Multimodal parameter comparison and recommended language generation system based on semantic comparison and structural difference analysis

Abstract

The invention relates to the technical field of intelligent foreign trade and international market data comparison, and provides a multi-modal parameter comparison and recommended language generation system based on semantic comparison and structural difference analysis. Comprising a data input module, a multi-input consistency control module, a field structure analysis module, a semantic aggregation and comparison module, a recommended language and market suggestion generation module, an output and touch module, a user configuration and editing module and a multi-language rendering and exporting module. The method is suitable for foreign trade enterprises to face scenes of transnational product comparison, quotation making, market preference identification and the like, a multi-language difference analysis result is generated through a multi-input fusion processing and consistency judgment mechanism in combination with modes of intelligent structure extraction, semantic vector comparison, authentication and price comparison, industry term association and the like; and a customized quotation template and personalized recommendation content are automatically formed. The method supports multi-format export and mail reach, improves foreign trade efficiency and customer communication quality, and has high expansibility and industry adaptability.

Description

Technical Field

[0001] This application relates to the technical field of intelligent quotation systems for foreign trade, and in particular to a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis. Background Technology

[0002] With the rapid development of global e-commerce and cross-border trade, foreign trade enterprises face increasingly complex challenges in product comparison and market communication when building pricing systems for overseas customers. However, existing technologies still have significant shortcomings in the following aspects: Differences in parameter structure make comparison difficult: Different countries and suppliers have different ways of describing product parameters, including dimensions, order, units, and language. Existing systems lack intelligent recognition and unified processing of structured fields, making it difficult to support automatic comparison or generate clear structured results.

[0003] Insufficient language diversity and semantic comprehension: Most current mainstream quotation tools are primarily in English and cannot effectively support input in multiple languages ​​such as Chinese, German, French, and Spanish. Even when translation is supported, the results are mostly literal translations, lacking contextual semantic consistency and failing to support accurate cross-language comparison and information fusion.

[0004] The comparison algorithm primarily uses string matching, ignoring semantic differences. Most platforms only make preliminary comparisons based on keywords or character similarity, which cannot understand the semantic relationship between "different expressions of the same product", resulting in low matching accuracy and difficulty in automatically identifying product differences.

[0005] Unable to automatically generate understandable market recommendations based on parameter differences. When preparing quotations, companies often need to manually write personalized suggestions for clients. Currently, there is a lack of systems that can automatically generate professional suggestions, improvement instructions, or supplementary clauses based on the differences in parameters between buyers and sellers.

[0006] Static pricing structures are not suitable for multi-dimensional comparison scenarios. Most existing price lists have a fixed structure, which cannot simultaneously showcase the differences between the products of buyers and sellers. They lack flexible display capabilities and editing interfaces, which limits users' comparison output and conversion efficiency.

[0007] Lack of multimodal input consistency control mechanism Existing systems typically require users to input comparison content in a single way (such as keywords or file uploads), lacking unified management capabilities for multiple input combinations such as "keywords + images + web pages + tables". If the semantics of multiple input sources are inconsistent (e.g., the keyword is "pet bag" while the uploaded image is "motor casing"), the system is prone to comparison errors, inaccurate recommendations, and chaotic output. Currently, there is no mature mechanism to support semantic consistency verification, dominant path setting, conflict alerts, and automatic correction under multiple input paths.

[0008] Therefore, there is an urgent need for an intelligent quotation system with cross-language semantic parsing, structural comparison, differentiated output, and multimodal input consistency control capabilities to achieve efficient and automated connection and communication, and improve the processing ability and professional performance of foreign trade business under complex language, structure, and market conditions.

[0009] It should be noted that the above content is not necessarily prior art, nor is it intended to limit the scope of patent protection of this application. Summary of the Invention

[0010] This application provides a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis to solve or alleviate one or more of the technical problems mentioned above.

[0011] The system includes: a data input module, a multi-input consistency control module, a field structure parsing module, a semantic aggregation and comparison module, a recommendation and market suggestion generation module, an output and outreach module, a user configuration and editing module, and a multi-language rendering and export module; wherein: The data input module is used to receive product-related data input from webpage link parsing, user-uploaded documents, tables, or manual entry. The multi-input consistency control module is used to: determine the semantic consistency of each input content when the user simultaneously inputs multiple data sources; identify potential conflicts and provide fusion paths or correction suggestions to ensure the accuracy and stability of subsequent comparison and generation processes; the data sources include keywords, images, web page links and documents; The field structure parsing module is used to: perform structured processing on the input data, extract core fields, and establish a standard field index structure; the core fields include product parameters, certification information, function descriptions, image data, unit specifications, and industry terms; The semantic aggregation and comparison module is used to: represent the core fields semantically based on an embedded model or an equivalent semantic vector model; identify the semantic similarity and differences between the core fields and generate structured comparison results; and is configured to support multilingual comparison. The recommendation and market suggestion generation module is used to: generate recommendation and market suggestions based on the structured comparison results and historical case data; the recommendation and market suggestions include difference prompts, recommended terms, market trend prompts, and industry suggestions; and is configured to support multi-industry template loading and intelligent context adaptation. The output and outreach module is used to: export the recommendations and market suggestions as a preset format file; the preset format includes Excel, PDF, API data stream or HTML web page format; and is configured with customizable functions to support field styles, language types and output logic; The user configuration and editing module is used to: provide a visual interface; and has functions that support field order adjustment, drag-and-drop structure configuration, content editing, language switching, version control, and historical comparison. The multilingual rendering and export module is used to: translate and replace the names, content descriptions, recommendations, and marketing tips of all fields according to the user-defined language combinations; it is also configured to support output in at least one target language to adapt to the corresponding market language requirements.

[0012] Optionally, the data input module includes multiple data acquisition sub-modules; wherein, it further includes: The URL parsing submodule is used to: parse the input webpage link and extract the product title, image, and structured parameter fields from the webpage; The document import submodule is used to: read uploaded Excel, CSV, or PDF files; identify and extract field information from the files; The manual data entry submodule is configured to support user input of missing fields or editing of extracted data. The language recognition submodule is used to: determine the language of the text based on a language detection model for translation path selection; the language detection model includes CLD3 and fastText; The terminology lexicon alignment submodule is used to perform field matching and translation preprocessing on user-uploaded professional terminology dictionaries to improve professional consistency. Translation quality assessment submodule: This module supports consistency assessment of translation results from multiple engines; it scores based on target metrics, including BLEU, BERTScore, and terminology coverage; it generates a credibility ranking based on a language model to select the best result; it acquires user-uploaded terminology lists as translation references to improve terminology stability and professional text expression; and it selects a combination of data collection submodules based on dimensions including data completeness, parsing accuracy, and user interaction burden to complete the initial import of product data.

[0013] Optionally, the field structure parsing module includes multiple structure processing sub-modules; wherein, it further includes: The field mapping submodule is used to: establish mapping relationships between input fields and standard fields based on rule matching, machine learning, or human feedback mechanisms; The field cleaning submodule is used to: obtain cleaning results based on preset field cleaning processes; the preset field cleaning processes include unifying unit formats, removing redundant characters, and standardizing capitalization and syntax structure; The field index building submodule is used to: generate a structured field index based on the cleaning results; and is configured to support fast semantic invocation. The web page crawling submodule is used to: perform preset processing based on web page parsing tools to load web page content and extract parameter information fields; the preset processing includes HTML DOM analysis and script rendering processing; The OCR recognition submodule is used to: convert unstructured image data into structured fields; the unstructured image data includes product images uploaded by users and scanned quotation sheets; The field merging and reconstruction submodule is used to aggregate fields with redundant, scattered, or overlapping semantics to generate an optimized structure for subsequent comparison or recommendation generation.

[0014] Optionally, the field structure parsing module further supports the structured reconstruction function of pages with mixed text and images. This function integrates OCR image recognition and HTML DOM structure analysis, and is used to perform block parsing and field annotation on web pages or documents that contain a mixture of images, paragraphs and tables, thereby automatically extracting and restoring product parameter fields and their corresponding values, thus improving the system's accuracy in recognizing and extracting structural fields in unstructured pages.

[0015] Optionally, the semantic aggregation and comparison module includes multiple comparison sub-modules; wherein the multiple comparison sub-modules include: The text-based semantic vector alignment submodule is used to: compare the semantic similarity between text fields based on the embedding model; The image-based visual feature comparison submodule is used to: extract image feature vectors based on unstructured image data; and calculate the visual similarity between the unstructured image data based on the image feature vectors. The field mapping submodule based on structure logic is used to: perform logical relationship mapping and matching on fields with different structures; The semantic embedding submodule is used to model text information using word vector and sentence vector embedding techniques to improve the accuracy of semantic recognition. The multimodal comparison submodule is used to compare and judge multimodal information that integrates text, images, and structural data. The regional adaptation submodule is used to: adjust the comparison results regionally according to a preset comparison strategy; the comparison strategy is determined based on the language, style, and regulations of the target country or market.

[0016] Optionally, the recommendation and market suggestion generation module includes multiple generation sub-modules; wherein the multiple generation sub-modules include: The recommended phrase template submodule is used to: load preset multilingual industry-specific phrase templates; The difference-driven generation submodule is used to: generate personalized suggestion statements based on the structured comparison results; The trend suggestion submodule is used to generate price range suggestions based on historical data, third-party market reference prices, and industry trend forecasts. The regulatory matching submodule is used to: compare the product's certification regulatory requirements with those of the target market, indicate any missing items, and generate compliance suggestions. The parameter merits and demerits scoring submodule is used to generate rating statements based on the evaluation results of our and our customers' products on core parameters. The language style adaptation submodule is used to adjust the recommended sentence structure and expression based on the language style of the target country or user preferences in order to improve communication effectiveness.

[0017] Optionally, the output and reach module includes multiple export and integration sub-modules; wherein the multiple export and integration sub-modules include: The format rendering submodule is used to: beautify and style the page by displaying the recommendations and market suggestions according to the preset format selected by the user; The Export Delivery submodule is used to: generate downloadable links, encrypted external links, or QR code preview links; The API output submodule is used to send structured results to external system interfaces or third-party applications. The email body generation submodule is used to: generate email body content with a complete structure and appropriate tone based on customer language preferences and product type; the email body content includes a product summary and suggestions; The behavior tracking submodule is used to: monitor whether customers exhibit preset behaviors; the preset behaviors include opening emails, clicking attachments, or replying to content; and generate behavior scores based on the preset behaviors to help sales determine customer intent. The CRM integration submodule is used to: synchronize quotation records and customer behavior analysis results to the customer relationship management system; and configure functions to support customer lead tagging and sales progress management.

[0018] Optionally, the output and outreach module can be configured to support integration with external system platforms, including but not limited to mainstream email platforms (such as Outlook and ProtonMail) and internal enterprise ERP, CRM, and OA systems. The system completes identity authentication and content push through OAuth or API interfaces, directly injecting the generated quotation documents or structured content into the target module, realizing an integrated closed loop of quotation generation, sending, and management processes, further improving the system's collaborative efficiency and automation level.

[0019] Optionally, the user configuration and editing module includes multiple interactive editing sub-modules; wherein, the multiple interactive editing sub-modules include: The visual editing submodule is used to configure functionality that allows users to adjust the field structure and order via drag and drop. The field customization submodule is used to configure functions such as adding fields, setting field grouping, or batch renaming. The version history submodule is used to: record every editing operation; and provide historical version comparison and rollback functions. The language switching and bilingual settings submodule is used to configure the output language combination, field name mapping method, and terminology translation source according to user preferences. The currency and unit of measurement configuration submodule is used to: configure functions that allow users to set currency and unit types; and perform conversions based on the market and display them uniformly. The template saving and reuse submodule is used to save the structure, format and display logic of the current field as a reusable template for customers or industry use.

[0020] Optionally, the multilingual rendering and export module includes multiple language processing sub-modules; wherein the multiple language processing sub-modules include: The language recognition and suggestion combination submodule is used to: identify the language type of the user input content; and recommend language combinations based on the language type of the user input content, the target customer's country, or browser configuration. The terminology recognition and translation submodule is used to: invoke multiple translation engines to translate technical terms in the field content; the multiple translation engines include DeepL, Google Translate, and Azure Translator. The bilingual output submodule is used to generate language combination output that retains the source language comparison content according to user settings; the language combination output includes Chinese-English, Chinese-German, and Chinese-French. The region adaptation submodule is used to adjust the field order, number format, and terminology according to the language expression style and unit of measurement format of different countries. The format export and packaging submodule is used to: export the rendering results to a preset format file; the preset format includes Word, Excel, PDF, HTML or JSON format; and configure support for local packaging and sharing of image resources and style files. The Language Sorting and Main Language Setting submodule is used to configure the display order of output languages, the position of the main language, and the translation priority strategy to support user-defined output languages.

[0021] Optionally, the multilingual rendering and export module is configured to support a terminology-priority matching mechanism. Before calling external translation engines (such as DeepL, Google Translate, or Azure Translator), the system builds a local cached vocabulary from the user-uploaded terminology dictionary and prioritizes terminology pre-matching and replacement operations before translation. This mechanism is suitable for industry scenarios with intensive technical terminology, such as the mechanical, medical, or textile fields, ensuring that terminology translation meets professional standards in terms of accuracy and consistency.

[0022] Optionally, the system configuration supports the function of user-defined field configuration schemes and saving them as templates; the template includes field structure, language preference, output format, visual style and outreach strategy configuration; based on the template, personalized output content is generated; the frequency of template calls, usage feedback and export results are recorded; and a recommended template list sorting mechanism is provided.

[0023] Optionally, the system configuration supports the function of allowing users to select the quotation structure type before exporting the results; the quotation structure types include: a main quotation structure containing only matching fields between the two parties, a supplementary quotation structure containing only supplementary fields from our side, and a recommended quotation structure generated based on market differences; the output content is dynamically rendered according to the selected quotation structure type.

[0024] Optionally, the system configuration supports the user's ability to select a pricing strategy in the output module; the pricing strategy includes a regular price, a discounted price, and a VIP price; the system then fills in the quotation field, outputs a recommendation message, and names the file according to the selected pricing strategy.

[0025] Optionally, the semantic aggregation and comparison module is also used to: configure the function to support the simultaneous loading of our product data and target customer product data; based on the dual-channel embedding model, align, compare and annotate the field structure of our product data and the field structure of the target customer product data; and configure the function to support the side-by-side display and visual marking of the difference fields.

