An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

Media objects are transformed into active, connected objects via identifiers embedded into them or their containers. In the context of a user's playback experience, a decoding process extracts the identifier from a media object and possibly additional context information and forwards it to a server. The server, in turn, maps the identifier to an action, such as returning metadata, re-directing the request to one or more other servers, requesting information from another server to identify the media object, etc. The linking process applies to broadcast objects as well as objects transmitted over networks in streaming and compressed file formats.

Connected audio and other media objects

The various methods and devices described herein relate to devices which, in at least certain embodiments, may include one or more sensors for providing data relating to user activity and at least one processor for causing the device to respond based on the user activity which was determined, at least in part, through the sensors. The response by the device may include a change of state of the device, and the response may be automatically performed after the user activity is determined.

Automated Response to and Sensing of User Activity in Portable Devices

A virtual assistant uses context information to supplement natural language or gestural input from a user. Context helps to clarify the user's intent and to reduce the number of candidate interpretations of the user's input, and reduces the need for the user to provide excessive clarification input. Context can include any available information that is usable by the assistant to supplement explicit user input to constrain an information-processing problem and/or to personalize results. Context can be used to constrain solutions during various phases of processing, including, for example, speech recognition, natural language processing, task flow processing, and dialog generation.

Using context information to facilitate processing of commands in a virtual assistant

A method for detecting against unauthorized transmission of digital works comprises the steps of maintaining a registry of information permitting identification of digital copyrighted works, monitoring a network for transmission of at least one packet-based digital signal, extracting at least one feature from the at least one digital signal, comparing the extracted at least one feature with registry information and applying business rules based on the comparison result.

Copyright detection and protection system and method

Study on 3D audio coding based on spatial auditory masking

A voice analysis-synthesis method comprising the steps of: dividing (911) an input voice signal on block-by-block basis and finding (912) pitch data in the block; converting (913) the voice signal on the block-by-block basis onto frequency axis and finding data on the frequency axis; dividing (914)the data on the frequency axis into plural bands on the basis of the pitch data; finding (915) power information for each of the divided bands and voiced/unvoiced decision information; transmitting (902) the pitch data, the power information for each band and the voiced/unvoiced decision information found in foregoing processes; predicting (922) a block terminal edge phase on the basis of the pitch data for each block obtained by transmission and a block initial phase; and modifying (924) the predicted terminal edge phase, using a noise having diffusion according to each of the bands.

Voice analysis-synthesis method

PROBLEM TO BE SOLVED: To attain N=M×2n (M is an odd number)-point FFT and IFFT for input data. SOLUTION: In step S1, (i) is set to 0 first and in step S2, M pieces of data are taken out. In step S3, M-point DFT of the data taken out in step S2 is performed. In step S4, the product of obtained y(k) and a twist coefficient w(i,k) is calculated and the result is stored in y(k). In step S5, the value of y(k) is overwritten to the array (x) where the original data were put. The mentioned processes are repeated N/M times for all pieces of data through steps S6 and S7. In step S9, N/M(=2n)-point FFT is carried out for 0<=k<M. Lastly, rearrangement is carried out in step S12.

Fast fourier transform method and fast inverse fourier transform method

Verfahren zur Rauschverminderung in einem Sprachsignal

PROBLEM TO BE SOLVED: To reduce the quantization error without deteriorating the quality and acoustic sense of the voice signal by obtaining the signal having strong correlation in the time base direction based on the prediction-coded signal, applying matrix quantization to it, then applying vector quantization to the signal. SOLUTION: LSP parameters for two frames are matrix-quantized by the first matrix quantization section 620, and the obtained quantization error is further matrix-quantized by the second matrix quantization section 6202 . The quantization error for two frames from the second matrix quantization section 6202 is inputted to a vector quantization section 640 constituted of the first and second vector quantization sections 6401 , 6402 . The quantization error from a matrix quantization section 620 is vector-quantized for each frame by vector quantization sections 650, 660. The obtained quantization error vector is further vector-quantized by vector quantization sections 670, 680.

Masayuki Nishiguchi

Papers

Study on 3D audio coding based on spatial auditory masking

Voice analysis-synthesis method

Fast fourier transform method and fast inverse fourier transform method

Verfahren zur Rauschverminderung in einem Sprachsignal

Voice coding method and voice encoder