48 results about "Generative process" patented technology

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

An apparatus includes at least one processing device including a processor coupled to a memory, wherein the at least one processing device is configured to modify first data obtained from one or more sources, wherein the modifying includes adding user context data to the first data to generate second data, the second data representing the first data supplemented with a per-user context and, in response to receipt of a query, generate a response to the query using at least one generative language model supplemented by a retrieval augmented generation process based on at least a portion of the second data.

The invention discloses a multi-modal-driven game 3D art resource stylization generation method, and relates to the technical field of artificial intelligence and game development, the method comprises the following steps: S1, obtaining multi-modal input data: receiving and analyzing various combined data input by a user, the data comprises but is not limited to natural language texts for describing roles and scenes, 2D concept original pictures and reference art style picture sets; according to the method, multi-modal input data is combined with feature extraction and semantic understanding, an automatic process from a concept to 3D model generation is realized, manual operation links are reduced, the production cycle of game 3D art resources is remarkably shortened, and the production efficiency is improved by automatically generating the 3D model, performing stylized rendering and optimizing engine suitability. Dependence on multiple professional workers is reduced, development cost is reduced, the reference art style picture set serves as style constraint, style consistency is kept in the generation process, and it is ensured that the generated 3D model and the overall art style of the project are highly unified.

The invention belongs to the technical field of natural language processing, provides a multi-dimensional confidence fusion large language model uncertainty evaluation method and system, designs a multi-dimensional confidence modeling mechanism, and can perform multi-dimensional confidence modeling on the basis of internal information in a large language model generation process without depending on an external knowledge base. And the reliability of the output content is effectively judged. According to the system, on the basis of a semantic clustering mechanism, a Token-level multi-dimensional confidence modeling method is innovatively introduced, a scoring system fusing factors such as probability centrality, context disturbance sensitivity, generation consistency and language rationality is constructed, the confidence structure of each part of content in model output can be evaluated from the Token level, and the evaluation efficiency is improved. And the discrimination capability of the system on the uncertainty difference in the generation process is obviously improved.

Provided is a method for modifying a machine-learning model. The method includes performing, by a machine learning model, a generative process to predict a first output, generating, via a processor, a latent space based on an input to the machine learning model, determining, via the processor, an intermediate decision parameter based on the latent space, based on the intermediate decision parameter, changing, via the processor, a structure of the machine learning model to generate a modified machine learning model to perform a modified generative process that is conditioned upon the intermediate decision parameter, and generating, by the modified machine learning model, a second output including content associated with the input.

The invention discloses a question and answer interaction method, device and equipment based on artificial intelligence and a medium, and relates to the technical field of artificial intelligence in the professional service fields of finance, insurance, medical treatment, banking and the like, and the method comprises the steps: segmenting a historical dialogue into segments with coherent themes, and constructing the segments into memory units; generating a semantic embedding vector and a time decay weight for each unit to form a memory bank; retrieving related memory units from a memory bank based on user query, comprehensive semantic relevance, theme consistency and time decay weight; the query and related units are combined into a context input generative language model, and a topic consistency constraint is applied during the generation process to generate a topic consistent reply. By optimizing the memory granularity and enhancing the memory representation and timeliness, multi-dimensional accurate retrieval and constraint generation are realized, and the system improves the accuracy, coherence and service consistency of question and answer interaction.

A method for suggesting prompts on a page of a workspace includes receiving an input and displaying a prompt block configured to initiate a generative process to create in-block content in response to the input. The prompt block is embedded as an in-page object on the page. The method includes causing a large language model (LLM) system to create a set of suggested prompts. Each prompt includes instructions configured to create generative content of a respective type by the LLM system. The set of suggested prompts is created based on in-page text content or a relative location of the prompt block on the page. The method includes displaying the set of suggested prompts as a set of control items of the workspace. Each of the set of control items is selectable to input as a prompt for generating content based on existing content of the workspace.

