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# Inventive Principles Extraction in Inventive Design Using Artificial Intelligence Methods

Masih Hanifi, Hicham Chibane, Remy Houssin, Denis Cavallucci

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Masih Hanifi, Hicham Chibane, Remy Houssin, Denis Cavallucci. Inventive Principles Extraction in Inventive Design Using Artificial Intelligence Methods. 22th International TRIZ Future Conference (TFC), Sep 2022, Warsaw, Poland. pp.178-186, 10.1007/978-3-031-17288-5_16. hal-04048438

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# Inventive principles extraction in inventive design using
Artificial Intelligence methods

Masih Hanifi<fnref n="1" />, <fnref n="2" />, <fnref n="3" />, Hicham Chibane<fnref n="2" />, <fnref n="3" />, Remy Houssin<fnref n="1" />, <fnref n="3" />, Denis Cavallucci<fnref n="2" />, <fnref n="3" />

**Abstract.** Today, companies are seeking effective approaches to improve their innovation cycle time. Among them, it is possible to mention Inventive Design Methodology (IDM) as a TRIZ-based systematic inventive design process. However, the application of this approach is time-consuming due to requesting a complete map of a problem situation at the initial phase of the inventive design process. To solve this drawback, the Inverse Problem Graph (IPG) method has been developed to increase the agility of the process. Nevertheless, authors of IPG did not mention how the designers could achieve the innovative solutions by using the formulated problems. The purpose of the research presented in this article is to integrate the doc2vec method and machine learning text classification algorithms as Artificial Intelligence methods into the IPG process. This integration helps introduce an automatic approach for the inventive design process, helping to formulate the contradictions among TRIZ parameters in the contradiction matrix and extract the inventive principles in their intersection. The capability of the proposed methodology is finally tested through its application in a case study.

**Keywords:** Inventive design, TRIZ, Text classification, Document embedding, Artificial Intelligence.

## 1 Introduction

In recent decades, many enterprises have been competing on decreasing the innovation cycle time due to its importance in their success [1]. To do so, these enterprises can apply TRIZ-based systematic approaches such as Inventive Design Methodology (IDM). This approach has been developed to complete the TRIZ body of knowledge with other theories such as graph theory [2]. Nevertheless, one of the criticisms often leveled is that Inventive Design Methodology lacks the essential agility [3]. This is mainly due to constructing a complete map of a problem situation at the start of the project, regardless of how effective it is in solving the problem. Consequently, the Inverse Problem Graph (IPG) method was developed by several authors to formulate a problem situation.

<footnote n="1">¹ Strasbourg University, 4 Rue Blaise Pascal, 67081 Strasbourg, France</footnote>

<footnote n="2">² INSA of Strasbourg, 24 Boulevard de la Victoire, 67000 Strasbourg, France</footnote>

<footnote n="3">³ ICUBE/University of Strasbourg, 4 Street Blaise Pascal, 67081 Strasbourg, France</footnote>
The IPG method was introduced to formulate the contradictions related to a problem situation [4] [5]. This method adds to the IDM process the characteristics such as the capability for iterative development and the capacity to generate rapid response to change to increase its agility [6]. Nevertheless, the manual extraction of the TRIZ parameters and TRIZ inventive principles within the IDM process reduces its agility. Therefore, it is necessary to utilize automatic information retrieval methods to solve this drawback. Among them, we can mention the machine learning algorithms and the doc2vec model. The main objective of this paper is to integrate machine learning algorithms and the doc2vec model into the process of Inventive Design Methodology. This integration can facilitate and accelerate the extraction of TRIZ parameters and inventive principles in the process.

The rest of the paper is organized as follows. In Section 2, we present the relevant literature review. Section 3 shows the evaluation of the reviewed machine learning algorithms. Section 4 describes the structure of the proposed method and its step. In Section 5, a case study is presented in which the proposed method is used to extract TRIZ parameters and the inventive principles related to the lattice structures. We present the discussion and conclusion in the last section.

# 2 Literature review and background

## 2.1 Inventive Design Methodology (IDM)

IDM framework has been developed for several years to solve the limitations of TRIZ [2]. This framework includes the phases below [7]:

1. Initial Analysis phase: In this phase, the relevant knowledge from patents, internal documents, tacit expert's know-how, and other documents is gathered. Then, the Problem Graph method is used to transform the knowledge into a graphical model to facilitate decision-making [2].

2. Contradictions Formulation phase: The physical and technical contradictions are formulated by applying the methods such as poly-contradiction template in the second phase of IDM framework [8].

3. Solution Concept Synthesis phase: In the third phase, various TRIZ tools are used to solve the formulated contradictions in the previous phase [9].

4. Solution Concept Selection phase: In the last phase, an evaluation grid is applied to measure the impact of each concept and select the most relevant one [2].

In the next sub-section, we will review several methods that has been introduced to automate information extraction in the Inventive Design Methodology framework.

