Method for providing commodity information, and electronic device

Abstract

Disclosed in embodiments of the present disclosure are a method for providing commodity information, and an electronic device. The method comprises: receiving a request for performing component analysis on a target commodity; and providing component analysis report information about the target commodity, wherein the component analysis report information is generated by an artificial intelligence (AI) large-scale parameter model by means of analysis and summarization on the basis of component data of the target commodity, and comprises information related to core components and core efficacies of the target commodity, mechanisms of action by which the core components contribute to the corresponding efficacies, and / or component safety analysis, and the component data comprises names and content or content sorting information of various components. The embodiments of the present disclosure can provide a more objective and more rational component analysis result, and assist a user in more rationally making a shopping decision.

Description

Methods and electronic devices for providing product information

[0001] This disclosure claims priority to Chinese Patent Application No. 202410488751.6, filed with the China Patent Office on April 22, 2024, entitled “Method and Electronic Device for Providing Product Information”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This disclosure relates to the field of information processing technology, and in particular to methods and electronic devices for providing product information. Background Technology

[0003] On product information service platforms, consumers typically obtain product information through descriptions on product detail pages and live demonstrations during the shopping process, and then make purchasing decisions based on this information. However, for functional products such as food and cosmetics, consumers may find it difficult to make rational decisions based solely on the information listed on the product detail page or the content of live demonstrations. For example, for a certain cosmetic product, the merchant claims that it has whitening effects, but consumers may need to be concerned about the actual whitening effect, safety, and suitability for sensitive skin before purchasing, etc.

[0004] Consumers typically cannot find answers to the above questions from merchants' promotional information. Although they can seek answers from recommendations or product reviews by experts or opinion leaders, these recommendations or reviews may not be entirely based on objective analysis, making it difficult for consumers to discern whether the content they share is truly fair and reliable. Summary of the Invention

[0005] This disclosure provides methods and electronic devices for providing product information, which can provide more objective and rational component analysis results, helping users make more rational shopping decisions.

[0006] This disclosure provides the following solutions:

[0007] A method for providing product information includes:

[0008] Receive a request to perform component analysis on the target product;

[0009] Provide a component analysis report on the target product. The component analysis report is generated by an artificial intelligence (AI) large-scale parameter model based on the component data of the target product. It includes information related to the core components, core efficacy, mechanism of action of the core components in achieving the corresponding efficacy, and / or component safety analysis of the target product. The component data includes the names of multiple components and their content or content ranking information.

[0010] The step of receiving a request to perform component analysis on the target product includes:

[0011] A page is displayed to provide component analysis services, and component analysis requests are received through the operation options provided on the page.

[0012] The operation options on the page include: a first operation option for inputting the product identifier of the target product and initiating a component analysis request, so as to receive the component analysis request through the first operation option.

[0013] The operation options on the page include: a second operation option for taking a photo of the packaging material of the target product to obtain a packaging material image or selecting a packaging material image of the target product from a local image collection, uploading the packaging material image, and then initiating a component analysis request, so as to receive the component analysis request through the second operation option; the image of the packaging material includes the component data of the target product.

[0014] The provision of the component analysis report information regarding the target product includes:

[0015] The packaging material image is identified to determine the names and content or content order of various ingredients included in the target product;

[0016] The AI ​​large-scale parameter model is used to analyze and summarize the names, contents, or content ranking information of the various components to generate the component analysis report information.

[0017] The step of receiving a request to perform component analysis on the target product includes:

[0018] The product details page of the target product provides an operation option for initiating ingredient analysis, so that the request can be received through this operation option.

[0019] The step of receiving a request to perform component analysis on the target product includes:

[0020] In response to a user's long-press operation on an image of a target product on any interface, a preliminary judgment is made as to whether the image is related to the product's ingredient data;

[0021] If relevant, provide operation options for initiating a component analysis request so that a component analysis request can be received through those operation options.

[0022] The information related to the core ingredients and core effects of the target product includes: comparative information between various different ingredients that can achieve the core effects.

[0023] The information related to the core ingredients and core functions of the target product includes: generating a reference price range for the target product based on the price attribute information of multiple products related to the core ingredients.

