Simultaneous multi-variable data acquisition and analysis equipment

Abstract

A system, method, and integrated hardware and software platform for multi-variable clinical data acquisition, analysis, and decision support, integrating IoT-enabled sensors, certified diagnostic devices, artificial intelligence, and secure communication protocols. The subject invention normalizes multi-source data, maps patient complaints to standardized medical classifications, and provides clinicians with real-time diagnostic and therapeutic recommendations. It improves diagnostic accuracy, reduces healthcare delivery time and costs, and ensures compliance with medical and data protection regulations.

Description

[0001] ABHI001

[0002] Simultaneous Multi-Variable Data Acquisition and Analysis Equipment FIELD OF DISCLOSURE

[0003] The present invention relates to the field of healthcare technology, and particularly to systems and methods for multi-variable data acquisition, analysis, and clinical decision support. More particularly, the invention integrates hardware, software, artificial intelligence, and loT-based medical devices into a single clinical terminal for simultaneous, efficient and accurate diagnostics, treatment planning, and patient management.

[0004] BACKGROUND OF INVENTION

[0005] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.

[0006] The delivery of primary healthcare is often hindered by fragmented systems that result in medical errors, increased costs, and delayed diagnosis. Current methods rely on serial processes for data collection, analysis, and consultation, leading to inefficiencies and clinical biases. The lack of real-time access to comprehensive patient data further complicates decision-making for healthcare providers as it introduces changes over time.

[0007] Existing solutions lack integration, as clinics, laboratories, and consultation services operate in silos. Furthermore, medical professionals face challenges due to incomplete data, asynchronous test results, and reliance on manual processes for treatment planning. These issues are compounded by a lack of standardization in processing patient data, insufficient safeguards for data security, and limited accessibility for remote consultations.

[0008] The present invention addresses these challenges by providing a unified platform that integrates multi-variable data acquisition, Al-based analytics, and real-time clinical decision support into a single terminal for simultaneous real time measurement. The subject invention, therefore, enhances diagnostic accuracy, reduce costs, and ensures efficient healthcare delivery.ABHI001

[0009] SUMMARY OF INVENTION

[0010] This summary is provided to introduce a selection of concepts in a simplified form to be further described below in the Detailed Description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

[0011] A. IMPROVED DIAGNOSITC ACCURACY DUE TO SIMULTANEOUS REAL TIME MUTIPLE DATA MEASUREMENTS RATHER THAN SERIAL MEASUREMENTS

[0012] The present invention is a system, method, and integrated hardware & software platform called “ZoyeMed”, designed for simultaneous multi-variable clinical data acquisition, analysis, and decision support. It integrates loT-based sensors, medical devices, artificial intelligence, and secure communication protocols to provide an end-to-end solution for realtime diagnostics and patient management. This improves the accuracy of diagnosis as it removes the time dependent noise in measurements. To explain further:

[0013] Current models use serial measurements or inputs from patients complaints expressed at the time of registering to symptoms and signs recorded by the clinician to investigation results after a significant period of time. This introduces noise from variables that change overtime. For example, a variable may appear later due to some other cause or the intervention of medical care like iatrogenic disease or nosocomial infection that can significantly alter the analysis wrongly. By enabling concurrent, simultaneous measurements of complaints, symptoms, signs, vitals, physiological and anthropological measurements, laboratory and other investigative tests at the same time and superimposing all will give a better and far more accurate result. It is explained below mathematically.

[0014] 1. Single Measurement Model

[0015] Assume each measurement yiy_iyi is noisy, modeled as:

[0016] yi=x+eiy_i = x + \epsilon_iyi=x+ei

[0017] where ei\epsilon_iei is independent noise with variance o2\sigmaA2o2, i.e., ei~N(0,o2)\epsilon_i \sim \mathcal{N}(0, \sigmaA2)ei~N(0,o2).ABHI001

[0018] 2. Serial (Averaged) Measurements

[0019] If we take NNN sequential measurements and compute their average: y”=lN^i=lNyi\bar{y} = \frac{l}{N} \sum_{i=l}A{N} y_iy"=Nli=l Nyi

[0020] then by the properties of variance:

[0021] Var(y")=o2N.\text{Var}(\bar{y}) = \frac{\sigmaA2} {N} .Var(y”)=No2.

[0022] Thus, increasing NNN reduces the noise variance by a factor of 1 / N1 / N1 / N, improving accuracy.

