Tool Object Analysis and ChatGPT Integration for Creative Problem-Solving Algorithm Development ^†

Abstract

TRIZ, the theory of inventive problem solving, can be used to solve contradictions in high-level problems in inventions and patents. Contradictions arise when two contradictory requirements or conditions cannot be satisfied simultaneously and resolving them can lead to innovative ideas. TRIZ experts have developed 76 standard solutions for applying TRIZ. Though TRIZ’s 76 standard solutions can be applied to a wide range of problem types, they are too complex. We developed a simplified tool object analysis algorithm using TRIZ’s 76 standard solutions. The analysis was used to analyze the interaction between components of a system to identify where problems occur. The interaction between a tool and an object was categorized into beneficial actions (positive relationship), no effect (zero relationship), and harmful actions (negative relationship). When beneficial actions, and gaps or beneficial actions and harmful actions occur in the interaction between a tool and an object, conflicting relationships arise in problem-solving. We classified the interaction between tools and objects into beneficial actions and gaps and beneficial actions and harmful actions to provide appropriate problem-solving strategies for each situation. The analysis has the advantage of specifying the causes and solutions of problem-solving. The analysis was used in creative problem-solving to train ChatGPT. By training ChatGPT for it, specific problem-solving solutions were obtained.

1. Introduction

There are many instances in the history of science and technology where contradictions have been used to achieve innovation. For example, James Watt solved the problem of a cylinder needing to be both hot and cold [1], Siemens resolved the issue of heating and cooling in the continuous production of plate glass [2], Florey and Chain solved Fleming’s problem of failing to extract penicillin by heating it, Auguste Piccard was the first to reach the stratosphere in a closed gondola, and Henry Bessemer developed a blast furnace that could introduce iron ore while blocking external air. All of these individuals resolved contradictions [3,4].

Despite the importance of contradiction problems, Western scholarship, which emphasizes rationality, has regarded contradictions as wrong or impossible to achieve [3]. On the other hand, Heinrich Altshuller and his colleagues in Russia, who were familiar with dialectics, easily accepted contradictions. They noticed that innovation occurred when contradictions were resolved, based on their analysis of creative patent specifications. They established TRIZ, an acronym derived from the Russian words for the theory of inventive problem-solving. Despite TRIZ’s unique perspective in viewing innovative problem-solving from the standpoint of contradiction resolution, TRIZ has developed its theories inductively rather than deductively [5,6].

By introducing symbols into the problem-solving process, problems are resolved easily without trial and error, as they can be succinctly represented. Symbols help to prevent differing interpretations and overcome language ambiguity. For example, the Pythagorean theorem (a² + b² = c²) clearly defines relationships in a right-angled triangle [7].

We used propositional logic in symbolic form to derive generalized solution strategies for various problems. Using this, solution strategies are identified appropriately for contradiction problems. By determining the correct problem-solving strategy, trials and errors can be minimized [7]. The butterfly model identifies and addresses contradiction problems using symbolic logic, offering specific solution strategies. This model reduces trial and error by accurately determining a problem-solving strategy and exploring available resources through the tool object analysis, leading to efficient problem resolution [8,9].

We developed a simplified tool object analysis algorithm. The analysis examines interactions between system components, classifying them as beneficial (positive), neutral (independent), or harmful (negative). By identifying these interactions and their conflicts, we provided tailored problem-solving strategies. This method is applied to creative problem-solving cases and is used to trained on ChatGPT 4.0 to offer specific solutions.

2. The Tool Object Analysis

In TRIZ’s tool object analysis, a system’s tool affects the attributes of the object, and the object is influenced by the tool. The interaction between the tool and the object involves the generation, absorption, and transfer of energy (field) [10,11]. The interactions between the tool and the object are categorized into (1) performing a desirable action, (2) performing an undesirable action, and (3) failing to perform a desirable action. When the tool performs a desirable action on the object, it is defined as a useful action. When the tool performs an undesirable action on the object, it is defined as a harmful action. Finally, when the tool fails to perform a desirable action on the object, it is defined as a null action. The interactions between the tool and the object are represented in Figure 1, with useful actions represented by solid arrows, harmful actions represented by wavy arrows, and null actions represented by dotted arrows.

In the interaction between the tool and the object, the function performed by the tool is analyzed in terms of syntax by combining the object and the transitive verb [12]—that is, it is expressed as ‘Who (tool) does what (object) how (action)’. When applying the components of Kipling’s Six Honest Serving Men to the analysis, ‘Who’ refers to the system user who operates the tool. Since the tool is the means and method by which the object is acted upon, ‘How’ corresponds to the tool. The object is the entity upon which the tool acts, so ‘What’ corresponds to the object. Therefore, the problem of toilet water wastage can be expressed as ‘The toilet (Who) blocks odors (What) because the pipe is S-shaped (How)’ or using the instrumental case marker ’by’, ‘The toilet blocks odors by the S-shaped pipe’. Here, the noun corresponding to the tool is ’pipe’. The nouns corresponding to the object are ‘odor’ and ‘amount of water’. ‘Why (cause and goal)’ is analyzed as preventing sewer odors from coming up to maintain cleanliness and not requiring a large amount of water for flushing (see also Figure 2). The analysis enables the application of the 76 standard solutions based on the substance-field structure and serves as a key to selecting the necessary solutions from the 40 inventive principles [6].

