56 results about "Automaticity" patented technology

The invention discloses a data processing method and system based on intelligent correction and electronic equipment, and relates to the technical field of educational informationization, and the method comprises the steps: analyzing a standard answer through natural language processing, extracting a standard knowledge point node and a connection edge, distributing a logic priority, calculating a node weight, and constructing a standard cognitive path map; analyzing student answers, mapping student knowledge point nodes through semantic matching, and constructing a student cognitive path map; comparing the two maps, and generating a score mark through a node matching state and sequence offset; detecting and complementing missing nodes and fracture paths, and constructing an atlas residual error scoring structure atlas; and calculating a structured score, and generating a report containing explanatory feedback. The system comprises five corresponding modules, and the equipment comprises a memory and a processor. The correction accuracy and interpretability are improved, the output report adapts to student feedback and teacher rechecking, and the method is suitable for education informatization automatic correction.

The invention discloses an engineering drawing compliance intelligent review method and system constructed based on a knowledge base and a large model. The method mainly comprises the following steps: intelligently analyzing a design specification in a natural language form by utilizing a large language model, and dynamically constructing a machine-readable rule base and a knowledge base; uniformly converting the multi-format engineering drawing into a structured intermediate format; functional areas and primitives in the drawing are recognized through a computer vision model, and natural language information describing attributes of the functional areas and the primitives is generated; and finally, in combination with the knowledge base and natural language description, performing compliance judgment by utilizing the reasoning ability of the large language model, and generating an interpretable review report. According to the method, computer vision and a large language model technology are fused, so that the defects that traditional manual review is low in efficiency and prone to making mistakes and a traditional automatic tool lacks semantic understanding ability are overcome, and efficient and accurate engineering drawing automatic compliance review with deep semantic understanding ability is achieved. Figure 1 of the abstract is a system architecture block diagram.

The invention relates to the technical field of artificial intelligence, in particular to an automatic deep thinking model selection training method for a large language model. Comprising the following steps: expanding a word segmentation device vocabulary, and adding a special mark lt; carrying out think gt; and lt; / (ginkgt); the identification module is used for identifying a content structure of the deep thinking mode; a dialogue template is designed, input and output formats of a common mode and a deep thinking mode are distinguished, and the deep thinking mode comprises lt; carrying out think gt; marking a guided reasoning process; a common mode training data set and a deep thinking mode training data set with the same scale are constructed, and joint training is performed on the model, so that the model has response capabilities of the two modes at the same time. The method comprises the following steps: adding a special mark (lt; carrying out think gt; and lt; / (ginkgt); and a dialogue template is customized, semantic distinguishing between a common mode and a deep thinking mode is achieved, and the model can accurately switch response strategies according to requirements.

The invention relates to a causal reasoning-based retrieval enhancement generative model fine tuning method and system, and the method comprises the following steps: A, modeling a causal relationship to reveal an influence mechanism of an input variable on related knowledge extraction and irrelevant knowledge filtering; step B, defining a knowledge revenue score (KGS), and calculating the knowledge revenue score through expert labeling or automatic evaluation to construct a causal enhancement data set (CED); c, designing a fine tuning strategy based on intervention and anti-factual reasoning, systematically performing knowledge variable intervention and anti-factual condition simulation, and enhancing the sensitivity of the model to related knowledge and the robustness of the model to irrelevant knowledge; and D, dynamically integrating the context and the knowledge to generate a high-quality response in a reasoning stage. The method is beneficial to improving the accuracy and stability of the dialogue generation model.

The invention relates to the technical field of learning education, in particular to an AR-based personalized learning education auxiliary method and system. By collecting and synchronously processing multi-modal behavior data such as eye movement, gestures and head orientation, time sequence characteristics capable of accurately reflecting the learning state of a user are constructed, and then a hidden cognitive state sequence is decoded by using a hidden Markov model and inflection points of the hidden cognitive state sequence are recognized; finally, dynamic and automatic adjustment of learning contents is realized by means of a reinforcement learning model, deep cognitive state changes can be captured from continuous and dynamic user behaviors, accurate teaching intervention is timely performed at'inflection points' of key transition of cognitive states, personalized adaptive learning path planning is realized, and the learning efficiency is improved. The pertinence and effectiveness of learning are obviously improved; the technical problem that an existing AR learning system is difficult to intervene in time at an inflection point where a user cognition state is changed during path planning, so that real personalized learning path planning is realized is solved.