[0023] 3. Simultaneous Multiple Measurements

[0024] Now, suppose we take MMM simultaneous, independent measurements of the same quantity xxx using different sensors or methods. If these measurements are combined optimally, say, in a weighted least squares or sensor fusion approach, the resulting estimate xA\hat{x}xAhas variance:

[0025] Var(xA)=o2M\text{Var}(\hat{x}) = \frac{\sigmaA2}{M}Var(xA)=Mo2

[0026] if all measurements have the same noise level.

[0027] 4. Comparison of Simultaneous vs. Serial

[0028] • In an ideal case (no external changes in xxx), serial measurements improve accuracy as lN\frac{l}{N}Nl.

[0029] • However, if the system being measured changes over time, sequential measurements may introduce additional variance due to temporal fluctuations.

[0030] • Simultaneous measurements avoid time-dependent variations, yielding a more accurate estimate in dynamic conditions.

[0031] 5. Generalized Model Using Covariances

[0032] If the noise has correlations across time (pt\rho_tpt) or across sensors (ps\rho_sps), then: Var(y_serial)=o2N(l+(N-l)pt)\text{Var}(\bar{y}_{\text{serial}}) = \frac{\sigmaA2}{N} ( 1 +(N - 1 )\rho_t) V ar(y”serial)=N o2( 1 +(N- 1 )pt)

[0033] Var(xAsimultaneous)=o2M(l+(M-l)ps)\text{Var}(\hat{x}_{\text{simultaneous}})= \frac{\sigmaA2}{M} (1 + (M-l) \rho_s)Var(xAsimultaneous)=Mo2(l+(M-l)ps)ABHI001

[0034] If time -dependent correlations (pt\rho_tpt) are high, simultaneous measurement is preferable.

[0035] Thus, the key inference is that if measurements are independent, both methods reduce variance similarly, but simultaneous measurements are superior in non-static environments due to avoiding correlated noise overtime.

[0036] B. DATA MODELLING FOR BETTER DISEASE MANAGEMENT

[0037] It adds all known variables in the planning of management of disease which are not possible for a human mind to process accurately. There are numerous variables like options for medicines, drug interactions, contraindications, special precautions that have to be iterated each time an option is exercised but this will take time for a human decision and needs a lot of information that will delay decision making. By bringing all known variables to bear on the issue at the same time, it allows more rational and safer therapy decisions. To explain further:

[0038] 1. Say the Set of Drugs be:

[0039] Let D={dl,d2,...,dn}D = \{ d_l, d_2, ots, d_n \}D={dl,d2,...,dn} be the set of all available drugs.

[0040] 2. The Variables for Each Drug:

[0041] Each drug did idi has multiple associated properties (variables):

[0042] • Drug interactions: Ii={ii 1 ,ii2, ... }I_I = \{i_{il}, i_{i2}, Wots \}Ii={ii l,ii2, ... } • Contraindications: Ci={cil,ci2,... }C_i = \{c_{il}, c_{i2},Wots \}Ci={cil,ci2,... } • Special precautions: Pi={pil,pi2,... }P_i = \{p_{il}, p_{i2}, Wots \}Pi={pil,pi2,... }

[0043] • Dosage safety range: Si=[smin ,smax ]S_i = [s_{\min}, s_{\max}]Si=[smin,smax]

[0044] • Stop use conditions: Ti={til,ti2,... }T_i = \{t_{il}, t_{i2}, Wots \}Ti={til,ti2,... } • Monitoring parameters: Mi={mil,mi2,... }M_i = \{m_{il}, m_{i2}, Wots \}Mi={mil,mi2,...}

[0045] Thus, each drug can be represented as a multidimensional vector:ABHI001

[0046] di=(Ii,Ci,Pi,Si,Ti,Mi)d_i = (I_i,C_i,P_i,S_i,T_i, M_i)di = (Ii,Ci,Pi,Si,Ti,Mi).

[0047] 3. The Prescription Complexity Function:

[0048] A safe prescription is a function f(D,P)f(D, P)f(D,P), where PPP represents patient-specific factors (e.g., allergies, comorbidities, existing medications). This function must optimize drug choice while ensuring safety:

[0049] fDxP^Safe Treatment Planf: D Times P \to \text{Safe Treatment Plan}f:D*P— >-Safe Treatment Plan

[0050] where

[0051] f(di,P)={safeif all constraints are satisfiedunsafeotherwisef(d_i, P) = \begin{cases} \text{safe} & \text{if all constraints are satisfied} \\ \text{unsafe} & \text{otherwise} \end{cases}f(di,P)={safeunsafe if all constraints are satisfiedotherwise.