[0024] This also includes:

[0025] Product recommendations are made based on the core efficacy, wherein the recommended products include: products with the same / similar core ingredients, and / or products with different core ingredients but the same core efficacy.

[0026] A method for providing product information includes:

[0027] Receive product search requests based on keywords, whereby the keywords describe the category and efficacy of the desired product;

[0028] The system provides search results generated by a large-scale AI parameter model, which include information on multiple optional ingredients that can achieve the stated efficacy, as well as product recommendations corresponding to each of the multiple optional ingredients.

[0029] The search results provided include:

[0030] Provides information on a variety of optional ingredients that can achieve the stated effects;

[0031] In response to a request to view detailed information about the target ingredient, an analysis report of the target ingredient is provided, as well as product information containing the target ingredient and achieving the stated efficacy. The analysis report of the target ingredient includes: the effectiveness and / or safety of the target ingredient in achieving the stated efficacy.

[0032] A computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of any of the preceding methods.

[0033] An electronic device, comprising:

[0034] One or more processors; and

[0035] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of any of the preceding methods.

[0036] A computer program product includes a computer program / computer executable instructions that, when executed by a processor in an electronic device, implement the steps of any of the preceding methods.

[0037] Based on the specific embodiments provided in this disclosure, the following technical effects are disclosed:

[0038] This disclosure provides a component analysis service for users. After a user requests a component analysis for a target product, a component analysis report can be generated. This report is based on the product's component data (names of multiple components and their content or content ranking information). It may include information related to the product's core components, core efficacy, the mechanism of action of these core components in achieving their corresponding efficacy, and / or component safety analysis. In this way, the product's component data can be interpreted and summarized to produce a corresponding analysis report. This report can be generated by AI models, rather than relying on the personal knowledge or experience of experts, thus providing more objective and rational analysis results and helping users make more informed purchasing decisions.

[0039] Of course, implementing any product of this disclosure does not necessarily require achieving all of the advantages described above at the same time. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 is a diagram of the system architecture provided in an embodiment of this disclosure;

[0042] Figure 2 is a flowchart of the first method provided in an embodiment of this disclosure;

[0043] Figure 3 is a schematic diagram of the first interface provided in an embodiment of this disclosure;

[0044] Figure 4 is a flowchart of the second method provided in an embodiment of this disclosure;

[0045] Figure 5 is a schematic diagram of the second interface provided in an embodiment of this disclosure;

[0046] Figure 6 is a schematic diagram of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0047] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure are within the scope of protection of this disclosure.

[0048] In this embodiment of the disclosure, for functional goods such as food and cosmetics, a service entry point can be provided to users to analyze the ingredients of the target product. Accordingly, this service can analyze the core ingredients and corresponding core effects of the product based on its ingredient data. For example, by analyzing the hyaluronic acid content of the target product, the user can be informed that the core effect of a certain skincare product is "moisturizing," and so on. Furthermore, based on the core ingredients, the price range of products with the same core ingredients and achieving the same effects can be determined to help users determine whether the current product has a brand premium, and so on. In addition, similar products can be recommended based on the analyzed core ingredients and core effects. For example, if the target product being queried is from a well-known brand, the recommended products may include "affordable alternatives" with the same core ingredients and achieving the same effects; or, if the target product being queried is from a lesser-known brand, the recommended products may include "high-end" products with the same core ingredients and achieving the same effects, and so on.

[0049] Alternatively, considering that some consumers may have specific health needs but are unsure how to choose products, a reverse-engineering product recommendation service based on health benefits could be offered. Specifically, after a user enters a product's health benefit as a keyword and initiates a search, a list of ingredients matching that benefit and related products can be recommended. For example, if the user searches for "moisturizing skincare," relevant ingredients (glycerin, petrolatum, hyaluronic acid, etc.) and corresponding products can be recommended. During this reverse-engineering process, analysis reports on specific ingredients can be provided, including the effectiveness and / or safety of the ingredients in achieving the desired health benefit. This helps users find products with the desired health benefits and understand which core ingredients achieve those benefits, their effectiveness, and safety. Having this comprehensive information helps users make more informed purchasing decisions.

