279 results about "Product Label" patented technology

The invention relates to an intelligent data transaction matching method. The method comprises the following steps: constructing a user portrait through personal information and historical behavior record information of a data transaction buyer, and generating a user tag set for describing user features; performing multi-dimensional analysis on each data product on the platform to generate a product label set for describing product features; adopting a natural language processing technology and a machine learning algorithm to calculate the matching degree between the user label and the product label; according to the label matching degree, generating a product recommendation list according to a preset weight algorithm; and dynamically adjusting the recommendation strategy according to user feedback. According to the method, more accurate personalized recommendation is realized by comprehensively analyzing the user tags and the product tags, the accuracy and the user satisfaction of a recommendation system can be remarkably improved, the probability of successful business matching is improved by accurately matching user interests and product characteristics, and compared with a traditional recommendation algorithm, the method has the advantage that the recommendation efficiency is improved. The method has obvious advantages in the aspects of product characteristic identification, recommendation strategy adjustment and the like.

The invention discloses a method for constructing an intelligent recommendation platform based on an artificial intelligence portrait system. The method is specifically implemented according to the following steps: step 1, constructing a customer portrait system; 2, constructing a financial product label portrait system; 3, matching the customer portrait system with the financial product label portrait system; and step 4, realizing accurate customer screening by adopting an intelligent recommendation technology, and completing financial product recommendation. According to the method, a more accurate customer portrait and financial product label portrait system is constructed, personalized financial products and services are provided for customers, and the problems that an existing recommendation platform construction method is low in efficiency and poor in accuracy are solved.

The invention discloses an artificial intelligence analysis method and system based on digital factory operation data, relates to the technical field of smart factories, and at least solves the problem of high production cost in a factory. The analysis method comprises the following steps: acquiring operation data of a digital factory, wherein the operation data comprises production equipment parameters, quality detection system parameters and historical repair data; obtaining abnormal information of the current abnormal product, wherein the abnormal information comprises an abnormal product label, an abnormal feature parameter and abnormal process node information; determining the repair cost and the scrap cost of the current abnormal product according to the operation data and the abnormal information; at least under the condition that the repair cost is smaller than the scrap cost, according to the abnormal process node information, returning the current abnormal product to the process node corresponding to the abnormal process node information for repair; and updating historical repair data corresponding to the production equipment parameters. The analysis method provided by the invention can prevent abnormal products from increasing the production cost of enterprises.

An online system receives a query from a user. An online system generates a prompt to provide to a first machine learning model to determine tags associated with the query. An online system provides the prompt to the first machine learning model. An online system receives as output a set of query tags associated with the query. An online system obtains a list of ranked product tags associated with the query, wherein the product tag is ranked according to a conversion rate of products matching the product tag when users submitted a historical search query. An online system identifies a set of candidate products for the search query. An online system, for each candidate product, provides the set of features to a second machine-learning model to generate a score for the candidate and selects at least a subset of the candidate products based on the generated scores for the candidate products.

An out-of-stock detection system notifies store management that a product is out of stock. The system captures images of a shelf and determines the position product labels thereon. For each product label, a bounding box is generated based on the position of each product label on the shelf. The system then identifies a product for each product label based on information within each product label and, for each product label, stores a product identified for each bounding box. Accordingly, the system performs an out-of-stock detection process that includes capturing additional image data of the shelf periodically that includes each bounding box, providing a portion of the additional image data for each bounding box to a model trained to determine whether the bounding box contains products, sending a notification for a product determined to be out of stock to a store client device based on output from the model.

