DESC:FORM 2
THE PATENTS ACT, 1970 (39 of 1970)
&
THE PATENTS RULES, 2003
COMPLETE SPECIFICATION
[See section 10, Rule 13]

1. TITLE OF THE INVENTION:
AN FTIR INTERFEROMETER, SYSTEM AND A METHOD FOR ANALYZING MILK SAMPLES
2. APPLICANT
a) Name: Beamoptics Scientific Private Limited
b) Nationality: Indian
c) Address: PAP-J-188, 2nd floor, Near Quality Circle Forum, Telco road, Mide Bhosari,
Pune - 411026, Maharashtra, India

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

FIELD OF THE INVENTION
[0001] The present invention relates to a system and method for analyzing liquid samples, more particularly relates to a system for analyzing milk samples especially those having high fat content and a system and method to do the same.

BACKGROUND OF THE INVENTION
[0002] India is one of the largest producers of milk, however exports from India of milk are relatively low, because quality is non-standard, primarily due to the disaggregate production of milk – the average dairy farmer in India, owns 2 livestock.
[0003] The milk from different farmers is collected at the village level in a Bulk milk collection center (BMCCs) and the farmer is compensated on two variables – amount of milk given and amount of fat in the milk.
[0004] Origin of milk, variants in milk and milk composition are not considered, this leads to a lot of adulteration at the farmer level as they only focus on the two parameters being tested, which at an aggregate level reduces exports due to non-standardized quality of milk.
[0005] Further, buffalo milk is unique to the Indian subcontinent and is generally characterized by higher fat and SNF percentages, making foreign testing machines difficult to adapt to Indian milk. within India, there are regional differences in milk due to local environmental and livestock feeding differences, these differences are neither tested nor taken into consideration throughout the milk testing process.
[0006] Currently, ultrasonic machines are mostly used in BMCCs to test for components in milk, the problem with ultrasonic milk analysers is that it cannot detect variants in milk, it can only measure Fat and SNF.
[0007] For measuring adulterants, currently the technology of choice is FTIR spectrometry as disclosed in Patent Application number WO2024175879A1. However, FTIR instruments are neither made to operate in the environment of a BMCC nor are they programmed to cater to the regional differences in milk which plays a major role in milk analysis. Due to this, variations in milk are often falsely detected as adulterants by the currently used devices and systems; and adulterants present are not adequately identified or detected.
[0008] Many FTIR instruments need homogenisers to ensure that fat droplets are uniformly spread through the sample, however it adds to the cost and complexity of the sample. The fat droplet size uniformity in milk handling is still a major bottleneck for reproducibility in operation.
[0009] There is, therefore, a need for an FTIR interferometer to operate in un-standardized conditions such as mentioned above while being robust, accurate and functional to analyze un-standardized samples especially those with high fat content, and a need for an FTIR interferometer and a method of working the same that can overcome at least one of the above-mentioned problems.
SUMMARY OF THE INVENTION
[0010] In an aspect of the present invention relates to an FTIR interferometer comprising a Michelson interferometer having
a processor configured to an inbuilt correction model
a moving mirror (212) to change the optical path;
at least a beam splitter (209,210,211);
a monochromatic radiation source (206, 217) for a reference beam; and
a divergent observation beam launched along its propagation path;
[0011] wherein the reference beam launched along its propagation path to be incident at the first face at the first angle (?) with respect to the propagation path of the observation beam which is less than or equal to the divergence half-angle (?) of the observation beam;
[0012] In another aspect of the present invention the employs a heated sample heater preferably using a foil heater and a slow flush technology where the sample is tested as it is heated and slowly flushed through the sample holder, wherein multiple spectra are “Averaged” for accurate readings;
[0013] In another aspect of the present invention the processor configured to an inbuilt correction model which is capable of correcting any errors associated with homogeneity, noise etc. thus overcoming the need for a homogenizer;
[0014] In yet another aspect of the present invention a system incorporating the FTIR instrument of the present invention is disclosed, said system having an algorithm, a controller, a processor configured to an inbuilt correction model and an AI/ML model having a training and task model, at least a display screen, a user application, a wireless connection and ancillary parts, a server (selected from physical, cloud based, etc.), Additionally the system having a digital twin model.
[0015] In yet another aspect of the present invention a method of operating an FTIR instrument of the present invention is disclosed said method being independent of a homogenizer, said method comprising:
i. Simultaneously launching a reference beam and a divergent observation beam towards a beam splitter;
ii. Testing sample using a heated sample holder and slow flush techniques;
iii. Generating an interferogram;
iv. Correcting data processed by processor by employing a correction algorithm using fat drop size estimation from average multiple spectra for lack of homogeneity;
v. The correction algorithm further cancels any errors in reading which are to be ignored for an accurate quantitation of the sample components;
vi. Correction model communicates to AI/ML model to improve accuracy with each test performed; and
vii. Communicating corrected results.

BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Reference will be made to embodiments of the invention, examples of which may be illustrated in the accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments.
[0017] FIG. 1a shows a line diagram 100 in the x/y plane of the Michelson type interferometer in accordance with an embodiment of the present invention;
[0018] FIG. 1b shows an exploded view of the the Michelson type interferometer in accordance with an embodiment of the present invention;
[0019] FIG. 2 shows a three-dimensional view 200 of the Michelson type interferometer in accordance with an embodiment of the present invention; and
[0020] FIG. 3 shows a flowchart demonstrating a method in accordance with an embodiment of the present invention.
[0021] FIG. 4 shows a flow chart diagram of the steps involved in the location based chemometric models in accordance with an embodiment of the present invention.
[0022] FIG. 5 shows a flow chart diagram of the steps involved in the working of the instrument in accordance with an embodiment of the present invention.
[0023] FIG.6 shows IR peak/ maxima position in accordance with an embodiment of the present invention.
[0024] FIG.7a shows sectioning of laser signal around the IR peak / maxima of the IR signal in accordance with an embodiment of the present invention.
[0025] FIG.7b shows the determining zero crossing length and mean for the sectioned laser signal in accordance with an embodiment of the present invention.
[0026] FIG.8 shows a system architectural drawing in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION
[0027] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise. Although any interferometer similar or equivalent to those described herein can be used to optimize the outcome of the present invention.
[0028] Glossary:
[0029] AI: shall mean “Artificial Intelligence” and may refer to a software / computing in which the simulation of human intelligence is processed by machines
[0030] ML: shall mean “Machine Learning” and may refer to a type of AI where the machine continually learns from data which is received, collected, processed, and stored by the system.
[0031] Digital Twin: may refer to a virtual model designed to accurately reflect a physical object being studied in order to make predictive analysis.
[0032] Server: A server may include or comprise, by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, one or more processors executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise, a defence facility, or any other facility that provides content.
[0033] Various embodiments of the invention provide an FTIR interferometer and a method for analyzing samples based on the principle of interference of light, particularly using a Michelson interferometer which has a moving mirror to change the optical path length. Further, the present invention discloses an improved mechanism to analyze milk samples containing high fat content.
[0034] An embodiment of the present invention discloses an FTIR interferometer (200) comprising a Michelson interferometer having
i. a processor configured to an inbuilt correction model
ii. a moving mirror (212) to change the optical path;
iii. at least a beam splitter (209,210,211);
iv. a monochromatic radiation source (206, 217) for a reference beam; and
v. a divergent observation beam launched along its propagation path;
Wherein the reference beam launched along its propagation path to be incident at the first face at the first angle (?) with respect to the propagation path of the observation beam which is less than or equal to the divergence half-angle (?) of the observation beam.
[0035] In a further embodiment, the FTIR interferometer (200) having a sample holder (203) for housing sample to be tested and at least two detectors to detect the interference in observation and reference beam respectively, after having travelled through the sample, the detectors are capable of recording the signal representing said interference in the form of an interferogram.
[0036] In an embodiment of the present invention, the detectors are capable of communicating the interferogram to processors programmed to calculate the sample components wherein the sample components are calculated quantitatively based on the difference between the incident observation beam and the transmitted observation beam and negating the difference between the incident reference beam and the transmitted reference beam to cancel any noise, natural deviations or errors in reading which are to be ignored for an accurate quantitation of the sample components.
[0037] In an embodiment, the FTIR of the present invention uses absorption of light at different wavelengths in the infrared spectrum to detect the presence of specific parameters in liquid samples like Fat, SNF and Protein. The samples for testing are preferably milk samples.
[0038] In another embodiment, a method of operating an FTIR instrument of the present invention is disclosed, comprising the steps of simultaneously launching a reference beam from the monochromatic radiation source said reference beam being laser of defined wavelength, preferably 850nm VCSEL laser and a divergent observation beam from the observation optical radiation source along respective propagation paths towards the first face of the beam splitter of the interferometer, the reference beam being launched along its propagation path to be incident at the first face at a first angle (?) with respect to the propagation path of the observation beam which is less than or equal to the divergence half-angle (?) of the observation beam.
[0039] In an embodiment, the FTIR interferometer of the present invention comprises of a heater in the cell where the milk sample flows through, for more reproducible measurements, the heater used is preferably a foil heater.
[0040] In an embodiment, the FTIR interferometer of the present invention employs a slow flush measurement technique which moves the sample to be tested slowly through the cuvette while the measurement happens and multiple spectra are “Averaged” said interferometer employs a heated sample holder (203) for housing sample which is optionally enabled to flow through such heated sample holder for a uniformity of sample testing wherein the heated sample holder and preferably employs at least a foil heater. In an aspect the sample flow is controlled to slowly moved through the cuvette while performing sample testing and averaging multiple spectra.