[0050] From a system architecture perspective, the component analysis service provided in this disclosure can have various specific implementation forms. For example, in one form, referring to Figure 1, an independent component analysis service can be provided. This service can be accessed through a commodity information service system, or it can be provided as a separate application (App), etc. After a user initiates a request to access the component analysis service, different functional options such as "Component Analysis" and "Efficacy Search" can be provided on the interface. If the user selects the former, they can enter information such as the name of the target product to initiate a component analysis request. Alternatively, they can take a picture of the component data portion of the target product's packaging material to obtain an image of the packaging material (or select an image of a product's packaging material from a local image collection, such as a local photo album on a mobile phone, which can store photos of the packaging material obtained by taking pictures or screenshots of a product beforehand) and then initiate a component analysis request, etc. If the user selects the "Efficacy Search" function, they can enter the desired product category and efficacy keywords in the displayed search input box. The service can then return the search results to the user. Unlike traditional searches that directly provide a list of products, this search result first presents a variety of optional ingredients that can achieve the desired effects for the user, and then recommends products based on the specific ingredients.

[0051] Alternatively, the aforementioned ingredient analysis service can be integrated into some standard processes within a product information service system. For instance, when a user visits a product's details page, they are typically interested in the product and need more information. While the details page may include ingredient lists, users may struggle to understand the technical terminology and cannot readily assess the effectiveness and safety of ingredients like professionals. Therefore, the details page could offer an option to initiate an ingredient analysis for the specific product. This allows users to access the analysis report to understand the product's core efficacy and / or ingredient safety, effectively aiding their purchasing decisions.

[0052] Alternatively, component analysis requests can be initiated from any page within the product information service system. In other words, component analysis-related functional modules can be embedded in any page of the system. In this case, if a user sees a product image on a page and needs to perform component analysis, they can initiate the request by long-pressing the product image. Of course, in this scenario, users may have other needs after seeing an image, such as translating the text within it. Therefore, to more accurately identify user needs, after detecting a long-press operation, a preliminary judgment can be made regarding the presence of component analysis-related data in the image. For example, this can be done through preliminary OCR recognition. If data is found, an option to initiate component analysis can be provided, allowing the user to perform the specific component analysis operation.

[0053] Specific component analysis reports for products can be pre-stored in a relevant database. When a user needs to query, the relevant analysis report can be directly retrieved from the database and displayed. Alternatively, for products not pre-stored in the database, especially when the user selects a packaging material image by taking a photo or choosing from a local image set, the analysis report can be generated only after receiving a user request. Specifically, when generating the analysis report, it can be based on AIGC (Artificial Intelligence General Computationalism), where a large-scale AI parameter model (referred to as the AI ​​big model) intelligently generates the specific component analysis report. The goal of AIGC is to establish a theoretical framework for better understanding and implementing artificial intelligence. Its core idea is that artificial intelligence systems can simulate the human brain and cognitive processes by using computational models, thereby achieving intelligent behavior. Based on the above theory, various generative models have emerged in the industry, including "text-to-text" models (generating text from text), "text-to-image" models (generating images from text), "image-to-text" models (generating text from images), and so on. In this embodiment, such an "image-to-text" AI big model can be used to generate content related to the component analysis report. Alternatively, one could first use computer vision models to recognize the text content of images of product packaging materials, and then use a "text-to-text" model to generate relevant content for a component analysis report, and so on.

[0054] The specific implementation schemes provided by the embodiments of this disclosure will be described in detail below.

[0055] Example 1

[0056] First, this first embodiment provides a method for providing product information, as shown in Figure 2, for situations where a user initiates a request for component analysis of a target product.

[0057] S201: Received a request to perform component analysis on the target product.

[0058] As mentioned earlier, user requests for component analysis of target products can be received in various ways. For example, one approach is to provide users with a standalone component analysis service, which can be embedded in a product information service system or provided as a separate app. When users use this service, a page providing the component analysis service can be displayed, and users can receive the component analysis request through the operation options provided on the page.

