IP Library › Granted Patent US 12711380

Granted Patent B1

US 12711380 · App. 18/774,852 · Granted Aug 18, 2026

Artificially intelligent systems, devices, and methods for learning and/or using a device's circumstances for autonomous device operation

Inventor: Jasmin Cosic (Melbourne, FL)

G06N3/08 G06N5/022

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Quick Facts

Patent No.: US 12711380
App. No.: 18/774,852
Granted: Aug 18, 2026
Kind: B1

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Abstract

Aspects of the disclosure generally relate to computing enabled devices and/or systems, and may be generally directed to devices, systems, methods, and/or applications for learning a device's operation in various circumstances, storing this knowledge in a knowledgebase (i.e. neural network, graph, sequences, etc.), and enabling autonomous operation of the device.

Claims (179)

1 . A first device comprising:

a knowledgebase that includes one or more inputs for inputting at least a portion of one or more object representations, wherein the one or more inputs are correlated with: one or more instruction sets for operating a second device, and one or more instruction sets for operating a third device;

one or more sensors;

one or more processors; and

one or more non-transitory machine-readable media storing machine readable code that, when executed by the one or more processors, causes the one or more processors to perform at least:

generating one or more object representations that represent a circumstance detected at least in part by the one or more sensors;

determining the one or more instruction sets for operating the second device at least by: inputting at least a portion of the generated one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the one or more instruction sets for operating the second device; and

at least in response to the determining, executing the one or more instruction sets for operating the second device, wherein the first device autonomously performs one or more operations defined by the one or more instruction sets for operating the second device.

2 . The first device of claim 1 , wherein the first device is a first vehicle, wherein the second device is a second vehicle, wherein the third device is a third vehicle.

3 . The first device of claim 2 , wherein at least a portion of the correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in a learning process, wherein at least a portion of a correlation between the one or more inputs and the one or more instruction sets for operating the third device is learned in the learning process.

4 . The first device of claim 2 , wherein at least a portion of the correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in a learning process, wherein at least a portion of a correlation between the one or more inputs and the one or more instruction sets for operating the third device is learned in another learning process.

5 . The first device of claim 2 , wherein a weight included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in a learning process, wherein a weight included in or related to a correlation between the one or more inputs and the one or more instruction sets for operating the third device is learned in the learning process.

6 . The first device of claim 2 , wherein a weight included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in a learning process, wherein a weight included in or related to a correlation between the one or more inputs and the one or more instruction sets for operating the third device is learned in another learning process.

7 . The first device of claim 2 , wherein at least a portion of the one or more instruction sets for operating the second device is learned in a learning process that includes operating the second device at least partially by a user, wherein at least a portion of the one or more instruction sets for operating the third device is learned in another learning process that includes operating the third device at least partially by another user.

8 . The first device of claim 2 , wherein the one or more instruction sets for operating the second device and their temporally corresponding one or more object representations that represent one or more objects detected by one or more sensors of the second device are learned in a learning process that includes operating the second device at least partially by a user, wherein the one or more instruction sets for operating the third device and their temporally corresponding one or more object representations that represent one or more objects detected by one or more sensors of the third device are learned in another learning process that includes operating the third device at least partially by another user.

9 . The first device of claim 2 , wherein the generated one or more object representations are multiple object representations that (i) include multiple object representation data structures that each comprises: a label for object type, coordinates for object location, and a computer model for object size, and (ii) represent multiple objects,

wherein the inputting the at least the portion of the generated one or more object representations into the one or more inputs includes inputting at least a portion of the multiple object representation data structures into the one or more inputs.

10 . The first device of claim 2 , wherein the generated one or more object representations include: one or more information about one or more distances of one or more objects relative to the first device, and one or more information about one or more angles of the one or more objects relative to the first device.

11 . The first device of claim 2 , wherein the generated one or more object representations include one or more three dimensional representations of one or more objects.

12 . The first device of claim 2 , wherein the generated one or more object representations include one or more digital pictures that depict one or more objects.

