Deoras
Anoop Deoras, San Jose, CA US
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20140222422 | SCALING STATISTICAL LANGUAGE UNDERSTANDING SYSTEMS ACROSS DOMAINS AND INTENTS - A scalable statistical language understanding (SLU) system uses a fixed number of understanding models that scale across domains and intents (i.e. single vs. multiple intents per utterance). For each domain added to the SLU system, the fixed number of existing models is updated to reflect the newly added domain. Information that is already included in the existing models and the corresponding training data may be re-used. The fixed models may include a domain detector model, an intent action detector model, an intent object detector model and a slot/entity tagging model. A domain detector identifies different domains identified within an utterance. All/portion of the detected domains are used to determine associated intent actions. For each determined intent action, one or more intent objects are identified. Slot/entity tagging is performed using the determined domains, intent actions, and intent object detector. | 08-07-2014 |
20140278355 | USING HUMAN PERCEPTION IN BUILDING LANGUAGE UNDERSTANDING MODELS - An understanding model is trained to account for human perception of the perceived relative importance of different tagged items (e.g. slot/intent/domain). Instead of treating each tagged item as equally important, human perception is used to adjust the training of the understanding model by associating a perceived weight with each of the different predicted items. The relative perceptual importance of the different items may be modeled using different methods (e.g. as a simple weight vector, a model trained using features (lexical, knowledge, slot type, . . . ), and the like). The perceptual weight vector and/or or model are incorporated into the understanding model training process where items that are perceptually more important are weighted more heavily as compared to the items that are determined by human perception as less important. | 09-18-2014 |
20150066496 | ASSIGNMENT OF SEMANTIC LABELS TO A SEQUENCE OF WORDS USING NEURAL NETWORK ARCHITECTURES - Technologies pertaining to slot filling are described herein. A deep neural network, a recurrent neural network, and/or a spatio-temporally deep neural network are configured to assign labels to words in a word sequence set forth in natural language. At least one label is a semantic label that is assigned to at least one word in the word sequence. | 03-05-2015 |
Anoop K. Deoras, San Jose, CA US
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20130304451 | BUILDING MULTI-LANGUAGE PROCESSES FROM EXISTING SINGLE-LANGUAGE PROCESSES - Processes capable of accepting linguistic input in one or more languages are generated by re-using existing linguistic components associated with a different anchor language, together with machine translation components that translate between the anchor language and the one or more languages. Linguistic input is directed to machine translation components that translate such input from its language into the anchor language. Those existing linguistic components are then utilized to initiate responsive processing and generate output. Optionally, the output is directed through the machine translation components. A language identifier can initially receive linguistic input and identify the language within which such linguistic input is provided to select an appropriate machine translation component. A hybrid process, comprising machine translation components and linguistic components associated with the anchor language, can also serve as an initiating construct from which a single language process is created over time. | 11-14-2013 |
Anoop Kiran Deoras, San Jose, CA US
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20130346066 | Joint Decoding of Words and Tags for Conversational Understanding - Joint decoding of words and tags may be provided. Upon receiving an input from a user comprising a plurality of elements, the input may be decoded into a word lattice comprising a plurality of words. A tag may be assigned to each of the plurality of words and a most-likely sequence of word-tag pairs may be identified. The most-likely sequence of word-tag pairs may be evaluated to identify an action request from the user. | 12-26-2013 |
Saurabh Deoras, Milpitas, CA US
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20140177090 | MEDIA TOPOGRAPHY DRIVEN FLYING HEIGHT MODULATION SENSING USING EMBEDDED CONTACT SENSOR - Approaches for a flying height control scheme in a hard-disk drive (HDD) device. The flying height control scheme utilizes an embedded contact sensor (ECS) to characterize the topography of a magnetic-recording disk at various flying heights of a head slider over a corresponding disk. A relation between a particular flying height and a corresponding ECS value which characterizes the media topography at that particular flying height is represented in disk topography data. The disk topography data is accessed and used for active flying height control for the head-disk interface in view of the current ECS value. | 06-26-2014 |
20140240871 | INTERFACE VOLTAGE CONTROL OPERATING POINT DETERMINATION IN A HARD DISK DRIVE - Approaches for an interface voltage control (IVC) system in a hard-disk drive (HDD), whereby the IVC operating point determination scheme utilizes non-contact spacing signals for calibration of IVC. While applying a series of input voltages to a slider, head-disk spacing signals are monitored, such as spacing signals from an embedded contact sensor or Wallace spacing loss spacing signals. Based on the relation between the spacing signal values and the series of input voltages, the IVC operating point is identified and stored within the HDD. The IVC operating point corresponds to the IVC input voltage necessary to neutralize the natural slider-disk voltage potential that would otherwise cause an electrostatic force that pulls the slider closer to the disk and can cause lubrication transfer from disk to slider. | 08-28-2014 |