17 results about "Self evaluation" patented technology

An object of a system according to an embodiment is to provide a hint and a way of thinking for a user to derive an answer by himself / herself.SOLUTION: A system according to an embodiment includes a hint provider, a concept presenter, and a self-evaluation promoter. The hint providing unit provides a hint. The concept presenting unit presents a concept. The self-evaluation promotion unit promotes the self-evaluation.SELECTED DRAWING: Figure 1

An object of a system according to an embodiment is to analyze a user's emotion or psychological state and propose personalized counseling or self-care activity.SOLUTION: A system includes a self-evaluation unit, an analysis unit, a proposal unit, and a tracking unit. The self-evaluation unit receives a self-evaluation of the user. The analysis unit analyzes the self-evaluation data received by the self-evaluation unit. The suggestion unit suggests a counseling session or a self-care activity based on the data analyzed by the analysis unit. The tracker tracks the user's progress and adjusts the plan as needed.SELECTED DRAWING: Figure 1

The invention provides a method and system for personal growth and accountability, addressing limitations of traditional therapy approaches. It teaches individuals to recognize cyclical patterns of victimhood, identify triggers, and analyze personal narratives. The method includes an Adult Circle Process for defining boundaries and acknowledging responsibility, and introduces “The Line,” a self-assessment tool for evaluating behaviors on a respect-disrespect scale. A Power Circle component encourages positive growth through recognizing positive triggers and developing empowering narratives. The invention can be implemented via digital platforms, workbooks, or guided sessions, offering concrete tools and measurable outcomes for users to foster accountability and shift focus from problem-solving to strength-building.

The application discloses a depression relapse risk intervention method, device, equipment and medium, and belongs to the medical technical field. The method comprises the following steps: integrating a gene risk score, a neural imaging brain region feature and clinical history data through a multi-modal fusion neural network to generate an individualized baseline risk score; dynamically correcting the baseline score based on self-evaluation data to enhance the real-time performance of risk assessment; analyzing the time sequence characteristics of physiological and behavioral data by using an LSTM time sequence model to output a short-term relapse early warning label; and combining real-time voice / text data emotion knowledge graph analysis and dynamic risk grading to trigger a hierarchical intervention strategy. The application solves the problems of single evaluation dimension and response lag of traditional methods through a multi-dimensional data fusion and dynamic calibration mechanism, realizes closed-loop management from risk warning to precise intervention on the premise of avoiding direct clinical diagnosis, and improves the comprehensiveness and timeliness of depression relapse prevention and control.

Systems and methods are provided for generating, managing, adapting, and delivering speech therapy to a user via a mobile device in an at-home or out-of-clinic setting. Users may be persons experiencing aphasia or other speech conditions. For example, prompts may be communicated to a user, and their spoken responses recorded and analyzed. Based on analysis of the responses, these systems and methods may determine a mode of response including but not limited to playback of the user's spoken response to allow the user to recursively self-assess and / or self-correct. User performance and improvement trends may be assessed, and utilized to adapt future therapy sessions.

The application belongs to the technical field of deep learning and computer vision, and discloses a small sample segmentation method and device based on fine tuning and self-evaluation. The method firstly initializes system parameters, then predicts pseudo labels for query images to obtain query prototypes, updates the query pseudo labels after combining with support prototypes, fine tunes the system parameters by using the pseudo labels, then repeats the process to realize multiple fine tuning of the system, finally determines the best fine tuning round by using the designed self-evaluation method, and obtains the final segmentation result. The application alleviates the problem that the existing system uses categories or support images in biased training, and improves the segmentation accuracy. The designed self-evaluation method can evaluate the optimal fine tuning round, and prevent the error accumulation in the fine tuning process from causing the problem of low accuracy. Compared with the existing method, the fine tuning process only needs about 1% of the time cost and computing power cost, and can more quickly and efficiently adapt to new categories and obtain high-quality segmentation results.

A system is provided.SOLUTION: The system includes a means for collecting data such as an operation history of a learner, a test result, and self-evaluation, a means for analyzing the collected data and identifying a thinking pattern and a cognitive style of the learner, a means for automatically generating individualized educational contents based on the analysis result, a means for delivering the automatically generated educational contents to the learner, and a means for dynamically adjusting an educational program according to the progress of the learner.SELECTED DRAWING: Figure 1

We provide a device that provides an integrated system that includes quantitative multiple-choice evaluation by category, combined self- and peer-evaluation, visualization of score trends, and action list presentation. [Solution] The organization comprises a display control means for displaying on a display unit, for each member of a plurality of categories, a screen for each member including one or more questions for each category and multiple choice answers to the questions; an input accepting means for inputting multiple choice answers by each member to each question; and an evaluation means for evaluating each member based on one or more questions for each of the plurality of categories and one or more answers corresponding to each of the one or more questions, wherein the categories include at least categories related to each member's skills, categories related to each member's condition, and categories related to engagement, and the display control means displays on the display unit a screen for each member including the results of the evaluation.

The application relates to the technical field of industrial equipment monitoring and defect diagnosis, and particularly discloses a data-adaptive equipment production process defect identification method and system. A multi-dimensional delay dynamic benchmark is constructed, and a joint decision based on a delay gradient and fluctuation entropy is adopted to realize intelligent classification and rapid routing of sudden failures, environmental disturbances and composite hidden dangers. The system adopts a three-layer nested diagnosis mechanism of abnormal dimension screening, cause-effect chain tracing and defect mode matching to accurately locate the root cause and drive a three-order adaptive response closed loop of instantaneous inhibition, parameter compensation and model correction to minimize production interruption. Meanwhile, the system periodically calculates a production line health index to realize self-evaluation and collaborative optimization of key parameters, so that the monitoring model can continuously evolve with changes in the equipment state and the environment, and finally form an integration of real-time sensing, intelligent diagnosis and adaptive optimization, thereby significantly improving the defect identification accuracy, system stability and overall operation efficiency.