The invention discloses a big language model causal reasoning method and system based on knowledge guidance, and the method comprises the steps: constructing a standard knowledge base of a target domain, obtaining an original observation corpus of the target domain, and matching J similar variables and variable types thereof in the original observation corpus based on the standard knowledge base of the target domain, according to the J similar variables and the variable types thereof, constructing a causal inference graph of the target domain, obtaining a to-be-inferred variable set, taking the to-be-inferred variable set as the input of the causal inference graph, outputting a corresponding causal inference path, and returning the causal inference path to the user side; according to the method, by introducing the positions of the variables in the knowledge fact tuple, the automatic determination of the reasoning direction is realized, and missing nodes and edges can be complemented as required by taking the part of variables as anchor points and combining a causal pairing relationship in a standard knowledge base, so that the complete candidate knowledge facts are recovered, and the reasoning efficiency is improved. And the adaptability in a data missing or information incomplete scene is improved.

The invention discloses a computer-aided old-age language erosion assessment method and assessment system, and relates to the technical field of old-age health assessment, and the method comprises the following steps: S1, collecting the voice data, text input data and interactive behavior data of an old-age user; s2, preprocessing the data, and extracting voice acoustic features, language structure features and cognitive behavior features; s3, inputting the extracted features into a pre-trained language erosion evaluation model, and outputting a language ability score and an erosion type classification result; and S4, generating a visual evaluation report, wherein the visual evaluation report comprises the language ability degradation degree, key obstacle points and intervention suggestions. According to the method, the score and classification result is automatically output through the multi-modal data acquisition and pre-trained deep learning model, so that the evaluation time is greatly shortened, the subjective deviation is eliminated, the result objectivity is ensured, the problems of low manual evaluation efficiency and high subjectivity are solved, and the effect of automatic evaluation is realized.

The invention provides an eBPF program automatic generation method, system and device based on retrieval enhancement and thinking chain reasoning, and the method comprises the steps: constructing an eBPF semantic knowledge base, and extracting and structuring a plurality of program examples with semantic annotations; after a natural language task description input by a user is received, the natural language task description is coded into a semantic vector, and accurate retrieval of related contexts is achieved in combination with semantic indexes; based on a retrieval result, constructing a structured prompt, and guiding a language model to gradually generate an eBPF program according to stages; an eBPF verifier is used for carrying out legality check on the generated program, and behavior testing is carried out in combination with task input; when structural errors or semantic deviations are found, the model is guided to be automatically repaired based on error information. The method can be operated on a multi-language model platform, supports the output of two styles of BCC and BPFtrace, and has the advantages of controllable structure, accurate semantics, stable deployment and the like. Experimental results show that the automatic generation efficiency and accuracy of the eBPF program can be remarkably improved.

The invention relates to the technical field of artificial intelligence question answering systems, and discloses an interactive question answering system based on multi-model parallel reasoning. The system comprises an interactive interface module, a query cognition construction module, a hierarchical index module, a multi-model parallel reasoning module, an interactive answer synthesis module and a tool calling adaptation module. The system constructs query cognition mapping by deeply analyzing a time sequence query stream containing texts and media of a user, and drives dynamic evolution of hierarchical indexes according to the query cognition mapping. The multi-model parallel reasoning is based on evolution strategy coordination processing, and knowledge slices with state vectors are generated. And finally, synthesizing a natural language answer attached with the interaction intention unit, and adapting the natural language answer to an external tool calling instruction. According to the system, the cognition and retrieval precision under complex query is improved through intention-driven dynamic indexing, and automatic closed loop from information question answering to business operation is realized through executable answers.

The application relates to a general field thought chain corpus generation method and medium, which comprises the following steps: acquiring general field corpora of different task types, converting the corpora into a unified standard format through structuring, and obtaining standardized corpora, wherein the task types include subjective types and objective types; generating initial thought chain texts and conclusions according to general matching reasoning links based on the standardized corpora of different task types; and verifying the initial thought chain texts and conclusions through a multi-dimensional automatic quality inspection mechanism to obtain final thought chain texts and conclusions. Compared with the prior art, the application has the advantages of generalization, process standardization, controllable quality and the like.

