Abstract: An electronic device and method for information extraction and user-oriented actions based on audio conversation are provided. The electronic device receives an audio signal that corresponds to a conversation associated with a first user and a second user. The electronic device extracts text information from the received audio signal based on at least one extraction criteria. The electronic device applies a machine learning model on the extracted text information to identify at least one type of information of the extracted text information. The electronic device determines a set of applications associated with the electronic device based on the identified at least one type of information. The electronic device selects a first application from the determined set of applications based on at least one selection criteria, and controls execution of the selected first application based on the text information.

Abstract: An audio source such as a display device configured to present AV content can present the video and send the audio in different languages to the respective devices of different listeners. For example, a device/TV/source can send audio in different languages to connected headphones/smartglasses with speakers/devices/sink. Furthermore, machine learning may be employed both to recognize listeners and correlate them to likely languages and to mimic voices in the played-back audio. Or, the source AV display device may send language in only the selected language of the display device to each listener device, with each receiving listener device converting the audio to the preferred language of the respective listener on the fly.

Abstract: A method of providing a remote-control interface on a smart device for an electronic device by operating an app, developed using machine learning (ML) comprising supervised learning, on the smart device to: use a camera in the smart device to capture an image of the electronic device, with minimal requirements on image quality; operate on the captured image to identify the electronic device in terms of make and model; select from a database of remote-control user interfaces a remote-control user interface corresponding to the identified make and model of the electronic device; and present the selected remote-control user interface on the smart device to a user. In some cases, the captured image is tagged with at least one of date, time, and global location information, allowing further identification in terms of at least one of version ID and region of sale.

Abstract: A personal voice model is created using a person's voice, essentially, voice cloning. When user wants to send a message to another person from his mobile phone or similar device, the user types the message and it is converted to a speech message using the voice model created. The speech message is delivered either to a voicemail or another medium which accepts a voice message. The text can be converted to a different language as well and send a voice message in a different language.

Abstract: An assembly includes a head mount such as smart glasses wearable on a head of a user. An imager is on the head mount and is configured to generate images of objects. A processor accesses the images responsive to a query and presents images of objects on a display of the head mount to assist the user in identifying, for example, where a lost object was last seen.

Abstract: An assembly includes a display configured to present video, a first head mount such as smart glasses wearable on a head of a first user, and a second head mount wearable on a head of a second user. A processor is configured to send closed captioning (CC) in a first language to the first head mount for presentation thereon while the first user is viewing the video on the display, and to send the CC in a second language to the second head mount for presentation thereon while the second user is viewing the video on the display.

Abstract: A personalized HRTF is generated from a generic HRTF by having a person don headphones, then select a generic HRTF model from a list. Test signals emulating sounds from a source at a specific virtual speaker location are played on the headphones, and the person indicates where he thinks the emulated sounds are coming from. Any difference between the virtual speaker location and the “heard” location is used to adjust the parameters (amplitude, phase, and frequency response) of the generic HRTF to render a HRTF personalized to the listening person.

Abstract: An apparatus includes a processor configured with instructions to identify a destination of a user, access a friends list of the user, and identify a first friend in the friends list as a carpool candidate. The processor outputs on a display an indication that the first friend is a carpool candidate.

Abstract: An assembly includes a head mount such as smart glasses wearable on a head of a user. An imager is on the head mount and is configured to generate images of objects. A processor accesses the images responsive to a query and presents images of objects on a display of the head mount to assist the user in identifying, for example, where a lost object was last seen.

Abstract: A vehicle includes a license plate frame. A processor is configured with instructions executable to present changing messages on the license plate frame. The processor may receive images of other vehicles and send a signal to a server pertaining the number of views of a message based on analysis of the images.

Abstract: A personalized room related transfer function (RRTF) for sound bars and speakers within a specific room is generated without the need for photographs of the listener's head or microphones in a headphone apparatus. A smart device aids the listener in creating his own RRTF by enabling the listener to select a general RRTF model based on the specific sound bar setup (e.g., sound bar; sound bar+subwoofer; sound bar+rear speakers+height; etc.) from a set of general RRTFs downloaded locally or from the cloud. A test signal is played from a specific driver of the sound bar, and the listener identifies a location at which he would like the perceived sound to originate. If the driver location signal and desired location are not the same, the app adjusts parameters and replays the test signal until the driver signal and the desired location are the same.

