CN111182162A - Telephone quality inspection method, device, equipment and storage medium based on artificial intelligence - Google Patents
Telephone quality inspection method, device, equipment and storage medium based on artificial intelligence Download PDFInfo
- Publication number
- CN111182162A CN111182162A CN201911367527.7A CN201911367527A CN111182162A CN 111182162 A CN111182162 A CN 111182162A CN 201911367527 A CN201911367527 A CN 201911367527A CN 111182162 A CN111182162 A CN 111182162A
- Authority
- CN
- China
- Prior art keywords
- neural network
- processed
- call
- input
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 64
- 238000007689 inspection Methods 0.000 title claims abstract description 54
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 34
- 238000013528 artificial neural network Methods 0.000 claims abstract description 82
- 238000005516 engineering process Methods 0.000 claims abstract description 47
- 238000003062 neural network model Methods 0.000 claims abstract description 32
- 230000011218 segmentation Effects 0.000 claims abstract description 11
- 238000012549 training Methods 0.000 claims description 27
- 230000006870 function Effects 0.000 claims description 25
- 210000002569 neuron Anatomy 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 20
- 238000003058 natural language processing Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 12
- 230000001537 neural effect Effects 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 5
- 238000003908 quality control method Methods 0.000 abstract 1
- 238000013461 design Methods 0.000 description 12
- 238000004590 computer program Methods 0.000 description 7
- 238000005070 sampling Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 208000035126 Facies Diseases 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000018109 developmental process Effects 0.000 description 2
- 238000012886 linear function Methods 0.000 description 2
- 238000007477 logistic regression Methods 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 238000012372 quality testing Methods 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 238000012550 audit Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 238000012797 qualification Methods 0.000 description 1
- 238000001303 quality assessment method Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000013519 translation Methods 0.000 description 1
Images
Classifications
-
- 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
- H04M3/51—Centralised call answering arrangements requiring operator intervention, e.g. call or contact centers for telemarketing
- H04M3/5175—Call or contact centers supervision arrangements
-
- 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
- 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
- G10L15/16—Speech classification or search using artificial neural networks
-
- 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
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
-
- 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
- H04M3/42221—Conversation recording systems
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Computational Linguistics (AREA)
- Acoustics & Sound (AREA)
- Signal Processing (AREA)
- Artificial Intelligence (AREA)
- Business, Economics & Management (AREA)
- Marketing (AREA)
- Evolutionary Computation (AREA)
- Machine Translation (AREA)
- Telephonic Communication Services (AREA)
Abstract
The invention relates to the field of artificial intelligence, and provides a telephone quality inspection method, a telephone quality inspection device, telephone quality inspection equipment and a storage medium based on artificial intelligence. The method comprises the following steps: acquiring a plurality of call records to be processed; marking a corresponding score for each call recording to be processed; converting a plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model; word segmentation is carried out on text information to be processed through Language Technology Platform (LTP) technologyObtaining a plurality of core keywords and the frequency corresponding to the core keywords; using the frequency of the core keyword as input xiAnnotated call score as ideal output yiInputting the data into a neural network to obtain a target neural network; deploying a target neural network to a client; and receiving the call information input by the user, calculating the score of the call information through the ideal weight, and returning the score of the call information to the user. The efficiency of phone quality control is promoted.
Description
Technical Field
The invention relates to the field of monitoring, in particular to a telephone quality inspection method, a telephone quality inspection device, telephone quality inspection equipment and a storage medium based on artificial intelligence.
Background
In the operation of the call center, various index data such as call completing rate, call duration, complaint rate, customer satisfaction and the like are very important. The data directly correspond to business quality differences among different personnel, at present, many monitoring and quality inspection are performed in a manual sampling inspection mode, quality inspection personnel need to be responsible for quality inspection in multiple links such as quality monitoring, process management, index management, field follow-up, report writing and culture construction, knowledge base management, business training, staff feedback guidance and the like, time and energy are consumed, only about 30% of telephone traffic quality inspection can be completed, and efficiency is low. The standards can be the same, the index setting can be the same, the results can be different, the key is that the purpose of using the standards by quality testing personnel is different, the method is different, the thought is different, and the accuracy of the quality testing result is lower.
Disclosure of Invention
The invention provides a telephone quality inspection method based on artificial intelligence, which improves the efficiency of telephone quality inspection.
In a first aspect, the present invention provides a telephone quality inspection method based on artificial intelligence, which includes:
acquiring a plurality of call records to be processed;
marking a corresponding score for each call recording to be processed;
converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model;
segmenting the text information to be processed by a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords;
taking the frequency corresponding to the core keyword as an input xiAnnotated said score as ideal output yiInputting the neural weight to an initial neural network, and training the neuron weight in the initial neural network through a loss function to obtain a target neural network;
deploying the target neural network to a client;
receiving call information input by a user, and inputting the call information into the target neural network through the client;
and calculating the score of the call information through the weight of the neuron in the target neural network, and returning the score of the call information to the user.
In some possible designs, before segmenting a word of text information to be processed by using a language technology platform LTP technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords, the method further includes:
generating a unique identification ID number for each text message to be processed;
constructing a hypertext transfer protocol (HTTP) request according to an Application Programming Interface (API) parameter of text information to be processed;
extracting Chinese word segmentation from the text information to be processed through an HTTP request and an LTP technology;
and matching the vocabularies recorded in the database, and deleting the vocabularies with the matching degree lower than the threshold value to obtain the vocabularies with the matching degree higher than the threshold value.