[0026] Optionally, the recommendation and market suggestion generation module is further used for: optimizing the output based on a multi-dimensional structural suggestion algorithm; the multi-dimensional structural suggestion algorithm combines multiple dimension parameters to select the recommendation generation path and structured output template; the multiple dimension parameters include field similarity, terminology matching degree, regional adaptation rules, and industry expression style. Optionally, the recommendation and market suggestion generation module may further include a corporate style modeling function: the system is configured with a user behavior analysis mechanism to record users' editing behavior and frequency of use of recommendation statements; based on this behavior data, the system trains a style model to form a corporate-specific language style package, which serves as a tone template and word choice basis in subsequent recommendation generation, thereby continuously optimizing the expression style and improving the consistency of statement output.

[0027] Optionally, the data input module is further configured to: configure a function that supports a multi-input fusion mode; the multi-input fusion mode allows users to simultaneously input content including keywords, product images, document files, and web page links, and any combination thereof; extract field information based on the received multi-path input content; and perform semantic fusion, field deduplication, and priority sorting processing on the field information to output a unified structured field result.

[0028] Optionally, the multi-input consistency control module includes the following sub-modules: a semantic classification comparison sub-module, a dominant input priority strategy sub-module, a similarity scoring and threshold judgment sub-module, and an automatic correction suggestion sub-module; wherein, The semantic classification and comparison submodule is used to: perform semantic tag determination on keywords, images, web links, or document content respectively; and issue consistency prompts when there are semantic conflicts between different input paths. The dominant input priority strategy submodule is used to: configure the function to support users in setting the dominant input channel; and perform semantic fusion and conflict judgment based on the dominant input channel. The similarity scoring and threshold judgment submodule is used to: perform semantic similarity calculation based on the embedding vector of the content of the dominant input channel, and judge whether the input content is consistent according to a preset threshold; The auto-correction suggestion submodule is used to provide keyword replacement suggestions, image update prompts, or guidance on switching the dominant input channel in the event of a semantic conflict.

[0029] The embodiments of this application employing the above-described technical solution may have the following advantages: Achieve cross-platform seller information aggregation capabilities in multilingual environments, breaking down language barriers and platform silos: By constructing a "data input module" and a "semantic aggregation and comparison module," this invention can automatically process multilingual input (Chinese, English, German, French, Spanish, etc.) and aggregate seller data from different global e-commerce platforms (such as Amazon, AliExpress, 1688, etc.). Relying on a semantic embedding vector model and a contextual semantic judgment mechanism, the system achieves cross-language and cross-platform seller entity recognition, significantly improving the accuracy of field normalization and aggregation recognition.

[0030] Provides structured field comparison and parameter-level semantic matching to improve the efficiency of product and quotation data integration: Through the collaborative work of the "Field Structure Parsing Module" and the "Semantic Aggregation and Comparison Module," the system can automatically parse unstructured page elements (such as HTML tables, images, and paragraphs) into standard field formats and complete the field-by-field alignment between our product and the target product overseas. It supports multi-layered mechanisms such as semantic vector matching, image embedding comparison, and logical field mapping, while also introducing field weight configuration and unit normalization mechanisms to achieve high-precision field-level alignment and anomaly detection.

[0031] It supports multi-dimensional quotation structure output with optional structure and configurable pricing strategies to meet the diverse needs of complex trading scenarios: Through the integrated configuration of the "User Configuration and Editing Module" and the "Multilingual Rendering and Export Module," the system allows users to freely select the output structure (main quote, supplementary quote, recommended quote) and pricing strategy (regular price, discounted price, VIP price) before exporting, and renders the output document content in real time according to the settings. This dynamic combination of quote structure and pricing content output significantly differs from the static template-based export mode of existing platforms, improving the flexibility and matching accuracy of cross-border quotes.

[0032] It possesses the ability to proactively generate recommendation messages and employs multi-dimensional structural suggestion algorithms, thereby enhancing intelligent assistance and difference recognition capabilities. The system includes a built-in "Recommendation and Market Suggestion Generation Module," which supports automatic triggering of recommendation output based on field comparison results. It covers various tone templates, including polite, emphatic, and optimization suggestion types, and intelligently switches expression styles according to country, industry, and customer type. Simultaneously, it introduces a "Multi-Dimensional Structure Suggestion Algorithm," integrating parameters such as field similarity, terminology consistency, regional adaptability, and language style to complete a closed loop from difference analysis to suggestion generation, compensating for blind spots in manual judgment and enhancing the professional output effect.

[0033] Enables integrated templated multilingual output and visual delivery, improving report professionalism and marketing efficiency: This system supports flexible combinations of bilingual / multilingual structures (such as Chinese-English, Chinese-German, and Chinese-English-French), and users can customize field styles, language order, and export templates (Excel / PDF / HTML). The system also embeds company branding and customized style templates, and integrates with mainstream email platforms or API interfaces to enable automatic naming, one-click preview, and sending of quotation documents, significantly improving response efficiency and conversion rates in cross-border business.

[0034] Enhanced multi-input fusion capabilities and semantic consistency control ensure the stability and accuracy of intelligent comparison. The system has added a "Multi-Input Consistency Control Module," which performs semantic classification and comparison, priority determination of dominant paths, similarity threshold detection, and correction suggestion generation for each input content in scenarios involving combinations of input paths such as keywords, images, documents, and web links. This avoids misjudgments, translation errors, and recommendation biases caused by input content conflicts. This module significantly enhances the system's fault tolerance and intelligent guidance capabilities in the multimodal information fusion process, providing strong support for cross-language and cross-platform parameter integration. Attached Figure Description

[0035] The accompanying drawings exemplify embodiments and form part of the specification, serving together with the textual description to explain exemplary implementations of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0036] Figure 1 The diagram illustrates the overall structure of the multimodal system of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 2 This illustration schematically shows a breakdown diagram of the multimodal system functional sub-modules of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 3 This illustration shows a multi-input path diagram of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 4 This illustration schematically shows the field structure parsing module and its sub-modules of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 5 This illustration shows a schematic diagram of the dual-link product comparison and structural output of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 6 This illustration schematically shows the email integration and behavior tracking flowchart of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 7 This illustration schematically shows the system integration process of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application with a mainstream email platform; Figure 7A This illustration schematically shows the structure of the recommendation and market suggestion generation module of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 8 This illustration schematically shows a general workflow diagram for a multi-industry scenario of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 9 This illustration schematically shows the email sending and platform integration structure of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 10 This illustration schematically shows a flowchart of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application, which performs customer behavior tracking and feedback analysis. Figure 11 This schematically illustrates the integrated structure of a CRM platform for a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 12 This diagram illustrates the configuration interface structure of a quotation template for a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 13 This schematically illustrates the multilingual rendering function interface structure of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 14 This illustration shows a flowchart of user behavior tracking data analysis for a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 15 This illustration schematically shows the customer behavior heatmap feedback interface structure of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 16This illustration schematically shows a cross-border platform and multi-tenant system deployment diagram of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 17 This illustration schematically shows a flowchart of the mobile interaction and offline use of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 18 This illustration schematically shows the system structure of a user behavior modeling and intelligent response system based on semantic comparison and structural difference analysis of a multimodal parameter comparison and recommendation generation system according to Embodiment 1 of this application; Figure 19 The flowchart of the multi-input consistency control module of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application is illustrated. Figure 19A This illustration shows a schematic diagram of the system prompts and correction interface in a scenario where the input of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis is inconsistent, according to Embodiment 1 of this application. Figure 19B This illustration schematically shows the interface of the extended function collection module of the multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application; Figure 20 This illustration schematically shows a multilingual quote output and email delivery interface of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Figure 21 The illustration shows a schematic diagram of the cross-market configuration suggestion and supplementary quotation generation interface of a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis according to Embodiment 1 of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0038] It should be noted that the descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0039] It should be noted that, in any stage of this application involving the collection, storage, use, transmission, and processing of data, each stage strictly adheres to the laws, regulations, industry standards, and regulatory requirements of the data source, usage location, and relevant countries and regions to ensure the legality and compliance of data activities. In the collection stage, the purpose, method, and scope of collection are clearly communicated to the data subject in a prominent manner. Collection is conducted only after obtaining the data subject's legal authorization, ensuring that the collection process follows the "minimum necessary" principle and does not exceed the scope of data collection. In the storage stage, storage periods are limited, and data is promptly deleted or anonymized / encrypted after the storage purpose is achieved. In the usage stage, a strict data security protection mechanism is implemented, using field-level desensitization technology and processing the original data according to preset desensitization rules. For different types of data, multiple desensitization strategies, such as data generalization, data anonymization, and data encryption, are employed to effectively mitigate the risk of sensitive information leakage and ensure that all data used is securely processed and desensitized, comprehensively protecting the rights and interests of data subjects and data security. In the transmission and processing stages, the confidentiality and security of data are ensured during transmission and processing.

[0040] In the description of this application, it should be understood that the numerical labels before the steps do not indicate the order of the steps, but are only used to facilitate the description of this application and to distinguish each step, and therefore should not be construed as a limitation of this application.

[0041] First, a definition of the terminology used in this application is provided: Data input module: This refers to a functional module that can identify the language of user input or web page content and translate it into a format that the system can understand. It supports automatic conversion of multiple languages ​​such as Chinese, English, German, French, and Spanish.

[0042] Structured field information: refers to content with clear semantic tags extracted from web pages, text, images or uploaded documents, such as standardized fields like product name, model, material, specifications, certifications, etc.

[0043] Semantic embedding models refer to algorithmic models that use machine learning methods to convert text into vector representations that can be used for semantic analysis, such as BERT, Sentence-BERT, Ernie, etc., which can be used to calculate the semantic similarity between texts.

[0044] Multi-input consistency control module: This module determines the semantic consistency between different inputs when users input information through various means such as keywords, images, web links, and documents. It supports the classification of semantic tags, determination of the dominant channel, calculation of similarity scores, and conflict alerts and corrections to ensure the accuracy and stability of the system's input data fusion.

[0045] Semantic classification and comparison submodule: This is a functional component used to identify and classify semantic tags for content extracted from different input paths (such as keywords, images, documents, etc.), and to determine whether there are semantic conflicts. For example, it can detect whether keywords and image descriptions belong to the same category.

[0046] Dominant Input Priority Strategy Submodule: This module is used to set or automatically select the dominant input channel. The system will use the dominant input content as the semantic core to perform information fusion and priority judgment, ensuring the consistency of the main control direction of key fields.

[0047] Similarity scoring and threshold judgment submodule: This module calculates the semantic similarity between paths based on semantic vectors generated from multiple input contents, and judges whether they are consistent or require correction based on a set threshold.

[0048] Automatic correction suggestion submodule: When the system detects semantic conflicts between multiple input information, it automatically generates replacement suggestions (such as replacing keywords, updating images, or adjusting the dominant path) and prompts the user to improve the quality of data before comparison.

[0049] Parameter comparison matrix: refers to a two-dimensional table structure formed by comparing two sets of product data item by item across multiple fields, used to display field consistency, degree of difference, or similarity score.

[0050] Master quote: refers to the preferred price list automatically generated by the system based on parameters of highly similar products. It is usually the set of matching options that is closest to the target buyer's current product.

[0051] Supplementary Quotes: These are alternative items automatically recommended by the system when there are inconsistencies or missing fields in the main quote, helping business personnel to present more comprehensive options.

[0052] Recommended quote: This refers to a price suggestion calculated by the system based on models such as market trends, average prices of similar products, and sales forecasts. It is for reference only and is not a rigid binding price.

[0053] Multilingual editable quotation: refers to a quotation document that supports bilingual display format in Chinese and English or Chinese + target language (such as German, French, etc.), and whose field content can be edited, annotated, or formatted online by the user.

[0054] Template configuration: This refers to the system's ability to preset and save the output document format, field order, and display style, and supports one-click calling of a unified style.

[0055] The tone adaptation module is a sub-module used to adjust the marketing language style according to cultural differences in the target country or region. For example, it can adjust the Chinese recommendation to a structure suitable for the German market.

[0056] Regulatory matching module: This refers to a sub-module used to identify the certification and compliance information that a target product needs to meet in the exporting country's market and automatically match recommended content, such as RoHS, CE, REACH and other standards.

[0057] Difference analysis scoring mechanism: This refers to the difference score mechanism calculated by the system based on the semantic comparison of fields. The lower the score, the higher the consistency of the fields. Threshold filtering and sorting are supported.

[0058] CRM system integration module: refers to the interface component used to automatically synchronize generated customer quotes to enterprise customer relationship management platforms (such as HubSpot, Zoho, Salesforce, etc.).

[0059] One-click output and outreach module: This refers to a functional component used to quickly generate standard file formats such as PDF, Excel, and Word from comparison and quotation content, supporting batch export and local storage.

[0060] Intelligent Recommendation Generation Module: This refers to a functional component that uses natural language generation models (such as the GPT series) to automatically generate product recommendation messages based on customer profiles, industry characteristics, and comparison results.

[0061] The Market Suggestions module is a sub-function of the "Output and Outreach Module." Based on a comprehensive analysis of differences in product parameters, price gradients, and functional characteristics between buyers and sellers, it provides automatically generated optimization suggestions, recommended alternatives, or supplementary pricing suggestions to assist users in developing more advantageous strategies during price negotiations, product optimization, or adding new proposals. This module is flexible and editable, supporting user-added custom suggestions or system-recommended commonly used phrases with multilingual output.

[0062] System Language Recognition Module: This module automatically identifies the main language type (such as zh-CN, en-US, de-DE) in the text, fields, or quotation documents uploaded by the user, and recommends a reasonable default language combination for subsequent translation and typesetting based on the customer's country or browser settings.

[0063] To enable those skilled in the art to clearly understand the technical content of the present invention, the technical composition of each functional module in the system of the present invention and their interrelationships will be specifically described and supplemented below with reference to the accompanying drawings.

[0064] The multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis integrates multiple input methods such as keywords, images, web page links, and document uploads. It also introduces a multi-input consistency control module to perform consistency processing on multi-channel input content, including semantic tag classification, dominant path judgment, similarity scoring, and conflict prompts, to ensure semantic consistency and comparison accuracy during the data fusion process.

[0065] The system integrates a data input module, a field structure parsing module, a semantic aggregation and comparison module, a recommendation and market advice generation module, an output and outreach module, a user configuration and editing module, and a multilingual rendering and export module. It can perform structured extraction and semantic alignment of product parameters from both buyers and sellers, identify differences, and generate corresponding recommendations, risk warnings, or market advice.

[0066] The system can ultimately output various quotation formats such as main quotation, supplementary quotation, and recommended quotation based on the user's set language combination, structure template, and pricing strategy. It supports export of multiple language combinations such as Chinese-English, Chinese-German, and Chinese-French, and can reach target customers with one click through email system or CRM platform, greatly improving the efficiency of cross-context and cross-platform business communication.

