Walmart Apollo, LLC patent applications on August 1st, 2024
Patent Applications by Walmart Apollo, LLC on August 1st, 2024
Walmart Apollo, LLC: 57 patent applications
Walmart Apollo, LLC has applied for patents in the areas of G06Q30/0601 (17), G06Q10/087 (8), G06Q30/0201 (4), G06Q30/0202 (4), G06F16/9538 (3) G06Q30/0631 (9), G06Q10/087 (5), G06Q10/0834 (2), G06F16/24578 (2), G06Q30/0629 (2)
With keywords such as: based, item, items, user, include, including, data, determining, respective, and processors in patent application abstracts.
Patent Applications by Walmart Apollo, LLC
20240253209. CLIMBING ROBOT WITH COMPLIANT PINION DRIVE_simplified_abstract_(walmart apollo, llc)
Inventor(s): William J. Fosnight of Windham NH (US) for walmart apollo, llc, John G. Lert, JR. of Wakefield MA (US) for walmart apollo, llc, Michael Duquette of Candia NH (US) for walmart apollo, llc, Martin R. Elliott of Bedford NH (US) for walmart apollo, llc, Julian D. Warhurst of Portsmouth RI (US) for walmart apollo, llc, Charles W. Su of Stratham NH (US) for walmart apollo, llc, Alan J. Grant of Nashua NH (US) for walmart apollo, llc
IPC Code(s): B25J9/10, B25J5/00
CPC Code(s): B25J9/1035
Abstract: an automated order fulfillment system and mobile robot are disclosed, where the mobile robot includes a compliant drive for moving between levels of a multilevel storage structure.
Inventor(s): Navin Naidu of Dublin CA (US) for walmart apollo, llc, Shankara Bhargava of Santa Clara CA (US) for walmart apollo, llc, Deepa Mohan of Los Altos CA (US) for walmart apollo, llc
IPC Code(s): G01C21/20, G06T7/73, G06T11/00, G06V20/20
CPC Code(s): G01C21/206
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.
Inventor(s): Mandeep Singh of Bangalore (IN) for walmart apollo, llc, Viraj Chimanlal Patel of Bentonville AR (US) for walmart apollo, llc, Ashish Gupta of Bangalore (IN) for walmart apollo, llc, Devanand Guruprasad Chintoju of Bentonville AR (US) for walmart apollo, llc, Abhishek Mishra of Bengaluru (IN) for walmart apollo, llc, Aaron Wayne Ray of Lowell AR (US) for walmart apollo, llc
IPC Code(s): G05B19/042, G06N20/20
CPC Code(s): G05B19/042
Abstract: a method including determining, via a machine learning model, a predicted energy load profile for a facility based at least in part on weather forecast data and sensor data for the facility. the sensor data can be received from one or more energy monitoring sensors for one or more devices in the facility. the method further can include determining one or more demand shedding time slots based at least in part on peak periods and the predicted energy load profile. moreover, the method can include determining one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots. the method additionally can include causing a respective performance of each of the one or more demand shedding events by the one or more devices during the one or more demand shedding time slots. other embodiments are disclosed.
Inventor(s): Rahul Radhakrishnan Iyer of Sunnyvale CA (US) for walmart apollo, llc, Malay Kumar Patel of Fremont CA (US) for walmart apollo, llc, Saurabh Kumar of Fremont CA (US) for walmart apollo, llc, Sushant Kumar of San Jose CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06F9/451, G06F40/40
CPC Code(s): G06F9/451
Abstract: systems and methods for context aware engagement are disclosed. a request for a user interface, including a user identifier, is received. a set of features associated with the user identifier are obtained and a user embedding is generated by applying an autoencoder to the set of features. a set of potential tasks associated with an enrollment portion of the user interface is obtained. a task embedding is generated for each task in the set of potential tasks. a user-task affinity is generated by comparing the user embedding to each task embedding. a ranked set of tasks is generated by ranking each task based on the user-task affinity. a set of interface elements related to the highest ranked tasks in the ranked set of tasks is generated. a user interface including interface elements is generated and transmitted to a device that requested the user interface.
Inventor(s): Onur Gungor of Sunnyvale CA (US) for walmart apollo, llc, Tri Cao of San Bruno CA (US) for walmart apollo, llc, Vineet Abhishek of Mountain View CA (US) for walmart apollo, llc
IPC Code(s): G06F16/21, G06F11/30, G06F11/34, G06F16/2457, G06F16/951, G06F16/9538
CPC Code(s): G06F16/217
Abstract: a system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: generating, using at least two different search algorithms, combined search results based on a search query, each search result of the combined search results having a respective rank based on at least one of the at least two different searching algorithms; determining a set of search results from the combined search results, wherein each result within the set of search results was respectively interacted with by at least one user; and determining a traffic impact for each result rank in the set of search results. other embodiments are described herein.
Inventor(s): Leonardo Lezcano of Rocklin CA (US) for walmart apollo, llc, Pankaj Appasaheb Adsul of WoodRidge NJ (US) for walmart apollo, llc, Jesus Perez Martin of Santiago (CL) for walmart apollo, llc, Jorge Gomez Robles of Mexico City (MX) for walmart apollo, llc, Krishna Sravanthi Rajanala Sai of Dublin CA (US) for walmart apollo, llc, Ciya Liao of Fremont CA (US) for walmart apollo, llc
IPC Code(s): G06F16/2452, G06F16/953, G06N5/022
CPC Code(s): G06F16/24522
Abstract: a method can include determining, via a translatability classifier module, a translatability class label for a cross-lingual search query received, via a computer network, from a user device for a user. the translatability classifier module can be trained to determine the translatability class label among multiple translatability class labels associated with user intentions for the cross-lingual search query based on a respective probability of the cross-lingual search query being associated with each of the multiple translatability class labels. the method further can include determining, via a language translator module, a class-associated search query for the cross-lingual search query based on the translatability class label, as determined. the method additionally can include transmitting, via the computer network, the class-associated search query to a monolingual search engine. other embodiments are disclosed.
Inventor(s): Junchao Zheng of Jersey City NJ (US) for walmart apollo, llc, Chen Song of Princeton NJ (US) for walmart apollo, llc, Jun Zhao of Jersey City NJ (US) for walmart apollo, llc, Zheng Yan of Short Hills NJ (US) for walmart apollo, llc
IPC Code(s): G06F16/2457, G06F16/9535, G06F16/9538, G06Q30/0601
CPC Code(s): G06F16/24578
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 functions relating to detecting trending search queries that are popular in a short-term time period. user engagement metrics are derived from historical search engine data. a trending analysis function executes statistical analyses on the user engagement to identify the trending search queries. other embodiments are disclosed herein.
Inventor(s): Afroza Ali of Los Altos CA (US) for walmart apollo, llc, Zhihao Huang of San Jose CA (US) for walmart apollo, llc, Abhimanyu Mitra of Cupertino CA (US) for walmart apollo, llc, Atul Kochhar of Bengaluru (IN) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06F16/2457, G06F9/451, G06F16/215, G06F16/248
CPC Code(s): G06F16/24578
Abstract: systems and methods for generating dynamic interfaces using explore-exploit models is disclosed. a first interface including a first set of content modules selected from a set of candidate content modules is generated and feedback data representative of one or more interactions with the first interface is received. the feedback data includes positive feedback and negative feedback. a set of weights for each content module is generated by a contextual explore-exploit model based on the feedback data. a request for a second interface including a context is received and a set of ranked content modules is selected for the context based on the weights of each of the content modules in the set of candidate content modules. a second interface including a second set of content modules is generated. the second set of content modules includes a predetermined number of top ranked content modules in the set of ranked content modules.
