Method and device for generating appointment conversation report based on large model

Abstract

The invention provides a conversation report generation method and device based on a large model. Presetting a conversation report template; preset filling positions are reserved in the appointment conversation person information column and the appointment conversation content column of the appointment conversation report template, and the reserved filling positions are filled with variables; voice recognition is carried out on voice content recorded in the appointment conversation process, and the recognized appointment conversation content in a text form is input into the large model; basic personnel information analyzed and output by the large model is used as a value of a variable reserved in an appointment talking person information column, and the appointment talking person information column in the appointment talking report template is filled with the value; and taking the appointment content key points analyzed and output by the large model as values of variables reserved in an appointment content column, and filling the appointment content column in the appointment report template with the appointment content key points, so as to obtain an appointment report corresponding to the appointment process. The conversation report generation method provided by the embodiment of the invention improves the intelligence, avoids the time and labor consumption of personnel, and improves the efficiency.

Description

Technical Field

[0001] One or more embodiments of the present invention relate to network communication technology, and more particularly, to a method and device for generating an interview report based on a large model. Background Art

[0002] In today's society, information technology is developing rapidly, and various industries often need to generate various reports. Staff need to spend a lot of energy to extract the content required for the report based on various forms of content and finally generate the report.

[0003] However, the current report generation method requires staff to manually analyze the content required for the report and fill in the content required for the report manually in order to generate the report. For example, staff need to interview relevant personnel for investigation and generate an interview report based on the interview content. It can be seen that this interview report generation method has problems such as lack of intelligence, time-consuming, labor-intensive, and inefficient. Summary of the invention

[0004] One or more embodiments of the present invention describe a method and device for generating an interview report based on a large model, which can solve at least one problem in the prior art.

[0005] According to the first aspect, a method for generating an interview report based on a large model is proposed, and the method includes:

[0006] The interview report template is pre-set to include an interviewer information column, an interview content column and a signature column, and corresponding labels are set for each of the interviewer information column, the interview content column and the signature column in the interview report template; predetermined filling positions are reserved in the interviewer information column and the interview content column of the interview report template, and the reserved filling positions in the interview report template are filled in through variables;

[0007] Access to audio recordings of the interview;

[0008] Performing voice recognition on the acquired voice content to obtain the interview content in text form;

[0009] Input the text-based interview content into the pre-trained large model;

[0010] The basic information of the personnel analyzed and output by the large model is used as the value of the variable reserved in the interviewer information column, and the value of the variable is filled into the interviewer information column in the interview report template using the label of the interviewer information column;

[0011] The key points of the interview content analyzed and output by the large model are used as the value of the variable reserved in the interview content column. The label of the interview content column is used to fill the value of the variable into the interview content column in the interview report template, thereby obtaining an interview report corresponding to the interview process.

[0012] After obtaining the interview report corresponding to the interview process, the method further includes:

[0013] The interview report corresponding to the interview process is provided to the manager in the form of an online editable document, so that the manager can confirm the key points of the interview content in the interview report. After confirmation, the manager prints the interview report and signs in the signature column of the interview report.

[0014] The training method of the large model includes:

[0015] Set the words in the set, and input the text content including the words in the set and the labels of the words in the set into the large model, so as to train the large model's ability to recognize the content of the interviewer's information column; wherein the words in the set include at least one of name, gender, age, position, address, and mobile phone number.

[0016] The training method of the big model includes: obtaining at least one conversation training sample, each conversation training sample including a conversation content in text form and interview content key points as labels of the conversation content; inputting the at least one conversation training sample into the big model so as to train the big model to use the deep learning and natural language processing capabilities of the big model to perform intelligent analysis on the text-based conversation content and automatically generate interview content key points.

[0017] The main points of the interview content include: at least one of asking questions, warning reminders, and making suggestions.