[0067] Research indicates that mainstream cross-border e-commerce platforms (such as Google Shopping, Alibaba, Amazon, and Bing) currently lack the capability to integrate multiple input methods, including keywords, images, web links, and documents, for consistent semantic recognition and structured pricing. Even platforms with image search or keyword query functions often limit their results to product displays or advertising recommendations, lacking a deep understanding of structural and semantic differences and the ability to recommend appropriate parameters. Therefore, the proposed combination mechanism of "multi-input fusion + consistency control + intelligent recommendation generation" offers significant technological breakthroughs and commercial viability, applicable to various entities including foreign trade enterprises, manufacturers, and platform providers.

[0068] The system includes, but is not limited to, the following core modules: The data input module is used to perform multilingual parsing of key fields (such as product name, description, and tags) in the input Chinese and foreign product information. This module can optionally call multiple translation sub-modules, including DeepL, GoogleTranslate, or Azure Translator, and automatically select the optimal translation engine based on response latency and translation accuracy to achieve unified semantic space mapping and improve the accuracy of cross-language parameter comparison.

[0069] The multi-input consistency control module automatically determines the semantic consistency of inputs when users combine multiple input methods (keywords, images, web links, documents), preventing input ambiguity from causing parameter comparison confusion or distorted recommendation results. This module includes a semantic classification comparison submodule, a dominant input priority strategy submodule, a similarity scoring and threshold judgment submodule, and an automatic correction suggestion submodule. These submodules respectively implement semantic classification of each input path, determination of the main channel, calculation of semantic similarity, and intelligent prompts and correction suggestions when inputs are inconsistent, ensuring accurate and reliable comparison results and a stable and consistent process. The introduction of this module significantly improves the system's robustness and semantic fusion capabilities in multimodal input environments, providing a fundamental guarantee for achieving high-quality recommendations and multi-source comparisons. The field structure parsing module is used to convert unstructured product information from Chinese and foreign company websites, Excel spreadsheets, or API data into structured fields. This module can optionally extract dimensional fields such as product model, size, material, and applicable scenarios through HTML DOM parsing, table field mapping, and OCR image recognition, and establish a field classification system to achieve parameter standardization. The semantic aggregation and comparison module is used to vectorize the content of structured fields based on semantic embedding models (such as BERT, SentenceTransformer, etc.) and compare domestic and foreign product fields in the semantic space. This module optionally supports strategies such as field-level similarity calculation, weight factor adjustment, and ignoring word order / language differences to automatically identify highly similar items and key differences. The system supports dual-end comparison functionality, that is, simultaneously loading domestic product fields and target competitor company fields, and performing field-level structural alignment and difference comparison under a dual-channel embedding model. The visual interface will highlight key difference fields, allowing business personnel to quickly identify core differences.

[0070] The recommendation and market suggestion generation module automatically generates differentiated suggestion statements that customers can understand based on semantic comparison results. Examples include "We suggest you adjust the product width to XX to meet the specifications of your target market" or "Our material is an upgraded environmentally friendly version that meets EU market standards." This module can optionally combine product category, target country, and user intent templates to generate recommendation statements, supporting dynamic editing and learning optimization. When generating recommendation statements, the system comprehensively applies a "multi-dimensional structure suggestion algorithm." This algorithm integrates dimensions such as field similarity, terminology matching, target country language preferences, and industry standard expression styles to automatically select the optimal language style and output template, achieving an intelligent closed loop from field comparison to suggestion output.

[0071] The output and outreach module is used to export the final generated comparison results and recommendations between Chinese and foreign products into editable or read-only formats. This module optionally supports output to Excel, PDF, HTML, or integration with third-party systems (such as CRM and ERP) via API, enabling the transformation and utilization of results and facilitating a closed-loop business process.

[0072] The user configuration and editing module allows users to manually adjust, add or delete fields, and modify recommendation templates by comparing results and suggestions. Optional features include drag-and-drop field sorting, synchronized updates for content modifications, and visual highlighting of differences, enabling users to flexibly intervene in the output.

[0073] The multilingual rendering and export module is used to automatically translate the output results into a combination of languages ​​such as Chinese-English, Chinese-German, Chinese-French, or pure English, based on the user's selection, while retaining the source language comparison information.

[0074] In some embodiments, when the user does not select a language combination, the system can analyze the language used in the uploaded fields, documents, or web pages (such as Chinese, English, German, etc.) through the language recognition module, and intelligently recommend a default language combination based on the user's country or browser language configuration. The language recognition module can also optionally support functions such as terminology dictionary binding, professional translation corpus backfilling, and rendering of specific language styles (such as formal German tone) to ensure that the content is professional, clear, and deliverable.

[0075] In some embodiments, before exporting the quotation, the system can provide selectable output structure options, supporting three types of quotation structures: a "main report" structure containing only matching fields, a "supplementary report" structure with our extended fields, and a "recommended report" structure generated based on field differences. This feature supports quick switching between multiple templates, enhancing user customization capabilities.

[0076] In some embodiments, the system can also support multiple pricing strategy options. Specifically, users can select different pricing templates such as "regular price," "discount price," and "VIP price" based on customer type or market policy. The system will automatically match the corresponding price information in the quotation field and recommendation text to ensure consistency in communication.

[0077] like Figure 12 As shown, in addition to supporting field structure adjustments, the quotation template configuration interface also allows users to select pricing strategies and output structure types (such as main quotation, supplementary quotation, and recommended quotation). The system will dynamically render the corresponding quotation document style accordingly, improving personalization and adaptability.

[0078] Optionally, the data input module supports users uploading custom terminology dictionaries. The technical approach includes building a local dictionary cache and a priority matching mechanism to pre-match the user's dictionary before calling external translation services, so as to improve the translation accuracy and consistency of specific industry terms. This is particularly suitable for industry scenarios with a large number of professional terms, such as medicine, machinery, and textiles.

[0079] Optionally, the multi-input consistency control module is used to perform semantic consistency verification on each input content when the user selects multiple input paths such as keywords, images, web links, and documents, to avoid deviations in the comparison results due to input conflicts. This module includes a semantic classification comparison submodule, a dominant input priority strategy submodule, a similarity scoring and threshold judgment submodule, and an automatic correction suggestion submodule, etc., to realize semantic fusion, conflict identification, and correction guidance between input paths, ensuring the accuracy and stability of the system's comparison output.

[0080] Optionally, the field structure parsing module can integrate image recognition and table structure restoration technologies. It extracts text information from images through an OCR engine, and combines HTML DOM structure analysis to re-block mixed text and image areas, automatically annotating parameter fields and their corresponding values, thereby supporting the structured extraction of unstructured pages.

[0081] Optionally, the semantic aggregation and comparison module supports custom field priority weight configuration. Users can assign different weights to fields such as "core parameters" and "minor indicators" through the front-end interface. The system then adjusts the field alignment, sorting, and matching confidence in the comparison algorithm using weight factors to achieve comparison results that better fit the user's business logic.

[0082] Optionally, the recommendation and market suggestion generation module has an adaptive model based on user behavior training. The system will record the suggestion statements that users have modified or adopted in the past as corpus, and generate an "enterprise-specific language style package" through clustering and style modeling, which will be used for the optimization of the writing style and the enhancement of personalized expression in future automatic suggestions.

[0083] Optionally, the recommendation module can interface with the target market's certification regulations database. For example, it can search for certification requirements such as RoHS, CE, and FDA in real time via API, and automatically generate a suggestion statement when the comparison finds that the customer's product lacks the corresponding certification, prompting "It is recommended to supplement CE certification to meet the compliance requirements of the European market", thereby enhancing the professionalism and practicality of the output content.

[0084] Optionally, the user configuration and editing module supports operations such as "locking", "hiding", and "remarking" for any field in the comparison results. This is achieved by embedding field status markers and display logic parameters in the output table structure. Users can control the displayed fields in real time in the editing interface to achieve customized management of external quotation content.

[0085] Optionally, the output and outreach module supports one-click generation of quotation templates with company logo and contact information. The technical approach includes: during the initialization phase, the system captures brand elements from the user's registration information and automatically embeds them into the template header area. When exporting to PDF or Excel, the system calls the image merging component to insert footer signatures, QR codes, and other content to enhance brand presentation.

[0086] Optionally, the system includes a version control mechanism that allows users to save multiple comparison versions and track their history. This mechanism uses a dual-channel approach—"result snapshots" and "parameter summary records"—to store the comparison logic and generated content. Users can view, compare, and trace back historical versions by timeline or project dimension, assisting in procurement and sales decisions.

[0087] Optionally, the system can integrate with third-party email platforms such as Outlook or ProtonMail to automate the sending process after generating a quotation. The system binds user email addresses through an OAuth authorization mechanism and supports automatically filling in recipient, subject, and body templates when the generated file is attached, thus shortening business response time.

[0088] Optionally, the system supports style fine-tuning of the target language output template. This is achieved by adding intonation parameters and sentence selection models to the language pack configuration. For example, passive voice and compound sentences are used by default in German output, while active voice and concise expressions are prioritized in English output to better match the business communication style of the target country.

[0089] Optionally, the system can provide an API interface to connect with mainstream ERP / OA systems, enabling automatic workflow after quotation generation. The interface output is packaged in JSON or Excel format, and includes field mapping and access control mechanisms, supporting cross-platform integration into enterprise processes such as procurement approval, customer relationship management, and sales forecasting.

[0090] Optionally, the system can support opening a "template development interface" for large enterprise customers, allowing them to upload exclusive quotation style templates (including font, color, and structure definition JSON files). The system will automatically render to the template structure during generation and generate a private quotation channel for the enterprise to meet high customization needs.

[0091] Example 1 like Figure 1As shown, this embodiment provides a multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis, including: a data input module, a multi-input consistency control module, a field structure parsing module, a semantic aggregation and comparison module, a recommendation and market suggestion generation module, an output and outreach module, a user configuration and editing module, and a multilingual rendering and export module; wherein: The data input module is used to receive product-related data input from webpage link parsing, user-uploaded documents, tables, or manual entry. The multi-input consistency control module is used to: determine the semantic consistency of each input content when the user simultaneously inputs multiple data sources; identify potential conflicts and provide fusion paths or correction suggestions to ensure the accuracy and stability of subsequent comparison and generation processes; the data sources include keywords, images, web page links and documents; The field structure parsing module is used to: perform structured processing on the input data, extract core fields, and establish a standard field index structure; the core fields include product parameters, certification information, function descriptions, image data, unit specifications, and industry terms; The semantic aggregation and comparison module is used to: represent the core fields semantically based on an embedded model or an equivalent semantic vector model; identify the semantic similarity and differences between the core fields and generate structured comparison results; and is configured to support multilingual comparison. The recommendation and market suggestion generation module is used to: generate recommendation and market suggestions based on the structured comparison results and historical case data; the recommendation and market suggestions include difference prompts, recommended terms, market trend prompts, and industry suggestions; and is configured to support multi-industry template loading and intelligent context adaptation. The output and outreach module is used to: export the recommendations and market suggestions as a preset format file; the preset format includes Excel, PDF, API data stream or HTML web page format; and is configured with customizable functions to support field styles, language types and output logic; The user configuration and editing module is used to: provide a visual interface; and has functions that support field order adjustment, drag-and-drop structure configuration, content editing, language switching, version control, and historical comparison. The multilingual rendering and export module is used to: translate and replace the names, content descriptions, recommendations, and marketing tips of all fields according to the user-defined language combinations; it is also configured to support output in at least one target language to adapt to the corresponding market language requirements.

[0092] This system supports user input of product information in various formats, including four main paths: keywords, product images, product description documents, and company websites. The system will automatically determine the type of input content and invoke the corresponding parsing and comparison modules to achieve intelligent recognition and semantic analysis of product information, generating appropriate structured fields and comparison results. For example... Figure 3 As shown, this module provides flexible access and a unified processing entry point for different input paths, significantly improving the system's applicability and user convenience.

[0093] In an optional embodiment, the data input module includes multiple data acquisition sub-modules; wherein, it further includes: The URL parsing submodule is used to: parse the input webpage link and extract the product title, image, and structured parameter fields from the webpage; The document import submodule is used to: read uploaded Excel, CSV, or PDF files; identify and extract field information from the files; The manual data entry submodule is configured to support user input of missing fields or editing of extracted data. The language recognition submodule is used to: determine the language of the text based on a language detection model for translation path selection; the language detection model includes CLD3 and fastText; The terminology lexicon alignment submodule is used to perform field matching and translation preprocessing on user-uploaded professional terminology dictionaries to improve professional consistency. Translation quality assessment submodule: This module supports consistency assessment of translation results from multiple engines; it scores based on target metrics, including BLEU, BERTScore, and terminology coverage; it generates a credibility ranking based on a language model to select the best result; it acquires user-uploaded terminology lists as translation references to improve terminology stability and professional text expression; and it selects a combination of data collection submodules based on dimensions including data completeness, parsing accuracy, and user interaction burden to complete the initial import of product data.

[0094] In an optional embodiment, the data input module is further configured to: configure a function that supports a multi-input fusion mode; the multi-input fusion mode allows users to simultaneously input content including keywords, product images, document files, and web page links, and any combination thereof; extract field information based on the received multi-path input content; and perform semantic fusion, field deduplication, and priority sorting processing on the field information to output a unified structured field result.

[0095] In some embodiments, the data input module can also be used to perform multilingual parsing of key fields (such as product name, description, and tags) in the input Chinese and foreign product information. The function of calling and selecting multiple translation paths is executed by the translation quality assessment submodule of the data input module. The system dynamically combines the calling paths based on language type, terminology suitability, and translation accuracy to improve the initialization processing effect of product fields and cross-language compatibility.

[0096] Specifically, such as Figure 3 As shown, the system can automatically recognize and extract key terms from multilingual texts including Chinese, English, German, and Japanese. With the built-in terminology library and contextual judgment algorithm, it performs standardized semantic modeling and vector encoding on keywords for subsequent structural comparison and suggestion generation.

[0097] In some implementations, the data input module can further support a "multi-input fusion mode." That is, users can choose one or more combinations of input methods, such as keywords, product images, document files, and web links. Upon receiving multiple types of input, the system will automatically trigger the corresponding parsing modules, such as the URL parsing submodule, image recognition submodule, OCR recognition submodule, and semantic extraction submodule, and extract the corresponding product field information accordingly.