Inventor(s): Ankur Bhardwaj of Sunnyvale CA (US) for walmart apollo, llc, Afroza Ali of () for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06F16/28, G06F16/22, G06F16/901
CPC Code(s): G06F16/288
Abstract: systems and methods of generating interfaces including recommended items selected by a graph-based cold-start (gcs) model are disclosed. a request for an interface is received and a set of interface items is generated for inclusion in the interface. the set of interface items is selected, at least in part, by a gcs model including a semantic similarity component and a viewed-also-viewed component. the set of interface items is generated based on a combination of an output of the semantic similarity component and an output of the viewed-also-viewed component. the interface including the set of interface items is generated and transmitted to a system that generated the request for the interface.
Inventor(s): Menkae Jeng of San Jose CA (US) for walmart apollo, llc, Shiqi Jia of San Jose CA (US) for walmart apollo, llc, Archit Agarwal of Mainpuri (IN) for walmart apollo, llc, Rahul Gupta of Fremont CA (US) for walmart apollo, llc
IPC Code(s): G06F16/31
CPC Code(s): G06F16/328
Abstract: a method including obtaining an index message at both (a) an index updater for an index and (b) a subindex updater for a subindex. the method also can include updating, by the index updater, the index based on the index message. the method additionally can include determining, at the subindex updater, whether the index message is eligible for the subindex. the method further can include updating, by the subindex updater, the subindex based on the index message when the index message is eligible for the subindex. other embodiments are described.
Inventor(s): Zigeng Wang of Santa Clara CA (US) for walmart apollo, llc, Jae Young Kim of Sunnyvale CA (US) for walmart apollo, llc, Yaotong Cai of Campbell CA (US) for walmart apollo, llc, Xia Zhao of Santa Clara CA (US) for walmart apollo, llc, Wei Shen of Pleasanton CA (US) for walmart apollo, llc
IPC Code(s): G06F16/33, G06F40/30
CPC Code(s): G06F16/3334
Abstract: systems and methods of item-specific keyword recommendation are disclosed. an item data structure including an item title is received and at least one item embedding is generated by applying a first trained semantic mapping model to the item title. the first trained semantic mapping model includes a first semantic mapping framework. the at least one item embedding is compared to a set of keyword embeddings representative of a set of platform-relevant keywords and a set of item-specific recommended keywords is selected from the set of platform-relevant keywords based on a similarity between the at least one item embedding and each embedding in the set of keyword embeddings. the item title is modified to include at least one of the set of item-specific recommended keywords and an interface including the modified item title is generated.
Inventor(s): Leonardo Lezcano of Rocklin CA (US) for walmart apollo, llc, Pankaj Appasaheb Adsul of WoodRidge NJ (US) for walmart apollo, llc, Jesus Perez Martin of Santiago (CL) for walmart apollo, llc, Jorge Gomez Robles of Mexico City (MX) for walmart apollo, llc, Krishna Sravanthi Rajanala Sai of Dublin CA (US) for walmart apollo, llc, Ciya Liao of Fremont CA (US) for walmart apollo, llc
IPC Code(s): G06F16/33, G06F40/263, G06F40/58
CPC Code(s): G06F16/3337
Abstract: a method can include determining a translatability class label and a class-associated search query for a cross-lingual search query. determining the translatability class label and the class-associated search query can include determining whether a first class-translation pair for the cross-lingual search query exists in a memory module. if the first class-translation pair exists in the memory module, the method further can include retrieving the first class-translation pair for the cross-lingual search query from the memory module. when no class-translation pair for the cross-lingual search query exists in the memory module, the method additionally can include: determining the translatability class label, and determining the class-associated search query based on the translatability class label. the method further can include determining search results for the cross-lingual search query based on the translatability class label and the class-associated search query, and transmitting the search results to a user based on the translatability class label. other embodiments are disclosed.
Inventor(s): Zigeng Wang of Santa Clara CA (US) for walmart apollo, llc, Jae Young Kim of Sunnyvale CA (US) for walmart apollo, llc, Tong Yao of West Lafayette IN (US) for walmart apollo, llc, Wei Shen of Pleasanton CA (US) for walmart apollo, llc
IPC Code(s): G06F16/35
CPC Code(s): G06F16/353
Abstract: systems and methods of generating keyword-specific content are disclosed. a request for including a keyword is received and the keyword is classified as one of catalog related or unrelated. when the keyword is catalog related, the keyword is categorized in a category associated with the catalog and at least one term in the keyword is categorized in a facet category associated with the catalog. a content template is obtained. the content template is a category specific template when the keyword is catalog related and a generic template when the keyword is catalog unrelated. the category specific template is populated with the at least one term at a position associated with the one of the plurality of facet categories. responsive content including the category specific template populated with the at least one term when the keyword is catalog related and the generic template when the keyword is catalog unrelated, is transmitted.
Inventor(s): Mossaab Bagdouri of Sunnyvale CA (US) for walmart apollo, llc, Nicole Isabel McNabb of Sunnyvale CA (US) for walmart apollo, llc, Krishan Sravanthi Rajanala Sai of Dublin CA (US) for walmart apollo, llc, Ciya Liao of Fremont CA (US) for walmart apollo, llc
IPC Code(s): G06F16/9532, G06F40/284, G06Q30/0601
CPC Code(s): G06F16/9532
Abstract: a system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain operations. the operations can include generating a first specificity score for a first query. the operations also can include propagating the first specificity score for the first query to generate a second specificity score for a second query. the operations additionally can include training a machine-learning classifier at least based on the first query and the second query. the operations further can include generating, using the machine-learning classifier, a third specificity score for a third query. other embodiments are described.
Inventor(s): Manik Goyal of Jersey City NJ (US) for walmart apollo, llc, Ari Kast of Glen Ridge NJ (US) for walmart apollo, llc, Rashad Eletreby of Burlington NJ (US) for walmart apollo, llc, Xinyi Liu of Sunnyvale CA (US) for walmart apollo, llc, Binwei Yang of Milpitas CA (US) for walmart apollo, llc, Cun Mu of New York NY (US) for walmart apollo, llc
IPC Code(s): G06F16/954, G06F16/583, G06F16/9538
CPC Code(s): G06F16/954
Abstract: a system including one or more processors and one or more non-transitory computer-readable storage devices storing computing instructions that, when run on the one or more processors, cause the one or more processors to execute operations: storing image hashes corresponding to images for items offered via an electronic platform; receiving, from a computing device, a search query via a search engine included on the electronic platform; executing a relevance function; executing a visual diversity ranking function that reorders the relevant set of search results to generate a ranked set of search results, wherein the visual diversity ranking function utilizes the image hashes corresponding to the subset of the items to reorder the relevant set of search results; and transmitting the ranked set of search results to the computing device. other embodiments are disclosed herein.
