • This invention relates to computer apparatus having a dialogue-based input system, and in particular to a computerised telephone answering system.
  • a computerised interface which is more user-oriented and more efficient in controlling call or enquiry routing.
  • the computer apparatus comprises means for storing a plurality of words and/or phrases, a user response detector, means for generating at least some of the linguistic outputs dynamically from the stored words and/or phrases as a function of user responses which are detected by the detector and are in response to earlier linguistic outputs provided to the user, wherein the generating means is operable, in generating each of a plurality of the dynamically generated linguistic outputs, to process an electrical representation of the user responses to a plurality of the respective earlier linguistic outputs in the dialogue in order to determine the content of the linguistic output.
  • the apparatus preferably includes an audio output device connectable to the linguistic output generating means for providing the linguistic outputs to the user as audio signals, and speech recognition means for detecting spoken user responses.
  • the apparatus may include a call switching circuit coupled to the command signal generating means and arranged to route a telephone call to call receiving means selected in response to the command signal and according to the said dialogue.
  • the apparatus may include a message generator coupled to the command signal generating means for providing the user with an information message which is determined according to the dialogue between the user and the apparatus.
  • the apparatus includes means for storing representations of objects as hereinafter defined from a domain of interest, together with their degree of membership of predetermined classes in the domain of interest, the stored words and/or phrases being related to the classes and objects.
  • the membership relationship between classes and objects may be seen as the relationship between fuzzy sets and their members.
  • Means may also be provided for storing a response history representing user responses, the means J for generating linguistic outputs including means for dynamically developing a set of the objects as being of interest to the user according to the stored response history, each object having its own degree of interest to the user. This is referred to hereinafter as the user or caller interest structure.
  • Selection means may be provided for allowing different subsets of the user interest structure to be selected according to detected user responses, and then offered to the user in the form of a further linguistic output for further consideration.
  • the linguistic output generating means may be arranged to select initially one class, to construct a question from the stored words and/or phrases, to provide a corresponding linguistic output to the user, to receive the detected user response and to construct an initial set of objects of interest, which is the user interest structure, using combination operations based on the fuzzy sets.
  • the output generating means may further be arranged to select a class repeatedly, which class in the appropriate combination with the user interest structure provides a new and more well-defined user interest structure, using functions such as fuzzy set union and intersection, to construct a question from the stored words and/or phrases, taking the dialogue history into account, to provide a corresponding linguistic output to the user, to receive the detected user response and to construct a new user interest structure as a function of the old user interest structure, the selected class and the user response.
  • functions such as fuzzy set union and intersection
  • the means for storing classes and objects include means for storing attributes relating to the said objects, the stored words and/or phrases including attribute words and/or phrases describing the attributes, and wherein the linguistic output means are arranged such that when the object of interest has been determined, linguistic outputs are generated containing the said attribute words and/or phrases to provide information to the user in response to the command signals.
  • the selected call receiving means are selected as a result of their association with the object of interest
  • objects means objects in the sense of things to which action is directed, abstract or mate ⁇ al things or persons of interest, or information of interest
  • the invention also includes a method of operating a computer to generate a command signal in response to a dialogue-based input sequence, as defined in the claims
  • the invention provides for the construction of dialogues for a computer-aided telephone answe ⁇ ng system handling calls to a large organisation
  • the apparatus is directed to handling ill-defined calls from callers unsure of the end destinations of their calls
  • the dialogues are negotiative in nature and designed to question the callers to ascertain their goals in making a call to the organisation
  • the words and/or phrases include grouping or basket words and/or phrases for use in dynamically building question sentences
  • the dialogue proceeds on the basis of hypotheses which the apparatus seeks to confirm or refute by approp ⁇ ately built questions
  • the apparatus is also capable, unlike p ⁇ or art systems, of changing its hypothesis so that if a caller's responses are falsely interpreted as indicating one class of objects of interest, the system can recover and follow an alternative hypothesis to reach a more appropnate class Put a different way the apparatus is operable to assign weightings to the classes of objects, the weightings being indicative of user interest, and to select a class which contains a sufficient number of sufficiently weighted members to be of interest to the user.
  • weightings can change to the extent that although, initially, a first class may contain a relatively high number of relatively highly weighted members, subsequently a different, second class can be selected, also having a relatively high number of relatively highly weighted members, according to user responses.