[0041] In yet another embodiment, a system comprising the FTIR instrument of the present invention is disclosed, wherein said instrument employs an algorithm, a controller, a processor configured to an inbuilt correction model and an AI/ML model, at least a display screen, a user application, a wireless connection and ancillary parts, a server (selected from physical, cloud based, etc.) an AI/ML model/block. In an aspect the processor is configured to an inbuilt correction model configured to the AI/ML model/block containing a training model such that the correction algorithm of the correction model corrects the data processed by the processor for the lack of homogenous milk sample, especially in high fat content milk algorithm for fat droplet size estimation and correction is derived from the FTIR spectra to correct for the lack of homogenization in milk sample. Wherein the algorithm comprises a correction algorithm to correct for the lack of homogenous milk sample, especially in high fat content milk.
[0042] The algorithm for fat droplet size estimation and correction is derived from the FTIR spectra to correct for the lack of homogenization in milk samples. The algorithm is thus capable of compensating for the lack of a homogenizer.
[0043] In a further embodiment of the present invention, the interferometer comprises of a homogenizer wherein the algorithm for fat droplet size estimation is employed to gauge effectiveness of the homogenizer.
[0044] In yet another embodiment of the present invention the AI/ML block includes a trained AI/ML model which may be provided with training data set which is preferably preset data which is updated by the training mode, said AI/ML model attempts various corrective algorithms based on the type of sample and infer if the data fits the trained AI model so as to improve accuracy of the testing method, in an embodiment the AI/ML model maps various corrective algorithms onto the type of sample and infers to confirm if the data fits the trained AI/ML model so as to improve accuracy with each test performed.
[0045] In an embodiment the iterative feedback loop increases the accuracy as well as eliminates manual intervention and for any data set, the ratio is identified automatically by intelligently learning from the provided data for providing best accuracy.
[0046] In yet another embodiment of the present invention the AI/ML block includes a task executor wherein an input dataset may be provided to or received by a task executor. The task executor, may be configured to forward the input dataset to the trained model. The trained model, may be configured to implement the AI model trained by model trainer.
[0047] In yet another embodiment of the present invention the AI/ML block includes a model trainer which may be configured to train the AI model and the ML model. Further the trained model, may be configured to generate results for the input data processed by the AI model. A tuner, may be configured to evaluate the results of the output. Further the tuner based on the feedback received on the results by evaluation is configured to optimize the training model.
[0048] Further in another embodiment of the present invention to obtain optimal ratio of training dataset to evaluation dataset or other training parameter values given while training the model to obtain the most accurate results from the AI/ML model, further to obtain the optimized ratio, the training parameters like learning rate, number of epochs, weight decay may be automatically adjusted.
[0049] Further in accordance with the exemplary embodiment of the present invention, interferometer and method may consider at least one of the factors, from data complexity, model architecture, available resources, and desired accuracy. In accordance with the exemplary embodiment a feedback-mechanisms may continuously monitor the interferometer's performance. Further dynamically adjusting parameters to the evaluation dataset based on real-time feedback, the method adapts to changes in data patterns and ensures optimal interferometer performance throughout the testing process to progressively learn about the variations in samples and adapts to such changes with every test performed by the interferometer.
[0050] In yet another embodiment, a system comprising the FTIR instrument of the present invention is disclosed, wherein said system employs a digital twin model capable of receiving data from each subsystem of each machine including subsystem level components, enabling predictive analysis of maintenance, operations, and to facilitate updates to various system models, units, subsystems, components, devices, applications inter alia. The data collected from various interferometers and tests are analyzed and stored locally, centrally, or both.
[0051] In yet another embodiment, a method of working the FTIR instrument is disclosed , said method comprising:
i. Simultaneously launching a reference beam and a divergent observation beam towards a beam splitter (301);
ii. Testing sample by passing the observation beam through a slow-moving sample placed in a heated sample holder to average multiple spectra to obtain a set of parameters including fat drop size (302);
iii. Generating an interferogram to calculate the difference between the incident and transmitted beams using detectors capable of detecting and communicating the same including average multiple spectra to the processor (303);
iv. Correcting processed data using correction model having correction algorithm using fat drop size estimation from average multiple spectra (304);
v. the correction algorithm corrects the data for lack of homogeneity, especially in high fat content sample algorithm for fat droplet size estimation and correction (305);
vi. The correction algorithm further cancels any noise, natural deviations or errors in reading which are to be ignored for an accurate quantitation of the sample components (306);