Inventor(s): Adithya Rajan of Edison NJ (US) for walmart apollo, llc, Weiqi Tong of Brooklyn NY (US) for walmart apollo, llc, Zheng Yan of Short Hills NJ (US) for walmart apollo, llc
IPC Code(s): G06F40/232, G06F16/2455
CPC Code(s): G06F40/232
Abstract: a method including extracting queries from historical search query data. the method also can include spellchecking each of the queries (i) using a first spellcheck model and (ii) using a second spellcheck model. a latency of the first spellcheck model is lower than a latency of the second spellcheck model, and an overall accuracy of the second spellcheck model is higher than an overall accuracy of the first spellcheck model. the method additionally can include determining first queries of the queries in which, for each of the first queries, a first respective spellcheck output from the first spellcheck model is different from a second respective spellcheck output from the second spellcheck model. the method further can include adding at least a portion of the first queries to a spellcheck cache for runtime spellchecking. other embodiments are described.
Inventor(s): Rahul Kumar Gupta of Ballia (IN) for walmart apollo, llc, Shilka Roy of Noida (IN) for walmart apollo, llc, Sujit Jos of Ernakulam (IN) for walmart apollo, llc
IPC Code(s): G06F40/279, G06F40/232, G06V30/14, G06V30/18, G06V30/244
CPC Code(s): G06F40/279
Abstract: a computer-implemented method including detecting respective one or more text boxes in each of multiple partial images of a text-bearing area. the method also can include determining respective one or more edge text boxes of the respective one or more text boxes in each of overlapping partial images of the multiple partial images, wherein each of the respective one or more edge text boxes comprise a respective incomplete text. the method additionally can include matching one or more pairs of corresponding edge text boxes from the respective one or more edge text boxes of two adjacent images of the overlapping partial images of the multiple partial images. the method also can include determining cross-image texts in the one or more pairs of the corresponding edge text boxes. the method further can include determining one or more entities in the text-bearing area based on entity texts of the cross-image texts and non-edge texts in respective one or more non-edge text boxes of the respective one or more text boxes in the multiple partial images. other embodiments are described.
Inventor(s): Arpit Sharma of Suisun City CA (US) for walmart apollo, llc
IPC Code(s): G06F40/35, G06F40/284, G06Q30/0601
CPC Code(s): G06F40/35
Abstract: a method can include upon receiving, from a computer network, a conversational input from a user device for a user, determining a context based on one or more contextual units. the one or more contextual units can be associated with immediate prior one or more conversational inputs relative to the conversational input. the method further can include determining an intent associated with the conversational input based on the context. moreover, the method can include determining one or more entities associated with the conversational input based on the context and one or more expected entities determined based on one or more predefined conversation flows. the method additionally can include determining an output based on the intent and the one or more entities. the method also can include transmitting, via the computer network, the output to be displayed on the user device. other embodiments are disclosed.
Inventor(s): Yue Ding of Mountain View CA (US) for walmart apollo, llc, Changzheng Liu of Sunnyvale CA (US) for walmart apollo, llc, Jixiang Huang of Pleasanton CA (US) for walmart apollo, llc, Boning Zhang of Santa Clara CA (US) for walmart apollo, llc, Georgios Rovatsos of San Francisco CA (US) for walmart apollo, llc, Wei Shen of Pleasanton CA (US) for walmart apollo, llc, Dongbo Zhang of Redwood City CA (US) for walmart apollo, llc
IPC Code(s): G06N3/08
CPC Code(s): G06N3/08
Abstract: systems and methods of deep learning-based multi-objective pacing content deployment are disclosed. a first set of input parameters is received and a first set of pacing parameters are generated by a trained pacing model that receives the first set of input parameters. the trained pacing model includes a k-nearest neighbor (knn) portion and neural basis expansion analysis for time series (n-beats) portion. in response to generating the first set of pacing parameters, a pacing pipeline is modified to incorporate the set of pacing parameters. the pacing pipeline is configured to generate deployment parameters. content is deployed to one or more content systems based on the deployment parameters and feedback data representative of the deployed content is received. a second set of pacing parameters is generated by the trained pacing model. the trained pacing model receives a second set of input parameters that are based at least in part on the feedback data.
Inventor(s): Reza Yousefi Maragheh of Santa Clara CA (US) for walmart apollo, llc, Ramin Giahi of Rego Park NY (US) for walmart apollo, llc, Aysenur Inan of Mountain View CA (US) for walmart apollo, llc, Hyun Duk Cho of San Francisco CA (US) for walmart apollo, llc, Kaushiki Nag of Sunnyvale CA (US) for walmart apollo, llc, Sushant Kumar of San Jose CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06N3/084
CPC Code(s): G06N3/084
Abstract: systems and methods for hybrid optimization of training ranking models is disclosed. a training dataset including a plurality of anchor items, a plurality of recommended item sets, and ground truth data is obtained from a database. a base machine learning model including a step function configured to determine a relevance score is iteratively trained to generate a trained ranking model. the plurality of anchor items and the plurality of recommended item sets are provided as an input to the base machine learning model and the ground truth is provided as a target output. the step function is trained using an adaptive step size according to a first order barzilai-borwein (bb) method and a line search method. the trained ranking model is stored in non-transitory memory.
Inventor(s): Neil Palleti of Cupertino CA (US) for walmart apollo, llc, Antriksh Shah of Gandhinagar (IN) for walmart apollo, llc, Ashraful Arefeen of Albany CA (US) for walmart apollo, llc, Saigopal Thota of Fremont CA (US) for walmart apollo, llc, Sreenaadh Sreekumar of Ernakulam (IN) for walmart apollo, llc, Mridul Jain of San Jose CA (US) for walmart apollo, llc, Nishad Kamat of Los Altos CA (US) for walmart apollo, llc, Rijul Magu of Atlanta GA (US) for walmart apollo, llc
IPC Code(s): G06N3/0895, G06N5/01
CPC Code(s): G06N3/0895
Abstract: a system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform functions comprising: generating pairs of identities from a plurality of sources; for each respective pair of identities of the pairs of identities: determining a match probability for the respective pair of identities using a deep-learning transformer-based binary classification model; and linking the respective pair of identities as nodes on a graph when the match probability meets a predetermined threshold, wherein a linkage between the nodes represents a match for the respective pair of identities; generating, using a connected component algorithm, clusters each containing identities representing a respective user; and generating a respective user profile for the respective user for each cluster. other embodiments are disclosed.
Inventor(s): Drishya Giri of Sunnyvale CA (US) for walmart apollo, llc, Kenneth David Kuhn of Pasadena CA (US) for walmart apollo, llc, Aravind Chiruvelli of Frisco TX (US) for walmart apollo, llc
IPC Code(s): G06N5/022, G06N3/04
CPC Code(s): G06N5/022
Abstract: systems and methods of optimizing digitally-represented space is disclosed. a request to optimize a digitally-represented space is received. the request includes a data structure storing the digitally-represented space and at least one optimization parameter. a set of elements for insertion into the digitally-represented space is obtained. each element in the set of elements includes at least one independent variable. a predicted function for the digitally-represented space is generated that represents a relationship between the at least one optimization parameter and the at least one independent variable. the predicted function is generated by a scaled neural multiplicative model (snmm) prediction model. an optimal allocation of a subset of the set of elements in the digitally-represented space is generated that maximizes the at least one optimization parameter. the data structure storing the digitally-represented space is updated to include the optimal allocation of the subset of the set of elements.
