WO2023035524A1 - Procédé et appareil de saut de nœud de traitement basés sur la reconnaissance d'intention, dispositif et support - Google Patents

Procédé et appareil de saut de nœud de traitement basés sur la reconnaissance d'intention, dispositif et support Download PDF

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Publication number
WO2023035524A1
WO2023035524A1 PCT/CN2022/071073 CN2022071073W WO2023035524A1 WO 2023035524 A1 WO2023035524 A1 WO 2023035524A1 CN 2022071073 W CN2022071073 W CN 2022071073W WO 2023035524 A1 WO2023035524 A1 WO 2023035524A1
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Prior art keywords
tag
label
intent
node
accumulation result
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PCT/CN2022/071073
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English (en)
Chinese (zh)
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陈欣
吴星
马骏
王少军
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平安科技（深圳）有限公司
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    • 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
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063—Training
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/30—Semantic analysis
    • 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
    • G10L15/08—Speech classification or search
    • 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
    • G10L15/08—Speech classification or search
    • G10L15/18—Speech classification or search using natural language modelling
    • 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
    • G10L15/26—Speech to text systems
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/14—Session management
    • H04L67/146—Markers for unambiguous identification of a particular session, e.g. session cookie or URL-encoding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present application relates to but not limited to the technical field of artificial intelligence, and in particular relates to a method, device, device and medium for jumping process nodes based on intent recognition.
  • a common intelligent customer service system usually pre-determines the business process. After obtaining the user's voice information, it determines the user's intention through speech recognition and natural language processing (Natural Language Processing, NLP) technology, and then determines the user's intention from the business process according to the user's intention. Determine the corresponding process node, and perform an intelligent response according to the preset response mode in the process node.
  • NLP Natural Language Processing
  • the user intention is usually determined based on the last voice information input by the user, and the process node is determined based on this.
  • the embodiment of the present application provides a process node jump method, device, device, and medium based on intent recognition, which can identify user intent in complex scenarios, improve the accuracy of process node jumps, and improve the reliability of the intelligent customer service system .
  • the embodiment of the present application provides a process node jump method based on intent recognition, including the following steps:
  • the business process is preset with at least two process nodes, and the process node is associated with a conditional tag set and an optional intent, and the optional
  • the intent is associated with identification information and an intent tag, the intent tags associated with different optional intents are different from each other, and the conditional tag set includes at least one conditional tag;
  • the embodiment of the present application also provides a process node jumping device based on intent recognition, including:
  • a node to be processed determining unit configured to determine the process node where the business process is currently located as a node to be processed, wherein the business process is preset with at least two process nodes, and the process node is associated with a conditional label set and Optional intents, the optional intents are associated with identification information and intent tags, the intent tags associated with different optional intents are different from each other, and the conditional tag set includes at least one conditional tag;
  • a speech processing unit configured to obtain speech information to be processed, input the speech information to be processed into a preset NLP model for semantic recognition, and obtain a recognition result output by the NLP model;
  • an intention exit determination unit configured to determine the optional intention whose identification information matches the identification result as a target intention
  • the first counting unit is configured to input the intent label of the target intention into a label counter, and obtain the label accumulation result obtained by the label counter, wherein the label accumulation result includes the label counter in the business process All said intent tags fetched after start;
  • a target process node determining unit configured to match the condition label set and the label accumulation result, and determine the process node associated with the condition label set that matches successfully as a target process node;
  • a node jumping unit configured to jump to the target process node, and determine the target process node as a new node to be processed.
  • the device for jumping process nodes based on intent identification further includes:
  • a label counter creation unit configured to create and initialize the label counter when the node to be processed is the first node of the business process.
  • the target process node determining unit further includes:
  • a first quantity acquiring unit configured to acquire the quantity of the conditional labels in the conditional label set to obtain the first quantity
  • a second quantity acquisition unit configured to acquire the quantity of the intended label of the label accumulation result to obtain a second quantity
  • a first matching unit configured to determine the set of conditions when the first quantity is the same as the second quantity, and the condition labels of the condition label set correspond one-to-one with the intent labels of the label accumulation result.
  • the label set of the above condition matches the accumulative result of the label successfully.