A method for suggesting prompts on a page of a workspace includes receiving an input and displaying a prompt block configured to initiate a generative process to create in-block content in response to the input. The prompt block is embedded as an in-page object on the page. The method includes causing a large language model (LLM) system to create a set of suggested prompts. Each prompt includes instructions configured to create generative content of a respective type by the LLM system. The set of suggested prompts is created based on in-page text content or a relative location of the prompt block on the page. The method includes displaying the set of suggested prompts as a set of control items of the workspace. Each of the set of control items is selectable to input as a prompt for generating content based on existing content of the workspace.

A system and method for generating multimedia artifacts with managed scene continuity in visual and multimedia using an AI-based and scene continuity aware media generation platform. The system receives a user or AI agent specification or simulation result(s), selects or trains generative models based on the specification, preprocesses relevant data, and generates scene narrative or frame-specific, sequence specific or broader continuity aware content using the selected or trained model(s). The generated content may be further enhanced using frame interpolation and view synthesis techniques to create smooth transitions or novel viewpoints or to aid in more efficient transmission or viewing or persistence of resultant content. The system enables efficient and customizable generation of high-quality scene continuity aware content for various applications in visual and multimedia production using neuro-symbolic and simulation enhanced compression, representation and generation processes.

This invention belongs to the field of computer vision technology, specifically a tree-structured video description generation method based on perceptual grammar knowledge. This invention explicitly utilizes semantic information inherent in language, using dependency structure analysis tools to transform sequential sentences into a syntax tree structure. By analyzing the connections between parent and child nodes in the tree, the dependency grammar structure within the sentence is explicitly modeled. A perceptual context attention network is used to model the contextual information generated along different paths during the generation process. Simultaneously, reinforcement learning and iterative generation training methods are introduced during the training phase to further improve model performance. Qualitative and quantitative experiments demonstrate that the model has the ability to generate more accurate and semantically richer descriptions.

The invention discloses a self-adaptive writing method and system based on support style feature extraction and template generation, and particularly relates to the technical field of self-adaptive writing. According to the method, the writing system has the active recognition and selection capability on the target style in the generation process, style drifting caused by context accumulation in long text and multi-round generation is avoided, meanwhile, a fixed semantic structure and a variable expression mode are combined based on a template constraint model automatically generated in a dominant style interval, and the method is more efficient and efficient. On the premise of ensuring logic integrity and expression normalization, the flexibility and adaptability of the writing process are improved, and a closed-loop self-adaptive regulation and control mechanism is formed by performing literary and physical feature consistency evaluation on the generated text and dynamically adjusting the template slot position weight.

The invention relates to the technical field of font generation in the field of computer mode recognition, in particular to an attribute-controllable font generation method based on a diffusion model. Preparing data including font images and font attributes; performing data preprocessing based on the font image and the attribute to obtain a standardized data set; a generation network based on a pre-training potential spatial diffusion model is constructed, a content encoder is introduced to extract font content features, an attribute encoder maps attribute vectors, an attribute control adapter is designed to integrate attribute information into the generation process, and effective decoupling of font content and style attributes is achieved; and finally, generating a high-quality font with a specified style by inputting a font content image and a target attribute vector, and supporting style conversion between any fonts based on an attribute value. The problems of high design cost, single style, inflexible attribute control and the like in the traditional font generation technology can be solved.

The present invention relates to a device and method for generating context-aware motion using text and emotion embeddings. An electronic device according to one embodiment of the present invention includes a memory and a processor connected to the memory, and the processor receives input information including a text prompt instructing the content of a character's action and emotion information to be assigned to the action for generating three-dimensional motion from a user terminal, and vectorizes the received text prompt and the emotion information, respectively, to generate a text embedding for the content of the action and an emotion embedding that determines the nuance of the action, and generates a motion sequence based on the text embedding using a Generative AI model, but reflects the emotion embedding as a condition of the generation process to assign an emotional context to the character's action, and can generate the motion sequence with the assigned emotional context as final motion data.