[0059] Specifically, the operation options on the page may include a first operation option for inputting the product identifier of the target product and initiating a component analysis request, so that the component analysis request can be received through the first operation option. For example, a user can input the name of a target product to initiate a request for component analysis of that target product.

[0060] Alternatively, the operation options on the page may include a second operation option: taking a photo of the packaging material of the target product to obtain an image of the packaging material, or selecting an image of the packaging material of the target product from a local image collection, uploading the image of the packaging material, and then initiating a component analysis request, so that the component analysis request can be received through the second operation option. In this way, the user can initiate a component analysis request by uploading a specific image of the packaging material. Of course, the image of the packaging material may include the component data of the target product.

[0061] It should be noted that the first and second operation options mentioned above can be parallel, and users can initiate a specific component analysis request by choosing either method. Alternatively, the second operation option can also supplement the first operation option. For example, if a user initiates an analysis request by entering the name of a specific product through the first operation option, and finds that there is no analysis report for that product in the database, nor is there any component data for that product in the identity system, then the user can initiate the specific analysis request by uploading an image of the product's packaging material through the second operation option.

[0062] Furthermore, as mentioned earlier, the specific target product can be a product published in a product information service system. In this case, the product details page provided by the product information service system for the target product can also offer an option to initiate ingredient analysis, allowing the user to receive the ingredient analysis request through this option. For example, a product details page typically includes text and image details, which may include an ingredient list. The aforementioned option to initiate an ingredient analysis request can be provided near the ingredient list. This way, if a user needs more detailed information about the effectiveness and / or safety of the ingredients while browsing the text and image details, they can initiate an analysis request through this option, and so on.

[0063] In addition to providing a separate page for ingredient analysis or initiating ingredient analysis from the product details page, the ingredient analysis function can also be triggered by long-pressing product images on other pages. Specifically, after a user long-presses an image of a target product on any interface, a preliminary judgment can be made as to whether the image is related to the product's ingredient data. If it is related, an option can be provided to initiate an ingredient analysis request, allowing the user to receive the request through this option.

[0064] S202: Provide a component analysis report on the target product. The component analysis report is generated by an AI large-scale parameter model after analyzing and summarizing the component data of the target product. It includes information related to the core components, core efficacy, mechanism of action of the core components in achieving the corresponding efficacy, and / or component safety analysis of the target product. The component data includes the names of multiple components and their content or content ranking information.

[0065] Upon receiving a request for ingredient analysis of a target product, an ingredient analysis report can be provided. This report can be generated by an AI model based on the product's ingredient data and may include information related to the product's core ingredients, core efficacy, the mechanism by which these ingredients achieve their efficacy, and / or ingredient safety.

[0066] Specific component data includes the names of various components and their content or content ranking information. Content information can be the specific value or percentage of each component. For example, some product packaging materials provide an ingredient list, which indicates the content value of each component (e.g., 10mg / 100g) or the content percentage (e.g., 10%). In this case, the core component of the product—that is, the component with the highest content percentage—can be analyzed using the specific content value or percentage. Alternatively, in another scenario, some product component data may only list the component names without specifying the content value or percentage. However, according to specific industry regulations, components are usually required to be arranged in descending order of their percentage. Therefore, the content ranking of each component can be determined based on the ranking information in the component data, with the components listed first typically being the core components, and so on. By analyzing and summarizing this type of component data, a specific component analysis report can be generated.

[0067] It's important to note that for some products, specific ingredient analysis reports can be pre-stored in a database. This means that after a user submits a request for ingredient analysis for a particular product, if a report exists in the database, it can be directly returned and displayed. For products where no analysis report is available in the database, the user can upload images of the product's packaging materials by taking a photo or selecting from a local image library. These images can then be used to identify the names, contents, or order of various ingredients in the target product. An AI model is then used to analyze and summarize these ingredient names, contents, or ordering information to generate the ingredient analysis report. The identification of the packaging material images can be performed directly by the AI ​​model, or it can be done using specialized models in the field of computer vision, etc.