  • the first counting unit also includes:
  • a label input unit for inputting the type label and the attribute label of the target intent into the label counter
  • the second counting unit is configured to obtain a first tag accumulation result and a second tag accumulation result through the tag counter, wherein the first tag accumulation result includes all the tag counters acquired after the business process starts For the type tag, the second tag accumulation result includes all the attribute tags acquired by the tag counter after the business process starts;
  • a counting result merging unit configured to combine the first tag cumulative result and the second tag cumulative result by the tag counter to obtain the tag cumulative result
  • the counting result obtaining unit is configured to obtain the tag accumulation result output by the tag counter.
  • the first matching unit further includes:
  • a candidate process node determining unit configured to determine a process node whose associated condition type label matches the type label of the first label accumulation result as a candidate process node
  • the second matching unit is configured to, when the condition attribute label associated with the candidate process node matches the attribute label corresponding to the second label accumulation result, determine the condition label set and the label The cumulative result matches successfully.
  • the unit to determine the node to be processed further includes:
  • a speech playing unit configured to play the speech information
  • a voice information acquiring unit configured to acquire the input voice information if a voice input is detected within a preset time range after the script information is played, or during the playback of the script information;
  • a voice determining unit configured to determine the acquired voice information as the voice information to be processed.
  • the embodiment of the present application also provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and operable on the processor, and the processor implements the following when executing the computer program:
  • the process node jumping method based on intent recognition includes: determining the current process node in the business process as the node to be processed, wherein, The business process is preset with at least two process nodes, and the process nodes are associated with a set of conditional tags and optional intents, and the optional intents are associated with identification information and intent tags, and the different optional intents are The associated intention tags are different from each other, and the condition tag set includes at least one condition tag; the speech information to be processed is obtained, and the speech information to be processed is input into a preset natural semantic processing NLP model for semantic recognition, and obtained The identification result output by the NLP model; determining the optional intention whose identification information matches the identification result as a target intention; inputting the
  • the embodiment of the present application further provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to perform the process node jump based on the recognition of intent as described in the first aspect.
  • the method for jumping process nodes based on intent recognition includes: determining the current process node in the business process as a node to be processed, wherein the business process is preset with at least two process nodes , the process node is associated with a conditional tag set and an optional intent, the optional intent is associated with identification information and an intent tag, the intent tags associated with different optional intents are different from each other, and the conditional tag
  • the set includes at least one condition label; acquire the speech information to be processed, input the speech information to be processed into a preset natural semantic processing NLP model for semantic recognition, and obtain the recognition result output by the NLP model;
  • the optional intention matching the recognition result is determined as the target intention; the intention label of the target intention is input into the label counter, and the label accumulation result obtained by the label
  • the embodiment of the present application includes: determining the process node where the business process is currently located as the node to be processed, wherein the business process is preset with at least two process nodes, and the process node is associated with a conditional label set and an optional Intent, the optional intent is associated with identification information and intent tags, the intent tags associated with different optional intents are different from each other, and the condition tag set includes at least one condition tag; to obtain the voice information to be processed, Input the speech information to be processed into a preset NLP model for semantic recognition, and obtain the recognition result output by the NLP model; determine the optional intention that the recognition information matches the recognition result as a target Intention: input the intent tag of the target intent into a tag counter, and obtain the tag accumulation result obtained by the tag counter, wherein the tag accumulation result includes the tag counter obtained after the business process starts All the intent tags; matching the condition tag set and the cumulative result of the tags, determining the process node associated with the successfully matched condition tag set as the target process node; jumping to the target process node,
  • different intent tags can be automatically accumulated through the operation of the process nodes without manual operation, and the target process node can be determined through the intent tags accumulated multiple times, which can effectively improve the target process in the case of complex logic Matching accuracy of nodes.
  • FIG. 1 is a flow chart of a process node jump method based on intent recognition provided by an embodiment of the present application
  • FIG. 2 is a flow chart of creating a tag counter provided by another embodiment of the present application.
  • Fig. 3 is a flowchart of determining a target process node provided by another embodiment of the present application.
  • Fig. 4 is the working flowchart of the label counter that another embodiment of the present application provides;
  • Fig. 5 is an example diagram of a business process provided by another embodiment of the present application.
  • Fig. 6 is a flow chart of determining a target process node provided by another embodiment of the present application.