[0098] like Figure 19 As shown, the data input module may further include a field fusion and priority sorting submodule, used to clean, deduplicate, resolve conflicts, and prioritize field information from different paths to construct a unified structured field result, improving the completeness and semantic accuracy of the input information. This fusion mechanism is particularly suitable for cross-border business scenarios where users provide incomplete information, ambiguous expressions, or mixed languages. The aforementioned language recognition, terminology dictionary matching, and translation quality assessment functions are handled by submodules 4 to 6 of the data input module, respectively, as detailed in [link to documentation]. Figure 3 The submodule structure is shown.

[0099] In another alternative implementation, such as Figure 19 As shown, the data input module further includes the following multi-input channel sub-module: Keyword extraction submodule: Used to perform language detection, term segmentation and standard category tag mapping on user-input keywords, and supports matching with industry keyword databases to improve accuracy; Image input parsing submodule: Used to receive product images uploaded by users and generate semantic tags through image recognition models (such as ResNet, CLIP, etc.) to assist in semantic consistency judgment; Document import submodule: used to parse uploaded documents in formats such as Excel and PDF, identify field structure, parameter values ​​and units, and perform unified encoding processing; The URL parsing submodule is used to extract information such as product titles, images, and parameter tables from web pages, filter out noisy content, and retain only the core fields.

[0100] The above sub-modules can be activated independently or used in conjunction with the multi-input consistency control module to achieve parallel processing and semantic fusion of multiple paths, providing standardized input for the field structure parsing and comparison module.

[0101] In some optional embodiments, the multi-input consistency control module includes the following sub-modules: a semantic classification and comparison sub-module, a dominant input priority strategy sub-module, a similarity scoring and threshold judgment sub-module, and an automatic correction suggestion sub-module; wherein, The semantic classification and comparison submodule is used to: perform semantic tag determination on keywords, images, web page links, or document content; and issue consistency prompts when semantic conflicts exist between different input paths. Specifically, the semantic classification and comparison submodule categorizes each input content into semantic tags. For example, it uses an image recognition model to identify the category of an image, extracts keyword tags using a keyword classification model or a web page text extraction model, and matches them against a standard category tree in the background. If an input item belongs to a different product category, the system automatically prompts the user of a semantic conflict.

[0102] The dominant input priority strategy submodule is used to: configure and support the function of users setting dominant input channels; and perform semantic fusion and conflict judgment based on the dominant input channels. Specifically, it can support users to manually set dominant channels such as "keyword priority" and "image priority". The system uses the semantics represented by the dominant channel as the core reference to perform consistency judgment and strategy adjustment on other inputs.

[0103] The similarity scoring and threshold judgment submodule is used to: perform semantic similarity calculation based on the embedding vector of the content of the dominant input channel, and determine whether the input content is consistent according to a preset threshold. Specifically, the semantic vectors of each input item can be processed in a unified manner (e.g., through BERT or Ernie models), and semantic consistency can be calculated using algorithms such as cosine similarity and Euclidean distance. When the score is lower than a set threshold (e.g., 0.6), the system prompts the user that there is a risk of inconsistency.

[0104] The auto-correction suggestion submodule is used to provide keyword replacement suggestions, image update prompts, or guidance on switching the dominant input path when semantic conflicts occur. Specifically, when a semantic conflict is detected, the system automatically recommends replacement keywords, prompts for image updates, or suggests switching the dominant input path based on the semantics of the dominant channel, thereby improving user interaction efficiency and system intelligence.

[0105] Specifically, the multi-input consistency control module can be used to determine the semantic consistency of each input content in scenarios where the user selects multiple input paths (such as keywords, images, documents, and web links) to combine inputs, thereby avoiding downstream output confusion caused by input conflicts.

[0106] In this embodiment, the collaborative use of multiple sub-modules of the multi-input consistency control module significantly enhances the robustness of the system in multi-input path fusion scenarios, ensuring that structured extraction, semantic comparison, and recommendation output are logically consistent and content-credible, thereby improving the system's adaptability and output quality under cross-language, cross-platform, and cross-format input conditions.

[0107] In one alternative implementation, such as Figure 19 As shown, the system supports users to input product information through various methods such as keywords, images, Excel / PDF documents, or product webpage links. The multi-input consistency control module will uniformly manage all input paths, specifically including steps such as input content type identification, semantic tag classification, dominant path confirmation, similarity scoring, and conflict judgment. If the system detects semantic conflicts among multiple input sources (e.g., the keyword is "pet backpack," while the image displays "electric lawnmower"), it will automatically pop up a prompt and pause the subsequent comparison process.

[0108] This module allows users to pre-define the dominant input path (e.g., "image priority"). Based on the semantics of the dominant path, it judges whether other input content matches. When the similarity is below a threshold (e.g., 0.6), the system provides correction suggestions, including options such as replacing keywords, re-uploading images, or switching the dominant path. Only after confirmation does the system distribute the input information to the corresponding parsing module to perform subsequent field extraction, structural modeling, and semantic comparison operations, ensuring the consistency, accuracy, and traceability of the comparison results under multi-channel fusion.

[0109] like Figure 19A As shown, when a user simultaneously inputs information from multiple sources (such as keywords, product images, web links, etc.), the system will first execute a semantic consistency judgment process to detect whether there are conflicts or differences between the content expressed by different input paths. If the system detects inconsistencies in the input content in terms of category tags, keyword semantics, or field structure, it will proactively pop up a prompt such as... Figure 19A The correction suggestion interface shown marks the conflicting items and provides optional action suggestions. For example, it may include the following steps: S1, Replace keywords (e.g., prompt: "coffee machine" does not match the uploaded image).

[0110] S2, upload more relevant product images.

[0111] S3, set the dominant channel (e.g., prioritize the image) to guide the subsequent comparison path.

[0112] In addition, the system will also simultaneously display the current semantic classification results and similarity scores (e.g., the keyword corresponds to "small kitchen appliances", the image is identified as "outdoor gardening tools", and the similarity score is only 0.42), and provide corresponding recommended path switching suggestions or keyword / image correction guidance.

[0113] This process relies on the collaborative work of the "semantic classification and comparison sub-module", "dominant channel priority strategy module", "similarity scoring and threshold judgment module" and "automatic correction suggestion module" to ensure semantic consistency judgment, path correction and robust control under multiple input modes. It significantly improves the stability and comparison accuracy of the system in complex input combination scenarios and avoids field matching errors or quotation result deviations caused by semantic conflicts.

[0114] In some optional embodiments, the field structure parsing module includes multiple structure processing sub-modules; wherein, it further includes: The field mapping submodule is used to: establish mapping relationships between input fields and standard fields based on rule matching, machine learning, or human feedback mechanisms; The field cleaning submodule is used to: obtain cleaning results based on preset field cleaning processes; the preset field cleaning processes include standardizing unit formats, removing redundant characters, and standardizing capitalization and syntax structure. It is used to perform unit standardization, word segmentation, and semantic format unification of field values, remove redundant characters, and output a comparable set of standard fields. This module optionally supports stop word removal and industry terminology extraction processing on field values. The field index building submodule is used to: generate a structured field index based on the cleaning results; and is configured to support fast semantic invocation. The web page crawling submodule is used to: perform preset processing based on web page parsing tools to load web page content and extract parameter information fields; the preset processing includes HTML DOM analysis and script rendering processing; The OCR recognition submodule is used to convert unstructured image data into structured fields; the unstructured image data includes user-uploaded product images and scanned price lists. It supports calling Tesseract to perform the image-to-text field extraction process. The field merging and reconstruction submodule is used to aggregate fields with redundant, scattered, or overlapping semantics to generate an optimized structure for subsequent comparison or recommendation generation.

[0115] The field structure parsing module can receive and parse product information from Excel files, product web pages, API interfaces, or databases, and automatically extract it into a standard field structure format. These fields include, but are not limited to: product name, model, size, material, color, applicable scope, price, minimum order quantity, delivery date, certification number, etc.

[0116] In specific implementation, such as Figure 3 As shown, the system can complete or rearrange missing fields through regular expression matching, keyword classification and field cleaning mechanisms, and in some embodiments, it supports user-defined field mapping rules and field merging logic to improve compatibility and flexibility.

[0117] In this embodiment, as Figure 4 As shown, the field structure parsing module transforms unstructured product information from Chinese and foreign company websites, Excel spreadsheets, or API data into structured fields. Optionally, this module extracts dimensional fields such as product model, size, material, and applicable scenarios through HTML DOM parsing, table field mapping, and OCR image recognition, and establishes a field classification system to achieve parameter standardization.

[0118] The aforementioned submodules are respectively used to perform processing steps such as standard mapping of structured fields, format cleaning, index building, web page field extraction, image text transcoding, and semantic field aggregation. The system will dynamically select the appropriate path based on the type, structural complexity, and industry adaptation rules of the input data to achieve standardized parsing and structural reconstruction of parameter fields. See the relevant module structure for details. Figure 4 As shown. The structured fields can originate from user-uploaded Excel, CSV, PDF, or image files. This type of uploaded input has already had its structure extracted and preprocessed by the document import submodule within the data input module.

[0119] In an optional embodiment, the semantic aggregation and comparison module includes multiple comparison sub-modules; wherein, the multiple comparison sub-modules include: The text-based semantic vector comparison submodule is used to compare the semantic similarity between text fields based on an embedding model. Specifically, semantic vector modeling of input and target text fields can be performed using embedding models (such as Sentence-BERT, Ernie, etc.), and semantic similarity can be calculated based on metrics such as cosine similarity to identify synonymous fields in different languages ​​or expressions.

[0120] The image-based visual feature comparison submodule is used to: extract image feature vectors from unstructured image data; and calculate the visual similarity between the unstructured image data based on the image feature vectors. Specifically, it can be used to extract feature vector information (such as contours, textures, color distribution, etc.) from product images and perform visual similarity calculations with target product images to determine whether they are similar products or substitutes.

[0121] The field mapping submodule based on structural logic is used to map and match logical relationships between fields with different structures. Specifically, it can be used to map and align the logical relationships between the input product field structure and the target field structure based on a preset field rule base or industry template, thus resolving comparison obstacles caused by different field names or structural groupings.

[0122] The semantic embedding submodule is used to model text information using word vector and sentence vector embedding techniques to improve the accuracy of semantic recognition. Specifically, it can be used to perform deep semantic modeling of structural field content using word vector, sentence vector, and other embedding techniques, enhancing the model's ability to understand terminological ambiguity, word order differences, and multilingual expressions, thereby improving the comparison accuracy.

[0123] The multimodal comparison submodule is used to compare and judge multimodal information that integrates text, images, and structural data. Specifically, it can be used to fuse multi-source information such as text, images, and structural fields, jointly model within the same semantic space, achieve comprehensive comparison and judgment of product features, and improve recognition capabilities and confidence in complex scenarios.

[0124] The regional adaptation submodule is used to: adjust the comparison results regionally according to a preset comparison strategy; the comparison strategy is determined based on the language, style, and regulations of the target country or market. Specifically, it can be used to adjust the semantic comparison logic according to the language style, certification regulations, or cultural preferences of the target country or region. For example, prioritizing the comparison of CE certification fields in the EU market, and emphasizing the accuracy of unit conversion in the Southeast Asian market.

[0125] The above six sub-modules can be combined and called as needed. The system will dynamically select the optimal comparison path based on factors such as input data type, industry template, and target market to achieve efficient aggregation and analysis of product fields, image information, and structural logic.

[0126] In an optional embodiment, the semantic aggregation and comparison module is further configured to: configure the function to support simultaneous loading of our product data and target customer product data; align, compare and annotate the field structure of our product data and the field structure of the target customer product data based on a dual-channel embedding model; and configure the function to support side-by-side display and visual marking of the difference fields.

[0127] Specifically, the semantic aggregation and comparison module can be used to identify, normalize, and compare field-level differences in parameter fields of Chinese and foreign product information after the field structure parsing is completed. For example... Figure 4 As shown, the system uses an HTML structure parsing and parameter extraction submodule to convert text, tables, images, and other content from web pages into standard field structures. Subsequently, a unit and numerical normalization submodule handles differences in measurement units (such as cm vs. inch, kg vs. lbs), and a field standard mapping submodule maps different expressions (such as "certification certificate" vs. "compliance mark") to unified field labels. Based on this, the system performs field-level alignment and difference identification, outputting a field correspondence table, a list of differing fields, and preliminary comparison summary results for subsequent recommendation generation and structured output.

[0128] In an optional embodiment, the semantic aggregation and comparison module is further configured to: configure the function to support simultaneous loading of our product data and target customer product data; align, compare and annotate the field structure of our product data and the field structure of the target customer product data based on a dual-channel embedding model; and configure the function to support side-by-side display and visual marking of the difference fields.

[0129] In one embodiment, when the system detects a difference between two products in the "Certification" field (e.g., one is CE and the other is empty), it can automatically mark the field as a "key difference item" and hand it over to the recommendation generation module for further processing.

[0130] In this embodiment, the semantic aggregation and comparison module can further call semantic embedding models (such as BERT, Sentence-BERT, SentenceTransformer, etc.) to perform vectorization processing on the parsed field content and complete the semantic matching and similarity calculation of Chinese and foreign product fields in a unified semantic space.

[0131] Optionally, the semantic aggregation and comparison module can further support field-level similarity analysis, dynamic weight adjustment mechanism and word order insensitive strategy, so as to more accurately identify semantically similar fields and key difference parameters.

[0132] Exemplary illustration, such as Figure 4As shown, the user-input product description field is "Main Material: Aluminum Alloy," while the target product webpage field is "Material: Stainless Steel." The system first calls the structure-logic-based field mapping submodule to match the two fields as belonging to the same dimension. Then, through a joint judgment by the text-based semantic vector comparison submodule and the semantic embedding submodule, it identifies the material difference between "aluminum alloy" and "stainless steel." Finally, through a regional adaptation strategy, it suggests: "It is recommended to upgrade the material to stainless steel to meet the standards of the target market."

[0133] Exemplary illustration, such as Figure 3 and Figure 4 As shown, a user uploads an image of a pet backpack and enters the keyword "pet backpack". The system calls the image-based visual feature comparison submodule to extract image features and identifies that the image has a 92% similarity to a high-selling product on Amazon.de. Subsequently, the multimodal comparison submodule fuses the image with structural fields (such as size, material, and load-bearing capacity) to complete the comparison. Finally, the system prompts "Our product is lighter and more portable. We suggest highlighting the 'foldable' attribute for differentiated marketing".

[0134] In this embodiment, through the collaborative work of the above six sub-modules, the semantic aggregation and comparison module realizes unified aggregation and analysis of multilingual text, product images, and structural fields, and based on the difference point-driven recommendation generation and regional market adaptation logic, it constitutes the key hub for the system's intelligent judgment and output.