In some possible designs, the converting the plurality of pending call records into the plurality of pending text messages by the preset sequential neural network model includes:
obtaining a plurality of training voice samples;
inputting the training voice sample into the preset sequence neural network model, and updating the neuron weight of the preset sequence neural network model through a Natural Language Processing (NLP) algorithm, the voice information and a text label corresponding to the voice information to obtain a target model;
adjusting the weight of the neuron of the target model, and updating the preset sequential neural network model;
and converting the plurality of call records to be processed into a plurality of text messages to be processed through the updated preset sequence neural network model.
In some possible designs, receiving call information input by a user, and inputting the call information into a target neural network through a client includes:
when prompting a user to input call information, starting a microphone application through the client;
after receiving the indication message, prompting a user to input call information through the client;
when the call information is obtained, removing noise, background sound and compressing the call information through the client to obtain the preprocessed call information;
judging whether the preprocessed call information conforms to an input preset rule or not through the client;
if the command does not accord with the preset rule, the command client is instructed to prompt the user to input again;
if the preset rule is met, uploading the preprocessed call information input by the user to a server through the client;
and inputting the preprocessed call information into the target neural network.
In some possible designs, before obtaining the plurality of pending call records, the method further comprises:
rejecting the call records with the call duration less than the threshold value;
adding a plurality of characteristic items to the call record to be processed, wherein the characteristic items at least comprise: whether the first call is made, the average duration of the industry recording, and the satisfaction of the user telephone rating.
In some possible designs, the method includes the steps of performing word segmentation on text information to be processed through a language technology platform LTP technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords, including:
frequency passing corresponding to core key words
Calculation of where FwIs the frequency of the core keyword, N is the number of sentences in which the core keyword appears, M isRefers to the number of sentences of the text information to be processed.
In some possible designs, the pass takes the frequency corresponding to the core keyword as input xiAnnotated said score as ideal output yiInputting the input data into an initial neural network, training neuron weights in the initial neural network through a loss function, and before obtaining a target neural network, after segmenting the text information to be processed through a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords, the method further comprises the following steps:
if the output y of the neural networkjWith n valid feature inputs x1，x2，…，xnThe weight of the corresponding connection is w1，w2，…，wnThen the neural network passes
And initializing the weight value.
In a second aspect, the present invention provides an artificial intelligence based phone quality inspection apparatus having a function of implementing a method corresponding to the artificial intelligence based phone quality inspection platform provided in the first aspect. The functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above functions, which may be software and/or hardware.
The artificial intelligence-based telephone quality inspection device comprises:
the input and output module is used for acquiring a plurality of call records to be processed;
the processing module is used for marking a corresponding score for each call record to be processed; converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model; segmenting the text information to be processed by a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords; the frequency corresponding to the core key words is processed by the input and output moduleRate as input xiAnnotated said score as ideal output yiInputting the neural weight to an initial neural network, and training the neuron weight in the initial neural network through a loss function to obtain a target neural network; deploying the target neural network to a client; receiving call information input by a user through the input and output module, and inputting the call information to the target neural network through the client; and calculating the score of the call information through the weight of the neuron in the target neural network, and returning the score of the call information to the user.
In some possible designs, the processing module is further to:
generating a unique identification ID number for each text message to be processed;
constructing a hypertext transfer protocol (HTTP) request according to the API parameters of the application programming interface of the text information to be processed;
extracting Chinese word segmentation from the text information to be processed through the HTTP request and the LTP technology;
and matching the vocabularies recorded in the database, and deleting the vocabularies with the matching degree lower than the threshold value to obtain the vocabularies with the matching degree higher than the threshold value.
In some possible designs, the processing module is further to:
obtaining a plurality of training voice samples;
inputting the training voice sample into the preset sequence neural network model, and updating the neuron weight of the preset sequence neural network model through a Natural Language Processing (NLP) technology, the voice information and a text label corresponding to the voice information to obtain a target model;
adjusting the weight of the neuron of the target model, and updating the preset sequential neural network model;
and converting the plurality of call records to be processed into a plurality of text messages to be processed through the updated preset sequence neural network model.
In some possible designs, the processing module is further to:
when prompting the user to input the call information, starting a microphone application through the client;
after receiving the indication message, prompting a user to input call information through the client;
when the call information is obtained, removing noise and background sound of the call information and compressing the call information through the client to obtain the preprocessed call information;
judging whether the preprocessed call information conforms to an input preset rule or not through the client;
if the preset rule is not met, the client is instructed to prompt the user to input again;
if the preset rule is met, uploading the preprocessed call information input by the user to a server through the client;
and inputting the preprocessed call information into the target neural network.
In some possible designs, the processing module is further to:
rejecting the call records with the call duration less than the threshold value;
adding a plurality of characteristic items to the call record to be processed, wherein the characteristic items at least comprise: whether the first call is made, the average duration of the industry recording, and the satisfaction of the user telephone rating.
In some possible designs, the processing module is further to:
the corresponding frequency of the core key words is passed
Calculation of where FwThe frequency of the core keywords, N the number of sentences in which the core keywords appear, and M the number of sentences in the text information to be processed.
In some possible designs, the processing module is further to:
if the output y of the neural networkjAnd n number of saidEffect feature input x1，x2，…，xnThe weight of the corresponding connection is w1，w2，…，wnThen the neural network passes
And initializing the weight value.