  • a dialogue-based input system for generating a command signal which depends on user responses to a plurality of linguistic outputs provided to the user by the computer apparatus
  • the apparatus comprises:- a dialogue generator for assembling linguistic outputs and for providing them to the user; a user response detector for detecting responses to the linguistic outputs; means for storing at least one selected class of objects as hereinbefore defined; means for storing a variable user interest structure dependent on earlier linguistic outputs provided to the user and of detected user responses; and an inference system coupled to the selected object class storing means and the user interest structure storing means, and operable repeatedly to select different object classes for storage and repeatedly to modify the user interest structure in response to the detected user responses; the dialogue generator including vocabulary storing means for storing a plurality of words and/or phrases and a message assembler for generating the linguistic outputs in response to the selected class stored in the selected object class storing means; the inference system further comprising evaluation means for evaluating the user
  • the apparatus may constitute or form part of a telephone answering system, with the user response detector comprising speech recognition means configured to recognise a plurality of predetermined spoken utterances such as "Yes", “No", and “don't know”
  • the apparatus includes means for storing a set of objects, a set of object classes, and a plurality of object/class relationships in the form of a fuzzy set or a plurality of fuzzy sets Normally, this data is constant du ⁇ ng a dialogue
  • the user interest structure storage means is preferably arranged to store the user interest structure as at least one dynamically variable fuzzy set relating different classes of objects as a function of the user responses
  • the contents of the user interest structure fuzzy set or sets change as the input sequence or dialogue progresses, the structure being updated together with the selected object class in response to at least some of the detected user responses
  • the dialogue generator is preferably concerned with the semantics of the linguistic outputs, and assembles messages on the basis not only of the existing content of the selected object class storing means, but also on the basis of stored user response information using a fixed vocabulary of words and/or phrases
  • the dialogue generator may be responsive to the command signal to generate an output information message for the user
  • the inference system typically includes combining means operable to perform combinauon operations such as fuzzy logic unions and intersections in order to update the user interest structure
  • the invention also includes a further method aspect as defined in claim 28
  • Figure 1 is a block diagram of a telephone answe ⁇ ng system in accordance with the invention
  • Figures 2A, 2B, 2C, and 2D are a goal and question matrix set for determining user goal variability
  • Figure 3 is a diagram illustrating a fragment of an associative network
  • Figure 4 is a fuzzy set representation of relationships between classes and objects.
  • Figure 5 is a block diagram of a portion of the telephone answering system of Figure 1.
  • a computerised telephone answering system in accordance with the invention has a call switching circuit 10 coupled to a processor 12 and storage means 14.
  • the switching circuit has an input portion 10A with several (here four) telephone line inputs 16 and a port 18 coupled to the processor 12.
  • the switching circuit includes an output switching portion 10B having an input port 20 coupled to an output port 22 of the processor 12.
  • the output switching portion has a large number of outputs 24 coupled to a corresponding number of call receivers (not shown).
  • calls received on lines 16 are initially routed to the processor 12 by the input switching portion 10A, the processor 12 including speech recognition means for detecting and decoding user responses to dynamically built sentences generated by the processor 12 and fed back to the user in a manner which will be described in more detail below.
  • the processor 12 may cause the switching circuit to connect the caller through to one of the output lines 24, the selection of line being performed according to the determined object of interest.
  • a message can be generated giving information to the caller.
  • the apparatus of Figure 1 forms the basis of a novel answering system involving dynamically creating dialogue responses which depend on a caller's utterances.
  • the system aims to assess the object of interest of a caller who is allowed to answer dialogue questions only using a limited set of responses such as "Yes", “No", and “don't know”
  • the apparatus can operate in this way simultaneously on several calls on the respective line inputs 16
  • the question sentences are dynamically built from a number of possible words and/or phrases in fuzzy sets based on a possible endpoint. A caller's goal can fall into several of these sets and move between them.
  • the organisation is represented by an associative network of organisational data which will be explained below.
  • the network consists of a series of interconnected nodes.
  • Each node contains information about an individual entity or class or entities within the organisation.
  • Facts about the organisation are decomposed into a set of nodes and relationships between those nodes.