The application discloses a Bloom cognitive hierarchy constraint-based achievement-oriented education diagnosis method and system. The method takes course outline text and student evaluation data as input, and realizes the automatic diagnosis of course goal achievement through three structural modifications in the large language model Transformer architecture: the logarithmic value of the cognitive hierarchy transfer matrix is embedded as a learnable bias item in the attention score calculation path to realize feature coding of cognitive hierarchy perception; the continuous differentiable relaxation technique is used to establish an end-to-end differentiable inference path for the course goal achievement weight; and the Bloom partial order constraint is coded as a training loss, so that the diagnosis result meets the cognitive hierarchy progressive relationship. The three modifications are optimized by a joint loss function, which significantly improves the diagnosis accuracy and promotes the interpretability and scientificity of the large language model diagnosis in the achievement-oriented education scene.

The invention discloses a style dynamic adaptation switching method for switching cue words along with a large model, and relates to the field of style dynamic adaptation switching of cue words, which comprises the following operation steps: S1, core hypothesis and benchmark establishment; s2, an input initialization stage; s3, performing a first reasoning and evaluation process; and S4, iteratively optimizing the process. According to the style dynamic adaptation switching method for switching the cue word along with the large model, style deviation of different models can be automatically recognized, the cue word of a system is corrected in real time through feedback circulation, an adaptation rule does not need to be written manually, intelligent optimization of the cue word is achieved, automatic adaptation can be achieved for model updating or supplier adding scenes, and the user experience is improved. According to the method, the cue word does not need to be manually readjusted, the human input is reduced, the maintenance cost of the cross-model cue word is remarkably reduced, the manual adaptation workload is reduced through automatic iterative optimization, the newly-added model access period is greatly shortened, and the time of several days of a traditional scheme is shortened to the hour level.

According to the verbal skill abstract generation method based on the pointer mechanism, paragraph segmentation and entity labeling are carried out on an original verbal skill problem in advance, and semantic analysis and information extraction can be carried out more accurately in the follow-up process. And then text enhancement is carried out on the preprocessed verbal skill text through a field enhancement model, so that insurance verbal skill technical terms, clause logics and complex relations between the insurance verbal skill technical terms and the clause logics in the verbal skill text can be better understood, and key information, most critical clause contents and logic structures in the verbal skill can be more accurately captured. According to the method, the pointer generation network is combined with the field enhanced text representation, so that the generated abstract can accurately reflect key information and core logic in insurance verbal skills, automatic key point extraction of complex insurance terms is realized, and seat service efficiency and compliance are improved. The method can be applied to insurance recommendation verbal skill scenes in the financial field and the medical field, and seat service efficiency and compliance are improved.

The invention provides a multi-modal large model fine-tuning corpus production method for the field of planning and natural resources, and aims at solving the problems that existing corpus lacks professional semantics, manual annotation is low in efficiency, and no standardized process exists. According to the method, an exclusive VQA corpus is generated through four core modules including construction of an industry exclusive cognitive task and VQA template library, image-text pairing generation, template-driven VQA sample generation and automatic quality control optimization in combination with an industry planning standard and a cognitive hierarchy, namely perception-reasoning-association-application. The method comprises the following steps: firstly, automatically extracting visual elements and policy texts of a planning graph, and matching a professional template to generate candidate Qamp; the method comprises the following steps: A, finally outputting a standardized corpus package through semantic detection and expert re-check optimization; according to the method, professional semantic accurate alignment is realized, the labor cost is greatly reduced, the corpus quality is reliable, the method can be expanded to similar fields, and efficient support is provided for field multi-mode large model fine adjustment and capability evaluation.

The application relates to the technical field of automation operation and maintenance, and discloses an intention recognition method and system based on dynamic ontology evolution and multi-agent, which comprises the following steps: receiving a natural language instruction, performing entity extraction and probabilistic linking by using a dynamic ontology knowledge base, and generating an initial intention based on predicate analysis; automatically completing missing key slots by using a probabilistic graph model, and generating a standardized intention; decomposing the standardized intention into an atomic subtask sequence, dynamically matching an execution agent based on an agent capability-demand matrix, generating a collaborative workflow, controlling the execution agent to call an atomic tool to execute a task, performing causal correlation analysis on multi-source results according to logical relations between ontology instances, generating a structured reasoning chain, and feeding back; and extracting a new treatment script based on execution feedback by using an evolution engine, and updating an ontology knowledge base and an agent confidence degree. The application can realize accurate understanding, automatic execution and adaptive evolution of a knowledge base of a fuzzy operation and maintenance intention.

