Abstract: In a communication network in which a plurality of equipments each having a function implementing unit having a plurality of functions and a communication control unit having communication function communicating with each other, the communication control unit includes a recording medium and stores on the recording medium in advance a plurality of pieces of function information related to at least one function held by the corresponding function implementing unit, and inquires function information of each of other equipments at the time of communication. Therefore, management of various equipments and function information of various equipments in correspondence with each other becomes unnecessary, load in processing including communication is relieved and functions of respective equipments can be controlled efficiently.

Abstract: Communication equipment comprises a communication section 3 performing communication with other equipment via a public line, a local area network, or an infrared communication network, a resource information storing section 5 storing what format data equipment can handle, a data storing section 6 storing data to be transmitted, a data conversion section 7 converting data so that the other party of communication can interpret it, a conversion table 8 storing whether currently held data can be converted to a form that the other party of communication can interpret, a conversion- means storing section 9 storing actual conversion means, and a negotiation section 10 knowing what the resource information of equipment of the other part is.

Abstract: The electronic secretary system comprises an input section (101) for inputting operation information; a processing section (102) for performing a predetermined processing in accordance with the input information; a data storing section (103) for storing the input information; an internal state memory section (104) for memorizing the state of processing; an action description storing section (106) for storing the action description of an electronic secretary; an action data storing section (107) for storing data, such as an action image, a voice, or the like of the electronic secretary; an electronic secretary generation section (105) for deciding and generating an action of the electronic secretary in accordance with the internal state; a display section (108) for displaying an output of the processing section (102) and an output of the electronic secretary generation section (105); an interface (109) for transmitting data with other equipment; and an interpreter section 110 for interpreting the transmitted d

Abstract: An information retrieving apparatus has a retrieving section, a security judging section and an input/display section. The retrieving section retrieves whether or not the security level of an inquirer is registered to a security DB section with respect to a retrieving request of retrieved data. When this security level of the inquirer is registered, the retrieving section retrieves retrieved data and a security level of the retrieved data stored to a data memory section. The security judging section judges whether the security level of the inquirer and the security level of the retrieved data are in conformity with each other or not. The input/display section displays the retrieving request, an inquirer's name, etc. when no security level of the inquirer is registered to the security DB section with respect to the retrieving request of desirable retrieved data. The input/display section can additionally store the security level of the inquirer to the security DB section.

Abstract: When performing learning for a neural network, a plurality of learning vectors which belong to an arbitrary category are used, and self-organization learning in the category is carried out. As a result, the plurality of learning vectors which belong to the category are automatically clustered, and the contents of weight vectors in the neural network are set to representative vectors which exhibit common features of the learning vectors of each cluster. Then, teacher-supervised learning is carried out for the neural network, using the thus set contents of the weight vectors as initial values thereof. In the learning process, an initial value of each weight vector is set to the representative vector of each cluster obtained by clustering. Therefore, the number of calculations required until the teacher-supervised learning is converged is greatly reduced.

Abstract: A learning apparatus for use in a neural network system which has a plurality of classes representing different meanings. The learning apparatus is provided for learning a number of different patterns, inputted by input vectors, and classified in different classes. The learning apparatus is constructed by a computer and it includes a section for producing a plurality of output vectors representing different classes in response to an input vector, a section for obtaining a first largest output vector of all the output vectors, a section for obtaining a second largest output vector of all the output vectors, and a section for setting predetermined weights to the first and second largest output vectors, respectively, such that the first largest output vector is made larger, and the second largest output vector is made smaller. Furthermore, a section for determining a ratio of the weighted first and second largest output vectors, respectively, is included.

Abstract: A character recongintion device has a subdivider, a features calculator and a character code recognition device. Image data for a single character area is extracted from scanned character image data and input to the subdivider. This subdivider divides the image data for the single character area into subregions. The features calculator calculates quantified features in each subregion based on a degree of resemblance between a template and image data in the subregions. When the features of each subregion are calculated for all subregions constituting the single character area, a character code corresponding to the scanned character image data is recognized by the character code recognition device based on the quantified features of each of all subregions.

Abstract: A neural network system and method of operating same wherein input data are initialized, then mapped onto a predetermined array for learning or recognition. The mapped information is divided into sub-input data or receptive fields, which are used for comparison of the input information with prelearned information having similar features, thereby allowing for correct classification of the input information. The receptive fields are shifted before the classification process, in order to generate a closest match between features which may be shifted at the time of input, and weights of the input information are updated based upon the closest-matching shifted position of the input information.

Abstract: A character recognition device has a subdivider, a features calculator and a character code recognition device. Image data for a single character area is extracted from scanned character image data and input to the subdivider. This subdivider divides the image data for the single character area into subregions. The features calculator calculates quantified features in each subregion based on a degree of resemblance between a template and image data in the subregions. When the features of each subregion are calculated for all subregions constituting the single character area, a character code corresponding to the scanned character image data is recognized by the character code recognition device based on the quantified features of each of all subregions.

Abstract: A recognizing apparatus is provided for recognizing a class to which an inputted characteristic pattern belongs from among a plurality of classes to be discriminated using a neural network. The classes are classified into a plurality of categories. The apparatus includes a network selecting portion for selecting a category to which the inputted characteristic pattern belongs and for selecting a neural network for use in discriminating the class to which the inputted characteristic pattern belongs in the selected category. The apparatus further includes a network memory portion, a network setting portion and a details discriminating portion. The network memory portion stores structures of a plurality of neural networks which have finished learning for respective categories, weights of the neural networks set by the learning and a plurality of discriminating algorithms to be used when the classes are discriminated by the neural networks.

Abstract: A self-extending shape neural-network is capable of a self-extending operation in accordance with the studying results. The self-extending shape neural-network has initially minimum number of the intermediate layers and the number of the nodes (units) within each layer by the self-extension of the network construction so as to shorten the studying time and the discriminating time. This studying may be effected efficiently by the studying being directed towards the focus when the studying is not focused.

Abstract: An acoustic processing unit for a speech recognition system is characterized in that the parameters are extracted from the speech data, including correlation functions, the zero crossing number of the original waveforms, the zero crossing number of the differential waveforms, and the average level of the waveforms. A suitable threshold is selected from a plurality of thresholds preliminarily stored, depending on the inputted speech volume level. The inputted speech volume level of the speaker is detected so that feedback of the detected volume level is obtained. The selected threshold set is then compared with each of the parameters to thereby make the phonemic classification. Because the plurality of threshold sets are thus automatically selected depending on the inputted speech volume level, flexible phonemic classification can be obtained to exactly detect the speech sections.

Abstract: An inputted sound signal is sampled at intervals over a period and cepstrum coefficients are calculated from the sampled values. Cepstrum sum, distance and/or power are calculated and compared with appropriately preselected threshold values to distinguish voice (vowel) intervals and noise intervals. The ratio of the length of the voice intervals to the sampling period is considered to determine whether the sampled inputted sound signal represents voice or noise.

Abstract: Inputted voice signals are analyzed in units of syllables. Each syllable is compared with standard syllables preregistered in a memory and thereby recognized but corrections may be made on erroneous recognitions by referencing a dictionary or entering a command. Each standard pattern is associated with phonological information on the neighborhood in a voice signal from where it was extracted and may be updated by another pattern having the same phonological information. Temporal sequences of correct and erroneous recognitions of individual syllables as well as whole syllables are stored and referenced in determining whether a standard pattern should be updated. A maximum pattern number may be set for each syllable.

Abstract: In a speech recognition system, logarithmic amplifiers avoid low level signal or noise problems by clamping all signals below an input threshold to a constant output level.