  • the system is focused on several nodes. In an inverse of the decomposition of facts, the system takes these several nodes to construct a question sentence.
  • Question sentences are constructed to produce responses which indicate what the user's goal is
  • the approach is to construct questions that incorporate basket terms for groups of goals. For example, the caller calling a hospital to determine the time of the "Fun Run” for their favou ⁇ te chanty might be asked “is it a medical matter 7 "
  • the term “medical” is a "basket” word which includes some references to medical staff and many other things
  • the system's model of the organisation Underlying the interaction between user and system is the system's model of the organisation Essentially this is a network with endpoints
  • the procedural role of the system is to build a model of the user goals and map it onto an endpoint Seen from the system's model, the same stated user goal - e g speaking to the person organising maternity classes - may be va ⁇ able between users
  • Two users may have two distinct specific goals in mind when both state "I wish to speak to the person organising maternity classes"
  • One may mean the person who is in charge of the content of the classes, the class plan and so
  • the other may mean the administrator, responsible for the paperwork, handling of finances etc
  • the desired endpoints of the system's models may also be different, although the path through the network to them may be common for the greater pan So this variability between actual goals, associated with the same stated goal, is taken into account, in the specification and design of the apparatus
  • the responses are tabulated as the matrices of Figures 2A to 2D.
  • Figure 2A tabulates “Yes” responses
  • Figure 2B tabulates “No” responses
  • Figure 2C tabulates "don't know” responses. Note that the figures for each cell position across the three matrices of Figures 2A to 2C add to 12, any response other than "Yes” or “No” being taken as “don't know”
  • the fourth matrix, that of Figure 2D shows how the model of the organisational data would score using a straight yes/no format and interpreting relevance through links in the network representation. The complete set of matrices can be used to assess responses to other questions, where the system uses the same relevance interpretation.
  • Associative networks otherwise known as conceptual, propositional, or semantic networks, have a long history in artificial intelligence, as well as in logic and reasoning. Strictly speaking, an associative network is distinct from its graphical representation, which is how they are usually represented. However, since the only other representations tend to be in pseudo-code or actual program code, we present in Figure 3 a graphical representation which is most strongly suggestive of the concept of an associative network.
  • Each class of objects may be a member - in which case it is called an instance -, a subset or superset of another.
  • Each instance has attributes, which may be a simple property of the object, or a relationship with another object.
  • the direction of the relationship is shown by the black disk at the end. So it can be seen that the class of doctors is concerned with medical matters.
  • Reflexive relationships, such as is are shown with a disk at each end of the connector. Most relations have some sort of inverse
  • the Fun Run has an organiser, who is organiser o/the Fun Run
  • the network in Figure 3 shows how some simple facts are decomposed For example the fact that "Jane Jones is a doctor" is represented by the instance node G833, together with the ⁇ s_a link to the doctor class node, and the has name link to a property node Nodes may be involved in several facts Jane Jones is organiser of a Fun Run on 20/11/94" centres on the three instance nodes G833, G942 and G27 The facts available may be more or less precise, and may overlap "Dr Jane Jones is organising a Fun Run" tell us both a little more and a little less than the previous fact
  • the associative network is generalised in one respect by abstracting out the is_a links in such as way that the is_a links for instances to classes receive weightings which are fuzzy set membership weightings, and the is a links between classes become implicit in the fuzzy set structure using the notion of fuzzy subsethood.
  • the relationships between objects and classes of objects is expressed in terms of degrees of membership or weightings.
  • an object which is unmistakably a member of a particular class can be allocated membership degree " 1 ", and one which is unmistakably not a member of that class has a membership degree "0" with respect to the class. In many cases, however, the membership degree is between "0" and "1 ". It follows that each class can be represented as a fuzzy set.
  • Fuzzy sets are the basis of fuzzy logic.
  • a signal can adopt the state “ 1 ", “0”, or a plurality of intermediate states such as 0.2, 0.6, etc., unlike binary logic in which, generally, only logic states “ 1 " and “0” are permitted.
  • Combinations of fuzzy sets can be performed in different ways.
  • Figure 4 contains a fragment of a fuzzy set representative of tourist activities in Oxford, England. This representation would be stored in the storage device 14 of the apparatus of Figure 1 if it is used as part of a telephone answering system for providing tourist information in Oxford.
