Systems, methods, apparatus, and platforms for the industrial internet of things

Abstract

In an exemplary embodiment, a method for detecting anomalies associated with a machine includes recording a dataset associated with the machine, determining, with a first machine learning model, a label associated with the dataset, determining whether the label should be reviewed, and if it is determined that the label should be reviewed, subjecting the dataset and label to review and updating the label based on the review.Alternatively or additionally, in an exemplary embodiment, a method for presenting an analysis of a machine included in an industrial facility includes generating a digital twin of the machine, determining at least one property of the digital twin based on a simulation of operation of the machine, and generating a representation of the industrial facility including a visualization of the digital twin and a visual indicator of the at least one property of the digital twin.

Claims

1. 1. A method for detecting anomalies associated with a machine, comprising: recording a data set associated with the machine; determining labels associated with the dataset with a first machine learning model; determining whether the label is to be reviewed; and If it is decided that the label will be reviewed, Subject the dataset and labels to review, and Update labels based on reviews, steps, A method comprising:

2. The step of recording the data set further comprises: receiving a sampled data set associated with the machine; downsampling the sampled dataset to generate a downsampled dataset; and recording a downsampled data set associated with the machine; The method of claim 1 , comprising:

3. The step of determining whether the label is to be reviewed may further include: applying a statistical anomaly detection analysis to the dataset and at least two other datasets related to the label; and characterizing the dataset as an outlier relative to at least two other datasets associated with the label based on a statistical anomaly detection analysis; The method of claim 1 , comprising:

4. 4. The method of claim 3, further comprising: comparing at least one statistical measure of at least one parameter of a dataset with typical statistical measures of at least one parameter of at least two other datasets to train a classifier model to characterize the dataset as an anomaly.

5. The step of determining whether the label is to be reviewed may further include: Presenting the labels and dataset to experts; and receiving an indication from an expert that the label should be reviewed; The method of claim 1 , comprising:

6. The method of claim 5 , wherein presenting the labels and the dataset to the expert further comprises presenting to the expert a confidence level of the labels associated with the dataset.

7. The step of determining whether the label is to be reviewed may further include: applying to the dataset a classifier model that has been trained to characterize the dataset as anomalous; and determining whether to review the label based on the output of the classifier model; The method of claim 1 , comprising:

8. The method further includes generating a report related to the labels using the machine learning model, the report comprising: A summary of findings related to the label, Diagnostics of the dataset related to the labels, or Next steps related to the dataset, The method of claim 1 , comprising one or more of:

9. Cluster analysis of the dataset, statistical minority oversampling of the dataset, or Comparison with expert-selected labels for the dataset, 10. The method of claim 1, further comprising training a second machine learning model based on one or more of:

10. generating a prediction associated with the machine based on the dataset and the labels, the prediction comprising: Predicting when machines will enter abnormal states, Predicting when a machine will enter an abnormal state, or Prediction of the type of abnormality that will cause the machine to enter an abnormal state The method of claim 1 , comprising at least one of:

11. 1. A system for detecting anomalies associated with a machine, the system comprising: a processor; When executed by the processor, the system: Recording machine-related data sets; A first machine learning model determines the labels associated with the dataset; Determine whether the label will be reviewed, If it is decided that the label will be reviewed, Subject the dataset and labels to review, and Update labels based on reviews, a memory for storing instructions for operation; A system including:

12. Recording the dataset further comprises: receiving a sampled data set associated with the machine; downsampling the sampled dataset to generate a downsampled dataset; and recording a downsampled data set associated with the machine; The system of claim 11 , comprising:

13. Determining whether a label will be reviewed further involves: applying a statistical anomaly detection analysis to the dataset and at least two other datasets related to the label; and characterizing the dataset as an outlier compared to at least two other datasets based on statistical anomaly detection analysis; The system of claim 11 , comprising:

14. 14. The system of claim 13, further comprising training a classifier model to characterize the dataset as an anomaly by comparing at least one statistical measure of at least one parameter of the dataset to typical statistical measures of at least one parameter of at least two other datasets.

