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Patent · US11151992B2 · B2 · US

Context aware interactive robot

(11) Publication number
US11151992B2
(21) Application number
16/227,474
(22) Filing date
2018-12-20
(30) Priority date
2017-04-06
(43) Publication date
2021-10-19
(45) Date of grant
2021-10-19
(51) IPC
B25J 11/00; B25J 13/00; B25J 9/16; G05D 1/00; G05D 1/02; G06K 9/00; G10L 15/18; G10L 15/22
(52) CPC
  • G10L Speech analysis techniques or speech synthesis; speech recognition; speech or voice processing techniques; speech or audio coding or decoding: 15/22, 15/1815, 2015/223
  • B25J Manipulators; chambers provided with manipulation devices: 11/0005, 13/003, 9/163, 9/1697
  • G05D Systems for controlling or regulating non-electric variables: 1/0016, 1/0231, 1/0274
  • G06K Graphical data reading; presentation of data; record carriers; handling record carriers: 9/00228, 9/00664, 9/00832
  • G06V Image or video recognition or understanding: 20/10, 20/59, 40/161
(73) Assignee
Aibrain Corp
(72) Inventors
Run Cui; Won Taek Chung; Hye Jun Yu; Hong Shik Shinn
(54) Title
Context aware interactive robot
(57) Abstract

A plurality of images captured using a camera included in a robotic system are analyzed. A spatial map is generated using a sensor included in the robotic system. A semantic location map is generated using at least the analyzed plurality of captured images and the generated spatial map. A natural language input referencing a desired product item is received from a user. A speech recognition result is recognized from the natural language input and sent to a reasoning engine. In response to sending the recognized speech recognition result, one or more commands for the robotic system are received from the reasoning engine. The received one or more commands are performed and feedback to the user based on at least one of the one or more commands is provided.

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Claims (20)