The invention further provides an artificial intelligence-based telephone quality inspection device, which comprises at least one connected processor, a memory and an input/output unit, wherein the memory is used for storing program codes, and the processor is used for calling the program codes in the memory to execute the method in each aspect.
Yet another aspect of the present invention provides a computer storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of the above-described aspects.
The invention converts the whole amount of the call voice into the text by the voice conversion technology, obtains the key core words and counts the core key words after the text is converted by the LTP technology, obtains the score of the call recording at this time by inputting the counted core key words into the neural network, leads the regular detection to be more objective and the quality inspection to be more accurate by the intelligent quality inspection, effectively saves the time cost of the intelligent quality inspection system, and completes the configuration of the optimization personnel.
Drawings
FIG. 1 is a schematic flow chart of a telephone quality inspection method based on artificial intelligence in an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an artificial intelligence-based phone quality inspection apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an artificial intelligence-based telephone quality inspection apparatus according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The terms "first," "second," and the like in the description and in the claims, and in the drawings described above, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or modules is not necessarily limited to those steps or modules expressly listed, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus, and the division of modules into blocks presented herein is merely a logical division that may be implemented in a practical application in a different manner, such that multiple blocks may be combined or integrated into another system, or some features may be omitted, or may not be implemented.
To solve the above technical problems, the present invention mainly provides the following technical solutions
The method converts the whole amount of call voice into text by a voice conversion technology, obtains key core words and counts the core keywords after converting the text by an LTP technology, and obtains the score of the call recording by inputting the counted core keywords into a neural network. Through intelligent quality inspection, the objective rule and accurate quality inspection are realized. The intelligent quality inspection system effectively saves time and cost and completes the configuration of optimization personnel. Through the real-time quality inspection of artificial intelligence, not only is the time of the artificial quality inspection saved, but also the specific requirements and complaint points of customers can be well counted and analyzed, and relevant rules can be summarized to assist the development of services and the solution of complaints.
Referring to fig. 1, the following illustrates an artificial intelligence-based phone quality inspection method according to the present invention, including:
101. and acquiring a plurality of call records to be processed.
Call recording refers to a technique or method by which voice communication signals on a telephone line are monitored and converted to a medium that can be stored and played back. The sampling indexes of the call recording comprise format, sampling frequency, sampling precision, sound channel, compression rate and data volume per second.
102. And marking corresponding scores for each call record to be processed.
The score is scored as the quality of the call recording, and the evaluation is performed through user input. Taking the telephone score as an example, such as customer service telephone quality assessment, there are several considerations regarding its call duration, call sound level, call words, and so on, which are called attributes or characteristics of the telephone. And for the call duration: 20 minutes, sound size: moderate, words for conversation: "you" appear 40 times, and a set of data we call an example or sample, and when each attribute of the phone expands as such, the resulting set can be called a data set. Where the specific values for those attributes are referred to as attribute values. The space of attribute composition we refer to as the "attribute space", "sample space" or "input space". For example: for telephone rating, he has three attributes: the call duration, the call sound size and the call word. And generating a three-dimensional coordinate space by taking each attribute as a coordinate. For each phone call, we can find their respective corresponding position in this formed three-dimensional space, hence, also called each instance a feature vector. The process of learning a model from data is called "learning" or "training" and the entire process is carried out by executing some learning algorithm. The data used in the training process is referred to as "training data", wherein each sample is referred to as a "training sample", and the set of all training samples is referred to as a "training set". The learned model corresponds to some underlying law on the data, and is therefore referred to as an "assumption"; this underlying law is called "true facies", and the learning process is to find or approximate the true facies. Because we eventually need to do something like 'predictive' that helps us judge whether the phone in front of us is qualified. We need to add a bit of label on the basis of the previous sample, that is, to satisfy the qualification called label here, and the labeled sample we refer to as the sample.
103. And converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model.
The preset sequential neural network model is used for converting vocabulary contents in human voice into computer-readable input. The sequential neural network is a recurrent neural network which takes sequential data as input, recurses in the evolution direction of the sequence and all nodes (cyclic units) are connected in a chain manner. The sequence neural network has memorability, shared parameters and perfect graphic, so the sequence neural network has certain advantages in learning the nonlinear characteristics of the sequence. The recurrent neural network has applications in the fields of natural language processing, such as speech recognition, language modeling, machine translation, and the like, and is also used for various types of time series prediction. The circular neural network constructed by introducing the convolutional neural network can process computer vision problems containing sequence input.
104. The method comprises the steps of performing word segmentation on text information to be processed through a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords.
The language technology platform provides rich, efficient and accurate natural language processing technology including Chinese word segmentation, part of speech tagging, named entity recognition, dependency syntactic analysis, semantic role tagging and the like. LTP provides a series of chinese natural language processing tools that users can use to work on chinese text for word segmentation, part-of-speech tagging, syntactic analysis, and so on. From an application perspective, LTP provides the following components for the user: and generating a tool for counting the machine learning model aiming at the single natural language processing task. And calling a programming interface for analyzing the model aiming at the single natural language processing task. All the analysis tools are combined in a pipeline mode to form a set of unified Chinese natural language processing system. The system can call the model file for Chinese language processing. And aiming at a single natural language processing task, a programming interface based on a cloud end is provided.
