What processes the input — the algorithm family, model class, or technique used to turn inputs into outputs.
Techniques that infer emotional state, mood, or sentiment from text, speech, facial expressions, or physiological signals. Used to gauge crowd sentiment, evaluate service experience, or screen for distress. The accuracy and cultural fairness of affect inference is contested, and some jurisdictions restrict its use in workplaces and public services.
Techniques that flag unusual events, patterns, or values that depart from a learned baseline of normal behavior. Used for equipment-failure prediction, fraud screening, intrusion detection, water-leak monitoring, and unusual crowd-flow alerts. Outputs are typically thresholded scores rather than direct decisions.
Techniques that identify or verify a specific person from biological signals — face, fingerprint, voice, iris, or gait. Distinct from generic computer vision because the output is tied to a named individual. Many jurisdictions restrict or prohibit biometric recognition in publicly accessible spaces.
Techniques that assign a label to an input or estimate a numeric outcome — including classifiers (spam vs. not spam, risk tier), regressors (predicted demand, expected wait), and time-series forecasters (next-hour ridership, energy load). Built from labeled historical data using statistical or machine-learning models.
Techniques that group people, places, or events into clusters based on similarity, without using pre-defined labels. Used to segment audiences, mobility patterns, energy-use profiles, or service-demand zones. Outputs are cluster assignments that downstream systems may treat as categories — even though clusters are inferred, not authored.
Techniques that interpret images or video — counting people, detecting objects, reading text in pictures, tracking motion, or segmenting scenes. Common in occupancy sensing, traffic and crowd monitoring, accessibility assistance, and signage inspection. Distinct from biometric recognition, which identifies specific individuals.
Models that read, write, summarize, translate, or hold a conversation in human language. Includes large language models (LLMs), chatbots, and generative-text systems. They work by predicting the most likely next tokens given the input and any prior context.
Techniques that search for the best assignment, route, schedule, or allocation under constraints. Includes vehicle routing, transit signal timing, energy and HVAC scheduling, staff rostering, and resource allocation. Works by exploring possibilities to maximize or minimize a stated objective such as cost, time, or emissions.
Techniques whose primary job is to transform input data so identifying details are removed, blurred, or replaced before any downstream use. Includes face blurring, license-plate masking, tokenization, k-anonymization, differential privacy, and synthetic-data generation. Acts as a barrier between raw collected data and the data used for analysis or decisions.
Techniques that suggest or order items for a person — services, listings, news, wayfinding options — based on preferences, behavior, or similarity to other people. Includes collaborative filtering, content-based recommenders, and learned ranking. Personalizes what each person sees from the same underlying catalog.
Techniques that find and surface relevant content from a body of documents, records, or media in response to a query. Includes semantic search, vector retrieval, and retrieval-augmented generation (RAG) pipelines that feed relevant passages into a language model. Used in document QA, public-records lookup, and library or archive search.
Techniques that convert between spoken words and other formats, or that classify non-speech sound. Includes speech-to-text (transcription), text-to-speech (synthetic voice), keyword spotting, and audio event detection. Voice-as-identity (recognizing who is speaking) belongs in Biometric Recognition.
Live API response from GET /schemas/dtpr@2026-09-01-beta/categories (this category) and GET /schemas/dtpr@2026-09-01-beta/elements?category_id=processing.
{
"id": "processing",
"name": [
{
"locale": "en",
"value": "Algorithm or Model"
}
],
"description": [
{
"locale": "en",
"value": "What processes the input — the algorithm family, model class, or technique used to turn inputs into outputs."
}
],
"prompt": [
{
"locale": "en",
"value": "What algorithm or model processes the data?"