vii. Correction model communicates to AI/ML model to map various corrective algorithms onto the type of sample and infers to confirm if the data fits the trained AI model configured to said AI/ML model, so as to improve accuracy with each test performed (307);
viii. Communicating corrected results (308).
[0052] In an embodiment of the present invention the method further includes comprising communicating to the processor, the interferogram recorded by the detectors and thereafter processing the data of the interferogram to quantitatively calculate sample components based on the difference between the incident observation beam and the transmitted observation beam and negating the difference between the incident reference beam and the transmitted reference beam to cancel any noise, natural deviations or errors in reading which are to be ignored for an accurate quantitation of the sample components.
[0053] Referring to FIG. 1a shows a line diagram 100 in the x/y plane of the Michelson type interferometer in accordance with an embodiment of the present invention. The interferometer 100 may comprise a moving mirror (101), a stationary mirror (102), a sample holder, detectors (103, 104), a beam splitter, at least a source (105);
[0054] Referring to FIG. 1b shows an exploded view of the Michelson type interferometer in accordance with an embodiment of the present invention. The interferometer may comprise IR Source (101b); Source Mirror (102b); Fixed Mirror (103b); Beam Splitter (104b); Compensator (105b); Moving Mirror (106b); Laser Source (107b) additionally Figure 1b shows the angles theta and alpha
[0055] Referring to FIG. 2 shows a three-dimensional view 200 of the Michelson type interferometer in accordance with an embodiment of the present invention. The interferometer 200 may comprise a moving mirror (212) controlled by a VCA, a stationary mirror (205), a sample holder (203), detectors (201, 208), a beam splitter (209,210,211), a source (206, 217)
[0056] Referring to FIG. 3 shows a flow chart of the method with detailed steps demonstrating how the sample is tested in a heated sample tested using slow flush method and generating an interferogram containing details of sample components including parameters for fat drop size estimation and an average multiple spectra, used by the processor configured to a correction model to process the data of interferogram and correct the results by a correction algorithm using fat drop size estimation received from FTIR spectra, the method further discloses an AI/ML model configured to a training model wherein the correction model communicates to AI/ML model to map various corrective algorithms onto the type of sample and infers to confirm if the data fits the trained AI model configured to said AI/ML model, so as to improve accuracy with each test performed.
[0057] Referring to FIG. 4 which shows a flow chart diagram of the steps involved in the location based chemometric models of data collection and processing. The steps involve Collecting data with lab reference values, the collected data is uploaded to a cloud computing platform such as AWS wherein the data is processed and prepared in a defined format, said processed data is compared with existing models to calculate accuracy; If the accuracy is detected to be within the given limit, no changes are made to the current model. However, if the accuracy is detected to be lower than the given limit, a new model is trained with a tuned limit of detection; said new model is then synced with the instrument when the network is available
[0058] Referring to Fig 5. Which shows a flow chart diagram of the steps involved in the working of the instrument of the present invention. Said steps comprise:
a. Capturing IR and Laser signal at defined VCA speeds;
b. Finding IR peak/ maxima position as shown in fig. 6;
c. Sectioning 1800 point / ± 900 points of laser signal around the IR peak / maxima of the IR signal as shown in fig. 7a;
d. Determining zero crossing length and mean for the sectioned laser signal as shown in fig.7b;
e. Generate calibration curve for speed v/s zero crossing length to get optimum speed at which laser signal gives a mean of 23.5-24.4 i.e Laser frequency of range 984-1022 Hz;
Generate calibration curve for mean v/s speed to get a coefficient/ correction factor by which the speed changes if the man is not within the range of 23.5-24.4.
[0059] Referring to Fig. 8 which as shown in the system architectural drawing shows a user (901) using the instrument (903) containing sample material for sample testing (902) to obtain information characteristic of the sample material as the testing parameters (904), obtained by processing data using a cloud based data processor (908) . Wherein the test parameters/readings obtained after passing the observation beam through the sample are communicated (905) to the cloud (907) to be evaluated against pre-existing data stored as “model(s)” taken from model library (909) located in the backend cloud (910) to identify a match, if a match is obtained, the test reading is categorised under said model and reported back/ synced (906) to the user on the instrument (903) or a user interface.
[0060] A person of ordinary skill in the art will readily ascertain that the aforementioned embodiments are set out to explain the present invention, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. The embodiments are presented herein for purposes of clarity and disclosure, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
,CLAIMS:CLAIMS
We claim,
Claim 1. An FTIR interferometer (200) for analysing samples comprising a Michelson interferometer having:
i. a processor configured to an inbuilt correction model
ii. a moving mirror (212) to change the optical path;
iii. at least a beam splitter (209,210,211);
iv. a monochromatic radiation source (206, 217) for a reference beam; and
v. a divergent observation beam launched along its propagation path;
wherein the reference beam launched along its propagation path to be incident at the first face at the first angle (?) with respect to the propagation path of the observation beam which is less than or equal to the divergence half-angle (?) of the observation beam.