13 . The first device of claim 2 , wherein the machine-readable code, when executed by the one or more processors, causes the one or more processors to further perform at least:

modifying the one or more instruction sets for operating the second device in the knowledgebase,

wherein the determining includes determining the modified one or more instruction sets for operating the second device at least by: the inputting the at least the portion of the generated one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the modified one or more instruction sets for operating the second device,

wherein the executing includes executing the modified one or more instruction sets for operating the second device,

wherein the first device autonomously performing includes the first device autonomously performing one or more operations defined by the modified one or more instruction sets for operating the second device.

14 . The first device of claim 2 , wherein the machine-readable code, when executed by the one or more processors, causes the one or more processors to further perform at least:

modifying a copy of the determined one or more instruction sets for operating the second device,

wherein the executing includes executing the modified the copy of the determined one or more instruction sets for operating the second device,

wherein the first device autonomously performing includes the first device autonomously performing one or more operations defined by the modified the copy of the determined one or more instruction sets for operating the second device.

15 . The first device of claim 2 , wherein the machine-readable code, when executed by the one or more processors, causes the one or more processors to further perform at least:

modifying the generated one or more object representations,

wherein the inputting the at least the portion of the generated one or more object representations into the one or more inputs includes inputting at least a portion of the modified the generated one or more object representations into the one or more inputs.

16 . The first device of claim 2 , wherein a server receives the one or more instruction sets for operating the second device from the second device, wherein the server further receives the one or more instruction sets for operating the third device from the third device, wherein the first device receives the one or more instruction sets for operating the second device from: the server, or another server.

17 . The first device of claim 2 , wherein the one or more inputs are correlated with: the one or more instruction sets for operating the second device using at least one or more connections, and the one or more instruction sets for operating the third device using at least one or more connections, wherein the using the correlation between the one or more inputs and the one or more instruction sets for operating the second device includes using at least one connection of the one or more connections between the one or more inputs and the one or more instruction sets for operating the second device.

18 . The first device of claim 2 , wherein the knowledgebase is a neural network,

wherein the one or more inputs are one or more input neurons of the neural network,

wherein the neural network further comprises one or more output neurons that include or are associated with: the one or more instruction sets for operating the second device, and the one or more instruction sets for operating the third device,

wherein the one or more input neurons are correlated with the one or more output neurons using at least one or more connections,

wherein the using the correlation between the one or more inputs and the one or more instruction sets for operating the second device includes using at least one connection of the one or more connections between the one or more input neurons and the one or more output neurons.

19 . The first device of claim 2 , wherein the knowledgebase further includes one or more outputs that include the one or more instruction sets for operating the second device,

wherein an information included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in a learning process that includes:

generating another one or more object representations that represent another circumstance detected at least in part by one or more sensors of the second device;

obtaining or receiving the one or more instruction sets for operating the second device;

inputting at least a portion of the generated another one or more object representations into the one or more inputs; and

applying the one or more instruction sets for operating the second device to the one or more outputs.

20 . The first device of claim 2 , wherein the generated one or more object representations include a representation of the first device.

21 . The first device of claim 2 , wherein the one or more instruction sets for operating the second device include one or more information about one or more states of at least a portion of the second device, wherein the one or more instruction sets for operating the third device include one or more information about one or more states of at least a portion of the third device.

22 . The first device of claim 2 , wherein the generated one or more object representations represent the circumstance detected at least in part by the one or more sensors during a time period.

23 . The first device of claim 2 , wherein the circumstance detected at least in part by the one or more sensors includes one or more objects detected at least in part by the one or more sensors, wherein the generated one or more object representations include: one object representation, multiple object representations, a collection of object representations, or a stream of collections of object representations, wherein the at least the portion of the generated one or more object representations includes: one portion of an object representation of the generated one or more object representations, multiple portions of an object representation of the generated one or more object representations, multiple portions of multiple object representations of the generated one or more object representations, one object representation of the generated one or more object representations, multiple object representations of the generated one or more object representations, or the entire generated one or more object representations, wherein at least a portion of the knowledgebase is stored in or on: at least one non-transitory machine readable medium of the one or more non-transitory machine readable media, or another one or more non-transitory machine readable media.

