Abstract: A method can include capturing, via a camera, an image for upload. The method further can include determining a polygon mask layer having one or more polygon masks for a user body in the image, comprising: (a) determining, via a body pose landmarking algorithm, key joint points for the user body based on joint landmarks defined in a configuration set; and (b) determining the polygon masks based on the key joint points and one or more regions of interest defined in the configuration set. The method additionally can include determining a skin mask layer with a skin mask for the image. The method also can include blending the one or more polygon masks and the skin mask into a final mask layer comprising a final mask for the image. The method further can include applying the final mask to the image. Other embodiments are disclosed.

Abstract: 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.

Abstract: Systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions configured to run on the one or more processors and perform: receiving a first set of facet information corresponding to a first marketplace; receiving a second set of facet information corresponding to a second marketplace; analyzing the first set of facet information to reduce a number of attributes to process; building a machine learning model to map candidate facets from the first set of facet information to facets from the second set of facet information; training the machine learning model based on the first set of facet information, the second set of facet information, and the mapping of candidate facets from the first set of facet information to facets from the second set of facet information; and displaying a selected number of re-ranked facets based on an output from the machine learning model. Other embodiments are disclosed herein.

Abstract: Systems and methods for multi-item searching for limited-channel interfaces are disclosed. In some embodiments, a disclosed method includes: receiving an input message from a requesting device via a limited-communication channel; identifying, via a first trained natural language model, a plurality of queries in the input message; implementing a plurality of search threads, wherein each of the plurality of search threads is configured to generate search results for a corresponding one of the plurality of queries; generating a responsive message comprising at least a portion of the search results for each of the corresponding one of the plurality of queries; and transmitting the responsive message to the requesting device.

Abstract: Example implementations relate to order prediction and automated cart creation. In an example, basket features, item features, and order features associated with a profile are inputted into a predictive model and daily order predictions are outputted by the predictive model. The daily order predictions are inputted into a grouping algorithm to output an order predictability. Profiles are segmented into respective order predictability cohorts of a plurality of order predictability cohorts based on respective order predictabilities. A cart may be automatically created for at least one daily order prediction associated with at least one profile when a respective order predictability cohort belongs to a predetermined order predictability cohort.

Abstract: Examples relate to data update requests for data items stored at a data store in a data system. In some examples, real-time data update requests to update data items at the data store via a plurality of data update nodes are received, where each data update node has a respective local map. In response to the real-time data update requests and for each respective data item, a respective data item is assigned to a respective data update node. The respective data item in the data store is updated and respective supplemental data of the respective data item including a historical time stamp indicating a historical generation time of the respective data item is stored in the respective local map of the respective data update node. A copy of the respective supplemental data of the respective data item is stored in a central database.

Abstract: There are provided systems and methods for picking up and transferring different types of merchandise. In one form, the system includes: a shopping facility with a merchandise order assembly area; an automated retrieval system to transport merchandise to the assembly area; a first robotic device at the assembly area for picking up and moving a first set of merchandise items; a second robotic device at the assembly area for picking up and moving a second set of merchandise items; and a merchandise database identifying the first and second sets of merchandise items. The system also includes a control circuit configured to: receive the merchandise order, identify a merchandise item being transported to the assembly area, determine the category of merchandise item, and direct the merchandise item to the first or second robotic device or a manual transfer area.

Abstract: A system and method for global and local category-based item location recognition filtering is provided. A plurality of candidate items associated with a current location within a retail environment are filtered using a global level filter that identifies and removes items associated with an item category that is not a confirmed category for the recognized location. A local level filter analyzes item count values for the remaining candidate items to identify and remove low-count items. If any single item count is below a threshold, the low count items are removed. If all the candidate items are low count items at the item level, a count values for all candidate items in the same category are aggregated into a category-level item count value which is used to identify low count items. The low-count items are filtered out to eliminate false positives for improved accuracy and reliability of the item-to-location mapping results.

Abstract: A method including determining respective training data for each of query-type-specific answer retrieval modules. The method further can include training each of the query-type-specific answer retrieval modules. The method additionally can include determining a query type of a query from a user device for a user. The method also can include determining, in real-time and based at least in part on the query type, an answer retrieval module from the query-type-specific answer retrieval modules, as trained. Moreover, the method can include determining, in real-time by the answer retrieval module, one or more answers for the query. Then, the method can include ranking, in real-time, the one or more answers based on a user profile of the user. Finally, the method can include transmitting, via a computer network and to the user device, at least one of the one or more answers, as ranked. Other embodiments are disclosed.

Abstract: In various embodiments, systems and methods for augmented reality guidance are disclosed. A first position of a local device and a second position of a first item within a predetermined locale are determined. Route data from the first position to the second position is generated and a position of the local device is tracked. In response to the position of the local device being within a predetermined distance of the second position, an image capture element of the local device is activated. Image data is received. When the first item is within the image data, an item highlighter indicating a position of the first item within the image data is generated and when the first item is not within the image data, a direction indicator indicating a position of the first item relative to a field of view of the image data is generated.