  1. A method, comprising: analyzing a plurality of images captured using a camera included in a robotic system; generating a spatial map using a sensor included in the robotic system; generating a semantic location map using at least the analyzed plurality of captured images and the generated spatial map; receiving a natural language input and a product photo input from a user referencing a desired product item; recognizing a speech recognition result from the natural language input; sending the speech recognition result to a reasoning engine, wherein the reasoning engine includes an adaptive interactive cognitive reasoning engine utilizing an artificial intelligence memory data structure that includes a robotic system part and a user part, and the artificial intelligence memory data structure is configured to store information associated with a conversation history and one or more cases for cased based reasoning; receiving one or more commands for the robotic system from the reasoning engine in response to sending the recognized speech recognition result; performing the received one or more commands, including by performing an object recognition of the product photo input at the robotic system in response to the one or more commands from the reasoning engine of a remote system; and providing a feedback to the user based on at least one of the one or more commands.
  2. The method of claim 1, wherein analyzing the plurality of images captured using the camera includes determining a tag recognition result on the plurality of captured images.
  3. The method of claim 2, wherein determining the tag recognition result includes outputting a tag layer including product information associated with a recognized tag.
  4. The method of claim 1, wherein analyzing the plurality of images captured using the camera includes determining an object recognition result using the plurality of captured images.
  5. The method of claim 4, wherein determining the object recognition result includes outputting an object layer containing product information associated with a recognized object.
  6. The method of claim 1, wherein generating the semantic location map includes using a place layer.
  7. The method of claim 6, wherein the place layer is curated using an input by a human operator.
  8. The method of claim 6, wherein the place layer identifies a location of a restroom, a food court, a rest area, a cashier, a changing room, an exit, an elevator, or an escalator.
  9. The method of claim 6, wherein generating the semantic location map includes performing a layer composition using the generated spatial map, a tag layer, an object layer, and the place layer.
  10. The method of claim 1, wherein the semantic location map includes product information and navigational information.
  11. The method of claim 1, wherein a costmap is created based on the desired product item.
  12. The method of claim 11, wherein the costmap includes cost values corresponding to complementary objects of the desired product item.
  13. The method of claim 1, wherein the sensor includes a lidar sensor.
  14. The method of claim 1, wherein the feedback to the user is generated using natural language generation.
  15. The method of claim 1, wherein the user is recognized using face recognition.
  16. The method of claim 1, wherein the reasoning engine is located on a remote computer server accessible via a network connection.
  17. The method of claim 1, wherein at least one of the one or more commands is used to control a motor to navigate the robotic system to a location of the desired product item.
  18. A method, comprising: receiving a natural language input and a product photo input from a user referencing a desired product item; recognizing a speech recognition result from the natural language input; sending the speech recognition result to a reasoning engine, wherein the reasoning engine includes an adaptive interactive cognitive reasoning engine utilizing an artificial intelligence memory data structure that includes a robotic system part and a user part, and the artificial intelligence memory data structure is configured to store information associated with a conversation history and one or more cases for cased based reasoning; receiving one or more commands for a robotic system from the reasoning engine in response to sending the recognized speech recognition result; performing an object recognition of the product photo input at the robotic system in response to the one or more commands from the reasoning engine of a remote system; receiving a path to a location of the desired product item, wherein the path is determined using a mapping that maps physical regions to weight values and at least a portion of the weight values is associated with objects complementary to the desired product item; and providing to the user a notice of an intent to provide a guidance to the location of the desired product item.
  19. A robotic system, comprising: a processor; a motorized base; a lidar sensor; a camera sensor; a microphone; a display; a network interface; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: analyze a plurality of images captured using the camera sensor; generate a spatial map using the lidar sensor; generate a semantic location map using at least the analyzed plurality of captured images and the generated spatial map; receive a natural language input via the microphone and a product photo input from a user referencing a desired product item; recognize a speech recognition result from the natural language input; send via the network interface the speech recognition result to a reasoning engine, wherein the reasoning engine includes an adaptive interactive cognitive reasoning engine utilizing an artificial intelligence memory data structure that includes a robotic system part and a user part, and the artificial intelligence memory data structure is configured to store information associated with a conversation history and one or more cases for cased based reasoning; receive via the network interface one or more commands from the reasoning engine in response to sending the recognized speech recognition result; perform the received one or more commands, including by being configured to perform an object recognition of the product photo input at the robot system in response to the one or more commands from the reasoning engine of a remote system; and provide a feedback to the user based on at least one of the one or more commands.
  20. The robotic system of claim 19, wherein at least one of the one or more commands is used to control a motor to navigate the robotic system to a location of the desired product item.

Description

The traditional retail shopping experience can be tedious and burdensome. For example, finding a desired product at a large retail location may be difficult. Inventory systems are often hard for both customers and work staff to access and finding the correct shelf location for a product can be challenging. Similarly, retail stores may often be understaffed or the staff poorly trained, which results in potential customers with unanswered questions. Therefore, there exists a need for a context aware interactive robot powered by an artificial intelligence reasoning agent that responds to conversational language requests to provide interactive customer and staff services to solve customer and staff problems.

Various embodiments of the invention are disclosed in the following detailed description and the accompanying drawings.

FIG. 1 is a flow diagram illustrating an embodiment of a process for responding to an input event using an adaptive, interactive, and cognitive reasoner.

FIG. 2 is a flow diagram illustrating an embodiment of a process for responding to voice input using an adaptive, interactive, and cognitive reasoner with a voice response.

FIG. 3 is a flow diagram illustrating an embodiment of a process for performing reasoning by an adaptive, interactive, and cognitive reasoner.

FIG. 4 is a flow diagram illustrating an embodiment of a process for identifying supporting knowledge.

FIG. 5A is a diagram illustrating an example of a memory graph data structure.

FIG. 5B is a diagram illustrating an example of a memory graph data structure.