105. Taking the frequency corresponding to the core keyword as input xiAnnotated score as ideal output yiAnd inputting the input to the initial neural network, and training the weight of the neuron in the initial neural network through a loss function to obtain the target neural network.
A loss function of
Wherein m is the number of the call voices to be processed, b and lambda are constants, | w | | laces1Is the L1 norm of w.
Neural networks refer to an algorithm that replicates such dense neuronal networks. By processing multiple data streams at once, the computer can significantly reduce the time required to process the data. Applying this technique to deep learning has produced artificial neural networks. These artificial neural networks consist of input nodes, output nodes, and a layer of nodes.
An input node for receiving data.
And the output node is used for outputting the result data.
And the node layer is used for converting the data input from the input node into the content which can be used by the output node. The node layer refers to a plurality of hidden nodes between the input node and the output node, and the node layer may also be a hidden layer. As data progresses through these hidden nodes, the neural network uses logic to decide to pass the data on to the next hidden node.
106. And deploying the target neural network to the client.
Deployment refers to the process of collecting, packaging, installing, configuring, publishing the configuration files, user manuals, help documents, etc. of the neural network. The main characteristics of the software deployment process are: process coverage, process variability, inter-process coordination, and model abstraction. Some abstract software deployment models have been proposed for efficiently guiding the deployment process, including application models, enterprise models, site models, product models, policy models, and deployment models.
107. And receiving call information input by a user, and inputting the call information into the target neural network through the client.
Each node of the input layer and each node of the hidden layer are subjected to point-to-point calculation, and the calculation method is weighted summation and activation. Each value calculated using the hidden layer is calculated using the same method and output layer. The hidden layer uses a logistic regression function as the activation function, and the output layer uses a linear function. This is because the linear function can maintain the previous value scaling in any range, which is convenient for comparison with the sample value, and the value range of the logistic regression can only be between 0 and 1. The value of the input layer is propagated to the hidden layer by network calculation, then propagated to the output layer in the same way, the final output value is compared with the sample value, and the error is calculated.
And 108, calculating to obtain the score of the call information through the weight of the neurons in the target neural network, and returning the score of the call information to the user.
The method converts the whole amount of call voice into text by a voice conversion technology, obtains key core words and counts the core keywords after converting the text by an LTP technology, and obtains the score of the call recording by inputting the counted core keywords into a neural network. Through intelligent quality inspection, the objective rule and accurate quality inspection are realized. The intelligent quality inspection system effectively saves time and cost and completes the configuration of optimization personnel. Through the real-time quality inspection of artificial intelligence, not only is the time of the artificial quality inspection saved, but also the specific requirements and complaint points of customers can be well counted and analyzed, and relevant rules can be summarized to assist the development of services and the solution of complaints.
In some embodiments, before segmenting a word of text information to be processed by using a language technology platform LTP technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords, the method further includes:
generating a unique identification ID number for each text message to be processed;
constructing a hypertext transfer protocol (HTTP) request according to an Application Programming Interface (API) parameter of text information to be processed;
extracting Chinese word segmentation from the text information to be processed through an HTTP request and an LTP technology;
and matching the vocabularies recorded in the database, and deleting the vocabularies with the matching degree lower than the threshold value to obtain the vocabularies with the matching degree higher than the threshold value.
In the above embodiment, the language technology platform is connected through the hypertext transfer protocol, and the text is segmented through the language technology platform.
In some embodiments, converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequential neural network model includes:
obtaining a plurality of training voice samples;
inputting a training voice sample into a preset sequence neural network model, and updating a neuron weight of the preset sequence neural network model through a Natural Language Processing (NLP) algorithm, voice information and a text label corresponding to the voice information to obtain a target model;
adjusting the weight of the neuron of the target model, and updating a preset sequence neural network model;
and converting the plurality of call records to be processed into a plurality of text messages to be processed through the updated preset sequence neural network model.
In the above embodiment, the trained target model may be used to convert the call recording into a corresponding text sequence through the computer.
In some embodiments, receiving call information input by a user, and inputting the call information into a target neural network through a client includes:
when prompting a user to input call information, starting a microphone application through a client;
after receiving the indication message, prompting a user to input call information through the client;
when the call information is obtained, removing noise, background sound and compressing the call information through the client to obtain the preprocessed call information;
judging whether the preprocessed call information conforms to an input preset rule or not through the client;
if the preset rule is not met, the client is instructed to prompt the user to input again;
if the preset rule is met, uploading the preprocessed call information input by the user to a server through the client;
and inputting the preprocessed call information into the target neural network.
In the above embodiment, the call information input by the user is received by opening the microphone and the client.
In some embodiments, before obtaining the plurality of call records to be processed, the method further comprises:
rejecting call records to be processed with call duration less than a threshold;
adding a plurality of characteristic items to the call record to be processed, wherein the characteristic items at least comprise: whether the first call is made, the average duration of the industry recording, and the satisfaction of the user telephone rating.
In the above embodiment, the invalid voice information input by the user is checked. The raw data should be audited mainly from both integrity and accuracy aspects. The integrity check mainly checks whether units or individuals to be checked have omission or not and whether all the check items or indexes are complete or not. The accuracy audit mainly comprises two aspects: firstly, whether the data material truly reflects the objective actual condition and whether the content accords with the reality is checked; and secondly, checking whether the data has errors or not, calculating whether the data is correct or not, and the like. The method for checking the data accuracy mainly comprises logic check and calculation check. The logic check mainly checks whether the data accords with the logic, whether the content is reasonable and whether the items or the numbers have the phenomenon of mutual contradiction. The calculation check is to check whether each item of data in the questionnaire has errors in the calculation result and the calculation method, and is mainly used for checking quantitative data.