15. Determining whether a label will be reviewed further involves: Presenting the labels and dataset to experts; and receiving an indication from an expert that the label should be reviewed; The system of claim 11 , comprising:

16. 16. The system of claim 15, wherein presenting the labels and the dataset to the expert further comprises presenting to the expert a confidence level of the labels associated with the dataset.

17. Determining whether a label will be reviewed further involves: applying to the dataset a classifier model that has been trained to characterize the dataset as anomalous; and determining whether the label is to be reviewed based on the output of the classifier model; The system of claim 11 , comprising:

18. Execution of the instructions further causes the system to generate a report associated with the labels by the machine learning model, the report comprising: A summary of findings related to the label, Diagnostics of the dataset related to the labels, or Next steps related to the dataset, The system of claim 11 , comprising one or more of:

19. Cluster analysis of the dataset, statistical minority oversampling of the dataset, or Comparison of the labels with labels selected for the dataset by experts, and further comprising training a second machine learning model based on one or more of: The system of claim 11.

20. Execution of the instructions further causes the system to generate a machine-related prediction based on the dataset and the label, the prediction comprising: Predicting when machines will enter abnormal states, Predicting when a machine will enter an abnormal state, or Prediction of the type of abnormality that will cause the machine to enter an abnormal state The system of claim 1 , comprising at least one of:

21. When executed by a processor, Recording machine-related data sets; determining labels associated with the dataset with a first machine learning model; Determine whether the label will be reviewed, and If it is decided that the label will be reviewed, Subject the dataset and labels to review, and Update labels based on reviews, A non-transitory computer-readable storage medium storing instructions for causing a processor to detect anomalies associated with a machine by detecting anomalies associated with the machine.

22. Recording the dataset further comprises: receiving a sampled data set associated with the machine; downsampling the sampled dataset to generate a downsampled dataset; and recording a downsampled data set associated with the machine; 22. The non-transitory computer-readable storage medium of claim 21, comprising:

23. Determining whether a label will be reviewed further involves: applying a statistical anomaly detection analysis to the dataset and at least two other datasets related to the label; and characterizing the dataset as an outlier compared to at least two other datasets associated with the label based on a statistical anomaly detection analysis; 22. The non-transitory computer-readable storage medium of claim 21, comprising:

24. further training a classifier model that characterizes the dataset as anomalous by comparing at least one statistical measure of the at least one parameter in the dataset with typical statistical measures of the at least one parameter in at least two other datasets; 24. The non-transitory computer-readable storage medium of claim 23, comprising:

25. The decision to review the label is further Presenting the labels and dataset to experts; and receiving an indication from an expert that the label should be reviewed; 22. The non-transitory computer-readable storage medium of claim 21, comprising:

26. 26. The non-transitory computer-readable medium of claim 25, wherein presenting the labels and the dataset to the expert further comprises presenting to the expert a confidence level of the labels associated with the data.

27. Determining whether a label will be reviewed further involves: applying to the dataset a classifier model that has been trained to characterize the dataset as anomalous; and determining whether the label is to be reviewed based on the output of the classifier model; 22. The non-transitory computer-readable storage medium of claim 21, comprising:

28. Execution of the instructions further configures the processor to generate a report related to the labels by the machine learning model, the report comprising: A summary of findings related to the label, Diagnostics of the dataset related to the labels, or Next steps related to the dataset, 22. The non-transitory computer-readable storage medium of claim 21, comprising one or more of:

29. Cluster analysis of the dataset, statistical minority oversampling of the dataset, or Comparison of the labels with labels selected for the dataset by experts, training a second machine learning model based on one or more of:

30. The non-transitory computer-readable storage medium of claim 28, further comprising:

30. Execution of the instructions further causes the processor to generate a machine-related prediction based on the dataset and the label, the prediction comprising: Predicting when machines will enter abnormal states, Predicting when a machine will enter an abnormal state, or Prediction of the type of abnormality that will cause the machine to enter an abnormal state 22. The non-transitory computer-readable storage medium of claim 21, comprising at least one of:

