Information processing device, information processing method, program, and data structure

Abstract

The objective is to provide an established information processing method for generating hypotheses or insights from large-scale text collections, going beyond mere information extraction and summarization. [Solution] An information processing device that generates hypotheses or insights from a data set including linguistic information, comprising: a conflict identification unit that identifies multiple conflicting claims; a condition analysis unit that generates structured data defining conditions under which the validity of each claim is maintained; and a hypothesis generation unit that generates at least one integrated interpretation model that encompasses or explains the validity conditions based on the structured data. The problem is solved by incorporating a new variable that explains the differences in the validity conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of natural language processing and knowledge discovery support. More specifically, using artificial intelligence such as large language models, from a large amount of text data groups in various domains such as medicine, economy, management, and earth science, it relates to an information processing device, an information processing method, a program, and a data structure for generating new hypotheses, theories, or strategic findings beyond simple information retrieval, summarization, or opinion adjustment.

Background Art

[0002] In recent years, with the development of large language models (LLMs), technologies for extracting and summarizing necessary information from huge amounts of text data have advanced by leaps and bounds. For example, in a technology called Retrieval-Augmented Generation (RAG), it is possible to search for documents related to a user's query and generate an answer based on the content. Also, technologies for referring to multiple documents, summarizing their common points, or extracting and organizing differences in opinions have been proposed.

[0003] For example, Patent Document 1 discloses a system that automatically detects the opposition of opinions of participants in a discussion and automatically generates proposals from a neutral perspective for opposing opinions in order to assist in opinion adjustment. Such a technology is useful for the purpose of reducing frictions in human relationships in meetings and chats and supporting smooth consensus formation.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in intellectual creative activities such as scientific discovery, complex market analysis, or sophisticated business strategy formulation, simply "summarizing" existing information or "neutrally adjusting (compromising)" conflicting opinions is sometimes insufficient. Innovation and paradigm shifts have arisen not by simply averaging and resolving contradictions and conflicts between existing theories and predictions, but by rigorously identifying the "limits of application (boundary conditions)" under which they hold true, and integrating them by introducing higher dimensions or new variables (so-called Aufhebung / sublation).

[0006] Conventional technologies, particularly systems like those described in Patent Document 1, viewed conflicts and contradictions as "frictions to be resolved" and focused primarily on finding a neutral compromise. As a result, they lacked the perspective that contradictions themselves are important signals for generating new insights, making it difficult to delve into the conditions (context and assumptions) under which the validity of each piece of contradictory information is maintained and to create a new interpretive model that integrates them. Furthermore, simply finding a neutral middle ground between conflicting opinions, while it may avoid friction in interpersonal relationships, is unlikely to lead to new discoveries or provide essential solutions to problems. In particular, in scientific discovery and high-level decision-making, uncovering the truth at the heart of conflict is more important than easy compromise.

[0007] This invention was made in view of the above problems, and actively utilizes the conflicts, contradictions, and even inconsistent relationships between claims contained in texts of all domains as a source of creativity. Through the analysis of the boundary conditions and scope of application for which each claim is valid, it generates a more explanatory and integrated hypothesis or insight that cannot be derived from the simple summation of existing information or neutral compromises. The objective is to achieve something. [Means for solving the problem]

[0008] To achieve the above objective, according to one aspect of the present invention, an information processing device is provided that generates hypotheses or insights from a data set containing linguistic information. This device comprises: a conflict identification unit that identifies a first claim included in the data set and a second claim that is logically opposed to it; a condition analysis unit that analyzes the validity conditions for each claim to be valid and generates structured data that defines the conditions under which the validity of each claim is maintained; and a hypothesis generation unit that generates an integrated interpretation model that can encompass or explain both sets of conditions based on the structured data. The resulting integrated interpretation model is characterized by being a hypothesis that allows both claims to coexist by introducing new variables that explain the differences in their respective conditions, rather than including an intermediate compromise or neutral perspective between the opposing claims.

[0009] Furthermore, the validity conditions may include at least one selected from the group consisting of physical conditions, environmental conditions, temporal scale, attributes of the target population, and the assumptions of the definition. Furthermore, if the validity conditions for each claim are mutually exclusive, a higher-level concept that resolves this exclusivity may be sought, and that higher-level concept may be treated as a new variable. Furthermore, the process may include reinterpreting opposing phenomena as including a phase transition point (tipping point) where the parameters irreversibly change from a first state to a second state when they exceed a certain threshold, and integrating them as phenomena in different phases. Furthermore, the process may include treating the divergence between claims itself as an important signal (such as uncertainty or risk) within the system and modeling it.

