AI tool system and device for automatically generating product introduction script

Abstract

The invention provides an AI tool system and device for automatically generating a product introduction script. Relates to the field of commodity text introduction, and the AI tool system for automatically generating the product introduction script comprises a keyword input module, a semantic analysis module, a generation module, an optimization module and a revision module, the keyword input module comprises a theme input module and a feature input module, the theme input module is used for inputting the name of a product, and the feature input module is used for inputting the feature of the product; the feature input module is used for inputting key data of a product; and the semantic analysis module is used for identifying semantic information of input product names and key data and determining a theme and a writing direction of an introduction script. According to the AI tool system for automatically generating the product introduction script, the introduction text of the product can be quickly and automatically generated, the introduction script of the product is intelligently generated through AI, and the workload of a user in the aspect is reduced.

Description

Technical Field

[0001] The present invention relates to the field of commodity text introduction, and in particular to an AI tool system and device for automatically generating product introduction scripts. Background Art

[0002] Currently, e-commerce live streaming is a common way of shopping. When selling products live, it is necessary to introduce the performance characteristics of the products being sold. A text introducing the products is needed to facilitate the seller to introduce the products during the live broadcast. This is especially true for digital human live streaming platforms, which use digital technology to create virtual characters to sell products live. Most of the digital human live broadcast introductions of products are made by users inputting introduction texts in advance, and the digital human will introduce the products based on the input introduction texts during the live broadcast.

[0003] Currently, users usually write and input product introduction texts by themselves, and need to edit the language themselves, which puts a certain test on the user's language expression ability. Moreover, when the user sells a wide variety of products, editing the product introduction texts by themselves is time-consuming and labor-intensive.

[0004] Therefore, it is necessary to provide an AI tool system that automatically generates product introduction scripts to solve the above technical problems. Summary of the invention

[0005] The present invention provides an AI tool system for automatically generating product introduction scripts, which solves the problem of time-consuming and labor-intensive editing of product introduction texts by users when they sell a wide variety of products.

[0006] In order to solve the above technical problems, the present invention provides an AI tool system for automatically generating product introduction scripts, including: a keyword input module, the keyword input module includes a theme input module and a feature input module, the theme input module is used to input the name of the product, and the feature input module is used to input the key data of the product; A semantic analysis module, which is used to identify the semantic information of the input product name and key data, and determine the theme and writing direction of the introduction script; A generation module, wherein the generation module generates an introduction script based on the understanding of key data and themes using a language system model; An optimization module, wherein the optimization module optimizes the sentences of the generated introduction script; A revision module is used by the user end to check and revise the generated introduction script.

[0007] Preferably, the construction of the language system model specifically includes the following steps: S11: collect text data and preprocess the text data; S12: Select model architecture; S13: Determine the parameters of the model; S14: Randomly initialize the model parameters; The preprocessed text data is input into the model, and the back propagation algorithm is used to update the model parameters by calculating the difference between the predicted results and the real text, so as to gradually optimize the performance of the model. S15: Select a suitable optimization algorithm to update the model parameters; S16: Repeat steps S11 to S15 multiple times to perform multiple rounds of training on the model; S17: Evaluate the language system model and make fine-tuning and improvements.

[0008] Preferably, the generation module includes a framework generation module and a content filling module. The framework generation module generates a framework of the introduction script based on the understanding of the product name and key data, and the content filling module utilizes the language system model and generates a specific introduction script according to the framework.

[0009] Preferably, the revision module includes a voice broadcast module and a text modification module, the voice broadcast module is used to voice broadcast the generated introduction script, and the text modification module is used to modify the text of the generated introduction script.

[0010] Preferably, the revision module also includes a reservation module. When the user revises the generated introduction script, the deleted text is collected in the reservation module; the reservation module includes a reserved box. When the user selects the modified part of the text for 2-5 seconds, the reserved box pops up, and the user can select all or part of the text in the reserved box to restore it to the introduction script, wherein each deletion of the text generates a corresponding reserved box.