31. 1. A method for deploying an agent in an environment, comprising: For agents, At least one action, At least one instinct that motivates the agent to act in the environment, and at least one goal related to the task in the environment; and associating associating at least one environment rule with the environment; A method comprising:

32. further allocating to the agent an agent memory configured to store at least one event occurring in the environment; 32. The method of claim 31 , comprising:

33. 33. The method of claim 32, wherein the agent memory comprises a short-term agent memory that stores events that occur within a short-term time frame.

34. 34. The method of claim 33, wherein the agent memory further comprises a long-term agent memory that contains all events stored in the short-term agent memory.

35. 32. The method of claim 31, further comprising transmitting sensor data detected by at least one sensor in the environment to the agent at a point in time.

36. Furthermore, the agent, the future state of at least one entity in the environment, or the outcome of the action based on at least one parameter of the environment; to predict at least one of:

32. The method of claim 31 , comprising:

37. Further comprising configuring the agent to determine whether to perform a behavior based on at least one parameter of the environment, the determination comprising: a cluster analysis of at least one label related to at least one parameter of the environment; or a classifier model trained to determine a label associated with at least one parameter of the environment; 32. The method of claim 31 , wherein the method is performed based on at least one of:

38. At least one instinct is Observation instinct, predictive instinct, Communication instinct, Prioritization instinct, Learning instinct, or Instinct to act, 32. The method of claim 31 , comprising at least one of:

39. Furthermore, the agent, assigning a priority to each goal of the at least one goal; and Choose the actions you will take to achieve each goal, 32. The method of claim 31, comprising configuring the selection of an action based on a priority assigned to each goal.

40. 32. The method of claim 31, wherein the agent further comprises control logic that motivates the agent to perform behaviors associated with the at least one instinct and the at least one goal.

41. 1. A system for deploying agents in an environment, comprising: a processor; When executed by the processor, the system: For agents, At least one action, At least one instinct that motivates the agent to act in the environment, and at least one goal related to the task in the environment; and Associating with the environment at least one environmental rule that triggers the agent to perform at least one behavior; a memory for storing instructions; A system including:

42. 42. The system of claim 41, wherein executing the instructions further comprises causing the system to allocate, to the agent, an agent memory configured to store at least one event occurring in the environment.

43. 43. The system of claim 42, wherein the agent memory includes a short-term agent memory that stores events that occur within a short-term time frame.

44. 44. The system of claim 43, wherein the agent memory further comprises a long-term agent memory containing all events stored in the short-term agent memory.

45. 42. The system of claim 41, wherein executing the instructions further comprises causing the system to transmit sensor data detected by at least one sensor in the environment to the agent at a particular point in time.

46. Executing the instructions further causes the system to: the future state of at least one entity in the environment, or the outcome of the action based on at least one parameter of the environment; 42. The system of claim 41, further comprising configuring the agent to predict at least one of:

47. Executing the instructions further causes the system to: configuring the agent to determine whether to perform an action based on at least one parameter of the environment, the determination comprising: a cluster analysis of at least one label related to at least one parameter of the environment; or a classifier model trained to determine a label associated with at least one parameter of the environment; 42. The system of claim 41, wherein the system is based on at least one of:

48. At least one instinct is Observation instinct, predictive instinct, Communication instinct, Prioritization instinct, Learning instinct, or Instinct to act, 42. The system of claim 41, comprising at least one of:

49. Executing the instructions further causes the system to: assigning a priority to each goal of the at least one goal; and Choose the actions you will take to achieve each goal, configuring the agent to 42. The system of claim 41, wherein the selection of an action is based on a priority assigned to each goal.

50. 42. The system of claim 41, wherein the agent further comprises control logic that motivates the agent to perform behaviors associated with the at least one instinct and the at least one goal.

