Semantic sensing system
A semantic sensing system includes a processor, a memory, a plurality of wireless communication enabled devices and at least one sensing element, the memory storing a plurality of mapped endpoints wherein the processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first endpoint supervisor in relation with the affirmative and non-affirmative determinations.
1. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type,
wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor;
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity; and
wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.
2. The semantic sensing system of claim 1 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first endpoint supervisor.
3. The semantic sensing system of claim 1 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.
4. The semantic sensing system of claim 1 , wherein the system infers a counter-measure and applies the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
5. The semantic sensing system of claim 1 , wherein the system infers an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one among the at least one non-affirmative semantic or the at least one affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
6. The semantic sensing system of claim 5 , wherein the system determines that the entropy between the at least one affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor is within a likeable interval.
7. The semantic sensing system of claim 6 , wherein the likeable interval is associated with a semantic time.
8. The semantic sensing system of claim 6 , wherein the likeable interval is associated with an affirmative semantic.
9. The semantic sensing system of claim 1 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on web content parsing.
10. The semantic sensing system of claim 1 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on an operating manual parsing.
11. The semantic sensing system of claim 1 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first-third activity.
12. The semantic sensing system of claim 11 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic comprises an activity semantic.
13. The semantic sensing system of claim 11 , wherein the second inferred semantic comprises a fourth activity and a determination that the second inferred semantic is highly entropic with the at least one non-affirmative semantic is based on a high entropy between the third activity and the fourth activity.
14. The semantic sensing system of claim 1 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.
15. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type,
wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor;
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity; and
wherein the second inferred semantic comprises a second activity and a determination that the second inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.
16. A semantic sensing system of claim 15 , wherein:
the memory further storing the at least one affirmative semantic in association with an object of the first object type;
wherein the system generates semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at the third time.
17. The semantic sensing system of claim 16 , wherein the system generates semantic augmentation based on a determination that the second inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic.
18. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
a wireless device comprising at least one wireless transceiver, the wireless device being comprised by a first object;
the memory further storing a plurality of interests associated with the first object published via the wireless transceiver by a semantic flux associated with the wireless device;
the memory further storing a plurality of capabilities associated with at least one sensor at a first endpoint; and
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to match the plurality of interests with the plurality of capabilities at the first endpoint based on semantic matching; wherein the system generates semantic augmentation based on a determination that the plurality of interests and capabilities are affirmatively matching at the first endpoint at a first time and that the plurality of interests and capabilities are non-affirmatively matching at the first endpoint at a second time and further, wherein the system generates semantic augmentation based on the determination that the plurality of interests are affirmatively matching the capabilities at the first endpoint at a third time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity.
19. The semantic sensing system of claim 18 , wherein the system generates semantic augmentation based on the determination that the plurality of interests are non-affirmatively matching the capabilities at the first endpoint at a fourth time.
20. The semantic sensing system of claim 18 , wherein the at least one among the first time and second time is a semantic time.
21. The semantic sensing system of claim 18 , wherein the interests are published via the wireless transceiver in association with a first semantic flux associated with the wireless device.
22. The semantic sensing system of claim 21 , wherein the first semantic flux publishing is controlled by an operator of the first semantic flux.
23. The semantic sensing system of claim 18 , wherein the capabilities at the first endpoint are published based on a configuration by the first endpoint supervisor.
24. The semantic sensing system of claim 23 , wherein the capabilities at the first endpoint are published in association with a semantic flux.
25. The semantic sensing system of claim 24 , wherein the interests published in association with the semantic flux are being discovered from an operating manual.
26. The semantic sensing system of claim 25 , wherein the interests published in association with the semantic flux are being discovered from an image.
27. The semantic sensing system of claim 25 , wherein the interests published in association with the semantic flux are being discovered from provider content.
28. The semantic sensing system of claim 24 , wherein the capabilities at the first endpoint are published in association with at least two semantic fluxes, wherein each semantic flux among the at least two semantic fluxes is associated with a semantic group of sensors.
29. The semantic sensing system of claim 28 , wherein the system associates a first semantic group of sensors with a first semantic flux among the at least two semantic fluxes based on an expertise factor, wherein the expertise factor is determined based on content associated with an operator of the first semantic flux.
30. The semantic sensing system of claim 28 , wherein each of the at least two semantic fluxes are operated by a distinct agent operator, each distinct agent operator having a distinct configured identity.
31. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor;
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity; and
wherein the system infers a counter-measure and applies the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
32. The semantic sensing system of claim 31 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first endpoint supervisor.
