Psychiatric Marker-Based Nourishment Program Generation
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Solution Overview
Problem
Nourishment selection is challenging due to unknown interactions and vast options, particularly when nutritional needs are influenced by psychiatric conditions, making it difficult to determine appropriate nutrient variations and nourishment programs.
Innovation Solution
A system and method using a computing device to retrieve psychiatric markers from DNA samples and subjective responses, identifying nutrient variations based on psychiatric impairment, and generating a nourishment program through machine learning, correlating psychiatric markers with nutrient variations and nourishment programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If psychiatric markers are used to identify nutrient variations, then the accuracy of nourishment recommendations is improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of nourishment recommendation into distinct components: psychiatric marker retrieval, nutrient variation identification, and nourishment program generation. Each component is handled by a separate module within the computing device, making the overall system more manageable and interpretable while maintaining high accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces psychiatric markers as intermediary elements that bridge the gap between user physiological data and nourishment recommendations. These markers serve as mediators that translate complex biological information into actionable nutritional guidance, reducing the direct complexity of mapping raw data to dietary prescriptions.
2Adaptability or versatility
If machine learning processes are trained on psychiatric markers and nutrient variations, then the personalization of nourishment programs is improved, but the computational resources required increase
Solution Approach 1:
The system performs preliminary actions by pre-training the machine learning process on comprehensive datasets of psychiatric markers and nutrient variations before actual use. This upfront investment in training creates a ready-to-use model that can quickly generate personalized recommendations without requiring intensive computational resources during runtime, thus balancing personalization with resource efficiency.
Solution Approach 2:
The patent utilizes parameter changes in the machine learning model to adapt to different user profiles and psychiatric conditions. By adjusting model parameters based on training data rather than retraining the entire system, the solution achieves high personalization while minimizing the computational resources required for each individual recommendation.
3Reliability
If DNA samples and subjective responses are analyzed to determine psychiatric conditions, then the comprehensiveness of nutritional assessment is improved, but the time required for processing increases
Solution Approach 1:
The system merges multiple data sources - DNA samples providing objective biological markers and subjective responses capturing patient-reported symptoms - into a unified psychiatric marker profile. This combination approach enhances the comprehensiveness and reliability of the assessment by leveraging complementary information from both objective and subjective measures simultaneously.
Solution Approach 2:
The patent implements preliminary processing of DNA samples and subjective responses to extract and standardize psychiatric markers before the main analysis. This pre-processing step organizes raw data into structured formats, reducing the time required for subsequent comprehensive analysis while maintaining assessment reliability through thorough evaluation of all input data.
Data Source
AI summary
A system and method for nourishment refinement using psychiatric markers including a computing device designed and configured to retrieve a psychiatric marker relating to a user, wherein the psychiatric marker includes a physical measurement obtained from physiological extraction including a deoxyribonucleic acid related sample indicative of psychiatric condition, and a subjective response indicative of a user's current emotional state related to a psychological energy level, identify a nutrient variation as a function of the psychiatric marker, wherein identifying further includes determining a degree of psychiatric impairment, establish a nourishment possibility as a function of the nutrient variation and the degree of psychiatric impairment, and generate a nourishment program by training a machine learning process as a function of a training set containing a plurality of psychiatric markers and nutrient variations as input correlated to a plurality of nourishment programs as output, and generating the nourishment program as a function of the psychiatric marker and the nourishment possibility, using the trained machine-learning process.