In the analysis, the S-shaped bent pipe contains water that blocks odors, which is a beneficial action. However, to flush waste, a large amount of water is needed to push the waste over the S-trap, which is a harmful action. A straight pipe (1-shaped) does not require a large amount of water for flushing waste due to the lack of height difference in the pipe, which is a beneficial action. However, a straight pipe cannot hold enough water to block odors, resulting in a null action where the system is exposed to odors.

3. Tool Object Analysis and ChatGPT Integration

We defined the types of interactions that occur between tools and objects in order to train for ChatGPT to perform the tool object analysis and develop solutions as follows:

• When the tool performs beneficial and neutral actions on the object.
• When the tool performs beneficial and harmful actions on the object.
• When the tool performs beneficial actions on the object, but the object performs harmful actions on the tool.
• When the tool performs beneficial actions on the object but performs harmful actions on itself.

The first case is exemplified by the relationship between a pencil and paper, the second case by the relationship between a tree support and a tree, the third case by the relationship between a paper cup and water, and the fourth case by the relationship between a rocket fuel tank and a rocket (Figure 3). We created a PDF file containing the above content to train ChatGPT.

The following two problems were then presented, and then ChatGPT was requested to perform the tool object analysis and provide specific solutions. As a result, the outcomes were obtained as shown in Figure 4 and Figure 5.

The first problem was defined as follows: “Now I will present a problem”. Based on the contents of the above PDF file, please conduct a tool object analysis and provide a specific solution. “A spoon can be used to eat soup, but it cannot pick up meat. Conversely, a fork can be used to pick up meat, but it cannot be used to eat soup. In this case, please suggest a way to enable both eating soup and picking up meat. Also, please conduct a tool object analysis”. The following solution is generated from ChatGPT: “To solve the problem of needing to eat both soup and meat with the same tool, we need to combine the functionalities of both the spoon and the fork. One practical solution is to create a hybrid tool commonly known as a “spork”, which is a combination of a spoon and a fork”. We confirmed that the tool object analysis and the solutions generated by ChatGPT were appropriate.

The second problem was as follows, “In 1930, Auguste Piccard designed a sealed, pressurized gondola that could safely carry him into the upper atmosphere to conduct scientific studies. The primary purpose of Piccard’s first ascent was cosmic ray investigation. In 1931, he ascended over 51,775 feet. Piccard faced the gondola problem as follow: To rise, the balloon canopy was inflated with lighter-than-air hydrogen. To descend, Piccard pulled a rope to release hydrogen from the top of the canopy, making the whole balloon heavier. The gondola of the balloon was hermetically sealed. A control rope for the outlet hydrogen-release valve of the balloon had to pass through the ceiling of the gondola. In order to keep the air inside the gondola, the gap between the rope and the ceiling plate should not have existed. But this would have prevented free motion of the rope. How did Piccard solve the rope problem? Perform the tool-and-object analysis and find the concrete solution of this problem” [13].

The solution generated by ChatGPT was as follows: “To solve this problem, Piccard needed a mechanism that maintained the hermetic seal of the gondola while allowing the rope to move freely. A possible concrete solution is to use a gland seal (stuffing box). A gland seal or stuffing box allows the control rope to pass through the ceiling plate. The gland seal consists of a flexible, airtight material (such as rubber or silicone) that can compress around the rope, creating a tight seal while allowing the rope to move freely”.

The analysis results and solutions generated by ChatGPT were appropriate. However, while ChatGPT suggested flexible materials such as rubber or silicone, the actual solution required mercury. This difference aside, we confirmed that ChatGPT performed the analysis accurately in generating specific solutions.

4. Conclusions

TRIZ suggests the separation principle and 40 inventive principles as methods for resolving contradictions in problems. However, beginners in problem-solving often find it difficult to apply TRIZ due to its complexity and vast content. To address this issue, this research developed an algorithm that defines contradiction problems and derives solutions using the tool object analysis of TRIZ. The analysis has the advantage of analyzing the problem’s goals, means of resolution, and the time and space where the problem occurs. During this process, it identifies the goal of the problem (Why), the object to be achieved (What), and the means to achieve the goal (How). It also helps in understanding the interactions between the components of the system. Based on this, this study proposed a tool object analysis method using ChatGPT to assist in resolving contradiction problems. The developed simplified tool object analysis algorithm analyzes the interactions between system components, classifying them as beneficial, neutral, or harmful, and identifies these interactions and conflicts to provide tailored problem-solving strategies. This method was applied to creative problem-solving cases and was used to be trained on ChatGPT, enabling it to generate specific solutions, which is the significance of this research.