Claim 2. The FTIR interferometer as claimed in claim 1 further having a sample holder (203) for housing sample to be tested and at least two detectors to detect the interference in observation and reference beam respectively, after having travelled through the sample, the detectors are capable of recording the signal representing said interference in the form of an interferogram.

Claim 3. The FTIR interferometer (200) as claimed in claim 1 further comprising a heated sample holder (203) for housing sample which is optionally enabled to flow through such heated sample holder for a uniformity of sample testing wherein the heated sample holder preferably employs at least a foil heater.

Claim 4. The FTIR interferometer as claimed in claim 1 further comprising a heated sample holder (203) for housing sample which is optionally enabled to flow through such heated sample holder for a uniformity of sample testing wherein the sample flow is controlled to slowly moved through the cuvette while performing sample testing and averaging multiple spectra.

Claim 5. The FTIR interferometer as claimed in claim 1, wherein the processor configured to an inbuilt correction model is configured to an AI/ML model containing a training model such that the correction algorithm of the correction model corrects the data processed by the processor for the lack of homogenous milk sample, especially in high fat content milk algorithm for fat droplet size estimation and correction is derived from the FTIR spectra to correct for the lack of homogenization in milk samples.

Claim 6. The FTIR interferometer as claimed in claim 1, wherein the processor configured to an inbuilt correction model is configured to an AI/ML model containing a training model such that the AI/ML model is provided with preset training data which is updated by the training model, said AI/ML model attempts to map various corrective algorithms onto the type of sample and infers to confirm if the data fits the trained AI model so as to improve accuracy with each test performed.