The invention discloses a Chinese text style migration method based on a diffusion model. According to the method, through multi-level style feature capture and accurate control, text style migration is carried out on the basis of ensuring original semantic content. The method comprises the following specific steps: firstly, preprocessing an input Chinese text, and mapping the input Chinese text to a continuous vector space by utilizing a pre-training language model to generate an initial representation of the text; then, multi-level style deviation items are introduced in the diffusion process, and vocabulary, syntax and semantic style levels of the text are controlled respectively; and in each time step, fine control of the style characteristics is realized by adjusting the control parameters of the style deviation item. Then, in a denoising stage, semantic embedding of the text is optimized by utilizing a semantic preserving mechanism, and the generated text semantics are ensured to be consistent with the original text; besides, the method is also combined with a DPM-Solver-v3 acceleration algorithm, so that sampling steps in the diffusion process are reduced, and the generation efficiency is remarkably improved. Finally, through multi-task joint training, style migration and semantic preserving tasks are balanced, and the generation effect is optimized. According to the method, the semantic consistency of the text can be kept while the style characteristics are finely controlled in a multi-level mode, the natural and smooth text conforming to the target style is generated, and the method is widely applied to the fields of text generation, sentiment analysis, advertisement copywriting generation and the like. In addition, the efficiency of the generation process is improved, the calculation overhead is reduced, and the method is suitable for large-scale text style migration tasks in practical application.

The invention discloses an AI teaching interaction method based on multi-modal emotion perception and generative strategy optimization, and the method comprises the steps: generating a comprehensive emotion state vector in a robust manner through multi-modal emotion analysis with uncertainty estimation and time sequence fusion based on attention; contexts such as teaching content semantics and cognitive load are combined; a generative model with strategy elements as a vocabulary is utilized, a composite interaction strategy sequence is generated through dynamic combination, and cognitive load constraint is carried out in the generation process; optimization is carried out through reinforcement learning integrating off-line evaluation and safety exploration, and strategy rehearsal is optionally carried out through a digital twin model. Deep understanding of student states and highly personalized and safe strategy generation and optimization in a complex and real teaching scene are realized, and the intelligent level and teaching effect of an AI teaching system are significantly improved.

Exact and interpretable computation is integrated into a large language model (LLM)- based generative process using an LEM in combination with a symbolic architecture that performs arithmetic, or other mathematical or domain-specific processing. Hidden states of a ELM are processed to control this symbolic architecture, and inputs to the symbolic architecture are automatically extracted from already generated words. A selection is made during an autoregressive generative process as to whether to use the ELM output or the output of the symbolic architecture to extend the word sequence. Because the use of the symbolic architecture is part of the autoregressive process, the computed result is incorporated into the generation of future words in the result. Advantages of this approach include reduced computation and higher accuracy, for example, by avoiding arithmetic or computation errors manifested in the generated word sequence.

The invention discloses an ancient language machine translation method for syntactic perception and knowledge enhancement, and belongs to the technical field of natural language processing. Aiming at the difficulties of complex sentence patterns and scarcity of historical knowledge of ancient Chinese, three innovation modules are designed: firstly, a dynamic dependency syntactic analysis module explicitly models an ancient Chinese syntactic structure by utilizing probability analysis and a graph network; secondly, a retrieval enhancement generation module accurately extracts related knowledge fragments from the high-quality ancient language corpus, and semantic comprehension is enhanced; and finally, the stream grammar constraint decoder fuses the source language syntax and the target language generation process through a double-stream mechanism and a stream mechanism, and loyalty and smooth modern text output is realized. The three modules are deeply fused, so that the accuracy and interpretability of ancient language translation are effectively improved, and a reliable technical path is provided for ancient book digitization.

The invention relates to the technical field of artificial intelligence, and discloses a music generation model training method, which comprises the following steps: S1, extracting harmony, rhythm and curve form theoretical characteristics of an input music sequence; s2, constructing a dynamic music theory constraint field, and fusing the spatio-temporal evolution law of the theoretical characteristics; s3, designing a gating constraint generator, and injecting the constraint field state into a music generation process; s4, establishing a dynamic optimization objective function containing theoretical constraint terms; s5, generating and screening a theoretical boundary sample through reverse disturbance; s6, implementing a double-loop iteration mechanism of generation training and theoretical evolution; s7, realizing generation capability migration of the cross-music theory system; the S1 comprises the following steps: extracting chord features by adopting a two-way circulation network; and coding the rhythm mode through depth separable convolution. According to the method, self-adaptive balance of theoretical guidance and artistic innovation is realized through a constraint field and chaotic scheduling, and the dual problems of stiff generated music and rule conflicts are solved.

