Systems and methods for automatically providing data and insight supports using a natural language model are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a support request seeking an insight about a data platform; retrieving, based on the support request, an original description from at least one data source associated with the data platform; computing, using a natural language model, a degree of relevancy of the original description regarding the support request; generating, according to the degree of relevancy, a context description based on the original description; generating, using the natural language model, an enhanced description based on the context description; and transmitting the enhanced description to the computing device.
Systems and methods for automatically identifying and resolving problem instances in data service workloads are disclosed. In some embodiments, a disclosed method includes: identifying a problem instance for a workload associated with a plurality of data service platforms; determining, using at least one machine learning model, a problem solution based on the problem instance and a catalog of problem solutions; executing the problem solution including operations across the plurality of data service platforms; and recovering the workload in accordance with a determination that the problem instance is resolved by the problem solution.
Systems and methods for automatically identifying and resolving problem instances in data service workloads are disclosed. In some embodiments, a disclosed method includes: monitoring a workload of at least one data service platform; determining, based on a catalog of problem patterns and metadata of the workload, whether a problem pattern exists in the workload using at least one machine learning model; identifying a problem instance for the workload in accordance with a determination that a problem pattern exists in the workload; creating a problem record for the problem instance; and storing the problem record in a database.
Some systems and methods are directed to a device agnostic architecture configured to control and/or manage the interactions between front end store systems (e.g., self checkout (SCO) systems) for capturing purchase items and backend systems (e.g., point of sale (POS) subsystems) for completing purchases. The device agnostic architecture can include a translation layer or translation component that mediates communications from and/or between the front end and backend systems. For example, the translation layer maps any commands received from any SCO and/or POS device into execution commands native to receiving systems. For example, back-end processing systems can be configured to control on-line identification of products and/or services for purchase, and manage execution of sales of any goods or services. The translation layer manages communication between SCO devices and the backend systems so each communicates with each other according to their respective formats (e.g., communication protocol and/or data format).
System and methods for analyzing contextual data of a user interface are disclosed. In some embodiments, a disclosed method includes: storing, in a database, historical customer data associated with a customer, receiving, from a user interface, an indication of a customer's interaction with a digital assistant and a webpage, receiving, using the digital assistant, a prompt from the customer, the prompt being in one or more of an audio format or a graphical format, parsing and extracting intent data from the prompt based on the historical customer data and contextual data from a webpage, and executing a task based on the intent data, the task associated with the customer's interaction with the webpage and the digital assistant.
Systems and methods of improved computer operation for optimizing feature values associated with a network application are disclosed. A set of weights for at least one offer associated with the network application is generated. A feature reduction goal for a first feature is obtained and a base feature value of the first feature for the at least one offer is received. A trained optimized feature value model is applied to determine a feature adjustment for the base feature value based at least in part on the feature reduction goal. The feature adjustment and the feature reduction goal are different. The feature adjustment is applied to the base feature value to generate an optimized feature value and the offer including the optimized feature value is transmitted to at least one user device associated with the network application.
09 - Scientific and electric apparatus and instruments
Goods & Services
Smartglasses; smart glasses and sunglasses that will connect to devices via Bluetooth technology; multi-surface headphone mount; TV mount installation toolkit; TV accessory storage kit
06 - Common metals and ores; objects made of metal
09 - Scientific and electric apparatus and instruments
16 - Paper, cardboard and goods made from these materials
18 - Leather and imitations of leather
20 - Furniture and decorative products
21 - HouseHold or kitchen utensils, containers and materials; glassware; porcelain; earthenware
Goods & Services
(1) Locks; Common metals and their alloys, ores; metal materials for building and construction; transportable buildings of metal; non-electric cables and wires of common metal; small items of metal hardware; metal containers for storage or transport; safes.
(2) Travel adaptors; luggage scales; Scientific, research, navigation, surveying, photographic,cinematographic, audiovisual, optical, weighing, measuring, signalling, detecting, testing,inspecting, life-saving and teaching apparatus and instruments; apparatus and instruments forconducting, switching, transforming, accumulating, regulating or controlling the distribution oruse of electricity; apparatus and instruments for recording, transmitting, reproducing orprocessing sound, images or data; recorded and downloadable media, computer software,blank digital or analogue recording and storage media; mechanisms for coin-operatedapparatus; cash registers, calculating devices; computers and computer peripheral devices;diving suits, divers' masks, ear plugs for divers, nose clips for divers and swimmers, gloves fordivers, breathing apparatus for underwater swimming; fire-extinguishing apparatus.