Inventor(s): Chuying Ma of Sunnyvale CA (US) for walmart apollo, llc, Jingru Zhou of San Jose CA (US) for walmart apollo, llc
IPC Code(s): G06N20/00
CPC Code(s): G06N20/00
Abstract: systems and methods of generating and deploying a final anomaly classification model are disclosed. a training dataset including data representative of a plurality of interactions is obtained and a plurality of anomaly detection models are generated. at least one of the plurality of anomaly detection models is generated by an unsupervised training process. a unified anomaly score is generated by combining outputs of a subset of the plurality of anomaly detection models and an augmented training dataset is generated by labeling at least one of the interactions in the plurality of interactions based on the unified anomaly score. the anomaly classification model is generated by applying a supervised training process including the augmented training dataset.
20240257028. SYSTEMS AND METHODS FOR ROUTE OPTIMIZATION_simplified_abstract_(walmart apollo, llc)
Inventor(s): Zheqi Zhang of San Ramon CA (US) for walmart apollo, llc, Siddhartha Maheshwary of Brookhaven GA (US) for walmart apollo, llc, Minhao Liu of Sunnyvale CA (US) for walmart apollo, llc, Ti Zhang of Rocklin CA (US) for walmart apollo, llc, Jing Huang of San Jose CA (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/083, G06Q10/0631, G06Q10/067
CPC Code(s): G06Q10/067
Abstract: systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving workload information corresponding to a workload, driver information corresponding to drivers, and one or more constraints; building a coordinate system based on the workload information, the driver information and the one or more constraints; analyzing the coordinate system to determine a respective efficiency metric for each driver of the drivers for the workload; identifying a driver of the drivers in which the respective efficiency metric for the driver satisfies an efficiency metric threshold; and assigning the workload to the driver to reduce driver workload waste. other embodiments are disclosed.
Inventor(s): Daniel Alberto Zuniga Vazquez of San Jose CA (US) for walmart apollo, llc, Ou Sun of Rancho Santa Margarita CA (US) for walmart apollo, llc, Ti Zhang of Rocklin CA (US) for walmart apollo, llc, Jing Huang of San Jose CA (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/08, G06Q10/047, G06Q10/067
CPC Code(s): G06Q10/08
Abstract: a system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors, to perform functions comprising: receiving, by an application programing interface (api), a change request for a route plan, wherein the change request is subject to a scenario specification and a load feasibility specification; determining an updated route plan based on whether the change request for the route plan is a feasible route based on the scenario specification and the load feasibility specification, and the route stop time is the route start time is flexible; and outputting the updated route plan and, when the route stop time is flexible, outputting the route start time. other embodiments are disclosed.
Inventor(s): Ming Ni of Pflugerville TX (US) for walmart apollo, llc, Kunlei Lian of Bentonville AR (US) for walmart apollo, llc, Yu Zheng of Bentonville AR (US) for walmart apollo, llc, Liqing Zhang of Humble TX (US) for walmart apollo, llc, Ti Zhang of Rocklin CA (US) for walmart apollo, llc, Jing Huang of San Jose CA (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/0834, G06Q10/047, G06Q10/087
CPC Code(s): G06Q10/0834
Abstract: a method including obtaining an optimization request at a coordinating engine. the method also can include triggering engines to process the optimization request. at least one of the engines divides the optimization request into subproblems. at least a portion of the engines solve the subproblems. respective instances of at least one of the engines are triggered to handle respective ones of the subproblems. each of the engines provides a dynamic algorithmic flow using modularized algorithmic solvers. the dynamic algorithmic flow is adjusted based on a respective input to each of the engines. the method additionally can include outputting, from the coordinating engine, one or more results in response to the optimization request, based on results for the subproblems generated by the engines. other embodiments are described.
Inventor(s): Tao Cao of San Bruno CA (US) for walmart apollo, llc, Soumita Saha of Kolkata (IN) for walmart apollo, llc, Minghui Liu of San Bruno CA (US) for walmart apollo, llc, Chenxin Ma of Belmont CA (US) for walmart apollo, llc, Yuan Wang of San Francisco CA (US) for walmart apollo, llc, Jing Huang of San Jose CA (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/0834, G06Q10/0631, G06Q10/083
CPC Code(s): G06Q10/0834
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 historical driver search information corresponding to a first offer publish time criterion, the first offer publish criterion including a driver lag time; building a machine learning model based on the driver search information to determine a first metric and a second metric; analyzing the first metric and the second metric with an optimization model to determine a second offer publish time criterion that reduces the driver lag time; receiving an order for a delivery for an item, the order including a delivery time window; transmitting the order to a driver search platform subject to the second offer publish time criterion to reduce the driver lag time and mitigate delivery outside of the delivery time window. other embodiments are disclosed herein.
Inventor(s): Xiaojie Wang of Redmond WA (US) for walmart apollo, llc, Ou Sun of Rancho Santa Margarita CA (US) for walmart apollo, llc, Ti Zhang of Rocklin CA (US) for walmart apollo, llc, Jing Huang of San Jose CA (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/0835, G06Q10/0832
CPC Code(s): G06Q10/08355
Abstract: a system comprising one or more processors; and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors to perform functions comprising: obtaining a route plan for outbound transport from a distribution center; processing multiple threads in parallel to obtain multiple improvements to the route plan, where each of the multiple threads customizes the route plan using one or more different parameters; selecting a lowest cost route solution from the multiple improvements to the route plan; and using the lowest cost route solution for the outbound transport from the distribution center. other embodiments are disclosed.
Inventor(s): Han Zhang of Allen TX (US) for walmart apollo, llc, Abhinav Pachauri of Kanpur (IN) for walmart apollo, llc, Raghava Balusu of Achanta (IN) for walmart apollo, llc, Ashlin Ghosh of Ernakulam (IN) for walmart apollo, llc, Avinash M. Jade of Bangalore (IN) for walmart apollo, llc, Lingfeng Zhang of Dallas TX (US) for walmart apollo, llc, Srinivas Muktevi of Bengaluru (IN) for walmart apollo, llc, Amit Jhunjhunwala of Bangalore (IN) for walmart apollo, llc, Zhaoliang Duan of Frisco TX (US) for walmart apollo, llc
IPC Code(s): G06Q10/087, G06V20/60
CPC Code(s): G06Q10/087
Abstract: systems and methods of updating templates for use in recognizing individual products in images captured at a product storage facility include an image capture device that captures one or more images of product storage structure at a product storage facility, a computing device in communication with the image capture device, and an electronic database that stores keyword model templates and feature model templates associated with images of previously recognized individual products detected at the product storage facility. the computing device obtains the keyword and feature model templates associated with a recognized product from the electronic database, extracts the keywords from the products associated with the obtained keyword model templates, identifies products that are similar to the recognized product, and updates the keyword model template for each of the products to include must keywords and negative keywords, facilitating recognition of products in subsequent images captured by the image capture device.
20240257044. UNSTACKING OPTIMIZATION_simplified_abstract_(walmart apollo, llc)
Inventor(s): Tiantian Nie of Fremont CA (US) for walmart apollo, llc, Xiaojie Wang of Redmond WA (US) for walmart apollo, llc, Ti Zhang of Rocklin CA (US) for walmart apollo, llc, Jing Huang of San Jose CA (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/087
CPC Code(s): G06Q10/087
Abstract: a system including one or more processors and one or more non-transitory computer-readable media storing computing instructions, that when executed on the one or more processors, cause the one or more processors, to perform: obtaining a first load plan, wherein the first load plan comprises a set of stacks assigned to floor spots of a trailer; determining that at least one of (a) there is an empty floor spot in the first load plan for the trailer or (b) the first load plan is infeasible; determining a target number of stacks for an updated load plan; iterating, via simulated annealing, through modifications to the updated load plan; terminating the iterating when a predetermined termination criteria is satisfied; and outputting the updated load plan. other embodiments are disclosed.