33. The semantic sensing system of claim 31 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.
34. The semantic sensing system of claim 31 , wherein the system infers a counter-measure and applies the counter-measure to reduce the high entropy between the at least one non-affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor.
35. The semantic sensing system of claim 31 , wherein the system infers an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor.
36. The semantic sensing system of claim 35 , wherein the system determines that the entropy between the at least one affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor is within a likeable interval.
37. The semantic sensing system of claim 36 , wherein the likeable interval is associated with a semantic time.
38. The semantic sensing system of claim 36 , wherein the likeable interval is associated with an affirmative semantic.
39. The semantic sensing system of claim 31 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on web content parsing.
40. The semantic sensing system of claim 31 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on an operating manual parsing.
41. The semantic sensing system of claim 31 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity.
42. The semantic sensing system of claim 41 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic comprises an activity semantic.
43. The semantic sensing system of claim 41 , wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.
44. The semantic sensing system of claim 31 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.
45. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor;
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity; and,
wherein the system infers a counter-measure and applies the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
46. A semantic sensing system of claim 45 , wherein:
the memory further storing the at least one affirmative semantic in association with an object of the first object type;
wherein the system generates semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at the third time.
47. The semantic sensing system of claim 46 , wherein the system generates semantic augmentation based on a determination that the second inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic.
48. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
a wireless device comprising at least one wireless transceiver, the wireless device being comprised by a first object;
the memory further storing a plurality of interests associated with the first object published via the wireless transceiver by a semantic flux associated with the wireless device;
the memory further storing a plurality of capabilities associated with at least one sensor at a first endpoint,
wherein the capabilities at the first endpoint are published in association with at least two semantic fluxes,
wherein each semantic flux among the at least two semantic fluxes is associated with a semantic group of sensors; and
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to match the plurality of interests with the plurality of capabilities at the first endpoint based on semantic matching; wherein the system generates semantic augmentation based on a determination that the plurality of interests and capabilities are affirmatively matching at the first endpoint at a first time and that the plurality of interests and capabilities are non-affirmatively matching at the first endpoint at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity.
49. The semantic sensing system of claim 48 , wherein the system generates semantic augmentation based on the determination that the plurality of interests are affirmatively matching the capabilities at the first endpoint at a third time.
50. The semantic sensing system of claim 48 , wherein the at least one among the first time and second time is a semantic time.
51. The semantic sensing system of claim 48 , wherein the interests are published via the wireless transceiver in association with a first semantic flux associated with the wireless device.
52. The semantic sensing system of claim 51 , wherein the first semantic flux publishing is controlled by an operator of the first semantic flux.
53. The semantic sensing system of claim 48 , wherein the capabilities at the first endpoint are published based on a configuration by the first endpoint supervisor.
54. The semantic sensing system of claim 53 , wherein the capabilities at the first endpoint are published in association with a semantic flux.
55. The semantic sensing system of claim 54 , wherein the interests published in association with the semantic flux are being discovered from an operating manual.
56. The semantic sensing system of claim 55 , wherein the interests published in association with the semantic flux are being discovered from an image.
57. The semantic sensing system of claim 55 , wherein the interests published in association with the semantic flux are being discovered from provider content.
58. The semantic sensing system of claim 54 , wherein the capabilities at a second endpoint are published in association with the at least two semantic fluxes.
59. The semantic sensing system of claim 58 , wherein the system associates a first semantic group of sensors with a first semantic flux among the at least two semantic fluxes based on an expertise factor, wherein the expertise factor is determined based on content associated with an he operator of the first semantic flux.
60. The semantic sensing system of claim 58 , wherein each of the at least two semantic fluxes are operated by a distinct agent operator, each distinct agent operator having a distinct configured identity.
61. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor;
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity; and
wherein the system infers an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one among the at least one affirmative semantic or the at least non-affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
62. The semantic sensing system of claim 61 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first endpoint supervisor.
63. The semantic sensing system of claim 61 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.
64. The semantic sensing system of claim 61 , wherein the system infers a counter-measure and applies the counter-measure to reduce the high entropy between the at least one non-affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor.
65. The semantic sensing system of claim 61 , wherein the system infers an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor.
66. The semantic sensing system of claim 65 , wherein the system determines that the entropy between the at least one affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor is within a likeable interval.
67. The semantic sensing system of claim 66 , wherein the likeable interval is associated with a semantic time.
68. The semantic sensing system of claim 66 , wherein the likeable interval is associated with an affirmative semantic.
69. The semantic sensing system of claim 61 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on web content parsing.
70. The semantic sensing system of claim 61 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on an operating manual parsing.