[0068] The specific component analysis report, in addition to including the specific core efficacy and information on the core components related to that efficacy, can also provide information on the mechanism of action of the core components in achieving the corresponding efficacy. In other words, the component analysis report provided by this disclosure can not only simply list the specific core components and their core efficacy, but also provide more detailed information on the mechanism of action of the specific components in achieving the corresponding efficacy, making the conclusions in the component analysis report (including the presence of a certain core efficacy or corresponding effects) more convincing. This information regarding the mechanism of action can be generated by an AI model, using paragraphs or sentences with coherent contextual expression, and is characterized by its ease of understanding, etc.

[0069] In addition, a detailed ingredient analysis report can include comparative information between various ingredients that can achieve the stated core efficacy. For example, both "glycerin" and "hyaluronic acid" can achieve "moisturizing" effects, but their mechanisms of action, effectiveness, safety, and price may differ. Comparing different ingredients from multiple perspectives makes it easier for users to make a choice.

[0070] Furthermore, a reference price range can be generated for the target product based on the price attribute information of multiple products related to the core ingredient. For example, assuming the core ingredient of the current target product is "hyaluronic acid," a price range can be determined for the user's reference based on the price attribute information of other similar products containing the same ingredient. This allows the user to know where the current target product's price falls within a price range, whether unnecessary expenses are incurred due to brand premiums, and so on.

[0071] In addition to providing the above-mentioned ingredient analysis report information, product recommendations can also be made based on the core efficacy. The recommended products include: products with the same core ingredients, and / or products with different core ingredients but the same core efficacy, etc.

[0072] For example, as shown in Figure 3(A), after a user takes a photo of the ingredient list on a product's packaging and uploads it, an ingredient analysis report as shown in Figure 3(B) can be displayed. This report includes core effects such as "deep moisturizing," core ingredients such as "squalane," and a price range of "¥115.00-180.00," etc. It can also include interpretations of these core effects and ingredients, such as: "This is a very effective moisturizer that can absorb and retain a large amount of moisture, keeping the skin hydrated..." Furthermore, it can include analysis results on ingredient safety, specifically providing results from multiple dimensions such as allergic reactions, hormonal effects, carcinogenicity, and environmental impact, with each dimension providing a risk level assessment. Moreover, it can also provide recommendations for related products, and so on.

[0073] In summary, this disclosure provides a component analysis service for users. After a user requests a component analysis for a target product, a component analysis report can be generated. This report is based on the product's component data (names of multiple components and their content or content ranking information). It may include information related to the product's core components, core efficacy, the mechanism of action of these core components in achieving their corresponding efficacy, and / or component safety analysis. In this way, the product's component data can be interpreted and summarized to produce a corresponding analysis report. This report can be generated by AI models, rather than relying on the personal knowledge or experience of experts, thus providing more objective and rational analysis results and helping users make more informed purchasing decisions.

[0074] Example 2

[0075] This second embodiment addresses a scenario where a user initiates a search for a specific function and then recommends products accordingly. It provides a method for offering product information, as shown in Figure 4. The method includes:

[0076] S401: Receive a product search request initiated based on keywords, whereby the keywords are used to describe the category and efficacy of the desired product.

[0077] Specifically, this can provide users with ingredient analysis services. One form of interaction for this service is providing a search entry point, allowing users to enter keywords related to efficacy, such as "skincare moisturizing," etc. For example, in the aforementioned example, an interactive entry point for ingredient analysis services could be provided in the product information service system. After entering the corresponding service interface, users can initiate the aforementioned search request through the search entry point provided within the service interface, and so on.

[0078] S402: Provide search results, which are generated by an AI large-scale parameter model, including information on multiple optional ingredients that can achieve the stated efficacy, and product recommendation results corresponding to each of the multiple optional ingredients.