Claim 7. The FTIR interferometer as claimed in claim 1 wherein the detectors are capable of communicating the interferogram to processors programmed to calculate the sample components wherein the sample components are calculated quantitatively based on the difference between the incident observation beam and the transmitted observation beam and negating the difference between the incident reference beam and the transmitted reference beam to cancel any noise, natural deviations or errors in reading which are to be ignored for an accurate quantitation of the sample components.

Claim 8. A method of operating an FTIR instrument comprising:
simultaneously launching (301) a reference beam from the monochromatic radiation source said reference beam being laser of defined wavelength and a divergent observation beam from the observation optical radiation source along respective propagation paths towards the first face of the beam splitter of the interferometer, the reference beam being launched along its propagation path to be incident at the first face at a first angle (?) with respect to the propagation path of the observation beam which is less than or equal to the divergence half-angle (?) of the observation beam, wherein the reference beam being laser of defined wavelength, preferably a laser of 850nm VCSEL.

Claim 9. The method as claimed in claim 8, said method comprising:
i. Simultaneously launching a reference beam and a divergent observation beam towards a beam splitter (301);
ii. Testing sample by passing the observation beam through a slow-moving sample placed in a heated sample holder to average multiple spectra to obtain a set of parameters including fat drop size (302);
iii. Generating an interferogram to calculate the difference between the incident and transmitted beams using detectors capable of detecting and communicating the same including average multiple spectra to the processor (303);
iv. Correcting processed data using correction model having correction algorithm using fat drop size estimation from average multiple spectra (304);
v. the correction algorithm corrects the data for lack of homogeneity, especially in high fat content sample algorithm for fat droplet size estimation and correction (305);
vi. The correction algorithm further cancels any noise, natural deviations or errors in reading which are to be ignored for an accurate quantitation of the sample components (306);
vii. Correction model communicates to AI/ML model to map various corrective algorithms onto the type of sample and infers to confirm if the data fits the trained AI model configured to said AI/ML model, so as to improve accuracy with each test performed (307);
viii. Communicating corrected results (308).

Claim 10. The method as claimed in claim 8 comprising communicating to the processor, the interferogram recorded by the detectors and thereafter processing the data of the interferogram to quantitatively calculate sample components based on the difference between the incident observation beam and the transmitted observation beam and negating the difference between the incident reference beam and the transmitted reference beam to cancel any noise, natural deviations or errors in reading which are to be ignored for an accurate quantitation of the sample components.

Claim 11. A system employing the FTIR instrument as claimed in claim 1; an algorithm, a controller, a processor configured to an inbuilt correction model and an AI/ML model, at least a display screen, a user application, a wireless connection and ancillary parts, a server (selected from physical, cloud based, etc.).

Claim 12. The system as claimed in claim 11 wherein the AI/ML block includes a model trainer which may be configured to train the AI model and the ML model. Further the trained model, may be configured to generate results for the input data processed by the AI model. A tuner, may be configured to evaluate the results of the output. Further the tuner based on the feedback received on the results by evaluation is configured to optimize the training model.

Claim 13. The system as claimed in claim 11 comprising the FTIR instrument as claimed in claim 1, wherein said system employs a digital twin model capable of receiving data from each subsystem of each machine including subsystem level components, enabling predictive analysis of maintenance, operations, and to facilitate updates to various system models, units, subsystems, components, devices, applications inter alia.

Claim 14. The system as claimed in claim 11 having a sample holder located in the instrument (903) containing sample material for sample testing (902) to obtain information characteristic of the sample material as the testing parameters (904), obtained by processing data using a cloud based data processor (908) ; wherein the test parameters obtained after passing the observation beam through the sample are communicated (905) to the cloud (907) to be evaluated against pre-existing data stored as “model(s)” taken from model library (909) located in the backend cloud (910) to identify a match, if a match is obtained, the test reading is categorised under said model and reported back (906) to the user (901) via the instrument (903) or a user interface.

Dated this on 9th day of April, 2024

For, Beamoptics Scientific Private Limited
Applicant’s Registered Agent

Ragini Shah
IN/PA/2898