Patents by Inventor Shengyang Zhang
Shengyang Zhang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11928897Abstract: In some examples, a system may include a first computing device communicatively coupled to a second computing device. Additionally, the first computing device is configured to obtain, from the second computing device, check-in data indicating an arrival of the user of the second computing device at a first location, and in response to obtaining the check-in data, determine current wait times. Moreover, the first computing device is configured to determine a first number of customers waiting for service, determine a first number of associates available to assist the first number of customers, and determine an expected wait time for the user operating the second computing device based at least on the current wait times. In some examples, the first number of customers waiting for service, and the first number of associates available. Further, the first computing device is configured to transmit the expected wait time to the second computing device.Type: GrantFiled: December 16, 2021Date of Patent: March 12, 2024Assignee: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Arun Prasad Nagarathinam, Apeksha Mehta, Pawan Kumar, Madhavan Kandhadai Vasantham, Ankit Jasuja, Surnaik Prakash Srivastava, Jennifer Chen, Vidyanand Krishnan
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Patent number: 11900313Abstract: 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 acts. The acts can include determining an estimated arrival time of a user at a physical store. The acts also can include generating an estimated wait time using a machine learning model and based on input data comprising the estimated arrival time and dynamic wait time data for the physical store. The acts additionally can include sending the estimated wait time to at least one of the physical store or a mobile device of the user. Other embodiments are disclosed.Type: GrantFiled: December 5, 2022Date of Patent: February 13, 2024Assignee: WALMART APOLLO, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20240029000Abstract: A second type computer assembly line balancing optimization method based on a migration genetic algorithm, and related to the technical field of assembly line balancing. The method uses assembly experience of similar assembly lines, the feasible solution set of the known assembly lines is transferred to the initial solution set of the assembly line balancing problem to be optimized, due to the migration of high-quality feasible solutions. The method can effectively reduce the sensitivity of the algorithm performance to the initial value and parameters, and improve the lower limit of the local optimal feasible solution of the heuristic algorithm to solve the assembly line balancing problem.Type: ApplicationFiled: December 3, 2021Publication date: January 25, 2024Applicant: Northeastern UniversityInventors: Hongrui GAO, Feng XUE, Yingwei ZHANG, Lin FENG, Shengyang ZHANG, Zubian LI
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Publication number: 20230105499Abstract: 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 acts. The acts can include generating, based on a trained machine learning model, one or more time-slot capacities for one or more pickup time slots at a physical store for a time period that has not yet occurred. The acts also can include, after the time period has occurred, determining when actual demand exceeded the one or more time-slot capacities to tune the trained machine learning model. Other embodiments are described.Type: ApplicationFiled: November 28, 2022Publication date: April 6, 2023Applicant: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20230095307Abstract: 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 acts. The acts can include determining an estimated arrival time of a user at a physical store. The acts also can include generating an estimated wait time using a machine learning model and based on input data comprising the estimated arrival time and dynamic wait time data for the physical store. The acts additionally can include sending the estimated wait time to at least one of the physical store or a mobile device of the user. Other embodiments are disclosed.Type: ApplicationFiled: December 5, 2022Publication date: March 30, 2023Applicant: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Patent number: 11521161Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform training a machine learning model based on historical input data for pickups by users that occurred during a historical time period and output data comprising actual wait times that occurred for the pickups by the users; receiving an order from a user for a pickup from a physical store during a selected time slot of a selected date; adding the order to a queue of assembled checked-in orders; determining an estimated arrival time based on geo-tracking; generating an estimated wait time using the machine learning model, as trained, and can be based on input data; and sending the estimated wait time to at least one of the physical store or the mobile device of the user. Other embodiments are disclosed.Type: GrantFiled: December 12, 2019Date of Patent: December 6, 2022Assignee: WALMART APOLLO, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Patent number: 11514404Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform: obtaining historical