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Literature Review And Prior Art Search

Learn Literature Review And Prior Art Search for free with explanations, exercises, and a quick test (for Applied Scientist).

Published: January 7, 2026 | Updated: January 7, 2026

Table of Contents

Why this matters

Applied Scientists are expected to design solutions that are novel, feasible, and evidence-based. Strong literature review and prior art search helps you:

  • Avoid reinventing the wheel and pick proven baselines.
  • Identify state-of-the-art methods, datasets, and metrics before proposing a solution.
  • Validate novelty for publications, patents, and internal approvals.
  • Spot risks early (bias, data leakage, IP conflicts, deployment pitfalls).
Real tasks you will do
  • Draft a background section for a project proposal with 8–12 key references.
  • Map prior art to confirm a feature idea isn’t already patented.
  • Summarize pros/cons of top-3 approaches and recommend one for a pilot.
  • Create an evidence matrix of methods, datasets, and reproducibility signals.

Concept explained simply

Literature review: systematically finding and understanding research about your problem. Prior art search: checking if ideas/implementations have already been disclosed (papers, patents, tech reports, standards, blogs).

Mental model: the research funnel (4R)

  • Retrieve: cast a wide net with smart queries.
  • Rapidly screen: skim titles/abstracts; discard off-topic items quickly.
  • Read deeply: evaluate a shortlist for methods, baselines, data, and limitations.
  • Record: capture notes, citations, and decisions in a structured matrix.
Cheat-sheet: Query building patterns
  • Combine core concepts with synonyms using AND; list synonyms with OR.
  • Use phrase quotes for multi-word terms: "contrastive learning".
  • Include common abbreviations: (LLM OR "large language model").
  • Add constraints: (benchmark OR dataset) AND (AUC OR F1) AND (2021..2024).
  • For patents: include functional verbs (detect, classify, segment) and domain nouns (sensor, camera, EHR) plus classification terms (CPC/IPC codes when known).

Step-by-step workflow

1. Define the scope
Problem, context, constraints, success metrics. Example: "Online recommendations; must handle cold-start; target metric: CTR uplift."
2. Draft initial queries
Split into concepts and synonyms. Example: ("recommendation" OR "ranking") AND ("cold start" OR "new item").
3. Retrieve results
Search academic portals, preprint servers, and patent databases. Save top 50–100 hits for screening.
4. Rapid screening
Title/abstract triage with inclusion/exclusion rules (domain, method relevance, recency). Discard duplicates.
5. Deep read
Extract: objective, method, data, baselines, metrics, results, compute, limitations. Note reproducibility signals (code, seeds, data access).
6. Citation chaining
Backward (references) and forward (who cited it) to find influential works you missed.
7. Record and synthesize
Maintain an evidence matrix and write a short narrative: What works, when, and why. Decide on baselines and novelty angle.
Evidence matrix (copy/paste template)
  • Paper/Patent:
  • Year:
  • Task/Domain:
  • Method summary:
  • Data/Scale:
  • Metrics/Results:
  • Baselines compared:
  • Compute/Cost:
  • Limitations/Risks:
  • Reproducibility (code/data?):
  • Relevance to our constraints:

Worked examples

Example 1: Fair ranking for recommendations

Goal: Reduce popularity bias while maintaining CTR.

How to search
  • Concepts: fairness, ranking, recommendation, exposure
  • Query: (fairness OR "fair exposure" OR debias*) AND (ranking OR recommender) AND (exposure OR popularity) AND (metric OR evaluation)
  • Screen out: unrelated fairness (e.g., only classification), outdated (pre-2015) unless seminal.
What to extract
  • Metrics: exposure disparity, NDCG, CTR proxy
  • Methods: re-ranking, regularization, counterfactual estimators
  • Risks: business trade-offs, cold-start creators

Example 2: Missing data in healthcare time series

Goal: Robust imputation for ICU vitals streams.

How to search
  • Concepts: time series, healthcare, imputation, irregular sampling
  • Query: ("time series" AND (imput* OR interpolation) AND (healthcare OR ICU OR EHR) AND (irregular OR sparse))
  • Add abbreviations: (RNN OR GRU OR TCN OR diffusion) for method breadth.
What to extract
  • Datasets: MIMIC-III/IV
  • Metrics: MAE, downstream AUROC on mortality task
  • Compute: training time and hardware
  • Limitations: failing patterns (e.g., long gaps)

Example 3: Prior art for visual defect detection on assembly line

Goal: Check novelty of an idea: contrastive pretraining + few-shot segmentation for surface defects.