The invention relates to the technical field of market analysis, in particular to a brand positioning optimization method based on big data, and the method comprises the steps: collecting multi-channel brand performance data, calculating a mean value, extracting a co-occurrence relation between emotion words and labels in comments, judging an emotion and image matching structure, and analyzing competing product label frequency difference and cognitive overlap. And identifying a user portrait and emotion offset, and merging expression gaps to generate an optimization suggestion scheme. According to the method, differentiated collection and unified coding are performed on multi-channel indexes, a brand expression structure capable of cross comparison is constructed, data definition is improved, comment words and brand labels form a co-occurrence matrix, an emotional expression structure under the value dimension is extracted, competitive product analysis locates a cognitive blank on the basis of label difference and coverage degree, and the competitive product analysis accuracy is improved. The emotion deviation value and the label acceptability are introduced as dynamic parameters by user division, and finally a fusion label missing and emotion space is output, expression reconstruction and crowd adaptation are completed, and dynamic linkage and positioning updating of a brand structure are realized.

A method is provided, the method including obtaining one or more vision machine learning—artificial intelligence (ML-AI) models associated with a product and obtaining one or more images of a product label of the product. The product label indicates a variable ink that changes colors based on environmental aspects. The method also includes determining condition information indicating a status of the product label based on the one or more vision ML-AI models and the one or more images of the product label indicating the variable ink, and outputting an indicator indicating the status of the product label based on the condition information.

The invention relates to the technical field of logistics data management, and discloses a logistics product positioning data management method and system based on cloud computing. The method comprises the following steps: acquiring video stream images and positioning images of different types of logistics products, and marking timestamps; performing association processing according to the video stream image and the positioning image to obtain an image association set; performing first feature extraction on the image association set to obtain a dynamic feature vector and a static feature vector; performing second feature extraction based on the three-dimensional scale feature point recognition model to obtain three-dimensional scale feature points; performing type matching on the three-dimensional scale feature points to obtain a matching result; performing parameter extraction according to a matching result to obtain a positioning parameter and a scale parameter; and performing parameter matching according to the positioning parameter and the scale parameter to obtain a corresponding logistics product label. According to the method, the accuracy of positioning and scale parameters of the logistics product can be improved, and efficient positioning of the logistics product is achieved.

The invention discloses a packaging material integrated code assigning method, system and device, a medium and a product, and relates to the field of two-dimensional code recognizing.The method comprises the steps that a marketing code of a marketing activity is obtained, the marketing code comprises a two-dimensional code and a logistics code, the logistics code correspondingly covers the two-dimensional code to obtain a finished product label, the finished product label is arranged on a product package, and the product package is packaged. A corresponding random code is generated according to the logistics code on the finished product label, the random code is arranged on the surface of the finished product label, and after the product arrives at a consumer, the logistics code is removed, the two-dimensional code is scanned, and the corresponding random code is input to participate in a marketing activity; according to the method, from the three aspects of printing, production packaging and sales channels, the two-dimensional code stealing scanning rate is comprehensively reduced, and the safety of marketing codes of marketing activities can be improved.

The invention provides an agricultural product traceability method and system based on the Internet of Things and a block chain, and relates to the technical field of agricultural product traceability, and the method comprises the steps: monitoring the production condition and the easy condition of an agricultural product, and obtaining a product label and an easy record; performing encryption processing by using a dynamic salt value hash algorithm to obtain first verification data and second verification data respectively; storing the product label, the easy record, the first verification data and the second verification data in a database, and constructing a mapping relationship to obtain block chain data; based on the block chain data, obtaining a corresponding product label and an easy record in a database; and processing the product label and the easy record in the database to obtain first generation verification data and second generation verification data respectively, and comparing the first generation verification data and the second generation verification data with the corresponding first verification data and second verification data to obtain a traceability verification result so as to complete traceability of the agricultural product. The problems of data counterfeiting, low reconciliation efficiency and poor stability of an existing traceability system are solved.

The system comprises of a security label engaged to a package. The label comprises of a first layer with a first code and a second layer with a second code. The first layer is disposed over the second layer in a way that at least a portion of the second code on the second layer is covered by the first layer. The second code on the second layer is revealed upon irreversibly disengaging the first layer from the package. The first code is associated with a first information and the second code is associated with a second information. The second code on the second layer is associated with the first code on the first layer. These codes are authentication-wise interlinked and as one single label which can be pre-applied on product-label and still extended authentication challenge like refill-evidence or tamper-evidence in addition to basic counterfeit-evidence.