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Record as JSON 
{
  "publication_number": "US11151992B2",
  "country": "US",
  "kind": "B2",
  "title": "Context aware interactive robot",
  "abstract": "A plurality of images captured using a camera included in a robotic system are analyzed. A spatial map is generated using a sensor included in the robotic system. A semantic location map is generated using at least the analyzed plurality of captured images and the generated spatial map. A natural language input referencing a desired product item is received from a user. A speech recognition result is recognized from the natural language input and sent to a reasoning engine. In response to sending the recognized speech recognition result, one or more commands for the robotic system are received from the reasoning engine. The received one or more commands are performed and feedback to the user based on at least one of the one or more commands is provided.",
  "claims": [
    "1. A method, comprising: analyzing a plurality of images captured using a camera included in a robotic system; generating a spatial map using a sensor included in the robotic system; generating a semantic location map using at least the analyzed plurality of captured images and the generated spatial map; receiving a natural language input and a product photo input from a user referencing a desired product item; recognizing a speech recognition result from the natural language input; sending the speech recognition result to a reasoning engine, wherein the reasoning engine includes an adaptive interactive cognitive reasoning engine utilizing an artificial intelligence memory data structure that includes a robotic system part and a user part, and the artificial intelligence memory data structure is configured to store information associated with a conversation history and one or more cases for cased based reasoning; receiving one or more commands for the robotic system from the reasoning engine in response to sending the recognized speech recognition result; performing the received one or more commands, including by performing an object recognition of the product photo input at the robotic system in response to the one or more commands from the reasoning engine of a remote system; and providing a feedback to the user based on at least one of the one or more commands.",
    "2. The method of claim 1, wherein analyzing the plurality of images captured using the camera includes determining a tag recognition result on the plurality of captured images.",
    "3. The method of claim 2, wherein determining the tag recognition result includes outputting a tag layer including product information associated with a recognized tag.",
    "4. The method of claim 1, wherein analyzing the plurality of images captured using the camera includes determining an object recognition result using the plurality of captured images.",
    "5. The method of claim 4, wherein determining the object recognition result includes outputting an object layer containing product information associated with a recognized object.",
    "6. The method of claim 1, wherein generating the semantic location map includes using a place layer.",
    "7. The method of claim 6, wherein the place layer is curated using an input by a human operator.",
    "8. The method of claim 6, wherein the place layer identifies a location of a restroom, a food court, a rest area, a cashier, a changing room, an exit, an elevator, or an escalator.",
    "9. The method of claim 6, wherein generating the semantic location map includes performing a layer composition using the generated spatial map, a tag layer, an object layer, and the place layer.",
    "10. The method of claim 1, wherein the semantic location map includes product information and navigational information.",
    "11. The method of claim 1, wherein a costmap is created based on the desired product item.",
    "12. The method of claim 11, wherein the costmap includes cost values corresponding to complementary objects of the desired product item.",
    "13. The method of claim 1, wherein the sensor includes a lidar sensor.",
    "14. The method of claim 1, wherein the feedback to the user is generated using natural language generation.",
    "15. The method of claim 1, wherein the user is recognized using face recognition.",
    "16. The method of claim 1, wherein the reasoning engine is located on a remote computer server accessible via a network connection.",
    "17. The method of claim 1, wherein at least one of the one or more commands is used to control a motor to navigate the robotic system to a location of the desired product item.",