71. The semantic sensing system of claim 61 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity.
72. The semantic sensing system of claim 71 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic comprises an activity semantic.
73. The semantic sensing system of claim 71 , wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.
74. The semantic sensing system of claim 61 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.
75. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor;
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity; and
wherein the system infers an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one among the at least one affirmative semantic or the at least non-affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
76. A semantic sensing system of claim 75 , wherein:
the memory further storing the at least one affirmative semantic in association with an object of the first object type;
wherein the system generates semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at a he third time.
77. The semantic sensing system of claim 76 , wherein the system generates semantic augmentation based on a determination that the second inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic.
78. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor; and
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is affirmative by semantically matching the second inferred semantic with the at least one affirmative semantic at a second time and further, wherein the system generates semantic augmentation based on a determination that a third inferred semantic is non-affirmative by semantically matching the third inferred semantic with the at least one non-affirmative semantic at a third time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity.
79. The semantic sensing system of claim 78 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is configured by the first endpoint supervisor.
80. The semantic sensing system of claim 78 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is associated with a semantic time.
81. The semantic sensing system of claim 78 , wherein the system infers a counter-measure and applies the counter-measure to reduce the high entropy between the at least one among the at least one affirmative semantic or the at least one non-affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
82. The semantic sensing system of claim 78 , wherein the system infers an affirmative measure and applies the affirmative measure to cause an affirmative entropy between the at least one among the at least one non-affirmative semantic or the at least one affirmative semantic in rapport with subsequent inferred semantics based on the inputs from the at least one sensor.
83. The semantic sensing system of claim 82 , wherein the system determines that the entropy between the at least one affirmative semantic and subsequent inferred semantics based on the inputs from the at least one sensor is within a likeable interval.
84. The semantic sensing system of claim 83 , wherein the likeable interval is associated with a semantic time.
85. The semantic sensing system of claim 83 , wherein the likeable interval is associated with an affirmative semantic.
86. The semantic sensing system of claim 78 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on web content parsing.
87. The semantic sensing system of claim 78 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is determined based on an operating manual parsing.
88. The semantic sensing system of claim 78 , wherein at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of a first activity.
89. The semantic sensing system of claim 88 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic comprises an activity semantic.
90. The semantic sensing system of claim 88 , wherein the first inferred semantic comprises a second activity and the determination that the first inferred semantic is highly entropic with the at least one affirmative semantic is based on a high entropy between the first activity and the second activity.
91. The semantic sensing system of claim 78 , wherein the at least one among the at least one affirmative semantic and the at least one non-affirmative semantic is indicative of an intrinsic orientation.
92. A semantic sensing system, comprising:
a memory storing a plurality of endpoints associated with physical locations;
the memory further storing a first identity of an assigned first supervisor of a first endpoint among the plurality of endpoints;
at least one sensor;
the memory further storing at least one affirmative semantic and at least one non-affirmative semantic associated with an object type;
the memory further storing the at least one affirmative semantic in association with an object of an object type;
at least one processor and a computer program operable by the at least one processor to cause the at least one processor to detect a first object of a first object type at the first endpoint from among the plurality of endpoints based on one or more inputs from the at least one sensor;
the computer program further being configured to cause the at least one processor to infer a first semantic and a second semantic at the first endpoint based on the one or more inputs from the at least one sensor; and
wherein the system generates semantic augmentation based on a determination that the first inferred semantic is affirmative by semantically matching the first inferred semantic with the at least one affirmative semantic at a first time and further based on a determination that the second inferred semantic is non-affirmative by semantically matching the second inferred semantic with the at least one non-affirmative semantic at a second time and further, wherein the system generates semantic augmentation based on a determination that a third inferred semantic is affirmative by semantically matching the third inferred semantic with the at least one affirmative semantic at a third time, wherein the semantic augmentation is directed to the assigned first supervisor based on the first identity.
93. A semantic sensing system of claim 92 , wherein:
the memory further storing the at least one non-affirmative semantic in association with an object of the first object type;
wherein the system generates semantic augmentation based on a determination that a fourth inferred semantic is non-affirmative by semantically matching the fourth inferred semantic with the at least one non-affirmative semantic at the third time.
94. The semantic sensing system of claim 93 , wherein the system generates semantic augmentation based on a determination that the second inferred semantic is non-affirmative by having a high entropy with respect to the at least one affirmative semantic.