(3) Passport holders; paper identification tags; Paper and cardboard; printed matter; bookbinding material; photographs; stationery and office requisites, except furniture; adhesives for stationery or household purposes; drawing materials and materials for artists; paintbrushes; instructional and teaching materials; plastic sheets, films and bags for wrapping and packaging; printers' type, printing blocks.
(4) Luggage; multi-piece sets and individual uprights; duffels and weekender bags; toiletrybags; luggage tags. Leather and imitations of leather; animal skins and hides; luggage andcarrying bags; umbrellas and parasols; walking sticks; whips, harness and saddlery; collars,leashes and clothing for animals.
(5) Pillows; non-metal locks; Furniture, mirrors, picture frames; containers, not of metal, forstorage or transport; unworked or semi-worked bone, horn, whalebone or mother-of-pearl;shells; meerschaum; yellow amber.
(6) Toiletry containers; Household or kitchen utensils and containers; cookware and tableware, except forks, knives and spoons; combs and sponges; brushes, except paintbrushes; brushmaking materials; articles for cleaning purposes; unworked or semi-worked glass, exceptbuilding glass; glassware, porcelain and earthenware.
Medical apparatus, namely, an ear wash device in the nature of a container to be filled with appropriate fluid by the user for flushing ear wax from the ear
Examples provide a query intent-aware search retrieval system using generative artificial intelligence (AI) and vector similarity search. A customized profanity filter performs a customized profanity check to maintain search retrieval system integrity and prevent misuse by malicious actors. This safeguard ensures that inappropriate or offensive language is effectively detected and mitigated, contributing to a secure and user-friendly experience. A customized prompt generator provides pertinent recall queries for a specific intent query or scenario. By employing a tailored approach, the system effectively narrows down the search scope, thereby providing relevant results corresponding to multiple intents inherent in the user's query. A multi-use case query classifier determines whether each query is single intent or multi-intent and ascertains the intent behind each query. By effectively differentiating between these scenarios, the classifier ensures that the appropriate search methodology is utilized, resulting in a more efficient retrieval process and improved user satisfaction.
09 - Scientific and electric apparatus and instruments
18 - Leather and imitations of leather
21 - HouseHold or kitchen utensils, containers and materials; glassware; porcelain; earthenware
28 - Games; toys; sports equipment
Goods & Services
Armbands specially adapted for personal electronic devices, namely, phones; Portable flashing safety lights for personal use; Personal security alarms; Safety products, namely, reflective safety bands to be worn on the body Backpacks compatible with personal hydration systems, sold empty Water bottle belts for running and hiking Mountaineering climbing belts
09 - Scientific and electric apparatus and instruments
18 - Leather and imitations of leather
21 - HouseHold or kitchen utensils, containers and materials; glassware; porcelain; earthenware
28 - Games; toys; sports equipment
Goods & Services
Portable flashing safety lights for personal use; Personal security alarms; Safety products, namely, reflective safety bands to be worn on the body Backpacks compatible with personal hydration systems, sold empty Water bottle belts for running and hiking Mountaineering climbing belts
Systems, apparatuses, devices, applications, and methods are provided herein useful to provide retrieval of products within a retail store and control a point of sale system to execute purchase of a product list in conjunction with a mobile device. A device control circuit of the mobile device is configured to receive a proposed product list from a respective customer; determine a suggested product list based on the proposed product list and a weighted average of a plurality of variables; determine a shopping route for the suggested product list, reduce the duration of the shopping route if possible, acquire payment from the respective customer for products on a final product list, and control the point of sale system to execute purchase of the products on the final product list on behalf of the respective customer.
A method can include determining one or more features associated with a user and also associated with recently viewed items for the user. The method further can include determining, at least in part by a machine learning model, a respective engagement score for each of the recently viewed items based on one or more first features of the one or more features. The one or more first features can be determined by a correlation analysis of the one or more features in a training process of the machine learning model. The method additionally can include ranking the recently viewed items based on the respective engagement score for each of the recently viewed items. The method also can include transmitting, via a computer network to a user device of the user, the recently viewed items, as ranked, for display on the user device. Other embodiments are disclosed.