  • objects of interest 30 are related to classes 32 of tourist activities. each class taking the form of a fuzzy set in that the objects 30 have different degrees of membership 34 or weightings in the different sets, as shown by the numerical values associated with the links between objects and classes. Note that in a number of cases, an object has several links, each to a different class In the general case, each object has links to all of the classes
  • FIG. 4 is, in some respects, a more generalised representation of the association between objects and classes than the associative network of Figure 3, but could equally be applied to the hospital telephone answe ⁇ ng system desc ⁇ bed above with reference to Figures 2A to 2D and Figure 3 by the above-descnbed integration of the weightings de ⁇ ved from the Goal Question Matnx Set into the associative network
  • the computer apparatus represented as processor 12 and storage means 14 in Figure 1 can be represented in more detail as an inference system 12IS and a dialogue generator 12DG both controlled by a controller 12C
  • Telephone calls ar ⁇ ving on lines 16 ( Figure 1) are routed to a caller response detector 12CD which, in practice, constitutes speech recognition means for detecting and decoding a limited number of caller responses such as "Yes", “No", and “don't know”
  • the number of permitted responses is 10 or less, preferably 5 or less, to obtain reliable operation with a variety of callers having different voice patterns, accents, etc , and in view of the limited reliability of cunent speech recognition systems in dealing with unknown callers, as is required of the present system
  • the storage means 14 ( Figure 1) includes, as shown in Figure 5, means for sto ⁇ ng the data shown in Figure 4, that is an objects store 14OS for sto ⁇ ng objects 30, and object classes store 14CS for sto ⁇ ng classes 32, and an object/class relationship store MRS for sto ⁇ ng the membership degrees 34
  • the storage means also includes a selected class store 14SC for classes selected during the dialogue, and a caller mterest structure (CIS) store 14CI for sto ⁇ ng a caller interest structure m the form of a fuzzv set or sets representing a summary of the dialogue history
  • CIS caller mterest structure
  • CIS caller mterest structure
  • an object class locator 12ISCL Associated with the combiner 12ISC in the inference system IS is an object class locator 12ISCL, the function of which is to select a new class C(QC + 1) potentially of interest to the caller in view of a combination of the cunent CIS and the set of object classes obtained from class store 14CS.
  • Both combiner 12ISC and class locator 12ISCL operate by performing union and intersection functions suitable for fuzzy logic processing, as will be described in more detail below.
  • Combiner 12ISC also periodically tests the CIS using fuzzy set measures such as fuzzy entropy which, when it attains a predetermined threshold, results in generation of a command signal COMMAND which is fed to the controller 12C for connecting the caller to. for example, a selected telephone receiver (not shown in Figure 5) or to actuate generation of an information message in the dialogue generator 12DG via command actuate line CA, this being transmitted to the caller as an audio signal by an audio output device 12OD comprising a speech synthesiser driven by the message generator 12DGM. Audio output device 12OD has an output (not shown) connectible to the relevant telephone line on which the incoming call is present.
  • fuzzy set measures such as fuzzy entropy which, when it attains a predetermined threshold, results in generation of a command signal COMMAND which is fed to the controller 12C for connecting the caller to.
  • a selected telephone receiver not shown in Figure 5
  • Audio output device 12OD has an output (not shown) connectible to the relevant telephone line on which the incoming call is
  • the primary function of the dialogue generator 12DG is to generate messages in the form of questions aimed at determining the caller's interest or otherwise in the selected class of objects C(QC) stored in selected class store 14SC, making use of the dialogue history so far as stored or summarised in the caller interest structure CIS.