  • Fig. 7 is a flow chart of determining target intent provided by another embodiment of the present application.
  • Fig. 8 is a flow chart of obtaining voice information to be processed provided by another embodiment of the present application.
  • Fig. 9 is a structural diagram of a process node jumping device based on intent recognition provided by another embodiment of the present application.
  • Fig. 10 is a structural diagram of an electronic device provided by another embodiment of the present application.
  • the present invention provides a process node jump method, device, device and medium based on intent recognition.
  • the method includes: determining the process node where the business process is currently located as the node to be processed, wherein the business process is preset with at least Two process nodes, the process nodes are associated with a conditional tag set and an optional intent, the optional intent is associated with identification information and intent tags, and the intent tags associated with different optional intents are different from each other Same, the condition label set includes at least one condition label; obtain the speech information to be processed, input the speech information to be processed into a preset NLP model for semantic recognition, and obtain the recognition result output by the NLP model; The optional intention whose identification information matches the identification result is determined as the target intention; the intention label of the target intention is input into the label counter, and the label accumulation result obtained by the label counter is obtained, wherein the The label accumulation result includes all the intent labels acquired by the label counter after the business process starts; match the condition label set with the label accumulation result, and match the condition label set associated with the successful The process
  • different intent tags can be automatically accumulated through the operation of the process nodes without manual operation, and the target process node can be determined through the intent tags accumulated multiple times, which can effectively improve the target process in the case of complex logic Matching accuracy of nodes.
  • AI artificial intelligence
  • digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.
  • Figure 1 is a process node jump method based on intent recognition provided by an embodiment
  • Step S110 determine the process node where the business process is currently located as the node to be processed, wherein the business process is preset with at least two process nodes, and the process node is associated with a conditional label set and an optional intent, and the optional intent is associated with identification information and intent tags, the intent tags associated with different optional intents are different from each other, and the conditional tag set includes at least one conditional tag;
  • Step S120 acquiring the speech information to be processed, inputting the speech information to be processed into a preset NLP model for semantic recognition, and obtaining the recognition result output by the NLP model;
  • Step S130 determining the optional intention whose identification information matches the identification result as the target intention
  • Step S140 input the intent tag of the target intent into the tag counter, and obtain the tag accumulation result obtained by the tag counter, wherein the tag accumulation result includes all the intent tags acquired by the tag counter after the business process starts;
  • Step S150 matching the condition label set and the label accumulation result, and determining the process node associated with the successfully matched condition label set as the target process node;
  • Step S160 jump to the target process node, and determine the target process node as a new node to be processed.
  • the intelligent customer service system that applies artificial intelligence usually serves enterprise users, and user consultation is usually highly subjective, so it is impossible to predict the specific business that users need to consult. Therefore, the business process It can cover all businesses that can provide intelligent customer service.
  • the business process can include balance business, credit card business, overdue business, etc., and set up multiple process nodes for each business to ensure full process coverage. Effectively improve user experience.
  • process nodes Through the setting of process nodes, the flexibility of intelligent customer service can be effectively improved.
  • process nodes When a certain business needs to modify the process, only the process nodes need to be adjusted, such as adding process nodes, deleting process nodes, or modifying the process
  • the condition label set and optional intent of the node are modified.
  • Those skilled in the art are familiar with how to adjust the configuration of the process node when the process node is set, and details will not be repeated here.
  • condition tag set can include one or more condition tags, and the condition tags can be configured in each process node in a preset way, and the matching conditions of the process nodes can be adjusted through the condition tag set, for example, in the process
  • the conditional label and the intent label can be the same label, that is, one label can be configured as an intent label and a conditional label at the same time. For example, taking label 1 as an example, when label 1 is set in the conditional label set, the label 1 can be It is determined as a conditional label. At the same time, label 1 can also be applied to the intent label, so that the intent label can be matched with the conditional label and provide a data basis for the automatic jump of the process node.
  • the number of optional intents can be arbitrary, which is not limited in this embodiment.
  • different intent tags can be set for the optional intents, so as to ensure that the matching target process node different from each other. It can be understood that different intent tags may have different specific content of the tag, or may be a combination of the same tag content and different numbers.