The invention relates to a method for the automated dynamic design of at least one technical component, comprising an input module (200), a reward module (300), a learning reinforcement module (400) with a learning reinforcement agent (410) and an action module (420), a design module (500) with an environment module (510) and a state module (540), and an output module (700), comprising the following steps: - Generating a design draft (520) for a design goal (390) in the form of a state (S i ) from the construction module (500); - Evaluating the generated state (S i ) from the reward module (300) with a target state (S t ) and determining an objective indicator value (340); - Generating a subjective indicator value (370) from subjective evaluations of the design (520) by a user; - Generating an emotional indicator value (380) from the user's emotional state data (290); - Calculating a reward (320) using a reward function (330), where the reward function (330) takes into account the objective indicator value (340), the subjective indicator value (370) and the emotional indicator value (380); - Selecting an action (A i ) by the learning reinforcement agent (410) due to the reward (320) and generating a modified design draft (520) in the form of a new state (S i+1 )

The present application relates to the technical field of intelligent evaluation, and more particularly to a patent creative intelligent screening and evaluation method and system based on multi-agent cooperation, comprising: S1: receiving a patent creative text by a creative intelligent agent and performing semantic analysis, extracting a technical application scenario, an existing scheme pain point, a creative description and a core innovation point; S2: calling a semantic retrieval engine by a retrieval intelligent agent, performing parallel retrieval in multiple databases, and generating a retrieval result set; S3: performing evaluation reasoning by an evaluation intelligent agent from multiple dimensions of novelty, practicality, technical effect, scene difference degree and technical path coverage, and generating an evaluation conclusion; S4: feeding back the evaluation conclusion to a management system by a cooperative intelligent agent and pushing an evaluation notification; S5: monitoring new creations and triggering cyclic execution by the cooperative intelligent agent according to a preset scheduling rule. The present application realizes full-process automatic closed-loop processing, evaluation standardization and conclusion explainability, and significantly improves the patent creative screening efficiency and management cooperation.

The invention relates to the technical field of artificial intelligence, in particular to an extensible large language model driven cognitive competence assessment system and method.One or more large language models are adjusted and optimized by using an example data set with a label, and questions in a project question bank are automatically classified by using the adjusted and optimized large language models, so that the accuracy of cognitive competence assessment is improved. Obtaining a classification result; constructing a cognitive competence test based on the classification result, generating a structured evaluation result, and storing the evaluation result in a historical record of the user; the method comprises the steps of obtaining an evaluation result of a user, based on historical records of the user and the evaluation result, activating a dialogue agent driven by a large language model, converting recommended items into a structured query request, integrating retrieval results and generating a customized course scheme exclusive to the user, and according to the method, the large language model (LLM) is deeply integrated into an education evaluation and course generation closed loop; and full-process automation of automatic question classification, dynamic cognitive competence diagnosis, continuous model optimization and personalized course delivery is realized.

The invention provides a multi-agent self-adaptive test question generation method and system based on cognitive constraints, and relates to the technical field of large language models, and the method comprises the steps: receiving a question setting request of a user; based on the question setting request, performing retrieval on a pre-constructed double-layer cognitive education knowledge graph to obtain an initial teaching method sub-graph; retrieving from a pre-constructed external vectorization test question bank to obtain candidate test questions; judging whether the context of the initial teaching method sub-graph is sufficient or not; if the context is insufficient, executing a self-adaptive context evolution loop, expanding the initial teaching method sub-graph, performing judgment again, and continuously expanding the loop until the context is sufficient; if the context is sufficient, a final context sub-graph is obtained, a graph constraint decoding algorithm is executed, the final context sub-graph serves as cognitive constraint, the candidate test questions are reconstructed, and a target test question is generated and output. The invention provides an automatic test question generation capability with high teaching law alignment and high knowledge loyalty.

The invention discloses a code review opinion generation method based on retrieval enhancement. The code review opinion generation method comprises the steps of 1, constructing a comparative learning training data set fusing code change structure features and review intention semantic features; 2, driving a universal pre-training code encoder to carry out transfer learning to a code review field by utilizing the comparative learning data set to obtain a review intention perception retriever; step 3, recalling a historical reference case as a reference context based on the retriever after fine tuning, and constructing a structured prompt template containing an anti-noise mechanism; and step 4, combining the prompt template, and performing instruction fine tuning on the large language model by using a low-rank adaptive technology (LoRA). Through cooperation of retrieval vector space remodeling and efficient parameter fine tuning, the problem that in the prior art, review opinions are seriously generalized is effectively solved, the pertinence and reference value of automatically generated opinions are remarkably improved, and the expert-level automatic code review opinion generation technology in the low-computing-resource environment is achieved.