[0018] According to the second aspect, a device for generating an interview report based on a large model is proposed, and the device comprises:

[0019] The interview report template generation module is configured to pre-set the interview report template to include an interviewer information column, an interview content column and a signature column, and set corresponding labels for each of the interviewer information column, the interview content column and the signature column in the interview report template; pre-reserved predetermined filling positions in the interviewer information column and the interview content column of the interview report template, and fill the reserved filling positions in the interview report template through variables;

[0020] A module for obtaining content to be identified, configured to obtain voice content recorded during the interview process;

[0021] A conversion module is configured to perform speech recognition on the acquired speech content, thereby obtaining the interview content in text form, and input the interview content in text form into a pre-trained large model;

[0022] The interview report generation module is configured to use the basic information of the personnel analyzed and output by the big model as the value of the variable reserved in the interviewer information column, and use the label of the interviewer information column to fill the value of the variable into the interviewer information column in the interview report template; use the key points of the interview content analyzed and output by the big model as the value of the variable reserved in the interview content column, and use the label of the interview content column to fill the value of the variable into the interview content column in the interview report template, so as to obtain an interview report corresponding to the interview process.

[0023] The device further includes a docking module;

[0024] The docking module is configured to provide the interview report obtained by the interview report generation module to the management personnel in the form of an online editable document, so that the management personnel can confirm the key points of the interview content in the interview report, print the interview report after confirmation, and sign in the signature column of the interview report.

[0025] The device further includes: a large model training module;

[0026] The large model training module is configured to execute:

[0027] Setting the words in the set, and inputting the text content including the words in the set and the labels of the words in the set into the large model, so as to train the large model to recognize the content of the interviewer information column; wherein the words in the set include at least one of name, gender, age, position, address, and mobile phone number;

[0028] and / or,

[0029] At least one conversation training sample is obtained, each conversation training sample includes a conversation content in text form and interview content key points as labels of the conversation content; the at least one conversation training sample is input into the big model so as to train the big model to use the deep learning and natural language processing capabilities of the big model to perform intelligent analysis on the textual conversation content and automatically generate interview content key points.

[0030] The main points of the interview content include: at least one of asking questions, warning reminders, and making suggestions.

[0031] According to a third aspect, a computing device is proposed, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method of any embodiment of the present invention is implemented.

[0032] The interview report generation method and device based on the big model provided by each embodiment of the present invention have at least the following beneficial effects:

[0033] 1. In the embodiment of the present invention, the format of the interview report template is pre-set. In order to meet the format characteristics of this template, that is, a predetermined filling position needs to be reserved, the filling position reserved in the interview report template is filled in by variables in the method of the embodiment of the present invention, thereby solving the formatting design problem of this template and obtaining a universal interview report template. And, subsequently, only the value of the variable needs to be filled in, and the corresponding interview content can be filled in the template. Furthermore, in the embodiment of the present invention, according to the label of each column, when a specific interview report is generated later, the position of the corresponding column in the template can be quickly found and located, thereby ensuring the generation of the interview report.

[0034] 2. The interview report generation method of the embodiment of the present invention uses speech-to-text technology to achieve accurate transcription of the conversation content, and then uses a large model to deeply mine and extract key information, and finally automatically weaves it into an interview report with a clear structure and prominent key points. This process not only greatly improves the efficiency and accuracy of the work, but also effectively reduces the burden of manual writing by the staff, making the work more intelligent and standardized. The interview report generation method of the embodiment of the present invention improves intelligence, avoids time and effort of personnel, and improves efficiency.

[0035] 3. Automatically generate interview reports through a variety of technologies (voice recognition, keyword extraction, and document generation) based on the big model, which greatly improves work efficiency and reduces the complexity of manual operations.

[0036] 4. Improve report accuracy and consistency: When extracting keywords and generating reports, the big model can follow preset rules and standards to ensure the accuracy and consistency of the report content. This standardized processing not only improves the quality of the report, but also makes the reports generated by different personnel comparable and traceable. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 It is a flow chart of a method for generating an interview report based on a large model in one embodiment of the present invention.