  • a message is assembled in message assembler 12DGM using words and phrases stored as a vocabulary in phraseology store 12DGP. and the output device 12OD synthesises it as an audio signal for transmission to the caller
  • the computer apparatus has two main components, the inference system 12IS and the dialogue generator 12DG
  • the two main roles of the inference system 12IS are (a) to determine which class of objects C(QC) to ask about next, p ⁇ or to the dialogue generator generating question number QC, and (b) to work out the next generation of the caller interest structure CIS(QC), given the reply to question number QC
  • the caller interest structure CIS(QC) is a summary, for the purposes of the inference system, of the dialogue history so far - I e after QC questions have been asked Thus it does not contain such items as the forms of question, etc It summarises in some form, the sequence of classes objects enquired about, and the responses to those enqui ⁇ es
  • a summary of a set of figures relating to, say, student course marks could vary from a simple average, through a sequence of histograms, based on different class sizes, to the o ⁇ ginal set of raw data
  • a very simple form of the caller interest structure is a fuzzv set, formed from approp ⁇ ate unions and intersections of the classes of objects enquired about More information about the dialogue history is retained if the CIS is a summary set of (weighted) fuzzy sets, which is a more robust form for a general purpose system
  • CIS(QC) is a function of the dialogue history after QC questions, and could, in p ⁇ n
  • the system is not necessarily constrained thereafter to a predetermined more limited set of questions as a result.
  • the dialogue progresses as an elaboration of a search path using a dynamically developed sequence of linguistic outputs, neither the search path nor the linguistic outputs beings explicitly built into the system at the start of the dialogue.
  • the complete set of linguistic outputs does not appear anywhere in the controlling software code or files.
  • the main roles of the dialogue generator are:- (i) given a class of objects C(QC), the dialogue history so far, semantic links between the classes of objects, as well as the (implicit) structural information contained in the fuzzy set representation, to generate a question message which is aimed at determining the caller's interest or otherwise in C(QC), which fits in with the rest of the dialogue, and which is designed to obtain as much information as possible through variation in (non- subject) content and style, and through reference to previously used classes of objects where appropriate; and
  • the dialogue generator welcomes the caller, explaining that there is a choice between using the system, or the alternatives of being put in a queue, ringing off. etc.
  • the dialogue generator asks the caller whether to run a sub -dialogue A to detail and offer the alternatives.' 3.4 The caller response with CAlt.
  • the dialogue generator asks the caller whether to run a sub-dialogue B to detail and offer the options 2
  • Sub-dialogue B is performed, during the course of which the default values concerning the final goal form may be altered.
  • the dialogue generator passes control to the inference system.
  • the inference system locates a plasible first class of objects C(0) for a question.
  • the initial caller interest structure CISCO is constructed from C(0)
  • the question count QC is set to 0.
  • a sub-dialogue D is performed, as a result of which C(QC) may be changed prior to return from D, or the system may be exited
  • the dialogue generator constructs a question, based on C(QC). and the previous dialogue history, and this question is put to the caller.
  • C(QC + 1) is set to C(QC) AND QC is set to QC + 1
  • the dialogue generator passes control to the inference system 11.5.8.3 Using the response, CIS(QC). C(QC) and the previous dialogue history the inference system computes a new caller interest group CISNext
  • CIS(QC) is set to CISNext 11.5.8.6
  • the inference system locates a plausible next class of objects, together with a combination function for that class and CIS(QC).
  • C(QC) for a query 11.5.8.7 NoDefiniteResponse is set to FALSE
  • the system may attempt to establish if the caller is experienced and wishes to work through an alternative system - e.g. a hierarchical menu structure
  • the caller may be able to select how many objects make up the goal set and the degree of homogeneity cr heterogeneity in the goal set
  • Fuzzy set entropy is a measure of the definiteness of the membeship degrees of a fuzzy.
  • a set having membership degrees near "1" and "0" e.g. (0.90, 0.15, 0.80, 0.05] has a higher entropy than one having membership degrees on average nearer 0.5, e.g. (0.65, 0.80, 0.40, 0.351.
  • the command signal is generated and an appropriate corresponding operation is performed, such as switching the call cr generating an information message (represented here as sub-dialogue C) .
  • the subsystem providing sub-dialogue C is conceptionally distinct from the mam dialogue system in the dialogue generator. It provides a straigt orward information service with a number of options. For example, t may inform the caller that half a dozen items of potential interest have been found and ask if the caller wants a summary, or to step through the l st, opting for full information on selected items, or have all the infomration with the option of cutting short the information provision. It should be noted that the system for generating sub-dialogue C is a relatively unintelligent system, in which the caller can select options regarding information delivery, the items of interest having been selected by the relatively intelligent activities of the inference system and the main dialogue system m the dialogue generator.
  • steps 1 - 5.5.2 of the pseudocode the system accepts inputs and provides linguistic outputs which form a series of preliminary dialogue exchanges prior to fuzzy logic operations to determine the true interest of the caller.