51. When executed by a processor, the processor: For agents, At least one action, At least one instinct that motivates the agent to act in the environment, and at least one goal associated with a task in the environment; Associate and Associating at least one environmental rule with the environment, the environmental rule triggering the agent to perform at least one behavior; By doing so, A non-transitory computer-readable storage medium storing instructions for deploying an agent within an environment.

52. 52. The non-transitory computer-readable storage medium of claim 51 , wherein executing the instructions further comprises causing the processor to allocate, to the agent, an agent memory configured to store at least one event that occurs in the environment.

53. 53. The non-transitory computer-readable storage medium of claim 52, wherein the agent memory comprises a short-term agent memory that stores events that occur within a short-term time frame.

54. 54. The non-transitory computer-readable storage medium of claim 53, wherein the agent memory further comprises a long-term agent memory that contains all events stored in the short-term agent memory.

55. 52. The non-transitory computer-readable storage medium of claim 51, wherein executing the instructions further includes causing the processor to transmit sensor data detected by at least one sensor in the environment to the agent at a point in time.

56. Executing the instructions further comprises causing the processor to: the future state of at least one entity in the environment, or The outcome of the behavior based on at least one parameter of the environment, 52. The non-transitory computer-readable medium of claim 51, further comprising configuring the agent to predict at least one of:

57. Executing the instructions further includes configuring the processor to determine whether to perform an action based on at least one parameter of the environment, the determination comprising: a cluster analysis of at least one label related to at least one parameter of the environment; or a classifier model trained to determine a label associated with at least one parameter of the environment; 52. The non-transitory computer-readable storage medium of claim 51, wherein the non-transitory computer-readable storage medium is configured to perform the non-transitory computer-readable storage medium according to at least one of the following:

58. At least one instinct is Observation instinct, predictive instinct, Communication instinct, Prioritization instinct, Learning instinct, or Instinct to act, 52. The non-transitory computer-readable medium of claim 51, comprising at least one of:

59. Executing the instructions further comprises causing the processor to: assigning a priority to each goal of the at least one goal; and Choose the actions you will take to achieve each goal, configuring the agent to 52. The non-transitory computer-readable medium of claim 51, wherein the selection of an action is based on a priority assigned to each goal.

60. 52. The non-transitory computer-readable storage medium of claim 51, wherein the agent further comprises control logic that motivates the agent to perform behaviors associated with the at least one instinct and the at least one goal.

61. 1. A method for displaying an analysis of machines in an industrial facility, comprising: Creating a digital twin of the machine; determining at least one property of the digital twin based on a simulation of the operation of the machine; and generating a representation of the industrial facility, the representation including a visualization of the digital twin and a visual indication of at least one property of the digital twin; A method comprising:

62. 62. The method of claim 61, wherein the digital twin is generated based on at least one sensor input received by at least one sensor associated with a machine in the industrial facility.

63. The step of generating a digital twin further includes: Determining the machine type of the machine; and retrieving, from a digital twin library, a stored representation of a digital twin corresponding to a machine type of the machine; 62. The method of claim 61, comprising:

64. The step of generating a digital twin further includes: Retrieving a stored representation of the machine's base digital twin from a digital twin library; determining at least one characteristic of the machine; and adjusting at least one characteristic of the digital twin based on at least one characteristic of the machine; 62. The method of claim 61, comprising:

65. 62. The method of claim 61, wherein the simulation of the operation of the machine is based on vibrations generated by at least one moving part of the machine and detected by a vibration sensor.

66. 62. The method of claim 61, wherein the representation of the industrial facility is generated by a graphics engine and the simulation of the machine operation is performed by a physics engine included in the graphics engine.

67. 62. The method of claim 61, wherein at least one property of the digital twin is based on a simulation of a second machine in the industrial facility.

68. 62. The method of claim 61 , wherein at least one property of the digital twin is based on a simulation of an operational interaction between a first component and a second component of the machine.