  • process node 1 can set optional intent 1-1 and optional intent 1-2, taking optional intent 1-1 as an example, you can set consulting tag 1, credit card tag 1, etc.; or determine the specific intent tag number for different trigger times of optional intent, as shown in Figure 5
  • optional intent 1 when optional intent 1 is triggered once, it can be determined as optional intent 1-1, and its corresponding tags can include, for example, consulting tag 1, credit card tag 1, balance tag 1, and overdue tag 1; when If it is triggered twice, for example, the consulting tag and the credit card tag are triggered, it can be determined as optional intent 1-2, and the corresponding intent tags are consulting tag 2, credit card tag 2, balance tag 2, and overdue tag 2.
  • the optional intent 1-2 has been successfully matched twice, and those skilled in the art have the motivation to adjust the type of the intent tag according to actual needs, which is not limited here.
  • the optional intention identification information may be a preset keyword or identification information, which can be used to match the identification result output by the NLP model, which is not limited in this embodiment.
  • the voice to be processed can be real-time voice obtained through the telephone.
  • Those skilled in the art are familiar with how to obtain the user's telephone voice, so I won't go into details here.
  • the NLP model is a natural language processing model based on artificial intelligence. Through the pre-trained NLP model, it can process the input speech information, including but not limited to semantic recognition, speech synthesis, speech recognition, etc., and output Recognition results, where the recognition results can be keywords or pre-set identification information, which can be used to match with the set identification information. Those skilled in the art are familiar with how to configure, train and select NLP models The specific types of output results need not be repeated here.
  • the intent tags are accumulated by the tag counter, and the target process node can be matched in combination with the intent tags determined by multiple process nodes, so as to improve the accuracy of the target process node matching.
  • the tag counter can be a functional module of the intelligent customer service system, which is used to accumulate and save the intent tags input by each process node, and output all the intent tags obtained after the business process starts, for example, at process node 1 Intent tag 1 is input, and intent tag 2 is input in process node 2, then the cumulative result of tags output for process node 2 is intent tag 1 and intent tag 2, which reflects the logical relationship between processes in a cumulative way, making intelligent customer service The response of the system is more in line with user needs.
  • the target process node can be determined as a new node to be processed, and steps S110 to S150 can be executed repeatedly, so as to realize the automatic operation of the business process without manual operation , improving the operating efficiency of intelligent customer service.
  • step S110 in the embodiment shown in FIG. 1 it also includes but is not limited to the following steps:
  • Step S210 when the node to be processed is the first node of the business process, create and initialize a label counter.
  • the label counter is used to accumulate intent labels, so the label counter can be created and initialized before the first intention label is obtained, for example, it is created when the node to be processed is determined to be the first node , the counter can also be pre-configured, and the specific creation time can be selected according to actual needs.
  • step S150 in the embodiment shown in FIG. 1 also includes but is not limited to the following steps:
  • Step S310 acquiring the number of conditional tags in the conditional tag set to obtain the first number
  • Step S320 acquiring the number of intent tags in the tag accumulation result to obtain the second number
  • step S330 when the first quantity is the same as the second quantity, and the condition labels of the condition label set correspond to the intent labels of the label accumulation result, it is determined that the condition label set matches the label accumulation result successfully.
  • conditional tag set is a set of conditional tags, including at least one conditional tag, and the cumulative result of tags is all the intent tags obtained after the business process starts, which is essentially equivalent to a set of intent tags. If the number of tags is different, it means that the tags included in the two cannot be completely matched, and the efficiency of tag matching can be improved. It can be understood that those skilled in the art know how to count the numbers in the set to obtain the above-mentioned first number and second number, and details are not repeated here.
  • condition tag and the intent tag may be the same tag, for example, the tag name or identification information is the same, so when the quantity matches, the specific content of the tag can be matched.
  • condition tags of the condition tag set 3 can include consulting tag 2, credit card tag 2, balance tag 2, and overdue tag 2, which can be successfully matched with the intent tags in optional intent 1-2 , , the specific matching process is a technology well known to those skilled in the art, and will not be repeated here.
  • the intent tag includes a type tag and an attribute tag.
