Methods, apparatus, systems and articles of manufacture are disclosed to identify media that has been pitch shifted, time shifted, and/or resampled. An example apparatus includes: memory; instructions in the apparatus; and processor circuitry to execute the instructions to: transmit a fingerprint of an audio signal and adjusting instructions to a central facility to facilitate a query, the adjusting instructions identifying at least one of a pitch shift, a time shift, or a resample ratio; obtain a response including an identifier for the audio signal and information corresponding to how the audio signal was adjusted; and change the adjusting instructions based on the information.

The failure frequency of first communication at the time of activation with a setting management server arranged in the same apparatus is reduced. A client switches the timing of executing the first communication at the time of activation by whether the server is the same apparatus. If the server is the same apparatus, activation of the server is delayed and the first communication at the time of activation for synchronization is performed after the delay. The delay period is, for example, for a predetermined time or for a period until a notification from the server is received.

A client device receives a request to stream a media item from a first position. Content corresponding to the media item includes samples identified in a container file for the media item. The client device obtains, independently of the container file, a seektable that is not included with the container file and that identifies a plurality of segments into which content corresponding to the media item is divided. Each segment of the plurality of segments includes multiple samples. The client device consults the seektable to determine a segment of the media item to retrieve in response to the request. The segment includes content at the first position. After consulting the seektable, the client device retrieves the segment of the media item and plays the content corresponding to the first position using the retrieved segment.

An intelligence-driven virtual assistant for automated documentation of new ideas is provided. During a brainstorming session, one or more user participants may discuss and identify one or more ideas. Such ideas may be tracked, catalogued, analyzed, developed, and further expanded upon through use of an intelligence-driven virtual assistant. Such virtual assistant may capture user input data embodying one or more new ideas and intelligently process the same in accordance with creativity tool workflows. Such workflows may further guide development and

expansion upon a given idea, while continuing to challenge ideas, provide opportunities to collaborate, and optimize scheduling of ideation sessions for efficiency, while continuing to document, analyze, and identify further aspects to develop and expand.

Disclosed herein is a memory aid method of selecting data watched by a user, recording the selected data along with situation/behavior data upon watching, providing recorded audio/video data in response to a situation condition. The memory aid method may include receiving audio/video data from a wearable device, separating unit data including only associated parts from the audio/video data, and extracting character or feature data from the unit data. Object data and context data are generated from the character/feature data. Importance of the generated object and context data is determined with reference to a personal information DB. Audio/video data is stored depending on the importance. The method can record audio/video data recognized by a user at normal times, extract data necessary for a current situation, and provide the extracted data to the user. Accordingly, a user can accurately remember information that was watched in the past.

A music recommendation method and apparatus are provided. When a music recommendation request of at least one terminal is received, a music preference of the at least one terminal is acquired, with one terminal corresponding to at least one music preference. According to the music preference of each terminal, a common music preference of the at least one terminal is generated; and music is recommended to the at least one terminal according to the common music preference.

A method of recorded message detection is provided. In this an audio restream is received and a set of landmark features is identified in a section of the audio stream. From these landmark features an audio finger print for the section of the audio stream is determined. This audio finger print is compared with at least one of the plurality of stored audio finger prints, each derived from a respective audio stream. It is determined that the received audio stream is a recorded message if a derived audio finger print is substantially equivalent to one of the plurality of stored audio finger prints representing a recorded message.

A method of recorded message detection is provided. In this an audio restream is received and a set of landmark features is identified in a section of the audio stream. From these landmark features an audio finger print for the section of the audio stream is determined. This audio finger print is compared with at least one of the plurality of stored audio finger prints, each derived from a respective audio stream. It is determined that the received audio stream is a recorded message if a derived audio finger print is substantially equivalent to one of the plurality of stored audio finger prints representing a recorded message.

An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.

An apparatus includes a memory and processor. The memory stores previous requests and corresponding previous responses. The processor determines that a user device transmitted a new voice request, converts the voice request into a first set of text, and transmits the text to an agent device. The processor applies the machine learning algorithm to the first set of text to generate suggested responses, by identifying patterns shared by the first set of text and a subset of the previous requests that are associated with the suggested responses. The processor transmits the suggested responses to the agent device. The processor then determines that the agent device transmitted voice signals responding to the new request. The processor converts these voice signals into a second set of text. The processor stores the first set of text as a previous request, and the second set of text as a corresponding previous response.