DESC:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
The Patent Rules, 2003
COMPLETE SPECIFICATION
(See sections 10 & rule 13)
1. TITLE OF THE INVENTION
A SMART MICROSCOPE FOR BACTERIA IDENTIFICATION
2. APPLICANT (S)
NAME NATIONALITY ADDRESS
DIVYASAMPARK IHUB ROORKEE FOR DEVICES MATERIALS AND TECHNOLOGY FOUNDATION IN Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, India.
3. PREAMBLE TO THE DESCRIPTION
COMPLETE SPECIFICATION

The following specification particularly describes the invention and the manner in which it is to be performed.

FIELD OF INVENTION:
[001] The present invention relates to the field of bacterial identification device to be used in pathology. The present invention in particular relates to a smart microscope system and method for bacterial identification.
DESCRIPTION OF THE RELATED ART:
[002] At present, the commonly used bacterial identification method mainly uses biochemical methods to identify bacteria based on the physiological activity characteristics of the bacteria themselves. Before identification, the bacteria are purified using culture medium to obtain pure colonies, and then the colonies are punctured into biochemical tubes to observe their color changes to determine the specific type of colonies. Alternatively, the colonies are made into a suspension and the suspension is identified using a special identification card and a special analyzer to determine the type of colonies.
[003] Reference may be made to the following:
[004] Publication No. IN202421058089 relates to a system which integrates the hardware for e.g. multiple stepper motors attached externally to the microscope, through camera lens eyepiece to intelligently detect, identify and count number of bacilli or cancerous cells in a single field. The learnings from the cited invention in terms of slide movement and image capture are carried out intelligently using the feedback control system from the AI module.
[005] Patent No. US11010610 relates to a microscope of the type used by a pathologist to view slides containing biological samples such as tissue or blood with the projection of enhancements to the field of view, such as a heatmap, border, or annotations, substantially in real time as the slide is moved to new locations or changes in magnification or focus occur. The enhancements assist the pathologist in characterizing or classifying the sample, such as being positive for the presence of cancer cells or pathogens.
[006] Publication No. CN106295572 relates to a bacteria identification method and a device. The method comprises steps that an image of a to-be-identified bacterium colony is acquired; the bacterium colony characteristic information is extracted from the to-be-identified bacterium colony; according to the extracted bacterium colony characteristic information and a pre-established bacterium colony characteristic database, the bacterial strain information corresponding to the to-be-identified bacterium colony is determined. Through a non-contact mode, the bacterial strain of the bacterium colony is identified, and safety of identification workers is improved; professional bacteria and medical requirements for the identification workers are quite low; an identification process can be rapidly accomplished through an automatic information processing process, an identification result can be further rapidly acquired, identification efficiency is greatly improved, and accuracy of the identification result is quite high; the image of the to-be-identified bacterium colony can be acquired through multiple modes, cost of the bacteria identification device is quite low, and identification cost is greatly reduced.
[007] Publication No. CN100593172 relates to a system and a method for identifying microorganism on the basis of microscopic image, the system and the method utilizes computer image processing technology to pre-process the microscopic images of the obtained grain-stored microorganism, automatically extracts mathematical statistics characteristics of the images, such as texture and geometric shapes, according to target areas of the microscopic images of the grain-stored microorganism, and then uses BP neural network to classify and identify, thus accurately identifying the microorganism in the grain. The achievement of the method can shorten the inspection period for the grain-stored microorganism and precisely forecast condition of the grain-stored microorganism, therefore enabling the staff can punctually take the prevention measures.
