Context-based smartphone sensor logic
Methods employ sensors in portable devices (e.g., smartphones) both to sense content information (e.g., audio and imagery) and context information. Device processing is desirably dependent on both. For example, some embodiments activate certain processor intensive operations (e.g., content recognition) based on classification of sensed content and context. The context can control the location where information produced from such operations is stored, or control an alert signal indicating, e.g., that sensed speech is being transcribed. Some arrangements post sensor data collected by one device to a cloud repository, for access and processing by other devices. Multiple devices can collaborate in collecting and processing data, to exploit advantages each may have (e.g., in location, processing ability, social network resources, etc.). A great many other features and arrangements are also detailed.
1. A method performed by a hardware system configured by software instructions, the method comprising the acts:
receiving first audio and/or visual information, and applying a classification procedure to the received first audio and/or visual information to identify its type from among plural possible types;
determining a first scenario type, based at least in part on one or more of time of day, day of week, location, calendar data, clock alarm status, motion sensor data, orientation sensor data, and information from a social networking service;
based on the identified type of the received first audio and/or visual information, and based on the determined first scenario type, selecting a first group of one or more recognition technologies from a set of available recognition technologies, and applying the selected first group of recognition technologies to the received first audio and/or visual information;
receiving second audio and/or visual information, and applying a classification procedure to the received second audio and/or visual information to identify its type from among plural possible types;
determining a second scenario type, based at least in part on one or more of time of day, day of week, location, calendar data, clock alarm status, motion sensor data, orientation sensor data, and information from a social networking service;
based on the identified type of the received second audio and/or visual information, and based on the determined second scenario type, selecting a second group of one or more recognition technologies from a set of available recognition technologies, and applying the selected second group of recognition technologies to the received second audio and/or visual information;
receiving third audio and/or visual information, and applying a classification procedure to the received third audio and/or visual information to identify its type from among plural possible types;
determining a third scenario type, based at least in part on one or more of time of day, day of week, location, calendar data, clock alarm status, motion sensor data, orientation sensor data, and information from a social networking service; and
based on the identified type of the received third audio and/or visual information, and based on the determined third scenario type, selecting a third group of one or more recognition technologies from a set of available recognition technologies, and applying the selected third group of recognition technologies to the received third audio and/or visual information;
wherein:
the selected first, second and third groups of one or more recognition technologies are different;
at least one of the selected groups of one or more recognition technologies includes two or more recognition technologies;
the first and second information are classified as the same type, and yet said first and second selected groups of recognition technologies are different, due to differences in the determined first and second scenario types; and
one of said first or second selected groups of recognition technologies includes a watermark-, fingerprint-, barcode-based, or optical character-recognition technology that is not included in the other of said first or second selected groups of recognition technologies.
2. The method of claim 1 in which the received first, second and third audio and/or visual information all comprises audio information.
3. The method of claim 1 in which the received first, second and third audio and/or visual information all comprises visual information.
4. The method of claim 1 in which at least one of the first, second and third scenario types is based on time of day.
5. The method of claim 1 in which at least one of the first, second and third scenario types is based on day of week.
6. The method of claim 1 in which at least one of the first, second and third scenario types is based on location.
7. The method of claim 1 in which at least one of the first, second and third scenario types is based on calendar data.
8. The method of claim 1 in which at least one of the first, second and third scenario types is based on clock alarm status.
9. The method of claim 1 in which at least one of the first, second and third scenario types is based on motion sensor data.
10. The method of claim 1 in which at least one of the first, second and third scenario types is based on orientation sensor data.
11. The method of claim 1 in which at least one of the first, second and third scenario types is based on information from a social networking service.
12. The method of claim 1 in which one of the first, second and third groups of one or more recognition technologies includes watermark decoding technology, and another of said first, second and third groups of one or more recognition technologies does not include watermark decoding technology.
13. The method of claim 1 in which one of said first, second and third groups of one or more recognition technologies includes fingerprinting technology, and another of said first, second and third groups of recognition technologies does not include fingerprinting technology.
14. The method of claim 1 in which one of said first, second and third groups of one or more recognition technologies includes barcode decoding technology, and another of said first, second and third groups of one or more recognition technologies does not include barcode decoding technology.
15. The method of claim 1 in which one of said first, second and third groups of one or more recognition technologies includes optical character recognition technology, and another of said first, second and third groups of one or more recognition technologies does not include optical character recognition technology.
16. The method of claim 1 that further includes:
receiving fourth audio and/or visual information, and applying a classification procedure to the received fourth audio and/or visual information to identify its type; determining a fourth scenario type, based at least in part on one or more of time of day, day of week, location, calendar data, clock alarm status, motion sensor data, orientation sensor data, and information from a social networking service; and
based on the identified type of the received fourth audio and/or visual information, and based on the determined fourth scenario type, selecting a fourth group of one or more recognition technologies, and applying the selected fourth group of recognition technologies to the received fourth audio and/or visual information;
wherein the selected first, second, third and fourth groups of one or more recognition technologies are different;
two of the received first, second, third or fourth audio and/or visual information comprise audio information; and
two of the received first, second, third or fourth audio and/or visual information comprise visual information.