24 . The first device of claim 2 , wherein the generated one or more object representations represent the circumstance detected at least in part by the one or more sensors at a first time,

wherein the machine-readable code, when executed by the one or more processors, causes the one or more processors to further perform at least:

generating another one or more object representations that represent another circumstance detected at least in part by the one or more sensors at a second time;

determining the one or more instruction sets for operating the third device at least by: inputting at least a portion of the generated another one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the one or more instruction sets for operating the third device; and

at least in response to the determining the one or more instruction sets for operating the third device, executing the one or more instruction sets for operating the third device, wherein the first device autonomously performs one or more operations defined by the one or more instruction sets for operating the third device.

25 . A system comprising:

a knowledgebase that includes one or more inputs for inputting at least a portion of one or more object representations, wherein the one or more inputs are correlated with: one or more instruction sets for operating a first device, and one or more instruction sets for operating a second device; and

one or more non-transitory machine-readable media storing machine readable code that, when executed, causes at least:

generating one or more object representations that represent a circumstance detected at least in part by one or more sensors of a third device;

determining the one or more instruction sets for operating the first device at least by: inputting at least a portion of the generated one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the one or more instruction sets for operating the first device; and

at least in response to the determining, executing the one or more instruction sets for operating the first device, wherein the third device autonomously performs one or more operations defined by the one or more instruction sets for operating the first device.

26 . The system of claim 25 , wherein at least a portion of the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein at least a portion of a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in the learning process.

27 . The system of claim 25 , wherein at least a portion of the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein at least a portion of a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in another learning process.

28 . The system of claim 25 , wherein a weight included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein a weight included in or related to a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in the learning process.

29 . The system of claim 25 , wherein a weight included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein a weight included in or related to a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in another learning process.

30 . The system of claim 25 , wherein at least a portion of the one or more instruction sets for operating the first device is learned in a learning process that includes operating the first device at least partially by a user, wherein at least a portion of the one or more instruction sets for operating the second device is learned in another learning process that includes operating the second device at least partially by another user.

31 . The system of claim 25 , wherein the one or more instruction sets for operating the first device and their temporally corresponding one or more object representations that represent one or more objects detected by one or more sensors of the first device are learned in a learning process that includes operating the first device at least partially by a user, wherein the one or more instruction sets for operating the second device and their temporally corresponding one or more object representations that represent one or more objects detected by one or more sensors of the second device are learned in another learning process that includes operating the second device at least partially by another user.

32 . The system of claim 25 , wherein the machine-readable code, when executed, further causes at least:

generating another one or more object representations that represent a circumstance detected at least in part by one or more sensors of a fourth device;

determining the one or more instruction sets for operating the second device at least by: inputting at least a portion of the generated another one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the one or more instruction sets for operating the second device; and

at least in response to the determining the one or more instruction sets for operating the second device, executing the one or more instruction sets for operating the second device, wherein the fourth device autonomously performs one or more operations defined by the one or more instruction sets for operating the second device.

33 . The system of claim 25 , wherein the generated one or more object representations are multiple object representations that (i) include multiple object representation data structures that each comprises: a label for object type, coordinates for object location, and a computer model for object size, and (ii) represent multiple objects,

34 . The system of claim 25 , wherein the generated one or more object representations include: one or more information about one or more distances of one or more objects relative to the third device, and one or more information about one or more angles of the one or more objects relative to the third device.

35 . The system of claim 25 , wherein the generated one or more object representations include one or more three dimensional representations of one or more objects.

36 . The system of claim 25 , wherein the generated one or more object representations include one or more digital pictures that depict one or more objects.

37 . The system of claim 25 , wherein the machine-readable code, when executed, further causes at least:

modifying the one or more instruction sets for operating the first device in the knowledgebase,

wherein the determining includes determining the modified one or more instruction sets for operating the first device at least by: the inputting the at least the portion of the generated one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the modified one or more instruction sets for operating the first device,

wherein the executing includes executing the modified one or more instruction sets for operating the first device,

wherein the third device autonomously performing includes the third device autonomously performing one or more operations defined by the modified one or more instruction sets for operating the first device.