  • the first of these operations is locating a plausible first class of objects C(0) for a query (step 7), and an initial caller interest set CIS(0) is constructed from C(0).
  • the inference system and dialogue generator operate within outer and inner loops until KeepAsking is set to FALSE (step 1 1.5.5.3).
  • the outer loop begins at step 1 1.1, while the inner loop starts at 1 1.5.1 and is followed so long as NoDefiniteResponse is TRUE (step 1 1.4).
  • step 11.5 Both loops end with step 11.5 ' 8.7. Accordingly, once the initial object class C(0) and caller interest structure CIS(O) have been computed, the outer loop is entered and the CIS is evaluated to see whether it has reached the fuzzy entropy threshold (step 11 2 1) If not, control is passed to the dialogue generator (step 11 3) and the inner loop is entered, which first checks whether the dialogue is not progressing (step 11 5 2 2), and then the system constructs a question based on C(QC) using the dialogue history and the vocabulary store to construct a user-friendly question aimed at finding out in the most efficient possible way whether the caller is interested in the class C(QC)
  • the caller's reponse CR(QC) is then checked to see whether it demands an exit (step 1 1 5 5 1 ) (in which case the system is exited), whether the caller has asked to backtrack (step 1 1 5 6 1 ) (in which case the last-but-one question is repeated), or whether the response is "don't know" (step 1 1 5 7 1), (in which case the dialogue generator puts the question based on C(QC) in a different way)
  • the NoDefiniteResponse flag remains TRUE and the system reverts to the beginning of the inner loop at step 1 1 5 1
  • the dialogue generator passes control to the inference system and two new fuzzy logic combinauon operations are performed, firstly, to generate a new caller interest structure CISNext and, secondly, to locate a plausible next class of objects C(QC)for a query
  • the CIS may be a single fuzzy set or a super set of weighted fuzzy sets
  • the case of the CIS being a simple fuzzy set is considered
  • This exemplary simple fuzzy set is over the same universal set as the fuzzy sets in the object class domain 34 ( Figure 4), and it has four elements from the universal class set, which are, taking the Oxford tounst guide example, ⁇ "The Biggs Museum", “The Lamb and Flag", “The Krump Tea Rooms", “Higgs Academy” ⁇
  • the CIS is ⁇ 0 3, 0 1, 0 0, 0 8 ⁇ , indicating that "The Biggs Museum” has membership degree 0 3, "The Lamb and Flag” has membership degree 0 6, "The Krump Tea Rooms” has membership degree 0 0, and "Higgs Academy” has membership degree
  • the inference system output indicates that the dialogue generator should generate a question aimed at a logical union with the class of Cultural Activities which is ⁇ 0 9, 0 2, 0 1, 0 6 ⁇ If the response is "Yes” then the CISNext is ⁇ 0 93, 0 28, 0 1, 0 92 ⁇ , using the union function x ⁇ *- y -xy If the response is "No", then the CIS is not changed
  • the new CIS for "Yes” is “sharper” (l e has a higher fuzzy entropy) than the old CIS and , if the goal structure indicates the caller has about two items of interest, this is taken as indicating it is appropriate to ask the caller about the two highly weighted items
  • Selection of a new object class C(QC) can be performed as follows
  • CIS intersection Cultural Activities is ⁇ 0 27, 0 02, 0 0, 0 48 ⁇
  • CIS union Food and D ⁇ nk is ⁇ 0 58, 1 0, 0 9, 0 8 ⁇
  • CIS intersection Cultural Activities is ⁇ 0 12, 0 01, 0 0, 0 0 ⁇
  • the inference system will infer that the most informative action is a "Yes" answer to a question posing a union of the cunent CIS and Cultural Activities Note that this is a very simple example in that the number of classes involved is very small (2) and the operations of union and intersection can be generalised and moderated by other functions