How to search
  • Keywords: (defect OR anomaly) AND (industrial OR manufacturing) AND (vision OR camera) AND (contrastive OR self-supervised) AND (few-shot OR low-shot)
  • Patents: include verbs and components: (detect OR segment) AND (surface OR weld OR scratch) AND (camera OR sensor) AND (contrastive)
  • Refine with classification terms if found relevant (e.g., CPC codes under computer vision inspection).
What to extract
  • Claim scope and embodiments in patents
  • Implementation specifics: augmentations, thresholding, post-processing
  • Datasets: DAGM, MVTec AD; reported metrics

Practical projects

  • Project 1: Baseline map for your team’s active problem
    • Deliver: 1-page narrative + evidence matrix (10–15 entries) + recommended baselines.
  • Project 2: Mini systematic review (lightweight)
    • Define inclusion/exclusion, run citation chaining, and produce a PRISMA-style count summary (numbers only).
  • Project 3: Prior art risk scan
    • Draft a 2-page brief comparing your proposed idea to 3–5 closely related patents/papers, highlighting differences.

Exercises

Anyone can take the exercises and test. Only logged-in users will see saved progress.

  1. Exercise 1: Build a search strategy
    Problem: "We need a robust method for detecting data drift in streaming tabular data with concept drift and limited labels."
    Task: Write 2 boolean queries: one for academic literature, one for patents. Include at least 3 synonym groups and 1 constraint (e.g., timeframe or evaluation).
    Submit: Your two queries and a one-sentence rationale each.
  2. Exercise 2: Rapid screening triage
    Given titles/abstract snippets (below), mark Include/Exclude and justify briefly:
    • A: "Unsupervised drift detection via adaptive windows in data streams"
    • B: "Image style transfer with transformers"
    • C: "Monitoring ML systems in production: a survey of drift and skew"
    • D: "Concept drift in non-stationary environments using KL divergence with labels"
    • E: "Real-time anomaly detection in network traffic using PCA"
    Submit: Your I/E decisions + 1-line reason each.
Completion checklist
  • Queries include core concept, synonyms, and constraints.
  • Screening decisions align with the defined problem (streaming tabular, drift, limited labels).
  • Reasons mention method-task fit and data/label constraints.

Common mistakes and self-check

  • Too narrow queries: You miss synonyms and adjacent fields. Self-check: Did you include 2–3 synonyms per core concept?
  • No stopping rule: Endless searching. Self-check: Stop when the last 10 quality sources add no new methods or datasets.
  • Ignoring patents/industry reports: Novelty risk. Self-check: Have you checked at least one patent database and one industry venue?
  • Weak screening: Keeping everything. Self-check: Apply clear inclusion/exclusion criteria and cap deep reads to a shortlist.
  • Poor notes: Can’t reproduce decisions. Self-check: Maintain an evidence matrix with decisions and rationale.
Quality bar for a "good enough" review
  • 8–12 high-quality, recent sources + 2–3 seminal works
  • At least 2 alternative methods compared head-to-head
  • Clear baseline and recommended path forward

Mini challenge

Pick any ML task you care about. In 45 minutes: draft one query, collect 15 hits, triage to 5, extract key points into the evidence matrix, and write a 3-sentence recommendation.

Who this is for

  • Applied Scientists and ML Engineers proposing solutions or writing internal/external research docs.
  • Data Scientists validating ideas before building prototypes.
  • Students preparing capstones or research statements.

Prerequisites

  • Basic understanding of ML tasks and metrics.
  • Comfort reading abstracts and method sections.
  • Ability to write simple boolean queries with AND/OR/quotes.

Learning path

  • Start: Learn problem scoping and success metrics.
  • This subskill: Literature review and prior art search.
  • Next: Experimental design and baseline selection.
  • Then: Risk, bias, and deployment considerations.

Next steps

  • Turn your evidence matrix into a short internal memo with recommended baselines.
  • Schedule a review with a teammate to sanity-check coverage and novelty.
  • Translate insights into an experiment plan: datasets, metrics, and compute budget.

Practice Exercises

2 exercises to complete

Instructions

Problem: "We need a robust method for detecting data drift in streaming tabular data with concept drift and limited labels."

Create two boolean queries:

  • Q1 (Academic): include at least 3 synonym groups and 1 constraint (e.g., timeframe or evaluation).
  • Q2 (Patent): include functional verbs and potential components.

Write 1 sentence explaining the intent of each query.

Expected Output
Two queries that cover drift detection in streaming tabular data, include synonyms (e.g., (drift OR shift), (stream* OR online), (unsupervised OR semi-supervised)), and a constraint (e.g., 2019..2026 or metric terms).

Literature Review And Prior Art Search — Quick Test

Test your knowledge with 8 questions. Pass with 70% or higher.

8 questions70% to pass
Translating Business Needs Into Research QuestionsScope And Feasibility Assessment

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