[0079] Upon receiving a search request, corresponding search results can be provided. Unlike traditional product search logic, this embodiment does not directly display a product list on the search results page. Instead, a specific search result can be generated by an AI-scaled parameter model. This search result can include information on multiple optional ingredients that achieve the stated efficacy, as well as product recommendations corresponding to each of these optional ingredients. This allows users to search for products based on specific ingredients. For example, assuming a user's search keyword is "skincare moisturizing," the returned search results can include information on multiple ingredients such as "glycerin" and "hyaluronic acid," as well as recommended product information for each ingredient. Furthermore, it can also include interpretations of various ingredients, such as the mechanism of action of specific ingredients in achieving their corresponding efficacy, allowing users to first select ingredients and then choose products with those ingredients as core components.

[0080] Specifically, search results can be provided in various ways. For example, different ingredients can correspond to different tabs, allowing users to view recommended product information for each ingredient on different tabs. Alternatively, the search results page can first provide information on multiple optional ingredients that can achieve the stated effects, along with brief descriptions of each ingredient to give users a preliminary understanding. Then, when a user clicks on an ingredient to request detailed information, an analysis report of the target ingredient and product information containing the target ingredient that can achieve the stated effects can be provided. The analysis report may include information on the effectiveness and / or safety of the target ingredient in achieving the stated effects, etc.

[0081] For example, as shown in Figure 5(A), assuming the user enters the search keyword "moisturizing," the search results page can first display various optional ingredients that can achieve this effect, as shown in Figure 5(B), such as squalane, hyaluronic acid, and glycerin. It can also include information explaining each ingredient, including its mechanism of action and representative products. If the user selects an ingredient and clicks on options such as "View More," then as shown in Figure 5(C), more detailed information about that ingredient can be displayed, including the price range of products with that ingredient as the core ingredient, and product recommendations with that ingredient as the core ingredient, etc.

[0082] In summary, through Embodiment 2 of this disclosure, users can initiate product searches using product efficacy as keywords. Subsequently, a large-scale AI parameter model can generate recommendations, including information on multiple optional ingredients that can achieve the stated efficacy, and corresponding product recommendations for each of these optional ingredients. This approach enables efficacy- and ingredient-based product recommendations. When selecting products, users make choices only after having a definitive understanding of the efficacy and ingredients, thus helping them make their final purchasing decisions.

[0083] It should be noted that the embodiments disclosed herein may involve the use of user data. In practical applications, user-specific personal data may be used in the scheme described herein within the scope permitted by applicable laws and regulations, provided that it complies with the applicable laws and regulations of the country in which it is located (e.g., with the user's explicit consent, with the user being properly notified, etc.).

[0084] Corresponding to Embodiment 1, this disclosure also provides an apparatus for providing product information, which may include:

[0085] The request receiving unit is used to receive requests for component analysis of the target product;

[0086] The analysis report providing unit is used to provide component analysis report information about the target product. The component analysis report information is generated by an artificial intelligence (AI) large-scale parameter model after analyzing and summarizing the component data of the target product. It includes information related to the core components, core efficacy, mechanism of action of the core components in achieving the corresponding efficacy, and / or component safety analysis of the target product. The component data includes the names of multiple components and their content or content ranking information.

[0087] Specifically, the request receiving unit can be used for:

[0088] A page is displayed to provide component analysis services, and component analysis requests are received through the operation options provided on the page.

[0089] The operation options on the page include: a first operation option for inputting the product identifier of the target product and initiating a component analysis request, so as to receive the component analysis request through the first operation option.

[0090] Alternatively, the operation options on the page include: a second operation option for taking a photo of the packaging material of the target product to obtain a packaging material image or selecting a packaging material image of the target product from a local image collection, and uploading the packaging material image, and then initiating a component analysis request, so as to receive the component analysis request through the second operation option; the image of the packaging material includes the component data of the target product.

[0091] At this point, the analysis report providing unit can specifically be used for:

[0092] The packaging material image is identified to determine the names and content or content order of various ingredients included in the target product;

[0093] The AI ​​large-scale parameter model is used to analyze and summarize the names, contents, or content ranking information of the various components to generate the component analysis report information.

[0094] In addition, the request receiving unit can also be used for:

[0095] The product details page of the target product provides an operation option for initiating ingredient analysis, so that the request can be received through this operation option.