[008] Publication No. CN110118771 relates to a smart microscopic examination device and method for active sludge microorganisms. The smart microscopic examination device comprises a chamber, wherein a locating device for fixing a glass slide loaded with an active sludge microorganism sample is arranged in the chamber; a camera device for shooting the active sludge microorganism sample is arranged above the locating device; the side edge of the locating device is provided with a cleaning device for cleaning the locating device and a blow-drying device for performing blow drying treatment on the locating device; and the camera device, the cleaning device and the blow-drying device are all connected to a central control device. Through adoption of the smart microscopic examination device, a shooting operation can be finished automatically, automatic detection, comparison and distinguishing can be implemented according to shot pictures without being influenced by objective factors, and a cleaning operation and a blow-drying operation are finished automatically after shooting. The structure is compact, and the whole set of device is small in size. The distinguishing, comparison and counting results can be recorded and printed automatically, so that traceability is achieved.
[009] Publication No. CN116694440 relates to a bacterial microorganism rapid detector based on big data analysis, the bacterial microorganism rapid detector comprises a detection box, a first motor is installed at the bottom of the detection box, and the first motor can drive a plurality of clamping seats for placing culture dishes to rotate through a turntable; the detection box is provided with a second motor, the second motor drives the detection microscope to move up and down and left and right through a lead screw, a sleeve, a second air cylinder and a guide rod, and the detection box is provided with a control panel, a data recognition and analysis module and a signal transmitting module. Meanwhile, the detected information is identified, analyzed and processed through the data identification and analysis module, and the identified and analyzed data is transmitted to a computer through the signal transmitting module, so that the identified and analyzed data is analyzed through subsequent big data, and the detection data of bacteria and microorganisms are rapidly collected.
[010] Publication No. CN110618130 relates to microbial detection, in particular to a bacterial morphology identification system, and aims to solve the problem that the identification of bacterial species and quantities mainly depends on subjective reading by human experts, and the process is time-consuming and error-prone. The technical point thereof is that the system comprises the following parts of a kit; a microscope used to micro-magnify the image of a test sample; a data management workstation in communication with the microscope and used to receive the microscopic image of the test sample, obtain its morphological characteristics after processing and analysis, compare the morphological characteristics with the pre-stored morphological characteristics of bacteria, determine the type of bacteria in the tested sample according to the judgment result, and generate a corresponding identification code; and a mobile terminal in communication with the data management station and used to get its basic information after receiving the identification code of the detection sample. The invention greatly reduces the workload of the staff, shortens the staff training period, reduces subjective errors, and makes the bacterial detection objective, systematic and standardized.
[011] Publication No. JPH11346758 relates to a slide glass coated with a most basic control bacterial strain for gram discrimination for bacteria identification tests, conducted by various areas, e.g. food, pharmaceutical and clinical care. This slide glass in which a bacterium of known gram characteristics and type is coated and immobilized for preventing release from the glass, is used for gram staining for gram discrimination. Competence of gram staining is confirmed, after the gram staining is completed, by visually observing with the aid of an optical microscope that the sample of known gram characteristics, as the standard for discrimination, shows adequate properties. Aptitude of gram discrimination of the sample of unknown gram characteristics, stained using the same slide glass, is indirectly guaranteed, when competence of the standard sample is guaranteed.
[012] The article entitled “Recent trends in smartphone-based detection for biomedical applications: a review” by Soumyabrata Banik, Sindhoora Kaniyala Melanthota, Arbaaz, Joel Markus Vaz, Vishak Madhwaraj Kadambalithaya, Iftak Hussain, Sibasish Dutta, Nirmal Mazumder; Anal Bioanal Chem.;413(9); 2389–2406; 2021 Feb 15 talks about the smartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of tele imaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices.
[013] Thus the traditional systems rely on expensive hardware setups and need expert person for bacterial identification. Hence there needed low-cost, portable system capable of real-time bacterial classification.
[014] In order to overcome above listed prior art, the present invention aims to provide a smart microscope for bacteria identification system that combines traditional microscopy with affordable embedded systems (Raspberry Pi) and deep learning method for the automated identification of bacteria.