69. 62. The method of claim 61 , wherein generating the representation of the industrial facility includes rendering a three-dimensional digital environment corresponding to the industrial facility using a game engine, and wherein the visualization of the digital twin is based on the three-dimensional digital model of the machine rendered by the game engine.

70. Visualization of digital twins a visual indicator of the location of at least one property; a visual indicator showing a measurement of at least one property; a visual indication of vibration associated with at least one property; a text description of at least one property, a visual indicator indicating the classification of at least one property; Media recording of the corresponding properties of the machine, a label associated with at least one property; A visual representation of the operation of machinery in an industrial facility, or Visual representation of the simulation of the machine's operation, 62. The method of claim 61, comprising at least one of:

71. 1. A system for displaying an analysis of machines contained within an industrial facility, comprising: processor, and When executed by the processor, the system: Creating a digital twin of the machine, determining at least one property of the digital twin based on a simulation of the operation of the machine; and generating a representation of the industrial facility, the representation including a visualization of the digital twin and a visual indication of at least one property of the digital twin; a memory for storing instructions for executing the Including, the system.

72. 72. The system of claim 71, wherein the digital twin is generated based on at least one sensor input received by at least one sensor associated with a machine in the industrial facility.

73. Creating a digital twin also: Determining the machine type of the machine; and retrieving, from a machine library, a stored representation of the machine's digital twin corresponding to the machine type; 72. The system of claim 71, comprising:

74. Creating a digital twin also: Retrieving a stored representation of the machine's base digital twin from a digital twin library; determining at least one characteristic of the machine; and adjusting at least one characteristic of the digital twin based on at least one characteristic of the machine; 72. The system of claim 71, comprising:

75. 72. The system of claim 71, wherein the simulation of the operation of the machine is based on vibrations generated by at least one moving part of the machine and detected by a vibration sensor.

76. 72. The system of claim 71, wherein the representation of the industrial facility is generated by a graphics engine and the simulation of the operation of the machine is performed by a physics engine included in the graphics engine.

77. 72. The system of claim 71, wherein at least one property of the digital twin is based on a simulation of a second machine in the industrial facility.

78. 72. The system of claim 71, wherein at least one property of the digital twin is based on a simulation of an operational interaction between a first component and a second component of the machine.

79. 72. The system of claim 71, wherein the step of generating a representation of the industrial facility includes rendering a three-dimensional digital environment corresponding to the industrial facility using a game engine, and wherein the visualization of the digital twin is based on the three-dimensional digital model of the machine rendered by the game engine.

80. Visualization of digital twins a visual indicator of the location of at least one property; a visual indicator showing a measurement of at least one property; a visual indication of vibration associated with at least one property; a text description of at least one property, a visual indicator indicating the classification of at least one property; Media recording of the corresponding properties of the machine, a label associated with at least one property; A visual representation of the operation of machinery in an industrial facility, or Visual representation of the simulation of the machine's operation, 72. The system of claim 71, comprising at least one of:

81. When executed by a processor, the processor: Creating a digital twin of the machine, determining at least one property of the digital twin based on a simulation of the operation of the machine; and generating a display including the industrial facility, the display including a visualization of the digital twin and a visual indication of at least one property of the digital twin; a non-transitory computer-readable storage medium storing instructions for displaying an analysis of a machine contained within an industrial facility by

82. 82. The non-transitory computer-readable medium of claim 81, wherein the digital twin is generated based on at least one sensor input received by at least one sensor associated with a machine in the industrial facility.

83. Creating a digital twin also: Determining the machine type of the machine; and retrieving, from a digital twin library, a stored representation of a digital twin corresponding to a machine type of the machine; 82. The non-transitory computer-readable storage medium of claim 81, comprising:

84. Creating a digital twin also: Retrieving a stored representation of the machine's base digital twin from a digital twin library; determining at least one characteristic of the machine; and adjusting at least one characteristic of the digital twin based on at least one characteristic of the machine; 82. The non-transitory computer-readable storage medium of claim 81, comprising:

85. 82. The non-transitory computer-readable medium of claim 81, wherein the simulation of the operation of the machine is based on vibrations generated by at least one moving part of the machine and detected by a vibration sensor.

