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公开(公告)号：US11436557B1
公开(公告)日：2022-09-06
申请号：US15939063
申请日：2018-03-28
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Dilip Kumar , Liefeng Bo , Nikhil Chacko , Robert Crandall , Nishitkumar Ashokkumar Desai , Jayakrishnan Kumar Eledath , Gopi Prashanth Gopal , Gerard Guy Medioni , Paul Eugene Munger , Kushagra Srivastava
IPC: G06Q10/08 , G01G19/387 , G06N7/00 , G01G19/40
Abstract: One or more load cells measure the weight of items on a shelf or other fixture. Weight changes occur as items are picked from or placed to the fixture. Output from the load cells is processed to produce denoised data. The denoised data is processed to determine event data representative of a pick or a place of an item. Hypotheses are generated using information about where particular types of items are stowed, the weights of those particular types of items, and the event data. A high scoring hypothesis is used to determine interaction data indicative of the type and quantity of an item that was added to or removed from the fixture. If ambiguity exists between hypotheses, additional techniques such as data about locations of weight changes and fine grained analysis may be used to determine the interaction data.
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公开(公告)号：US11308442B1
公开(公告)日：2022-04-19
申请号：US15939080
申请日：2018-03-28
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Dilip Kumar , Liefeng Bo , Nikhil Chacko , Robert Crandall , Nishitkumar Ashokkumar Desai , Jayakrishnan Kumar Eledath , Gopi Prashanth Gopal , Gerard Guy Medioni , Paul Eugene Munger , Kushagra Srivastava
IPC: G06Q10/08 , G01G19/387 , B65G1/137 , G01G19/42 , G06Q20/20
Abstract: Sensor data from load cells at a shelf is processed using a first time window to produce first event data describing coarse and sub-events. Location data is determined that indicates where on the shelf weight changes occurred at particular times. Hypotheses are generated using information about where items are stowed, weights of those of items, type of event, and the location data. If confidence values of these hypotheses are below a threshold value, second event data is determined by merging adjacent sub-events. This second event data is then used to determine second hypotheses which are then assessed. A hypothesis with a high confidence value is used to generate interaction data indicative of picks or places of particular quantities of particular types of items from the shelf.
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公开(公告)号：US11301684B1
公开(公告)日：2022-04-12
申请号：US15839734
申请日：2017-12-12
Applicant: Amazon Technologies, Inc.
Inventor： Dilip Kumar , Liefeng Bo , Keunhong Park , Gerard Guy Medioni , Nikhil Chacko , Jayakrishnan Kumar Eledath , Nishitkumar Ashokkumar Desai
IPC: G06K9/00
Abstract: This disclosure describes systems and techniques for detecting certain activity in image data, such as frames of video data. For example, the systems and techniques may create and utilize an activity classifier for detecting and classifying certain human activity in video data of a facility. In some instances, the classifier may be trained to identify, from the video data, certain predefined activity such as a user picking an item from a shelf, a user returning an item to a shelf, a first user passing an item to a second user, or the like. In some instances, the techniques enable activity detection using only video data, rather than in addition to data acquired by other sensors.
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公开(公告)号：US10318917B1
公开(公告)日：2019-06-11
申请号：US14675167
申请日：2015-03-31
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Michel Leonard Goldstein , Ramanathan Palaniappan , Fan Sun , Liefeng Bo , Ohil Krishnamurthy Manyam , Navid Shiee , Gerard Guy Medioni
Abstract: An inventory location such as a shelf may be used to stow different types of items. Interactions may take place, such as the pick or place of one or more items from the inventory location. Image data may be acquired from cameras viewing the shelf and weight data may be acquired from weight sensors coupled to the shelf. Hypotheses may be determined that indicate possible interactions with the inventory location, such as pick or place of an item with regard to the inventory location, and the probability that those interactions are correct. The hypotheses and their associated probabilities may be aggregated. From the aggregated hypotheses, a hypothesis with a highest confidence value may be deemed a solution.
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公开(公告)号：US11468400B1
公开(公告)日：2022-10-11
申请号：US15938992
申请日：2018-03-28
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Dilip Kumar , Liefeng Bo , Nikhil Chacko , Robert Crandall , Nishitkumar Ashokkumar Desai , Jayakrishnan Kumar Eledath , Gopi Prashanth Gopal , Gerard Guy Medioni , Paul Eugene Munger , Kushagra Srivastava
IPC: G06Q10/08 , G01G19/387 , G06N7/00 , G01G19/40
Abstract: One or more load cells measure the weight of items at a fixture. Weight changes occur as items are picked from or placed to the fixture and may be used to determine when the item was picked or placed, quantity and so forth. Individual weights for a type of item may vary. A set of data comprising weight changes associated with interactions involving a single one of a particular type of item is gathered. These may be weight changes due to picks, places, or both. A model, such as a probability distribution, may be created that relates a particular weight of that type of item to a probability. The model may then be used to process other weight changes and attempt to determine what type of item was involved in an interaction.