The utility model relates to the technical field of slitting devices for printed matter labels, and discloses a slitting device which comprises two fixing plates, a fixing frame and a cutting seat, and a label roller is fixedly mounted between the two fixing plates through a servo motor; a connecting roller is movably installed in the fixing frames, a cutting roller is arranged between the fixing frames, the cutting roller is sleeved with a cutting tool apron, grooves are symmetrically formed in the outer portion of the cutting roller, sleeve rods are fixedly installed in the grooves, and arc-shaped electromagnetic blocks are fixedly installed at the tops of the sleeve rods. A supporting plate is fixedly installed on one side of the cutting base, two guide plates are fixedly installed on the portion, close to the upper portion of the supporting plate, of one side of the cutting base, two sets of electric push rods are fixed to the upper ends of the guide plates in a penetrating mode, a pressing plate is fixedly installed at the lower end of one set of electric push rods, and a cutting knife is fixedly installed at the lower end of the other set of electric push rods. The pressing plate is located over the supporting plate. The slitting device for the printed matter labels is good in slitting effect and convenient to use.

An analyzing system for identifying and measuring a solar panel module is provided. The solar panel module includes a product label that is attached thereon. The analyzing system includes a housing, a carrying unit, and an image recognition unit. The carrying unit is mounted on the housing and is configured to carry the solar panel module and to measure a weight of the solar panel module that is carried by the carrying unit. The image recognition unit is mounted on the housing and is configured to move relative to the carrying unit and, while moving relative to the carrying unit that is carrying the solar panel module, to capture an image of the solar panel module. The image recognition unit is configured to process the image thus captured to obtain dimension data and product information of the solar panel module, where the product information is related to the product label.

The invention relates to the technical field of product label extraction, in particular to a product label extraction method based on Internet big data and an AI big language model. The method comprises the following steps: S1, capturing text data of a product on the Internet by using a crawler technology; s2, determining important vocabularies in the text data by adopting a TF-IDF algorithm, capturing semantic association among the vocabularies in combination with a Skip-Gram model, and introducing a weight reflecting user browsing frequency and a behavior feature vector of a user in a process of capturing the semantic association among the vocabularies to optimize a capturing process; s3, based on the extracted important vocabularies and semantic association information between the vocabularies, generating product labels by using a large-scale pre-trained language model; and S4, positioning and classifying product labels by combining a sequence labeling model BERT and a conditional random field CRF, and outputting the finally extracted product labels. According to the technology, the BERT model and the conditional random field (CRF) layer are combined, so that product labels can be effectively positioned and classified.

A system for capturing a product tag image, extracting product information, and validating information. The extracted information is then utilized to populate a universal (brand independent) product registration database. To incentivize end users to register their new and in service products, entry is simplified by the use of an application on a smartphone, tablet or personal computer. A user downloads an application and enters their information then takes at least one picture / scan / image of the product label(s) that contain: 1) manufacturer name (and / or brand name), 2) model number, 3) serial number; and 4) date of production. Said information is extracted by Optical Character Recognition (OCR) and then validated before entering said database.

The invention relates to the technical field of artificial intelligence, can be applied to business system platforms of medical health, financial science and technology and the like, and discloses a product label identification method and device, computer equipment and a storage medium, various sample data related to product label identification are collected, and the sample data comprise text data, picture data and voice data; preprocessing all the sample data to obtain a fine tuning data set, and performing fine tuning on a pre-trained large language model by using the fine tuning data set to generate a product label identification model; obtaining description information of a target product, wherein the description information is generated by utilizing one or more of text information, picture information and voice information related to the target product; formatting the description information to obtain target information, and generating a label identification result of the target product through the product label identification model; therefore, efficient and accurate label identification of the target product can be realized.

