183 results about "Product Label" patented technology

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.

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 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.

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.

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.

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.

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.

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.

The invention provides a product anti-counterfeiting traceability detection system based on machine vision and a working process, belongs to the technical field of product anti-counterfeiting traceability detection, and aims to provide the product anti-counterfeiting traceability detection system based on machine vision and the working process. The product anti-counterfeiting traceability detection device can be installed on an assembly line or any stable cross beam, on one hand, the first industrial camera is used for collecting product labels for anti-counterfeiting traceability detection, on the other hand, the second industrial camera can shoot label images at different angles to enrich a data set, and the accuracy of watermark extraction is improved. Meanwhile, the detection system is matched with a printing-shooting-resistant image watermark embedding and extracting method based on deep learning, so that a machine vision system has strong image recognition and analysis capabilities, and powerful technical support is provided for anti-counterfeiting traceability of products.

1. The name of the design product: label (Aojiutian yeast oligosaccharide defense peptide product). 2. The use of the design product: for product labels. 3. The design points of the design product: the combination of shape and pattern. 4. The picture or photo that best indicates the design points: front view. 5. The design product is a flat product, omitting left view, right view, top view, and bottom view.

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.

1. The name of this design product: label (Shenger Mengduo Collection). 2. Purpose of this design product: This design is used for product labels. 3. The design key points of this appearance design product are: the main view. 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 the back is not common, so the rear view, left view, right view, top view and bottom view are omitted.

1. Name of the Design Product: Label (for the packing box of straw ropes). 2. Use of the Design Product: For product labels. 3. Design Key Points of the Design Product: The combination of the shape and the pattern. 4. Picture or Photograph that Best Illustrates the Design Key Points: Front view. 5. This is a flat product, so the perspective view, top view, rear view, left view, right view and bottom view are omitted.

The invention belongs to the technical field of data analysis and processing, and relates to a multi-dimensional data analysis method and system based on WeChat nickname information extraction. The analysis method comprises the steps that a user WeChat nickname is acquired and preprocessed; calling a pre-trained BERT model to perform semantic coding on the preprocessed nickname text, and extracting multi-dimensional features including gender tendency, region marks, interest tags, emotional tendency and social attributes; inputting the extracted feature vectors of each dimension into a KMeans clustering algorithm to obtain a plurality of user groups, and generating a tag portrait for each group according to a clustering result; and matching the user group tag with the commodity tag, and calling a recommendation system interface to realize accurate commodity recommendation for different user groups. According to the multi-dimensional data analysis method and system based on WeChat nickname information extraction, interests and attributes can be mined through user nicknames, so that the precision of personalized recommendation and the user satisfaction degree are improved.

1. Name of the designed product: label (1). 2. Use of the designed product: used for product label. 3. Design points of the designed product: combination of shape and pattern. 4. Picture or photo that best shows the design points: front view.

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.

1. Name of the product in this design: Label (1). 2. Purpose of this design: This design is intended for use on product labels. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: the front view. 5. This design is a two-dimensional product, so the left view, right view, top view, and bottom view are omitted. The rear view is omitted because it has no design features.

The invention discloses label attaching equipment which comprises a cabinet, moving wheels are assembled at the bottom of the cabinet, a first support is mounted at the top of the cabinet, a touch panel is mounted at the front end of the first support, and a driving mechanism is arranged at the top of the cabinet and can drive a label to move; a positioning and attaching mechanism is arranged at the top of the machine cabinet and can drive a label to be attached to a product; the top of the machine cabinet is provided with a pressing mechanism, the pressing mechanism can press a product label after the product label is attached, and the attaching equipment is composed of a driving mechanism, a positioning attaching mechanism and the pressing mechanism in a matched mode, the label attaching operation is automatically conducted, rapid and accurate attaching to an attaching object is achieved, and the attaching efficiency is improved. Meanwhile, the equipment has the advantages of being simple in structure, convenient to operate, high in stability and the like, and efficient and accurate automatic attaching and pressing operation can be achieved.

The invention relates to a multi-model-based product label auditing method, system and device and a medium, and the method comprises the steps: obtaining a product label picture of a preset product, and the product label picture comprises an image label and a text label; performing text recognition on the text tag, and extracting tag text information of the preset product; on the basis of the image tag, content correction is carried out on the tag text information through a multi-modal model and a first large language model, and corrected tag text information is obtained; and based on the label text information, performing compliance auditing on the product label of the preset product through the second large language model. Through application of the method and the device, identification and auditing of the label information are realized by combining the product image label and the text label under a multi-model architecture of a multi-modal model and a large language model, the identification and auditing accuracy of the label information is effectively improved, and the problem of how to improve the identification and auditing accuracy of the product label information is solved.

1. Name of the product in this design: RFID tag inlay (1). 2. Purpose of this design: This product is used for the identification and tracking of items, such as logistics management, inventory control, or anti-counterfeiting verification. 3. The key design features of this product are its pattern, which consists of two symmetrical vertical stripes, a central rectangular element, a serpentine line pattern connecting the rectangular elements, and two dots (one below the rectangular element and one in the serpentine line). 4. The image or photograph that best illustrates the design's key features: the front view. 5. This design is a two-dimensional product, so the left, right, top, and bottom views are omitted; the back of this design is a part that is not easily seen or cannot be seen during use, so the rear view is omitted. 6. Other situations requiring explanation: In the design product (RFID tag inlay) of this application, the tag body and the circle are an integral part of the product and will be attached together to the surface of the target item.