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Item management system using weight

  • US 10,332,066 B1
  • Filed: 03/30/2015
  • Issued: 06/25/2019
  • Est. Priority Date: 03/30/2015
  • Status: Active Grant
First Claim
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1. A system comprising:

  • a shelf to hold a type of item in one of a plurality of partitioned areas;

    a plurality of weight sensors, each weight sensor coupled to the shelf at different points;

    a camera with a field-of-view that includes at least a portion of the plurality of partitioned areas; and

    a computing device comprising;

    a memory, storing computer-executable instructions; and

    a hardware processor configured to execute the computer-executable instructions to;

    access first weight data acquired by the plurality of weight sensors at a first time;

    access second weight data acquired by the plurality of weight sensors at a second time;

    determine, using the first weight data, a first weight distribution of the shelf at the first time;

    determine, using the second weight data, a second weight distribution of the shelf at the second time;

    determine, using a difference between the first weight distribution and the second weight distribution, a location of weight change indicative of a location with respect to the shelf;

    determine a partitioned area corresponding to the location of weight change;

    determine measured weight change data indicative of a difference between a first total weight at the first time and a second total weight at the second time;

    access item data indicative of weight of a single one of the type of item and placement of the type of item within the one of the plurality of partitioned areas;

    determine a plurality of hypotheses with each hypothesis indicative of a predicted quantity of the type of item and one or more of;

    a predicted weight change for the predicted quantity corresponding to the each hypothesis,a predicted weight distribution based on the predicted weight change and the placement of the type of item within the one of the plurality of partitioned areas, ora predicted change in weight distribution based on the predicted weight change and the placement of the type of item within a predicted partitioned area;

    determine one or more selected hypotheses from the plurality of hypotheses for which;

    the location of weight change is within the one of the plurality of partitioned areas indicated by the item data for the type of item, anda difference between the measured weight change data and the predicted weight change is less than a threshold value;

    access image data acquired by the camera;

    determine, using the image data, activity data indicative of a hand at the one of the plurality of partitioned areas;

    determine one of the one or more selected hypotheses for which the predicted partitioned area matches;

    the one of the plurality of partitioned areas that the location of weight change is within, andthe one of the plurality of partitioned areas indicated in the activity data;

    generate, based on the one of the one or more selected hypotheses, interaction data indicative of one or more of pick or place of a quantity of the type of item at the one of the plurality of partitioned areas; and

    update, using the interaction data, a quantity stored in the item data.

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