The invention discloses a multi-modal large language model generation method and system based on visual information backtracking, and belongs to the technical field of artificial intelligence and multi-modal information processing. The implementation method comprises the following steps: 1, acquiring an image token sequence and a text token sequence; 2, obtaining the real attention of the model on the input image; screening the ROI set by using overlapping penalty; 3, generating statement sequence token prediction distribution and obtaining hierarchical normalized entropy; 4, when the normalized entropy of the hierarchy is greater than an entropy threshold and the screened ROI set injection is triggered for the first time, performing feedforward network residual injection; 5, the ROI set is injected into a feed-forward layer of the multi-modal large language model, visual information in the model is updated, and then attention-enhanced visual features are obtained; compared with the prior art, the method has the advantage that the phenomenon that the image is inconsistent with the fact in the image-based generation process due to strong priori brought by text corpora in the pre-training process of the model is solved.

The invention relates to a computer-implemented speech synthesis method comprising the steps: generating at least one simulated natural speech sequence, the generation comprising providing at least one physical speaker attribute as input to a generative artificial intelligence model; output from the generative artificial intelligence model forming said at least one simulated natural speech sequence, the generative artificial intelligence model having been previously trained on the basis of a training dataset comprising at least one natural speech sequence, each natural speech sequence being associated with at least one physical attribute of the corresponding speaker; extracting a set of speech features from each generated simulated natural speech sequence; and configuring a speech synthesis engine according to the extracted set of speech features. Figure 1

The application relates to the technical field of space automatic layout generation and generation process control, and discloses a multi-room consistency layout generation method and system based on generation process constraint modulation. The method comprises the following steps: acquiring space structure information of a target residential space; constructing a multi-room generation state model; constructing and updating a multi-room consistency constraint set in a layout gradual generation process; performing consistency constraint modulation processing on a candidate layout action in a current generation step, so that each room shares a consistency control condition in the generation stage; and outputting a multi-room overall layout scheme satisfying the multi-room consistency constraint set. Compared with the prior art in which each room is independently generated and then subjected to rule checking or scoring screening to realize consistency, the application cooperatively controls consistency relationships such as layout density, furniture scale and space blank proportion in the generation stage, so that the structural rationality, stability and generation efficiency of the multi-room overall layout result are improved.

The invention relates to the technical field of image generation, in particular to a severe weather image generation method based on a generative diffusion model, and the method comprises the steps: obtaining the multi-scale features of an original image through ViT; carrying out statistics on the real severe weather image set to obtain prompt basic information; performing instance sampling, scene construction processing and scene description processing on the prompt basic information to obtain scene information, and performing scene description processing on the scene information by adopting a large language model to generate a text prompt; converting the text prompt into text embedding, and enabling the text embedding to be consistent with the multi-scale feature dimension; performing multi-step diffusion on the text embedding and multi-scale features by adopting a diffusion model, updating a text prompt and updating the corresponding text embedding by adopting the LLM module in each step of diffusion process, and finally obtaining a severe weather image; according to the method, the controllability and the accuracy of the diffusion model generation process can be improved.

This provides an information provision method that enables the easy generation of optimal landscape plans by utilizing generation AI. [Solution] The information provision method performed by the information provision device 3 includes: a question reception process that receives question information D1, which is construction target information relating to the construction target of the landscaping work; a prompt generation process that generates prompt information D3 that instructs the generative artificial intelligence model 20 to output answer information D4, which is plan information indicating a plan for carrying out landscaping work on the construction target, based on the question information D1; and an answer provision process that provides the answer information D4 output from the generative artificial intelligence model 20 by inputting the prompt information D3 to the generative artificial intelligence model 20.