OBJECTS OF THE INVENTION:
[015] The principal object of the present invention is to provide a smart microscope for bacteria identification that combines traditional microscopy with affordable embedded systems (Raspberry Pi) and deep learning method for the automated identification of bacteria.
[016] Another object of the present invention is to provide a cost-effective, portable, and efficient microscope for bacteria identification.
[017] Yet another object of the present invention is to provide a microscope for bacteria identification which does not require high-end computing resources or specialized laboratory infrastructure.
SUMMARY OF THE INVENTION:
[018] The present invention relates smart microscope for bacteria identification system that combines traditional microscopy with affordable embedded systems (Raspberry Pi) and deep learning method for the automated identification of bacteria.
[019] The smart microscope for bacteria identification is a system comprises an optical microscope with a Raspberry Pi 4 Model B, equipped with a high-quality camera and a deep learning model, to automatically identify bacteria species in real-time. This system streamlines the bacterial identification process, which is critical in clinical and microbiological research, by automating it using artificial intelligence and IoT technologies.
BREIF DESCRIPTION OF THE INVENTION
[020] It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered for limiting of its scope, for the invention may admit to other equally effective embodiments.
[021] Figure 1 shows smart microscope for bacteria identification system according to the present invention;
[022] Figure 2 shows flowchart according to the present invention.
DETAILED DESCRIPTION OF THE INVENTION:
[023] The present invention provides a smart microscope for bacteria identification system that combines traditional microscopy with affordable embedded systems (Raspberry Pi) and deep learning method for the automated identification of bacteria.
[024] Figure 1 shows smart microscope for bacteria identification system according to the present invention. The smart microscope for bacteria identification is a system comprises an optical microscope with a Raspberry Pi based control unit (1), equipped with a high-quality camera and a deep learning model, to automatically identify bacteria species in real-time. This system streamlines the bacterial identification process, which is critical in clinical and microbiological research, by automating it using artificial intelligence and IoT technologies.
[025] The lightweight, real-time deep learning model with a Raspberry Pi-based embedded system, coupled with an optical microscope, to perform automated bacterial species identification directly on-device—without the need for external computing infrastructure.
[026] Unlike existing systems that either rely on high-end lab equipment or offload computation to cloud/PC-based systems, this invention enables edge-based bacterial classification using a compact and low-cost setup. The optimization and deployment of deep learning models tailored for Raspberry Pi’s limited computational resources, along with real-time inference and wireless communication capabilities, make the system suitable for remote, point-of-care, and resource-limited environments.
[027] This system demonstrates a technical advancement and non-obvious synergy of AI, embedded systems, and microscopy.
[028] The microscope lens (2) is attached to a Raspberry Pi high-quality camera (HQ camera). The Raspberry Pi acts as the central processing unit (1). It runs the trained deep learning model and processes the images captured from the microscope. The bacterial identification relies on a lightweight and highly accurate model, making it suitable for real-time applications in various research and medical settings.
[029] Figure 1 depicts a schematic representation of the smart microscope system for bacteria identification, in accordance with the present invention. The system integrates conventional microscopy with embedded computing and wireless communication to facilitate automated microbial analysis. The smart microscope system consists of a compound microscope, with minor, non-invasive structural modifications, high-quality digital camera module is mounted onto the eyepiece or trinocular port of the microscope using a custom-designed adapter or holder, which aligns the optical path with the camera sensor, Raspberry Pi and associated control electronics form a separate embedded unit, physically attached or housed adjacent to the microscope. This unit is connected to the camera and processes the images in real time.
[030] Thus, the invention provides the microscope with AI-powered control unit, making the system both cost-effective and adaptable to existing microscopy setups.
[031] A prepared bacterial sample is first placed onto a glass slide, which is then mounted onto a conventional optical microscope. The microscope is augmented with a camera module connected to a Raspberry Pi, enabling digital image acquisition of the magnified bacterial specimen. The Raspberry Pi functions as the central processing unit of the system and is configured to capture, preprocess, and analyze the microscopic images using pre-trained deep learning models embedded within its memory.