The invention provides an automatic learning follow-up system and method, and the system comprises a student management module which is used for collecting the full-life-cycle data of a student, and constructing a student portrait of the student based on the full-life-cycle data; the learning plan making module is used for generating a personalized learning plan based on the trainee portrait, the full life cycle data and a multi-modal learning content recommendation algorithm; the learning condition analysis module is used for determining whether the trainees are abnormal or not based on the trainee portraits, the full life cycle data and the personalized learning plans; and the learning plan making module is also used for optimizing the personalized learning plan based on the abnormal data, the student portrait, the full-life-cycle data and a multi-modal learning content recommendation algorithm when the student is abnormal, so as to obtain an optimized learning plan. According to the invention, targeted formulation and progress tracking of the personalized learning plan are realized, and the passing rate of the examination is improved.

The application discloses a fusion large model intelligent teaching quality evaluation improvement system and relates to the technical field of wisdom education.The application realizes automatic identification of skill response behavior by means of a disturbance group modeling module and a behavior characteristic difference set of students between original questions and candidate comparison questions.System no longer depends on subjective judgment of teachers, but analyzes answer stability of students when facing semantically equivalent and structurally stable questions through quantitative index.When it is detected that the behavior path of students deviates by a high amplitude, the system can immediately determine that there are skill dependence and concept misplacement problems.For example, after answering the disturbance question, the correct rate of the answer decreases, which indicates that there is a strategic answer instead of concept reasoning, and the objectivity and traceability of the teaching quality evaluation are further improved through the knowledge graph of the original question stem.

The application discloses an automatic penetration testing method and system based on a cognitive decision model, relates to the technical field of information security and data processing, and realizes deep cognitive modeling of a penetration environment by constructing a multilayer state space composed of an explicit state layer, a structural state layer and a potential cognitive state layer, directly records observable attributes through the explicit state layer, infers network topology and component correlation through graph analysis through the structural state layer, and speculates unknown factors based on a probability model through the potential cognitive state layer to form conditional probability representation, so that the modeling mode enables the system to construct complete environmental cognition from fragmented information, maintains decision stability through probabilistic reasoning when the information is incomplete, meanwhile, penetration experience trajectories are parsed into unified state-action sequences through a semantic mapping function from natural language penetration records, so that the trajectories ensure that expert reasoning logic is reflected, and a test system with environmental cognition and experience internalization is formed.

The invention relates to the field of medicine, psychology and psychotherapy, as well as to computer information technology. It is based on the 8-phase psychotherapy technique of F. Shapiro for treating stress and post-traumatic stress disorder in patients. The technique involves eight consecutive phases (EMDR Association (Eye Movement Desensitization and Reprocessing), https: / / emdr.ru / news / 2018 / emdr-terapiya-obzor-razvitiya-i-mekhanizmy-deystviya). The prototype of the claimed invention is the method described in 'Intelligent virtual environment for treating anxiety exploring the Eye Movement Desensitization and Reprocessing technique' (https: / / www.researchgate.net / publication / 357606075_Intelligent_virtual_environment_for_treati ng_anxiety_exploring_the_Eye_Movement_Desensitization_and_Reprocessing_technique). A disadvantage of the prototype is that artificial intelligence is used in a limited fashion merely to determine the level of anxiety and adjust the movement of the ball in accordance therewith, and to record information. Furthermore, the prototype presupposes the involvement of a therapist, albeit to a lesser extent than in the previous version. A virtual therapist interacts with patients by means of speech, and responses to questions from a real therapist, in addition to anxiety level and heart rate, must be logged by the real therapist with the aid of a keyboard during communication with patients in virtual reality. A further disadvantage of the prototype is its focus on solving a limited number of patient problems (relieving stress and PTSD), i.e. only one protocol is used, and the fact that it cannot be used without the involvement of a therapist. The prototype is unable to provide a diagnostic assessment of problematic states of a wide range of psychological problems, select a corrective protocol, monitor the effectiveness of correction, and propose additional methods of correction. The claimed invention solves the technical problem of broadening the range of negative and problematic states that can be diagnosed (not just the level of PTSD) and making it possible to select and implement the specific protocol required to correct the problem identified, as well as to assess the effectiveness of correction and to propose additional solutions, wherein all of these aims are achieved without involving a therapist. A significant defining feature of the proposed method is the possibility of identifying a problem state in an initial step and selecting a suitable software-automated protocol tailored to each identified category of problem.