  • Step S140 in the embodiment shown in FIG. 1 also includes but is not limited to the following steps:
  • Step S410 inputting the type label and attribute label of the target intent into the label counter
  • Step S420 using the tag counter to obtain the first tag accumulation result and the second tag accumulation result, wherein the first tag accumulation result includes all types of tags acquired by the tag counter after the business process starts, and the second tag accumulation result includes the tag counter in All attribute tags obtained after the business process starts;
  • Step S430 combining the first tag accumulation result and the second tag accumulation result by the tag counter to obtain the tag accumulation result
  • Step S440 acquiring the tag accumulation result output by the tag counter.
  • the type label can represent the user's needs.
  • the user intention indicates that the user needs to consult, and the type label can be the consultation label shown in Figure 5.
  • the user intention indicates that the user expresses an affirmative answer, then the type The label can be the positive label shown in Figure 5, and the specific type can be adjusted according to actual needs.
  • attribute tags can represent specific attributes of user intentions, such as balance tags and overdue tags shown in Figure 5.
  • user intent can be distinguished from two dimensions, which is convenient for management .
  • the tag counter can accumulate the input tags, and in order to count the type tags and attribute tags separately, the tag technology device can also be pre-configured with a classification function, that is, after the tags are input, they are classified according to the category, so as to realize the type Tags and attribute tags are accumulated separately to obtain the first tag accumulation result and the second tag accumulation result.
  • the specific accumulation method and principle can refer to the description of the embodiment shown in FIG. 1 , and will not be repeated here.
  • the condition tag includes a condition type tag and a condition attribute tag.
  • Step S320 in the embodiment shown in FIG. 3 also includes but is not limited to the following steps:
  • Step S610 determining the process node whose associated condition type label matches the type label of the first label accumulation result as a candidate process node
  • Step S620 when the condition attribute label associated with the candidate process node matches the attribute label corresponding to the second label accumulation result, it is determined that the condition label set matches the label accumulation result successfully.
  • the cumulative result of the first label determines the candidate process node, and then further refines the matching according to the cumulative result of the second label.
  • the cumulative result of the first label can be determined first, such as counting all the consulting categories obtained after the business process starts
  • the type label can be determined as the consultation label 2
  • the process node 3 can be determined as the candidate process node.
  • process node 3 can be determined as the target process node, which can effectively reduce the number of matching times and improve the matching efficiency of the target process node.
  • the recognition result includes first keyword information
  • the recognition information includes second keyword information.
  • Step S130 in the embodiment shown in FIG. 1 also includes but is not limited to the following steps:
  • Step S710 the first keyword information is the same as the second keyword information
  • Step S720 the first keyword information and the second keyword information represent the same semantics.
  • the intelligent customer service system its purpose is to provide customer service, so the first keyword information can be used for the services, objects, operations, etc. that the process node can provide, such as the corresponding Keywords can include “query”, “repayment”, etc., keywords corresponding to objects can include “account”, “credit card”, etc., keywords corresponding to operations can include “report loss”, “cancel”, etc. Technicians are motivated to select different keywords as the first keyword information according to the actual needs of process nodes.
  • the second keyword information is obtained from the voice to be processed input by the user, for example, the real-time voice from the customer phone, and the NLP model is used for semantic recognition to propose at least one second keyword information.
  • the NLP model can be pre-trained.
  • the NLP model can be trained with the first keyword information as a sample output.
  • the number of keyword information can be arbitrary.
  • the identification information can be a plurality of preset second keyword information, and the first keyword information in the recognition result can be compared with the second keyword information. If at least one of them matches, the match can be determined to be successful.
  • the recognition results obtained may be different. If the recognized second keyword information is the same as the first keyword information, then It can be determined by simple text matching, so I won’t go into details here; if the second keyword information is different from the first keyword information in text, but may be the same in semantics, for example, the first keyword information is "Balance", the second keyword information is "remaining amount", it can be determined that the two represent the same semantics, therefore, after the recognition result is determined, the first keyword information and the second keyword information can be determined through common word meaning recognition methods Whether the two keyword information are similar in meaning, if so, it can be determined that the recognition is successful, which can improve the scope of application of the intelligent customer service system.
  • the process node also includes speech information.
  • Step S120 in the embodiment shown in FIG. 1 also includes but is not limited to the following steps:
  • Step S810 playing the speech information
  • Step S820 if voice input is detected within the preset time range after the script information is played, or during the playback of the script information, the input voice information is acquired;
  • Step S830 determining the acquired voice information as voice information to be processed.