86. 82. The non-transitory computer-readable medium of claim 81, wherein the representation of the industrial facility is generated by a graphics engine and the simulation of the machine operation is performed by a physics engine included in the graphics engine.

87. 82. The non-transitory computer-readable medium of claim 81, wherein at least one property of the digital twin is based on a simulation of a second machine in the industrial facility.

88. 82. The non-transitory computer-readable storage medium of claim 81, wherein at least one property of the digital twin is based on a simulation of an operational interaction between a first component and a second component of the machine.

89. 82. The non-transitory computer-readable storage medium of claim 81, wherein generating the representation of the industrial facility includes rendering a three-dimensional digital environment corresponding to the industrial facility with a game engine, and wherein the visualization of the digital twin is based on the three-dimensional digital model of the machine rendered by the game engine.

90. Visualization of digital twins a visual indicator of the location of at least one property; a visual indicator showing a measurement of at least one property; a visual indication of vibration associated with at least one property; a text description of at least one property, a visual indicator indicating the classification of at least one property; Media recording of the corresponding properties of the machine, a label associated with at least one property; A visual representation of the operation of machinery in an industrial facility, or Visual representation of the simulation of the machine's operation, 82. The non-transitory computer-readable medium of claim 81, comprising at least one of:

91. 1. A method for generating content related to an Industrial Internet of Things (IIoT) environment, comprising: receiving prompts related to the IIoT environment, the GAIE being trained to generate content based on the prompts received as input; processing the prompt and generating a response by the GAIE; and outputting, by the GAIE, a response responsive to a prompt related to the IIoT environment; A method comprising:

92. 92. The method of claim 91, wherein the GAIE is configured to interface with the machine learning engine to receive the data, and receiving the prompt further comprises generating the prompt based on the received data.

93. The prompt processing is further GAIE sends a question related to the prompt to the machine learning engine; receiving answers to questions from the machine learning engine; and processing the answers in combination with the prompt to generate a response to the prompt; 92. The method of claim 91, comprising:

94. 92. The method of claim 91, wherein the prompt includes content of at least one content type, and processing the prompt further includes processing the prompt and the content to generate a response to the prompt.

95. At least one content type is natural language expressions generated by individuals, data from at least one natural language source; data received from at least one other device; data received from other components of the device running GAIE, at least one output of at least one other machine learning model; at least one image associated with at least one conversation partner; at least one audio recording associated with at least one conversation partner; At least one video recording associated with at least one conversation partner; or 95. The method of claim 94, comprising at least one synthetic data generated based on an extension of the training data set.

96. 92. The method of claim 91, wherein the GAIE is further configured to store an internal state, and processing the prompt further comprises processing the internal state of the GAIE with the prompt to generate a response to the prompt.

97. 92. The method of claim 91, further comprising the step of further training the GAIE based on the prompts and the acceptable outputs corresponding to the prompts, wherein further training the GAIE causes the GAIE to generate an output similar to the acceptable output for a prompt similar to the prompt.

98. 92. The method of claim 91, further comprising the step of generating, by the GAIE, a request for further training based on the prompt, wherein the further training causes the GAIE to generate, for a prompt similar to the prompt, an output similar to the acceptable output corresponding to the prompt.

99. 92. The method of claim 91, further comprising generating, by the GAIE, at least one new machine learning model, the at least one new machine learning model configured to process data to prove additional prompts received as input by the GAIE.

100. 92. The method of claim 91, wherein the prompts are received from at least one conversation partner, and the prompts received by the GAIE and the responses generated by the GAIE are part of a conversation with the at least one conversation partner.

101. 101. The method of claim 100, wherein processing the prompt further comprises processing the current prompt and at least one previous prompt in the conversation to generate a response to the current prompt.