## 2.2 Automatic extraction methods to assist designers

In the literature, various methods have been introduced to support the designers in the framework of Inventive Design Methodology. For example, [10] introduced an approach to extract problems and partial solutions from the patent. Besides, in order to improve the final output of the IDM-related information extraction tools, the authors in [11] proposed to use claims hierarchical structure. In addition, [12] developed a tool
within the IDM framework to extract the parameters, problems and partial solutions from scientific articles. However, these approaches retrieve the information beyond the needs of designers. This makes analysis of the extracted information time-consuming and quite laborious.

## 2.3 Document embedding techniques

The authors in [13] have proposed the word2vec model to create a word embedding representation. This model includes the two model architectures that are Skip-gram and Continuous bag-of-words [14]. Word2vec permits deriving similar words semantically. However, this model loses the word order information [15]. To overcome this drawback, the researchers proposed an unsupervised method called doc2vec.

The doc2vec method is an extension of word2vec, which has been proposed by Le et al [16]. This method includes the following architectures [17]: 1) Distributed Memory (PV-DM) and 2) Distributed Bag of Words (PV-DBOW). Doc2vec can extract the word order information from the text [18]. Besides, it can express variable-length text, ranging from sentences to large documents, as a vector [19]. Furthermore, doc2vec is used to calculate document similarity and classify the given texts. To extract the TRIZ parameters from the sentences, it is necessary to use also the machine learning text classification algorithms, reviewed in the next section.

## 2.4 Text classification algorithms

The task of classifying a text into predefined classes is defined as Text classification [20]. There are two main groups of machine learning algorithms to automate text classification, as the following [21]: 1) Supervised learning and 2) Unsupervised learning. In our proposed method, we used the algorithms related to the first group. Below, we brought some of these algorithms:

Logistic regression (LR) is a supervised machine learning classification technique developed by David Cox in 1958 [21]. There are two types of LR, as the following [22]: 1) Binomial logistic model and 2) Multinomial logistic regression. This technique serves well to anticipate categorical results [23].

The Multilayer Perceptron (MLP) algorithm is a feed-forward neural network proposed by Rosenblatt in 1958 [24] [25]. MLP maps input data to the outputs. This algorithm is constructed of an output layer, one or several hidden layers, and an input layer [26]. In this algorithm, the input layer obtains an external activation vector including the values {X1, X2,..., Xn}. Subsequently, these values are transferred via weighted connections to the nodes of the first hidden layer. Then, the layer calculates their activation and spreads them to the nodes in the subsequent layer [27]. MLP is used to predict and classify problems [28].
## 3 Experiment and evaluation

In this section, we evaluate the accuracy of the reviewed machine learning algorithms for the “TRIZ Parameters” data set.

TRIZ Parameters data set is used for the second evaluation of machine learning algorithms. This data set consists of 3607 sentences and 2 following groups of parameters:

1) The “Positive TRIZ Parameters” group includes 40 labels (39 TRIZ parameters labels + nan label), and 2) The “Negative TRIZ Parameters” group consists of 40 labels (39 TRIZ parameters + nan label). To evaluate the reviewed machine learning algorithms by the TRIZ parameters data set, we first selected 4 highest labels in the first and second group as the following:

1) “Positive TRIZ Parameters” group (469 sentences, Parameters: 15.77 % Strength, 14.07 % Object-generated harmful factors, 13.21 % Temperature, 12.36 % Use of energy by stationary object, ...).

2) “Negative TRIZ Parameter” group (410 sentences, Parameters: 13.90 % Strength, 13.41 % Loss of energy, 12.92 % Temperature, 12.68 % Loss of substance, ...).

Then, we perform a train-test split using the 80-20 rule, where 80 % of data is used for training, and the remaining 20% is used for testing. Subsequently, the reviewed machine learning algorithms in the literature were trained using the training data and tested on the test set for their accuracies. Tables 1 display the accuracy for each machine learning algorithm related to the TRIZ parameter dataset. We used the algorithms with the highest accuracy to train the model in our case study section.

<table><caption>Table 1: Evaluation of machine learning algorithms for the “Positive TRIZ Parameter” and the “Negative TRIZ Parameter” groups</caption><thead><tr><th colspan="4">Positive TRIZ Parameter</th></tr><tr><th>Algorithms</th><th>F1 scores</th><th>Precision scores</th><th>Recall scores</th></tr></thead><tbody><tr><td>Logistic regression</td><td>83.35 %</td><td>85.24 %</td><td>83.67 %</td></tr><tr><td>MLP</td><td>78.62 %</td><td>81.79 %</td><td>79.59 %</td></tr></tbody><thead><tr><th colspan="4">Negative TRIZ Parameter</th></tr><tr><th>Algorithms</th><th>F1 scores</th><th>Precision scores</th><th>Recall scores</th></tr></thead><tbody><tr><td>Logistic regression</td><td>80.60 %</td><td>84.09 %</td><td>80 %</td></tr><tr><td>MLP</td><td>80.66 %</td><td>83.42 %</td><td>80 %</td></tr></tbody></table>

## 4 Proposed method

In this section, we used doc2vec and machine learning algorithms to extract TRIZ parameters from the sentences. This enabled us to introduce a new method for the inventive design process. Figure 1. shows different steps of our proposal. These steps are as the following.