The invention discloses a method for enhancing a code abstract based on an LLM enhanced data set and an AST hierarchical perception model, and relates to the field of software engineering, and the method can automatically generate an abstract described by a natural language for a code. Firstly, an innovative method fusing code quality evaluation and optimization is provided, and according to the method, code defects in a data set are automatically corrected in the generation process of a large language model through a prompt engineering mechanism based on code quality feedback, and the method is used for follow-up model training. And then designing a directed syntax graph (DSG) with a hierarchical semantic retention capability based on AST, proposing a hierarchical perception code abstract model (HiSum), and realizing grammar hierarchical structure coding through a directed graph convolutional network (dirGCN). The directed syntax graph encoded representation is then fed into a decoder with an attention replication mechanism to generate a code abstract. A large number of experiments on Java and Python data sets prove the effectiveness of the model provided by the invention.

The invention belongs to the technical field of artificial intelligence, and relates to an AI-based performance analysis optimization method and related equipment thereof, and the method comprises the steps: collecting multi-source isomerized performance analysis data from a target system; analyzing and identifying system performance defects; inputting the system performance defects into the generative large language model, and generating a system performance optimization scheme according to system performance defect repair or optimization data in the generative large language model; and analyzing the system performance optimization scheme, and performing performance optimization on the target system. In the process of data acquisition, data analysis and subsequent system performance optimization scheme generation, an AI processing component is fully utilized, so that a large amount of manpower consumption is saved, manual analysis steps are reduced, and the accuracy of an analysis result can be ensured as much as possible. When the performance analysis optimization method is applied to system performance data analysis, especially in a financial business scene with a large and miscellaneous business data volume, the analysis efficiency can be remarkably improved, and business performance abnormity can be found in time.

This invention relates to the field of natural language generation technology, specifically to a text generation system and method based on visual topology and color orthogonal modulation. In this invention, a graph neural network is used to calculate node association weights and generate node state vectors, ensuring that character interactions and plot progression in the generated text always adhere to strict topological constraints, significantly improving narrative coherence and structural integrity. By introducing a support vector machine to calculate the correlation between content vectors and style vectors and adjusting the update direction when limits are exceeded, precise control of the emotional tone and linguistic style of the generated content is achieved without interfering with the core semantic expression, avoiding the risks of repeated deadlocks and semantic drift in the generation process. Furthermore, by using style-controlled context vectors and entity-consistent context vectors to expand the paths of candidate lexical sequences and accumulate evaluation values, repeated loops and logical deadlocks in the generation process are avoided, thus producing digital media content with both high logical rigor and rich stylistic expressiveness.

The invention discloses a GEO anti-fraud method and system based on multi-source contact information conflict modeling, and mainly relates to the technical field of generative artificial intelligence. Comprising the following steps: extracting a structured triple from multi-modal content associated with a result returned by a generative engine; constructing a cross-modal heterogeneous conflict graph by taking each triple as a node; giving an initial confidence coefficient based on a source authority identifier to each node in the conflict graph, executing belief propagation of conflict perception on the conflict graph, iteratively updating the confidence coefficient of each node, and performing fraud judgment based on the confidence coefficient of each contact way under the same brand obtained after propagation; and based on a fraud judgment result, carrying out dynamic intervention on a retrieval enhancement generation process of the generative large model, and reducing contact ways judged to be fraud from appearing in a finally generated answer. The method has the beneficial effects that high-precision fraud identification can be realized by dynamically inhibiting a propagation path of mutually exclusive false information.

This invention discloses a multimodal creative content generation method and system. The method includes: receiving multimodal input data such as text, images, and audio and converting it into feature vectors; calculating the cross-modal correlation between features of each modality using a self-attention mechanism and performing weighted fusion to obtain a unified multimodal fusion representation; generating creative content based on this representation through a generator network; collecting multi-dimensional feedback information from user ratings, market testing, or sentiment analysis; dynamically adjusting the weights of each modality in subsequent fusion processes based on this feedback, and outputting optimized creative content. This invention achieves deep cross-modal information understanding and fusion through a self-attention mechanism and introduces a dynamic feedback loop, enabling the creative content generation process to have adaptive optimization capabilities, significantly improving the relevance, diversity, and market adaptability of creative content.