[0135] After performing semantic comparison and field matching, this system will automatically trigger a proactive recommendation generation mechanism based on the comparison results, generating various styles of recommendations (such as polite, emphatic, and optimization suggestion types) for fields with inconsistent structures. The recommendation module supports automatic adjustment of language style and expression based on country / industry / customer type. The system also incorporates multiple indicators such as field similarity, degree of difference, and regional adaptability to form a multi-dimensional structural recommendation algorithm, realizing a closed loop from "field comparison" to "output recommendation".

[0136] In optional embodiments, such as Figure 7A As shown, the recommendation and market suggestion generation module includes multiple generation sub-modules; wherein, the multiple generation sub-modules include: The "Recommendation Templates" submodule is used to: load preset multilingual industry-specific script templates. This allows for quick filling in of suggestion formats.

[0137] The difference-driven generation submodule is used to generate personalized suggestion statements, such as "Our material is more environmentally friendly," based on the structured comparison results.

[0138] The trend indicator submodule is used to generate price range suggestions based on historical data, third-party market reference prices, and industry trend forecasts. It combines historical quotes and third-party market data to generate price range suggestions or market trend analysis statements.

[0139] The regulatory matching submodule is used to: compare the product against the certification regulatory requirements of the target market, indicate any missing items, and generate compliance recommendations. It identifies whether our products meet the certification standards (such as CE, RoHS, FDA) of the target country and generates compliance recommendations.

[0140] The parameter performance rating submodule is used to generate rating statements based on the evaluation results of our products and the customer's products on core parameters. Specifically, it can output "Excellent / Good / Average" ratings and reasons for recommendation by comparing the parameter strengths and weaknesses of our products and the customer's products.

[0141] The language style adaptation submodule is used to: adjust the recommended sentence structure and expression based on the target country's language style or user preferences to improve communication effectiveness. It adjusts the recommended sentence structure according to the client's country or personal preferences, including adjusting honorific style, word order, or strengthening marketing language.

[0142] The system obtains the click-through rate, adoption rate, and suggestion acceptance score of the recommendation text from the above-mentioned multiple generation sub-modules under different target industries, language scenarios, and user feedback. Based on the comprehensive evaluation results, it dynamically selects the optimal generation path and expression style to improve the relevance, professionalism, and conversion effect of the suggested content.

[0143] The recommendation and market suggestion generation module can automatically generate personalized recommendation messages and market tips based on semantic comparison results, field difference analysis, and historical case data. These include, but are not limited to: difference-driven statements, compliance suggestions, industry trend analysis, and multilingual content.

[0144] In an optional embodiment, the recommendation and market suggestion generation module is further configured to: optimize the output based on a multidimensional structural suggestion algorithm; the multidimensional structural suggestion algorithm combines multiple dimension parameters to select a recommendation generation path and a structured output template; the multiple dimension parameters include field similarity, terminology matching degree, regional adaptation rules, and industry expression style.

[0145] Optionally, the recommendation and marketing suggestion generation module can support extended use of large language models (such as GPT and ERNIE) to generate more personalized and persuasive recommendation content. Combined with product selling point extraction, pricing strategy analysis, and optimization suggestion output mechanisms, it enhances the commercial value and conversion rate of the recommendations. Additionally, it can automatically identify missing packaging information, accessory suggestions, or certification items in the quotation and supplement them in the email body or quotation remarks to enhance content completeness.

[0146] like Figure 7A As shown, the six sub-modules of the recommendation and market suggestion generation module constitute independently callable functional units within the system. The system dynamically combines and generates paths based on field comparison results and market context to achieve the automated construction of intelligent recommendation and supplementary information.

[0147] like Figure 6 As shown, the recommendation and marketing suggestion generation module can work in conjunction with the output and outreach module after the content is generated. It can adjust the format of the generated content, embed the content, and optimize the delivery based on user configuration and delivery platform requirements, and perform tasks such as filling email body, generating attachments, and tracking replies.

[0148] In optional embodiments, such as Figure 6 As shown, the output and reach module includes multiple export and integration sub-modules; wherein, the multiple export and integration sub-modules include: The format rendering submodule is used to beautify and style the recommendations and market suggestions according to the user-selected preset format. Specifically, it can be used to beautify and format the processing results in Excel, PDF, HTML and other formats, and supports font scaling, multi-language switching and print preview functions, so that users can proofread and personalize the content before sending.

[0149] The export delivery submodule is used to generate downloadable links, encrypted external links, or QR code preview links. Specifically, it can be used to generate editable or read-only attachment files from quotation content, supporting one-click packaging of Excel quotations, product catalogs, and other content, and delivering them via downloadable links, encrypted links, or QR codes.

[0150] The API output submodule is used to send structured results to external system interfaces or third-party applications. Specifically, it directly pushes structured data to external system interfaces (such as ERP and BI platforms) or integration platforms to achieve data synchronization and business closed loop.

[0151] The email body generation submodule is used to generate a complete and appropriately toned email body based on customer language preferences and product type. The email body includes a product summary and suggestions. Specifically, it can generate email body content based on customer language, industry background, and preferred style, supporting various tones such as formal, friendly, and marketing, and automatically embedding customer salutations, product summaries, personalized suggestions, and attachment descriptions.

[0152] The behavior tracking submodule is used to: monitor whether customers exhibit preset behaviors; these preset behaviors include opening emails, clicking attachments, or replying to content; and generate behavior scores based on these preset behaviors to help sales assess customer intent. Specifically, for example... Figure 10 As shown, the behavior tracking submodule can monitor whether emails are opened and attachments are clicked, count click time, frequency and dwell time, and output behavior scores for sales to assess customer interest.

[0153] The CRM integration submodule is used to: synchronize quotation records and customer behavior analysis results to the customer relationship management system; and configure functions to support customer lead tagging and sales progress management. Specifically, such as... Figure 11 As shown, email sending records, customer behavior, and feedback tags can be automatically synchronized to CRM systems such as Notion, Zoho, and Airtable, supporting customer card binding, stage progress marking, and subsequent sales follow-up.

[0154] During the actual execution process, the output path can be dynamically combined from the above sub-modules according to the delivery method set by the user, platform permissions, and customer interaction status, and user operation logs can be recorded in real time, including information such as sender account, target email, sending time, and attachment list, to ensure business tracking, auditing, and compliance requirements.

[0155] The output and outreach module can be used to display, export, and send the generated intelligent quotes in various ways, and integrate with external communication tools and customer relationship management (CRM) systems to build a one-stop closed-loop path for quote generation, content distribution, and customer feedback. For example... Figure 6 As shown, the output and outreach modules can also be integrated with platforms such as Outlook, Gmail, and corporate email, supporting OAuth login, one-click sending, automatic synchronization of sending records, and draft saving. Email feedback analysis results can also automatically generate tags such as "inquiry," "hesitation," and "customization" through emotion recognition and keyword extraction technology, providing data support for subsequent sales follow-up.

[0156] In an optional embodiment, the user configuration and editing module includes multiple interactive editing sub-modules; wherein, the multiple interactive editing sub-modules include: The visual editing submodule is used to configure and support the function of allowing users to adjust the field structure and order through drag and drop. This submodule provides a WYSIWYG editing interface, allowing users to adjust the field structure and order through drag and drop, and preview the layout in real time. It is suitable for generating output documents with a clear structure and logical presentation.

[0157] The field customization submodule is used to configure functions such as adding new fields, setting field groups, and batch renaming. In practical applications, users can customize new fields, set field groups, and batch rename fields to meet the personalized needs of different customers or industries.

[0158] The version history submodule is used to: record every editing operation; and provide historical version comparison and rollback functions.

[0159] The language switching and bilingual settings submodule is used to configure the output language combination, field name comparison method, and terminology translation source according to user preferences. Specifically, it supports user configuration of language combinations such as Chinese-English and Chinese-German, field names can be displayed in a comparative format, and terminology consistency is optimized by combining a terminology database.

[0160] The currency and unit of measurement configuration submodule is used to: configure the function to support users in setting currency and unit types; and perform conversions based on the market and display them uniformly. Specifically, users can set currencies and units such as USD↔CNY, cm↔inch, and the system will automatically complete the conversion and field synchronization.

[0161] The template saving and reuse submodule is used to save the structure, format, and display logic of the current field as a reusable template for customers or industry use. Specifically, it allows users to save field structure, style layout, and language settings as industry templates or customer templates for quick use in subsequent tasks.

[0162] In practical applications, such as Figure 12 As shown, users can first select the template type (industry template or customer template), then adjust the field order, control the display status, edit field remarks, preview multi-language format styles, and finally save and export it as a reusable template.

[0163] In one specific embodiment, users can select "Pet Supplies Industry Template" in the quotation template configuration interface. The system automatically loads the field structure, such as "Product Name / Material / Size / Certification Requirements," and supports dragging to hide or adjust the field order. After completing the field settings, users can save it as a "Standard Pet Supplies Quotation Template" for quickly generating comparative quotations for similar products in the future, improving efficiency and consistency.

[0164] The system dynamically combines and configures paths among the above sub-modules based on user-defined frequency, field complexity, and industry preference parameters. It also supports templated calls and editing rollback, improving system flexibility, customization capabilities, and user operation efficiency.

[0165] In an optional embodiment, the multilingual rendering and export module includes multiple language processing sub-modules; wherein the multiple language processing sub-modules include: The language recognition and suggestion combination submodule is used to: identify the language type of the user input content; and recommend language combinations based on the language type of the user input content, the target customer's country, or browser configuration. Specifically, the language recognition and suggestion combination submodule can automatically identify the main language types (such as Chinese, English, German, and French) in the user input field, document, or webpage, and intelligently recommend suitable language combinations based on the user's browser configuration or the target customer's regional settings.

[0166] The terminology recognition and translation submodule is used to: invoke multiple translation engines to translate technical terms in the field content; these multiple translation engines include DeepL, Google Translate, and Azure Translator. Specifically, it can automatically translate field titles and key field content according to the target country and language settings, combining a terminology database, AI translation models (such as DeepL and GPT), and a system dictionary to ensure accurate presentation of technical terms.

[0167] The bilingual output submodule is used to generate language combination output that retains the source language comparison content according to user settings; the language combination output includes Chinese-English, Chinese-German, and Chinese-French. Specifically, it can support the side-by-side display of bilingual fields, including field name bracket comparison, custom arrangement, etc., to improve multilingual readability and adaptability to international customers, while retaining the source language comparison content.

[0168] The region adaptation submodule is used to adjust the field order, number format, terminology, and interface layout style according to the language expression style and unit format of different countries to ensure the consistency of output and localization compliance.

[0169] The format export and packaging submodule is used to: export the rendering results to files in preset formats, including Word, Excel, PDF, HTML, or JSON; and configure support for local packaging and sharing of image resources and style files. It can export the rendering results to formats such as Word, Excel (with embedded images), PDF (with cover watermark), HTML pages, or JSON, and supports local packaging of image resources and style files for offline archiving or external sharing.

[0170] The Language Sorting and Main Language Setting submodule is used to configure and support user-defined output language display order, main language position, and translation priority strategy. By customizing the output language arrangement order, main language position, and translation priority strategy, users can adapt to the language display preferences of different regions and clients.

[0171] Specifically, the multilingual rendering and export module can render compared and edited product pricing information into professional-format documents that support multiple languages, and provides a one-click export function. The system supports automatic generation of bilingual or multilingual combinations such as Chinese-English, Chinese-German, and Chinese-French, with output formats including Excel, PDF, HTML email body, and API JSON, meeting the application needs of different business scenarios (such as email sending, platform uploading, and system integration). The system supports multilingual display; users can select a language in the interface, and the interface content will automatically switch, making Chinese-English bilingual display the default, and it can be extended to other languages. The interface design can consider differences in language length and layout style to ensure a consistent and clean language experience across various terminal devices.

[0172] Exemplary illustration, such as Figure 13 As shown, when configuring the template, if the user selects "Chinese-English Bilingual Output," the system automatically loads a terminology lookup table and arranges field names in "Chinese (English)" format, while simultaneously enabling the currency and unit configuration module. Based on the user-defined "EUR + cm" output standard, the system can automatically convert the "USD + inch" field in the customer's product to the corresponding unit and annotate the original value, achieving bidirectional adaptation between language and unit, improving the reading comprehension efficiency for international buyers. The multilingual rendering module supports switching between Chinese, English, and other languages, with synchronized interface display. This structure encompasses processes such as language recognition, text switching, field rendering, and interface display adaptation, ensuring the consistency and readability of system content in a multilingual environment.

[0173] In this embodiment, the multilingual rendering and export module can translate the output content into combinations such as Chinese-English, Chinese-German, Chinese-French, or pure English, depending on the user's selection, while retaining the source language comparison. By supporting the binding of terminology dictionaries, referencing professional translation corpora, and applying specific language styles (such as formal German tone), it ensures that the content is clear, professional, and deliverable.

[0174] In specific implementations, such as Figure 13 As shown, the following functional enhancement modules can also be optionally configured: a text and image mixed layout and table style engine submodule (supporting language direction adjustment and terminal adaptation), an automatic naming and version control submodule (supporting file naming rule setting and version tracking), and an export trigger and permission control submodule (providing permission verification and export preview functions), etc., to enhance the system's controllability and professionalism in multi-language rendering and delivery scenarios.

[0175] In some embodiments, to accommodate different customers' pricing habits, the system can also be configured to support freely combinable language export templates, such as "Chinese-English main and auxiliary comparison," "pure German pricing," and "Chinese-English-French trilingual arrangement." Simultaneously, users can select the pricing template style (such as table width, field order, and mixed text and image layout) during export, and the system will instantly preview and export the corresponding format, achieving parallel support for flexible structural configuration and language switching. This feature differs from existing systems on the market that only support static single-language export, improving the efficiency of multilingual adaptation in cross-border business.

[0176] In an optional embodiment, the system configuration supports the function of user-defined field configuration schemes and saving them as templates; the template includes field structure, language preference, output format, visual style and outreach strategy configuration; based on the template, personalized output content is generated; the frequency of template calls, usage feedback and export results are recorded; and a recommended template list sorting mechanism is provided.

[0177] In this embodiment, personalized output content generation can be achieved by supporting custom template configuration and call records. Combined with a recommendation and sorting mechanism, user operating efficiency can be improved.

[0178] In an optional embodiment, the system configuration supports the function of allowing users to select the quotation structure type before exporting the results; the quotation structure types include: a main quotation structure containing only matching fields between the two parties, a supplementary quotation structure containing only supplementary fields from our side, and a recommended quotation structure generated based on market differences; the output content is dynamically rendered according to the selected quotation structure type.