Inventor(s): Jiewen Wang of San Jose CA (US) for walmart apollo, llc, Kenneth Kuhn of Pasadena CA (US) for walmart apollo, llc, Saipraveen Vabbilisetty of Bentonville AR (US) for walmart apollo, llc
IPC Code(s): G06Q10/087
CPC Code(s): G06Q10/087
Abstract: systems and methods for automatic planogram generation and optimization are disclosed. in some embodiments, an exemplary method includes: based on recommended assortment information regarding future products to be displayed in a retailer store, determining a plurality of candidate layouts each of which corresponds to a two-dimensional arrangement of the future products; for each candidate layout, computing an objective function based on current product location information, a set of parameters and at least one of: a minimum area ratio, a first unprotected rank, a combined propensity score, or a contiguity score; generating, from the plurality of candidate layouts, an optimal layout that optimizes the objective function; generating a planogram based on the optimal layout for the retailer store; and transmitting the planogram to a computing device associated with the retailer store for placing at least some of the future products in the retailer store according to the planogram.
Inventor(s): Arash Asadi-Shahmirzadi of San Bruno CA (US) for walmart apollo, llc, Amin Gholami of San Jose CA (US) for walmart apollo, llc, Kunlei Lian of Bentonville AR (US) for walmart apollo, llc, Mingang Fu of Palo Alto CA (US) for walmart apollo, llc
IPC Code(s): G06Q10/087, G06Q10/04
CPC Code(s): G06Q10/087
Abstract: systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving truck load configuration information corresponding to a number of items to be positioned in one or more trucks based on a delivery to be transported from a first location to a second location; analyzing, using a load generation simulator, the truck load configuration information to determine a first simulated configuration for the number of items in the one or more trucks; analyzing the first simulated configuration for the number of items in the one or more trucks to determine whether the first simulated configuration is feasible; in response to determining the first simulated configuration is not feasible, analyzing the truck load configuration information using a load shrinking model or a load expanding model to determine a second simulated configuration for the number of items; and transmitting the second simulated configuration for the number of items to the vendor to prepare the one or more trucks for the delivery. other embodiments are disclosed.
Inventor(s): Raghava Balusu of Achanta (IN) for walmart apollo, llc, Siddhartha Chakraborty of Kolkata (IN) for walmart apollo, llc, Ashlin Ghosh of Ernakulam (IN) for walmart apollo, llc, Avinash M. Jade of Bangalore (IN) for walmart apollo, llc, Lingfeng Zhang of Dallas TX (US) for walmart apollo, llc, Amit Jhunjhunwala of Bangalore (IN) for walmart apollo, llc
IPC Code(s): G06Q10/087, G06V10/762
CPC Code(s): G06Q10/087
Abstract: 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.
Inventor(s): Siva Charan Muraharirao of Bangalore (IN) for walmart apollo, llc, Hiral Roshan Salvi of Bangalore (IN) for walmart apollo, llc, Shubham Mangla of Yamunanagar (IN) for walmart apollo, llc, Christopher J. Hendrick of Rogers AR (US) for walmart apollo, llc
IPC Code(s): G06Q20/20, G07F17/32
CPC Code(s): G06Q20/20
Abstract: there are provided systems and methods relating to reconciling lottery transactions. in one form, the system includes point-of-sale (pos) systems at retail store for inputting of first lottery transaction data for lottery transactions occurring at the retail stores in which the lottery transactions correspond to lottery games being operated by a third party. it includes a pos server for extracting the first lottery transaction data and a retailer server for receiving second lottery transaction data from a server of the third party. further, it includes a control circuit that conducts one-to-one matching of entries of the two data sets, determines whether a discrepancy exists based on the one-to-one matching, conducts one-to-many matching by iteratively aggregating different combinations of entries of one data set and comparing them with individual entries of the other data set, and determines whether a discrepancy exists based on the one-to-many matching.
Inventor(s): Xuan Zhang of Fremont CA (US) for walmart apollo, llc, Fei Ye of San Carlos CA (US) for walmart apollo, llc, Shihui Jia of Cupertino CA (US) for walmart apollo, llc
IPC Code(s): G06Q20/40
CPC Code(s): G06Q20/4016
Abstract: systems and methods for evaluating risks for physical locations are disclosed. a request for risk evaluation of transactions associated with a physical location located within a predetermined region including a plurality of neighboring locations is received. a spatial model is implemented to generate a first risk score based on chargeback data associated with the physical location and chargeback data of the plurality of neighboring locations. a temporal model is implemented to generate a second risk score for the physical location. a final risk score for the physical location is generated based on the first risk score and the second risk score. the final risk score is transmitted to a computing device for detecting fraudulent transactions associated with the physical location in response to the request.
Inventor(s): Anshul Lakhani of Sunnyvale CA (US) for walmart apollo, llc, Taizhou Li of San Jose CA (US) for walmart apollo, llc, Santhosh Kumar Shanmugam of San Jose CA (US) for walmart apollo, llc, John Penfield Bowman of El Cerrito CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0202, G06N20/00, G06Q10/087
CPC Code(s): G06Q30/0202
Abstract: systems and methods for estimating in-store demand based on online data are disclosed. in some embodiments, a machine learning model is trained based on data of shared items that are being sold both online and in-store by a retailer. for a physical store of the retailer, inference items are determined from items being sold online but not in-store. an estimated demand is computed for each inference item to be offered for sale in the physical store in a future time period, based on the trained machine learning model and online data of the inference item. based on the estimated demands for the inference items, recommended assortment data is generated for the physical store in the future time period, and is transmitted to a computing device associated with the physical store for assortment refresh.
20240257170. SYSTEMS AND METHODS FOR ANOMALY DETECTION_simplified_abstract_(walmart apollo, llc)
Inventor(s): Akshit Sarpal of Santa Clara CA (US) for walmart apollo, llc, Qiwen Kang of Santa Clara CA (US) for walmart apollo, llc, Sherry Lijie Wan of Mountain View CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0201, G06N20/20
CPC Code(s): G06Q30/0206
Abstract: systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving historical marketplace information corresponding to one or more categories, wherein the historical marketplace information is for a merchant in a marketplace; determining a respective anchor for each of the one or more categories of the historical marketplace information; receiving an observed price for an item in the marketplace, wherein the observed price corresponds to an item in the one or more categories; normalizing the anchors for each of the one or more categories based on the observed price and the historical marketplace information; determining if the observed price is an anomaly based on the anchors, as normalized; and transmitting an alert to the merchant in the marketplace when an anomaly is detected. other embodiments are disclosed.