  • the above system may be summa ⁇ sed as an automatic telephone answe ⁇ ng system uses fuzzy set combinations to generate a command signal which causes switching of a caller to a telephone receiver associated with a person most likely to give the information required by the caller or to generate automatically an audio message signal containing information useful to the caller
  • the system produces, as part of a telephone dialogue with the caller, linguistic outputs which are dynamically va ⁇ able, each output being assembled according to real-time processing of dialogue history data based on a plurality of the previous caller responses in the dialogue
  • the system progressively generates a user interest structure representing a model of the caller's goals
  • the use of fuzzy logic operations results in a system which is capable of connecting the caller to a suitable information source more quickly and with a greater possibility of avoiding human intervention than with pnor automated answering systems

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Telephonic Communication Services (AREA)

Abstract

Système de répondeur téléphonique automatique faisant intervenir des combinaisons d'ensembles flous pour générer un signal d'ordre provoquant la commutation de l'appel d'un demandeur vers un récepteur téléphonique associé à une personne qui est la plus susceptible de fournir l'information recherchée par le demandeur ou pour générer automatiquement un signal de message sonore contenant des informations utiles à l'appelant. Le système produit, au cours d'un dialogue téléphonique avec le demandeur, des sorties linguistiques, variables dynamiquement, chaque sortie étant assemblée en fonction du traitement en temps réel des données historiques du dialogue fondé sur une pluralité de réponses préalables du demandeur au cours du dialogue. Le système génère progressivement une structure des intérêts de l'usager représentant un modèle de ses objectifs. Le recours à des opérations de logique floue donne un système capable de relier le demandeur à une source d'information appropriée plus rapidement et avec une plus grande possibilité d'éviter l'intervention humaine qu'avec les systèmes précédents de répondeur téléphonique automatisé.
PCT/GB1995/002887 1994-12-09 1995-12-08 Appareil informatise muni d'un systeme d'entree base sur dialogue WO1996018260A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU41825/96A AU4182596A (en) 1994-12-09 1995-12-08 Computer apparatus with dialogue-based input system

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GB9424887.9 1994-12-09
GBGB9424887.9A GB9424887D0 (en) 1994-12-09 1994-12-09 Computer apparatus with dialogue-based input system

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WO1996018260A1 true WO1996018260A1 (fr) 1996-06-13

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Country Link
AU (1) AU4182596A (fr)
GB (1) GB9424887D0 (fr)
WO (1) WO1996018260A1 (fr)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998010413A1 (fr) * 1996-09-03 1998-03-12 Siemens Aktiengesellschaft Systeme et procede de traitement de la parole
EP1026871A2 (fr) * 1999-02-01 2000-08-09 Siemens Information and Communication Networks, Inc. Système de réponse vocale interactif avec des blocs polyvalents
WO2001091110A1 (fr) * 2000-05-23 2001-11-29 Thomson Licensing S.A. Dispositif autonome comprenant un systeme de reconnaissance vocale
EP1164575A2 (fr) * 2000-06-17 2001-12-19 Alcatel Diffusion d'annonces vocales
WO2002009094A1 (fr) * 2000-07-20 2002-01-31 British Telecommunications Public Limited Company Dialogues interactifs
WO2002051108A3 (fr) * 2000-12-18 2002-11-07 Deutsche Telekom Ag Equipement interactif pour interaction homme-machine avec des dispositifs interactifs cooperants
WO2002101720A1 (fr) * 2001-06-08 2002-12-19 Mende Speech Solutions Gmbh & Co.Kg Procede de reconnaissance d'informations parlees
US7143040B2 (en) 2000-07-20 2006-11-28 British Telecommunications Public Limited Company Interactive dialogues
US8540517B2 (en) 2006-11-27 2013-09-24 Pharos Innovations, Llc Calculating a behavioral path based on a statistical profile