In some embodiments, the segmenting the text information to be processed by using the LTP technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords includes:
frequency passing corresponding to core key words
Calculation of where FwThe frequency of the core keywords, N the number of sentences in which the core keywords appear, and M the number of sentences in the text information to be processed.
In the above embodiment, the ratio of the number of times each object appears to the total number of times is the frequency.
In some embodiments, the frequency corresponding to the core keyword is used as the input xiAnnotated score as ideal output yiInputting the input data into an initial neural network, training neuron weights in the initial neural network through a loss function, and performing word segmentation on text information to be processed through a Language Technology Platform (LTP) technology before obtaining a target neural network to obtain a plurality of core keywords and frequencies corresponding to the core keywords, wherein the method further comprises the following steps:
if the output y of the neural networkjWith n valid feature inputs x1，x2，…，xnThe weight of the corresponding connection is w1，w2，…，wnThen the neural network passes
And initializing the weight value.
In the above embodiment, training of the neural network is accelerated by initializing the weight of the neural network.
Fig. 2 is a schematic structural diagram of an artificial intelligence-based phone quality inspection apparatus 20, which can be applied to artificial intelligence-based phone quality inspection. The artificial intelligence based telephone quality inspection device in the embodiment of the invention can realize the steps corresponding to the artificial intelligence based telephone quality inspection method executed in the embodiment corresponding to the figure 1. The functions implemented by the artificial intelligence-based telephone quality inspection device 20 can be implemented by hardware, and can also be implemented by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, which may be software and/or hardware. The artificial intelligence based phone quality inspection apparatus may include an input/output module 201 and a processing module 202, and the processing module 202 and the input/output module 201 may refer to operations performed in the embodiment corresponding to fig. 1 for realizing the functions, which are not described herein again. The input-output module 201 may be used to control input, output, and acquisition operations of the input-output module 201.
In some embodiments, the input/output module 201 may be configured to obtain a plurality of call records to be processed;
the processing module 202 may be configured to label a score corresponding to each call recording to be processed; converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model; segmenting the text information to be processed by a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords; using the frequency corresponding to the core keyword as an input x through the input and output moduleiAnnotated said score as ideal output yiInputting the neural weight to an initial neural network, and training the neuron weight in the initial neural network through a loss function to obtain a target neural network; deploying the target neural network to a client; receiving call information input by a user through the input and output module, and inputting the call information to the target neural network through the client; and calculating the score of the call information through the weight of the neuron in the target neural network, and returning the score of the call information to the user.
In some embodiments, the processing module 202 is further configured to:
converting the plurality of call records to be processed into text information to be processed through a preset sequence neural network model;
generating a unique identification ID number for each text message to be processed;
constructing a hypertext transfer protocol (HTTP) request according to the API parameters of the application programming interface of the text information to be processed;
extracting Chinese word segmentation from the text information to be processed through the HTTP request and the LTP technology;
and matching the vocabularies recorded in the database, and deleting the vocabularies with the matching degree lower than the threshold value to obtain the vocabularies with the matching degree higher than the threshold value.
In some embodiments, the processing module 202 is further configured to:
obtaining a plurality of training voice samples;
inputting the training voice sample into the preset sequence neural network model, and updating the neuron weight of the preset sequence neural network model through a Natural Language Processing (NLP) technology, the voice information and a text label corresponding to the voice information to obtain a target model;
adjusting the weight of the neuron of the target model, and updating the preset sequential neural network model;
and converting the plurality of call records to be processed into a plurality of text messages to be processed through the updated preset sequence neural network model.
In some embodiments, the processing module 202 is further configured to:
when prompting the user to input the call information, starting a microphone application through the client;
after receiving the indication message, prompting a user to input call information through the client;
when the call information is obtained, removing noise and background sound of the call information and compressing the call information through the client to obtain the preprocessed call information;
judging whether the preprocessed call information conforms to an input preset rule or not through the client;
if the preset rule is not met, the client is instructed to prompt the user to input again;
if the preset rule is met, uploading the preprocessed call information input by the user to a server through the client;
and inputting the preprocessed call information into the target neural network.
In some embodiments, the processing module 202 is further configured to:
rejecting the call records with the call duration less than the threshold value;
adding a plurality of characteristic items to the call record to be processed, wherein the characteristic items at least comprise: whether the first call is made, the average duration of the industry recording, and the satisfaction of the user telephone rating.
In some embodiments, the processing module 202 is further configured to:
the corresponding frequency of the core key words is passed
Calculation of where FwThe frequency of the core keywords, N the number of sentences in which the core keywords appear, and M the number of sentences in the text information to be processed.
In some embodiments, the processing module 202 is further configured to:
if the output y of the neural networkjWith n of said valid feature inputs x1，x2，…，xnThe weight of the corresponding connection is w1，w2，…，wnThen the neural network passes
And initializing the weight value.