    "18. A method, comprising: receiving a natural language input and a product photo input from a user referencing a desired product item; recognizing a speech recognition result from the natural language input; sending the speech recognition result to a reasoning engine, wherein the reasoning engine includes an adaptive interactive cognitive reasoning engine utilizing an artificial intelligence memory data structure that includes a robotic system part and a user part, and the artificial intelligence memory data structure is configured to store information associated with a conversation history and one or more cases for cased based reasoning; receiving one or more commands for a robotic system from the reasoning engine in response to sending the recognized speech recognition result; performing an object recognition of the product photo input at the robotic system in response to the one or more commands from the reasoning engine of a remote system; receiving a path to a location of the desired product item, wherein the path is determined using a mapping that maps physical regions to weight values and at least a portion of the weight values is associated with objects complementary to the desired product item; and providing to the user a notice of an intent to provide a guidance to the location of the desired product item.",
    "19. A robotic system, comprising: a processor; a motorized base; a lidar sensor; a camera sensor; a microphone; a display; a network interface; and a memory coupled with the processor, wherein the memory is configured to provide the processor with instructions which when executed cause the processor to: analyze a plurality of images captured using the camera sensor; generate a spatial map using the lidar sensor; generate a semantic location map using at least the analyzed plurality of captured images and the generated spatial map; receive a natural language input via the microphone and a product photo input from a user referencing a desired product item; recognize a speech recognition result from the natural language input; send via the network interface the speech recognition result to a reasoning engine, wherein the reasoning engine includes an adaptive interactive cognitive reasoning engine utilizing an artificial intelligence memory data structure that includes a robotic system part and a user part, and the artificial intelligence memory data structure is configured to store information associated with a conversation history and one or more cases for cased based reasoning; receive via the network interface one or more commands from the reasoning engine in response to sending the recognized speech recognition result; perform the received one or more commands, including by being configured to perform an object recognition of the product photo input at the robot system in response to the one or more commands from the reasoning engine of a remote system; and provide a feedback to the user based on at least one of the one or more commands.",
    "20. The robotic system of claim 19, wherein at least one of the one or more commands is used to control a motor to navigate the robotic system to a location of the desired product item."
  ],
  "description_excerpt": "The traditional retail shopping experience can be tedious and burdensome. For example, finding a desired product at a large retail location may be difficult. Inventory systems are often hard for both customers and work staff to access and finding the correct shelf location for a product can be challenging. Similarly, retail stores may often be understaffed or the staff poorly trained, which results in potential customers with unanswered questions. Therefore, there exists a need for a context aware interactive robot powered by an artificial intelligence reasoning agent that responds to conversational language requests to provide interactive customer and staff services to solve customer and staff problems.\n\nVarious embodiments of the invention are disclosed in the following detailed description and the accompanying drawings.\n\nFIG. 1 is a flow diagram illustrating an embodiment of a process for responding to an input event using an adaptive, interactive, and cognitive reasoner.\n\nFIG. 2 is a flow diagram illustrating an embodiment of a process for responding to voice input using an adaptive, interactive, and cognitive reasoner with a voice response.\n\nFIG. 3 is a flow diagram illustrating an embodiment of a process for performing reasoning by an adaptive, interactive, and cognitive reasoner.\n\nFIG. 4 is a flow diagram illustrating an embodiment of a process for identifying supporting knowledge.\n\nFIG. 5A is a diagram illustrating an example of a memory graph data structure.\n\nFIG. 5B is a diagram illustrating an example of a memory graph data structure.",
  "cpc": [
    "G10L 15/22",
    "B25J 11/0005",
    "B25J 13/003",
    "B25J 9/163",
    "B25J 9/1697",
    "G05D 1/0016",
    "G05D 1/0231",
    "G05D 1/0274",
    "G06K 9/00228",
    "G06K 9/00664",
    "G06K 9/00832",
    "G06V 20/10",
    "G06V 20/59",
    "G06V 40/161",
    "G10L 15/1815",
    "G10L 2015/223"
  ],
  "ipc": [
    "B25J 11/00",
    "B25J 13/00",
    "B25J 9/16",
    "G05D 1/00",
    "G05D 1/02",
    "G06K 9/00",
    "G10L 15/18",
    "G10L 15/22"
  ],
  "assignees": [
    "Aibrain Corp"
  ],
  "inventors": [
    "Run Cui",
    "Won Taek Chung",
    "Hye Jun Yu",
    "Hong Shik Shinn"
  ],
  "filing_date": "2018-12-20",
  "publication_date": "2021-10-19",
  "grant_date": "2021-10-19",
  "priority_date": "2017-04-06",
  "application_number": "US-201816227474-A",
  "family_id": "67059763",
  "cited_by_count": 28,
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}

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