The present invention provides an multi-stage end to end integrated inventory and multi-factor authentication based logistics management method to ensure secure and reliable operations such as intact delivery of products from the original equipment manufacturers in the market and to eliminate counterfeiting issues and to revolutionize the authentication and tracking processes within the logistics landscape, enhancing customer trust and brand loyalty comprise steps of, a) serialization of a product label (1), b) authentication of said product label (1); and c) data updation.

The utility model aims to overcome the defects, provides novel profiling equipment for side wing assembly, provides positioning and supporting for a product, and solves the problems that the product is bent and deformed and the surface of a skin is scratched and damaged due to mounting force when the side wing is assembled. The printer is placed on the profiling equipment frame and used for printing product labels. A table top in the upper profiling supporting frame is fixed to the profiling equipment frame through bolts, a floor support in the lower profiling supporting frame is fixed to the profiling equipment frame through bolts, and the right side wing and the left side wing are placed on the upper profiling supporting frame and the lower profiling supporting frame respectively. The utility model has the advantages and positive effects that the platform of the assembly equipment is realized, the product change is realized, a plurality of products can be met only by updating the upper profiling support frame and the lower profiling support frame, meanwhile, the equipment is low in manufacturing cost, spare parts are easy to replace, and the cost is saved for enterprises.

Consumer product labels sometime include 2D machine readable codes, such as digital watermark patterns. When such label artwork is printed on a plastic sleeve that is then heat-shrunk to conform to a plastic container, the code can become distorted and unreadable. Various techniques are detailed to cope with this problem. Additionally, methods are disclosed to assess—before a heat-shrink label is applied to a product—the expected final readability of any machine readable code included in the label artwork. A variety of other features and arrangements are also detailed.

1. Name of the design product: Bottle label (Small Boiled Pear Soup). 2. Use of the design product: For product labels. 3. Design points of the design product: The combination of shape and pattern. 4. The picture or photo that best shows the design points: Front view. 5. The back is the part that is not easily seen or not seen during use, and it is blank without a pattern, so the rear view is omitted; Since this design product is a flat product, the left view, right view, top view, and bottom view are omitted.

The present invention relates to the technical field of product label extraction, and specifically, to a product label extraction method based on Internet big data and AI large language models. It includes the following steps: S1. Use web crawler technology to capture the text data of products on the Internet; S2. Adopt the TF-IDF algorithm to determine the important words in the text data, and combine the Skip-Gram model to capture the semantic associations between words. In the process of capturing the semantic associations between words, introduce the weight reflecting the user browsing frequency and the user behavior feature vector to optimize the capturing process; S3. Based on the extracted important words and the semantic association information between words, use a large-scale pre-trained language model to generate product labels; S4. Combine the sequence annotation model BERT and the conditional random field CRF to locate and classify the product labels, and output the finally extracted product labels. The technology of the present invention can effectively locate and classify product labels by combining the BERT model and the conditional random field (CRF) layer.

1. The name of this design product: middle part of syringe. 2. Purpose of the product of this design: used for injection of drugs, etc., and is a medical device. Topical use: The middle of the syringe is used to adhere product labels. 3. The design highlights of this design product are: the combination of shape and pattern in Design 1, and the combination of shape, pattern and color in Design 2. 4. The picture or photo that best illustrates the key points of the design: 3D drawing of Design 1. 5. Designate Design 1 as the base design. 6. Other circumstances that require explanation: Design 2 requires protection of color; the parts indicated by dotted lines in each view of Design 1‑2 do not require protection, and the remaining parts require protection.

1. The name of this design product: label. 2. Purpose of this design product: used as a product label. 3. The design key point of this appearance design product lies in the pattern. 4. The picture or photo that best illustrates the key points of the design: main view. 5. This design product is a flat product and other views are omitted.

The present invention relates to a system and method for analyzing videos. The system comprises a video section and a tabs section, wherein the video section comprises a video and the tabs section comprises at least one tab with items or information related to the video. In most embodiments one of the tabs is a products tab which lists items in the video which can be purchased. These items are associated with specific time points in the video, generally when the item is visible in the video. When a user watches the video, the individual products will be highlighted as their time points are viewed. The user can additionally scroll through the list of products without effecting video playback. The method comprises the gathering and uploading of information associated with the video.

