Method and device for generating verbal skill process by using GPT instruction

Abstract

The invention relates to a method and a device for generating a verbal skill process by using a GPT instruction. The method comprises the following steps: 1) corpus construction: firstly, constructing a corpus of a specific scene; 2) data pre-processing: pre-processing the data before the data is sent into the model; 3) model training: training a GPT model by using the processed data; 4) instruction generation: after the model is trained, generating a verbal skill process of a specific scene by using a GPT instruction; (5) after the dialogue is generated, customized modification and adjustment are conducted on the dialogue according to actual needs; 6) after the customized generation of the verbal skill, exporting the verbal skill as a text file or storing the verbal skill in a local database; compared with the prior art, the method has the advantages that different verbal skills can be generated according to different scenes and different customer requirements, the method has high individuation ability, meanwhile, the working efficiency is remarkably improved, and the AI solving ability of intelligent product application is improved.

Description

[Technical field]

[0001] The present invention relates to the field of information processing technology, and in particular to a method and device for generating a speech flow using GPT instructions. [Background Technology]

[0002] The existing speech process configuration platform has the following main shortcomings. Specifically, without the technology of using GPT instructions to generate speech processes, enterprises may face the following problems and shortcomings:

[0003] (1) Time and cost constraints: Manually writing the script for each scenario takes a lot of time and costs a lot of money. Companies need to assign professionals to write, modify, and review the script, which not only increases the company's operating costs but also reduces work efficiency. In addition, as the company's business continues to expand and change, the updating and optimization of the script will become more cumbersome and time-consuming.

[0004] (2) Difficulty in ensuring consistency and accuracy: Manually written scripts are prone to grammatical errors, spelling errors, and inaccurate expressions, which may affect customers' impression and trust in the company. At the same time, due to differences in writing styles and word usage habits among different people, the scripts may lack consistency in expression, which can also lead to an unstable customer experience.

[0005] (3) Difficulty in achieving personalized services: Companies hope to provide personalized services and scripts to each customer to meet the customer's specific needs and preferences. However, manually written scripts are difficult to customize for each customer, and companies can often only provide limited choices or standard answers, which makes the customer experience suboptimal.

[0006] (4) Difficulty in responding quickly to customer needs: In a highly competitive market, responding quickly to customer needs is crucial; however, the process of manually writing scripts is often cumbersome and requires multiple levels of review and confirmation, making it difficult for companies to provide customers with satisfactory responses and solutions in a short period of time.

[0007] (5) Difficulty in knowledge management and inheritance: Enterprises need to accumulate and pass on their own knowledge and experience to continuously improve service quality and efficiency. However, manually writing scripts makes it difficult to integrate the knowledge and experience of the enterprise, making it difficult for employees to share and pass on these valuable resources, which also limits the growth and development of the enterprise.

[0008] To sum up, if the technology of generating speech flow from GPT instructions is not used, enterprises may face problems and shortcomings such as time and cost constraints, difficulty in ensuring consistency and accuracy, difficulty in providing personalized services, difficulty in responding quickly to customer needs, and difficulty in knowledge management and inheritance. The introduction of the technology of generating speech flow from GPT instructions can help enterprises overcome these problems, improve work efficiency and customer satisfaction, and thus achieve better development in the fierce market competition. [Summary of the invention]

[0009] The purpose of the present invention is to solve the above-mentioned deficiencies and provide a method for generating a speech flow using GPT instructions, which can generate different speech according to different scenarios and different customer needs, has strong personalization capabilities, and significantly improves work efficiency and the AI ​​solution capability of smart product applications.

[0010] In one aspect, the present invention provides a method for generating a speech flow using a GPT instruction, comprising the following steps:

[0011] 1) Corpus construction: First, build a corpus for a specific scenario;

[0012] 2) Data preprocessing: Before feeding the data into the model, preprocess the data;

[0013] 3) Model training: Use the processed data to train the GPT model;

[0014] 4) Instruction generation: After the model is trained, use GPT instructions to generate the speech flow for specific scenarios.

[0015] As an embodiment, in step 2), first, the text data is segmented and tagged with parts of speech; then, the text data is converted into a format that can be processed by the model, that is, tokenized.

[0016] As an embodiment, in step 3), during the training process, the model learns a large number of language patterns and grammatical rules, and learns how to generate coherent text that conforms to the context; after the training is completed, the model is evaluated using test data to ensure that its performance meets the expected level.

[0017] As an embodiment, in step 4), after the instruction is input, the GPT model automatically generates a set of speech templates that match the scenario.