US8540515B2 (en) 2006-11-27 2013-09-24 Pharos Innovations, Llc Optimizing behavioral change based on a population statistical profile
US8540516B2 (en) 2006-11-27 2013-09-24 Pharos Innovations, Llc Optimizing behavioral change based on a patient statistical profile

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2165969A (en) * 1984-10-19 1986-04-23 British Telecomm Dialogue system

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2165969A (en) * 1984-10-19 1986-04-23 British Telecomm Dialogue system

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
B.B.CHAUDHURI ET AL: "FUZZY SETS AND POSSIBILITY THEORY IN RELIABILITY STUDIES OF MAN MACHINE SYSTEMS", CONF. PROC. OF THE SYMPOSIUM ON POLICY ANALYSIS AND INFORM. SYSTEMS, 28 June 1980 (1980-06-28) - 30 June 1980 (1980-06-30), DURHAM(US), pages 267 - 273, XP000565439 *
K. BROWNSEY ET AL: "FUZZY MODELLING FOR AUTOMATIC TELEPHONE ANSWERING SYSTEMS", PROCEEDINGS OF ISUMA-NAFIPS 95-THE THIRD INT. SYMP. ON UNCERTAINTY MODELING AND ANALYSIS AND ANNUAL CONF. OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY-, 17 September 1995 (1995-09-17) - 20 September 1995 (1995-09-20), COLLEGE PARK, MARYLAND (US), pages 182 - 187, XP000566003 *
K.BROWNSEY ET AL: "STRUCTURE FOR USER-ORIENTED DIALOGUES IN COMPUTER-AIDED TELEPHONY", INTERACTING WITH COMPUTERS, vol. 6, no. 4, UK, pages 433 - 449, XP000566010 *
M.ZAJICEK ET AL: "DESIGN AND EVALUATION OF NEGOTIATIVE DIALOGUES FOR COMPUTER AIDED TELEPHONY", ECCE-7 SEVENTH EUROPEAN CONFERENCE ON COGNITIVE ERGONOMICS, 5 September 1994 (1994-09-05) - 8 September 1994 (1994-09-08), BONN(DE), pages 47 - 56, XP000565500 *
M.ZAJICEK ET AL: "METHODS FOR TRAVERSING A PRE-RECORDED SPEECH MESSAGE NETWORK TO....", PROCEEDINGS EUROSPEECH 1993, pages 1351 - 1354, XP000566209 *

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998010413A1 (fr) * 1996-09-03 1998-03-12 Siemens Aktiengesellschaft Systeme et procede de traitement de la parole
CN100401375C (zh) * 1996-09-03 2008-07-09 西门子公司 语音处理系统及方法
US7286989B1 (en) 1996-09-03 2007-10-23 Siemens Aktiengesellschaft Speech-processing system and method
EP1026871A3 (fr) * 1999-02-01 2004-05-19 Siemens Information and Communication Networks Inc. Système de réponse vocale interactif avec des blocs polyvalents
US7031440B1 (en) 1999-02-01 2006-04-18 Ssimens Communications Inc. Interactive voice response systems with general-purpose blocks
EP1026871A2 (fr) * 1999-02-01 2000-08-09 Siemens Information and Communication Networks, Inc. Système de réponse vocale interactif avec des blocs polyvalents
WO2001091110A1 (fr) * 2000-05-23 2001-11-29 Thomson Licensing S.A. Dispositif autonome comprenant un systeme de reconnaissance vocale
EP1164575A3 (fr) * 2000-06-17 2004-02-11 Alcatel Diffusion d'annonces vocales
EP1164575A2 (fr) * 2000-06-17 2001-12-19 Alcatel Diffusion d'annonces vocales
US7143040B2 (en) 2000-07-20 2006-11-28 British Telecommunications Public Limited Company Interactive dialogues
WO2002009094A1 (fr) * 2000-07-20 2002-01-31 British Telecommunications Public Limited Company Dialogues interactifs
WO2002051108A3 (fr) * 2000-12-18 2002-11-07 Deutsche Telekom Ag Equipement interactif pour interaction homme-machine avec des dispositifs interactifs cooperants
US7437292B2 (en) 2000-12-18 2008-10-14 Deutsche Telekom Ag Dialog system for a man-machine interaction having cooperating dialog devices
WO2002101720A1 (fr) * 2001-06-08 2002-12-19 Mende Speech Solutions Gmbh & Co.Kg Procede de reconnaissance d'informations parlees
US8540517B2 (en) 2006-11-27 2013-09-24 Pharos Innovations, Llc Calculating a behavioral path based on a statistical profile
US8540515B2 (en) 2006-11-27 2013-09-24 Pharos Innovations, Llc Optimizing behavioral change based on a population statistical profile
US8540516B2 (en) 2006-11-27 2013-09-24 Pharos Innovations, Llc Optimizing behavioral change based on a patient statistical profile

Also Published As

Publication number Publication date
AU4182596A (en) 1996-06-26
GB9424887D0 (en) 1995-02-08

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