The above describes the artificial intelligence based telephone quality inspection apparatus in the embodiment of the present invention from the perspective of the modular functional entity, and the following describes an artificial intelligence based telephone quality inspection apparatus from the perspective of hardware, as shown in fig. 3, which includes: a processor, a memory, an input-output unit (which may also be a transceiver, not identified in fig. 3), and a computer program stored in the memory and executable on the processor. For example, the computer program may be a program corresponding to the telephone quality inspection method based on artificial intelligence in the embodiment corresponding to fig. 1. For example, when the computer device implements the functions of the artificial intelligence based telephone quality inspection apparatus 20 shown in fig. 2, the processor executes the computer program to implement the steps of the artificial intelligence based telephone quality inspection method executed by the artificial intelligence based telephone quality inspection apparatus 20 in the embodiment corresponding to fig. 2. Alternatively, the processor implements the functions of the modules in the artificial intelligence based telephone quality inspection apparatus 20 according to the embodiment corresponding to fig. 2 when executing the computer program. For another example, the computer program may be a program corresponding to the telephone quality inspection method based on artificial intelligence in the embodiment corresponding to fig. 1.
The processor may be a Central Processing Unit (CPU), other general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like which is the control center for the computer device and which connects the various parts of the overall computer device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the computer device by running or executing the computer programs and/or modules stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The input-output unit may also be replaced by a receiver and a transmitter, which may be the same or different physical entities. When they are the same physical entity, they may be collectively referred to as an input-output unit. The input and output may be a transceiver.
The memory may be integrated in the processor or may be provided separately from the processor.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM), and includes instructions for causing a terminal (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The present invention is described in connection with the accompanying drawings, but the present invention is not limited to the above embodiments, which are only illustrative and not restrictive, and those skilled in the art can make various changes without departing from the spirit and scope of the invention as defined by the appended claims, and all changes that come within the meaning and range of equivalency of the specification and drawings that are obvious from the description and the attached claims are intended to be embraced therein.
Claims (10)
1. A telephone quality inspection method based on artificial intelligence is characterized by comprising the following steps:
acquiring a plurality of call records to be processed;
marking a corresponding score for each call recording to be processed;
converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model;
segmenting the text information to be processed by a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords;
taking the frequency corresponding to the core keyword as an input xiAnnotated said score as ideal output yiInputting the neural weight to an initial neural network, and training the neuron weight in the initial neural network through a loss function to obtain a target neural network;
deploying the target neural network to a client;
receiving call information input by a user, and inputting the call information into the target neural network through the client;
and calculating the score of the call information through the weight of the neuron in the target neural network, and returning the score of the call information to the user.
2. The method according to claim 1, wherein before the segmenting the text information to be processed by using a language technology platform LTP technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords, the method further comprises:
generating a unique identification ID number for each text message to be processed;
constructing a hypertext transfer protocol (HTTP) request according to the API parameters of the application programming interface of the text information to be processed;
extracting Chinese word segmentation from the text information to be processed through the HTTP request and the LTP technology;
and matching the words recorded in the database, and deleting the words with the matching degree lower than the threshold value to obtain the words with the matching degree higher than the threshold value.
3. The method of claim 1, wherein the converting the plurality of pending call records into a plurality of pending text messages via a pre-configured sequential neural network model comprises:
obtaining a plurality of training voice samples;
inputting the training voice sample into the preset sequence neural network model, and updating the neuron weight of the preset sequence neural network model through a Natural Language Processing (NLP) algorithm, the voice information and a text label corresponding to the voice information to obtain a target model;
adjusting the weight of the neuron of the target model, and updating the preset sequential neural network model;
and converting the plurality of call records to be processed into a plurality of text messages to be processed through the updated preset sequence neural network model.
4. The method of claim 1, wherein the receiving the call information input by the user, and the inputting the call information into the target neural network through the client comprises:
when prompting the user to input the call information, starting a microphone application through the client;
after receiving the indication message, prompting a user to input call information through the client;
when the call information is obtained, removing noise and background sound of the call information and compressing the call information through the client to obtain the preprocessed call information;
judging whether the preprocessed call information conforms to an input preset rule or not through the client;
if the preset rule is not met, the client is instructed to prompt the user to input again;
if the preset rule is met, uploading the preprocessed call information input by the user to a server through the client;
and inputting the preprocessed call information into the target neural network.
5. The method of claim 1, wherein prior to obtaining the plurality of pending call records, the method further comprises:
rejecting the call records with the call duration less than the threshold value;
adding a plurality of characteristic items to the call record to be processed, wherein the characteristic items at least comprise: whether the first call is made, the average duration of the industry recording, and the satisfaction of the user telephone rating.
6. The method according to claim 1, wherein the segmenting the text information to be processed by using a language technology platform LTP technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords comprises:
the corresponding frequency of the core key words is passed
Calculation of where FwThe frequency of the core keywords, N the number of sentences in which the core keywords appear, and M the number of sentences in the text information to be processed.
7. The method according to any of claims 1-6, wherein the method is performed by taking a frequency corresponding to the core keyword as an input xiAnnotated said score as ideal output yiInputting the input data into an initial neural network, training neuron weights in the initial neural network through a loss function, and before obtaining a target neural network, after segmenting the text information to be processed through a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords, the method further comprises the following steps:
if the output y of the neural networkjWith n of said valid feature inputs x1，x2，…，xnThe weight of the corresponding connection isw1,w2,…，wnThen the neural network passes
And initializing the weight value.