[0096] Alternatively, the request receiving unit may also be used for:

[0097] In response to a user's long-press operation on an image of a target product on any interface, a preliminary judgment is made as to whether the image is related to the product's ingredient data;

[0098] If relevant, provide operation options for initiating a component analysis request so that a component analysis request can be received through those operation options.

[0099] The information related to the core ingredients and core effects of the target product includes: comparative information between various different ingredients that can achieve the core effects.

[0100] Information related to the core ingredients and core efficacy of the target product includes: generating a reference price range for the target product based on the price attribute information of multiple products related to the core ingredients.

[0101] Additionally, the device may also include:

[0102] The product recommendation unit is used to recommend products based on the core efficacy, wherein the recommended products include: products with the same / similar core ingredients, and / or products with different core ingredients but the same core efficacy.

[0103] Corresponding to Embodiment 2, this disclosure also provides an apparatus for providing product information, which may include:

[0104] A product search request receiving unit is used to receive product search requests initiated based on keywords, wherein the keywords are used to describe the category and efficacy of the desired product;

[0105] The search result providing unit is used to provide search results generated by an AI large-scale parameter model, which includes information on multiple optional ingredients that can achieve the stated efficacy, as well as product recommendation results corresponding to each of the multiple optional ingredients.

[0106] Specifically, the search result providing unit can be used for:

[0107] Provides information on a variety of optional ingredients that can achieve the stated effects;

[0108] In response to a request to view detailed information about the target ingredient, an analysis report of the target ingredient is provided, as well as product information containing the target ingredient and achieving the stated efficacy. The analysis report of the target ingredient includes: the effectiveness and / or safety of the target ingredient in achieving the stated efficacy.

[0109] In addition, this disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0110] And an electronic device, comprising:

[0111] One or more processors; and

[0112] A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0113] A computer program product includes a computer program / computer executable instructions that, when executed by a processor in an electronic device, implement the steps of the method described in the foregoing method embodiments.

[0114] Figure 6 illustrates the architecture of an electronic device, such as a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet, medical device, fitness equipment, personal digital assistant, aircraft, etc.

[0115] Referring to FIG6, device 600 may include one or more of the following components: processing component 602, memory 604, power supply component 606, multimedia component 608, audio component 610, input / output (I / O) interface 612, sensor component 614, and communication component 616.

[0116] Processing component 602 typically controls the overall operation of device 600, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods provided in this disclosure. Furthermore, processing component 602 may include one or more modules to facilitate interaction between processing component 602 and other components. For example, processing component 602 may include a multimedia module to facilitate interaction between multimedia component 608 and processing component 602.

[0117] Memory 604 is configured to store various types of data to support the operation of device 600. Examples of this data include instructions for any application or method operating on device 600, contact data, phonebook data, messages, pictures, videos, etc. Memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0118] Power supply component 606 provides power to various components of device 600. Power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 600.

[0119] Multimedia component 608 includes a screen that provides an output interface between device 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 608 includes a front-facing camera and / or a rear-facing camera. When device 600 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0120] Audio component 610 is configured to output and / or input audio signals. For example, audio component 610 includes a microphone (MIC) configured to receive external audio signals when device 600 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 604 or transmitted via communication component 616. In some embodiments, audio component 610 also includes a speaker for outputting audio signals.

[0121] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0122] Sensor assembly 614 includes one or more sensors for providing status assessments of various aspects of device 600. For example, sensor assembly 614 may detect the on / off state of device 600, the relative positioning of components such as the display and keypad of device 600, changes in the position of device 600 or a component of device 600, the presence or absence of user contact with device 600, the orientation or acceleration / deceleration of device 600, and temperature changes of device 600. Sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 614 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 614 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0123] Communication component 616 is configured to facilitate wired or wireless communication between device 600 and other devices. Device 600 can access wireless networks based on communication standards, such as WiFi, or mobile communication networks such as 2G, 3G, 4G / LTE, and 5G. In one exemplary embodiment, communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 616 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0124] In an exemplary embodiment, device 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0125] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, which can be executed by a processor 620 of device 600 to perform the method provided by the present disclosure. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0126] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this disclosure can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this disclosure, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this disclosure.

