Module 2: R&D+i Typologies, Intellectual Property, and Scientific Writing

1. Typological Distinction: Investment vs. R&D+i

In the field of Process Engineering, it is imperative to distinguish between a traditional investment project and a Research, Development, and Innovation (R&D+i) project.

• Investment Project: Aims to create or expand installed capacity to produce goods or services using knowntechnologies. The risk is primarily commercial or operational.

• R&D+i Project: Aims to generate new knowledge, develop prototypes, or significantly improve processes. The risk is technological and epistemic (Minciencias, 2023).

According to the Documento de Tipología de Proyectos (Version 7) used in the Colombian scientific ecosystem, projects are classified as follows:

1.1 Scientific Research

Systematic creative work undertaken to increase the stock of knowledge.

• Basic Research: Theoretical or experimental work without a specific practical application in view.

• Applied Research: Original investigation directed towards a specific practical objective (e.g., optimizing a kinetic reaction for a specific polymer).

1.2 Technological Development

The application of research findings to a plan or design for the production of new or substantially improved materials, products, or processes before commercial production.

• Key Deliverables: Prototypes, pilot plants, beta versions.

1.3 Innovation

The introduction of a new or significantly improved product (good or service) or process to the market or the organization.

• Process Innovation: Implementation of new production or delivery methods (e.g., changing from batch to continuous flow chemistry).

• Product Innovation: Introduction of a good/service with significantly improved characteristics.

2. Intellectual Property (IP) and Protection Strategy

Intellectual Property refers to the creations of the mind. In engineering, IP protection mechanisms are strategic assets that turn intangible knowledge into a monopoly right.

2.1 Industrial Property (Patents)

A patent is an exclusive right granted for an invention. To be patentable, an invention must meet three rigorous criteria (Superintendencia de Industria y Comercio; WIPO):

1. Novelty: The invention must be new worldwide (not part of the "State of the Art").

2. Inventive Step: It must not be obvious to a "person skilled in the art" (PHOSITA).

3. Industrial Application: It must be capable of being made or used in an industry.

Types:

• Invention Patent: Protection for 20 years. High inventive step.

• Utility Model: Protection for 10 years. Known as "petty patents," applicable to tools or devices with a functional advantage derived from their shape or arrangement.

2.2 The Disclosure Dilemma: Patent vs. Paper

A critical error in R&D drafting is publishing a scientific article before filing the patent application.

• Rule: Public disclosure (article, conference, thesis) destroys Novelty.

• Strategy: File the patent application first $\rightarrow$ Publish the scientific article second.

3. Drafting Technical Documents: Projects, Articles, and Patents

Each document type serves a different purpose and follows a specific structure.

Feature	R&D Project Proposal	Scientific Article (Paper)	Patent Application
Objective	Secure funding and approval for execution.	Disseminate knowledge to the scientific community	Define a legal monopoly over technology
Key Audience	Evaluators, Investors, Managers	Peers, Researchers (Reviewers)	Patent Examiners, Lawyers, Competitors
Structure	Problem $→$ Objectives $→$ Methodology $→$ Budget $→$ Impact.	IMRaD (Introduction, Methods, Results, Discussion).	Description $→$ Claims (Reivindicaciones) $→$ Drawings.
Language	Persuasive, future tense ("We will develop...").	Descriptive, past tense ("We observed...").	Legal-technical, broad scope ("A device comprising...")

3.1 Drafting the "State of the Art" (Background)

• In Projects: Focus on the gap in current knowledge or the market failure that justifies the investment.

• In Articles: Focus on the theoretical framework and how this work fills a knowledge gap.

• In Patents: Focus on "Prior Art" (Anterioridades) to demonstrate that existing solutions are insufficient or different from the proposed invention.

4. Mathematical Modeling: Technology Adoption (Bass Model)

When formulating innovation projects, predicting the speed of market adoption is crucial for financial evaluation. The Bass Diffusion Model is the standard differential equation for this purpose.

4.1 Theoretical Formulation

The model assumes adoption is driven by two coefficients:

1. Innovation (p): External influence (advertising).

2. Imitation (q): Internal influence (word of mouth).

$$\frac{dN(t)}{dt} = \left[ p + \frac{q}{M} N(t) \right] [M - N(t)]$$

Where:

• N(t): Cumulative adopters at time t.

• M: Total market potential.

4.2 Python Implementation

import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

# 1. Define the Differential Equation
def bass_model(N, t, p, q, M):
    """
    dN/dt = Rate of adoption at time t
    p: Innovation coefficient (External)
    q: Imitation coefficient (Internal/Word of Mouth)
    M: Total Market Potential
    """
    dNdt = (p + q * N / M) * (M - N)
    return dNdt

# 2. Simulation Parameters (Scenario: New Bio-reactor Technology)
M = 500         # Total potential industrial clients
p = 0.015       # Slow initial adoption (conservative advertisement)
q = 0.30        # Strong word-of-mouth (industry reputation)
t = np.linspace(0, 15, 100) # 15-year horizon
N0 = 0          # Start from zero

# 3. Solve ODE
solution = odeint(bass_model, N0, t, args=(p, q, M))
cumulative_adopters = solution.flatten()
adoption_rate = np.gradient(cumulative_adopters, t) # Sales per year

# 4. Visualization
plt.figure(figsize=(10, 6))
plt.plot(t, adoption_rate, 'g-', linewidth=2.5, label='Adoption Rate (Sales/Year)')
plt.plot(t, cumulative_adopters, 'b--', linewidth=1.5, label='Cumulative Installed Base')
plt.axvline(x=t[np.argmax(adoption_rate)], color='k', linestyle=':', alpha=0.5, label='Peak Adoption')

plt.title(f'Bass Diffusion Model: Innovation Adoption (p={p}, q={q})')
plt.xlabel('Years')
plt.ylabel('Number of Adopters (Units)')
plt.legend()
plt.grid(True, alpha=0.3)
plt.show()

# Peak Analysis
peak_time = t[np.argmax(adoption_rate)]
print(f"Peak adoption occurs at year: {peak_time:.2f}")

References:

• Minciencias. (2023). Documento de Tipología de Proyectos de Carácter Científico, Tecnológico o de Innovación. Versión 7.

• OECD. (2015). Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development.

• Superintendencia de Industria y Comercio. (n.d.). Nuevas Creaciones.