1. Name of the designed product: product label. 2. Use of the designed product: for showing the relevant information of the product. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view 1.

1. The name of this design product: label. 2. Purpose of this design product: used as a product label. 3. The design key point of this appearance design product lies in the pattern. 4. The picture or photo that best illustrates the key points of the design: main view. 5. This design product is a flat product and other views are omitted.

This invention discloses a product classification and recognition method and system based on AI technology, belonging to the field of AI recognition technology. By constructing a semantic alignment master vector Edis, the system achieves structured extraction and ambiguity resolution of the overall semantic representation of the product. On this basis, the system introduces the construction of a product classification label semantic vector set Gvec and a semantic similarity score set Sset, enabling the system to perform fine-grained matching of multiple classification labels based on contextual semantic features. This constructs a confidence score Conf reflecting the classification credibility, which is then compared with a preset classification judgment threshold Thrs to ensure that product labels are output only when the classification judgment credibility is sufficient. Through the connection of the above processes and the linkage of output results, this method not only significantly reduces the probability of misjudgment caused by expression ambiguity and word order reversal, but also provides a reliable rejection mechanism when accurate classification is not possible, effectively improving the overall robustness and practical value of the classification system.

The invention relates to a label data processing method and device, equipment and a storage medium. The method comprises the following steps: performing label mining processing on a plurality of data sources associated with a product service, determining a plurality of first product labels, and respectively determining product categories to which the plurality of first product labels belong; and for each product category, determining respective tag popularity values of the plurality of first product tags belonging to the product category on the plurality of data sources, and screening out at least one second product tag from the first product tags belonging to the product category according to the tag popularity values. And obtaining a product description template matched with the product category, obtaining label discrimination information for each second product label, the combined product description template and the second product label, carrying out label consistency discrimination processing on the label discrimination information, and taking the second product label as product label information of the product category when determining that the labels have consistency. By adopting the method, the mining and distinguishing efficiency of the product labels can be improved.

The utility model relates to the technical field of packaging paper products, and discloses a packaging paper product printing label pasting assembly which comprises a bottom plate, the top of the bottom plate is fixedly connected with a supporting frame, the top of the supporting frame is fixedly connected with a supporting rod, one side of the supporting rod is provided with a paper ring, and the top of the bottom plate is fixedly connected with a pasting base. A pasting assembly is arranged at the top of the supporting frame and comprises an air cylinder and a sliding plate, the bottom of the air cylinder is fixedly connected to the top of the supporting frame, the top of the sliding plate is fixedly connected to the output end of the air cylinder, and a soft pressing block is fixedly connected to the bottom of the sliding plate. The output end of the motor drives the second guide wheel to rotate, the second guide wheel and the first guide wheel drive label paper to move, the output end of the air cylinder drives the sliding plate to slide on the outer wall of the sliding rail, the sliding plate drives the bottom soft pressing block to press downwards, the effect that paper products of different shapes and sizes can be pasted is achieved, and therefore the pasting efficiency is improved.

An analyzing system (200) for identifying and measuring a solar panel module (9) is provided. The solar panel module (9) includes a product label (93) that is attached thereon. The analyzing system (200) includes a housing (3), a carrying unit (4), and an image recognition unit (5). The carrying unit (4) is mounted on the housing (3) and is configured to carry the solar panel module (9) and to measure a weight of the solar panel module (9) that is carried by the carrying unit (4). The image recognition unit (5) is mounted on the housing (3) and is configured to move relative to the carrying unit (4) and, while moving relative to the carrying unit (4) that is carrying the solar panel module (9), to capture an image of the solar panel module (9). The image recognition unit (5) is configured to process the image thus captured to obtain dimension data and product information of the solar panel module (9), where the product information is related to the product label (93).