[032] The processed data, including bacterial classification results, is not transmitted wirelessly to output devices. Instead, the Raspberry Pi hosts a web interface and uses port forwarding to make the system accessible over the internet. External devices such as desktop computers and smartphones can access the diagnostic output (4) in real-time by visiting the corresponding web URL. This architecture allows remote users to securely view and interpret results through a browser-based interface, without the need for direct wireless data transmission from the device itself.
[033] The system, provides a compact, low-cost, and intelligent solution for bacterial identification, with potential applications in clinical diagnostics, research laboratories, and remote field settings. The wireless transmission capability further enhances the system’s portability and scalability, making it suitable for deployment in decentralized and resource-constrained environments.
[034] Figure 2 illustrates the complete operational workflow of the smart microscope system developed for bacterial species identification. The process begins with the placement of a prepared bacteria slide under a conventional compound microscope. A camera module, mounted on the microscope, is then activated to initiate real-time image acquisition.
[035] The captured microscopic image is transmitted to a Raspberry Pi unit for processing. The Raspberry Pi performs essential image preprocessing steps such as resizing, normalization, and enhancement to prepare the input for deep learning analysis.
[036] Once the image is preprocessed, it is forwarded to a lightweight deep learning model running on the Raspberry Pi. The model analyzes the image and outputs a classification result, identifying the bacterial species present on the slide.
[037] Instead of transmitting the output wirelessly, the classification result and the live camera feed are made available through a web interface. This is achieved by hosting a local server on the Raspberry Pi and exposing it to the internet via port forwarding or dynamic tunneling services.
[038] Users can then access the system remotely using a desktop computer or smartphone browser to view both the predicted bacterial class and the live microscope feed in real time. This enables efficient remote diagnostics and monitoring, eliminating the need for direct physical access to the microscope.
[039] The system is equipped with wireless capabilities, allowing the Raspberry Pi to transmit processed data and results to a remote PC or server (4). The remote display of bacterial species identification results enables researchers and clinicians to view the data without direct interaction with the microscope, increasing convenience and efficiency.
[040] The system incorporates wireless network functionality, enabling seamless communication between the Raspberry Pi and remote output devices. Once the bacterial species have been identified through image processing and classification, the results are made accessible over a network. This allows the processed data to be transmitted to a remote PC, server, or web interface without requiring physical connections.
[041] Such a design ensures that researchers and clinicians can view both the live microscope feed and classification outcomes in real time from a distant location. By eliminating the need for direct interaction with the microscope setup, the system enhances user accessibility, simplifies diagnostic workflows, and improves operational efficiency—especially in remote diagnostics and collaborative environments.
[042] The results are displayed on a remote computer through a user-friendly interface, which can visualize the identified bacterial species, provide confidence scores, and facilitate further analysis.
[043] The system integrates cutting-edge artificial intelligence (AI)-driven diagnostics with affordable and accessible hardware, offering a cost-effective, portable, and efficient alternative to traditional bacterial identification methods, which often require expensive machinery and specialized expertise. By leveraging deep learning models, the system ensures rapid and accurate identification of microorganisms, reducing the time and effort required for manual analysis.
[044] The proposed system represents a significant advancement in microbiological diagnostics by integrating state-of-the-art artificial intelligence (AI) techniques with low-cost, widely available hardware components such as the Raspberry Pi and standard camera modules. This combination results in a highly portable, budget-friendly, and operationally efficient solution compared to conventional bacterial identification methods, which typically rely on sophisticated laboratory equipment and trained personnel.
[045] By utilizing deep learning models for automated image-based classification, the system facilitates rapid and precise identification of microorganisms. This significantly minimizes the dependency on manual interpretation and reduces the overall diagnostic turnaround time. Consequently, the system not only democratizes access to advanced diagnostics in resource-limited settings but also enhances the scalability and practicality of bacterial detection in both clinical and research environments.