Systems and methods are provided for generating, managing, adapting, and delivering speech therapy to a user via a mobile device in an at-home or out-of-clinic setting. Users may be persons experiencing aphasia or other speech conditions. For example, prompts may be communicated to a user, and their spoken responses recorded and analyzed. Based on analysis of the responses, these systems and methods may determine a mode of response including but not limited to playback of the user's spoken response to allow the user to recursively self-assess and / or self-correct. User performance and improvement trends may be assessed, and utilized to adapt future therapy sessions.

The embodiment of the invention provides an image editing method, a virtual fitting method, a model training method and equipment. In the method, the image editing model can be called to edit the initial image to obtain the intermediate image, and whether the editing effect of the intermediate image is matched with the image editing instruction can be automatically determined, so that self-evaluation is realized; and when the editing effect of the intermediate image is not matched with the image editing instruction, the image correction instruction can be generated to correct the intermediate image until the target image conforming to the image editing instruction is obtained, so that self-correction is realized. The image editing model forms a closed loop of'editing-evaluating-correcting ', initial editing errors of the image editing model can be actively recognized and corrected, the image editing effect is improved, and therefore the accuracy of generating the target image through the image editing model is improved.

A system and method for using retrieval-augmented generation (RAG), in a large language model (LLM) having additional content to enhance relevance of LLM generated content. In operation, a user asks a question and the LLM determines whether additional content needs to be retrieved from a data management system to answer the question. If so, the LLM is provided with the user question and said additional content and asked to score its ability to confidently answer the question with the provided content. If the score is below a predefined threshold, the LLM is instructed to generate an alternate response using the retrieved additional content. Additionally, the system generates follow-up suggestions to assist the user in gaining more in-depth knowledge on the subject at hand. If the score is below a predefined threshold for a selected suggestion, the system will record this to assist to fill gaps in available content.

The application relates to the technical field of personalized education, and discloses a personalized learning monitoring system based on user behavior data, which comprises a data collection module, a learning target setting module, a self-evaluation and feedback module and an emotion monitoring module.The data collection module is used for collecting learning behavior data and physiological data of a target user and integrating interactive log data in a learning platform.The learning target setting module sets a personalized learning target system comprising short-term and long-term targets.The self-evaluation and feedback module guides the target user to regularly perform self-evaluation and helps the target user to understand current learning progress.The emotion monitoring module identifies current emotional fluctuations of the target user.A dynamic task adjustment module dynamically adjusts the difficulty, content and rhythm of learning tasks based on the personalized learning target system, current learning progress and current emotional fluctuations, and generates real-time adaptive learning task flows.The application automatically identifies state changes, pushes matching tasks, and greatly reduces cognitive burden and management cost.

An object of the system according to the embodiment is to provide learning content customized in accordance with interests and learning styles of children.SOLUTION: A system according to an embodiment includes an interaction unit, a content generation unit, a gamification unit, an evaluation unit, and a mobile application providing unit. The dialogue unit dialogues with the children and grasps their interests and learning styles. The content generation unit generates learning content customized based on the information grasped by the dialogue unit. The gamification unit introduces gamification elements into the learning content. The evaluator provides self-evaluation and feedback on the learning content. The mobile App providing unit provides learning content through the mobile App.SELECTED DRAWING: Figure 1

The invention provides a valuation table processing method and an RPA robot, which can be used in the financial field or other fields, and the method comprises the following steps: receiving a valuation table processing request; if the processing mode in the valuation table processing request is valuation table reconciliation, reconciliation data are obtained according to the valuation table processing request, and the reconciliation data comprise a to-be-processed party valuation table, a to-be-processed local party valuation table, a local party subject name and to-be-compared elements; according to the reconciliation data, reconciliation is carried out on the to-be-processed party valuation table and the to-be-processed local valuation table, and valuation table processing corresponding to the valuation table processing request is completed. According to the invention, the automation degree and accuracy of valuation table processing can be improved.

This invention provides an intelligent operation and maintenance fault diagnosis method based on problem reconstruction and consensus reasoning. The method includes: constructing a fault description rewriting model based on Direct Preference Optimization (DPO) by leveraging the generation capabilities and self-evaluation feedback of a large language model; reconstructing the original fault description in diverse ways based on the fault description rewriting model, and filtering non-standard fault descriptions into a set of fault query statements through a rejection sampling mechanism based on perplexity level (PPL); performing multi-perspective hybrid consensus diagnostic decision-making based on the real-time collected fault descriptions and fault query statement set, and outputting intelligent operation and maintenance fault diagnosis results corresponding to the real-time collected fault descriptions, including root cause localization and remediation suggestions. This invention uses the PPL rejection sampling mechanism to filter high-quality candidate rewrites that conform to operation and maintenance specifications, and utilizes a hybrid majority voting strategy to eliminate comprehension biases from a single perspective, achieving highly robust automated fault localization and diagnosis.

Systems and methods include improved planning and reasoning capabilities of a web agent, which interacts with a real world website. The agent improves with autonomous experience and requires limited supervision. An MCTS-based search routine is deployed over web pages, a base model is used as an initial distribution, and a number of rationales and possible web actions are explored. AI feedback and self-criticism are used, and the model provides self-evaluation at each node.