Examples provide improved methods for generating search recommendations in response to a user-initiated search. Examples include receiving a search request including search terms; identifying one or more product categories as output from a machine learning classification model; identifying products that are assigned to those product categories, including product titles short descriptions in a natural language; applying the product titles and short descriptions as input to a second machine learning model that is configured to generate recommended searches; scoring each recommended search of the plurality of recommended searches; selecting one or more recommended searches of the plurality of recommended searches based on the scoring; and causing the one or more recommended searches to be displayed as user-interactable components on a graphical user interface, each user-interactable component being configured to execute a second search request upon user interaction with the user-interactable component.
Examples provide improved methods for refining an initial search request of a user, as may be performed by a search recommendations system. The system may receive an initial search request via a graphical user interface and identify relevant products for the initial search request. The relevant products for the initial search request may be associated with refinement filters for refining the initial search request. The system may assign scores to the refinement filters based at least on historical search queries of the user or historical interactions of the user. The system may select refinement filter(s) based on the scores assigned to the refinement filters, and display refined search request(s) as user-interactable component(s) on the graphical user interface. The refined search request may be based on refining the initial search request using the selected refinement filter.
A system including a processor and a non-transitory computer-readable media storing computing instructions that, when executed on the processor, cause the processor to perform operations comprising receiving user session information for a current session for a user; generating, using a ranking model, a first listing of items based on the user session information; generating, using a query model, a query intent measurement based on the user session information; generating, using a cart context model, a cart context measurement based on the user session information; generating a second listing of items based on the first listing of items, the query intent measurement, and the cart context measurement; and displaying the second listing of items in a graphical user interface to the user. Other embodiments are described.
A method can include upon receiving, from a policy update engine, one or more hold-time recommendations, selectively determining, based on one or more selection rules, one or more selected hold-time values of the one or more hold-time recommendations. The method further can include implementing the one or more selected hold-time values, as determined. The method additionally can include after implementing the one or more selected hold-time values, determining one or more effects associated with the one or more selected hold-time values. The method also can include transmitting the one or more selected hold-time values and the one or more effects to the policy update engine for retraining. Other embodiments are disclosed.
G06Q 10/0637 - Strategic management or analysis, e.g. setting a goal or target of an organisationPlanning actions based on goalsAnalysis or evaluation of effectiveness of goals
G06Q 10/0631 - Resource planning, allocation, distributing or scheduling for enterprises or organisations
G06Q 30/0202 - Market predictions or forecasting for commercial activities
In some embodiments, apparatuses and methods are provided herein useful to presenting information to customers. In some embodiments, an augmented reality system for presenting information to customers comprises a personalization server configured to store personalized data for the customers, receive an indication of a customer, receive a product identifier for a product, retrieve personalized data for the customer, and transmit the personalized data for the customer, an application configured to be executed by the mobile device, the application when executed by the mobile device causing the mobile device to capture images of products in a retail facility, receive user input to select the product from the images of products, receive the personalized data for the customer, generate an augmented reality presentation, and present the augmented reality presentation, and a control circuit configured to identify the product, and determine the product identifier for the product.
G06Q 30/0207 - Discounts or incentives, e.g. coupons or rebates
G06V 10/22 - Image preprocessing by selection of a specific region containing or referencing a patternLocating or processing of specific regions to guide the detection or recognition
G06V 10/26 - Segmentation of patterns in the image fieldCutting or merging of image elements to establish the pattern region, e.g. clustering-based techniquesDetection of occlusion
G06V 20/20 - ScenesScene-specific elements in augmented reality scenes
68.
IN-SCOPE AND OUT-OF-SCOPE RFID-BASED ITEM MANAGEMENT
Examples provide a system for managing inventory updates based on RFID data using product in-scope and out-of-scope rules (PISOS). The PISOS system adjusts on-hand inventory count values for an item upward based on item scan data if the scan data identifies a higher number of instances of a given item than is recorded in on-hand inventory. The PISOS system does not permit adjusting on-hand inventory downward based on the scan data indicating fewer instances of the given item within an item display area than is recorded in on-hand inventory unless a set of PISOS rules indicate the given item is in-scope for downward adjustments. If the item is out-of-scope, no downward adjustments to on-hand inventory are made based on the number of items detected during the scan.