38 . The system of claim 25 , wherein the machine-readable code, when executed, further causes at least:

modifying a copy of the determined one or more instruction sets for operating the first device,

wherein the executing includes executing the modified the copy of the determined one or more instruction sets for operating the first device,

wherein the third device autonomously performing includes the third device autonomously performing one or more operations defined by the modified the copy of the determined one or more instruction sets for operating the first device.

39 . The system of claim 25 , wherein the machine-readable code, when executed, further causes at least:

modifying the generated one or more object representations,

40 . The system of claim 25 , further comprising:

a server that receives the one or more instruction sets for operating the first device from the first device,

wherein the server further receives the one or more instruction sets for operating the second device from the second device,

wherein the third device receives the one or more instruction sets for operating the first device from: the server, or another server.

41 . The system of claim 25 , wherein the one or more inputs are correlated with: the one or more instruction sets for operating the first device using at least one or more connections, and the one or more instruction sets for operating the second device using at least one or more connections, wherein the using the correlation between the one or more inputs and the one or more instruction sets for operating the first device includes using at least one connection of the one or more connections between the one or more inputs and the one or more instruction sets for operating the first device.

42 . The system of claim 25 , wherein the knowledgebase is a neural network,

wherein the one or more inputs are one or more input neurons of the neural network,

wherein the neural network further comprises one or more output neurons that include or are associated with: the one or more instruction sets for operating the first device, and the one or more instruction sets for operating the second device,

wherein the one or more input neurons are correlated with the one or more output neurons using at least one or more connections,

wherein the using the correlation between the one or more inputs and the one or more instruction sets for operating the first device includes using at least one connection of the one or more connections between the one or more input neurons and the one or more output neurons.

43 . The system of claim 25 , wherein the knowledgebase further includes one or more outputs that include the one or more instruction sets for operating the first device,

wherein an information included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process that includes:

generating another one or more object representations that represent another circumstance detected at least in part by one or more sensors of the first device;

obtaining or receiving the one or more instruction sets for operating the first device;

inputting at least a portion of the generated another one or more object representations into the one or more inputs; and

applying the one or more instruction sets for operating the first device to the one or more outputs.

44 . The system of claim 25 , wherein the generated one or more object representations include a representation of the third device.

45 . The system of claim 25 , wherein the one or more instruction sets for operating the first device include one or more information about one or more states of at least a portion of the first device, wherein the one or more instruction sets for operating the second device include one or more information about one or more states of at least a portion of the second device.

46 . The system of claim 25 , wherein the generated one or more object representations represent the circumstance detected at least in part by the one or more sensors of the third device during a time period.

47 . The system of claim 25 , wherein the circumstance detected at least in part by the one or more sensors of the third device includes one or more objects detected at least in part by the one or more sensors of the third device, wherein the generated one or more object representations include: one object representation, multiple object representations, a collection of object representations, or a stream of collections of object representations, wherein the at least the portion of the generated one or more object representations includes: one portion of an object representation of the generated one or more object representations, multiple portions of an object representation of the generated one or more object representations, multiple portions of multiple object representations of the generated one or more object representations, one object representation of the generated one or more object representations, multiple object representations of the generated one or more object representations, or the entire generated one or more object representations, wherein at least a portion of the knowledgebase is stored in or on: at least one non-transitory machine readable medium of the one or more non-transitory machine readable media, or another one or more non-transitory machine readable media, wherein the machine readable code is executed by one or more processors that cause the generating, the determining, and the executing.

48 . The system of claim 25 , wherein the generated one or more object representations represent the circumstance detected at least in part by the one or more sensors of the third device at a first time, wherein the machine-readable code, when executed, further causes at least:

generating another one or more object representations that represent another circumstance detected at least in part by the one or more sensors of the third device at a second time;

determining the one or more instruction sets for operating the second device at least by:

inputting at least a portion of the generated another one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the one or more instruction sets for operating the second device; and

at least in response to the determining the one or more instruction sets for operating the second device, executing the one or more instruction sets for operating the second device, wherein the third device autonomously performs one or more operations defined by the one or more instruction sets for operating the second device.