[0011] Preferably, the AI ​​tool system for automatically generating product introduction scripts also includes a style selection module, through which introduction scripts of different styles are generated.

[0012] Preferably, the construction of the style selection module specifically includes the following steps: S21: Collect text data of various styles and pre-process the text data; S22: Perform style annotation on text data; S23: Randomly mix the preprocessed data of different styles together to form a comprehensive dataset; S24: Select the model architecture and initialize the multi-style language model; S25: define training parameters, input the mixed data into the multi-style language model in batches, calculate the loss between the prediction results of the multi-style language model and the real text, and use the optimization algorithm to update the parameters of the multi-style language model according to the loss; S26: During the training process, regularly evaluate the performance of the multi-style language model on the validation set, analyze the performance of different style data on the validation set, and understand the model's learning of various styles; S27: Evaluate the trained multi-style language model on the test set to determine the comprehensive performance of the multi-style language model on multiple styles of data.

[0013] Preferably, the AI ​​tool system for automatically generating product introduction scripts also includes a comparison module, which is used to add comparison products, and form comparison information of multiple products in the generated product introduction script by inputting the names and key data of the comparison products.

[0014] Preferably, the comparison module includes an input module, a classification module and a data analysis module. The input module is used to input the name and key data of the comparison product. The classification module classifies the key data of the input product to facilitate the comparison of the same type of data between different products. The data analysis module is used to perform comparative analysis on the same type of key data.

[0015] The present invention also provides an AI device for automatically generating product introduction scripts, including a storage device and a processing device, wherein the processing device is used to execute the AI ​​tool system for automatically generating product introduction scripts stored in the storage device to realize the function of automatically generating product introduction scripts by inputting product names and key data.

[0016] Compared with related technologies, the AI ​​tool system for automatically generating product introduction scripts provided by the present invention has the following beneficial effects: The present invention provides an AI tool system and device for automatically generating product introduction scripts. When writing a text introduction for a product, the name of the corresponding product and the key data of the product are input through a keyword input module, the name and key data of the product are identified through a semantic analysis module, the theme and writing direction of the introduction script are determined, and then the introduction script for the product is automatically generated based on a language system model. After the introduction script is generated, the system optimizes the sentences of the introduction script, adjusts the language expression, corrects grammatical errors, and improves the readability of the article. Finally, the user can read and revise the generated introduction script, so that the introduction text for the product can be quickly and automatically generated, reducing the user's workload in this regard. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1A functional block diagram of the AI ​​tool system for automatically generating product introduction scripts provided by the present invention; Figure 2 A functional block diagram of a keyword input module provided by the present invention; Figure 3 A flowchart of the steps for constructing the language system model provided by the present invention; Figure 4 A principle block diagram of a generation module provided by the present invention; Figure 5 A functional block diagram of a revision module provided by the present invention; Figure 6 A flowchart of the steps for constructing the style selection module provided by the present invention; Figure 7 This is a principle block diagram of the comparison module provided by the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] The present invention provides an AI tool system for automatically generating product introduction scripts.

[0020] Please refer to Figure 1 to Figure 2 In one embodiment of the present invention, the AI ​​tool system for automatically generating product introduction scripts includes: a keyword input module, the keyword input module includes a theme input module and a feature input module, the theme input module is used to input the name of the product, and the feature input module is used to input the key data of the product; A semantic analysis module is used to identify the semantic information of the input product name and key data, and determine the theme and writing direction of the introduction script.

[0021] A generation module, wherein the generation module generates an introduction script based on the understanding of key data and themes using a language system model; An optimization module, wherein the optimization module optimizes the sentences of the generated introduction script; A revision module is used by the user end to check and revise the generated introduction script.