Inventor(s): Tanay Kumar Saha of San Jose CA (US) for walmart apollo, llc, Yanbing Xue of Sunnyvale CA (US) for walmart apollo, llc, Xiaobo Peng of Bellevue WA (US) for walmart apollo, llc, Jayanth Korlimarla of Sunnyvale CA (US) for walmart apollo, llc, Musen Wen of Mountain View CA (US) for walmart apollo, llc, Wei Shen of Pleasanton CA (US) for walmart apollo, llc, Rajesh Garigipati of San Bruno CA (US) for walmart apollo, llc, Anant Furia of San Jose CA (US) for walmart apollo, llc, Valeriy Pelyushenko of San Jose CA (US) for walmart apollo, llc, Chintan Jagdish Rita of Sunnyvale CA (US) for walmart apollo, llc, Ergin Guney of San Bruno CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0242, G06Q30/0273, G06Q30/0601
CPC Code(s): G06Q30/0244
Abstract: systems and methods for automatically retrieving and providing digital advertisements from multiple channels with improved relevancy to a search query are disclosed. in some embodiments, based on a query, a first set of sponsored items is retrieved and ranked based on an optimization of conversion rate, and a second set of sponsored items is retrieved and ranked based on an optimization of click-through rate. based on the first set of sponsored items and the second set of sponsored items, a ranked list of recommended items is generated for display based on an advertisement auction mechanism, in response to the query.
Inventor(s): Fanglida Yan of Milpitas CA (US) for walmart apollo, llc, Tanay Kumar Saha of San Jose CA (US) for walmart apollo, llc, Musen Wen of Mountain View CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0251, G06Q30/0201, G06Q30/0273
CPC Code(s): G06Q30/0256
Abstract: systems and methods for automatically determining and providing digital advertisements with ensured relevancy to a search query are disclosed. in some embodiments, based on historical user session data, a query is determined to be a head query, torso query, or tail query. for each sponsored item, a relevance score is generated to represent a degree of relevancy between the sponsored item and the query, and it is determined whether the sponsored item is eligible to be recommended in response to the query based on: (a) comparing the relevance score to a first threshold when the query is a head or torso query, or (b) comparing the relevance score to a second threshold when the query is a tail query. based on all sponsored items that are eligible to be recommended, a ranked list of recommended items is generated for display based on an auction mechanism.
Inventor(s): Hangjian Li of Issaquah WA (US) for walmart apollo, llc, Yuan Feng of Los Altos CA (US) for walmart apollo, llc, Dong Xu of Sunnyvale CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0273, G06Q30/0202, G06Q30/0242
CPC Code(s): G06Q30/0273
Abstract: systems and methods for estimating advertisement impressions needed for detecting sales lift are disclosed. in some embodiments, based on an impression estimation request received from a computing device, a disclosed system determines an item set including items proposed to be advertised with an advertising campaign, and a time period proposed for the advertising campaign. based on at least one model and historical transaction data associated with the item set, control sales data in the time period is computed for the item set without the advertising campaign. based on a trained model and additional data associated with the item set, the system computes a number of impressions needed to detect a target sales lift compared to the control sales data in the time period for the item set with the advertising campaign. based on the number of impressions, the system generates and transmits estimated impression data to the computing device.
Inventor(s): Yijie Sun of Santa Clara CA (US) for walmart apollo, llc, Chittaranjan Tripathy of Sunnyvale CA (US) for walmart apollo, llc, Asheem Sinha of Sammamish WA (US) for walmart apollo, llc, Nita Malani of Thornhill CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06Q10/087
CPC Code(s): G06Q30/0623
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.
20240257204. SYSTEMS AND METHODS FOR PRODUCT ANALYSIS_simplified_abstract_(walmart apollo, llc)
Inventor(s): Karthick Sivakumar of Chennai (IN) for walmart apollo, llc, Radhika Raghu of Chennai (IN) for walmart apollo, llc, Sreeraman Krishnan of Chennai (IN) for walmart apollo, llc, Karunashree Saproo of Sunnyvale CA (US) for walmart apollo, llc, Amitha Krishnappa of Los Altos CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0629
Abstract: systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving historical engagement information for products in a marketplace; clustering a first subset of the products based on at least one clustering criterion and based on a set of attributes; identifying a second subset of the products that are similar to the first subset of the products based on at least one similarity criterion; determining a third subset of the products by filtering the second subset of the products based on the historical engagement information for the second subset of the products; determining at least one benchmark for the first subset of the products based on the historical engagement information for the first subset of the products; and determining a lift score for the third subset of the products based on the at least one benchmark. other embodiments are disclosed.
Inventor(s): Kritika Upreti of Sunnyvale CA (US) for walmart apollo, llc, Yanbing Xue of Sunnyvale CA (US) for walmart apollo, llc, Rithvik Reddy Ananth of Santa Clara CA (US) for walmart apollo, llc, Mohit Prakash Patel of San Jose CA (US) for walmart apollo, llc, Jayanth Korlimarla of Sunnyvale CA (US) for walmart apollo, llc, Musen Wen of Mountain View CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0629
Abstract: systems and methods of generating interfaces including variant item recommendations are disclosed. a request for an interface and a set of candidate items selected from an item catalog are received. at least one of the candidate items is representative of two or more variant items. a variant score is determined for each variant item related to the at least one of the candidate items and a set of recommended items is generated by independently ranking each item in the set of candidate items and each of the two or more variant items. the set of recommended items is generated by a variant-aware ranking model configured to receive the variant score for each variant item and based on the variant score and a relevancy score for each variant item. the interface including the set of recommended items is generated and transmitted to a system that generated the request for the interface.
Inventor(s): Jingbo Liu of Princeton NJ (US) for walmart apollo, llc, Jie Zhao of Rego Park NY (US) for walmart apollo, llc, Junchao Zheng of Jersey City NJ (US) for walmart apollo, llc, Jun Zhao of Jersey City NJ (US) for walmart apollo, llc, Cun Mu of New York NY (US) for walmart apollo, llc, Zheng Yan of Short Hills NJ (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06N20/20
CPC Code(s): G06Q30/0631
Abstract: a method including receiving a request from a user to view a page. the page is one of a search results page or a browse shelf page. the method also can include obtaining a respective brand affinity score for the user for each of one or more product types associated with the request. the method additionally can include generating a respective brand affinity signal for the user for each respective item in a baseline list of items to be displayed on the page, based on the request and the respective brand affinity score for the user for a product type of the one or more product types associated with the respective item. the method further can include generating a reranking of the items to be displayed on the page, based on a machine learning model and based on factors comprising the respective brand affinity signals for the user for the items and other rerank signals. the method additionally can include outputting the reranking of the items. other embodiments are described.
Inventor(s): Zigeng Wang of Santa Clara CA (US) for walmart apollo, llc, Bohan Zhai of Emeryville CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06Q30/0202, G06Q30/0251
CPC Code(s): G06Q30/0631
Abstract: systems and methods for generating a machine learning model to support multiple tasks for semantic retrieval, embedding and classification are disclosed. in some embodiments, a disclosed method includes: obtaining a training data set generated based on search related data and item related data associated with a website; training, based on the training data set, a machine learning model using a two-tower structure to generate an optimized set of model parameters, wherein the optimized set of model parameters minimizes a total loss function computed based on at least one of: a query classification loss, an item classification loss, and a similarity loss; and transmitting the trained machine learning model to be utilized for at least one of the following tasks: semantic item retrieval, query classification, item classification, query clustering, and item clustering.
Inventor(s): Shiqin Cai of Mountain View CA (US) for walmart apollo, llc, Sinduja Subramaniam of San Jose CA (US) for walmart apollo, llc, Yijie Cao of Harrison NJ (US) for walmart apollo, llc, Rahul Sridhar of Santa Clara CA (US) for walmart apollo, llc, Evren Korpeoglu of San Jose CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0631
Abstract: systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving historical interaction information corresponding to a user in a marketplace; identifying a shopping journey and a basket type for the user based on the cart context and items in a cart for the user for a current user session; identifying a price threshold for the cart for the user; building a machine learning model for the current user session in real-time based on the historical interaction information, the cart context, the basket type and the price threshold to determine a ranking of new items to display to the user to add to the cart for the current user session, wherein the new items satisfy the price threshold; re-ranking the ranking of the new items to display to the user in the current user session based on item attributes of the new items; and transmitting the re-ranked ranking of the new items to the user via a graphical user interface (gui) during the current user session. other embodiments are disclosed.