49 . The system of claim 25 , wherein the first device is a first vehicle, wherein the second device is a second vehicle, wherein the third device is a third vehicle.

50 . The system of claim 25 , wherein the first device is a first robot, wherein the second device is a second robot, wherein the third device is a third robot.

51 . The system of claim 25 , wherein the first device is a first fixture, wherein the second device is a second fixture, wherein the third device is a third fixture.

52 . The system of claim 25 , wherein the first device is a first appliance, wherein the second device is a second appliance, wherein the third device is a third appliance.

53 . The system of claim 25 , wherein the first device is a first control device, wherein the second device is a second control device, wherein the third device is a third control device.

54 . A method comprising:

accessing a knowledgebase that includes one or more inputs for inputting at least a portion of one or more object representations, wherein the one or more inputs are correlated with: one or more instruction sets for operating a first device, and one or more instruction sets for operating a second device;

generating one or more object representations that represent a circumstance detected at least in part by one or more sensors of a third device;

at least in response to the determining, executing the one or more instruction sets for operating the first device; and

autonomously performing, by the third device, one or more operations defined by the one or more instruction sets for operating the first device.

55 . The method of claim 54 , wherein at least a portion of the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein at least a portion of a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in the learning process.

56 . The method of claim 54 , wherein at least a portion of the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein at least a portion of a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in another learning process.

57 . The method of claim 54 , wherein a weight included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein a weight included in or related to a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in the learning process.

58 . The method of claim 54 , wherein a weight included in or related to the correlation between the one or more inputs and the one or more instruction sets for operating the first device is learned in a learning process, wherein a weight included in or related to a correlation between the one or more inputs and the one or more instruction sets for operating the second device is learned in another learning process.

59 . The method of claim 54 , wherein at least a portion of the one or more instruction sets for operating the first device is learned in a learning process that includes operating the first device at least partially by a user, wherein at least a portion of the one or more instruction sets for operating the second device is learned in another learning process that includes operating the second device at least partially by another user.

60 . The method of claim 54 , wherein the one or more instruction sets for operating the first device and their temporally corresponding one or more object representations that represent one or more objects detected by one or more sensors of the first device are learned in a learning process that includes operating the first device at least partially by a user, wherein the one or more instruction sets for operating the second device and their temporally corresponding one or more object representations that represent one or more objects detected by one or more sensors of the second device are learned in another learning process that includes operating the second device at least partially by another user.

61 . The method of claim 54 , further comprising:

generating another one or more object representations that represent a circumstance detected at least in part by one or more sensors of a fourth device;

at least in response to the determining the one or more instruction sets for operating the second device, executing the one or more instruction sets for operating the second device; and

autonomously performing, by the fourth device, one or more operations defined by the one or more instruction sets for operating the second device.

62 . The method of claim 54 , wherein the generated one or more object representations are multiple object representations that (i) include multiple object representation data structures that each comprises: a label for object type, coordinates for object location, and a computer model for object size, and (ii) represent multiple objects,

63 . The method of claim 54 , wherein the generated one or more object representations include: one or more information about one or more distances of one or more objects relative to the third device, and one or more information about one or more angles of the one or more objects relative to the third device.

64 . The method of claim 54 , wherein the generated one or more object representations include one or more three dimensional representations of one or more objects.

65 . The method of claim 54 , wherein the generated one or more object representations include one or more digital pictures that depict one or more objects.

66 . The method of claim 54 , further comprising:

modifying the one or more instruction sets for operating the first device in the knowledgebase,

wherein the executing includes executing the modified one or more instruction sets for operating the first device,

wherein the autonomously performing includes autonomously performing, by the third device, one or more operations defined by the modified one or more instruction sets for operating the first device.

67 . The method of claim 54 , further comprising:

modifying a copy of the determined one or more instruction sets for operating the first device,

wherein the executing includes executing the modified the copy of the determined one or more instruction sets for operating the first device,

wherein the autonomously performing includes autonomously performing, by the third device, one or more operations defined by the modified the copy of the determined one or more instruction sets for operating the first device.