[0022] When writing a text introduction for a product, the name of the corresponding product and the key data of the product are input through the keyword input module. The semantic analysis module identifies the name and key data of the product, determines the theme and writing direction of the introduction script, and then automatically generates an introduction script for the product based on the language system model. After the introduction script is generated, the system optimizes the sentences in the introduction script, adjusts the language expression, corrects grammatical errors, and improves the readability of the article. Finally, the user can read and revise the generated introduction script, so that the introduction text of the product can be quickly and automatically generated. The introduction script for the product is generated intelligently by AI, reducing the user's workload in this regard.

[0023] And for users with poor language expression ability, the quality of product introduction text can be improved.

[0024] When applied to the digital human live broadcast platform, the AI ​​tool system that automatically generates product introduction scripts is loaded into the digital human live broadcast platform, and the script for product introduction is generated similarly. Then, the text is converted into speech through the text-to-speech (TTS) service and broadcast by the digital human.

[0025] And when the buyer interacts with the digital human live and asks questions related to the product, the digital human platform system can directly search for content related to the product from the introduction script, so that it can answer quickly.

[0026] Among them, for the input of keywords, the name of the food, such as apple, is entered in the subject input module, and key data such as origin information, taste information, sweetness information, price information, size information, special processing steps in the apple planting process, etc. are entered in the feature input module.

[0027] An introduction script is generated based on these key data, and information can be expanded during the introduction based on the key data. For example, after entering the origin information, the system can search for local environmental information, such as introducing information such as soil and rain, and explaining the suitability for apple cultivation.

[0028] Please refer to Figure 3 As an optional method of this embodiment, the construction of the language system model specifically includes the following steps: S11: Collect text data and pre-process the text data; the text data comes from the Internet, books, news articles, academic papers, etc.; clean and pre-process the collected data, including deleting special characters, converting to a unified encoding format, and segmenting (segmenting the text into words or terms). S12: Select a model architecture; the language model architecture may be a Transformer architecture, etc.; S13: Determine the parameters of the model, such as the number of layers, heads, and hidden units; S14: Randomly initialize the model parameters; The preprocessed text data is input into the model, and the difference between the predicted result and the real text (loss function) is calculated, and the back propagation algorithm is used to update the parameters of the model to gradually optimize the performance of the model. S15: Choose a suitable optimization algorithm to update the model parameters; such as stochastic gradient descent (SGD) and its variants (such as Adam optimizer) to update the model parameters; S16: Repeat steps S11 to S15 multiple times to train the model for multiple rounds until the performance of the model on the validation set is no longer significantly improved; S17: Evaluate the language system model and make fine-tuning and improvements.

[0029] Among them, commonly used evaluation indicators include accuracy, recall, F1 value, etc. According to the evaluation results, the model is fine-tuned, such as adjusting hyperparameters, increasing the amount of data, using a more complex model structure, etc.

[0030] As an optional method of this embodiment, the construction of the semantic recognition module specifically includes the following steps: S31: Collect a large amount of text data containing various semantic expressions, such as the Internet, books, news, social media, etc. S32: Clean, segment, tokenize, and process the collected data to convert the text into a machine-understandable form; S33: Extract meaningful features from the preprocessed text. These features can be bag-of-words model, word vector, part-of-speech tagging, named entity recognition, etc. S34: Choose a model suitable for semantic recognition tasks. Common models include convolutional neural networks (CNN), recurrent neural networks (RNN) and their variants such as long short-term memory networks (LSTM) and gated recurrent units (GRU), as well as Transformer architectures. S35: Use the extracted features and annotated semantic data to train the model; During the training process, the model learns the patterns and regularities of different semantic expressions; S36: Select appropriate optimization algorithms (such as stochastic gradient descent, Adagrad, Adadelta, etc.) to adjust the model parameters to minimize the loss function and improve the performance of the model; S37: Use the test set to evaluate the trained model. Common evaluation indicators include accuracy, recall, F1 value, etc. to determine the performance of the model. S38: According to the evaluation results, adjust the model, such as increasing the amount of data, adjusting the model structure, changing the training parameters, etc., to further improve the performance.