Inventor(s): Sonal Bathe of Sunnyvale CA (US) for walmart apollo, llc, Rahul Sridhar of Santa Clara CA (US) for walmart apollo, llc, Sinduja Subramaniam of San Jose CA (US) for walmart apollo, llc, Evren Korpeoglu of San Jose CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06Q30/0202
CPC Code(s): G06Q30/0631
Abstract: systems and methods including one or more processors and one or more non-transitory computer readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: receiving historical marketplace information for a user in a marketplace corresponding to products previously purchased by the user; processing the products to group the products into one or more product-type clusters; analyzing the one or more product-type clusters to determine respective inter-purchase interval (ipi) likelihood scores for each product in each of the one or more product-type clusters; identifying one or more candidate products from the one or more product-type clusters that have respective ipi likelihood scores that satisfy one or more thresholds; determining a respective time and a respective duration for a respective re-purchase notification for the user based on the respective ipi likelihood score for each of the one or more candidate products; ranking the one or more candidate products based on the respective ipi likelihood scores for the one or more candidate products; and transmitting a re-purchase notification to the user via a graphical user interface (gui) that includes at least a subset of the one or more candidate products, the gui including a first section that includes a first portion of the at least the subset of the one or more candidate products and a second section that includes a second portion of the at least the subset of the one or more candidate products. other embodiments are disclosed.
Inventor(s): Zhihao Huang of San Jose CA (US) for walmart apollo, llc, Afroza Ali of Los Altos CA (US) for walmart apollo, llc, Abhimanyu Mitra of Cupertino CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0631
Abstract: systems and methods for improving diversification in online item recommendations are disclosed. in some embodiments, a disclosed method includes: receiving, from a computing device, a recommendation request seeking recommendations to be displayed on a webpage of a website to a user; obtaining user session data identifying website activities of users on the website; determining weights of a set of content elements associated with the website based on posterior distributions utilizing the user session data; modifying the weights by at least one predetermined parameter to generate updated weights based on contextual feature data of the set of content elements; generating, from the set of content elements, a ranked list of content elements as recommended content based on the updated weights of the set of content elements; and transmitting, to the computing device, the recommended content in response to the recommendation request.
Inventor(s): Varun Joshi of Jersey City NJ (US) for walmart apollo, llc, Cun Mu of New York NY (US) for walmart apollo, llc, Zheng Yan of Short Hills NJ (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06Q30/0201
CPC Code(s): G06Q30/0631
Abstract: systems and methods including one or more processors and one or more non-transitory storage devices storing computing instructions executed by the one or more processors can perform functions comprising: storing a category classification hierarchy that classifies items into a plurality of browse categories; monitoring user engagement metrics for each of the items; using the user engagement metrics to compute shelf importance signals for the items; executing a ranking engine that generates a ranked item listing for a browse category based, at least in part, on the shelf importance signals for the items; and transmitting the ranked item listing for the browse category to a computing device. other embodiments are disclosed herein.
Inventor(s): Shanu Vashishtha of San Francisco CA (US) for walmart apollo, llc, Abhay Kumar of Sunnyvale CA (US) for walmart apollo, llc, Lalitesh Morishetti of San Jose CA (US) for walmart apollo, llc, Kaushiki Nag of Sunnyvale CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0631
Abstract: systems and methods for providing personalized item recommendations with personalized title are disclosed. in some embodiments, each user and each item of a website is represented as an embedding in a same heterogeneous graph. based on an anchor embedding representing an anchor item in the heterogeneous graph, a set of items are determined. at least one feature aspect is selected from a set of feature aspects based on at least one implicit intent of a user, each feature aspect characterizing a respective aspect of features common to the set of items. from the set of items, a ranked list of items are generated as recommended items based on the at least one feature aspect. a recommendation title describing the at least one feature aspect is generated for the recommended items. the recommended items and the recommendation title are transmitted to be displayed together with the anchor item to the user.
Inventor(s): Rahul Sridhar of Santa Clara CA (US) for walmart apollo, llc, Luyi Ma of San Jose CA (US) for walmart apollo, llc, Sinduja Subramaniam of San Jose CA (US) for walmart apollo, llc, Shiqin Cai of Mountain View CA (US) for walmart apollo, llc, Jianpeng Xu of Cupertino CA (US) for walmart apollo, llc, Nikhil Shripad Thakurdesai of Milpitas CA (US) for walmart apollo, llc, Evren Korpeoglu of San Jose CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06Q30/0201
CPC Code(s): G06Q30/0631
Abstract: a computer-implemented method including determining an anchor product type for an anchor item. the method further can include determining at least one associated product type for the anchor product type. determining the at least one associated product type for the anchor product type further can include: (a) determining at least one complementary product type for the anchor product type; (b) determining an anchor-product-type-name vector for an anchor-product-type name of the anchor product type; (c) determining a respective complementary-product-type-name vector for a respective complementary-product-type name of each of the at least one complementary product type; (d) determining a respective product-type-name similarity score between the anchor-product-type-name vector and the respective complementary-product-type-name vector for each of the at least one complementary product type; and (e) determining the at least one associated product type based at least in part on a product-type-level threshold and the respective product-type-name similarity score for each of the at least one complementary product type. the method also can include determining at least one associated item for the anchor item based at least in part on the at least one associated product type and at least one recommended item for the anchor item. the method further can include transmitting, via a computer network, the at least one associated item to be displayed on a user interface for a user. other embodiments are described.
Inventor(s): Najmeh Forouzandehmehr of Saratoga CA (US) for walmart apollo, llc, Behzad Shahrasbi of San Jose CA (US) for walmart apollo, llc, Evren Korpeoglu of San Jose CA (US) for walmart apollo, llc, Kannan Achan of Saratoga CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601
CPC Code(s): G06Q30/0631
Abstract: a computer-implemented method including determining, based on an anchor item, at least one look template from a plurality of look templates. the at least one look template can include an anchor super product type for the anchor item, one or more remaining non-accessory super product types, and one or more accessory super product types. the method also can include determining one or more respective complementary items for the anchor item in each of the one or more remaining non-accessory super product types to generate one or more preliminary looks. the method additionally can include determining, via a machine learning module, at least one respective accessory recommendation for the anchor item for each of the one or more preliminary looks based at least in part on respective visual compatibility of the at least one respective accessory recommendation with respective existing items of each of the one or more preliminary looks to create one or more looks. the method further can include transmitting, via a computer network, the one or more looks to be displayed on a user interface for a user. other embodiments are described.