[0039] Figure 2 It is a flowchart of a method for generating an interview report based on a large model in another embodiment of the present invention.

[0040] Figure 3It is a structural diagram of an interview report generating device based on a large model in one embodiment of the present invention.

[0041] Figure 4 It is a structural diagram of an interview report generating device based on a large model in another embodiment of the present invention. DETAILED DESCRIPTION

[0042] First of all, it should be noted that the terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates otherwise.

[0043] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0044] Figure 1 FIG. 1 is a flow chart of a method for generating an interview report based on a large model in one embodiment of the present invention. Figure 1 In one embodiment of the present invention, a method for generating an interview report based on a large model includes:

[0045] Step 101: Pre-set the interview report template.

[0046] The specific process of this step 101 may include: pre-setting the interview report template to include an interviewer information column, an interview content column and a signature column, and setting corresponding labels for each column in the interviewer information column, the interview content column and the signature column in the interview report template; pre-reserving predetermined filling positions in the interviewer information column and the interview content column of the interview report template, and filling the reserved filling positions in the interview report template through variables, thereby obtaining a universal interview report template.

[0047] The interview report template usually contains three core parts: the interviewer information column, the interview content column and the signature column. In the interviewer information column, a corresponding space will be reserved for subsequent filling of relevant information; the interview content column is used to record the specific communication content during the interview process; and the signature column is designed to be filled in by hand to ensure its authenticity and seriousness. Therefore, during the template preparation stage, the signature column does not need to be pre-filled with content.

[0048] Step 103: Acquire the voice content recorded during the interview.

[0049] Step 105: Perform voice recognition on the acquired voice content to obtain the interview content in text form.

[0050] For example, in the process of an interview, audio recordings are usually made to ensure the accuracy and completeness of the information. These recordings are processed by speech recognition technology and efficiently converted into text. Advanced technology can ensure the clarity of the recordings and make the converted text content accurate.

[0051] Step 107: Input the text-based interview content into the pre-trained large model.

[0052] Step 109: Use the basic information of the personnel analyzed and output by the large model as the value of the variable reserved in the interviewer information column, and use the label of the interviewer information column to fill the value of the variable into the interviewer information column in the interview report template.

[0053] Step 111: Use the interview content key points analyzed and output by the large model as the value of the variable reserved in the interview content column, and use the label of the interview content column to fill the value of the variable into the interview content column in the interview report template, so as to obtain an interview report corresponding to the interview process.

[0054] For the above steps 107, 109, and 111, from the interview content in the converted text form, it is necessary to further use the big model keyword extraction capability to extract key information in the text, such as basic personnel information such as name, age, gender, and key points of the interview content such as questions, warnings, and suggestions. For example, for step 111, the deep learning and natural language processing capabilities of the big model are used to intelligently analyze the extracted questions, warnings, and suggestions, and automatically generate interview content that complies with the standards, has clear logic, and accurate content. These generated contents are automatically filled into the interview content column.

[0055] according to Figure 1 It can be seen from the process shown that the format of the interview report template is pre-set in the embodiment of the present invention. In order to meet the format characteristics of this template, that is, to reserve a predetermined filling position, the method in the embodiment of the present invention fills in the filling position reserved in the interview report template through variables, thereby solving the formatting design problem of this template and obtaining a universal interview report template. And, subsequently, only the value of the variable needs to be filled in, and the corresponding interview content can be filled into the template. Furthermore, in the embodiment of the present invention, according to the label of each column, when a specific interview report is generated later, the position of the corresponding column in the template can be quickly found and located, thereby ensuring the generation of the interview report.

[0056] The interview report generation method of the embodiment of the present invention uses speech-to-text technology to achieve accurate transcription of the conversation content, and then uses a large model to deeply mine and extract key information, and finally automatically weaves it into an interview report with a clear structure and prominent key points. This process not only greatly improves the efficiency and accuracy of the work, but also effectively reduces the burden of manual writing by the staff, making the work more intelligent and standardized. The interview report generation method of the embodiment of the present invention improves intelligence, avoids time and effort of personnel, and improves efficiency.