8. An artificial intelligence based telephone quality inspection device, the device comprising:
the input and output module is used for acquiring a plurality of call records to be processed;
the processing module is used for marking a corresponding score for each call record to be processed; converting the plurality of call records to be processed into a plurality of text messages to be processed through a preset sequence neural network model; segmenting the text information to be processed by a Language Technology Platform (LTP) technology to obtain a plurality of core keywords and frequencies corresponding to the core keywords; using the frequency corresponding to the core keyword as an input x through the input and output moduleiAnnotated said score as ideal output yiInputting the neural weight to an initial neural network, and training the neuron weight in the initial neural network through a loss function to obtain a target neural network; deploying the target neural network to a client; receiving call information input by a user through the input and output module, and inputting the call information to the target neural network through the client; and calculating the score of the call information through the weight of the neuron in the target neural network, and returning the score of the call information to the user.
9. An artificial intelligence based telephone quality inspection apparatus, wherein the computer apparatus comprises:
at least one processor, a memory, and an input-output unit;
wherein the memory is configured to store program code and the processor is configured to invoke the program code stored in the memory to perform the method of any of claims 1-7.
10. A computer storage medium comprising instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911367527.7A CN111182162B (en) | 2019-12-26 | 2019-12-26 | Telephone quality inspection method, device, equipment and storage medium based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911367527.7A CN111182162B (en) | 2019-12-26 | 2019-12-26 | Telephone quality inspection method, device, equipment and storage medium based on artificial intelligence |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111182162A true CN111182162A (en) | 2020-05-19 |
| CN111182162B CN111182162B (en) | 2023-04-25 |
Family
ID=70652188
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911367527.7A Active CN111182162B (en) | 2019-12-26 | 2019-12-26 | Telephone quality inspection method, device, equipment and storage medium based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111182162B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723204A (en) * | 2020-06-15 | 2020-09-29 | 龙马智芯（珠海横琴）科技有限公司 | Method and device for correcting voice quality inspection area, correction equipment and storage medium |
| CN111753994A (en) * | 2020-06-22 | 2020-10-09 | 深圳鲲云信息科技有限公司 | Data processing method and device of AI chip and computer equipment |
| CN112163434A (en) * | 2020-10-20 | 2021-01-01 | 腾讯科技（深圳）有限公司 | Text translation method, device, medium and electronic equipment based on artificial intelligence |
| CN112911072A (en) * | 2021-01-28 | 2021-06-04 | 携程旅游网络技术（上海）有限公司 | Call center volume identification method and device, electronic equipment and storage medium |
| CN113132523A (en) * | 2021-04-19 | 2021-07-16 | 广州绿怡信息科技有限公司 | Call detection model training method and call detection method |
| CN113179352A (en) * | 2021-05-25 | 2021-07-27 | 北京捷通华声科技股份有限公司 | Call quality determination method, call quality determination device, and computer-readable storage medium |
| CN113724708A (en) * | 2021-07-23 | 2021-11-30 | 山东环维通讯科技有限公司 | Quality control system for converting voice into text intelligent checklist |
| CN115499545A (en) * | 2022-09-13 | 2022-12-20 | 上海浦东发展银行股份有限公司 | Telephone customer service performance scoring method, device, equipment and medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106776806A (en) * | 2016-11-22 | 2017-05-31 | 广东电网有限责任公司佛山供电局 | The methods of marking and system of call center's quality inspection voice |
| US20170154293A1 (en) * | 2014-06-16 | 2017-06-01 | Panasonic Intellectual Property Management Co., Ltd. | Customer service appraisal device, customer service appraisal system, and customer service appraisal method |
| CN107885726A (en) * | 2017-11-06 | 2018-04-06 | 广州杰赛科技股份有限公司 | Customer service quality evaluating method and device |
| CN108806667A (en) * | 2018-05-29 | 2018-11-13 | 重庆大学 | The method for synchronously recognizing of voice and mood based on neural network |
| CN108962282A (en) * | 2018-06-19 | 2018-12-07 | 京北方信息技术股份有限公司 | Speech detection analysis method, apparatus, computer equipment and storage medium |
| CN109327632A (en) * | 2018-11-23 | 2019-02-12 | 深圳前海微众银行股份有限公司 | Intelligent quality inspection system, method and the computer readable storage medium of customer service recording |
| CN109961801A (en) * | 2019-01-16 | 2019-07-02 | 深圳壹账通智能科技有限公司 | Intelligent Service evaluation method, computer readable storage medium and terminal device |
| CN110310663A (en) * | 2019-05-16 | 2019-10-08 | 平安科技（深圳）有限公司 | Words art detection method, device, equipment and computer readable storage medium in violation of rules and regulations |
| CN110378587A (en) * | 2019-07-08 | 2019-10-25 | 上海辰尚信息科技有限公司 | Intelligent quality detecting method, system, medium and equipment |
-
2019
- 2019-12-26 CN CN201911367527.7A patent/CN111182162B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170154293A1 (en) * | 2014-06-16 | 2017-06-01 | Panasonic Intellectual Property Management Co., Ltd. | Customer service appraisal device, customer service appraisal system, and customer service appraisal method |
| CN106776806A (en) * | 2016-11-22 | 2017-05-31 | 广东电网有限责任公司佛山供电局 | The methods of marking and system of call center's quality inspection voice |