Inventor(s): Timothy J. Pincumbe of Richardson TX (US) for walmart apollo, llc, Benjamin T. Ford of Redmond OR (US) for walmart apollo, llc, Michael A. Guin of Los Angeles CA (US) for walmart apollo, llc, Rongjin Hong of Santa Clara CA (US) for walmart apollo, llc, Lars C. Rehder of San Bruno CA (US) for walmart apollo, llc, Priyanka Paliwal of Grapevine TX (US) for walmart apollo, llc, Sheetal Patel of McKinney TX (US) for walmart apollo, llc, Khasan Shirmatov of McKinney TX (US) for walmart apollo, llc, Roman Sentsov of Sacramento CA (US) for walmart apollo, llc, Artem Pechenkin of Los Angeles CA (US) for walmart apollo, llc
IPC Code(s): G06Q30/0601, G06Q20/20, G06Q20/32, G06Q20/38, G06Q20/40
CPC Code(s): G06Q30/0643
Abstract: some embodiments provide systems to device in purchasing retail items, comprise: a product database; a transaction database; and a mobile purchase control circuit configured to: receive a session notification from a mobile customer computing device to initiate an additional purchase session; establish, in response to the session notification, a second purchase session, and link the first and second purchase sessions as a single transaction; incorporate into a second virtual cart, associated with the second purchase session and different than a first virtual cart associated with the first purchase session, item identification information for each of one or more items identified, through the customer computing device, by the customer and intended to be purchased by the customer in association with the second purchase session; and cause a final authentication of the single transaction in authenticating at a single time of both the first session and the second session.
Inventor(s): Arup Kumar Das of Bengaluru (IN) for walmart apollo, llc, Rajat Gupta of Hazipura Shamli (IN) for walmart apollo, llc, Raviteja Uppalapati of San Francisco CA (US) for walmart apollo, llc, Samrat Kokkula of Hyderabad (IN) for walmart apollo, llc
IPC Code(s): G06Q50/18
CPC Code(s): G06Q50/184
Abstract: a computer-implemented method including determining a feature-embedding vector for a listing item based on textual feature data and imagery feature data for the listing item. the method also can include determining, via a machine learning module, an intellectual property infringement prediction associated with a genuine item based on a feature-embedding vector for the genuine item and the feature-embedding vector for the listing item. furthermore, the method can include upon determining that the intellectual property infringement prediction is positive, causing a take-down of the listing item from a retailer platform. other embodiments are described.
Inventor(s): Wei Wang of Dallas TX (US) for walmart apollo, llc, Lingfeng Zhang of Dallas TX (US) for walmart apollo, llc, Han Zhang of Allen TX (US) for walmart apollo, llc, Avinash M. Jade of Bangalore (IN) for walmart apollo, llc, Mingquan Yuan of Flower Mound TX (US) for walmart apollo, llc, Zhaoliang Duan of Frisco TX (US) for walmart apollo, llc, Siddhartha Chakraborty of Kolkata (IN) for walmart apollo, llc, Benjamin R. Ellison of San Francisco CA (US) for walmart apollo, llc, William Craig Robinson, JR. of Centerton AR (US) for walmart apollo, llc, Eric W. Rader of Plano TX (US) for walmart apollo, llc
IPC Code(s): G06T7/70, B25J5/00, G06T7/90, G06V10/10, G06V10/56
CPC Code(s): G06T7/70
Abstract: systems and methods of detecting support members of product storage structures that store products at a product storage facility include an image capture device that captures images of a product storage structure including vertical and horizontal support members. a computing device including a control circuit is configured to: obtain the images of the product storage structure captured by the image capture device, stitch the obtained images together to generate a stitched image that depicts the product storage structure, generate a color distribution map of the stitched image of the product storage structure to detect individual ones of the horizontal and vertical support members of the product storage structure.
Inventor(s): Navin Naidu of Dublin CA (US) for walmart apollo, llc, Shankara Bhargava of Santa Clara CA (US) for walmart apollo, llc
IPC Code(s): G06T19/00, G06V10/44
CPC Code(s): G06T19/006
Abstract: systems and methods of generating shared virtual maps include generating interfaces including augmented map data. a request for an interface is received from a local system and base map data including a virtual representation of a physical environment and augmented map data including at least one visible interface element associated with an augmented reality interaction is obtained. the at least one visible interface element is associated with a zone of the virtual representation. an interface including the virtual representation of the physical environment and the at least one visible interface element is generated and provided to the local system. a position of the local system within the physical environment is tracked and the augmented reality interaction is executed when the position of the local system is within the zone associated with the augmented reality interaction.
20240259209. NESTED BLOCKCHAIN SYSTEM_simplified_abstract_(walmart apollo, llc)
Inventor(s): Christopher M. Johnson of Bentonville AR (US) for walmart apollo, llc
IPC Code(s): H04L9/32, G06F16/27, G06Q20/38, H04L9/00
CPC Code(s): H04L9/3236
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.
- Walmart Apollo, LLC
- B25J9/10
- B25J5/00
- CPC B25J9/1035
- Walmart apollo, llc
- G01C21/20
- G06T7/73
- G06T11/00
- G06V20/20
- CPC G01C21/206
- G05B19/042
- G06N20/20
- CPC G05B19/042
- G06F9/451
- G06F40/40
- CPC G06F9/451
- G06F16/21
- G06F11/30
- G06F11/34
- G06F16/2457
- G06F16/951
- G06F16/9538
- CPC G06F16/217
- G06F16/2452
- G06F16/953
- G06N5/022
- CPC G06F16/24522
- G06F16/9535
- G06Q30/0601
- CPC G06F16/24578
- G06F16/215
- G06F16/248
- G06F16/28
- G06F16/22
- G06F16/901
- CPC G06F16/288
- G06F16/31
- CPC G06F16/328
- G06F16/33
- G06F40/30
- CPC G06F16/3334
- G06F40/263
- G06F40/58
- CPC G06F16/3337
- G06F16/35
- CPC G06F16/353
- G06F16/9532
- G06F40/284
- CPC G06F16/9532
- G06F16/954
- G06F16/583
- CPC G06F16/954
- G06F40/232
- G06F16/2455
- CPC G06F40/232
- G06F40/279
- G06V30/14
- G06V30/18
- G06V30/244
- CPC G06F40/279
- G06F40/35
- CPC G06F40/35
- G06N3/08
- CPC G06N3/08
- G06N3/084
- CPC G06N3/084
- G06N3/0895
- G06N5/01
- CPC G06N3/0895
- G06N3/04
- CPC G06N5/022
- G06N20/00
- CPC G06N20/00
- G06Q10/083
- G06Q10/0631
- G06Q10/067
- CPC G06Q10/067
- G06Q10/08
- G06Q10/047
- CPC G06Q10/08
- G06Q10/0834
- G06Q10/087
- CPC G06Q10/0834
- G06Q10/0835
- G06Q10/0832
- CPC G06Q10/08355
- G06V20/60
- CPC G06Q10/087
- G06Q10/04
- G06V10/762
- G06Q20/20
- G07F17/32
- CPC G06Q20/20
- G06Q20/40
- CPC G06Q20/4016
- G06Q30/0202
- CPC G06Q30/0202
- G06Q30/0201
- CPC G06Q30/0206
- G06Q30/0242
- G06Q30/0273
- CPC G06Q30/0244
- G06Q30/0251
- CPC G06Q30/0256
- CPC G06Q30/0273
- CPC G06Q30/0623
- CPC G06Q30/0629
- CPC G06Q30/0631
- G06Q20/32
- G06Q20/38
- CPC G06Q30/0643
- G06Q50/18
- CPC G06Q50/184
- G06T7/70
- G06T7/90
- G06V10/10
- G06V10/56
- CPC G06T7/70
- G06T19/00
- G06V10/44
- CPC G06T19/006
- H04L9/32
- G06F16/27
- H04L9/00
- CPC H04L9/3236