68 . The method of claim 54 , further comprising:

modifying the generated one or more object representations,

69 . The method of claim 54 , wherein a server receives the one or more instruction sets for operating the first device from the first device, wherein the server receives the one or more instruction sets for operating the second device from the second device, wherein the third device receives the one or more instruction sets for operating the first device from: the server, or another server.

70 . The method of claim 54 , wherein the one or more inputs are correlated with: the one or more instruction sets for operating the first device using at least one or more connections, and the one or more instruction sets for operating the second device using at least one or more connections, wherein the using the correlation between the one or more inputs and the one or more instruction sets for operating the first device includes using at least one connection of the one or more connections between the one or more inputs and the one or more instruction sets for operating the first device.

71 . The method of claim 54 , wherein the knowledgebase is a neural network,

wherein the one or more inputs are one or more input neurons of the neural network,

wherein the one or more input neurons are correlated with the one or more output neurons using at least one or more connections,

72 . The method of claim 54 , wherein the knowledgebase further includes one or more outputs that include the one or more instruction sets for operating the first device,

generating another one or more object representations that represent another circumstance detected at least in part by one or more sensors of the first device;

obtaining or receiving the one or more instruction sets for operating the first device;

inputting at least a portion of the generated another one or more object representations into the one or more inputs; and

applying the one or more instruction sets for operating the first device to the one or more outputs.

73 . The method of claim 54 , wherein the generated one or more object representations include a representation of the third device.

74 . The method of claim 54 , wherein the one or more instruction sets for operating the first device include one or more information about one or more states of at least a portion of the first device, wherein the one or more instruction sets for operating the second device include one or more information about one or more states of at least a portion of the second device.

75 . The method of claim 54 , wherein the generated one or more object representations represent the circumstance detected at least in part by the one or more sensors of the third device during a time period.

76 . The method of claim 54 , wherein the circumstance detected at least in part by the one or more sensors of the third device includes one or more objects detected at least in part by the one or more sensors of the third device, wherein the generated one or more object representations include: one object representation, multiple object representations, a collection of object representations, or a stream of collections of object representations, wherein the at least the portion of the generated one or more object representations includes: one portion of an object representation of the generated one or more object representations, multiple portions of an object representation of the generated one or more object representations, multiple portions of multiple object representations of the generated one or more object representations, one object representation of the generated one or more object representations, multiple object representations of the generated one or more object representations, or the entire generated one or more object representations, wherein the method further comprising: generating, using means for processing, another one or more object representations that represent a circumstance detected at least in part by one or more sensors of a fourth device; determining, using means for processing, the one or more instruction sets for operating the second device at least by: inputting at least a portion of the generated another one or more object representations into the one or more inputs, and using a correlation between the one or more inputs and the one or more instruction sets for operating the second device; at least in response to the determining the one or more instruction sets for operating the second device, executing the one or more instruction sets for operating the second device; and autonomously performing, by the fourth device, one or more operations defined by the one or more instruction sets for operating the second device.

77 . The method of claim 54 , wherein the generated one or more object representations represent the circumstance detected at least in part by the one or more sensors of the third device at a first time,

wherein the method further comprising:

generating another one or more object representations that represent another circumstance detected at least in part by the one or more sensors of the third device at a second time;

at least in response to the determining the one or more instruction sets for operating the second device, executing the one or more instruction sets for operating the second device; and

autonomously performing, by the third device, one or more operations defined by the one or more instruction sets for operating the second device.

78 . The method of claim 54 , wherein the first device is a first vehicle, wherein the second device is a second vehicle, wherein the third device is a third vehicle.

79 . The method of claim 54 , wherein the first device is a first robot, wherein the second device is a second robot, wherein the third device is a third robot.

80 . The method of claim 54 , wherein the first device is a first fixture, wherein the second device is a second fixture, wherein the third device is a third fixture.

81 . The method of claim 54 , wherein the first device is a first appliance, wherein the second device is a second appliance, wherein the third device is a third appliance.

82 . The method of claim 54 , wherein the first device is a first control device, wherein the second device is a second control device, wherein the third device is a third control device.