1. An ethylene-vinyl alcohol copolymer, represented by the formula (1):
3
wherein n represents a number in the range of 15 to 15,000.
2. An ethylene-vinyl alcohol copolymer according to claim 1, wherein said copolymer is derived by hydroxylating a cis-1,4-polybutadiene.
3. An ethylene-vinyl alcohol copolymer according to claim 1, wherein said copolymer is derived by hydroxylating a trans-1,4-polybutadiene.
4. A method of producing an ethylene-vinyl alcohol copolymer, wherein an 1,4-polybutadiene is hydroborated in non-gel state in a solvent, followed by oxidation to be hydroxylated so as to derive an ethylene-vinyl alcohol copolymer represented by the formula (1):
4
wherein n represents a number in the range of 15 to 15,000.
The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.
1. A computer-implemented method, comprising:
capturing a media content item by a recording device;
receiving location data indicating one or more users of nearby devices within a particular distance of the recording device when the media content item is captured; and
tagging the media content item with at least a portion of the location data.
2. The computer-implemented method as recited in claim 1, wherein the tagging further comprises:
inferring a presence of at least one of the one or more users in the media content item based on the location data; and
tagging the media content item with an identity of the at least one user.
3. The computer-implemented method as recited in claim 1, further comprising generating facial recognition data by performing facial recognition on the media content item to identify one or more users in the media content item.
4. The computer-implemented method as recited in claim 3, wherein the tagging further comprises:
inferring a presence of at least one of the one or more users in the media content item based on the location data and the facial recognition data; and
tagging the media content item with an identity of the at least one user.
5. The computer-implemented method as recited in claim 1, obtaining calendar data from a calendar associated with a user of the recording device.
6. The computer-implemented method as recited in claim 5, wherein the tagging further comprises:
inferring a presence of at least one of the one or more users in the media content item based on the location data and the calendar data; and
tagging the media content item with an identity of the at least one user.
7. The computer-implemented method as recited in claim 1, wherein the media content item comprises an image or a video.
8. The computer-implemented method as recited in claim 1, wherein the location data is received from a web service.
9. The computer-implemented method as recited in claim 8, wherein the web service maintains current geographical locations of the nearby devices.
10. A computer storage device comprising instructions stored thereon that, responsive to execution by a processor on a computing device, perform operations comprising:
performing facial recognition on facial data corresponding to a media content item captured by the computing device;
obtaining location data about one or more users of nearby devices;
comparing the facial data to the location data about the one or more users of nearby devices;
inferring presence of the one or more users in the media content item based at least on the location data and the facial data; and
tagging the data with an identity of the one or more users in the media content item.
11. The computer storage device as recited in claim 10, wherein the location data is obtained from a web service.
12. The computer storage device as recited in claim 11, wherein the web service maintains a current geographical location of the one or more users of nearby devices.
13. The computer storage device as recited in claim 10, wherein the media content item comprises one of an image or a video.
14. A mobile device comprising:
at least a memory and a processor to implement:
an acquisition component, the acquisition component configured to acquire a first type of information and a second type of information; and
a tag component configured to utilize the first type of information and the second type of information to tag data captured by the mobile device to facilitate at least one of subsequent location, filtration, or organization of the data.
15. The mobile device of claim 14, wherein the data corresponds to at least one of captured images, audio, or video.
16. The mobile device of claim 1, wherein the memory and the processor are further configured to implement an inference component, the inference component configured to infer one or more tags as a function of the acquired first type of information and the acquired second type of information.
17. The mobile device of claim 14, wherein the first type of information comprises location data indicating one or more users of mobile devices that are within a particular distance of the mobile device when the data is captured.
18. The mobile device of claim 17, wherein the location data is acquired from a web service.
19. The mobile device of claim 17, wherein the second type of information comprises facial recognition data acquired by performing facial recognition on the data captured by the mobile device.
20. The mobile device of claim 17, wherein the second type of information comprises calendar data acquired from a calendar of a user of the mobile device.