Abstract: An identification validation system and method may include detecting that a first age-restricted item has been selected for purchase at a first point of sale by a user, determining that an age validation is not enabled, requiring that the user submit an identification instrument to verify an age of the user, after the purchase is complete, prompting the user to enable the age validation feature of the mobile wallet application of the user mobile device, directing a software application loaded on the user mobile device to access at least one biometric data input functionality of the user mobile device to capture the at least one biometric of the user, and comparing biometric features of the user captured by at least one camera.

Abstract: A computer implemented method including determining an expected click-through-rate (CTR) of a query-item pair with a first machine learning model by using content-based features. The computer implemented method can also include, determining a click engagement (CE) feature by determining a Bayesian inference based on the expected CTR and a historical CTR for the query-item pair. The computer implemented method can further include, determining a rerank score of the query-item pair with a second machine learning model by using the content-based features and the CE feature. The computer-implemented method can additionally include reranking the query-item pair based in part on the rerank score and the expected CTR. Other embodiments are described.

Abstract: Some embodiments provide systems to identify products comprising: product vector database; a plurality of portable computing devices comprising a camera and a control circuit configured to: access an image captured by the camera; perform an optical character recognition on the image; apply a vector modeling rule to key text, generate a first query product vector and wirelessly communicate the first query product vector to the product recommendation system; the product recommendation system is configured to apply a vector evaluation rule to the first query product vector to identify a first product; and wirelessly communicate to the portable computing device the first product identifier; wherein the control circuit receives the first product identifier, accesses a product information, causes product information to be displayed; and causes the first product to be virtually added to a virtual cart.

Abstract: Examples enable pallet tag tracking and cluster voting for more accurate pallet tag management using images of a selected pallet. A tag manager tracks a pallet through multiple images of the pallet to ensure the same pallet appears in every image. If the pallet tag is absent from all the images, a tag missing confidence score is generated that indicates the degree of confidence that the tag is missing from the pallet and not merely out of view. The score is used to prioritize handling of pallet tag missing exceptions. If the pallet tag is present in the images, optical character recognition (OCR) results for each tag image are aggregated into a tag cluster with a confidence score calculated for each result. A pallet tag identification (ID) number is predicted based on the result having the highest confidence score to ensure the pallet tag ID is complete and accurate.

Abstract: A system comprises one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions configured to run on the one or more processors and cause the one or more processors to perform operations comprising: receiving a set of images of respective different portions of a label on a package. The operations also can include: determining, using contrast, one or more respective locations in the images of the set of images that are associated with the respective different portions of the label on the package. The operations further can include: providing, for display on a user interface, a flattened reconstruction of the respective different portions of the label on the package based at least in part on the one or more respective locations that have been determined. Other embodiments and features are also disclosed.

Abstract: A system is provided including a processor and a non-transitory computer-readable medium storing computing instructions that, when executed on the processor, cause the processor to perform operations including: obtaining a plurality of query-item pairs associated with a query, wherein the plurality of query-item pairs associated with the query are initially ranked at least partially based on user engagement metrics; obtaining, for each query-item pair of the plurality of query-item pairs associated with the query, first temporal-behavioral features, second temporal-behavioral features, and context-aware features, wherein the context-aware features include a vertical of the query; generating, using a Gradient Boosted Decision Tree (GBDT) model, respective query-item pair GBDT output values based on respective inputs for each query-item pair of the plurality of query-item pairs associated with the query, wherein the respective inputs for each query-item pair of the plurality of query-item pairs associated with

Abstract: Described in detail herein is a nested blockchain system. The nested blockchain system can include distributed nodes in a network. A node is configured to generate a master cryptographically verifiable distributed ledger represented by a sequence of blocks. The node spawns a first sub cryptographically verifiable ledger represented by a first genesis block containing a hash value referencing the first block generated in the master cryptographically verifiable ledger, in response to a first event. The node purges the first sub cryptographically verifiable ledger, in response to a second event.

Abstract: A method can include computing, a dimension of a product using a machine learning model, a dimension of a product. The computing can include, transforming preprocessed data into numerical values using a vectorize layer. The computing can also include, condensing the numerical values using an embedding layer. The predicting can further include, processing the numerical values, as condensed, using a global average pooling layer. The predicting can also include, concatenating the numerical values, as processed into a layer. The predicting can further include processing the layer using a first dense layer. The predicting can additionally include, standardizing the layer, as processed, using a batch normalization layer. The predicting can also include, transforming the layer, as standardized, using an activation function. The predicting can additionally include, regularizing the layer, as transformed, using a dropout function.

Abstract: Example implementations related to next state prediction and action selection are disclosed. In an example, an initial state is estimated based on a set of priors including at least one engagement opportunity. A diminishing effect of the at least one engagement opportunity is determined using a non-linear transformation and a set of likelihood estimates for the initial state is generated using a Bayesian steady state filter model that receives the initial state and the diminishing effect of the at least one engagement opportunity. A next state is predicted based on the initial state and the set of likelihood estimates.