demand data for pickup time slots at a physical store for a first time period; training a machine learning model to create a trained model based on the historical demand data for the pickup time slots over the first time period; generating, using the trained model, a projected demand for future pickup time slots at the physical store for a second time period; generating a time-slot capacity for each of the future pickup time slots at the physical store for the second time period based at least in part on the projected demand; and sending the time-slot capacities for the future pickup time slots to the physical store. Other embodiments are described.Type: GrantFiled: December 12, 2019Date of Patent: November 29, 2022Assignee: WALMART APOLLO, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20220108579Abstract: In some examples, a system may include a first computing device communicatively coupled to a second computing device. Additionally, the first computing device is configured to obtain, from the second computing device, check-in data indicating an arrival of the user of the second computing device at a first location, and in response to obtaining the check-in data, determine current wait times. Moreover, the first computing device is configured to determine a first number of customers waiting for service, determine a first number of associates available to assist the first number of customers, and determine an expected wait time for the user operating the second computing device based at least on the current wait times. In some examples, the first number of customers waiting for service, and the first number of associates available. Further, the first computing device is configured to transmit the expected wait time to the second computing device.Type: ApplicationFiled: December 16, 2021Publication date: April 7, 2022Inventors: Shengyang ZHANG, Mingang FU, Arun Prasad NAGARATHINAM, Apeksha MEHTA, Pawan KUMAR, Madhavan KANDHADAI VASANTHAM, Ankit JASUJA, Surnaik Prakash SRIVASTAVA, Jennifer CHEN, Vidyanand KRISHNAN
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Publication number: 20220063005Abstract: The present disclosure relates to a tool for a double-angle chamfering cutter, including a cutter handle and a cutter body. The cutter handle is connected to one end of the cutter body via a snap-fit connection device. The cutter body is provided with chamfering blades located symmetrically on both sides of the cutter body. One end of the cutter body is provided with a sliding track perpendicular to the cutter body. A sliding track forms a cavity with the side of the cutter body. A spring is arranged inside the cavity. The bump is fixedly connected to the sliding track. A hole is provided inside the cutter handle. One side of a wall is provided with a groove channel. The cutter body enters along a hole in the cutter handle. The bump corresponding to the cavity of the cutter body falls into the groove channel.Type: ApplicationFiled: August 30, 2021Publication date: March 3, 2022Inventors: Changhong Feng, Han Liu, Bingning Li, Chaofeng Ma, Zebo Ying, Zheng Yuan, Ao Xu, Jianna Li, Shengyang Zhang
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Patent number: 11244529Abstract: A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.Type: GrantFiled: October 28, 2019Date of Patent: February 8, 2022Assignee: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Arun Prasad Nagarathinam, Apeksha Mehta, Pawan Kumar, Madhavan Kandhadai Vasantham, Ankit Jasuja, Surnaik Prakash Srivastava, Jennifer Chen, Vidyanand Krishnan
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Patent number: 11113751Abstract: 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 acts of: providing, via an electronic platform, access to one or more order placement user interfaces; collecting order placement information associated with the one or more order placement user interfaces; analyzing, by a conversion determination network of a machine learning architecture, the order placement information; generating actual conversion information for client sessions based on the actual availability of the order placement options during the client sessions; generating predicted conversion information for the client sessions based on a full availability of all of the order placement options during the client sessions; and generating lost demand information based, at least in part, on the actual conversion information and the predicted conversion information. Other embodiments are disclosed herein.Type: GrantFiled: January 28, 2020Date of Patent: September 7, 2021Assignee: WALMART APOLLO, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20210233153Abstract: 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 acts of: providing, via an electronic platform, access to one or more order placement user interfaces; collecting order placement information associated with the one or more order placement user interfaces; analyzing, by a conversion determination network of a machine learning architecture, the order placement information; generating actual conversion information for client sessions based on the actual availability of the order placement options during the client sessions; generating predicted conversion information for the client sessions based on a full availability of all of the order placement options during the client sessions; and generating lost demand information based, at