[0057] After obtaining the interview report corresponding to the interview process in step 111, the method of the embodiment of the present invention may further include:

[0058] Step 113: Provide the interview report corresponding to the interview process to the manager in the form of an online editable document, so that the manager can confirm the key points of the interview content in the interview report. After confirmation, the manager prints the interview report and signs in the signature column of the interview report.

[0059] Through the processing of the above step 113, the generated interview content is formed into an online editable document. The staff will carefully check and modify this document to ensure the accuracy and completeness of its content. After confirming that it is correct, they can download this interview report and directly use it in subsequent work and decision-making.

[0060] according to Figure 1 It can be seen from the process shown that in the embodiment of the present invention, a large model needs to be pre-trained.

[0061] In one embodiment of the present invention, the training process of the big model includes: setting the words in the set, inputting the text content including the words in the set and the labels of the words in the set into the big model, so as to train the big model's recognition ability of the content of the interviewer information column; wherein the words in the set include at least one of name, gender, age, position, address, and mobile phone number.

[0062] The content of the interviewee information column is relatively fixed. Later in the interview process, the staff can be asked to "cooperate" with the voice recognition system and try to say "in-set words", such as name, gender, age, etc. For the conversations in the interview content column, first collect a large number of conversation recordings as voice data sets to ensure the generalization ability of the large model; then, preprocess the voice data sets, including removing silent segments, segmenting long recordings, and distinguishing between speech and non-speech, to reduce noise and redundancy in the training process of the large model; finally, use data enhancement and transfer learning and other technologies to train the large model. Through this training process, the large model can more accurately obtain the various information required in the interviewee information column from the text content.

[0063] In another embodiment of the present invention, the training method of the large model includes:

[0064] At least one conversation training sample is obtained, each conversation training sample includes a conversation content in text form and interview content key points as labels of the conversation content; the at least one conversation training sample is input into the big model so as to train the big model to use the deep learning and natural language processing capabilities of the big model to perform intelligent analysis on the textual conversation content and automatically generate interview content key points.

[0065] In one embodiment of the present invention, the key points of the interview content include at least one of: asking questions, warning reminders, and making suggestions. For example, in the above step 111, for the conversation content such as asking questions, warning reminders, and making suggestions, the big model can classify and extract the interview topic information, simplify the conversation content, and extract the core content.

[0066] Figure 2 FIG. 1 is a flow chart of a method for generating an interview report based on a large model in another embodiment of the present invention. Figure 2 , the method comprising:

[0067] (1) Audio to text conversion: First, use advanced speech recognition technology to accurately convert the audio content of the interview process into text format.

[0068] (2) Extract keywords: Next, conduct an in-depth analysis of the converted text and use professional keyword extraction technology to accurately identify and classify basic personnel information keywords (such as name, gender, department, etc.) and interview content summary keywords (such as conversation focus, problem feedback, suggested measures, etc.).

[0069] (3) Fill in the information column: Then, the extracted basic personnel information keywords are directly filled into the interviewer information column of the interview report template.

[0070] (4) Generate interview content: Utilize powerful document generation capabilities to logically integrate and optimize the language of the interview content summary keywords to generate a clear and standardly formatted interview content.

[0071] (5) Fill in the interview content column: accurately fill the generated interview content into the interview content column of the interview report template.

[0072] (6) Improve and print the report: Finally, the staff will carefully check and improve the generated interview report to ensure the accuracy and completeness of the report content. After confirmation, the report will be printed out for subsequent work and decision-making.