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Patent Agency Ranking
公开(公告)号：US11263583B1
公开(公告)日：2022-03-01
申请号：US15939061
申请日：2018-03-28
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Dilip Kumar , Liefeng Bo , Nikhil Chacko , Robert Crandall , Nishitkumar Ashokkumar Desai , Jayakrishnan Kumar Eledath , Gopi Prashanth Gopal , Gerard Guy Medioni , Paul Eugene Munger , Kushagra Srivastava
Abstract: Load cells measure the weight of items on a shelf. Weight changes occur as items are picked from or placed to the fixture. Information about these weight changes is used to determine an estimated location on the shelf of a weight change. Hypotheses are generated using information about where particular types of items are stowed, the weights of those particular types of items, information about the weight changes, and the estimated locations of the weight changes. A model is used to produce confidence values in the hypotheses based on a change in weight measured at a first side and a change in weight measured at a second side of the shelf. A hypothesis with a confidence value that exceeds the threshold may be selected and used to determine interaction data indicative of a quantity picked or placed, type of item, and location on the shelf.
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公开(公告)号：US11117744B1
公开(公告)日：2021-09-14
申请号：US14963204
申请日：2015-12-08
Applicant: AMAZON TECHNOLOGIES, INC.
Inventor： Gerard Guy Medioni , Haichao Zhang , Saurabh Sehgal , Liefeng Bo
Abstract: A user may place an item at an inventory location in an incorrect location, such as the wrong lane on the shelf. An untidy score is generated that is indicative of whether an item is where it is supposed to be. A correct item verification (CIV) score is determined that is indicative of how similar the returned item is to the type of item that is supposed to be stored at that location. An invalid item recognition (IIR) score is determined that is indicative of similarity of the returned item to a set of items associated with the user, that set excluding the item that is supposed to be stored at that location. The CIV and the IIR are combined to generate an untidy score. Based on the untidy score, someone may be dispatched to clean up the inventory location.
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公开(公告)号：US10657411B1
公开(公告)日：2020-05-19
申请号：US14225240
申请日：2014-03-25
Applicant: Amazon Technologies, Inc.
Inventor： Ohil Krishnamurthy Manyam , Minmin Chen , Liefeng Bo , Xiaofeng Ren , Dilip Kumar
IPC: G06K9/62
Abstract: This disclosure describes a system for utilizing multiple image processing techniques to identify an item represented in an image. In some implementations, one or more image processing algorithms may be utilized to process a received image to generate item image information and compare the item image information with stored item image information to identify the item. When a similarity score identifying the similarity between the item image information and at least one of the stored item image information is returned, a determination may be made as to whether the similarity score is high enough to confidently identify the item. If it is determined that the similarity score is high enough to confidently identify the item, the other algorithms may be terminated and the determined identity of the item returned.
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公开(公告)号：US10713614B1
公开(公告)日：2020-07-14
申请号：US14225196
申请日：2014-03-25
Applicant: Amazon Technologies, Inc.
Inventor： Ohil Krishnamurthy Manyam , Minmin Chen , Liefeng Bo , Xiaofeng Ren , Dilip Kumar
Abstract: This disclosure describes a system for processing an image of an item and correctly identifying the item from a group of candidate items. In one implementation, as item image information for a new item is added to an item images data store, a determination is made as to the weight of the item represented by the image, and the item may be associated with a weight class. Each weight class represents items within a defined weight range. Item image information for items in the same weight class may then be used when new items are added to inventory and/or when identifying an item represented in an image.
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公开(公告)号：US10169660B1
公开(公告)日：2019-01-01
申请号：US14578021
申请日：2014-12-19
Applicant: Amazon Technologies, Inc.
Inventor： Xiaofeng Ren , Avishkar Misra , Ohil Krishnamurthy Manyam , Liefeng Bo , Sudarshan Narasimha Raghavan , Christopher Robert Towers , Gopi Prashanth Gopal , Yasser Baseer Asmi
Abstract: Described is a system for counting stacked items using image analysis. In one implementation, an image of an inventory location with stacked items is obtained and processed to determine the number of items stacked at the inventory location. In some instances, the item closest to the camera that obtains the image may be the only item viewable in the image. Using image analysis, such as depth mapping or Histogram of Oriented Gradients (HOG) algorithms, the distance of the item from the camera and the shelf of the inventory location can be determined. Using this information, and known dimension information for the item, a count of the number of items stacked at an inventory location may be determined.
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