[0010] Furthermore, according to another aspect of the present invention, an information processing method and program for causing a computer to perform the above processing are provided, as well as a data structure generated by the above processing that stores in association the conflicting claims and their conditions for validity, and the interpretive model that integrates them. [Effects of the Invention]

[0011] According to the present invention, "contradictions," "conflicts," and "inconsistencies" scattered throughout a large amount of text data can be utilized not as errors or noise, but as "clues (signals)" for new discoveries. Specifically, instead of treating conflicting claims as a simple "which is right?" dichotomy, this approach breaks them down into conditional descriptions of "under what conditions is it right?" and analyzes them, generating higher-order models that integrate them. This allows for the efficient creation of high-quality hypotheses and insights that transcend the framework of existing knowledge systems. This expands human creativity in all fields requiring advanced intellectual productivity, such as medicine, earth science, economics, management, and biology. [Brief explanation of the drawing]

[0012] [Figure 1] This is a block diagram showing the functional configuration of an information processing device according to one embodiment of the present invention. [Figure 2] This is a conceptual diagram of the information processing process (compromise) in conventional technology. [Figure 3] This is a conceptual diagram of the information processing process (integration) in this embodiment. [Modes for carrying out the invention]

[0013] Embodiments of the present invention will be described in detail below. The information processing device according to this embodiment (hereinafter referred to as "the System") utilizes artificial intelligence technologies such as large-scale language models (LLMs) to generate emergent hypotheses from an input text corpus by integrating contradictions. In this specification, "text corpus" or "data set containing linguistic information" refers to a collection of single or multiple documents, etc., that contain linguistic information related to the theme being analyzed. Refers to integration. The format of the data is not particularly limited, and includes files in text format (e.g., TXT, HTML, XML), files in binary format (e.g., DOCX, PDF), files in image format (e.g., JPEG, TIFF), or the text transcription of audio data, etc., including all forms of data that contain text information internally or from which text information can be extracted.

[0014] [Overall Configuration of Information Processing Apparatus] Functionally, the information processing apparatus includes a data input unit, an opposition identification unit, a condition analysis unit, a hypothesis generation unit, and an output unit. However, the input unit and the output unit do not necessarily have to be always provided by the information processing apparatus, and they may be connected when necessary for function expansion. These are realized by general computer hardware (processor, memory, storage, etc.) and software programs. [[ID=!]]

[0015] FIG. 1 is a block diagram showing the functional configuration of an information processing apparatus according to one embodiment. The information processing apparatus 100 has an input unit 10, an output unit 20, and an information processing unit 30. The information processing unit has an opposition identification unit 31, a condition analysis unit 32, and a hypothesis generation unit 33. The information processing unit may have other functions in addition to these.

[0016] [1. Input Unit] The input unit has the function of collecting or receiving a data group (corpus) containing language information to be analyzed from one or more sources based on a theme specification from the user or a direct input of a data group. The corpus to be analyzed is papers, patents, reports, etc. related to the theme to be analyzed. The corpus input unit may be a keyboard or a microphone for directly inputting the corpus into the system, or may be a receiver for receiving corpus input from the outside through a communication function. Also, the input unit 10 may be connected to an external AI to collect information related to the specified theme by the external AI and use this information as the corpus, or in addition to the corpus.

[0017] [2. Opposition Identification Unit] The opposition identification unit comprehensively analyzes a corpus using artificial intelligence, and identifies pairs or groups of multiple claims (at least a first claim and a second claim) that are logically opposed regarding the theme to be analyzed. Here, "opposing claims" include not only claims with 180-degree different directions, but also conflicting claims and claims in an inconsistent relationship.

[0018] Conventional information processing presents the majority opinion as the correct answer or excludes conflicting information as having low reliability. However, in this form, it is the opposing claims (information) that are regarded as "signals suggesting knowledge gaps, unexplored areas, or phase transitions of the system" and extracted as important analysis targets.

[0019] To perform the processing by the opposition identification unit, for example, the following instructions (prompts) are given to the artificial intelligence. "Analyze the following corpus and identify pairs or combinations of opposing claims, conflicting claims, and claims in an inconsistent relationship among the major claims contained therein."

[0020] [3. Condition Analysis Unit] For each of the opposing claims identified by the opposition identification unit, the condition analysis unit analyzes the validity conditions under which each claim holds and generates structured data defining the conditions under which the validity of each claim is maintained. That is, it analyzes "under what conditions that claim can have validity". Validity conditions include, for example, boundary conditions, application ranges, premises, and the like. Propositions and claims are rarely absolute truths, and are subject to specific environments, time axes, market situations Alternatively, they often possess "contextual validity," meaning they only hold true within a specific organizational context. Therefore, the conditional analysis unit meticulously analyzes the context of the original text containing each claim, extracting constraint parameters such as boundary conditions, scope, and assumptions that support the claim's validity, such as "under high-temperature conditions," "when the market is in an expansion phase," or "from a short-term perspective." It then generates structured data (for example, a data structure such as JSON format) that combines each extracted claim with its conditions for validity. Through this process, what might appear to be a complete contradiction—"A is true" and "A is not true"—is transformed into a conditional description: "A is true under condition α" and "A is not true (B is true) under condition β."

[0021] To perform processing using the condition analysis unit, for example, the artificial intelligence is given instructions (prompts) such as the following. "Create structured data by parameterizing the context in which each argument is made, based on identified pairs or combinations of opposing claims."

[0022] Based on these instructions, the artificial intelligence generates structured data that organizes the points of contention between claims present in the corpus. This structured data includes any format that can be interpreted by the computer in subsequent processing, such as lists, tables, or data consisting of key-value pairs. In a preferred embodiment, the user may be prompted to summarize the main common views (consensus) within the corpus, in addition to the above. This can be useful as contextual information for interpreting identified inconsistencies, etc. Furthermore, a preferred process may include dividing the data set into multiple batches corresponding to the multiple subdomains and generating first-level structured data for each batch, and integrating the multiple first-level structured data generated for different batches to generate second-level structured data that identifies conflicts or inconsistencies between the subdomains.

[0023] [4. Hypothesis Generation Section] The hypothesis generation unit generates at least one integrated interpretation model that encompasses or explains both the first and second validity conditions, using artificial intelligence with text generation capabilities, based on the generated structured data. To perform processing using the hypothesis generation unit, you can give instructions such as the following: "Generate at least one unified interpretation model that encompasses or explains both the first and second validity conditions shown in the structured data." Alternatively, data including all or part of the specification, claims, drawings, or abstract of the patent application in question can be used as part of the prompt. This method has the effect of eliminating definitional ambiguities and improving the accuracy of generation.

[0024] The artificial intelligence generates candidate integrated interpretation models. The important point here is that these integrated interpretation models do not simply generate "a middle ground between claim A and claim B" or "a compromise that averages the two." The artificial intelligence focuses on the differences between the "first validity condition" and the "second validity condition" extracted by the condition analysis unit, and searches for higher-order logical models and mechanisms that can comprehensively explain these differing conditions. In one example, when the first validity condition and the second validity condition are mutually exclusive, a mediating factor or higher-level concept that resolves this exclusivity is searched for, and the integrated interpretation model is generated by introducing that factor or concept as a new variable. Specifically, the condition analysis unit extracts differences in validity conditions as parameters, and the hypothesis generation unit processes these parameters to capture changes. For example, when the change in the parameters exceeds a certain threshold, a phase transition point occurs where the state irreversibly changes from the first state to the second state (T). By interpreting this as including a "stopping point," the seemingly contradictory first and second claims are integrated as phenomena in different state phases. That is, a dynamic model (phase transition model) in which the phenomenon transitions from A to B is introduced, along with a new dimension of variables (mediating factors) that explain that A and B are actually different aspects of the same phenomenon. This is referred to as "dynamic integration" in this specification.

[0025] [5. Output Section] The output unit presents the integrated interpretation model generated by the hypothesis generation unit in a format that is easy for the user to interpret. The output unit only needs to be able to display the received candidate hypotheses, and may be a display device such as a display, or an output device such as a printer. The output unit should not only display the conclusion, but also visually relate (for example, in the form of metadata, a conflict structure diagram, or a conditional branching tree (branching condition rules for selecting a scenario)) "which conflict (contradiction) the hypothesis was generated to resolve" and "what differences in conditions caused that conflict." In other words, it is preferable to output the integrated interpretation model generated by the hypothesis generation unit, the conflict resolved by the model, and the first and second validity conditions that formed the basis of the conflict, in a visually related manner. It is also preferable to output the conditions for the first and second validity conditions to be met, and / or the conditions for their refutation.

[0026] [6. Artificial intelligence with text generation capabilities] Artificial intelligence can be embedded within an information processing system or exist as an external API. General-purpose generative AI can be applied. Furthermore, processing that can be done without artificial intelligence does not necessarily require its use.

[0027] In this embodiment, the information processing device may execute the functions of the conflict identification unit, the condition analysis unit, and the hypothesis generation unit sequentially or integrally in response to a single instruction from the user. The process that performs this series of functions can be viewed as an information processing device that, as a whole, generates structured data that recognizes logical problems (conflicts, contradictions, etc.) from the data set under analysis, and then generates new ideas (hypotheses, insights, etc.) that explain or integrate those problems, thereby transitioning to a higher-order second data state.

[0028] Furthermore, if an artificial intelligence model is incorporated into this information processing device, the model may be trained or fine-tuned using the information processing method of this embodiment (for example, paired data of numerous conflicts / contradictions and hypotheses for resolving them) to acquire the ability to generate conflict / contradiction-resolving hypotheses for specific inputs. Furthermore, in this embodiment, the generated structured data and integrated interpretation models may include additional information to facilitate infringement detection or aid user understanding. For example, the generated integrated interpretation model is clearly associated with the corresponding conflict information in the structured data by linkage information such as identifiers or pointers that indicate which conflicts or contradictions the model was generated to explain or integrate. In addition, the identified conflicts or contradictions and the generated integrated interpretation model may be accompanied by citation information (source information) indicating which parts of which documents (e.g., book title, page, paragraph, highlighted text, etc.) in the input data set each relies on. Preferably, the output unit utilizes this linkage information and citation information to provide a display device such as a user interface that graphically displays conflicting source texts and hypotheses that integrate them, or makes them mutually referential via hyperlinks, so that the user can intuitively verify the validity of the integrated interpretation model.

[0029] Another embodiment of the present invention is an information processing method using the above-described information processing apparatus. Specifically, a computer-based information processing method for generating hypotheses or insights from a data set containing linguistic information, comprising the steps of: analyzing the data set and identifying a first claim and a second claim that logically contradicts the first claim; analyzing a first validity condition for the first claim to be valid and a second validity condition for the second claim to be valid, and generating structured data that defines the conditions under which the validity of each claim is maintained; The process includes the step of generating at least one integrated interpretation model that encompasses or explains both the first and second validity conditions, using artificial intelligence with text generation capabilities based on the structured data, The method for generating the integrated interpretation model is characterized by introducing a new variable that explains the difference between the first and second validity conditions, thereby generating a hypothesis or insight that allows both the first and second claims to coexist under specific conditions, and the hypothesis or insight does not include an intermediate compromise or neutral perspective between the first and second claims.

[0030] Furthermore, the data structure generated by the above-described information processing method is also an embodiment of the present invention.

[0031] The following shows an example of a specific application of this embodiment, including anticipated deliverables. However, the scope of application of the present invention is not limited in any way to these examples. The information processing device of this embodiment will be referred to as the "system" below. [Example 1: Application in the medical and healthcare field] As a concrete example of hypothesis generation using this system, we present the process of resolving the scientific debate surrounding the pathogenesis of long COVID. Information from related papers and other sources was collected and compiled into a corpus. (Step 1: Identify the conflict) The system identified two opposing claims regarding microclots: Claim A (affirmative studies) which asserts that "thrombosis is the primary cause," and Claim B (critical studies) which argues that "thrombosis is a nonspecific phenomenon and not the cause." (Step 2: Conditional Analysis) The system analyzed the background of each claim and extracted that pathogenicity is asserted when the thrombus is in a state of being "difficult to break down (resistible)," and that "mere presence" is considered nonspecific. (Step 3: Generation of an integrated model) The system focused on "neutrophil extracellular traps (NETs)" as a new variable that reconciles both sides' claims, and generated an integrated interpretation model in which "the thrombus becomes physically stable and pathogenic only when NETs bind to it (condition α)." Furthermore, the system organized multiple pathological hypotheses (thrombosis, neuroinflammation, metabolic dysfunction) using "gating rules (conditional branching)" and generated an "endotype-stratified model" that switched the applicable model according to each patient's biomarker values. This successfully unified and explained conflicting experimental results.

[0032] [Example 2: System Stability Integration in the Field of Earth Sciences] Next, we will show an example of applying this system to Earth system science. (Step 1: Identify the conflict) The system identified a seemingly contradictory "paradox of stability and instability" regarding the Earth's environment: Claim A, which states that the Earth is stable due to its remarkable self-regulating abilities (Gaia hypothesis), and Claim B, which states that the Earth has experienced catastrophic fluctuations many times in the past (mass extinctions). (Step 2: Conditional Analysis) The system extracted that claim A refers to "homeostasis under specific boundary conditions," while claim B refers to "a transitional period in which the boundary conditions themselves change." (Step 3: Generating the integrated model) The system integrated these elements to generate a new theoretical model that posits that "Earth's stability is not singular but hierarchical." Specifically, it defined three hierarchies: "oscillating modes (movements between metastable states)," "tipping modes (irreversible transitions beyond critical points)," and "reboot modes (rewriting of the rules themselves by life)." It then output an integrated interpretation model that explains these mode transitions uniformly using a variable called state-dependency.

[0033] [Example 3: Narrative Analysis in the Economic and Financial Fields] This section presents an example of applying this system to information analysis in financial markets. (Step 1: Identify the conflict) The system identified a narrative divergence regarding a company's future prospects, specifically between "an optimistic outlook by management" and "a cautious assessment by securities analysts." (Step 2: Conditional Analysis) The system recognizes that this discrepancy is not merely a difference of opinion, but rather a difference in "focus" and "intention." We analyzed that it is composed of two dimensions: the difference in "opinion" and "perspective." (Step 3: Generating the integrated model) Instead of predicting "moderate growth" by taking the middle ground, the system generated a model that quantifies this discrepancy itself as a signal indicating "information uncertainty" or "agency risk." It then produced a new investment decision model (integrated interpretation model) based on narrative asymmetry, stating that "if only management is disseminating negative information, the information is highly reliable (strong sell signal)," and "if both are negative, it suggests a structural problem."

[0034] [Example 4: Paradox Integration in the Field of Business Strategy] This section presents an example of applying this system to organizational management theory. (Step 1: Identify the conflict) The system identified strategic requirements for the sustainable growth of companies that involve a trade-off relationship in resource allocation between "knowledge exploration (new business development)" and "knowledge deepening (efficiency improvement of existing businesses)." (Step 2: Conditional Analysis) The system analyzed that these conflicts only occur within the same timeframe and organizational structure (partial validity). (Step 3: Generating the integrated model) The system generated a model called the "dynamic integration process." This is a cognitive process model for "ambidextrous management" that does not resolve contradictions, but rather uses contradictions as a driving force for growth, by not viewing conflicts as fixed, but instead allowing them to coexist through temporal and structural separation, and by building a "self-reinforcing loop" in which the outcomes of one become resources for the other.

[0035] [Example 5: Integration of origin disputes in the field of biology] This section presents an example of applying this system to scientific reasoning about the origin of life. (Step 1: Identify the conflict) The system identified a long-standing conflict regarding the origin of life: the theory that "genetic information was established first (gene-first theory)" and the theory that "metabolic systems were established first (metabolic-first theory)." (Step 2: Conditional Analysis) The system analyzed that each theory emphasizes different aspects essential for life support: "information preservation" and "energy supply." It also summarized the advantages of the locations where organisms originate: "deep sea (rich in chemical energy)" and "land (rich in physical cycles)." (Step 3: Generating the integrated model) The system focused on the fact that "RNA template replication (genetics)" itself possesses the properties of "autocatalytic chemical reactions (metabolism)," and generated an integrated model that posits the inseparable co-evolution of the two. Furthermore, it outputted a spatially integrated scenario that includes a global material cycle, in which precursors generated in the deep sea are polymerized and replicated by the de- and de-wet cycle on land.

[0036] [Conceptual differences from conventional technology: Planar adjustment versus three-dimensional integration] Referring to Figures 2 and 3, we will detail the qualitative differences between the hypothesis generation approach of the present invention (this system) and the approach of the prior art (for example, the AI ​​facilitator represented in Patent Document 1). Figure 2 illustrates a conceptual model of conflict resolution in conventional technology. In conventional technology, the conflict between the first claim A and the second claim B is perceived as "friction" that needs to be resolved. Conventional systems treat both claims on the same dimension (plane) and perform processing to find a middle ground or compromise. As a result, the generated proposals tend to be inoffensive and neutral, and even useful information is removed as noise (information degradation). In contrast, Figure 3 shows a conceptual model of integrated interpretation in this system. This system views conflict not as friction, but as "differences in conditions (boundary conditions)" for each claim to be valid. Furthermore, instead of mixing the opposing claims A and B on a plane, it attempts to integrate them by "raising the dimension (a three-dimensional approach)." Specifically, this system introduces a new variable (dimension) and rearranges Claim A as "a phenomenon in the domain of condition α" and Claim B as "a phenomenon in the domain of condition β". As a result, the generated integrated interpretation model (hypothesis C) becomes a higher-level law that encompasses both Claim A and Claim B as particular solutions. In this process, far from degrading information, new structures and theories that were not visible from individual events are discovered (emergence of information). Thus, this system performs information processing that is fundamentally different from conventional technologies in that it performs "synthesis" rather than "compromise". [Explanation of Symbols]

[0037] 100 Information Processing Devices 10 Input section 20 Output section 30 Information Processing Department 31 Conflict Special Department 32 Condition analysis section 33 Hypothesis Generation Unit

Claims

1. An information processing device that generates hypotheses or insights from a data set containing linguistic information, A conflict identification unit analyzes the aforementioned data set and identifies a first claim included in the data set and a second claim that logically contradicts the first claim, A condition analysis unit analyzes the first validity condition for the first claim to be true and the second validity condition for the second claim to be true, and generates structured data that defines the conditions under which the validity of each claim is maintained. A hypothesis generation unit that generates at least one integrated interpretation model capable of encompassing or explaining both the first and second validity conditions, using artificial intelligence with text generation capabilities based on the aforementioned structured data; Equipped with, The integrated interpretation model is characterized by being a hypothesis or insight that allows both the first and second claims to coexist under specific conditions by introducing a new variable that explains the difference between the first and second validity conditions, and the hypothesis or insight does not include an intermediate compromise or neutral viewpoint between the first and second claims, and is an information processing device.

2. The information processing apparatus according to claim 1, wherein the first validity condition and / or the second validity condition includes at least one selected from the group consisting of physical conditions, environmental conditions, temporal scale, attributes of the target group, and definitional assumptions.

3. The information processing apparatus according to claim 1, wherein the hypothesis generation unit searches for a higher-level concept that resolves the mutually exclusive relationship between the first validity condition and the second validity condition, and generates the integrated interpretation model by introducing the concept as the new variable.

4. Claim 1: The condition analysis unit extracts differences in validity conditions as parameters, and the hypothesis generation unit interprets that the parameters include a phase transition point (tipping point) where, when a certain threshold is exceeded, the first state irreversibly changes to a second state, thereby integrating the seemingly contradictory first and second claims as phenomena in different state phases. The information processing device described above.

5. The structured data is used for each conflict point identified by the conflict identification unit. The content of each opposing argument, Metadata indicating the first or second validity conditions associated with each claim, The information processing apparatus according to claim 1, having a data structure that stores in association a conditional branching rule for selecting a scenario to be applied based on the validity conditions.

6. The information processing apparatus according to claim 1, further comprising an output unit that visually relates and outputs the integrated interpretation model generated by the hypothesis generation unit, the conflict resolved by the model, and the first and second validity conditions that formed the basis of the conflict.

7. The information processing apparatus according to claim 6, wherein the output unit outputs the conditions for fulfilling the first validity condition and the second validity condition, and / or the conditions for disproving them.

8. A computer-based information processing method that generates hypotheses or insights from a set of data containing linguistic information, The steps include analyzing the aforementioned data set and identifying a first claim included in the data set and a second claim that logically contradicts the first claim, The steps include: analyzing the first validity condition for the first claim to be true and the second validity condition for the second claim to be true, and generating structured data that defines the conditions under which the validity of each claim is maintained; The steps include generating at least one integrated interpretation model that encompasses or explains both the first and second validity conditions, using artificial intelligence with text generation capabilities based on the structured data, Includes, An information processing method characterized by generating a hypothesis or insight that allows both the first and second claims to coexist under specific conditions by introducing a new variable that explains the difference between the first and second validity conditions in the generation of the integrated interpretation model, wherein the hypothesis or insight does not include an intermediate compromise or neutral perspective between the first and second claims.

9. A program for causing a computer to function as an information processing device according to any one of claims 1 to 7.

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