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
69.
SYSTEMS AND METHODS OF MAPPING AN INTERIOR SPACE OF A PRODUCT STORAGE FACILITY
Systems and methods for use in mapping an interior space of a product storage facility include at least one sensor that captures distance measurement data with respect to an interior space of the product storage facility. A computing device obtains a first image representing a 2-dimensional map of the interior space of the product storage facility and processes this image to define a boundary of the interior space of the product storage facility and detect individual structures located within the interior space of the product storage facility. Then, the computing device defines separate department areas, assigns a department label to each of the separate department areas, and converts the 2-dimensional map representing the detected structures and the defined separate department areas and the department labels assigned to the separate department areas into a second image representing a 3-dimensional map of the interior space of the product storage facility.
Systems and methods for forecasting energy utilization of charging stations to determine charging station allocation are disclosed. In some embodiments, a disclosed method includes: receiving a forecast request seeking utilization of electric vehicle (EV) charging stations at a location in a future time period; determining at least one EV related feature based on the forecast request; computing at least one forecasted feature value for the at least one EV related feature associated with the location in the future time period; generating, using a utilization model, forecasted utilization data based on the at least one forecasted feature value; and transmitting the forecasted utilization data to a computing device.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
H02J 3/00 - Circuit arrangements for ac mains or ac distribution networks
72.
SYSTEMS AND METHODS OF USING CACHED IMAGES TO DETERMINE PRODUCT COUNTS ON PRODUCT STORAGE STRUCTURES OF A PRODUCT STORAGE FACILITY
Systems and methods of detecting and recognizing products on product storage structures of a product storage facility include an image capture device that moves about and captures images of the product storage structures at the product storage facility. A computing device processes the obtained images to detect and identify the products on the product storage structure, crops each of the identified individual products from the image to generate a plurality of cropped images and generates an image histogram template, feature vector template and location information template for each of the cropped images. The cropped images are stored in an electronic database and represent a reference model for each of the identified individual products and are stored in association with the generated image histogram template, feature vector template and location information template to facilitate recognition of products subsequently captured on the product storage structure by the image capture device.
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06V 10/50 - Extraction of image or video features by performing operations within image blocksExtraction of image or video features by using histograms, e.g. histogram of oriented gradients [HoG]Extraction of image or video features by summing image-intensity valuesProjection analysis
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 20/62 - Text, e.g. of license plates, overlay texts or captions on TV images
Systems and methods for image retrieval are disclosed. In an example, sets of catalog images are received, wherein each set of catalog images is associated with a catalog item of a plurality of catalog items. Respective catalog embeddings representing each set of catalog images are generated. Query images associated with a query item are received. Query embeddings representing the query images are generated. Based on comparisons of the query images and the catalog images, select a candidate set of catalog items from the plurality of catalog items. Based on a comparison of the query embeddings and respective catalog embeddings associated with respective catalog items of the candidate set, generate respective similarity scores. Based on the similarity scores, determine that the query item is similar to a candidate catalog item, and in response identify the query item for review.
Examples provide active learning for effective computer vision (CV) item detection labeling using foundation models to generate updated training data for retraining CV item detection models. Raw image data of shopping carts in a retail facility are analyzed by a pretrained CV item detection model to identify items in the carts. The detected items are labeled and enclosed in bounding boxes. A set of foundation models mask the detected items in the cart images. Predicted labels for the undetected and unmasked items in the cart images are generated. Predicted bounding boxes enclosing the unmasked items undetected by the CV item detection model are generated. The predicted bounding boxes and predicted labels are merged with the detected items bounding boxes and labels to generate updated training data for dynamically retaining the CV item detection model to detect future occurrences of the undetected items in cart images with greater accuracy and efficiency.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
75.