  • the speech information can refer to the method shown in Figure 5, and the speech information is preset for each process node.
  • the mapping method of the speech information can also be set in the process node, such as setting the speech number, Read the corresponding voice information to be played from the database through the script number and play it, or it can be the script text stored in the process node, and play it according to the script text through the artificial intelligence broadcast system.
  • the process node is determined to be After the target process node, it is sufficient to play the corresponding speech information, and the specific setting method of the speech information in this embodiment is not limited too much.
  • the preset time can be set in advance If the voice input is detected within the preset time range, the voice information will be obtained normally, and the specific time range can be adjusted according to actual needs.
  • the user may make the next answer after listening to it, or may directly interrupt the playback of the speech information.
  • the playback of the speech information is interrupted and the speech to be processed is obtained.
  • the specific method of obtaining the user's voice can be obtained through a common telephone recording method, which is not limited in this embodiment.
  • an embodiment of the present invention also provides a process node jumping device based on intent recognition, including:
  • the node to be processed determining unit 910 is configured to determine the process node where the business process is currently located as the node to be processed, wherein the business process is preset with at least two process nodes, and the process node is associated with a conditional label set and an optional intent, which can be
  • the selected intents are associated with identification information and intent tags, and the intent tags associated with different optional intents are different from each other, and the conditional tag set includes at least one conditional tag;
  • the voice processing unit 920 is configured to obtain voice information to be processed, input the voice information to be processed into a preset NLP model for semantic recognition, and obtain a recognition result output by the NLP model;
  • An intention exit determination unit 930 configured to determine a target intention, where the target intention is an optional intention whose identification information matches the identification result;
  • the first counting unit 940 is configured to input the intent tag of the target intent into the tag counter, and obtain the tag accumulation result obtained by the tag counter, wherein the tag accumulation result includes all the intent tags acquired by the tag counter after the business process starts;
  • the target process node determination unit 950 is configured to match the condition label set and the label accumulation result, and determine the process node associated with the successfully matched condition label set as the target process node;
  • the node jumping unit 960 is configured to jump to a target process node and determine the target process node as a new node to be processed.
  • the process node jumping device 900 based on intent recognition further includes:
  • the tag counter creation unit 970 is configured to create and initialize a tag counter when the node to be processed is the first node of the business process.
  • the target process node determination unit 950 further includes:
  • the first quantity acquiring unit 951 is configured to acquire the first quantity, the first quantity is the quantity of the conditional labels of the conditional label set;
  • the second quantity acquisition unit 952 is configured to acquire a second quantity, the second quantity being the quantity of the intended label of the label accumulation result;
  • the first matching unit 953 is configured to determine that the conditional label set matches the label accumulation result successfully when the first quantity is the same as the second quantity, and the condition label of the condition label set corresponds to the intent label of the label accumulation result.
  • the first counting unit 940 further includes:
  • a label input unit 941 configured to input the type label and attribute label of the target intent to the label counter;
  • the second counting unit 942 is used to obtain the first label accumulation result and the second label accumulation result through the label counter, wherein the first label accumulation result includes all types of labels acquired by the label counter after the business process starts, and the second label accumulation result The result includes all attribute tags obtained by the tag counter after the business process starts;
  • the counting result merging unit 943 is used to combine the first tag cumulative result and the second tag cumulative result through the tag counter to obtain the tag cumulative result;
  • the counting result obtaining unit 944 is configured to obtain the label accumulation result output by the label counter.
  • the first matching unit 953 further includes:
  • the second matching unit 955 is configured to determine that the conditional label set matches the label accumulation result successfully when the condition attribute label associated with the candidate process node matches the attribute label corresponding to the second label accumulation result.
  • the node to be processed determining unit 910 further includes:
  • the script playing unit 911 is used to play the script information
  • the voice information acquiring unit 912 is configured to acquire the input voice information if a voice input is detected within a preset time range after the script information is played, or during the playback of the script information;
  • the voice determining unit 913 is configured to determine the acquired voice information as voice information to be processed.
  • an embodiment of the present application also provides an electronic device.
  • the electronic device 1000 includes: a memory 1010 , a processor 1020 , and a computer program stored in the memory 1010 and operable on the processor 1020 .
  • the processor 1020 and the memory 1010 may be connected through a bus or in other ways.