[0031] See also Figure 4 As an optional method of this embodiment, the generation module includes a framework generation module and a content filling module. The framework generation module generates a framework of the introduction script based on the understanding of the product name and key data, and the content filling module uses the language system model to generate a specific introduction script according to the framework.

[0032] The framework generation module generates the framework of the introduction script, including the paragraph structure and the distribution of the main content. For example, multiple paragraphs can be generated for different types of keywords. According to the order of the keywords, the information corresponding to the keywords is determined in the order of the introduction script.

[0033] The content filling module uses the language system model to expand key data information and organically connect multiple keywords to generate a specific introduction script.

[0034] By first generating the script framework and then filling in the content, the introduction script can be gradually completed, making the generation of the script more organized and improving the efficiency and quality of the introduction script generation.

[0035] See also Figure 5 As an optional method of this embodiment, the revision module includes a voice broadcast module and a text modification module, the voice broadcast module is used to voice broadcast the generated introduction script, and the text modification module is used to modify the text of the generated introduction script.

[0036] By setting up a voice broadcast module, after the introduction script is generated, when the user checks whether there is any problem with the introduction script, the voice broadcast module can be used to voice broadcast the introduction script without the user having to read it by themselves. The user can also read the introduction script by themselves, or voice broadcast and reading can be done simultaneously.

[0037] When the user thinks that part of the sentence needs to be modified, the text sentence is modified and edited through the text modification module.

[0038] As a preferred method of this embodiment, the revision module also includes a reservation module. When the user revises the generated introduction script, the deleted text is collected in the reservation module; the reservation module includes a reserved box. When the user selects the modified part of the text for 2-5 seconds, the reserved box pops up, and the user can select all or part of the text in the reserved box and restore it to the introduction script, wherein each deletion of the text generates a corresponding reserved box.

[0039] By setting up a reserved module, when the user deletes or modifies some sentences or paragraphs in the originally generated introduction script, these deleted sentences or paragraphs will be temporarily stored in the reserved module. When the user deletes by mistake or wants to use the deleted sentence after deletion, the user can select the modified part for 2-5 seconds. At this time, a reserved box will pop up, and the originally deleted sentences or paragraphs will be located in the reserved box. The entire text or part of the text in the reserved box can be selected, and then restored to the introduction script, thereby improving the convenience of using the system.

[0040] A reserved box is generated for each deleted location. When the text of the modified part is not selected, the reserved box will not be displayed and will pop up 2-5 seconds after the selection.

[0041] Preferably, it pops up 3 seconds after selection, and the reserved box and the text inside it have a certain degree of transparency when popping up.

[0042] In one embodiment, when a user deletes a statement and re-edits the corresponding text, the portion selected for modification is the portion of text selected for re-editing. When the deleted statement is restored, the portion re-edited by the user is not automatically deleted and is handled by the user at his / her own choice.

[0043] In another embodiment, when the user only deletes a sentence and does not edit it any more, the part selected for modification can be the previous sentence or the next sentence, the previous paragraph or the next paragraph of the deleted sentence or paragraph.

[0044] The user can choose to delete the text content in the reserved box again in the reserved box.

[0045] Among them, in this embodiment, when the terminal device is a computer, the operation of selecting the modification part is to keep the mouse cursor on the modification part; when the terminal device is a mobile phone, tablet computer, etc., the operation of selecting the modification part is to keep the user's finger on the modification part.

[0046] See also Figure 1 The AI ​​tool system for automatically generating product introduction scripts also includes a style selection module, through which introduction scripts of different styles are selected and generated.

[0047] By setting up the style selection module, you can choose introduction scripts with different editing styles according to user needs, such as formal, humorous, literary and other styles. Subsequently, you can cooperate with digital human broadcasts in corresponding styles to increase the attractiveness of product sales.