SYSTEMS AND METHODS OF CONTROLLING RETAIL PRODUCT ALLOCATION AND RETAIL MARKET VARIATIONS BASED ON CUSTOMIZED INSIGHT
Some embodiments provide systems to control customized retail product performance information, comprising: a linkage mapping system to define and update linkings within a knowledge graph; a personalization recommendation system controlling different display systems to control graphical user interfaces presenting customized anomaly notification information specific to intended recipients as a function of the linkings; and a community detection system applying a set of machine learning community detection models to identify additional relationships between two or more of the entity nodes, based on feedback data from multiple intended recipients, and cause the linkage mapping system to update the multi-level linkages to embed one or more additional association links between the two or more of the entity nodes; wherein the personalization recommendation system is configured to control, based on the updated additional association links, a first graphical user interface to present first customized anomaly notification information specific to a first intended recipient.
Examples provide a system and method for dynamically filtering candidate item identifiers (IDs) from a pool of item IDs in real-time for automatic labeling of images for use as training data used to train computer vision (CV) models. Images of carts are paired with item receipts. Candidate item IDs are extracted from the receipts. Item recognition inference results generated by CV models are used to pair images of individual items with item IDs identifying the item in each item image. As each candidate item ID is assigned to an item image, the item ID is dynamically filtered. Any candidate item IDs remaining after filtering are assigned to any item images failing to pair with an item ID based on the infer results. The results are presented for review and status update via a user interface device for faster and more accurate auto-labeling of training data for CV models.
Examples provide a system for generating image-based training data using progressive data curation. An anchor image of a selected item and historical receipts including the selected item generated during a dynamic receipt retrieval time period are obtained. Images of the carts including the selected item paired with the receipts are analyzed and cropped to isolate the selected item from each cart image. An embedding model generates embeddings representing the anchor image and the cropped images of the selected item. A similarity of the cropped image embeddings to the anchor image embedding is calculated using a similarity metric. The cropped image embeddings are ranked based on the calculated similarity to the anchor image. The images having the highest rank and greatest similarity to the anchor image are selected for inclusion in training data used to train computer vision models to detect and/or recognize the selected item in images of various objects.
In some embodiments, apparatuses and methods are provided herein useful to processing captured images. In some embodiments, there is provided a system for processing captured images of objects including a memory and a control circuit executing a trained machine learning model. The memory may be configured to store a plurality of images comprising first images and second images. The control circuit may be configured to: allocate each of the first images into one of a plurality of datasets; cluster each image in the dataset into one of a plurality of groups; select a sample from at least one of the plurality of groups; cluster each of the second images into one of dominant product identifier group and a non-dominant product identifier group; select a sample from the dominant product identifier group and a sample from the non-dominant product identifier group; and output the selected sample.
G06Q 10/087 - Inventory or stock management, e.g. order filling, procurement or balancing against orders
G06V 10/762 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
79.
SYSTEM AND METHOD FOR DETECTING OBJECT COLLISIONS IN AUGMENTED REALITY IMAGES
A method can include capturing, in real-time via a camera, an image of a real environment. The method further can include determining, using the image, a primary plane in the real environment. The method additionally can include rendering, in real-time on a display device for a user, a virtual object in the image of the real environment. The method also can include detecting a collision by the virtual object in the image, comprising: (a) projecting the virtual object onto the primary plane as a 2D resting plane for the virtual object; (b) determining tracking rays for the virtual object, wherein the tracking rays connect vertices of the 2D resting plane to a viewpoint from the camera; and (c) determining the collision based on whether the tracking rays intersect with a plane of one or more existing physical objects in the image. The method further can include upon determining that the collision is detected, outputting a haptic effect indicating the collision. Other embodiments are disclosed.
Examples a digital tag integration system using modular data to populate digital tags with item data. In some examples, a tag manager obtains a tag unique identifier (UID) for each digital tag associated with a modular display in accordance with a customized scan sequence. Each digital tag is temporarily matched to an item UID having the same position in an item placement sequence as the digital tag without requiring a user to scan the UID on the physical items. The temporary tag-item pairing can be undone while setting the modular is in-progress. When the number of tag-item pairs is equal to an expected number for a portion of the modular display, the digital tags are linked to the paired items. Linked digital tags are populated with item data for linked items. When the linked digital tag is reset, the digital tag is updated back to an initial unlinked state.
G09F 3/20 - Casings, frames, or enclosures for labels for adjustable, removable, or interchangeable labels
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
Garden ornaments made primarily of textiles; accessories for lawn ornaments made primarily of textiles or plastic, namely, outfits, costumes, or apparel for geese lawn ornaments