102. 101. The method of claim 100, wherein the conversation includes at least one sub-conversation associated with at least one sub-conversation prompt and at least one sub-conversation response responsive to the at least one sub-conversation prompt.

103. 101. The method of claim 100, wherein at least one of a conversation partner or a GAIE is associated with at least one role in the conversation context.

104. 101. The method of claim 100, wherein the GAIE is associated with an avatar, and further comprising displaying, by the GAIE, the avatar with a facial expression corresponding to at least one of the prompt or a response to the prompt.

105. GAIE is Body language indicators relevant to the individual, gaze related to the individual, Attitudes related to individuals, the pitch of a voice associated with an individual, or the volume of the voice associated with the individual; 105. The method of claim 104, further configured to display an avatar based on at least one of:

106. 92. The method of claim 91, wherein the GAIE includes at least one generative pre-trained Transformer element configured as a language model, and further comprising generating, by the GAIE, the chat interface system based on a language associated with the language model.

107. 92. The method of claim 91, wherein at least one of the prompts or responses is associated with a workflow, and the GAIE is further configured to generate an output associated with a robotic process automation (RPA) automated process corresponding to the workflow.

108. The method of claim 107, wherein the GAIE is further configured to observe interactions between the conversation partner and at least one other individual or at least one other device, and responses to prompts generated by the GAIE are included in at least one RPA automated process corresponding to the workflow.

109. 92. The method of claim 91, wherein the prompt includes a request to generate at least a portion of a story, and the response generated by the GAIE includes at least a portion of the requested story.

110. 110. The method of claim 109, wherein the prompts include content to be incorporated into a story, and at least a portion of the story generated by the GAIE incorporates the content.

111. 110. The method of claim 109, wherein the prompts include comments about the story, and wherein at least some of the stories generated by the GAIE modify the story based on the comments.

112. 92. The method of claim 91, wherein the response includes a request to generate at least a portion of a variant of the first story, and the response generated by the GAIE includes at least a portion of a second story that is a variant of the first story.

113. 92. The method of claim 91, wherein the GAIE is trained to perform an operation related to an IIoT environment, the prompts include at least one input related to the IIoT environment, and the GAIE is further configured to perform an operation related to the IIoT environment based on the at least one input.

114. The processing performed by GAIE is at least one user behavior model; Group clustering and / or similarity, personality classification, Governance of inputs, Explanation of GAIE's knowledge base, Intelligent Agents, Voice assistant, Transaction Agent, Opportunity Miner, User interface, Hybrid content generators, or A set of selected data sources, 92. The method of claim 91, wherein the method is associated with at least one of:

115. 92. The method of claim 91, wherein the prompt is associated with at least one robotic process automation (RPA) workflow, and the GAIE response includes a summary of a course of action associated with the RPA workflow.

116. 92. The method of claim 91, wherein the GAIE is pre-trained in a context, the pre-training causing the response generated by the GAIE to be personalized based on the context.

117. By GAIE Why the response to the prompt is appropriate; A description of the internal state of the GAIE associated with the response, or an indication of at least one characteristic of the prompt that is relevant to the response; 92. The method of claim 91, further comprising outputting at least one of:

118. 92. The method of claim 91, further comprising identifying at least one piece of evidence regarding at least one fact included in the response, wherein the response includes a description of the at least one piece of evidence regarding the at least one fact included in the response.

119. 92. The method of claim 91, wherein the response is based on information received from at least one external source, the response including at least a portion of said information.

120. 92. The method of claim 91, wherein the GAIE is further configured to maintain context awareness throughout the chat interaction, and wherein responses are generated based on the prompts and the chat interaction.

121. The response is provided in the first chat interaction; a prompt included in a second chat interaction that precedes the first chat interaction; or a response to a prompt included in a second chat interaction that precedes the first chat interaction; 121. The method of claim 120, comprising at least one reference to:

122. 92. The method of claim 91, wherein the GAIE is further configured to detect at least one activity of the individual related to the prompt, and the response is generated based on the prompt and the at least one activity of the individual related to the prompt.

