118 results about "Content Model" patented technology

Methods and systems for automatically determining game content based upon dynamically adjusted individual skill levels are provided. Example embodiments provide an Electronic Gaming Engine (“EGE”), which includes a Dynamic Challenge Level Adjuster for supporting multi-player, individualized skill-based games. In one embodiment, the EGE comprises game flow logic; game content models, for example, question and answer (“Q&A”) challenge models; a Dynamic Challenge Level Adjuster; one or more scoring modules; challenge data; participant data; and an input/output interface. These components cooperate to determine and assign skill level indexes on an individual basis and to automatically present game content appropriate to each individual player's skill level.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

The invention provides a news recommendation system and method based on an FOLFM model. Based on a content recommendation method, a news content model is expressed abstractly through a latent class model and content characteristics, and an individual latent class preference model is built for each user. Real-time training is carried out on a real-time behavior record of a user to obtain preference, on certain latent class news, of the user, whether the news is recommended to the user is determined through calculation, and a final news recommendation list is obtained after a series of processing processes. The news recommendation system and method based on the FOLFM model deeply excavate the interest of the user, improve recommendation accuracy and satisfaction of the user, avoid a cold starting problem of the news, and guarantee performance under the condition that the recommendation effect is improved as much as possible. The experiment shows that the news recommendation system and method based on the FOLFM model not only guarantee the requirements for high accuracy and high speed, but also realize visual real-time recommendation for the user.

The invention belongs to the field of image processing and computer vision, and more particularly discloses an image style transfer method based on a convolutional neural network. Based on a high-level semantic representation in the convolutional neural network, an image content model and an image style model are established, then an initial image is optimized to have a content representation similar to a content image and a style representation similar to a style image in the same convolutional neural network, an image fused with the content of the content image and the style of the style image is therefore generated, and the style transfer function is realized. The method of the invention can achieve style transfer for any style image.

An intelligent content assistance system generates context-sensitive content recommendation during business processes. Information needs of a user in context are dynamically monitored and detected. Content-recommendation templates are retrieved to match the detected information needs, and instantiated using information from user input, context, interaction history, system-learned query and content models, and external sources. Context, interaction history, and system-learned models are dynamically updated for learning and adaptation. The system and methods are applicable to multiple domains and applications.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

The invention discloses a method for starting up a mobile terminal through a voice password, wherein a user voice print model, a pronunciation content model and a pronunciation rhythm model which are pre-established are stored in the mobile communication terminal. The method comprises the following steps: extracting the acoustic feature from the sound wave of the mobile terminal user and forming a voice print feature vector sequence, a pronunciation content feature vector sequence and a pronunciation rhythm feature vector sequence which are to be identified; matching and comparing the pronunciation content feature vector sequence to be identified with the pronunciation content model one by one, and obtaining a pronunciation content matching score A; matching and comparing the voice print feature vector sequence with the voice print model one by one, and obtaining a voice print matching score B; matching and comparing the pronunciation rhythm feature vector sequence with the pronunciation rhythm model one by one, and obtaining a pronunciation rhythm matching score C; performing operation to the comprehensive matching degree of the pronunciation content matching score A, the voice print matching score B and the pronunciation rhythm matching score C, and starting up the mobile terminal when the obtained comprehensive matching degree Z is equal to or larger than a preset threshold value.

Methods and apparatus for a recommendation system based on collaborative filtering is provided. Explicit and implicit ratings of items by network users are used to create a contextual model. The explicit ratings comprise different rating types regarding different item attributes. The implicit ratings comprise different rating types derived from different user events and may include recency, intensity, or frequency ratings. The contextual model may be optimized for a specific objective function, such as click-through-rate or conversion rate. In other embodiments, item information is used to produce a content model where item information for an item is encoded as metadata into a document that represents the item. The contextual or content model is used to recommend one or more items to a current user. The basic unit of the recommendation system may be an item set of two or more items or a particular sequence of two or more items.

Various embodiments of the present disclosure relate generally to systems and methods for generating multi-view interactive digital media representations in a virtual reality environment. According to particular embodiments, a plurality of images is fused into a first content model and a first context model, both of which include multi-view interactive digital media representations of objects. Next, a virtual reality environment is generated using the first content model and the first context model. The virtual reality environment includes a first layer and a second layer. The user can navigate through and within the virtual reality environment to switch between multiple viewpoints of the content model via corresponding physical movements. The first layer includes the first content model and the second layer includes a second content model and wherein selection of the first layer provides access to the second layer with the second content model.