The application discloses a kind of big language model causal inference method and system based on knowledge guidance, comprising: the standard knowledge base of target field is constructed, the original observation corpus of target field is acquired, based on the standard knowledge base of target field, J similar variables and its variable types are matched in original observation corpus, according to J similar variables and its variable types, the causal inference graph of target field is constructed, the set of variables to be reasoned is acquired, the set of variables to be reasoned is taken as the input of causal inference graph, the corresponding causal inference path is output, and the causal inference path is returned to user end;The application realizes the automatic determination of reasoning direction by introducing the position of variable in knowledge fact tuple, and can use the part of variable as anchor point, combine the causal pairing relationship in standard knowledge base, complete the missing node and edge on demand, so as to restore the complete candidate knowledge fact, improve the adaptability in data missing or incomplete information scene.

This invention relates to the field of artificial intelligence technology, specifically to an automatic construction method and system for large-scale language model intelligent agents. The system includes: S1 Modular representation and knowledge construction, decomposing the intelligent agent into standardized functional modules and establishing a module knowledge base and a historical experience base; S2 Initial intelligent agent automatic assembly, generating an initial configuration from the language model based on task description and dual-base retrieval results; S3 Execution and hybrid evaluation, collecting execution data and generating performance feedback through holistic evaluation of the language model and game theory module contribution evaluation; S4 Dynamic module reorganization and optimization, performing module replacement or optimization based on feedback to achieve knowledge accumulation; S5 Iterative optimization, cyclically executing evaluation and optimization to output the optimal configuration. The system comprises four main units: module management, assembly, execution and evaluation, and optimization, used to implement the above method. This invention achieves automated construction and dynamic optimization of intelligent agents, improving interpretability, cross-task adaptability, and continuous optimization capabilities, while reducing development costs.

The invention discloses a workflow arrangement method, an execution method, a device, a system, equipment and a product, and relates to the technical field of artificial intelligence. A plurality of operation steps in a target workflow and description information and instruction prompts of each operation step are obtained; and inputting the description information and instruction prompts of the multiple operation steps into the large language model in sequence to obtain the intelligent verbal skill of each operation step output by the large language model, thereby realizing arrangement of the target workflow. And inputting the intelligent verbal skills of a plurality of operation steps in the target workflow into the large language model in sequence in an intelligent conversation form to obtain an execution result of the target workflow. Workflow arrangement and execution are achieved through intelligent dialogue with the large language model, complex programs or scripts do not need to be written to support various automatic tasks and system interaction of the target workflow, the development workload of workflow arrangement is reduced, and the workflow arrangement and execution efficiency is improved.

The present application relates to artificial intelligence. More specifically, the present application provides an automated method and an automated system integrating large language models and formal reasoning. The method comprises: transforming / converting / encoding, by a trained first large language model, an input natural language description for describing a problem into a structured formal representation for representing the problem; and performing automated formal reasoning on the formal representation to provide a solution to the problem. In the method, by combining the capability of the large language model in language processing with automated formal reasoning, the first large language model is allowed to convert the natural language describing the problem into a structured formal representation that can accurately represent the problem, and rigorous logical reasoning is applied to the formal representation to provide a correct solution to the problem, thereby enhancing the analytical reasoning capability of the second large language model. The inherent limitations of large language models in analytical reasoning are solved, thereby ensuring the correctness, consistency and coherence of the output of the second large language model.

Systems and methods for automated adaptive learning framework for large language models are provided. A method includes receiving one or more input parameters, including at least one of a user query, an automated response, or a validated response. The automated response is based on a prompt. The method further includes providing the one or more input parameters and an input analysis prompt to a first large language model, and receiving an analysis report from the first large language model. Furthermore, the method includes providing the one or more input parameters and the analysis report to one of the first large language model or a second large language model, and receiving a learned memory from one of the first large language model or the second large language model. The method also includes generating an updated prompt through memory injection of the learned memory into the prompt.