ADJUSTING ALARMS BASED ON SLEEP ONSET LATENCY

    公开(公告)号:US20210345948A1

    公开(公告)日:2021-11-11

    申请号:US17382547

    申请日:2021-07-22

    Applicant: Apple Inc.

    Abstract: In some implementations, a mobile device can adjust an alarm siting based of the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

    Performing actions associated with task items that represent tasks to perform

    公开(公告)号:US11120372B2

    公开(公告)日:2021-09-14

    申请号:US16895944

    申请日:2020-06-08

    Applicant: Apple Inc.

    Abstract: Techniques for processing task items are provided. A task item is electronic data that represents a task to be performed, whether manually or automatically. A task item includes one or more details about its corresponding task, such as a description of the task and a location of the task. Specifically, techniques for generating task items, organizing task items, triggering notifications of task items, and consuming task items are described. In one approach, a task item is generated based on input from a user and context of the input. In another approach, different attributes of task items are used to organize the task items intelligently into multiple lists. In another approach, one or more criteria, such as location, are used to determine when to notify a user of a task. In another approach, actions other than generating notifications are enabled or automatically performed, actions such as emailing, calling, texting, and searching.

    ADJUSTING ALARMS BASED ON SLEEP ONSET LATENCY

    公开(公告)号:US20200345298A1

    公开(公告)日:2020-11-05

    申请号:US16934983

    申请日:2020-07-21

    Applicant: Apple Inc.

    Abstract: In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

    Exemplar-based natural language processing

    公开(公告)号:US10417344B2

    公开(公告)日:2019-09-17

    申请号:US16194069

    申请日:2018-11-16

    Applicant: Apple Inc.

    Abstract: Systems and processes for exemplar-based natural language processing are provided. In one example process, a first text phrase can be received. It can be determined whether editing the first text phrase to match a second text phrase requires one or more of inserting, deleting, and substituting a word of the first text phrase. In response to determining that editing the first text phrase to match the second text phrase requires one or more of inserting, deleting, and substituting a word of the first text phrase, one or more of an insertion cost, a deletion cost, and a substitution cost can be determined. A semantic edit distance between the first text phrase and the second text phrase in a semantic space can be determined based on one or more of the insertion cost, the deletion cost, and the substitution cost.

    EXEMPLAR-BASED NATURAL LANGUAGE PROCESSING
    38.
    发明申请

    公开(公告)号:US20190102381A1

    公开(公告)日:2019-04-04

    申请号:US16194069

    申请日:2018-11-16

    Applicant: Apple Inc.

    CPC classification number: G06F17/2785 G06F17/2211

    Abstract: Systems and processes for exemplar-based natural language processing are provided. In one example process, a first text phrase can be received. It can be determined whether editing the first text phrase to match a second text phrase requires one or more of inserting, deleting, and substituting a word of the first text phrase. In response to determining that editing the first text phrase to match the second text phrase requires one or more of inserting, deleting, and substituting a word of the first text phrase, one or more of an insertion cost, a deletion cost, and a substitution cost can be determined. A semantic edit distance between the first text phrase and the second text phrase in a semantic space can be determined based on one or more of the insertion cost, the deletion cost, and the substitution cost.

    Training an at least partial voice command system

    公开(公告)号:US09922642B2

    公开(公告)日:2018-03-20

    申请号:US14213878

    申请日:2014-03-14

    Applicant: Apple Inc.

    CPC classification number: G10L15/063 G10L15/22 G10L2015/223 G10L2015/227

    Abstract: An electronic device with one or more processors and memory includes a procedure for training a digital assistant. In some embodiments, the device detects an impasse in a dialog between the digital assistant and a user including a speech input. During a learning session, the device utilizes a subsequent clarification input from the user to adjust intent inference or task execution associated with the speech input to produce a satisfactory response. In some embodiments, the device identifies a pattern of success or failure associated with an aspect previously used to complete a task and generates a hypothesis regarding a parameter used in speech recognition, intent inference or task execution as a cause for the pattern. Then, the device tests the hypothesis by altering the parameter for a subsequent completion of the task and adopts or rejects the hypothesis based on feedback information collected from the subsequent completion.

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