[046] The system integrated of Raspberry Pi with a microscope for real-time AI-based bacterial classification: While microscopes and AI have been used separately in various domains, this invention uniquely combines them by using a Raspberry Pi 4, which runs a pre-trained deep learning model directly on captured images. The processing happens in real-time, and no such specific system currently exists where a Raspberry Pi serves as a central unit for direct bacterial species identification through microscopic imaging.
[047] The present invention introduces a integration of a Raspberry Pi with a conventional microscope to perform real-time, AI-based bacterial classification.
[048] The Raspberry Pi with optical microscope to enable automated, AI-based bacterial classification in real-time. A high-resolution camera is attached to the microscope’s eyepiece or imaging port to capture microscopic images of bacterial specimens. These images are processed by the Raspberry Pi, which runs a lightweight, pre-trained deep learning model optimized for embedded systems. The system performs on-device inference to classify bacterial species without needing external computing resources. This setup eliminates manual analysis, reducing human error and speeding up diagnostics. The Raspberry Pi also supports wireless connectivity, enabling remote monitoring and data transmission. The system is designed to be compact, affordable, and adaptable to existing microscopes without altering their structure. It brings advanced microbiological analysis to resource-limited or field settings. The combination of AI, embedded processing, and conventional microscopy makes the system intelligent and scalable. Overall, the invention enhances the usability of traditional microscopes with modern smart capabilities.
[049] While both microscopic imaging and artificial intelligence have been independently employed in diverse diagnostic and research applications, this system uniquely unifies these technologies into a single, compact solution. Specifically, a Raspberry Pi 4 functions as the core computational unit, executing a pre-trained deep learning model locally on images captured via a connected camera module.
[050] This enables on-device processing of microscopic images without reliance on external servers or high-end computing resources. The classification occurs in real-time, allowing immediate identification of bacterial species as the image is acquired. To the best of current knowledge and literature, no existing solution directly incorporates a Raspberry Pi for autonomous, AI-driven bacterial species detection using live microscopic imagery. This innovation thus offers a low-cost, scalable, and portable diagnostic tool, particularly valuable in settings with limited infrastructure.
[051] This is a low-cost, computing device, for running complex AI-based bacterial identification method. The invention offers an accessible alternative for researchers and clinicians without requiring high-end computing resources or specialized laboratory infrastructure.
[052] It wirelessly transmits the classification results from the Raspberry Pi to a remote system, enabling real-time access to diagnostic results, introduces a new feature in bacterial identification tools. This remote data access can support telemedicine or remote research collaboration, for bacterial identification systems. This invention provides a low-cost, AI-powered bacterial identification system using a Raspberry Pi, eliminating the need for expensive computing or lab equipment. It enables real-time, remote access to diagnostic results via wireless transmission, making it ideal for telemedicine and collaborative research in resource-limited settings.
[053] This system allows for continuous learning by retraining the AI model as new bacterial species emerge. The flexibility to update the model without requiring significant hardware changes makes this invention stand out as a future-proof, scalable solution. This invention provides an automated, user-friendly system where even non-specialists can identify bacteria, a feature that does not exist in microbial identification tools available today.
[054] This invention has a broad range of applications, from clinical diagnostics to educational and research settings. Its compact design, low cost, and ability to provide real-time, accurate bacterial identification. The portability of the system, combined with its wireless data transmission capabilities, opens up opportunities for remote diagnostics and telemedicine, particularly in resource-limited areas.
[055] The invention automates the entire workflow, eliminating the need for manual intervention, a feature not addressed in the cited invention. It integrates a high-quality imaging setup with a deep learning model to classify bacteria in real-time, a capability that extends beyond assisting pathologists to providing a standalone diagnostic tool.