[0018] As an embodiment, after step 4) the instruction is generated, a personalized generation step is also included: after the dialogue is generated, the dialogue is customized and adjusted according to actual needs.

[0019] As an embodiment, after the personalized generation, the steps of exporting and using the speech are also included: after the customized speech is generated, the speech is exported as a text file or saved in a local database.

[0020] In another aspect, the present invention provides a device for generating a speech flow using a GPT instruction, comprising:

[0021] The corpus construction module is used to build a corpus for a specific scenario;

[0022] The data preprocessing module is used to preprocess the data before feeding it into the model;

[0023] Model training module, used to train the GPT model using the processed data;

[0024] The instruction generation module is used to generate the speech flow for a specific scenario using GPT instructions after the model is trained;

[0025] Personalized generation module, which is used to modify and adjust the dialogues according to actual needs after the dialogues are generated;

[0026] The script export and use module is used to export the script as a text file or save it to the local database after the customized script is generated.

[0027] As an embodiment, in the data preprocessing module, the text data is first segmented and tagged with parts of speech, and then the text data is converted into a format that can be processed by the model, that is, tokenized.

[0028] As an embodiment, during the training process of the model training module, the model learns a large number of language patterns and grammatical rules, and learns how to generate coherent text that conforms to the context; after the training is completed, the model is evaluated using test data to ensure that its performance reaches the expected level.

[0029] In a third aspect of the present invention, a computer device is proposed, comprising: a processor, a memory and a bus; the processor and the memory are connected via the bus; the memory is used to store programs, the processor is used to run programs, and the above method is executed when the program is run.

[0030] Compared with the prior art, the present invention has the following advantages:

[0031] (1) Improve efficiency: The present invention can significantly improve work efficiency by using GPT instructions to generate a script flow. The time and effort previously required to manually write scripts will be greatly reduced. At the same time, the automatic generation of the GPT model can also reduce errors and omissions, improve the quality and consistency of the scripts, and greatly improve the efficiency of related configuration work.

[0032] (2) Improving the AI ​​solution capability of smart product applications: The GPT of the present invention can generate high-quality text, thereby simulating a more realistic dialogue scenario, making the interaction between smart products and users more natural and smooth; compared with traditional keyword matching and rule engines, GPT can better understand the user's intentions and needs, and generate more intelligent replies and solutions; GPT can generate text that meets the requirements from a large amount of text data based on the keywords and context entered by the user, thereby showing a certain degree of originality and creativity, which will make smart products more outstanding in content generation and creativity, and can provide more imaginative solutions; the application of GPT will be able to improve the AI ​​solution capability of smart product applications, so that smart products can perform better in dialogue interaction, semantic understanding, knowledge field expansion and creative creativity, and provide users with more intelligent, efficient and comprehensive services.

[0033] (3) Enhanced personalization capabilities: The GPT instruction generation process of the present invention has strong personalization capabilities and can generate different scripts according to different scenarios and different customer needs. For example, for customers from different industries, different regions, and different personalities, GPT can automatically generate highly targeted script content, which will make customer service more personalized and customized. The GPT model is trained with a large amount of text data to better understand semantics and extract key information from user input, which will enable smart products to better understand user problems and needs and provide more accurate personalized solutions. [Drawings]

[0034] Figure 1 It is a schematic diagram of the process structure of the method of the present invention;

[0035] Figure 2 It is a schematic diagram of the structure of the device of the present invention;

[0036] Figure 3 It is a principle block diagram of the present invention;

[0037] Figure 4 It is a structural schematic diagram of the computer device of the present invention. [Specific implementation method]

[0038] References to "one embodiment" or "some embodiments" etc. described in this specification mean that a particular feature, structure or characteristic described in conjunction with the embodiment is included in one or more embodiments of the present invention. Thus, the phrases "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appearing in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0039] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described below in conjunction with the accompanying drawings and specific embodiments:

[0040] As attached Figure 1 As shown, in one embodiment, the present invention provides a method for generating a speech flow using GPT instructions, the purpose of which is to provide a method for generating a speech flow in a specific scenario using a GPT model, establishing a diversified interactive mode, a personalized service experience, a convenient business processing flow, comprehensively improving the user experience, sustainable development, integrating new technology capabilities, creating new applications, and establishing a speech flow library. The technical solution proposed by the present invention uses the GPT model to generate a speech flow in a specific scenario, and the specific steps are as follows:

[0041] 1. Corpus construction:

[0042] First, you need to build a corpus for a specific scenario. The corpus is the basis of natural language processing. It contains a large amount of text data for training and optimizing models. In this solution, you need to collect conversation data for specific scenarios, such as customer complaints, after-sales service, customer return visits, and other scenarios.