Step 1: Determine the initial problem of the project: In the first step, designers determine the initial problem of the project by considering its objective.
Step 2: Extract the cause sentences from the corpus: The cause sentences are extracted from the corpus in the second step of the process. To do this, designers formulate the what-cause question at the beginning of this step. Then, they use it to extract the cause sentences from the corpus.

Step 3: Extract TRIZ parameters to formulate the contradictions: In the third step of the process, the system extracts the positive and negative TRIZ parameters from the cause sentences to formulate their related contradictions. For this purpose, it is necessary to train the doc2vec model and machine learning algorithms by applying the provided data samples, including TRIZ parameters.

Step 4: Connect the formulated contradictions to the inventive principles: In the final step, the system connects the formulated contradictions in the previous step to the inventive principles of contradiction matrix.

<figure><img src="image_3.png" /><figcaption>Figure 1. Agile Automated System to extract TRIZ parameters and TRIZ inventive principles</figcaption></figure>
## 5 Application of the method

In this section, we used the proposed method “Agile Automated System to Extract TRIZ Parameters” to the Lattice Structure (LS) case study. This application enables us to evaluate its applicability. The energy absorption of LS has always been an interesting subject for engineers and research scientist because of its wide usage in energy-absorbing application [29] [30]. In this case study, we will determine the factors that influence the energy absorption of the lattice structure. In the following, we will describe the steps of this application.

In the first step, we determined the initial problem, which was “The energy absorption of LS is decreased”, by considering the project objective.

After defining the initial problem, we had to identify the causes of this problem in the second step. To do so, we first formulated our question, which was “What reduces the energy absorption of the lattice structure?”. Then, we used the most similar method and doc2vec to uncover the cause sentences nearest to our formulated question. Table 2. displays some of these sentences. We should also mention that the minimum threshold for this case study is considered to be 0.65.

We extracted the TRIZ parameters in the third step. For this purpose, we first trained our machine learning algorithm with the highest accuracy presented in the “Experiment and Evaluation” section. Next, this algorithm allowed us to extract the TRIZ parameters. Table 2. also shows the extracted parameters related to the selected causes.

In the fourth step, we connected the inventive principles of the contradiction matrix to the formulated contradiction in the previous step. Table 2 also displays the inventive principles related to formulated contradictions. In what follows, we use the principles to develop our final solution.

<table><caption>Table 2. The selected cause sentences related to the formulated question, their extracted TRIZ parameters, and the related inventive principles.</caption><thead><tr><td>Sentences</td><td>TRIZ Positive Parameter</td><td>TRIZ Negative Parameter</td><td>IP</td></tr></thead><tbody><tr><td>When the volume fraction is reduced from 40.4% to 12.5%, the maximum energy absorption efficiency increases from 35.20% to 46.95%, an increase of 11.75%.</td><td>Shape</td><td>nan</td><td>-</td></tr><tr><td>However, the increase in strength is at the expense of energy absorption, as observed experimentally.</td><td>Strength</td><td>Shape</td><td>10, 30, 35, 40</td></tr><tr><td>It indicated that as the volume fraction of the lattice increases, the maximum energy absorption efficiency decreases.</td><td>nan</td><td>Shape</td><td>-</td></tr></tbody></table>
<table><tr><td>But lower energy absorption due to the increase in stiffness.</td><td>Strength</td><td>Shape</td><td>10, 30, 35, 40</td></tr></table>

# 6 Discussion and conclusion

In this study, we proposed a new method for extracting TRIZ parameters in the inventive design process. To propose our method, we first reviewed some of the leading document embedding techniques to highlight their advantages and limitations. Next, we reviewed several machine learning algorithms. In the end, we proposed to apply doc2vec and the machine learning algorithms to develop a new approach, helping to extract TRIZ parameters from the sentences. We then tested the capacity of our proposal by applying it to the case study of Lattice Structure. Based on this application, we realized that our proposed method could facilitate and accelerate the formulation of contradictions through automatic extraction of TRIZ parameters.

The contribution of this work to the inventive design process is reflected in several aspects. First, the system can retrieve TRIZ parameters from the cause sentences through the application of machine learning algorithms. The existence of this capability can facilitate and accelerate the formulation of contradictions in the inventive design process. As the second, the formulated contradictions can be automatically connected to the inventive principles on the contradiction matrix. This can reduce the amount of time to develop inventive solutions for the designers. The analysis of the results related to the case study reveals some limitations. As the most important of them, we can mention that providing a complete data sample for our supervised machine learning algorithms is a time-consuming task.

Further research is necessary to appreciate our proposal. To solve the drawback related to providing data samples for our used machine learning algorithms, it is possible to use transfer learning. This can decrease the amount of resources and labelled to train the models.

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