[056] The system processes individual bacterial cells, enabling more precise results, and offers real-time analysis with wireless connectivity, features not included in this prior art. This is a fully integrated solution for real-time bacterial classification, eliminating the need for separate computational resources. It prioritizes precision and integrates wireless communication for real-time diagnostics.
[057] The high efficiency is achieved without requiring external computational infrastructure or extensive data processing, making our system highly deployable in resource-limited settings. It accelerates the identification process, enhances versatility, and removes the dependency on pre-prepared slides, making it more practical for diverse microbiological applications.
[058] Figure 2 shows flowchart according to the present invention. This flowchart illustrates the step-by-step method employed in the invention for real-time bacterial classification using a microscope, Raspberry Pi, and a deep learning model. Each block represents a distinct process in the system:
[059] The system is initialized and made ready for image acquisition and analysis. A microscope slide containing a bacterial sample is prepared and positioned under the lens for observation. A high-resolution camera connected to the Raspberry Pi activates automatically and captures microscopic images of the slide. The live image feed from the camera is sent to the Raspberry Pi for further processing.
[060] The raw image is preprocessed using standard computer vision techniques to make it suitable for input into the AI model. A lightweight deep learning model is trained to identify specific bacterial species. The Raspberry Pi sends the output—live image feed and bacterial classification result—to an online platform or web server through port forwarding. The results are accessible remotely in real-time, allowing researchers or clinicians to monitor the classification output via a web interface. The process concludes, and the system can be restarted for a new analysis cycle.
[061] The system integrates real-time deep learning-based bacterial classification system within a low-cost Raspberry Pi setup, directly coupled to a microscope.
[062] Thus the system is running the entire deep learning model locally on a Raspberry Pi without offloading to cloud or PC. Real-time bacterial identification and remote web-based result are accessible without high-end computing. It is a standalone unit ccombining artificial intelligence n=based microscopy with IoT (via port forwarding/website). This is used in remote, rural, or low-resource areas for real-time tele microbiology.
[063] Numerous modifications and adaptations of the system of the present invention will be apparent to those skilled in the art, and thus it is intended by the appended claims to cover all such modifications and adaptations which fall within the true spirit and scope of this invention.
,CLAIMS:WE CLAIM:
1. A smart microscope for bacteria identification comprises an optical microscope (2) with a control unit with deep learning model characterized in that Raspberry Pi based central processing unit (1), a camera mounted over the microscope's eyepiece to capture live microscopic images and sends them directly to the control unit, to automatically identify bacteria species in real-time and with wireless communication capabilities, allowing the control unit to transmit processed data and results to a output device remote PC or server (3a, 3b) wherein Raspberry Pi-based embedded system that serves as the central processing unit which receives live image feed from the camera mounted on the microscope and runs a deep learning model optimized for low-resource environments, which performs real-time classification of bacterial species; wireless Communication Module Integrated within the control unit (1), this module allows the processed data and classification results to be transmitted wirelessly to external devices.
2. The smart microscope for bacteria identification, as claimed in claim 1, wherein the control unit runs a pre-trained deep learning model to classify the bacterial species and when a sample is placed under the microscope and an image is captured, this image is sent to the Raspberry Pi, pre-trained model then analyzes the image in real-time and automatically classifies the bacterial species based on its learned features.
3. The smart microscope for bacteria identification, as claimed in claim 1, wherein the output devices are remote PC or server (3a), which receives the classified results and processed images via wireless communication, and mobile device (3b), that can similarly receive and display the output data from the control unit.
4. The smart microscope for bacteria identification, as claimed in claim 1, wherein the camera-microscope interface is in a non-invasive manner, ensuring compatibility with standard optical microscopes without requiring structural modifications.
5. The smart microscope for bacteria identification, as claimed in claim 1, wherein the system automates the entire workflow, eliminating the need for manual intervention including the prepared bacterial slide is placed under the microscope, the camera module captures the image, and the control unit preprocesses it, classification using the AI model, and result transmission.