Abstract: An audio source such as a display device configured to present AV content can present the video and send the audio in different languages to the respective devices of different listeners. For example, a device/TV/source can send audio in different languages to connected headphones/smartglasses with speakers/devices/sink. Furthermore, machine learning may be employed both to recognize listeners and correlate them to likely languages and to mimic voices in the played-back audio. Or, the source AV display device may send language in only the selected language of the display device to each listener device, with each receiving listener device converting the audio to the preferred language of the respective listener on the fly.

Abstract: A personal voice model is created using a person's voice, essentially, voice cloning. When user wants to send a message to another person from his mobile phone or similar device, the user types the message and it is converted to a speech message using the voice model created. The speech message is delivered either to a voicemail or another medium which accepts a voice message. The text can be converted to a different language as well and send a voice message in a different language.

Abstract: A personalized room related transfer function (RRTF) for sound bars and speakers within a specific room is generated without the need for photographs of the listener's head or microphones in a headphone apparatus. A smart device aids the listener in creating his own RRTF by enabling the listener to select a general RRTF model based on the specific sound bar setup (e.g., sound bar; sound bar+subwoofer; sound bar+rear speakers+height; etc.) from a set of general RRTFs downloaded locally or from the cloud. A test signal is played from a specific driver of the sound bar, and the listener identifies a location at which he would like the perceived sound to originate. If the driver location signal and desired location are not the same, the app adjusts parameters and replays the test signal until the driver signal and the desired location are the same.

Abstract: A personalized HRTF is generated from a generic HRTF by having a person don headphones, then select a generic HRTF model from a list. Test signals emulating sounds from a source at a specific virtual speaker location are played on the headphones, and the person indicates where he thinks the emulated sounds are coming from. Any difference between the virtual speaker location and the “heard” location is used to adjust the parameters (amplitude, phase, and frequency response) of the generic HRTF to render a HRTF personalized to the listening person.

Abstract: Each of plural devices includes a laser emitter and a camera for detecting reflections of laser light emitted by the device, so that plural of the devices can generate their own depth maps showing images within the field of view of their cameras. The resolution of each depth map may be improved by accessing a data store of prior images of objects and combining the prior images using super-resolution with current images of the depth maps.

Abstract: A head mounted display (HMD) device that includes a plurality of sensors, an actuator, a plurality of protective mechanisms, and a processor configured to identify a situational state of a user of the HMD device within a proximity of the user, based on sensor input received from the plurality of sensors. A risk-level of a potential injury to the user from a component of the HMD device is determined based on the identified situational state and the sensor input. A protective mechanism is selected from the plurality of protective mechanisms based on the identified situational state and the determined risk-level. The actuator is controlled to deploy the selected protective mechanism to mitigate injury to the user from the component of the HMD device, based on the identified situational state and the determined risk-level.

Abstract: Implementations generally relate to automated equalizer adjustments based on audio characteristics. In some implementations, a method includes detecting music that is being currently played on an audio device. The method further includes adjusting one or more equalizer settings of an equalizer device based at least in part on a music genre associated with the music. The method further includes outputting the music based at least in part on the adjusting of the one or more equalizer settings of the equalizer device.

Abstract: Machine learning is applied to both 2D images from an infrared imager imaging laser reflections from an object and to the 3D depth map of the object that is generated using the 2D images and time of flight (TOF) information. In this way, the 3D depth map accuracy can be improved without increasing laser power or using high resolution imagers.

Abstract: Implementations generally relate to automated equalizer adjustments based on audio characteristics. In some implementations, a method includes detecting music that is being currently played on an audio device. The method further includes adjusting one or more equalizer settings of an equalizer device based at least in part on a music genre associated with the music. The method further includes outputting the music based at least in part on the adjusting of the one or more equalizer settings of the equalizer device.