[0043] 2. Data preprocessing:

[0044] Before feeding the data into the model, a series of data preprocessing is required. First, the text data needs to be segmented and tagged. Then, the text data needs to be converted into a format that the model can process, i.e. tokenization. Tokenization is the process of converting text into symbols that the model can understand. It can convert continuous text into independent tokens.

[0045] 3. Model training:

[0046] The GPT model is trained using the processed data. During the training process, the model will learn a large number of language patterns and grammatical rules, and learn how to generate coherent text that conforms to the context. After the training is completed, the model can be evaluated using test data to ensure that its performance meets the expected level.

[0047] 4. Instruction generation:

[0048] After the model is trained, you can use GPT instructions to generate a specific scenario's speech flow. The specific instruction format can be referred to as follows: "Generate [scenario name] speech". After entering the instruction, the GPT model will automatically generate a set of speech templates that match the scenario. For example, if you enter "Generate customer complaint speech", the model will generate a set of customer complaint speech templates, including "Dear [customer name], thank you for your support to our [company name]... Based on your feedback, we have arranged for someone to handle this matter. Please wait patiently for our reply." and other statements.

[0049] 5. Personalized generation:

[0050] After the script is generated, it can be customized and adjusted according to actual needs. For example, you can add a message of condolence to the customer or a message of thanks for the customer's feedback, etc. You can also customize the script by adding variables such as the customer's name and company name according to the needs of different customers.

[0051] 6. Export and use of speech techniques:

[0052] After the customized speech is generated, the speech can be exported as a text file or saved to a local database. In actual use, the exported speech can be used directly for communication, or the content of the speech can be modified and adjusted in real time during communication to better meet actual needs.

[0053] When the speech process configuration device of the present invention receives a user's question and answer (or behavior) that cannot trigger the trigger rule of a node, a prompt can be generated for GPT to process, thereby generating a speech question and answer that has flexibility and high accuracy.

[0054] In some other embodiments, the present invention provides a device for generating a speech flow using a GPT instruction, comprising:

[0055] The corpus construction module is used to build a corpus for a specific scenario;

[0056] The data preprocessing module is used to preprocess the data before feeding it into the model;

[0057] Model training module, used to train the GPT model using the processed data;

[0058] The instruction generation module is used to generate the speech flow for a specific scenario using GPT instructions after the model is trained;

[0059] Personalized generation module, which is used to modify and adjust the dialogue according to actual needs after the dialogue is generated;

[0060] The script export and use module is used to export the script as a text file or save it to a local database after the customized script is generated.

[0061] As a further embodiment, in the data preprocessing module, the text data is first segmented and tagged with parts of speech, and then the text data is converted into a format that can be processed by the model, that is, tokenized.

[0062] As a further embodiment, during the training process of the model training module, the model learns a large number of language patterns and grammatical rules, and learns how to generate coherent text that conforms to the context; after the training is completed, the model is evaluated using test data to ensure that its performance reaches the expected level.

[0063] In some other embodiments, the Figure 4 The figure is a schematic diagram of a computer device of the present invention. The computer device includes a processor, a memory and a bus; the processor and the memory are connected via a bus, the memory is used to store programs, and the processor is used to run programs, and when the programs are run, the method for generating speech flow using GPT instructions provided by the present invention is executed. Furthermore, the present invention also provides a computer-readable storage medium, the computer-readable storage medium includes a stored program, and the program executes the method for generating speech flow using GPT instructions provided by the present invention.

[0064] The computer device may be a desktop computer, a notebook computer, a PDA, a cloud server, or other computing devices. Figure 4 It is only an example of a computer device and does not constitute a limitation of the computer device. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0065] The processor may be a central processing unit CPU, or other general-purpose processors, digital signal processors DSP, application-specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device.

[0066] The memory can be used to store computer programs and / or modules / units. The processor realizes various functions of the computer device by running or executing the computer programs and / or modules / units stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the computer device (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a 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, at least one disk storage device, a flash memory device or other volatile solid-state storage device.

[0067] If the functions described in the method of the embodiment of the present invention are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the part of the embodiment of the present invention that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, mobile computing device or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention; and the aforementioned storage medium includes various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories, random access memories, disks or optical disks. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0068] In some other embodiments, a company needs to improve the quality and efficiency of its customer service and hopes to quickly respond to customer questions and needs by generating a speech flow through GPT instructions.