least in part, on the actual conversion information and the predicted conversion information. Other embodiments are disclosed herein.Type: ApplicationFiled: January 28, 2020Publication date: July 29, 2021Applicant: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20210125445Abstract: A system is provided and generally includes a server, an associate computing device, and a customer computing device. The server may receive data from the customer computing device indicating that a customer is picking up items from a predetermined location. The server may compute an estimated wait time for the customer based on one or more machine learning processes. In some examples, a number of unexpected customers that may arrive is determined. The machine learning process may compute the estimated wait time based on the number of unexpected customers. The machine learning process may be trained with historical data. The estimated wait time is transmitted to the customer computing device, and is displayed to the customer. In some examples, the server sends a list of customers waiting to be serviced to the associate computing device. The list may be prioritized based on estimated wait times for those customers.Type: ApplicationFiled: October 28, 2019Publication date: April 29, 2021Inventors: Shengyang ZHANG, Mingang FU, Arun Prasad NAGARATHINAM, Apeksha MEHTA, Pawan KUMAR, Madhavan KANDHADAI VASANTHAM, Ankit JASUJA, Surnaik Prakash SRIVASTAVA, Jennifer CHEN, Vidyanand KRISHNAN
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Publication number: 20200325668Abstract: A water pre-storable toilet includes a drain valve located between an injection duct and a water tank, and a float connected to the drain valve is further provided in the water tank. The float is configured to apply an upward pulling force to the drain valve at least when a water level of the water tank reaches a maximum. The float is also configured in such a way that when the float applies the upward pulling force to the drain valve, a buoyancy of the float may not overcome a water pressure caused by the water in the water tank to the drain valve, a weight of the drain valve itself, and a negative pressure formed when the pre-stored water in the injection duct reaches the highest water level regardless of water level of a water in the water tank and thus can't open the drain valve.Type: ApplicationFiled: October 4, 2019Publication date: October 15, 2020Applicant: JOMOO KITCHEN&BATH CO., LTD.Inventors: Xiaofa LIN, Xiaoshan LIN, Shengyang ZHANG, Guanqiao HUANG
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Publication number: 20200250626Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform: obtaining historical demand data for pickup time slots at a physical store for a first time period; training a machine learning model to create a trained model based on the historical demand data for the pickup time slots over the first time period; generating, using the trained model, a projected demand for future pickup time slots at the physical store for a second time period; generating a time-slot capacity for each of the future pickup time slots at the physical store for the second time period based at least in part on the projected demand; and sending the time-slot capacities for the future pickup time slots to the physical store. Other embodiments are described.Type: ApplicationFiled: December 12, 2019Publication date: August 6, 2020Applicant: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20200242553Abstract: A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions configured to run on the one more processors and perform training a machine learning model based on historical input data for pickups by users that occurred during a historical time period and output data comprising actual wait times that occurred for the pickups by the users; receiving an order from a user for a pickup from a physical store during a selected time slot of a selected date; adding the order to a queue of assembled checked-in orders; determining an estimated arrival time based on geo-tracking; generating an estimated wait time using the machine learning model, as trained, and can be based on input data; and sending the estimated wait time to at least one of the physical store or the mobile device of the user. Other embodiments are disclosed.Type: ApplicationFiled: December 12, 2019Publication date: July 30, 2020Applicant: Walmart Apollo, LLCInventors: Shengyang Zhang, Mingang Fu, Madhavan Kandhadai Vasantham
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Publication number: 20200065715Abstract: Systems and methods for obtaining resource information for a resource identifier are disclosed. A resource identifier associated with one of a plurality of resource providers is received from a source system. A clustering model including a plurality of clusters each associated with one of the plurality of resource providers is selected from a plurality of clustering models. A cluster in the clustering model having a least distance from the resource identifier is selected. A request for resource information including the resource identifier is generated and provided to a system associated with the one of the plurality of resource providers associated with the selected cluster.Type: ApplicationFiled: August 27, 2018Publication date: February 27, 2020Inventors: Mingang Fu, Madhavan Kandhadai Vasantham, Shengyang Zhang, Anurag Gupta