  • the non-transitory software programs and instructions required to realize the method for jumping process nodes based on intent recognition in the above embodiments are stored in the memory 1010, and when executed by the processor 1020, the process nodes in the above embodiments are executed based on intent recognition Jump method, for example, execute method step S110 to step S160 in Fig. 1 described above, method step S210 in Fig. 2, method step S310 to step S330 in Fig. 3, method step S410 to step S440 in Fig. 4 , the method step S610 to step S620 in FIG. 6 , the method step S710 to step S720 in FIG. 7 , and the method step S810 to step S830 in FIG. 8 .
  • the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • the process node where the business process is currently located is determined as the node to be processed, wherein the business process is preset with at least two process nodes, and the process nodes are associated with a conditional label set and an optional intent, and the Optional intents are associated with identification information and intent tags, and the intent tags associated with different optional intents are different from each other, and the condition tag set includes at least one condition tag; to obtain the voice information to be processed, the pending Processing the speech information and inputting it into a preset NLP model for semantic recognition, and obtaining the recognition result output by the NLP model; determining the optional intention matching the recognition information with the recognition result as the target intention; Input the intent tag of the target intent into the tag counter, and obtain the tag accumulation result obtained by the tag counter, wherein the tag accumulation result includes all the intents obtained by the tag counter after the business process starts label; match the condition label set with the label accumulation result, determine the process node associated with the condition label set that matches successfully as the target process node; jump to the target process node,
  • different intent tags can be automatically accumulated through the operation of the process nodes without manual operation, and the target process node can be determined through the intent tags accumulated multiple times, which can effectively improve the target process in the case of complex logic Matching accuracy of nodes.
  • an embodiment of the present application also provides a computer-readable storage medium
  • the computer-readable storage medium may be non-volatile or volatile
  • the computer-readable storage medium stores the computer-executable Instructions
  • the computer-executable instructions are executed by a processor or a controller, for example, executed by a processor in the above-mentioned device embodiment, so that the above-mentioned processor executes the process node jumping method based on the recognition of intent in the above-mentioned embodiment, For example, the method step S110 to step S160 in FIG. 1 described above, the method step S210 in FIG. 2 , the method step S310 to step S330 in FIG. 3 , the method step S410 to step S440 in FIG.
  • a processor such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit .
  • Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media).
  • computer storage media includes both volatile and nonvolatile media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. permanent, removable and non-removable media.
  • Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer.
  • communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .
  • the application can be used in numerous general purpose or special purpose computer system environments or configurations. Examples: personal computers, server computers, handheld or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, including A distributed computing environment for any of the above systems or devices, etc.
  • This application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • the application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer storage media including storage devices.

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Abstract

L'invention concerne un procédé et un appareil de saut de nœud de traitement basés sur la reconnaissance d'intention, un dispositif et un support, le procédé consistant à : déterminer, à partir d'un traitement de service qui est prédéfini avec au moins deux nœuds de traitement, un nœud à traiter, les nœuds de traitement étant associés à un ensemble d'étiquettes conditionnelles et à une intention facultative, l'intention facultative étant associée à des informations de reconnaissance et une étiquette d'intention unique et l'ensemble d'étiquettes conditionnelles comprenant au moins une étiquette de condition (S110) ; acquérir des informations vocales à traiter et les entrer dans un modèle NLP prédéfini afin d'obtenir un résultat de reconnaissance (S120) ; entrer l'étiquette d'intention d'une intention cible déterminée en fonction du résultat de reconnaissance dans un compteur d'étiquettes et obtenir un résultat d'accumulation d'étiquettes constitué de toutes les étiquettes d'intention obtenues après que le traitement de service a commencé (S140) ; et mettre en correspondance un nœud de traitement cible en fonction du résultat d'accumulation d'étiquettes et réaliser un saut (S160). Un nœud de traitement cible est déterminé au moyen d'une étiquette d'intention accumulée sans opération manuelle et la précision de correspondance du nœud de traitement cible peut être améliorée lorsqu'il se trouve dans une condition de logique complexe.
PCT/CN2022/071073 2021-09-10 2022-01-10 Procédé et appareil de saut de nœud de traitement basés sur la reconnaissance d'intention, dispositif et support WO2023035524A1 (fr)

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