[0069] The following are the specific implementation steps:

[0070] (1) Determine needs and goals: First, you need to identify your target customer groups and understand their needs and problems so that you can provide them with better services.

[0071] (2) Data collection and analysis: Collect the problems and needs that customers often encounter, and analyze the data to understand their needs and problems.

[0072] (3) Formulate GPT instructions: Based on the analysis results, formulate GPT instructions, including input and output, and how to generate corresponding answers based on customer questions and needs.

[0073] (4) Develop the GPT model: Use the prepared GPT instructions to train the GPT model so that the model can learn to generate corresponding answers based on customer questions and needs.

[0074] (5) Testing and optimization: After the model is developed, a lot of testing and optimization work is carried out to ensure that the accuracy and stability of the model are optimal.

[0075] (6) Deploy to the production environment: Finally, the GPT model is deployed to the production environment and integrated with the company’s customer service system to provide better services to customers.

[0076] From this specific case, we can see that by using GPT instructions to generate the speech process, the company's customer service quality and efficiency have been significantly improved, with the following effects:

[0077] 1. Fast response: Since the GPT model responds very quickly, companies can respond to customer questions and needs more quickly;

[0078] 2. Improve efficiency: The GPT model can automatically generate script content, reducing the time and cost of manual writing and improving work efficiency;

[0079] 3. Improved customer satisfaction: Customer satisfaction is improved because the company can respond to customer questions and needs faster and more accurately;

[0080] 4. Reduce costs: By using the GPT model, the company can reduce the number of customer service staff and reduce labor costs;

[0081] 5. Improve the quality of speech: The speech content of the GPT model is more standardized, accurate, and professional, which improves the quality of speech.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit it. Under the idea of ​​the present invention, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other changes in different aspects of the present invention as described above. Although the present invention is described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the technical solutions described in the above embodiments can still be modified, or some of the technical features can be replaced by equivalents. However, these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0083] The present invention is not limited to the above-mentioned implementation modes, and any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement modes and shall be included in the protection scope of the present invention.

Claims

1. A method for generating a speech flow using GPT instructions, characterized in that: The following steps are involved: 1) Corpus construction: First, build a corpus for a specific scenario; 2) Data preprocessing: Before feeding the data into the model, preprocess the data; 3) Model training: Use the processed data to train the GPT model; 4) Instruction generation: After the model is trained, use GPT instructions to generate the speech flow for specific scenarios.

2. The method according to claim 1, characterized in that: In step 2), first, the text data is segmented and tagged with parts of speech; then, the text data is converted into a format that can be processed by the model, that is, tokenized.

3. The method according to claim 1, characterized in that: In step 3), during the training process, the model learns a large number of language patterns and grammatical rules, and learns how to generate coherent text that fits the context; after the training, the model is evaluated using test data to ensure that its performance meets the expected level.

4. The method according to claim 1, characterized in that: In step 4), after entering the command, the GPT model automatically generates a set of speech templates that match the scenario.

5. The method according to claim 1, characterized in that Step 4) After the instruction is generated, a personalized generation step is also included: after the dialogue is generated, the dialogue is customized and adjusted according to actual needs.

6. The method according to claim 1, characterized in that After personalized generation, there are also steps for exporting and using the script: after customized generation of the script, export the script as a text file or save it to a local database.

7. A device for generating a speech flow using GPT instructions, characterized in that: include: The corpus construction module is used to build a corpus for a specific scenario; The data preprocessing module is used to preprocess the data before feeding it into the model; Model training module, used to train the GPT model using the processed data; The instruction generation module is used to generate the speech flow for a specific scenario using GPT instructions after the model is trained; Personalized generation module, which is used to modify and adjust the dialogues according to actual needs after the dialogues are generated; The script export and use module is used to export the script as a text file or save it to the local database after the customized script is generated.

8. The device according to claim 7, characterized in that: In the data preprocessing module, the text data is first segmented and tagged with parts of speech, and then the text data is converted into a format that can be processed by the model, that is, tokenized.

9. The device according to claim 7, characterized in that: During the training process of the model training module, the model learns a large number of language patterns and grammatical rules, and learns how to generate coherent text that conforms to the context; after the training is completed, the model is evaluated using test data to ensure that its performance meets the expected level.

10. A computer device, characterized in that: include: processor, memory, and bus; The processor is connected to the memory via the bus; The memory is used to store a program, and the processor is used to run the program. When the program is run, the method according to any one of claims 1 to 6 is executed.

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