[0073] according to Figure 1 , Figure 2It can be seen from the process that the present invention significantly reduces the workload of staff by efficiently converting the content of the conversation into an interview report. Specifically, this process utilizes advanced speech recognition, keyword extraction of large models, generation of official documents and other technologies, which can automatically convert the recorded content during the interview process into text form. Subsequently, through intelligent analysis and processing, key information is extracted and automatically filled into the interview report template to generate an interview report with a complete structure and accurate content. In this way, staff no longer need to manually organize the content of the recording and write reports, which greatly saves time and energy and achieves a significant improvement in work efficiency.

[0074] One embodiment of the present invention provides a device for generating an interview report based on a large model, see Figure 3 , the device comprises:

[0075] The interview report template generation module 301 is configured to pre-set the interview report template to include an interviewer information column, an interview content column and a signature column, and set corresponding labels for each of the interviewer information column, the interview content column and the signature column in the interview report template; pre-reserved predetermined filling positions in the interviewer information column and the interview content column of the interview report template, and fill the reserved filling positions in the interview report template through variables;

[0076] The to-be-recognized content acquisition module 302 is configured to acquire the voice content recorded during the interview process;

[0077] The conversion module 303 is configured to perform speech recognition on the speech content obtained by the to-be-recognized content acquisition module 302, thereby obtaining the interview content in text form, and input the interview content in text form into the pre-trained large model;

[0078] The interview report generation module 304 is configured to use the basic information of the personnel analyzed and output by the large model as the value of the variable reserved in the interviewer information column, and use the label of the interviewer information column to fill the value of the variable into the interviewer information column in the interview report template; use the key points of the interview content analyzed and output by the large model as the value of the variable reserved in the interview content column, and use the label of the interview content column to fill the value of the variable into the interview content column in the interview report template, thereby obtaining an interview report corresponding to the interview process.

[0079] In one embodiment of the device of the present invention, the device further comprises a docking module 401;

[0080] The docking module 401 is configured to provide the interview report obtained by the interview report generation module 304 to the management personnel in the form of an online editable document, so that the management personnel can confirm the key points of the interview content in the interview report, print the interview report after confirmation, and sign in the signature column of the interview report.

[0081] In one embodiment of the apparatus of the present invention, the apparatus further comprises: a large model training module;

[0082] The large model training module is configured to execute: setting a set of words, inputting text content including the words in the set and labels of the words in the set into the large model, so as to train the large model to recognize the content of the interviewer information column; wherein the words in the set include at least one of name, gender, age, position, address, and mobile phone number;

[0083] and / or,

[0084] The big model training module is configured to perform the following steps: obtaining at least one conversation training sample, each conversation training sample including a conversation content in text form and interview key points as labels for the conversation content; inputting the at least one conversation training sample into the big model so as to train the big model to use the big model's deep learning and natural language processing capabilities to intelligently analyze the text-based conversation content and automatically generate interview key points.

[0085] It should be noted that the above-mentioned devices are usually implemented on the server side, and can be set on independent servers respectively, or some or all of the devices can be combined and set on the same server. The server can be a single server or a server cluster composed of multiple servers. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system. The above-mentioned devices can also be implemented on a computer terminal with strong computing power.

[0086] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed in a computer, the computer is caused to execute a method in any one of the embodiments in the specification.

[0087] An embodiment of the present invention provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method in any embodiment of the specification is implemented.

[0088] It is to be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the device of the embodiment of the present invention. In other embodiments of the specification, the above-mentioned device may include more or fewer components than those shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0089] The various embodiments of the present invention are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0090] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the present invention can be implemented by hardware, software, widgets, or any combination thereof. When implemented by software, these functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0091] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for generating an interview report based on a large model, characterized in that: The method includes: The interview report template is pre-set to include an interviewer information column, an interview content column and a signature column, and corresponding labels are set for each of the interviewer information column, the interview content column and the signature column in the interview report template; predetermined filling positions are reserved in the interviewer information column and the interview content column of the interview report template, and the reserved filling positions in the interview report template are filled in through variables; Access to audio recordings of the interview; Performing voice recognition on the acquired voice content to obtain the interview content in text form; Input the text-based interview content into the pre-trained large model; The basic information of the personnel analyzed and output by the large model is used as the value of the variable reserved in the interviewer information column, and the value of the variable is filled into the interviewer information column in the interview report template using the label of the interviewer information column; The key points of the interview content analyzed and output by the large model are used as the value of the variable reserved in the interview content column. The label of the interview content column is used to fill the value of the variable into the interview content column in the interview report template, thereby obtaining an interview report corresponding to the interview process.