[0179] In this embodiment, by supporting the function of selecting the quotation structure type before exporting, it is possible to flexibly respond to quotation requirements under different business scenarios, achieve accurate matching of output content, and thus improve the efficiency of result export.

[0180] In an optional embodiment, the system configuration supports the user's ability to select a pricing strategy in the output module; the pricing strategy includes a regular price, a discounted price, and a VIP price; the system then fills in the quotation field, outputs a recommendation message, and names the file according to the selected pricing strategy.

[0181] In this embodiment, by selecting a pricing strategy, the pricing content can be automatically adjusted according to different customer positioning, thereby improving the relevance and professionalism of the pricing and enhancing the customer's user experience.

[0182] Optionally, this system can be deployed on a local server, private cloud, or SaaS platform. It has horizontal scalability and heterogeneous data source compatibility mechanism, supports data input and output in the form of API, and also supports online fine-tuning of cross-language AI models to improve industry adaptability.

[0183] The following, in conjunction with the accompanying drawings and typical usage scenarios, further illustrates the specific implementation process and system operation logic of the present invention, but is not limited thereto.

[0184] Exemplary illustration, such as Figure 3 As shown, when a user enters the keyword "pet backpack" or uploads a local product image, the system first uses a web crawling module (such as a crawler module) to automatically crawl relevant product data about "hund rucksack" or "katzen transporttasche" from e-commerce platforms such as Amazon.de and Zooplus.de, and then filters out the 10 most similar target products by comparing image feature vectors.

[0185] In this process, the data cleaning submodule will automatically remove invalid information from the webpage (such as advertising code, navigation bar, translation button, etc.), retaining only core fields such as "product title", "feature description" and "selling point keywords", and uniformly converting them into UTF-8 encoding format and storing them in the local cache database to improve processing efficiency.

[0186] In another optional embodiment, the file upload submodule not only supports regular .xlsx or .csv files, but also embeds support for uploading product quotation drafts in the form of image scans or mobile phone photos. In this case, the system will automatically call the OCR module to perform image recognition, and extract information such as product name, description, price, and certification through the key field extraction model before performing structured modeling.

[0187] In an optional implementation, the output and delivery module can be integrated with mainstream email systems such as Microsoft Outlook, Gmail, and ProtonMail, enabling passwordless sending via OAuth 2.0 and supporting users selecting multiple recipients and attaching descriptive text. The system can record all email sending history and provide feedback indicators such as "viewed" and "attachments downloaded" for behavior tracking. Figure 7 As shown, the system can achieve end-to-end integration of quotation output and user email behavior analysis.

[0188] In multi-industry quotation processes, the system can, according to Figure 8 The shown path completes the structural comparison, recommendation generation, and multilingual output. It also synchronizes the quotation to customer management systems such as Notion, Airtable, and Zoho CRM via the CRM interface submodule, enabling subsequent follow-up and response recording. The above description is only a preferred embodiment of the present invention. Those skilled in the art can make other changes and modifications within the spirit of the present invention, and all such changes and modifications should be covered within the protection scope of the present invention.

[0189] To make this invention clearer and easier to understand, six exemplary applications are provided below, each corresponding to a typical process and action path of the system in different usage scenarios: Example Application 1: Keyword + Image Input in the Pet Supplies Industry Exemplary illustration, such as Figure 3 As shown, when a user enters the keyword "pet backpack" and uploads a local product image, the system first uses a web crawler module to automatically retrieve relevant product data for "hundrucksack" or "katzen transporttasche" from e-commerce platforms such as Amazon.de and Zooplus.de, and then filters out the 10 most similar target products by comparing image feature vectors.

[0190] S1: The user enters keywords and uploads product images on the system front end; the system first performs semantic consistency judgment on the keyword and image input to ensure that "pet backpack" and the category identified in the image (such as cat carrier) belong to the same standard category, avoiding information conflict that may lead to mismatch.

[0191] S2: The system starts web page crawling and image semantic recognition.

[0192] S3: Connect to the database to perform semantic comparison and similarity ranking.

[0193] S4: Output structured comparison results and generate preliminary suggested quotes.

[0194] Example Application 2: PDF Scan Recognition in the Maternal and Infant Industry like Figure 4 As shown, exporters of maternal and infant products can upload a scanned copy of a product quotation in PDF format. The system automatically calls the OCR module for image recognition and extracts information such as product name, description, price, and certifications through the key field extraction model before performing structured modeling.

[0195] S1: Users upload a quotation sketch in PDF or image format.

[0196] S2: The system parses key data using OCR and field extraction models.

[0197] S3: Perform data cleaning and field mapping.

[0198] S4: Automatically generate structured product fields and incorporate them into the matching process.

[0199] Example Application 3: Keyword upload for apparel companies + Excel file upload like Figure 3As shown in the clothing category scenario, a textile and apparel export company enters the keyword "children's hooded jacket" and uploads an Excel file containing product specifications and material descriptions. The system automatically completes matching and difference analysis through field structure parsing and keyword comparison mechanisms.

[0200] S1: The user enters keywords and uploads a product table. The system pre-determines whether the keywords and the corresponding fields (such as size, fabric) in the table content belong to the same category tag. If a conflict is found between the fields and keywords, a prompt suggestion will be triggered.

[0201] S2: The system identifies table fields, standardizes the format, and compares them with product parameters.

[0202] S3: Extract key features and compare them with market products.

[0203] S4: Output editable suggested quotes and optional copy descriptions.

[0204] Example Application 4: Multilingual Output and Email Outreach like Figure 20 As shown, in the output stage, users can choose to generate quotations in multiple languages, such as Chinese and English, or Chinese and German, and send them to the target customer's email address via the integrated OAuth protocol email interface. The system supports recording the email delivery status and can track whether the target customer has viewed or downloaded the quotation.

[0205] S1: Before exporting, users can select the target language combination and quotation template layout (such as bilingual vertical / horizontal, comparative structure, or overseas only structure).

[0206] S2: The system calls the language translation module, template rendering module, and typesetting engine to complete language conversion and structured format generation.

[0207] S3: The system automatically connects to the selected email service (such as Gmail or Outlook) and generates a visual package of email content and attachments.

[0208] S4: Complete the delivery and send back the delivery result. The system uses the email tracking module to monitor whether the email has been opened, downloaded, or replied to, which serves as one of the bases for subsequent behavior tracking and recommendations.

[0209] Example Application 5: Cross-Category Intelligent Configuration Suggestions and Supplementary Quotes like Figure 21 As shown, combined with Figure 3 Input module Figure 4 The parsing process and Figure 7 The system employs a comparison mechanism that, after generating structured quotes, further utilizes an AI configuration suggestion engine to combine historical transaction data with target market requirements to propose configuration optimization solutions for user products.

[0210] S1: Users upload product parameter description documents, product images, or configuration tables (such as certification documents, packaging plans, accessory structures, etc.).

[0211] S2: The system analyzes the historical preferences, platform requirements, and compliance certification status of the target market (such as Europe / Middle East) for this type of product.

[0212] S3: Automatically generate a set of feasible configuration optimization suggestions (such as suggesting adding CE certification, reducing carton volume, and including a power adapter).

[0213] S4: Automatically incorporate the above suggestions into the "Supplementary Quotation" appendix, or allow users to add them as remarks, forming a structured value-added path.

[0214] Example Application 6: Dual-link structured alignment + multilingual difference output like Figure 5 As shown, the system supports users inputting target product information through various methods such as keywords, images, documents, or web links. The system first uses an input parsing module to uniformly receive and recognize the input content, converting it into a standardized field format. Subsequently, the system enters a semantic consistency determination module to verify the consistency of the semantic tags, category classification, and field structure of the current input content. The system uses semantic vector modeling and category tree alignment to determine whether the input belongs to the same semantic category. If the semantic judgment result is consistent, the system will directly perform parameter extraction and proceed to the next stage of structured comparison. If the judgment result is inconsistent, the system will proactively display a consistency conflict warning and call a correction suggestion submodule to recommend that the user change keywords, replace images, or switch the dominant input path to ensure that the comparison logic is based on a unified category and semantic tags.

[0215] Specifically, it includes the following steps: S1: Input parsing and field standardization User-input keywords, images, web links, or PDF documents will be uniformly processed by the input parsing module to extract fields such as product model, certification, packaging, and price, and to clean the format and normalize the language.

[0216] S2: Semantic consistency judgment and correction mechanism triggering The system categorizes the input content through the semantic classification comparison submodule. If it finds that the categories are inconsistent or the similarity score is lower than the threshold (e.g., 0.6), the similarity score and threshold judgment module will issue a prompt and call the automatic correction suggestion module to generate keyword or image replacement suggestions to prompt the user to optimize the input combination.

[0217] S3: Structural Field Comparison and Difference Identification After confirming that the input content is semantically consistent, the system performs two-way field matching between the extracted fields and the comparison targets in the database, comparing structural differences, unit offsets and parameter gaps item by item, and generating a field difference matrix and annotation results.

[0218] S4: Quotation Template Structure Generation Based on the field matching results, the system automatically calls the quotation template structure generation module to output the main quotation, supplementary quotation, or recommended quotation structure. The field sorting, price priority, and annotation method within each structure template can be configured by the user in advance.

[0219] S5: Multilingual Output and Rendering Finally, the quotation is generated into multiple languages, such as Chinese-English, Chinese-German, or Chinese-French, through a multilingual output and rendering module. It supports simultaneous output of editable Excel spreadsheets, PDF attachments, and HTML page preview formats. Users can choose to edit manually or submit with one click.

[0220] This embodiment is particularly suitable when users need to quickly connect product fields between buyers and sellers, highlight parameter differences, and generate structured suggestions. Figure 5 As shown in the process diagram, the system provides a closed-loop process from input recognition, semantic consistency judgment, structural comparison, template generation to multilingual rendering, which significantly improves the efficiency of quotation preparation, language adaptability and customer response professionalism.

[0221] In addition to the main implementation methods and exemplary applications described above, this application may also include the following optional embodiments to further enhance the flexibility and intelligence of the system.

[0222] Optional Implementation Example 1: Industry-Customized Module In one optional implementation, the system further includes an industry customization module, used to dynamically adjust the layout of product fields and pricing content based on the user's industry (such as clothing, home appliances, pet supplies, etc.). This module automatically determines the industry type by analyzing the industry category information submitted by the user during initial registration or the attributes of the products uploaded by the user, and automatically calls the matching field template.

[0223] Exemplary illustration, such as Figure 6 As shown, when the system identifies a user's main product as "clothing," the quotation template will automatically add fields such as "size range," "fabric composition," and "color options," and extract matching information from the source webpage to fill in the fields. Furthermore, if the system detects that the user is selling "maternity and baby products," it will automatically activate field templates such as "EU CE certification" and "material safety level" to ensure content industry compatibility.

[0224] Optional Implementation Example 2: Buyer Company Website Parsing Module In one optional implementation, it further includes: a buyer company website parsing module, used to: automatically analyze the structure of the customer company's official website after the user enters the customer company's website address, identify key product pages and contact information modules, and extract historical purchase product features to assist in matching our products.

[0225] Exemplary illustration, such as Figure 7 As shown, after a user enters the URL "https: / / www.bimport.de", the system uses DOM tree structure analysis and keyword focusing technology to identify pages such as "ProductCatalogue", "Shop", and "Contact" on the website, and extracts keywords such as "pet travel bag", "dog food", and "cat tree" to reflect the user's purchasing preferences. The system will then prioritize matching and recommending its own products that are semantically closest to the user's preferences, thereby improving the relevance of the match.

[0226] Optional Implementation Example 3: Image and Text Joint Recognition Module In one optional implementation, it further includes: an image-text joint recognition module, used to: simultaneously perform multimodal joint modeling of product images and accompanying text descriptions to improve the accuracy of fuzzy matching in complex situations such as inconsistencies between images and text, and different languages.

[0227] Exemplary illustration, such as Figure 8 As shown, a user uploads a photo of a "pet backpack" with a Chinese tag and the English description "PetTravelBackpack". The system extracts both the image and text vectors and uses a cross-attention fusion mechanism for semantic matching. This multimodal comparison method can significantly improve recall and precision on cross-border platforms compared to traditional keyword matching.

[0228] Optional Implementation Example 4: Quotation Pre-approval and Compliance Check Module In one optional implementation, the system further includes a quotation pre-review and compliance check module, which is used to automatically scan the quotation fields for any violations or missing key information before the user clicks to export the quotation, and prompt the user to complete the compliance correction.

[0229] Exemplary illustration, such as Figure 9 As shown, when a user generates a pet supplies quote for a German customer, the system detects that a product is missing the "GS certification" field, automatically highlighting it as a yellow warning and suggesting the user add the relevant certification information. Simultaneously, if the unit price of a product is significantly lower than the market average for that type of product, the system will trigger a price anomaly warning mechanism and request user confirmation to avoid subsequent risks due to pricing errors.

[0230] Optional Implementation Example 5: Multi-channel Interconnection and Feedback Closed-Loop Module In one optional implementation, it also includes a multi-channel integration and feedback closed-loop module, used to: synchronously distribute the generated quotation to third-party platforms such as WhatsApp, LinkedIn, and Gmail through the API interface provided by the system, and track customer behavior feedback in real time, such as actions such as opening, downloading, and forwarding.

[0231] Exemplary illustration, such as Figure 10 As shown, after a user sends a quotation to a customer via the Gmail API, the system can record information such as the first time the quotation is opened, the total number of views, and whether attachments are downloaded in the background. If a customer clicks on a product hyperlink in the quotation, the system can also record the click path and push the behavioral data to the CRM module to form a customer behavior profile and support subsequent marketing strategy optimization.

[0232] Optional Implementation Example 6: Intelligent Recommendation and Quotation Assistance Module In one optional implementation, the system further includes an intelligent recommendation quotation assistance module, which is used to intelligently recommend reference values ​​based on historical data or an AI-trained model when the user has not filled in all quotation fields (such as FOB price, MOQ, remarks, etc.).

[0233] Exemplary illustration, such as Figure 11 As shown, the system recognizes that the current user is filling out a price quote for a "pet dog backpack." The system's historical records show that the average price for similar products is USD 6.50, with an MOQ of 200 pieces. If the current field is empty, the system will display "AI suggestion: USD 6.50; MOQ: 200," which the user can directly adopt or manually modify, significantly improving form-filling efficiency and standardization.