A former foundation method of chemistry component content to plant fibrin material gathers close infrared spectroscopic to plant fibrin material sample and sets up quantitative analysis former according the chemistry component content to the plant fibrin material sample based on the close infrared spectroscopic by adopting chemistry metrology method. A part of the said plant fibrin material sample is at least mixed plant fibrin material sample. The said mixed plant fibrin material sample is at least one of the mixtures of plant fibrin material sample with known chemistry component content. According the former establishing method

The present invention is a distributed system comprising client- and server-side components that, together, allow XML schemas or the like to be managed in a collaborative way across an enterprise environment. The server-side component, or server, is used to assemble a collection of one or more schemas. The server exposes the collection of schemas to one or more client components that connect to the server. Such connections may be made over a computer network. The client component preferably exports a display interface through which a user can visualize dependencies between schemas, e.g., by dragging them into a workspace and viewing all related schemas and how they are connected, as well as what elements and other components they define. In this way, the client component provides a high-level view of a larger (e.g., enterprise-wide) information architecture.

The invention discloses a shale oil content forecasting method for LogR-delta T logging evaluation and belongs to the technical field of logging exploration. The shale oil content forecasting method comprises the following steps: (1) preprocessing logging data; (2) performing minimum value joint identification; (3) performing logging curve leveling processing; (4) calibrating forecasted shale stratum oil content S1 and chloroform bitumen 'A' formula parameters; and (5) evaluating non-measured data shale stratum shale oil S1 and chloroform bitumen 'A' content in a quantitative mode, and achieving continuous forecasting and evaluation on the shale stratum shale oil content S1 and the chloroform bitumen 'A' without sampling. By means of the shale oil content forecasting method, fast calibration on a content model of the shale oil content S1 (or chloroform bitumen 'A') can be achieved by performing calibrating fitting on measured data directly through processed interval transit time and resistance data and forecasting on S1 (or chloroform bitumen 'A') of the non-measured oil content S1 (or chloroform bitumen 'A') can be achieved through logging data.

The invention provides a method and a device for determining the content of adsorbed gas in a clay shale reservoir and belongs to the technical field of prediction of gas content of the clay shale reservoir. The method comprises steps as follows: influence factors of the adsorbed gas in the clay shale reservoir are obtained; the actually measured adsorbed gas content is acquired through a preset adsorption model according to physical property parameters and isothermal adsorption experimental data of a to-be-measured clay shale reservoir; a scatter diagram indicating that the actually measured adsorbed gas content changes along with the adsorbed gas influence factors is determined according to the adsorbed gas influence factors and the actually measured adsorbed gas content; the scatter diagram is subjected to fitting, a correlation curve and the fitting degree of the adsorbed gas content changing along with the predetermined influence factors are acquired, and the influence factors with the fitting degree larger than a preset value are determined as main controlling factors of the adsorbed gas content; a multiple linear regression model is established according to the main controlling factors of the adsorbed gas content; an adsorbed gas content model is determined through the multiple linear regression model according to the adsorbed gas influence factors and the actually measured adsorbed gas content. The method and the device have the characteristic of higher accuracy of a quantitative prediction result.

A method for constructing a highly optimized linear-sized validation plan, the method comprising: providing a schema having a plurality of schema components; compiling the schema in three stages: a first stage in which the schema is read and modeled in terms of abstract schema components; a second stage in which the schema components are augmented with a set of derived components and properties by (i) synthesizing content models from the plurality of schema components by adding additional components including one or more synthetic content-model components and one or more synthetic elements and one or more synthetic types to the one or more of the plurality of schema components and (ii) computing the derived set of properties on the components; and a third stage in which the schema is traversed in order to generate recursive-descent validation code for each of the plurality of schema components by generating the highly optimized linear-sized validation plan directly from the plurality of schema components.

The invention discloses a method for pre-warning forest fire based on satellite remote sensing data, and solves the problem that existing forest fire danger forecasting is insufficient in precision. The method comprises the following steps: obtaining image data, and carrying out radiation correction, geometric correction and projection transformation; using processed image data to calculate surface temperature; using processed image data to calculate normalized differential vegetation index so as to calculate vegetation coverage rate; obtaining a panchromatic image and a multispectral image, carrying out geometric correction on the panchromatic image and the multispectral image, fusing the panchromatic image and the multispectral image for vegetation classification, constructing combustible moisture content models of different types of vegetations in combination with meteorological factors, and calculating the moisture contents of different types of vegetations; calculating dynamic hazard indexes of forest fire through the surface temperature, the vegetation coverage rate and the moisture contents of different types of vegetations, and classifying fire danger classes according to the dynamic hazard indexes of forest fire. According to the method, the precision of a combustible moisture content model is much higher than that of a model determined through remote sensing satelliteparameters directly.