123. 92. The method of claim 91, wherein the GAIE is further configured to discover domain-specific knowledge in a domain associated with the prompt, and wherein the response is generated based on the prompt and the domain-specific knowledge in the domain associated with the prompt.

124. 92. The method of claim 91, wherein the GAIE is trained for use by and / or collaborative interaction with the digital twin engine, and wherein processing the prompt and generating a response includes generating an interaction by the GAIE with the digital twin engine to generate the response.

125. 125. The method of claim 124, wherein the digital twin engine is associated with at least one of a role or a user, and the response is generated by the GAIE based on the role or user to which the response to the prompt is delivered.

126. 125. The method of claim 124, wherein the GAIE is further configured to connect at least one real-time data source to the digital twin engine, and the response is based on real-time data received from the at least one real-time data source.

127. 125. The method of claim 124, wherein the GAIE is further configured to participate in conversational interactions with a digital twin associated with the digital twin engine.

128. 125. The method of claim 124, wherein the response includes a summary of the content, the summary being based on the granularity of the data associated with the entity associated with the digital twin engine.

129. 125. The method of claim 124, further comprising the step of GAIE training the digital twin engine, the training being based on a difference between a predicted reaction to a response by the digital twin engine and an actual reaction to a response by at least one individual associated with the digital twin engine, and the training reducing the difference between the predicted reaction to a response by the digital twin engine and an actual reaction to a response by at least one individual associated with the digital twin engine.

130. GAIE further states, A description of at least one failure prediction related to the IIoT environment, or Feedback related to failure prediction in IIoT environments, 92. The method of claim 91, wherein the method is configured to generate at least one of:

131. 92. The method of claim 91, wherein the IIoT environment includes at least one manufacturing process, and further comprising completing, by the GAIE, at least one instance of the at least one manufacturing process based on the prompt.

132. 92. The method of claim 91, wherein the IIoT environment includes at least one energy resource, and further comprising managing, with the GAIE, at least one energy-related process associated with the at least one energy resource.

133. 92. The method of claim 91, wherein the prompt is associated with at least one invention and the response includes at least one aspect of a patent related to the invention.

134. 92. The method of claim 91, wherein the prompt is based on data received from at least one real-world sensor and the response includes synthetic data for the training data set based on data received from the at least one real-world sensor.

135. 92. The method of claim 91, further comprising training, by the GAIE, at least one conversational agent based on the responses and reactions to the responses by the at least one individual.

136. 92. The method of claim 91, further comprising training, by the GAIE, at least one governance system based on at least one of the prompts or responses to the prompts.

137. 92. The method of claim 91, wherein the prompt is associated with at least one risk associated with the at least one process, and the response includes an analysis of the at least one risk associated with the at least one process.

138. 92. The method of claim 91, wherein the prompt includes at least one image depicting at least one scene, and the response includes a description of the at least one scene depicted in the at least one image.

139. 92. The method of claim 91, wherein the GAIE includes an artificial intelligence model that predicts the next token, and processing the prompt and generating a response includes iteratively generating each token of the response by the GAIE, and each token of the response is determined by the artificial intelligence model that predicts the next token based on at least one previous token of the response.

140. 92. The method of claim 91, wherein the GAIE is included in a generative artificial intelligence (AI) platform, and further comprising updating at least one component of the generative AI platform based on the response to the prompt.

141. The generative AI platform Task-independent next token prediction engine, a set of discipline-specific pre-training examples and / or prompts; Pre-training optimization module, Storage facility for specialized GAIE instances, Neural machine translation engine, closed problem set solving prediction engine, logic-based AI systems, image and / or video analysis engines; Expert Review and Approval Portal, training data generation facilities, Citation module, Interpretability engine, A new skills development system for data science, or Ongoing pre-training modules, 141. The method of claim 140, comprising at least one of:

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