2. The method according to claim 1, characterized in that After obtaining the interview report corresponding to the interview process, the method further includes: The interview report corresponding to the interview process is provided to the manager in the form of an online editable document, so that the manager can confirm the key points of the interview content in the interview report. After confirmation, the manager prints the interview report and signs in the signature column of the interview report.

3. The method according to claim 1, characterized in that The training method of the large model includes: Set the words in the set, and input the text content including the words in the set and the labels of the words in the set into the large model, so as to train the large model's ability to recognize the content of the interviewer's information column; wherein the words in the set include at least one of name, gender, age, position, address, and mobile phone number.

4. The method according to claim 1, characterized in that: The training method of the big model includes: obtaining at least one conversation training sample, each conversation training sample including a conversation content in text form and interview content key points as labels of the conversation content; inputting the at least one conversation training sample into the big model so as to train the big model to use the deep learning and natural language processing capabilities of the big model to perform intelligent analysis on the text-based conversation content and automatically generate interview content key points.

5. The method according to claim 1, characterized in that The main points of the interview content include: at least one of asking questions, warning reminders, and making suggestions.

6. A device for generating interview reports based on a large model, characterized in that: The device includes: The interview report template generation module is configured to pre-set the interview report template to include an interviewer information column, an interview content column and a signature column, and set corresponding labels for each of the interviewer information column, the interview content column and the signature column in the interview report template; pre-reserved predetermined filling positions in the interviewer information column and the interview content column of the interview report template, and fill the reserved filling positions in the interview report template through variables; A module for obtaining content to be identified, configured to obtain voice content recorded during the interview process; A conversion module is configured to perform speech recognition on the acquired speech content, thereby obtaining the interview content in text form, and input the interview content in text form into a pre-trained large model; The interview report generation module is configured to use the basic information of the personnel analyzed and output by the big model as the value of the variable reserved in the interviewer information column, and use the label of the interviewer information column to fill the value of the variable into the interviewer information column in the interview report template; use the key points of the interview content analyzed and output by the big model as the value of the variable reserved in the interview content column, and use the label of the interview content column to fill the value of the variable into the interview content column in the interview report template, so as to obtain an interview report corresponding to the interview process.

7. The device according to claim 6, characterized in that The device further includes a docking module; The docking module is configured to provide the interview report obtained by the interview report generation module to the management personnel in the form of an online editable document, so that the management personnel can confirm the key points of the interview content in the interview report, print the interview report after confirmation, and sign in the signature column of the interview report.

8. The device according to claim 6, characterized in that The device further includes: a large model training module; The large model training module is configured to execute: Setting the words in the set, and inputting the text content including the words in the set and the labels of the words in the set into the large model, so as to train the large model to recognize the content of the interviewer information column; wherein the words in the set include at least one of name, gender, age, position, address, and mobile phone number; and / or, At least one conversation training sample is obtained, each conversation training sample includes a conversation content in text form and interview content key points as labels of the conversation content; the at least one conversation training sample is input into the big model so as to train the big model to use the deep learning and natural language processing capabilities of the big model to perform intelligent analysis on the textual conversation content and automatically generate interview content key points.

9. The device according to claim 6, characterized in that The main points of the interview content include: at least one of asking questions, warning reminders, and making suggestions.

10. A computing device comprising a memory and a processor, wherein the memory stores executable codes, and when the processor executes the executable codes, the method according to any one of claims 1 to 5 is implemented.

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