[0234] Optional Implementation Example 7: Localized Regulatory Matching Module In one optional implementation, it further includes: a localized regulatory matching module, used to: automatically query the applicable import regulations, product certification standards and tax requirements based on the country or region where the target customer is located, and display prompt information in the quotation.

[0235] Exemplary illustration, such as Figure 12 As shown, when the target customer is an Italian company and the product is "pet food", the system automatically calls the regulatory database to confirm that it must have the "EU FEED mark" and the corresponding "customs tariff number", and automatically generates a warning in the quotation remarks column: "It is recommended that this product comply with EC Regulation No. 767 / 2009" to avoid import compliance risks.

[0236] Optional Implementation Example 8: Historical Quotation Data Comparison Module In one optional implementation, it further includes: a historical price data comparison module, used to: compare the currently generated price content with the historical prices of similar products in the system, and mark price trends, changes in authentication fields, etc. in a visual chart.

[0237] Exemplary illustration, such as Figure 13 As shown, the user intends to generate a price quote for a certain model of "folding pet stroller." The system extracts information such as price, MOQ, and certification fields from the past 6 months' price records, generating line and bar charts. If the currently entered value is higher or lower than the normal range, the system will indicate this in red to assist the user in making adjustments.

[0238] Optional Implementation Example 9: Language Style Conversion Submodule In one optional implementation, it further includes: a language style conversion submodule, used to: automatically switch the language expression style of the quotation based on the customer's country, business culture and enterprise attributes, including various writing style templates such as formal, technical and marketing.

[0239] Exemplary illustration, such as Figure 14 As shown, if the customer is a French B2C e-commerce distributor, the system will use the "marketing-oriented + concise style" template and translate the "product features" field as: "Solution innovante pour les déplacements confortables des animaux de compagnie." (Innovative solutions to improve the comfort of pets traveling), avoiding the use of rigid technical jargon and improving communication effectiveness.

[0240] Optional Implementation Example 10: Buyer Behavior Heatmap Feedback Module In one optional implementation, it further includes: a buyer behavior heatmap feedback module, used to: record the buyer's page browsing path, dwell time, mouse movement trajectory, etc., after the buyer accesses the quotation through a link, and generate a heatmap for user review and analysis.

[0241] Exemplary illustration, such as Figure 15 As shown, the system detected that a certain buyer spent the most time in the "MOQ" and "Product Certification" sections and moved the mouse multiple times in the "Remarks" section. A heatmap was generated in the background to display the hot spots. This data will be automatically stored in the "Customer Behavior Analysis" module, prompting users to pay attention to compliance and supply conditions, which helps optimize subsequent communication strategies.

[0242] Optional Implementation Example 11: Cross-border Platform and Multi-tenant Deployment Module In an optional implementation, it also includes: a cross-border platform and multi-tenant deployment module, used to: connect the system with mainstream cross-border e-commerce platforms (such as Amazon, eBay, Aliexpress, Shopee, etc.) and support SaaS multi-tenant mode deployment.

[0243] Exemplary illustration, such as Figure 16 As shown, the system can directly retrieve the corresponding seller's product information through the platform API to realize the cross-border price comparison function; at the same time, enterprise users can deploy it in isolation under subdomains to realize personalized customization of interface, template and data, and support white label service (WhiteLabelSaaS).

[0244] Optional Implementation Example 12: Mobile Interaction and Offline Usage Module In an optional implementation, it also includes a mobile interaction and offline usage module, which supports access to the system by mobile devices such as iPad and Android, and allows users to generate quotation drafts offline when the network is poor.

[0245] Exemplary illustration, such as Figure 17 As shown, the system features a responsive front-end architecture and a local caching mechanism, enabling business personnel to complete quotations and send emails via mobile phones or tablets during exhibitions and field visits. The data is automatically synchronized to the cloud system after connecting to the internet, ensuring data continuity.

[0246] Optional Implementation Example 13: User Behavior Modeling and Intelligent Customer Service Module In an optional implementation, it also includes a user behavior modeling and intelligent customer service module, which is used to automatically build customer interest profiles based on user browsing behavior, click patterns and price feedback, and guide first-time users to quickly complete operations through an AI multilingual assistant.

[0247] Exemplary illustration, such as Figure 18 As shown, the system records the time customers spend on the quotation and the areas they frequently click on, forming a "preference graph" for subsequent recommendation optimization. At the same time, the AI ​​assistant can answer questions such as "How to upload product images" and "How to set the minimum order quantity" in languages ​​such as Chinese, English, German, and French, improving the first-time user experience.

[0248] Optional Implementation Example 14: Extended Function Collection Module In one alternative implementation, the system further includes an extended function suite module for integrating various other auxiliary functions to enhance system flexibility, compatibility, and scalability, supporting users in performing intelligent quotation management on different platforms and in different environments.

[0249] Exemplary illustration, such as Figure 19B As shown, the module may include, but is not limited to, the following sub-functions: Image recognition browser plugin: When a user right-clicks on a product image on a client's website, the browser plugin can automatically recognize the image and pop up a quote suggestion window, enabling instant quotes.

[0250] Tax rate matching module: Supports identification of tax rate structures in countries and regions with multiple tax systems, such as Canada and Switzerland. The system automatically estimates the after-tax price based on the product category and local tax regulations, and alerts users to compliance risks.

[0251] Quotation version control module: Used to record the historical versions of each quotation, supporting team functions such as version switching, annotation, and collaborative approval, suitable for multi-person collaborative quotation processes.

[0252] API and Webhook integration module: Open data interfaces for external platforms such as CRM, ERP, and email systems to call, while also supporting event-driven automatic push notifications to messaging platforms such as WeChat Work and Slack, enhancing the system's ecosystem integration capabilities.

[0253] Through this extended feature set module, the system can adapt to more diverse use cases and provide enterprise customers with flexible deployment, rapid integration and refined management capabilities, significantly enhancing the system's engineering value and commercial scalability.

[0254] In addition to the optional embodiments described above, this system may also include the following extended functional modules to further enhance its practicality, deployability, and intelligence capabilities. Specifically, these include: Image recognition browser plugin: When a user right-clicks on a product image on a client's website, a pop-up window with a quote suggestion will be automatically triggered.

[0255] Tax rate matching module: Supports identification of multiple tax systems in regions such as Canada and Switzerland, and automatically estimates the final after-tax price.

[0256] Quotation version control module: Supports historical version recording and collaborative approval processes, suitable for team use scenarios.

[0257] API and Webhook integration module: Open interfaces allow external CRM, ERP, and email systems to connect; supports automatic push notifications to WeChat, Slack, etc.

[0258] The scope of protection of this invention is not limited to the above embodiments. For each module, step, and functional unit in this application, those skilled in the art can implement at least thirty equivalent or alternative implementation methods without creative effort. All the following solutions and combinations should be considered part of the scope of protection of this invention. For ease of explanation, typical alternative solutions are categorized and shown below according to functional logic: I. Language Processing and AI Interface Alternative Paths The multilingual translation module can replace ChatGPT multilingual dialogue interface, Amazon Translate, Baidu Translate, and other equivalent services.

[0259] The image recognition module can be replaced with existing image parsing solutions such as Google Vision API and Tencent Cloud Image Recognition.

[0260] The multilingual semantic matching module can be replaced by a word vector model with a new generation of multilingual large models based on BERT, mT5, GEMINI, etc.

[0261] The customer intent recognition module can integrate large language models to intelligently determine users' purchasing intentions and automatically filter out inefficient customers.

[0262] User behavior logs and historical data can be used to train lightweight local models to enable personalized template recommendations and product suggestions.

[0263] II. Deployment Structure and System Environment Extension Path This system can be deployed on any mainstream public cloud platform such as AWS, Alibaba Cloud, Azure, and Google Cloud.

[0264] In enterprise environments with sensitive information or weak network conditions, the system supports local private deployment and can run all functions without the need for a public network.

[0265] SaaS services can be deployed using a multi-tenant architecture, with subdomains divided by customer, and support white label service customization.

[0266] The system supports embedding into other enterprise-level systems such as ERP, CRM, and OA, allowing them to be called as nested modules.

[0267] III. Replacement Path for Functional Modules and Interface Technologies The Excel format output module can be extended to PDF template rendering, HTML page export, or RESTful API structure output.

[0268] The email sending module can be integrated with third-party SMTP services such as Outlook and Zoho Mail, or handled by the enterprise's local mail server.

[0269] The template management module can be upgraded from manually configuring forms to an automatic template recommendation mechanism based on AI style preference learning.

[0270] Industry standard fields (such as HS code, RoHS, REACH) can be automatically mapped by the system to quotation table structure fields.

[0271] In addition to manual uploading, product data can also be automatically obtained by synchronizing with the company's ERP / PDM system.

[0272] The system supports API calls and Webhook push notifications, and can be integrated with external systems such as WeChat, Slack, and Teams.

[0273] In addition to REST API, inter-module communication can also be achieved using mechanisms such as WebSocket, gRPC, and cache synchronization.

[0274] IV. Data Output Formats and Alternative Paths for Tracking Functions Quotation data can be output in any structured or unstructured format such as Word, PPT, JSON, and XML to adapt to external systems.

[0275] The quotation supports embedded electronic signatures, meeting contract signing and legal compliance requirements.

[0276] Each product suggestion can be accompanied by a unique tracking ID for subsequent clicks, customer feedback, and conversion rate analysis.

[0277] The quotation output can be accompanied by a blockchain hash signature to achieve timestamp notarization and tamper protection.

[0278] V. Areas for Enhancing User Experience and Interaction It supports A / B testing, allowing you to quote the same product in different languages ​​or using different templates to test differences in customer response.

[0279] The attachment upload module can be expanded into a format conversion module, such as automatically converting PSD and AI files into PDFs and attaching them to the quotation.

[0280] The image recognition plugin can be integrated into the browser, allowing users to directly extract product images from target websites and generate quotation suggestions.

[0281] The system supports responsive design and is compatible with devices such as iPad and Android, meeting the needs of mobile business scenarios.

[0282] The system has a built-in AI customer service module that supports languages ​​such as Chinese, English, German, and French, guiding users through key operations such as uploading products and generating quotes.

[0283] VI. Compliance, Security and Compatibility Enhancement Path User behavior tracking can be integrated with third-party systems such as Mailtrack, Mixpanel, and Hotjar to achieve accurate recording and feedback analysis.

[0284] Data caching mechanisms can be deployed based on mainstream databases or memory structures such as Redis, MongoDB, and MySQL.

[0285] Users can define pricing rules, such as the lowest price for a product, default service items, and free shipping to specific countries.

[0286] All fields support mapping to a JSON Schema structure, facilitating two-way data synchronization and validation.

[0287] The system can be integrated with AI risk control models to monitor sensitive keywords, prohibited items, or potential fraudulent activities, ensuring the platform's security and compliance.

[0288] It should be noted that the scope of protection of this invention is not limited to the above embodiments. For each module, step, and functional unit in this application, those skilled in the art can implement at least thirty equivalent or alternative implementation methods without creative effort. All the following solutions and combinations should be considered part of the scope of protection of this invention. For ease of explanation, typical alternative solutions are categorized and presented below according to functional logic: I. Language Processing and AI Interface Alternative Paths 1. The multilingual translation module can replace existing translation APIs such as ChatGPT multilingual dialogue interface, Amazon Translate, and Baidu Translate.

[0289] 2. The image recognition module can be replaced with models such as Google Vision API, Tencent Cloud Image Recognition, and CLIP.

[0290] 3. The multilingual semantic matching module can be replaced by a word vector model with a new generation of multilingual large models based on BERT, mT5, GEMINI, Qwen, etc.

[0291] 4. The customer intent recognition module can be integrated with LLM (Large Language Model) for purchasing intent analysis and field pre-filling.

[0292] 5. User historical data can be used to fine-tune the local model to achieve personalized recommendation templates, language configurations, and pricing styles.

[0293] II. Deployment Structure and System Environment Extension Path 6. This system can be deployed on any mainstream public cloud platform such as AWS, Azure, Alibaba Cloud, and Google Cloud.

[0294] 7. In weak network or classified environments, the system supports local private deployment and can run without a public network.

[0295] 8. SaaS services can be deployed using a multi-tenant architecture, supporting white-label services, self-branded embedding, and permission isolation.

[0296] 9. The system can be integrated as a nested module into enterprise systems such as ERP, CRM, PLM, and OA.

[0297] 10. Data input can be automatically synchronized through the enterprise PDM / ERP, eliminating the need for manual uploading.

[0298] III. Replacement Path for Functional Modules and Interface Technologies 11. The Excel output module can be extended to PDF rendering, HTML embedding, JSON data stream and other formats.

[0299] 12. The email module can be integrated with any SMTP or API service provider such as Outlook, Zoho, and Sendgrid.

[0300] 13. The template rendering module supports Markdown, LaTeX, or Word template rendering paths.

[0301] 14. Parameter fields can be automatically mapped to industry standards (such as HS codes, RoHS, REACH, FDA certification, etc.).

[0302] 15. The recommendation module can be replaced with a prompt word builder combined with AI generation tools to output suggested sentence patterns.

[0303] 16. Figure 19B The API and Webhook modules can be extended to channels such as Slack, WeChat Work, Lark, and Teams.

[0304] IV. Data Output and Tracking Function Path 17. The output quotation document supports any structured format such as Word, PPT, XML, SVG, CSV, YAML, etc.

[0305] 18. Supports embedding electronic signatures and timestamps to meet audit compliance requirements.

[0306] 19. The system supports unique quote ID tracking and integrates with the customer behavior analysis module to monitor clicks / views / conversion rates.

[0307] 20. Quotations may include a blockchain hash signature for cross-border transaction credentials and data tamper protection.

[0308] 21. Output documents can automatically add QR codes / short links and bind external resources such as product manuals and video demonstrations.

[0309] V. Path to Enhance User Experience and Interaction 22. Supports A / B testing mechanism, allowing the same product to be tested for response differences under different language templates.

[0310] 23. The attachment upload module can be expanded with format conversion functions, such as PSD→PDF, AI→SVG and other design document compression and export.

[0311] 24. The image recognition plugin can be directly deployed as a browser extension, generating a draft quotation by clicking on a product image on any website.

[0312] 25. Supports responsive design and adapts to multi-screen display on mobile devices (iOS, Android), tablets, and TVs.

[0313] 26. The system has a built-in AI guide assistant that supports languages ​​such as Chinese, English, German, French, and Japanese to help upload products and generate templates.

[0314] VI. Security, Compliance and Compatibility Expansion Path 27. The system supports the integration of DLP security modules and risk control identification models to prevent the leakage of sensitive information.