[0048] See also Figure 6 As an optional method of this embodiment, the construction of the style selection module specifically includes the following steps: S21: Collect text data of various styles and pre-process the text data; The data comes from online articles, books, social media, academic papers, news reports, etc., and the collected data covers a variety of different styles, such as formal, colloquial, literary, technical, humorous, etc. S22: Perform style annotation on text data so that data of different styles can be distinguished in subsequent analysis and evaluation; S23: Randomly mix the preprocessed data of different styles together to form a comprehensive dataset; S24: Select the model architecture and initialize the multi-style language model; it can be a language model of the Transformer architecture, such as the GPT series, ELMO, etc.; S25: Define training parameters, set learning rate, number of training rounds, batch size and other training parameters; input the mixed data into the multi-style language model in batches, calculate the loss between the prediction results of the multi-style language model and the real text, and use an optimization algorithm (such as Adam optimizer) to update the parameters of the multi-style language model according to the loss; S26: During the training process, regularly evaluate the performance of the multi-style language model on the validation set, such as perplexity, accuracy and other indicators, analyze the performance of different style data on the validation set, and understand the model's learning of various styles; S27: Evaluate the trained multi-style language model on the test set to determine the comprehensive performance of the multi-style language model on multiple styles of data.

[0049] Finally, the trained model can be deployed to practical applications and further improved based on actual feedback.

[0050] See also Figure 7 The AI ​​tool system for automatically generating product introduction scripts also includes a comparison module, which is used to add comparison products. By inputting the names and key data of the comparison products, comparison information of multiple products is formed in the generated product introduction script.

[0051] By setting up the comparison module, users can add comparison products, enter the names and key data of the comparison products, and integrate the comparison product data into the generated product introduction script to compare the key data with the user's own products, thereby highlighting the advantages of the user's own products.

[0052] Among them, one or more comparison products can be added.

[0053] See also Figure 7As an optional method of this embodiment, the comparison module includes an input module, a classification module and a data analysis module. The input module is used to input the name and key data of the comparison product. The classification module classifies the key data of the input product to facilitate the comparison of the same type of data between different products. The data analysis module is used to compare and analyze the same type of key data.

[0054] The name and key data of the comparison module are input through the input module. The classification module classifies the types of the key data of the comparison product and the key data of the user's own product, that is, the same type of data is classified together to facilitate subsequent comparison; the data analysis module is used to compare and analyze the same type of key data, and obtain the difference data after comparison, etc., to facilitate subsequent editing and output in the introduction script.

[0055] Preferably, the keyword input module and the input module in the comparison module will automatically recommend product types after the product name is entered. For example, if the name entered is apple, the origin, taste, price, etc. will be automatically popped up. When the corresponding item needs to be filled in, click the corresponding item and then fill in the information corresponding to the specific item; when the type item that pops up automatically does not meet the user's needs, the user can customize the edited type and fill in the corresponding information, and the customized edited type system will automatically retain it. When the user enters a similar product again next time, the customized edited type can be popped up; By defining each type, after the user enters the key data, the system can quickly identify and find the data of the corresponding type between the user's own product and the comparison product, and then perform comparative analysis.

[0056] The present invention also provides an AI device for automatically generating product introduction scripts, including a storage device and a processing device, wherein the processing device is used to execute the AI ​​tool system for automatically generating product introduction scripts stored in the storage device to realize the function of automatically generating product introduction scripts by inputting product names and key data.

[0057] Among them, the AI ​​device that automatically generates product introduction scripts can be carried on terminal devices such as computers, mobile phones, and tablets.

[0058] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An AI tool system for automatically generating product introduction scripts, characterized in that: include: A keyword input module, the keyword input module includes a theme input module and a feature input module, the theme input module is used to input the name of the product, and the feature input module is used to input the key data of the product; A semantic analysis module, which is used to identify the semantic information of the input product name and key data, and determine the theme and writing direction of the introduction script; A generation module, wherein the generation module generates an introduction script based on the understanding of key data and themes using a language system model; An optimization module, wherein the optimization module optimizes the sentences of the generated introduction script; A revision module is used by the user end to check and revise the generated introduction script.