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"required": false,
"order": 9,
"datachain_type": "dtpr",
"shape": "circle",
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"value": "Description"
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"required": false,
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}[
{
"id": "affect_emotion_analysis",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Affect & Emotion Analysis"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that infer emotional state, mood, or sentiment from text, speech, facial expressions, or physiological signals. Used to gauge crowd sentiment, evaluate service experience, or screen for distress. The accuracy and cultural fairness of affect inference is contested, and some jurisdictions restrict its use in workplaces and public services."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "processing_sentiment-analysis",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
}
],
"required": false,
"kind": "localized_text"
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],
"shape": "circle",
"icon_variants": [
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},
{
"id": "anomaly_detection",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Anomaly Detection"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that flag unusual events, patterns, or values that depart from a learned baseline of normal behavior. Used for equipment-failure prediction, fraud screening, intrusion detection, water-leak monitoring, and unusual crowd-flow alerts. Outputs are typically thresholded scores rather than direct decisions."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "dm_anomaly-detection",
"variables": [
{
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"label": [
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"required": false,
"kind": "localized_text"
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],
"shape": "circle",
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},
{
"id": "biometric_recognition",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Biometric Recognition"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that identify or verify a specific person from biological signals — face, fingerprint, voice, iris, or gait. Distinct from generic computer vision because the output is tied to a named individual. Many jurisdictions restrict or prohibit biometric recognition in publicly accessible spaces."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "personal",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
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"required": false,
"kind": "localized_text"
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],
"shape": "circle",
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},
{
"id": "classification_prediction",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Classification & Prediction"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that assign a label to an input or estimate a numeric outcome — including classifiers (spam vs. not spam, risk tier), regressors (predicted demand, expected wait), and time-series forecasters (next-hour ridership, energy load). Built from labeled historical data using statistical or machine-learning models."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "processing_time-series-forecasting",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
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],
"required": false,
"kind": "localized_text"
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],
"shape": "circle",
"icon_variants": [
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},
{
"id": "clustering_segmentation",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Clustering & Segmentation"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that group people, places, or events into clusters based on similarity, without using pre-defined labels. Used to segment audiences, mobility patterns, energy-use profiles, or service-demand zones. Outputs are cluster assignments that downstream systems may treat as categories — even though clusters are inferred, not authored."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "social",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
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"required": false,
"kind": "localized_text"
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],
"shape": "circle",
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},
{
"id": "computer_vision",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Computer Vision"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that interpret images or video — counting people, detecting objects, reading text in pictures, tracking motion, or segmenting scenes. Common in occupancy sensing, traffic and crowd monitoring, accessibility assistance, and signage inspection. Distinct from biometric recognition, which identifies specific individuals."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "pixel_based_image",
"variables": [
{
"id": "additional_description",
"label": [
{
"locale": "en",
"value": "Description"
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],
"required": false,
"kind": "localized_text"
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],
"shape": "circle",
"icon_variants": [
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},
{
"id": "language_models",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Language Models"
}
],
"description": [
{
"locale": "en",
"value": "Models that read, write, summarize, translate, or hold a conversation in human language. Includes large language models (LLMs), chatbots, and generative-text systems. They work by predicting the most likely next tokens given the input and any prior context."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "processing_llm",
"variables": [
{
"id": "additional_description",
"label": [
{
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"value": "Description"
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],
"required": false,
"kind": "localized_text"
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],
"shape": "circle",
"icon_variants": [
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},
{
"id": "optimization",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Optimization"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that search for the best assignment, route, schedule, or allocation under constraints. Includes vehicle routing, transit signal timing, energy and HVAC scheduling, staff rostering, and resource allocation. Works by exploring possibilities to maximize or minimize a stated objective such as cost, time, or emissions."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "processing_optimization-algorithm",
"variables": [
{
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"label": [
{
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"value": "Description"
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"required": false,
"kind": "localized_text"
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],
"shape": "circle",
"icon_variants": [
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},
{
"id": "privacy_transformation",
"category_id": "processing",
"title": [
{
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"value": "Privacy-Preserving Transformation"
}
],
"description": [
{
"locale": "en",
"value": "Techniques whose primary job is to transform input data so identifying details are removed, blurred, or replaced before any downstream use. Includes face blurring, license-plate masking, tokenization, k-anonymization, differential privacy, and synthetic-data generation. Acts as a barrier between raw collected data and the data used for analysis or decisions."
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],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "mask",
"variables": [
{
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"label": [
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"value": "Description"
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"required": false,
"kind": "localized_text"
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"shape": "circle",
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},
{
"id": "recommendation_ranking",
"category_id": "processing",
"title": [
{
"locale": "en",
"value": "Recommendation & Ranking"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that suggest or order items for a person — services, listings, news, wayfinding options — based on preferences, behavior, or similarity to other people. Includes collaborative filtering, content-based recommenders, and learned ranking. Personalizes what each person sees from the same underlying catalog."
}
],
"authoring_guidance": [],
"examples": [],
"sources": [],
"symbol_id": "processing_recommendation-systems",
"variables": [
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{
"id": "search_retrieval",
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"title": [
{
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"value": "Search & Retrieval"
}
],
"description": [
{
"locale": "en",
"value": "Techniques that find and surface relevant content from a body of documents, records, or media in response to a query. Includes semantic search, vector retrieval, and retrieval-augmented generation (RAG) pipelines that feed relevant passages into a language model. Used in document QA, public-records lookup, and library or archive search."
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],
"authoring_guidance": [],
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"symbol_id": "search_retrieval",
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{
"id": "speech_audio",
"category_id": "processing",
"title": [
{
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"value": "Speech & Audio"
}
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"description": [
{
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"value": "Techniques that convert between spoken words and other formats, or that classify non-speech sound. Includes speech-to-text (transcription), text-to-speech (synthetic voice), keyword spotting, and audio event detection. Voice-as-identity (recognizing who is speaking) belongs in Biometric Recognition."
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],
"authoring_guidance": [],
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"sources": [],
"symbol_id": "waveform",
"variables": [
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]