[0127] The various embodiments in this disclosure are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the description of the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0128] The method and electronic device for providing product information provided in this disclosure have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this disclosure. The descriptions of the embodiments above are only for the purpose of helping to understand the method and its core ideas. Furthermore, those skilled in the art will recognize that, based on the ideas of this disclosure, there will be changes in the specific implementation methods and application scope. Therefore, the content of this disclosure should not be construed as a limitation of this disclosure.

Claims

1. A method for providing product information, wherein, include: Receive a request to perform component analysis on the target product; Provide a component analysis report on the target product. The component analysis report is generated by an artificial intelligence (AI) large-scale parameter model based on the component data of the target product. It includes information related to the core components, core efficacy, mechanism of action of the core components in achieving the corresponding efficacy, and / or component safety analysis of the target product. The component data includes the names of multiple components and their content or content ranking information.

2. The method according to claim 1, wherein, Receiving a request to perform component analysis on the target product includes: A page is displayed to provide component analysis services, and component analysis requests are received through the operation options provided on the page.

3. The method according to claim 2, wherein, The operation options on the page include: a first operation option for entering the product identifier of the target product and initiating a component analysis request, so as to receive the component analysis request through the first operation option.

4. The method according to claim 2 or 3, wherein, The operation options on the page include: a second operation option for taking a photo of the packaging material of the target product to obtain a packaging material image or selecting a packaging material image of the target product from a local image collection, and uploading the packaging material image, and then initiating a component analysis request, so as to receive the component analysis request through the second operation option; the image of the packaging material includes the component data of the target product.

5. The method according to claim 4, wherein, The provision of the component analysis report information regarding the target product includes: The packaging material image is identified to determine the names and content or content order of various ingredients included in the target product; The AI ​​large-scale parameter model is used to analyze and summarize the names, contents, or content ranking information of the various components to generate the component analysis report information.

6. The method according to claim 1, wherein, Receiving a request to perform component analysis on the target product includes: The product details page of the target product provides an operation option for initiating ingredient analysis, so that the request can be received through this operation option.

7. The method according to claim 1, wherein, Receiving a request to perform component analysis on the target product includes: In response to a user's long-press operation on an image of a target product on any interface, a preliminary judgment is made as to whether the image is related to the product's ingredient data; If relevant, provide operation options for initiating a component analysis request so that a component analysis request can be received through those operation options.

8. The method according to any one of claims 1 to 7, wherein, Information related to the core ingredients and core efficacy of the target product includes: comparative information between various different ingredients that can achieve the core efficacy.

9. The method according to any one of claims 1 to 8, wherein, Information related to the core ingredients and core functions of the target product includes: generating a reference price range for the target product based on the price attribute information of multiple products related to the core ingredients.

10. The method according to any one of claims 1 to 9, wherein, Also includes: Product recommendations are made based on the core efficacy, wherein the recommended products include: products with the same / similar core ingredients, and / or products with different core ingredients but the same core efficacy.

11. A method for providing product information, wherein, include: Receive product search requests based on keywords, whereby the keywords describe the category and efficacy of the desired product; The system provides search results generated by a large-scale AI parameter model, which include information on multiple optional ingredients that can achieve the stated efficacy, as well as product recommendations corresponding to each of the multiple optional ingredients.

12. The method according to claim 11, wherein, The search results provided include: Provides information on a variety of optional ingredients that can achieve the stated effects; In response to a request to view detailed information about the target ingredient, an analysis report of the target ingredient is provided, as well as product information containing the target ingredient and achieving the stated efficacy. The analysis report of the target ingredient includes: the effectiveness and / or safety of the target ingredient in achieving the stated efficacy.

13. A computer-readable storage medium having a computer program stored thereon, wherein, When executed by a processor, the program implements the steps of the method described in any one of claims 1 to 12.

14. An electronic device, wherein, include: One or more processors; as well as A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 12.

15. A computer program product comprising a computer program / computer-executable instructions, wherein, When the computer program / computer-executable instructions are executed by a processor in an electronic device, they implement the steps of the method according to any one of claims 1 to 12.

Images