2. The AI ​​tool system for automatically generating product introduction scripts according to claim 1, characterized in that: The construction of the language system model specifically includes the following steps: S11: collect text data and preprocess the text data; S12: Select model architecture; S13: Determine the parameters of the model; S14: Randomly initialize the model parameters; The preprocessed text data is input into the model, and the back propagation algorithm is used to update the model parameters by calculating the difference between the predicted results and the real text, so as to gradually optimize the performance of the model. S15: Select a suitable optimization algorithm to update the model parameters; S16: Repeat steps S11 to S15 multiple times to perform multiple rounds of training on the model; S17: Evaluate the language system model and make fine-tuning and improvements.

3. The AI ​​tool system for automatically generating product introduction scripts according to claim 1, characterized in that: The generation module includes a framework generation module and a content filling module. The framework generation module generates a framework of an introduction script based on an understanding of a product name and key data, and the content filling module generates a specific introduction script based on the framework using a language system model.

4. The AI ​​tool system for automatically generating product introduction scripts according to claim 1, characterized in that: The revision module includes a voice broadcast module and a text modification module. The voice broadcast module is used to voice broadcast the generated introduction script, and the text modification module is used to modify the text of the generated introduction script.

5. The AI ​​tool system for automatically generating product introduction scripts according to claim 4 is characterized in that: The revision module also includes a reservation module. When the user revises the generated introduction script, the deleted text is collected in the reservation module; the reservation module includes a reservation box. When the user selects the modified part of the text for 2-5 seconds, the reservation box pops up, and the user can select all or part of the text in the reserved box to restore it to the introduction script, wherein each deletion of the text generates a corresponding reserved box.

6. The AI ​​tool system for automatically generating product introduction scripts according to claim 1, characterized in that: The AI ​​tool system for automatically generating product introduction scripts also includes a style selection module, through which introduction scripts of different styles are selected and generated.

7. The AI ​​tool system for automatically generating product introduction scripts according to claim 6, characterized in that: The construction of the style selection module specifically includes the following steps: S21: Collect text data of various styles and pre-process the text data; S22: Perform style annotation on text data; S23: Randomly mix the preprocessed data of different styles together to form a comprehensive dataset; S24: Select the model architecture and initialize the multi-style language model; S25: define training parameters, input the mixed data into the multi-style language model in batches, calculate the loss between the prediction results of the multi-style language model and the real text, and use the optimization algorithm to update the parameters of the multi-style language model according to the loss; S26: During the training process, regularly evaluate the performance of the multi-style language model on the validation set, analyze the performance of different style data on the validation set, and understand the model's learning of various styles; S27: Evaluate the trained multi-style language model on the test set to determine the comprehensive performance of the multi-style language model on multiple styles of data.

8. The AI ​​tool system for automatically generating product introduction scripts according to claim 1, characterized in that: The AI ​​tool system for automatically generating product introduction scripts also includes a comparison module, which is used to add comparison products. By inputting the names and key data of the comparison products, comparison information of multiple products is formed in the generated product introduction script.

9. The AI ​​tool system for automatically generating product introduction scripts according to claim 8, characterized in that: The comparison module includes an input module, a classification module and a data analysis module. The input module is used to input the name and key data of the comparison product. The classification module classifies the key data of the input product to facilitate the comparison of the same type of data between different products. The data analysis module is used to compare and analyze the same type of key data.

10. An AI device for automatically generating product introduction scripts, characterized in that: It includes a storage device and a processing device, wherein the processing device is used to execute the AI ​​tool system for automatically generating product introduction scripts as described in any one of claims 1 to 9 stored in the storage device, so as to realize the function of automatically generating product introduction scripts by inputting product names and key data.

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