| CN107885726A (en) * | 2017-11-06 | 2018-04-06 | 广州杰赛科技股份有限公司 | Customer service quality evaluating method and device |
| CN108806667A (en) * | 2018-05-29 | 2018-11-13 | 重庆大学 | The method for synchronously recognizing of voice and mood based on neural network |
| CN108962282A (en) * | 2018-06-19 | 2018-12-07 | 京北方信息技术股份有限公司 | Speech detection analysis method, apparatus, computer equipment and storage medium |
| CN109327632A (en) * | 2018-11-23 | 2019-02-12 | 深圳前海微众银行股份有限公司 | Intelligent quality inspection system, method and the computer readable storage medium of customer service recording |
| CN109961801A (en) * | 2019-01-16 | 2019-07-02 | 深圳壹账通智能科技有限公司 | Intelligent Service evaluation method, computer readable storage medium and terminal device |
| CN110310663A (en) * | 2019-05-16 | 2019-10-08 | 平安科技（深圳）有限公司 | Words art detection method, device, equipment and computer readable storage medium in violation of rules and regulations |
| CN110378587A (en) * | 2019-07-08 | 2019-10-25 | 上海辰尚信息科技有限公司 | Intelligent quality detecting method, system, medium and equipment |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111723204A (en) * | 2020-06-15 | 2020-09-29 | 龙马智芯（珠海横琴）科技有限公司 | Method and device for correcting voice quality inspection area, correction equipment and storage medium |
| CN111753994A (en) * | 2020-06-22 | 2020-10-09 | 深圳鲲云信息科技有限公司 | Data processing method and device of AI chip and computer equipment |
| CN111753994B (en) * | 2020-06-22 | 2023-11-03 | 深圳鲲云信息科技有限公司 | Data processing method and device of AI chip and computer equipment |
| CN112163434A (en) * | 2020-10-20 | 2021-01-01 | 腾讯科技（深圳）有限公司 | Text translation method, device, medium and electronic equipment based on artificial intelligence |
| CN112163434B (en) * | 2020-10-20 | 2024-03-08 | 腾讯科技（深圳）有限公司 | Text translation method, device, medium and electronic equipment based on artificial intelligence |
| CN112911072A (en) * | 2021-01-28 | 2021-06-04 | 携程旅游网络技术（上海）有限公司 | Call center volume identification method and device, electronic equipment and storage medium |
| CN113132523A (en) * | 2021-04-19 | 2021-07-16 | 广州绿怡信息科技有限公司 | Call detection model training method and call detection method |
| CN113132523B (en) * | 2021-04-19 | 2023-05-26 | 广州绿怡信息科技有限公司 | Call detection model training method and call detection method |
| CN113179352A (en) * | 2021-05-25 | 2021-07-27 | 北京捷通华声科技股份有限公司 | Call quality determination method, call quality determination device, and computer-readable storage medium |
| CN113179352B (en) * | 2021-05-25 | 2023-04-07 | 北京捷通华声科技股份有限公司 | Call quality determination method, call quality determination device, and computer-readable storage medium |
| CN113724708A (en) * | 2021-07-23 | 2021-11-30 | 山东环维通讯科技有限公司 | Quality control system for converting voice into text intelligent checklist |
| CN115499545A (en) * | 2022-09-13 | 2022-12-20 | 上海浦东发展银行股份有限公司 | Telephone customer service performance scoring method, device, equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111182162B (en) | 2023-04-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111182162B (en) | Telephone quality inspection method, device, equipment and storage medium based on artificial intelligence | |
| WO2022095380A1 (en) | Ai-based virtual interaction model generation method and apparatus, computer device and storage medium | |
| CN106446045B (en) | User portrait construction method and system based on dialogue interaction | |
| CN107766511A (en) | Intelligent answer method, terminal and storage medium | |
| CN111046132A (en) | Customer service question and answer processing method and system for retrieving multiple rounds of conversations | |
| CN109885810A (en) | Nan-machine interrogation's method, apparatus, equipment and storage medium based on semanteme parsing | |
| CN110910283A (en) | Method, device, equipment and storage medium for generating legal document | |
| CN109345282A (en) | A kind of response method and equipment of business consultation | |
| CN109857846B (en) | Method and device for matching user question and knowledge point | |
| CN107704453A (en) | A kind of word semantic analysis, word semantic analysis terminal and storage medium | |
| CN103593412B (en) | A kind of answer method and system based on tree structure problem | |
| CN111694940A (en) | User report generation method and terminal equipment | |
| CN111694937A (en) | Interviewing method and device based on artificial intelligence, computer equipment and storage medium | |
| CN108549697A (en) | Information-pushing method, device, equipment based on semantic association and storage medium | |
| CN113239147A (en) | Intelligent conversation method, system and medium based on graph neural network | |
| CN111930914A (en) | Question generation method and device, electronic equipment and computer-readable storage medium | |
| US20070156406A1 (en) | Voice user interface authoring tool | |
| CN117056481A (en) | Cloud service industry dialogue help system based on large model technology and implementation method | |
| CN116431806A (en) | Natural language understanding method and refrigerator | |
| CN113627194B (en) | Information extraction method and device, and communication message classification method and device | |
| CN114065720A (en) | Conference summary generation method and device, storage medium and electronic equipment | |
| CN112966076A (en) | Intelligent question and answer generating method and device, computer equipment and storage medium | |
| CN110782221A (en) | Intelligent interview evaluation system and method | |
| CN114579751A (en) | Emotion analysis method and device, electronic equipment and storage medium | |
| CN114462376A (en) | RPA and AI-based court trial record generation method, device, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |