Information processing system

Abstract

This system provides an information processing system that can propose improvements to the spatial health of a given space. [Solution] An information processing system that proposes ways to improve the spatial health of a predetermined space, comprising: a spatial health calculation unit that calculates the spatial health of the space; a gap analysis unit that calculates the difference between the spatial health and a preset target value for spatial health; and an intervention means selection unit that selects an intervention means based on the difference. The spatial health is calculated based on one or more selected from a group consisting of the amount of microorganisms contained in the space, the proportion of human-derived microorganisms in the microbial community, the proportion of pathogenic microorganisms, and microbial diversity.

Description

Technical Field

[0001] The present invention relates to an information processing system.

Background Art

[0002] A method of redefining, redesigning, and reconstructing a field ecosystem using backcast thinking to improve the economic value of land in a state of low economic value has been disclosed (Patent Document 1).

[0003] A space evaluation system capable of quantitatively evaluating the degree to which an unknown space to be evaluated is close to a natural environment has been disclosed (Patent Document 2).

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] There is a need for a technology that proposes to improve the spatial health of a predetermined space.

[0006] Therefore, an object of the present invention is to provide an information processing system capable of proposing to improve the spatial health of a predetermined space.

Means for Solving the Problems

[0007] As a result of intensive studies to solve the above problems, the present inventors have found that the spatial health can be calculated based on the characteristics of the microbial community contained in the space, the difference between the spatial health and the target value can be analyzed, and the intervention means can be selected based on the difference, and thus have completed the present invention.

[0008] In other words, one aspect of the present invention is an information processing system that proposes ways to improve the spatial health of a given space, comprising: a spatial health calculation unit that calculates the spatial health of the space; a gap analysis unit that calculates the difference between the spatial health and a preset target value for spatial health; and an intervention means selection unit that selects intervention means based on the difference analyzed by the gap analysis unit, wherein the spatial health is calculated based on one or more selected from a group consisting of the amount of microorganisms in the microbial community contained in the space, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms in the microbial community contained in the space, and the microbial diversity in the microbial community contained in the space.

[0009] Further issues and solutions disclosed in this application will be made clear in the section on embodiments of the invention and in the drawings. [Effects of the Invention]

[0010] According to the present invention, it is possible to provide an information processing system that can objectively and quantitatively evaluate the microbial environment of a given space and propose improvements to the health of that space based on the evaluation.

[0011] Furthermore, according to the present invention, by selecting intervention measures based on the difference between the spatial health level and the target value, it becomes possible to propose appropriate improvement measures tailored to the conditions of each space. This is expected to enable detailed environmental improvement that is tailored to the characteristics of the space, rather than a uniform approach.

[0012] Furthermore, by implementing the present invention, it becomes possible to evaluate the microbial environment of a space from multiple perspectives, including microbial mass, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity. It is expected that this will enable comprehensive spatial environmental management, including not only the removal of pathogenic microorganisms but also the maintenance and improvement of microbial diversity.

[0013] Furthermore, according to the present invention, it becomes possible to automate or semi-automate the evaluation of spatial health and the proposal of intervention measures, and it is expected that even users without specialized knowledge will be able to easily perform appropriate spatial environment management. [Brief explanation of the drawing]

[0014] [Figure 1] This figure shows an example of the overall configuration of an information processing system. [Figure 2] This figure shows an example of the hardware configuration of management server 2. [Figure 3] This figure shows an example of the software configuration for management server 2. [Figure 4] This is a diagram illustrating the processing flow in an information processing system. [Figure 5] This diagram illustrates the processing flow, including the measurement of the effects of the intervention and any additional interventions after the intervention has been implemented. [Modes for carrying out the invention]

[0015] <Background of the Invention>

[0016] In modern society, people spend the majority of their time indoors within buildings. The interior spaces of various buildings—residences, offices, commercial facilities, educational institutions, medical facilities, and so on—are the primary places of human activity. The environmental quality of these indoor spaces directly impacts the health of the people who live or work within them.

[0017] Traditionally, physicochemical elements such as temperature, humidity, illuminance, ventilation rate, and chemical concentration have been the focus of attention when considering the components of the indoor environment. However, recent research is revealing that the types and composition of microorganisms present in indoor spaces also have a significant impact on human health. In particular, modern buildings are often designed to be highly airtight, which limits natural ventilation and results in an imbalance in the indoor microbial flora.

[0018] In indoor spaces, there are microorganisms with diverse origins, such as those that invade from outdoor air, are released from building materials and furniture, are derived from plants and animals, and are from humans themselves. The types, quantities, and compositions of these microorganisms vary depending on various factors such as the building structure, architectural style, outdoor land use, ventilation conditions, activities of the occupants, cleaning methods, and others.

[0019] The so-called hygiene hypothesis is the hypothesis that the more opportunities one has to be exposed to diverse microorganisms during childhood, the more appropriately the immune system develops, and the lower the risk of developing allergic and autoimmune diseases. Based on this hypothesis, appropriately maintaining microbial diversity in the indoor environment may contribute to the development and maintenance of the immune function of humans living in that space.

[0020] On the other hand, when pathogenic microorganisms accumulate in the indoor environment, the risk of infectious diseases may increase. Also, when microorganisms derived from the human body surface, oral cavity, intestinal tract, etc. become dominant indoors, the risk of these microorganisms reinfecting humans increases, and at the same time, the opportunity for beneficial immune stimulation by diverse microorganisms derived from the natural environment such as soil and plants may decrease. In modern indoor environments, especially in highly airtight buildings and indoor spaces in urban areas with high population density, human-derived microorganisms continuously released from the occupants tend to accumulate, while it is difficult for microorganisms derived from the natural environment to flow in. As a result, there is a tendency for human-derived microorganisms to be excessive. Such a bias in the microbial composition raises concerns about potentially having an adverse impact on the health of humans living or active in that space.

[0021] Conventionally, as a method for hygienic management of the indoor environment, methods using chemical disinfectants and bactericides have been widely adopted. However, such methods may not only remove pathogenic microorganisms but also beneficial microorganisms at the same time, resulting in a potential decrease in microbial diversity. There are also concerns about the environmental load caused by chemical substances and the risk of the emergence of drug-resistant bacteria.

[0022] Against this backdrop, the importance of comprehensively evaluating and appropriately managing the microbial environment in indoor spaces has become recognized. However, practical means for objectively evaluating the indoor microbial environment and proposing specific improvement measures have not been sufficiently established.

[0023] Therefore, there is a growing demand for systems that can comprehensively evaluate the microbial environment of indoor spaces and propose appropriate intervention measures based on that evaluation. In particular, there is a need for comprehensive spatial environment management that includes not only the removal of pathogenic microorganisms but also the maintenance and improvement of microbial diversity.

[0024] This invention was made in view of the above background, and provides an information processing system that can quantitatively evaluate the microbial environment of a space, calculate an index called "space health," and propose specific intervention measures for environmental improvement based on this index.

[0025] The background of the invention described above is merely an example of some of the specific problems that the present invention aims to solve, and the problems that the present invention aims to solve are not limited to these.

[0026] <Summary of the Invention>

[0027] The information processing system of the present invention proposes ways to improve the spatial health of a given space. The information processing system of the present invention comprises a spatial health calculation unit, a gap analysis unit, and an intervention means selection unit as its main components.

[0028] The spatial health calculation unit has the function of calculating the spatial health of the target space. Spatial health is calculated based on one or more indicators selected from a group consisting of the amount of microorganisms in the microbial community contained in the space, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity.

[0029] The gap analysis unit has the function of calculating the difference between the spatial health level calculated by the spatial health level calculation unit and a pre-set target value for spatial health level. This difference serves as an indicator of how much the microbial environment of the space deviates from the target state.

[0030] The intervention selection unit has the function of selecting an intervention to improve the spatial health of the space based on the difference calculated by the gap analysis unit. The selected intervention is output to the user as a suggestion.

[0031] In addition to the basic configuration described above, the information processing system of the present invention may include additional components such as a target setting unit for setting target values ​​for spatial health, an intervention means database for recording intervention means, an environmental information acquisition unit for acquiring spatial environmental information, and an output unit for outputting processing results.

[0032] The present invention can be implemented as a server, cloud computing system, edge computing device, personal computer, mobile device, embedded system, or a combination thereof. Furthermore, the present invention can also be realized as an information processing method, information processing program, recording medium storing the program, or integrated circuit.

[0033] <Basic methodology>

[0034] The basic methodology for calculating spatial health in this invention involves analyzing the characteristics of the microbial community contained in a sample taken from the target space, and quantifying the spatial health based on the results of this analysis.

[0035] Various methods can be used to analyze the characteristics of microbial communities, including culture methods, microscopic observation methods, and molecular biological methods. In particular, metagenomic analysis using next-generation sequencing (NGS) can be suitably used in this invention because it can comprehensively detect and identify the diverse microorganisms present in the sample.

[0036] In this invention, four indicators can be used to describe the characteristics of a microbial community: microbial mass, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms (the proportion of pathogenic bacteria themselves, the proportion of drug resistance genes and pathogenicity genes), and microbial diversity. These indicators can be used individually or in combination of two or more. When combining multiple indicators, it is desirable to calculate an integrated overall score by appropriately weighting each indicator.

[0037] The target value for spatial health can be set considering various factors such as the use of the space, user attributes, geographical conditions, and seasonal conditions. The target value can be set fixedly in advance, or it can be set dynamically based on the attribute information of the space.

[0038] The selection of intervention methods can be made by considering various factors such as the magnitude of the difference between spatial health and the target value, the main factors causing the difference, the physical characteristics of the space, and the available resources. Intervention methods can be selected from a pre-defined set of options, or they can be dynamically generated using methods such as machine learning.

[0039] <Definition of Terms> The main terms used in this invention are defined below.

[0040] In this specification, "information processing system" means a system having one or more functions among information input, processing, storage, and output. The information processing system of the present invention has at least the processing functions of calculating spatial health, gap analysis, and selection of intervention means.

[0041] The information processing system of the present invention may be configured by a single computer or device, or it may be configured as a distributed system in which multiple computers or devices are connected via a network. When configured as a distributed system, each component may be located in the same physical location or in different locations.

[0042] The information processing system of the present invention can also be implemented in a cloud computing environment. In this case, some or all of the components, such as the spatial health calculation unit, gap analysis unit, and intervention means selection unit, may be implemented as software that runs on a cloud server.

[0043] The information processing system of the present invention can also be implemented as an edge computing device. In this case, a device located near the target space can perform spatial health calculations and select intervention methods. Implementation using edge computing is advantageous for applications requiring real-time performance and in environments with unstable network connectivity.

[0044] In this specification, “space” means a three-dimensional area that is physically demarcated or can be demarcated. Space does not need to be completely enclosed by physical boundaries such as walls, floors, and ceilings, and may be partially open.

[0045] Specific examples of spaces include the interiors of buildings (living rooms, offices, shops, hospital rooms, school classrooms, factory workshops, etc.), common areas of buildings (corridors, entrances, elevator interiors, etc.), interiors of transportation systems (car interiors, train interiors, airplane interiors, ship interiors, space stations, etc.), agricultural facilities (greenhouses, livestock barns, etc.), underground spaces (underground shopping malls, underground parking lots, tunnels, etc.), rooftop spaces of buildings (building rooftops, rooftop gardens, rooftop terraces, etc.), and connecting spaces between buildings in urban areas (pedestrian decks, connecting passages, skywalks, etc.). However, these are merely examples, and spaces are not limited to these.

[0046] Spaces can also be classified according to their purpose. For example, they can be classified as residential spaces, office spaces, commercial spaces, educational spaces, medical spaces, industrial spaces, agricultural spaces, and transportation spaces. The purpose of a space can be a factor to consider when setting target values ​​for spatial health.

[0047] Spaces can also be classified according to their openness. For example, they can be classified into closed spaces with limited ventilation to the outside air, semi-open spaces with some ventilation to the outside air, and open spaces that are largely open to the outside air. The openness of a space can be a factor that influences the composition of the microbial community.

[0048] In this specification, "spatial health" refers to an index indicating whether the microbial environment in a given space is in a favorable state for the health of humans living or working in that space. Spatial health is preferably expressed as a quantitative numerical value, but may also be expressed as a qualitative evaluation (for example, a rank such as good, normal, or needs improvement).

[0049] In the present invention, spatial health can be calculated based on one or more indicators selected from the group consisting of the amount of microorganisms in the microbial community contained in the space, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity. How these indicators are combined and what formula is used to calculate spatial health can be appropriately set according to the embodiment of the present invention.

[0050] One method for calculating spatial health involves scoring the measured values ​​of each indicator against predetermined criteria, and then adding or multiplying these scores with appropriate weights to calculate an overall score. For example, scores based on microbial mass, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity can be calculated separately, and these can be weighted averaged to determine spatial health.

[0051] Another method for calculating spatial health involves using machine learning models to predict spatial health from multiple indicators. In this case, a model trained on past data can calculate spatial health from new measurement data.

[0052] Spatial health may be expressed as an absolute value or as a relative value (e.g., a percentage or standard score relative to a baseline). Furthermore, spatial health may be expressed as a single numerical value or as a multidimensional index showing multiple aspects.

[0053] In this specification, "microbial community" refers to a collection of microorganisms present in a given space or sample. Microbial communities include various types of microorganisms such as bacteria, archaea, fungi, protists, and viruses. While the present invention primarily focuses on bacteria, other types of microorganisms may also be targeted.

[0054] Molecular biological methods such as amplicon sequencing targeting the 16S rRNA gene, shotgun metagenomic analysis, and metatranscriptome analysis can be used to analyze the composition of microbial communities. These methods allow for the determination of the types, relative abundances, and functional characteristics of microorganisms present in the sample.

[0055] In this specification, "microbial mass" refers to an indicator of the amount of microorganisms present in a given space or sample. Microbial mass can be expressed as the number of microbial cells, biomass, or a measured value correlated therewith. Microbial mass may be expressed as an absolute amount, or as an amount per unit area, per unit volume, or per unit time.

[0056] Microbial mass may be measured as the total amount of all microorganisms present in the sample, or as the amount of microorganisms belonging to a specific taxonomic group such as a species, genus, or family. Furthermore, microbial mass may be calculated by summing the amounts of microorganisms belonging to functional or originary categories such as human-derived microorganisms, naturally occurring microorganisms, and pathogenic microorganisms. For example, the total amount of human-derived microorganisms, pathogenic microorganisms, and naturally occurring microorganisms can be measured individually and used to evaluate spatial health. Thus, the target of microbial mass measurement can be appropriately set according to the purpose of the evaluation.

[0057] Various methods can be used to measure microbial mass, including culture methods, molecular biological methods, biochemical methods, and physical methods.

[0058] One culture method involves forming colonies on an agar plate and counting the colony-forming units (CFU).

[0059] Molecular biological methods include measuring the copy number of the 16S rRNA gene using quantitative PCR (qPCR) or digital PCR (dPCR) to estimate bacterial cell count. The target gene used for estimating microbial mass is not limited to the 16S rRNA gene; the 23S rRNA gene, rpoB gene, gyrB gene, and other highly conserved genes can also be targeted. Furthermore, for fungi, the ITS region or the 18S rRNA gene can be targeted. Additionally, methods for estimating microbial mass based on read count and genome coverage obtained by shotgun metagenomic sequencing can also be used. When using shotgun metagenomic analysis, methods such as estimation based on the detection frequency of single copy marker genes or absolute quantification using spike-in controls can also be employed.

[0060] Biochemical methods include measuring the amount of ATP derived from microorganisms using the ATP measurement method (ATP bioluminescence method), and quantifying cell wall components such as lipopolysaccharide (LPS) and peptidoglycan.

[0061] Physical methods include directly counting cells using flow cytometry (cell sorter), visualizing and counting cells using a fluorescence microscope with DAPI staining, and estimating microbial mass by turbidity measurement (OD measurement). These measurement methods can be used individually or in combination, depending on the purpose and situation.

[0062] Microbial mass can be used to assess the health of a space. For example, a space with an extremely low microbial mass may have undergone excessive sterilization and may be an undesirable environment from the perspective of microbial diversity. On the other hand, a space with an extremely high microbial mass may have hygiene problems. Maintaining an appropriate range of microbial mass is often desirable from the perspective of space health.

[0063] In this specification, "human-derived microorganisms" refers to microorganisms whose primary habitat is the human body (body surface, oral cavity, digestive tract, respiratory system, urogenital system, etc.) and which are released into the environment as a result of human activities. Human-derived microorganisms mainly consist of microorganisms that make up the normal human flora, but also include microorganisms that have temporarily colonized humans.

[0064] Human-derived microorganisms are released into the environment through human skin, hair, saliva, breath, and excrement. A high proportion of human-derived microorganisms in an indoor environment suggests that the space is frequently used by humans and that ventilation and cleaning may be insufficient.

[0065] Spaces with a high proportion of human-derived microorganisms may be undesirable from a health perspective for the following reasons: Firstly, human-derived microorganisms may include pathogenic ones, posing a risk of reinfection in humans. Secondly, environments dominated by human-derived microorganisms may reduce opportunities for exposure to diverse microorganisms from the natural environment, potentially diminishing beneficial stimulation of the immune system.

[0066] The determination of whether or not a microorganism is of human origin can be made based on its taxonomic classification. For example, microorganisms belonging to a specific family, genus, or species can be classified as microorganisms of human origin. The classification of which taxonomic groups constitutes microorganisms of human origin can be determined based on publicly available databases and literature.

[0067] In the present invention, human-derived microorganisms may include, for example, microorganisms belonging to the following bacterial families: Abyssicoccaceae, Acidaminococcaceae, Actinomycetaceae, Acutalibacteraceae, Aerococcaceae, Akkermansiaceae, Aliterellaceae, Aminobacteriaceae, Anaeroplasmataceae, Anaerotignaceae, Anaerovoracaceae, Arenimicrobiaceae, Atopobiaceae, Azospirillaceae, B12-WMSP1, BD7-11, Barnesiellaceae, Bianqueaceae, Bifidobacteriaceae, Brachyspiraceae, Brucellaceae, Butyricicoccaceae, C86, Campylobacteraceae, Cardiobacteriaceae, Carnobacteriaceae, Cellulosilyticaceae, Chlamydiaceae, Christensenellaceae, Clostridiaceae, Coprobacillaceae, Coriobacteriaceae, Corynebacteriaceae, DS-100, Desulfovibrionaceae, Dethiosulfovibrionaceae, Dialisteraceae, Eggerthellaceae, Elusimicrobiaceae, Enterobacteriaceae, Enterococcaceae, Erysipelotrichaceae, Eubacteriaceae, F082, Filifactoraceae, Fusobacteriaceae, Gottschalkiaceae, Hafniaceae, Helicobacteraceae, Hungateiclostridiaceae, Intestinicryptomonaceae, JGI_0000069-P22, Lachnospiraceae, Lactobacillaceae, Lawsonellaceae, Leptotrichiaceae, Listeriaceae, Metamycoplasmataceae,Methanobacteriaceae, Micrococcaceae, Monolobaceae, Moraxellaceae, Morganellaceae, Muribaculaceae, Mycobacteriaceae, Mycoplasmataceae, Mycoplasmoidaceae, Negativicoccaceae, Neisseriaceae, Odoribacteraceae, Oscillospiraceae, PB19, Pasteurellaceae, Peptococceae, Peptoniphilaceae, Peptostreptococcaceae, Porph Yromonadaceae, Prevotellaceae, Propionibacteriaceae, Rikenellaceae, Ruminococcaceae, Selenomonadaceae, Staphylococcaceae, Streptococcaceae, Succinivibrionaceae, Sutterellaceae, Synergistaceae, Tannerellaceae, Treponemataceae, Tropherymataceae, Turicibacteraceae, UA11, UCG-010, Veillonellaceae, Victivallaceae. However, these are illustrative examples, and human-derived microorganisms are not limited to these.

[0068] In this specification, "naturally occurring microorganisms" refers to microorganisms whose primary habitat is natural environments such as soil, water bodies, forests, and grasslands. Naturally occurring microorganisms can enter indoor environments via the outside air, soil, plants, etc. The presence of naturally occurring microorganisms may increase the microbial diversity of the indoor environment and provide beneficial stimuli to the human immune system.

[0069] The proportion of human-derived microorganisms can also be expressed as the ratio of human-derived microorganisms to naturally occurring microorganisms. For example, a ratio of human-derived microorganisms to naturally occurring microorganisms of 2:8 indicates that 20% of the microorganisms in a space are of human origin, and 80% are of naturally occurring origin.

[0070] In this specification, "pathogenic microorganism" refers to a microorganism that has the potential to infect humans or animals and cause disease. Pathogenic microorganisms include bacteria, fungi, protozoa, viruses, and the like. While the present invention primarily focuses on pathogenic bacteria, other types of pathogenic microorganisms may also be targeted, or combinations thereof may be used.

[0071] Spaces with a high proportion of pathogenic microorganisms are considered environments where people living or working in those spaces are at high risk of contracting infectious diseases. In particular, spaces with a high proportion of pathogenic microorganisms can pose a health risk to individuals with weakened immune systems (such as the elderly, infants, and those with underlying medical conditions).

[0072] The determination of whether or not a microorganism is pathogenic can be made based on its taxonomic classification. For example, microorganisms belonging to a specific family, genus, or species can be classified as pathogenic. The classification of which taxonomic groups constitute pathogenic microorganisms can be determined based on publicly available databases and literature.

[0073] In the present invention, pathogenic microorganisms may include, for example, microorganisms belonging to the following fungal families: Brucellaceae, Campylobacteraceae, Clostridiaceae, Corynebacteriaceae, Enterobacteriaceae, Enterococcaceae, Helicobacteraceae, Listeriaceae, Moraxellaceae, Morganellaceae, Mycobacteriaceae, Mycoplasmoidaceae, Neisseriaceae, Pasteurellaceae, Peptostreptococcaceae, Staphylococcaceae, Streptococcaceae, and Treponemataceae. However, these are illustrative examples, and pathogenic microorganisms are not limited to these.

[0074] In evaluating pathogenic microorganisms, it is possible to consider not only their relative prevalence but also the degree of pathogenicity, transmission routes, and presence or absence of drug resistance of each microorganism. For example, it may be desirable to give greater weight to highly pathogenic microorganisms or those with drug resistance when calculating spatial health.

[0075] In this specification, "microbial diversity" refers to an indicator that shows the diversity of microbial communities present in a given space or sample. Ideally, microbial diversity should be an indicator that reflects species richness, species evenness, and genetic distance.

[0076] Known diversity indicators such as the Shannon index, Simpson index, observed features, phylogenetic diversity, and Chao1 estimator can be used to assess microbial diversity. These indicators can be used individually or in combination.

[0077] Phylogenetic diversity is a diversity index that takes into account the phylogenetic distance between microorganisms in a microbial community. In evaluating phylogenetic diversity, a phylogenetic tree constructed based on sequence information such as 16S rRNA genes is used, and diversity is quantified as the sum of branch lengths in that tree. High phylogenetic diversity means that there are many phylogenetically distantly related microorganisms, indicating the presence of a wider range of taxonomic groups within the space.

[0078] Specific examples of methods for evaluating phylogenetic diversity include Faith's phylogenetic diversity (Faith's PD), weighted UniFrac distance, and unweighted UniFrac distance. Faith's PD is calculated as the sum of the branch lengths of the phylogenetic tree occupied by the microorganisms present in the sample. UniFrac distance is an index that evaluates the phylogenetic similarity of microbial communities between samples; weighted UniFrac considers the abundance of each microorganism, while unweighted UniFrac considers only the presence or absence of each microorganism. By using these indices, it is possible to evaluate not only the number of species and their uniformity, but also the phylogenetic structure of the microbial community.

[0079] Evaluating spatial health from the perspective of phylogenetic diversity has the following significance: In spaces where human-derived microorganisms are dominant, there are many microorganisms that inhabit the human body surface, oral cavity, intestinal tract, etc., and these tend to be biased towards phylogenetically related taxonomic groups. Introducing naturally occurring microorganisms into such spaces adds phylogenetically diverse microorganisms that inhabit soil, plants, aquatic bodies, etc., thus improving phylogenetic diversity. Exposure to phylogenetically diverse microorganisms may provide a broader stimulus to the immune system and contribute to the development and maintenance of immune function.

[0080] Spaces with high microbial diversity may be beneficial to health for the following reasons: Firstly, exposure to diverse microorganisms may properly stimulate and develop the immune system. Secondly, environments with diverse microbial communities may suppress the dominance of specific pathogenic microorganisms. Thirdly, environments with high microbial diversity are closer to natural environments and may be well-suited to the evolutionary background of humans.

[0081] In this specification, "spatial health calculation unit" refers to a component that has the function of calculating the spatial health of a space. The spatial health calculation unit can be implemented by hardware, software, or a combination thereof.

[0082] When the spatial health calculation unit is implemented by software, a processor such as a CPU or GPU functions as the spatial health calculation unit by executing a program for calculating spatial health. This program may be stored in a storage device such as ROM, RAM, hard disk, or SSD.

[0083] The spatial health calculation unit calculates spatial health based on the input data (e.g., microbial flora composition data, environmental information, etc.) using a predetermined algorithm or model. The calculated algorithm or model may be rule-based or machine learning-based.

[0084] In this specification, "gap analysis unit" refers to a component that has the function of calculating the difference between spatial health and the target value of spatial health. The gap analysis unit can be implemented by hardware, software, or a combination thereof.

[0085] The "difference" calculated by the gap analysis unit may be a value obtained by simple subtraction, or it may be a value obtained by more complex calculations. For example, the absolute difference, relative difference (percentage), squared difference, etc., between spatial health and the target value can be used as the difference.

[0086] When spatial health is comprised of multiple components (e.g., a score based on microbial mass, a score based on the proportion of human-derived microorganisms, a score based on the proportion of pathogenic microorganisms, and a score based on microbial diversity), it is desirable for the gap analysis unit to calculate not only the overall difference but also the differences for each component. This allows the intervention selection unit to select a more appropriate intervention.

[0087] In this specification, "intervention method selection unit" refers to a component that has the function of selecting an intervention method based on the difference calculated by the gap analysis unit. The intervention method selection unit can be implemented by hardware, software, or a combination thereof.

[0088] The intervention selection unit should preferably select an appropriate intervention from a pre-prepared set of intervention options to eliminate or reduce the difference. The selection of an intervention may be performed by a rule-based algorithm, a machine learning model, or an optimization algorithm.

[0089] The intervention selection unit can select a single intervention or a combination of multiple interventions. When selecting multiple interventions, it is desirable to also present the priority order and implementation sequence of each intervention.

[0090] In this specification, “intervention measures” means any action, operation, or measure taken to improve the spatial health of a space. Intervention measures may be physical, chemical, biological, or a combination thereof.

[0091] The following provides a detailed explanation of specific intervention methods and their mechanisms of action.

[0092] (Spraying or applying a composition containing microorganisms) Interventions involving the dispersal or application of compositions containing microorganisms into a space are expected to improve microbial diversity by directly introducing naturally occurring microorganisms into the space. Examples of microorganisms used for dispersal or application include non-pathogenic bacteria derived from soil (e.g., Bacillus bacteria), lactic acid bacteria, and yeasts. These microorganisms can survive in the space for a certain period and alter the composition of the microbial flora. Furthermore, it is expected that the introduction of non-pathogenic microorganisms will become dominant in the space, competitively suppressing the growth of pathogenic microorganisms (competitive exclusion effect). In addition, an increase in the proportion of naturally occurring microorganisms is expected to decrease the relative proportion of human-derived microorganisms, contributing to an improvement in the health of the space.

[0093] In this specification, "composition containing microorganisms" means a formulation or material containing one or more microorganisms as an active ingredient for improving microbial diversity. The dosage form of the composition is not particularly limited and may be in solid form such as powder, granules, or tablets; in liquid form such as a solution, suspension, or emulsion using a solvent that can disperse without killing bacteria; or in semi-solid form such as a gel or paste. Furthermore, the state of the microorganisms contained in the composition is not particularly limited and may be vegetative cells (proliferating cells) or spores (dormant cells), or a mixture of these. In addition, the composition may contain carriers, excipients, protective agents, nutrients, etc., to maintain the activity and viability of the microorganisms. Specific methods of application or coating include spraying, chemical spraying devices, sprayers, humidifiers, brush or roller application of liquids, or powder scattering.

[0094] (Installation of plants or soil) Interventions involving the placement of ornamental plants, soil, or planters combining these into a space are expected to have the effect of continuously supplying diverse microorganisms that inhabit plants and soil into the space. Soil is home to an extremely diverse range of microorganisms, and by installing soil, the microbial diversity in the space can be significantly improved. In addition, diverse microorganisms inhabit the leaves and rhizosphere of plants, and their release into the space contributes to the improvement of microbial diversity. Because plants and soil continuously supply microorganisms, a long-term effect can be expected compared to temporary spraying. In addition, plants also have secondary effects such as air purification, humidity regulation, and psychological relaxation. In particular, installing plant planters in spaces where human-derived microorganisms are dominant is effective from the perspective of phylogenetic diversity. Human-derived microorganisms mainly belong to specific taxonomic groups such as the Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes phyla, whereas microorganisms inhabiting soil and plants include many taxonomic groups that are phylogenetically distant from human-derived microorganisms, such as the Acidobacteria, Verrucomicrobia, Plantomycetes, and Chloroflexi phyla. Therefore, by installing plant planters, phylogenetically different microorganisms from existing human-derived microorganisms are added to the space, and it is expected that phylogenetic diversity will be improved. Exposure to phylogenetically diverse microorganisms may provide more diverse stimuli to the immune system and contribute to the regulation of immune function.

[0095] (Improvements or changes to the operation of the ventilation system) Interventions that improve ventilation systems or modify their operation are expected to have the effect of introducing naturally occurring microorganisms contained in the outside air into the space. Outside air, especially in areas near green spaces and parks, contains a diverse range of microorganisms originating from soil and plants. By increasing the ventilation rate or optimizing the ventilation route, these naturally occurring microorganisms can be efficiently introduced into the space. Furthermore, improved ventilation is expected to dilute and expel human-derived microorganisms accumulated in the space, thus reducing the proportion of human-derived microorganisms. Modifications to the operation of the ventilation system include optimizing ventilation times, increasing ventilation rates, changing the location of outside air intakes, and changing the type of filter. However, since some types of filters may excessively remove microorganisms, care must be taken in selecting filters from the perspective of maintaining microbial diversity.

[0096] (Changes to building materials or interior finishes) Interventions involving the modification of building or interior materials are expected to contribute to the maintenance and improvement of microbial diversity by creating an environment conducive to microbial colonization. Natural materials (wood, diatomaceous earth, plaster, cork, natural fibers, etc.) have surface characteristics that make them more favorable for microbial colonization compared to synthetic materials, and can serve as habitats for a diverse range of microorganisms. Furthermore, natural materials themselves may contain microorganisms, which can contribute to the microbial flora within a space. On the other hand, building and interior materials treated with antibacterial agents may inhibit microbial colonization and reduce microbial diversity, so it may be desirable to refrain from using them from the perspective of spatial health.

[0097] Specific examples of "building materials or interior materials" include mycelial materials made from mycelium, and biodegradable building materials using wood, bamboo, hemp fibers, etc. These biodegradable materials have complex microstructures that facilitate the establishment and habitation of microorganisms, and are expected to contribute to improving microbial diversity indoors. Mycelial materials, in particular, are expected to continuously supply diverse microorganisms derived from mycelial networks into the space. Furthermore, biodegradable materials have the advantage of having a low environmental impact after use and not impairing microbial diversity compared to antibacterial building materials, making them a promising option from the perspective of improving the health of the space.

[0098] (Changes to cleaning methods or cleaning frequency) Interventions that modify cleaning methods or frequency are expected to suppress excessive sterilization and disinfection, and maintain microbial diversity. Cleaning with chemical disinfectants removes not only pathogenic microorganisms but also beneficial microorganisms, potentially reducing microbial diversity. Furthermore, the continuous use of strong disinfectants may create selective pressure on drug-resistant bacteria. Changes to cleaning methods include reducing the frequency of disinfectant use, changing the type of disinfectant (switching to milder products), and adopting physical cleaning (wiping with water, vacuuming, etc.). However, in spaces with a high proportion of pathogenic microorganisms or spaces with users who have weakened immune systems, it is also important to maintain appropriate sterilization and disinfection, and it is desirable to select a balanced cleaning method that is appropriate to the characteristics of the space.

[0099] In this specification, "target value" refers to the value set as the target for spatial health. It is desirable that the target value be set considering various factors such as the use of the space, the attributes of the users, geographical conditions, and seasonal conditions.

[0100] Target values ​​may be set fixedly or dynamically based on the attribute information of the space. For example, in a medical facility, a strict target value for the proportion of pathogenic microorganisms may be set, while in a childcare facility, a high target value for microbial diversity may be set; thus, target values ​​can be set according to the use of the space.

[0101] Target values ​​may be set based on previously accumulated microbial environment data. For example, target values ​​can be set based on statistical values ​​(mean, median, distribution range, etc.) of the microbial composition contained in a database that has accumulated microbial flora data collected and analyzed from various spaces such as indoor spaces, urban spaces, and natural environments. Such a database may be constructed by organizing and reanalyzing data obtained from public databases, or it may be a custom database constructed by conducting its own sampling, or it may be a combination of these.

[0102] When using a custom database, it becomes possible to set appropriate target values ​​according to the characteristics of the target space by selectively referencing data that matches specific conditions such as a particular region, specific use, or specific season. Furthermore, by prioritizing the reference of microbial composition data that has been confirmed to be associated with health outcomes, it is possible to set more appropriate target values ​​from the perspective of maintaining and promoting health.

[0103] In this specification, "target setting unit" refers to a component that has the function of setting a target value for spatial health. The target setting unit can set the target value based on input from the user, or it can automatically set the target value based on the attribute information of the space.

[0104] In this specification, "intervention database" refers to a database that records information about intervention methods. It is desirable that the intervention database records information such as the name, content, mechanism of action, application conditions, effects, cost, and implementation method of each intervention method.

[0105] In this specification, "target value database" refers to a database that records information regarding target values ​​for spatial health. It is desirable that the target value database contains target values ​​corresponding to various conditions such as the use of the space (office, medical facility, educational facility, residence, etc.), user attributes (age group, health status, etc.), geographical conditions, and seasonal conditions. The target setting unit can refer to the target value database based on the input spatial attribute information and obtain target values ​​appropriate for that space.

[0106] In this specification, "human-derived microorganism database" refers to a database that records a list of microorganisms classified as human-derived microorganisms. It is desirable that the human-derived microorganism database includes taxonomic information (phylum, class, order, family, genus, species, etc.) of microorganisms classified as human-derived, the main habitat of each microorganism (body surface, oral cavity, intestinal tract, etc.), detection frequency, and information on health impacts. The spatial health calculation unit can calculate the proportion of human-derived microorganisms in a target space by comparing microbial composition data with the human-derived microorganism database. The human-derived microorganism database may be constructed based on data obtained from a known database (e.g., the Human Microbiome Project) or based on original research results.

[0107] In this specification, "pathogenic microorganism database" refers to a database that records a list of microorganisms classified as pathogenic. It is desirable that the pathogenic microorganism database includes taxonomic information (phylum, class, order, family, genus, species, etc.) of microorganisms classified as pathogenic, the type of disease caused by each microorganism, transmission routes, degree of pathogenicity, presence or absence of drug resistance, possession of drug resistance genes, and possession of virulence factors. The spatial health calculation unit can calculate the proportion of pathogenic microorganisms in a target space by comparing microbial composition data with the pathogenic microorganism database. The pathogenic microorganism database may be constructed based on data obtained from publicly known databases (e.g., Virulence Factor Database, CARD (Comprehensive Antibiotic Resistance Database), etc.) or based on original research results.

[0108] In this specification, "microbiome database" refers to a database that records microbiome data from various spaces that have been analyzed in the past. It is desirable that the microbiome database record information such as the microbial composition data of samples taken from each space, attribute information of the space (purpose, size, geographical location, architectural style, etc.), environmental information (temperature, humidity, ventilation conditions, etc.), sampling date and time, and analysis method. The microbiome database can be referenced in setting target values ​​and can also be used as a comparison point in evaluating spatial health. The microbiome database may be constructed by organizing and reanalyzing data obtained from public databases, or it may be a custom database constructed by conducting independent sampling, or a combination of these. It is expected that the more data accumulated in the microbiome database, the more accurate target value setting and spatial health evaluation will become possible.

[0109] In this specification, "environmental information" refers to information relating to the physical, chemical, or usage conditions of a space. Specific examples of environmental information include temperature, humidity, illuminance, ventilation rate, number of occupants, pedestrian flow, size of the space, arrangement of windows and doors, type of air conditioning equipment, and cleaning frequency.

[0110] Environmental information may be desirable to consider when calculating spatial health. For example, even with the same microbial composition, different temperatures and humidity levels may result in different growth rates and metabolic activity of microorganisms, which may also affect spatial health.

[0111] Environmental information should also be considered when selecting intervention methods. For example, in a closed space with limited ventilation, planting plants or soil may be a more appropriate intervention than improving ventilation.

[0112] <Implementation Details> Next, embodiments of the present invention will be described in detail.

[0113] In the information processing system of the present invention, input data for calculating spatial health can be obtained in various formats. Most directly, compositional data obtained as a result of microbiome analysis of a sample taken from the target space is used as input.

[0114] Methods for collecting samples include air sampling, surface swabbing, suction sampling, and airborne microorganism collection. Air sampling allows for the collection of microorganisms from the air using devices such as impactors, impingers, and filters. Surface swabbing allows for the collection of microorganisms by wiping surfaces such as walls, floors, furniture, and equipment. Suction sampling allows for the collection of microorganisms by suctioning dust from surfaces such as floors and ground.

[0115] By extracting DNA or RNA from a collected sample and subjecting it to various detection methods, microbial composition data in the sample can be obtained. Detection methods include amplicon sequencing targeting the 16S rRNA gene, shotgun metagenomic sequencing, detection using microarrays, detection using quantitative PCR (qPCR), and detection using digital PCR (dPCR). Amplicon sequencing and shotgun metagenomic sequencing are suitable for comprehensively understanding the composition of microbial communities, while microarrays, qPCR, and digital PCR are suitable for rapid and highly sensitive detection of specific microorganisms or microbial groups. These detection methods can be used individually or in combination, depending on the purpose and situation.

[0116] Sequence data obtained through sequencing is processed through a bioinformatics pipeline. Typically, this involves removing low-quality reads, removing chimeric sequences, clustering or denoising, and taxonomy assignment. This yields relative abundance data for each taxonomic group (phylum, class, order, family, genus, species, etc.).

[0117] The information processing system of the present invention may be configured to receive data directly from sequencers and analysis devices, or it may be configured to accept data from analyses performed externally as input. In the former case, it is desirable that the information processing system of the present invention be configured to cooperate with sequencers and analysis devices via a network or direct connection.

[0118] In calculating spatial health, the proportion of human-derived microorganisms can be calculated based on microbial composition data. Specifically, the proportion of human-derived microorganisms can be calculated by summing the relative abundances of microorganisms that belong to a predefined list of human-derived microorganisms (for example, a list of specific fungal families).

[0119] Similarly, the proportion of pathogenic microorganisms can be calculated by summing the relative abundances of microorganisms that fall under a predefined list of pathogenic microorganisms. In evaluating pathogenic microorganisms, it may also be desirable to weight each pathogenic microorganism according to its degree of pathogenicity.

[0120] Microbial diversity can be evaluated by calculating diversity indices such as the Shannon index and the Simpson index based on microbial composition data. Furthermore, evaluating phylogenetic diversity using phylogenetic tree information is also useful.

[0121] When calculating spatial health as a score, it is desirable to convert the measured values ​​of each indicator (microbial mass, percentage of human-derived microorganisms, percentage of pathogenic microorganisms, and microbial diversity) into a score according to predetermined criteria. For example, a score of 1 could be assigned if the percentage of human-derived microorganisms is between 0% and 20%, and a score of 2 if it is between 21% and 40%, with scores assigned according to the range of percentages.

[0122] Methods for integrating the scores of each indicator to calculate the overall spatial health can include simple addition, weighted addition, multiplication, maximum value selection, and minimum value selection. The method to be adopted can be appropriately set according to the embodiment of the present invention.

[0123] The gap analysis unit calculates the difference by comparing spatial health and target values ​​using the same scale. If spatial health and target values ​​are of the same type (for example, both are percentages or scores), the difference can be calculated by simple subtraction.

[0124] When spatial health is comprised of multiple components, it is desirable for the gap analysis unit to calculate the differences for each component in addition to the overall difference. For example, difference information for each component, such as "microbial abundance is lower than the target," "the proportion of human-derived microorganisms is 10% higher than the target," "the proportion of pathogenic microorganisms is within the target range," and "microbial diversity is lower than the target," is useful in selecting intervention measures.

[0125] The selection of interventions in the intervention selection section should preferably be based on the characteristics of the difference. For example, if the proportion of human-derived microorganisms is high, it is desirable to select interventions that increase naturally occurring microorganisms (e.g., planting plants and soil, improving ventilation). If the proportion of pathogenic microorganisms is high, it is desirable to select interventions that reduce pathogenic microorganisms (e.g., increasing cleaning frequency, sterilization treatment). If microbial diversity is low, it is desirable to select interventions that introduce diverse microorganisms (e.g., microbial spraying, planting plants).

[0126] When selecting intervention methods, it is desirable to consider the physical characteristics and usage of the space. For example, in enclosed spaces where improving ventilation is difficult, it is desirable to prioritize the installation of plants or soil. Conversely, in open spaces where plant installation is difficult, it is desirable to optimize the ventilation pattern.

[0127] <System Implementation Details> Next, we will describe in detail how to implement the present invention as an information processing system.

[0128] <Overall Structure> Figure 1 is a diagram showing an example of the overall configuration of an information processing system according to one embodiment of the present invention. As shown in Figure 1, the information processing system of this embodiment is composed of a sensor 1, a management server 2, and a user terminal 3. The sensor 1, the management server 2, and the user terminal 3 are connected to each other so as to be able to communicate with each other via a communication network.

[0129] Sensor 1 is a device installed in a target space to acquire environmental information of that space. Examples of environmental information acquired by Sensor 1 include temperature, humidity, illuminance, CO2 concentration, atmospheric pressure, and the number of occupants. Sensor 1 transmits the acquired environmental information to the management server 2 via a communication network. Note that Sensor 1 is not an essential component of the information processing system of the present invention and can be omitted in embodiments that do not use environmental information.

[0130] Furthermore, environmental information equivalent to the data acquired by sensor 1 may be acquired by a standalone sensor that is not connected to a communication network. In this case, the environmental information acquired by the standalone sensor may be transmitted to the management server 2 by the user manually inputting it into user terminal 3, or by transferring it to user terminal 3 using means such as a USB cable, memory card, or short-range wireless communication (Bluetooth, NFC, etc.). Such a configuration is useful when evaluating spatial health in spaces where a network environment is not available or where it is difficult to permanently install sensors.

[0131] Management Server 2 is a server device that calculates spatial health, performs gap analysis, and selects intervention measures. Management Server 2 receives environmental information from Sensor 1 and accepts input such as microbial composition data from User Terminal 3. Based on this data, Management Server 2 calculates spatial health, performs gap analysis, selects intervention measures, and transmits the results to User Terminal 3.

[0132] The management server 2 may consist of a single physical server or it may be configured in a distributed manner using multiple physical servers. Furthermore, the management server 2 may be a general-purpose computer, such as a workstation or personal computer, or it may be logically implemented through cloud computing.

[0133] User terminal 3 is a terminal device used by users of the information processing system of the present invention. Examples of user terminal 3 include smartphones, tablet devices, and personal computers. User terminal 3 transmits microbial composition data, spatial attribute information, etc., to management server 2, and receives and displays the calculation results of spatial health, proposed intervention measures, etc., from management server 2.

[0134] The communication network is a network that interconnects sensor 1, management server 2, and user terminal 3. The communication network can be, for example, the internet, intranet, LAN, WAN, or mobile communication network. It may also be constructed using public telephone networks, mobile phone networks, wireless communication channels, Ethernet (registered trademark), etc. It is desirable that communication within the communication network be protected by appropriate security protocols.

[0135] Note that the configuration shown in Figure 1 is just one example, and the information processing system of the present invention can be implemented in other configurations. For example, it can be implemented as an edge computing device in which the sensor 1 and the management server 2 are integrated. Alternatively, some or all of the functions of the management server 2 can be executed on the user terminal 3.

[0136] <Management Server Hardware Configuration> Figure 2 shows an example of the hardware configuration of the management server 2. As shown in Figure 2, the management server 2 consists of a processor 21, memory 22, storage 23, communication interface 24, and bus 25.

[0137] The processor 21 is a computing device that performs various processes in the management server 2. The processor 21 can be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), or a combination thereof. The processor 21 functions as a spatial health calculation unit, a gap analysis unit, an intervention means selection unit, etc., by reading programs stored in the storage 23 into the memory 22 and executing them.

[0138] Memory 22 is the main memory used by the processor 21 when executing processes. Volatile memory such as RAM (Random Access Memory) can be used as memory 22. The program executed by the processor 21 and the data necessary for the process are temporarily stored in memory 22.

[0139] Storage 23 is an auxiliary storage device that permanently stores programs and data. Storage 23 can be an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a combination thereof. Storage 23 stores programs for functioning as a spatial health calculation unit, a gap analysis unit, and an intervention selection unit, as well as an intervention database, a target value database, a list of human-derived microorganisms, a list of pathogenic microorganisms, and the like.

[0140] The communication interface 24 is an interface for communicating with other devices via a communication network. The communication interface 24 can be an Ethernet adapter, a wireless LAN adapter, a mobile communication module, or the like.

[0141] Bus 25 is a transmission path that interconnects the processor 21, memory 22, storage 23, and communication interface 24, and transfers data.

[0142] Note that the hardware configuration shown in Figure 2 is just one example, and the management server 2 can be implemented with other hardware configurations. For example, the management server 2 may be configured to include additional hardware such as input devices (keyboard, mouse, etc.), output devices (display, printer, etc.), and external storage devices (USB memory, external HDD, etc.).

[0143] <Management Server Software Configuration> Figure 3 shows an example of the software configuration of the management server 2. As shown in Figure 3, the software of the management server 2 is composed of a spatial health calculation unit 31, a gap analysis unit 32, an intervention means selection unit 33, a target setting unit 34, a storage unit 35, and a communication control unit 36.

[0144] The spatial health calculation unit 31 has the function of calculating the spatial health of the target space based on the input microbial composition data and environmental information. The spatial health calculation unit 31 calculates indicators such as microbial mass, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity from the microbial composition data, and calculates the spatial health based on these indicators.

[0145] The gap analysis unit 32 has the function of calculating the difference between the spatial health calculated by the spatial health calculation unit 31 and the target value set by the target setting unit 34. In addition to the overall difference, the gap analysis unit 32 can also calculate the difference for each indicator (microbial mass, proportion of human-derived microorganisms, proportion of pathogenic microorganisms, and microbial diversity).

[0146] The intervention selection unit 33 has the function of selecting an intervention to improve spatial health based on the difference calculated by the gap analysis unit 32. The intervention selection unit 33 refers to the intervention database included in the database group 35 and selects an appropriate intervention according to the characteristics of the difference.

[0147] The target setting unit 34 has the function of setting target values ​​for spatial health. Based on the attribute information of the space (purpose, size, user attributes, etc.) input from the user terminal 3, the target setting unit 34 can set target values ​​appropriate for the space. In addition, the target setting unit 34 can also set target values ​​by referring to the target value database 352 contained in the storage unit 35.

[0148] The storage unit 35 is a component that stores various data and databases used in the information processing system of the present invention. The storage unit 35 stores databases such as an intervention means database 351, a target value database 352, a human-derived microorganism database 353, a pathogenic microorganism database 354, and a microbiome database 355. The storage unit 35 may also store programs for functioning as a spatial health calculation unit 31, a gap analysis unit 32, an intervention means selection unit 33, a target setting unit 34, and a communication control unit 36.

[0149] Examples of data recorded in various databases include: the intervention database 351, which records information such as the name, content, mechanism of action, application conditions, effects, cost, and implementation method of each intervention; the target value database 352, which records target values ​​according to the use of the space, user attributes, etc.; the human-derived microorganism database 353, which records a list of microorganisms classified as human-derived microorganisms; the pathogenic microorganism database 354, which records a list of microorganisms classified as pathogenic microorganisms; and the microbiome database 355, which records microbiome data from various spaces analyzed in the past and is referenced when setting target values ​​and evaluating the health of the space.

[0150] The communication control unit 36 ​​has the function of controlling communication between the sensor 1 and the user terminal 3. The communication control unit 36 ​​receives environmental information from the sensor 1, receives input such as microbial composition data from the user terminal 3, and transmits calculation results and proposed intervention measures to the user terminal 3.

[0151] Note that the software configuration shown in Figure 3 is just one example, and the software for the management server 2 can be implemented in other configurations. For example, the spatial health calculation unit 31, the gap analysis unit 32, and the intervention means selection unit 33 can be implemented as a single integrated module. Also, part or all of the storage unit 35 can be placed on an external database server.

[0152] <Processing Flow>

[0153] Figure 4 is a diagram illustrating the processing flow in an information processing system according to one embodiment of the present invention. The processing flow will be described below in reference to Figure 4.

[0154] First, in step S1, the information processing system of the present invention receives input data related to the target space. The input data includes microbial flora composition data (sequence analysis results), environmental information of the space (temperature, humidity, etc.), and attribute information of the space (purpose, size, etc.). The microbial flora composition data is transmitted from the user terminal 3 to the management server 2. The environmental information may be transmitted from the sensor 1 to the management server 2, or it may be input from the user terminal 3.

[0155] The microbial flora composition data may be input as data that has already undergone sequence analysis, or as raw sequence data output from a sequencer. In the latter case, it is desirable that the information processing system of the present invention be equipped with a function to analyze the sequence data and generate composition data.

[0156] Next, in step S2, the spatial health calculation unit 31 calculates the spatial health. Based on the input microbial composition data, the spatial health calculation unit 31 calculates indicators such as the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity, and calculates the spatial health from these indicators. If microbial quantity information is input, it is desirable to calculate the spatial health considering the microbial quantity information as well. Furthermore, if environmental information is input, it is desirable to calculate the spatial health considering the environmental information as well.

[0157] Next, in step S3, the gap analysis unit 32 calculates the difference between the spatial health level and the target value. The target value may be one that has been set in advance, or it may be one that has been set by the target setting unit 34 based on the spatial attribute information entered in step S1.

[0158] Next, in step S4, the intervention means selection unit 33 selects an intervention means based on the difference calculated by the gap analysis unit 32. The intervention means selection unit 33 selects an appropriate intervention means from among the intervention means recorded in the intervention means database 351 included in the database group 35 to eliminate or reduce the difference.

[0159] Finally, in step S5, the selected intervention is output. The output is transmitted to the user terminal 3 via the communication control unit 36 ​​and displayed on the interface of the user terminal 3. It can also be output in the form of a report, notification to an external system, etc. In addition to the selected intervention, it is desirable that the output includes the spatial health value, the difference from the target value, and information on how to implement the intervention.

[0160] <Measurement of effects after intervention and additional interventions> Figure 5 illustrates the process flow, including effect measurement and additional interventions after the implementation of intervention measures. The process flow shown in Figure 5 includes steps S6 and S7 in addition to steps S1 to S5 shown in Figure 4.

[0161] In step S6, the information processing system of the present invention measures the change in spatial health after the implementation of the intervention. Specifically, after the user implements the intervention output in step S5, a sample is taken from the space after the intervention and microbial composition data is obtained. The obtained microbial composition data is input to the management server 2 in the same manner as in step S1, and the spatial health calculation unit 31 calculates the spatial health after the intervention. By comparing the spatial health before and after the intervention, it becomes possible to quantitatively evaluate the effect of the implemented intervention.

[0162] The results of the effectiveness measurement in step S6 may be fed back into the intervention means database 351 of the storage unit 35 and stored as information regarding the effectiveness of each intervention means. The stored effectiveness information is expected to be referenced in future intervention means selection and contribute to the selection of more effective intervention means.

[0163] In step S7, if the spatial health level after the intervention has not reached the target value, the intervention selection unit 33 selects an additional intervention. Specifically, the gap analysis unit 32 calculates the remaining difference between the spatial health level after the intervention and the target value, and the intervention selection unit 33 selects an additional intervention based on this remaining difference. The selected additional intervention is output to the user terminal 3, similar to step S5.

[0164] Steps S6 and S7 may be repeated until the spatial health level reaches the target value, or they may be terminated at any point at the user's discretion. In this way, the information processing system of the present invention can gradually improve the spatial health level by repeatedly executing a cycle of measuring the spatial health level, proposing intervention measures, executing the intervention, measuring its effect, and proposing additional interventions.

[0165] Furthermore, the period from the implementation of an intervention to the measurement of its effects should be appropriately set according to the type of intervention and the characteristics of the space. For example, for interventions with immediate effects, such as increasing ventilation, it is appropriate to measure the effects over a short period (a few hours to a few days), while for interventions that take time to show effects, such as the installation of plants or soil, it may be appropriate to measure the effects over a long period (a few weeks to a few months).

[0166] The processing flow described above is an example, and the order and content of each step can be changed without departing from the spirit of the present invention. For example, steps S2 and S3 can be integrated and executed as a single process. In addition, in step S4, multiple candidate interventions can be generated, and the user's selection can be accepted before the final output is produced. Furthermore, steps S6 and S7 shown in Figure 5 may be repeated multiple times until the spatial health reaches the target value. Through such iterative processing, even if the target value is not reached with a single intervention, it is possible to gradually improve the spatial health and eventually reach the target value.

[0167] <Specific implementation examples and variations> The following describes specific embodiments of the present invention. These are examples illustrating the applicability of the present invention and do not limit the technical scope of the present invention.

[0168] <Example 1: Application to an office space> This section describes an example of applying the information processing system of the present invention to an office space. Office spaces are often places where many people stay for long periods of time, and ventilation is controlled by air conditioning equipment. In such spaces, the proportion of human-derived microorganisms tends to be high, and microbial diversity tends to decrease due to the high degree of airtightness.

[0169] Surface swabs and air samples are collected from multiple locations within the office space (work areas, conference rooms, break rooms, restrooms, etc.), and amplicon sequencing of the 16S rRNA gene is performed. When the obtained composition data is input into the information processing system of the present invention, the spatial health calculation unit calculates the spatial health based on the microbial mass, the proportion of human-derived microorganisms, the proportion of pathogenic microorganisms, and microbial diversity.

[0170] In the case of office spaces, the target setting unit can set target values ​​considering the characteristics of the workspace. For example, target values ​​can be set such as a percentage of human-derived microorganisms of 30% or less, a percentage of pathogenic microorganisms of 10% or less, and a Shannon index of 4.0 or higher (for example, when there are 10,000 reads per sample).

[0171] If the gap analysis reveals that the proportion of human-derived microorganisms exceeds the target value and microbial diversity falls below the target value, the intervention selection unit should ideally select interventions that increase naturally occurring microorganisms and improve microbial diversity. Specifically, possible options include installing ornamental plants, increasing ventilation, and introducing a soil-based biofiltration system.

[0172] <Example 2: Application to medical facilities> This section describes an example of applying the information processing system of the present invention to medical facilities (hospitals, clinics, etc.). In medical facilities, where patients with weakened immune systems stay, the management of pathogenic microorganisms is particularly important. On the other hand, excessive sterilization and disinfection can reduce microbial diversity and increase the selective pressure on drug-resistant bacteria.

[0173] In healthcare facilities, the target setting department can set stricter targets for the proportion of pathogenic microorganisms. For example, a target of 5% or less of pathogenic microorganisms may be set. Furthermore, for specific high-risk pathogenic microorganisms (such as MRSA, VRE, and drug-resistant Gram-negative bacteria), it may be desirable to set individual targets of below the detection limit.

[0174] If the gap analysis reveals that the proportion of pathogenic microorganisms exceeds the target value, the intervention selection unit selects an intervention to reduce pathogenic microorganisms. However, if microbial diversity is already low, it is desirable to select an intervention that controls pathogenic microorganisms while maintaining microbial diversity, such as the spraying of non-pathogenic microorganisms that are expected to have a competitive exclusion effect, rather than chemical fungicides.

[0175] <Example 3: Application to educational facilities> This paper describes an example of applying the information processing system of the present invention to educational facilities (nursery schools, kindergartens, schools, etc.). In educational facilities, especially those used by infants and young children, exposure to diverse microorganisms is considered important for the development of the immune system. On the other hand, managing pathogenic microorganisms is also important in order to prevent outbreaks of infectious diseases.

[0176] In educational institutions, the goal-setting department can set relatively high target values ​​for microbial diversity. For example, a target value of 5.0 or higher for the Shannon index (e.g., when there are 10,000 reads per sample) may be set. At the same time, it is desirable to set a target of maintaining low percentages of specific pathogenic microorganisms (e.g., bacteria that cause infectious gastroenteritis, influenza viruses).

[0177] If the gap analysis reveals that microbial diversity is below the target value, the intervention selection unit will select interventions to improve microbial diversity. Specifically, possible selections may include recommending outdoor activities, introducing playground equipment made from natural materials, improving the soil environment of the playground, and planting plants.

[0178] <Example 4: Application to a residence> This section describes an example of applying the information processing system of the present invention to residences (detached houses, apartment buildings, etc.). Residences are the spaces where residents spend the most time and can directly affect their health. Modern residences are often designed to be highly airtight, which tends to reduce microbial diversity.

[0179] In the case of residential properties, the target setting unit can set target values ​​considering the attributes of the residents (age, health status, allergy history, etc.). For example, a high target value for microbial diversity can be set for households with infants, and a strict target value for pathogenic microorganisms can be set for households with immunocompromised residents.

[0180] Possible interventions in homes include improving ventilation (recommending opening windows for ventilation, changing the operation of ventilation equipment, etc.), installing indoor plants, keeping pets, changing cleaning methods (reducing the excessive use of disinfectants, etc.), and selecting building materials and interior materials (adopting natural materials, etc.).

[0181] <Example 1: Real-time monitoring> In the embodiments described above, a method of periodically collecting and analyzing samples was explained, but the present invention can also be implemented using a real-time monitoring method. In this modified example, a microbial sensor is installed in the target space to detect the amount of microorganisms and the presence of specific microorganisms in real time. The detected data is continuously transmitted to the information processing system of the present invention, and the health level of the space is calculated and displayed in real time.

[0182] Real-time monitoring systems can include features to detect and warn of sudden changes in spatial health, as well as features to analyze patterns of spatial health fluctuations based on time of day and day of the week. Furthermore, they can be integrated with building management systems to automatically adjust ventilation rates based on spatial health.

[0183] <Example 2: Application of Machine Learning> In the above-described embodiment, a method for calculating spatial health and selecting intervention methods using a rule-based algorithm was explained, but the present invention can also be implemented using machine learning. In this modified version, past data (microbial composition data, environmental information, results of intervention methods, etc.) are used as training data to construct a predictive model for spatial health and a predictive model for the effectiveness of intervention methods.

[0184] By utilizing machine learning, it is expected that we can calculate spatial health by capturing the complex relationship between microbial composition and health outcomes, and select the optimal intervention methods according to the characteristics of the space. Furthermore, by continuously updating the model based on accumulated data, we expect to improve the accuracy of predictions.

[0185] <Variation 3: Integrated management of multiple spaces> Although the above-described embodiment explained a method targeting a single space, the present invention can also be implemented using a method that comprehensively manages multiple spaces. In this modified example, multiple spaces such as an entire building, a group of facilities, or an entire city are targeted, and the spatial health of each space is calculated, followed by overall trend analysis and comparative analysis.

[0186] In an integrated management system for multiple spaces, it is possible to incorporate functions to analyze the transmission routes of microorganisms between spaces and to recommend spaces where specific intervention measures should be prioritized. Furthermore, by accumulating geographical and temporal data, it becomes possible to obtain epidemiological insights.

[0187] The embodiments described above are merely illustrative to facilitate understanding of the present invention and are not intended to limit its interpretation. The present invention can be modified and improved without departing from its spirit, and it goes without saying that the present invention includes equivalents thereof.

[0188] <Disclosure Items> Furthermore, this disclosure also includes the following configurations. [Item 1] An information processing system that makes suggestions for improving the spatial health of a given space, A spatial health calculation unit that calculates the spatial health of the aforementioned space, A gap analysis unit calculates the difference between the aforementioned spatial health level and a predetermined target value for spatial health level. An intervention means selection unit selects an intervention means based on the difference analyzed by the gap analysis unit, Equipped with, An information processing system in which the spatial health is calculated based on one or more selected from a group consisting of the amount of microorganisms contained in the space, the proportion of human-derived microorganisms in the microbial community, the proportion of pathogenic microorganisms in the microbial community contained in the space, and the microbial diversity in the microbial community contained in the space. [Item 2] The information processing system described in item 1, wherein the spatial health calculation unit further calculates the spatial health by taking into account the environmental information of the space. [Item 3] Furthermore, the information processing system according to item 1, having a target setting unit for setting the target value of the spatial health level. [Item 4] Furthermore, the information processing system described in item 1 has an intervention database in which the intervention means are recorded. [Item 5] An information processing method for making suggestions to improve the spatial health of a given space, A step of calculating the spatial health of the aforementioned space, A step of calculating the difference between the aforementioned spatial health level and a predetermined target value for spatial health level, The steps include selecting an intervention measure based on the aforementioned difference, Includes, An information processing method in which the spatial health is calculated based on one or more selected from a group consisting of the amount of microorganisms contained in the space, the proportion of human-derived microorganisms in the microbial community contained in the space, the proportion of pathogenic microorganisms in the microbial community contained in the space, and the diversity of the microbial community contained in the space. [Item 6] A program that enables a computer to function as an information processing system that makes suggestions for improving the spatial health of a given space, The computer comprises a spatial health calculation unit that calculates the spatial health of the space, The aforementioned spatial health level, A gap analysis unit calculates the difference between the pre-set target value for spatial health and the actual value. It functions as an intervention means selection unit that selects an intervention means based on the aforementioned difference. A program in which the spatial health is calculated based on one or more of the following groups: the amount of microorganisms contained in the space, the proportion of human-derived microorganisms in the microbial community contained in the space, the proportion of pathogenic microorganisms in the microbial community contained in the space, and the microbial diversity in the microbial community contained in the space. [Explanation of Symbols]

[0189] 1 sensor 2 Management Server 3. User terminals 21 processors 22 memory 23 Storage 24 Communication Interfaces 25 buses 31 Spatial health level calculation section 32 Gap Analysis Section 33 Intervention Method Selection Section 34 Goal Setting Department 35 Storage section 351 Intervention Methods Database 352 Target Value Database 353 Human-derived microorganism database 354 Pathogenic Microorganism Database 355 Microbial Community Database 36 Communication Control Unit

Claims

1. An information processing system that makes suggestions for improving the spatial health of a given space, A spatial health calculation unit that calculates the spatial health of the aforementioned space, A gap solution is calculated to determine the difference between the aforementioned spatial health level and a predetermined target value for spatial health. Analysis part, A database of intervention methods that records the intervention methods, Based on the difference analyzed by the gap analysis unit, an intervention means selection unit selects an intervention means from among the intervention means recorded in the intervention means database to eliminate or reduce the difference. Equipped with, The aforementioned spatial health is calculated based on the amount of microorganisms contained in the space or the microbial diversity within the microbial community contained in the space. An information processing system in which the improvement is either increasing the amount of microorganisms present in the space or increasing microbial diversity.

2. The information processing system according to claim 1, wherein the spatial health calculation unit calculates the spatial health of the space based on at least the composition data of the microbial flora.

3. Furthermore, the information processing system according to claim 1 or 2, further comprising a target setting unit for setting a target value for spatial health.

4. An information processing method for making suggestions to improve the spatial health of a given space, A step of calculating the spatial health of the aforementioned space, A step of calculating the difference between the aforementioned spatial health level and a predetermined target value for spatial health level, The steps include recording the intervention methods in advance, Based on the aforementioned difference, the step of selecting an intervention means to eliminate or reduce the aforementioned difference from among the intervention means recorded in the step of pre-recording the intervention means, This is an information processing method that enables the following: The aforementioned spatial health is calculated based on the amount of microorganisms contained in the space or the microbial diversity within the microbial community contained in the space. A computer-based information processing method in which the improvement is either increasing the amount of microorganisms present in the space or increasing microbial diversity.

5. A program that enables a computer to function as an information processing system that makes suggestions for improving the spatial health of a given space, The computer comprises a spatial health calculation unit that calculates the spatial health of the space, A gap analysis unit calculates the difference between the aforementioned spatial health level and a predetermined target value for spatial health level. A database of intervention methods that records the intervention methods and Based on the aforementioned difference, it functions as an intervention means selection unit that selects an intervention means from among the intervention means recorded in the intervention means database to resolve or reduce the aforementioned difference. The aforementioned spatial health is calculated based on the amount of microorganisms contained in the space or the microbial diversity within the microbial community contained in the space. The improvement is a program that either increases the amount of microorganisms present in the space or enhances microbial diversity.

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