[0315] 28. All field structures can be mapped to JSON Schema, facilitating data validation and synchronization with external interfaces.

[0316] 29. In addition to REST, inter-module communication can also be replaced by high-performance methods such as WebSocket, gRPC, and Kafka.

[0317] 30. Optional configuration items support enterprise business rule customization, such as minimum price limits for products, shipping policies, or default service items.

[0318] In summary, the "Multi-dimensional Intelligent Parameter Comparison and Market Recommendation System" proposed in this application has the following outstanding advantages in actual deployment: 1. Comprehensive parameter comparison dimensions: Supports comparison of multiple key fields such as product name, description, image, price, unit of measurement, and certification qualifications, building a semantically consistent comparison framework; 2. Strong multilingual support and bilingual output capabilities: Natively supports combined output in Chinese, English, German, French, Spanish, etc., improving the efficiency of cross-border business communication; 3. Intelligent matching and difference recognition mechanism is accurate: By combining structural analysis and semantic judgment, the system can automatically identify inconsistencies in parameters and generate corresponding recommendations or market suggestions; 4. Flexible and highly customizable output configuration: Users can independently choose the output structure, language template, price type, and layout to meet various business styles; 5. Strong system scalability: Supports integration with mainstream cross-border platforms (such as Amazon, AliExpress, 1688, etc.) and can be linked with ERP, CRM and other systems; 6. Intuitive interface with zero learning cost: The WYSIWYG configuration and preview interface allows ordinary business personnel to quickly master the operation; 7. Modular structure and plug-in expansion design: such as Figure 19B As shown, the system supports a wealth of extensions such as image recognition plugins, automatic tax rate matching, version control, and webhook push notifications, meeting enterprise-level deployment and customization needs.

[0319] Therefore, this system performs excellently in multiple dimensions such as functional completeness, intelligence level, user experience, platform adaptability and commercial scalability, providing a new generation of intelligent quotation solutions for global foreign trade business, and has broad practical application value and industrial extension potential.

[0320] It should be noted that the scope of protection of this application is not limited to the above embodiments. For each module, step and functional unit in this application, those skilled in the art can make various equivalent substitutions or combinations based on the content of this specification without creative effort. All the following technical paths and any combination thereof are considered reasonable extensions of this invention and constitute the legal protection scope of this application.

[0321] The above equivalent implementation methods include, but are not limited to, various implementation paths such as module replacement, interface expansion, platform deployment changes, output format adjustment, and interaction method reconstruction. For details, please refer to the typical alternatives listed later in this specification. Regardless of whether it adopts a module division or implementation method that is completely consistent with this application, as long as it achieves the core technical objective of this invention, it should be included within the scope of protection.

[0322] Obviously, those skilled in the art will understand that the above modules or steps can be implemented by general-purpose computer equipment, can be centrally deployed on a single device, or can be distributed through a network; they can be executed by program code or implemented by integrated circuits; their execution order can be adjusted or run in parallel as needed. Therefore, this invention is not limited to any specific hardware form or software implementation.

[0323] Furthermore, the multilingual parameter comparison, structural difference identification, and intelligent recommendation mechanisms of this application have achieved flexible combination and pluggable calling through modular design, providing a solid foundation for subsequent platform integration, mobile terminal adaptation, and multi-system linkage. All technical improvements, structural changes, or cross-industry applications made based on the content of this application's specification and figures should be covered within the protection scope of this application.

Claims

1. A multimodal parameter comparison and recommendation generation system based on semantic comparison and structural difference analysis, characterized in that, include: The module comprises: a data input module, a multi-input consistency control module, a field structure parsing module, a semantic aggregation and comparison module, a recommendation and market suggestion generation module, an output and outreach module, a user configuration and editing module, and a multi-language rendering and export module; among which: The data input module is used to receive product-related data input from webpage link parsing, user-uploaded documents, tables, or manual entry. The multi-input consistency control module is used to: determine the semantic consistency of each input content when the user simultaneously inputs multiple data sources; identify potential conflicts and provide fusion paths or correction suggestions to ensure the accuracy and stability of subsequent comparison and generation processes; the data sources include keywords, images, web page links and documents; The field structure parsing module is used to: perform structured processing on the input data, extract core fields, and establish a standard field index structure; the core fields include product parameters, certification information, function descriptions, image data, unit specifications, and industry terms; The semantic aggregation and comparison module is used to: represent the core fields semantically based on an embedded model or an equivalent semantic vector model; identify the semantic similarity and differences between the core fields and generate structured comparison results; and is configured to support multilingual comparison. The recommendation and market suggestion generation module is used to: generate recommendation and market suggestions based on the structured comparison results and historical case data; the recommendation and market suggestions include difference prompts, recommended terms, market trend prompts, and industry suggestions; and is configured to support multi-industry template loading and intelligent context adaptation. The output and outreach module is used to: export the recommendations and market suggestions as a preset format file; the preset format includes Excel, PDF, API data stream or HTML web page format; and is configured with customizable functions to support field styles, language types and output logic; The user configuration and editing module is used to: provide a visual interface; and has functions that support field order adjustment, drag-and-drop structure configuration, content editing, language switching, version control, and historical comparison. The multilingual rendering and export module is used to: translate and replace the names, content descriptions, recommendations, and marketing tips of all fields according to the user-defined language combinations; it is also configured to support output in at least one target language to adapt to the corresponding market language requirements.

2. The system according to claim 1, characterized in that, The data input module includes multiple data acquisition sub-modules; among which, it also includes: The URL parsing submodule is used to: parse the input webpage link and extract the product title, image, and structured parameter fields from the webpage; The document import submodule is used to: read uploaded Excel, CSV, or PDF files; identify and extract field information from the files; The manual data entry submodule is configured to support user input of missing fields or editing of extracted data. The language recognition submodule is used to: determine the language of the text based on a language detection model for translation path selection; the language detection model includes CLD3 and fastText; The terminology lexicon alignment submodule is used to perform field matching and translation preprocessing on user-uploaded professional terminology dictionaries to improve professional consistency. Translation quality assessment submodule: This module supports consistency assessment of translation results from multiple engines; it scores based on target metrics, including BLEU, BERTScore, and terminology coverage; it generates a credibility ranking based on a language model to select the best result; it acquires user-uploaded terminology lists as translation references to improve terminology stability and professional text expression; and it selects a combination of data collection submodules based on dimensions including data completeness, parsing accuracy, and user interaction burden to complete the initial import of product data.

3. The system according to claim 1, characterized in that, The field structure parsing module includes multiple structure processing sub-modules; among which, it also includes: The field mapping submodule is used to: establish mapping relationships between input fields and standard fields based on rule matching, machine learning, or human feedback mechanisms; The field cleaning submodule is used to: obtain cleaning results based on preset field cleaning processes; the preset field cleaning processes include unifying unit formats, removing redundant characters, and standardizing capitalization and syntax structure; The field index building submodule is used to: generate a structured field index based on the cleaning results; and is configured to support fast semantic invocation. The web page crawling submodule is used to: perform preset processing based on web page parsing tools to load web page content and extract parameter information fields; the preset processing includes HTML DOM analysis and script rendering processing; The OCR recognition submodule is used to: convert unstructured image data into structured fields; the unstructured image data includes product images uploaded by users and scanned quotation sheets; The field merging and reconstruction submodule is used to aggregate fields with redundant, scattered, or overlapping semantics to generate an optimized structure for subsequent comparison or recommendation generation.

4. The system according to claim 1, characterized in that, The semantic aggregation and comparison module includes multiple comparison sub-modules; wherein, the multiple comparison sub-modules include: The text-based semantic vector alignment submodule is used to: compare the semantic similarity between text fields based on the embedding model; The image-based visual feature comparison submodule is used to: extract image feature vectors based on unstructured image data; and calculate the visual similarity between the unstructured image data based on the image feature vectors. The field mapping submodule based on structure logic is used to: perform logical relationship mapping and matching on fields with different structures; The semantic embedding submodule is used to model text information using word vector and sentence vector embedding techniques to improve the accuracy of semantic recognition. The multimodal comparison submodule is used to compare and judge multimodal information that integrates text, images, and structural data. The regional adaptation submodule is used to: adjust the comparison results regionally according to a preset comparison strategy; the comparison strategy is determined based on the language, style, and regulations of the target country or market.

5. The system according to claim 1, characterized in that, The recommendation and market suggestion generation module includes multiple generation sub-modules; wherein, the multiple generation sub-modules include: The recommended phrase template submodule is used to: load preset multilingual industry-specific phrase templates; The difference-driven generation submodule is used to: generate personalized suggestion statements based on the structured comparison results; The trend suggestion submodule is used to generate price range suggestions based on historical data, third-party market reference prices, and industry trend forecasts. The regulatory matching submodule is used to: compare the product's certification regulatory requirements with those of the target market, indicate any missing items, and generate compliance suggestions. The parameter merits and demerits scoring submodule is used to generate rating statements based on the evaluation results of our and our customers' products on core parameters. The language style adaptation submodule is used to adjust the recommended sentence structure and expression based on the language style of the target country or user preferences in order to improve communication effectiveness.

6. The system according to claim 1, characterized in that, The output and reach module includes multiple export and integration sub-modules; wherein, the multiple export and integration sub-modules include: The format rendering submodule is used to: beautify and style the page by displaying the recommendations and market suggestions according to the preset format selected by the user; The Export Delivery submodule is used to: generate downloadable links, encrypted external links, or QR code preview links; The API output submodule is used to send structured results to external system interfaces or third-party applications. The email body generation submodule is used to: generate email body content with a complete structure and appropriate tone based on customer language preferences and product type; the email body content includes a product summary and suggestions; The behavior tracking submodule is used to: monitor whether customers exhibit preset behaviors; the preset behaviors include opening emails, clicking attachments, or replying to content; and generate behavior scores based on the preset behaviors to help sales determine customer intent. The CRM integration submodule is used to: synchronize quotation records and customer behavior analysis results to the customer relationship management system; and configure functions to support customer lead tagging and sales progress management.

7. The system according to claim 1, characterized in that, The user configuration and editing module includes multiple interactive editing sub-modules; wherein, the multiple interactive editing sub-modules include: The visual editing submodule is used to configure functionality that allows users to adjust the field structure and order via drag and drop. The field customization submodule is used to configure functions such as adding fields, setting field grouping, or batch renaming. The version history submodule is used to: record every editing operation; and provide historical version comparison and rollback functions. The language switching and bilingual settings submodule is used to configure the output language combination, field name mapping method, and terminology translation source according to user preferences. The currency and unit of measurement configuration submodule is used to: configure functions that allow users to set currency and unit types; and perform conversions based on the market and display them uniformly. The template saving and reuse submodule is used to save the structure, format and display logic of the current field as a reusable template for customers or industry use.

8. The system according to claim 1, characterized in that, The multilingual rendering and export module includes multiple language processing sub-modules; wherein, the multiple language processing sub-modules include: The language recognition and suggestion combination submodule is used to: identify the language type of the user input content; and recommend language combinations based on the language type of the user input content, the target customer's country, or browser configuration. The terminology recognition and translation submodule is used to: invoke multiple translation engines to translate technical terms in the field content; the multiple translation engines include DeepL, Google Translate, and Azure Translator. The bilingual output submodule is used to generate language combination output that retains the source language comparison content according to user settings; the language combination output includes Chinese-English, Chinese-German, and Chinese-French. The region adaptation submodule is used to adjust the field order, number format, and terminology according to the language expression style and unit of measurement format of different countries. The format export and packaging submodule is used to: export the rendering results to a preset format file; the preset format includes Word, Excel, PDF, HTML or JSON format; and configure support for local packaging and sharing of image resources and style files. The Language Sorting and Main Language Setting submodule is used to configure the display order of output languages, the position of the main language, and the translation priority strategy to support user-defined output languages.

9. The system according to claim 1, characterized in that, The system configuration supports users to customize field configuration schemes and save them as templates; the templates include field structure, language preferences, output format, visual style, and outreach strategy configurations. Based on the template, generate personalized output content; Record the frequency of template calls, usage feedback, and export results; Provides a sorting mechanism for the recommended template list.

10. The system according to claim 1, characterized in that, The system configuration supports users in selecting the quotation structure type before exporting the results; the quotation structure types include: the main quotation structure containing only the matching fields of both parties, the supplementary quotation structure containing only the supplementary fields of our side, and the recommended quotation structure generated based on market differences; the output content is dynamically rendered according to the selected quotation structure type.

11. The system according to claim 1, characterized in that, The system configuration supports users in selecting pricing strategies in the output module; the pricing strategies include regular price, discounted price, and VIP price; the system fills in the quotation field, outputs the recommendation text, and names the file according to the selected pricing strategy.

12. The system according to claim 1, characterized in that, The semantic aggregation and comparison module is also used to: configure the function to support the simultaneous loading of our product data and target customer product data; based on the dual-channel embedding model, align, compare and mark the differences between the field structure of our product data and the field structure of the target customer product data; and configure the function to support the side-by-side display and visual marking of the differences.

13. The system according to claim 1, characterized in that, The recommendation and market suggestion generation module is also used for: optimizing the output based on a multi-dimensional structural suggestion algorithm; the multi-dimensional structural suggestion algorithm combines multiple dimension parameters to select the recommendation generation path and structured output template; the multiple dimension parameters include field similarity, terminology matching degree, regional adaptation rules, and industry expression style.

14. The system according to claim 1, characterized in that, The data input module is also used to: configure a function that supports a multi-input fusion mode; the multi-input fusion mode allows users to simultaneously input content including keywords, product images, document files, and web page links, and any combination thereof; extract field information based on the received multi-path input content; and perform semantic fusion, field deduplication, and priority sorting processing on the field information to output a unified structured field result.

15. The system according to claim 1, characterized in that, The multi-input consistency control module includes the following sub-modules: The system includes a semantic classification and comparison submodule, a dominant input-priority strategy submodule, a similarity scoring and threshold judgment submodule, and an automatic correction suggestion submodule. The semantic classification and comparison submodule is used to: perform semantic tag determination on keywords, images, web links, or document content respectively; and issue consistency prompts when there are semantic conflicts between different input paths. The dominant input priority strategy submodule is used to: configure the function to support users in setting the dominant input channel; and perform semantic fusion and conflict judgment based on the dominant input channel. The similarity scoring and threshold judgment submodule is used to: perform semantic similarity calculation based on the embedding vector of the content of the dominant input channel, and judge whether the input content is consistent according to a preset threshold; The auto-correction suggestion submodule is used to provide keyword replacement suggestions, image update prompts, or guidance on switching the dominant input channel in the event of a semantic conflict.

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