[0052] 4. Computational Complexity:

[0053] Since checking all constraints involves evaluating multiple interactions across drugs and patient-specific conditions, this problem scales exponentially. If there are nnn drugs and each has mmm constraints, the number of evaluations required is O(2m n)O(2A{m \cdot n})O(2m n), making it computationally infeasible for the human brain.

[0054] Conclusion: The function f(D,P)f(D,P)f(D,P) is non-trivial and computationally complex, making it impractical for the human mind to compute accurately in real time. This highlights the need for Al-driven decision support systems to optimize drug prescriptions safely.

[0055] C. COMPUTING WITH SUFFICIENT CONFIDENCE PROBABILITY OF DIAGNOSIS

[0056] In a given set of diagnostic challenge, there may be multiple variables like patient complaints, symptoms, signs, vitals, anthropomorphic data, physiological data, clinical investigations and while all data may not always be present but it is possible to create a mathematical model using Al to reach a probabilistic diagnosis using available data and give the probability of the diagnosis with reasonable assurance.ABHI001

[0057] 1. Define the Diagnostic Variables

[0058] Let XXX be a set of diagnostic features:

[0059] X={Xl,X2,...,Xn}X = \{X_1, X_2, Wots, X_n\}X={Xl,X2,...,Xn}

[0060] where each XiX_iXi represents a diagnostic factor:

[0061] • Patient complaints (CCC)

[0062] • Symptoms (SSS)

[0063] • Signs (PPP)

[0064] • Vital signs (VW)

[0065] • Anthropometric data (AAA)

[0066] • Physiological measurements (YYY)

[0067] • Clinical investigations (III)

[0068] Since not all data points may be available for every case, let Xobs£XX_{\text{obs}} \subseteq XXobs^X represent the observed subset for a given patient.

[0069] 2. Define the Set of Possible Diagnosis

[0070] Let DDD be the set of possible diagnoses:

[0071] D={dl,d2,...,dm}D = \{ d_l, d_2, ..., d_m \}D={dl,d2,...,dm}

[0072] Our goal is to compute the probability of each diagnosis given the observed data.

[0073] 3. Probabilistic Diagnosis Using Bayesian Inference

[0074] Using Bayes' theorem, the probability of a diagnosis djdjdj given the observed data XobsX_{\text{obs}}Xobs is:

[0075] P(dj|Xobs)=P(Xobs|dj)P(dj)P(Xobs)P(dJ | X_{\text{obs}}) = \frac{P(X_{\text{obs}} |dj) P(dJ)}{P(X_{\text{obs}})}P(dj|Xobs)=P(Xobs)P(Xobs|dj)P(dj)

[0076] where:ABHI001

[0077] • P(Xobs|dj)P(X_{\text{obs}} | dJ)P(Xobsldj) is the likelihood function, representing how likely it is to observe the given patient data if the diagnosis djdjdj were true.

[0078] • P(dj)P(dJ)P(dj) is the prior probability of diagnosis djdjdj, obtained from population statistics or historical data.

[0079] • P(Xobs)P(X_{\text{obs}})P(Xobs) is the evidence, ensuring that probabilities sum to 1 across all possible diagnoses: P(Xobs)= k=lmP(Xobs|dk)P(dk)P(X_{\text{obs}}) = \sum_{k=l}A{m} P(X_{\text{obs}} | d_k) P(d_k)P(Xobs)=k=l JmP(Xobs|dk)P(dk)

[0080] Since P(Xobs)P(X_{\text{obs}})P(Xobs) is the same for all diagnoses, we can compute a normalized probability:

[0081] P(dj|Xobs)ocP(Xobs|dj)P(dj)P(dJ | X_{\text{obs}}) \propto P(X_{\text{obs}} | dj) P(dJ)P(dj|Xobs)ocP(Xobs|dj)P(dj).

[0082] 4. AI-Based Diagnosis Model

[0083] A machine learning model can learn to approximate this probability function: f0:Xobs^P(D)f_{\theta}: X_{\text{obs}} \to P(D)f0:Xobs^P(D)

[0084] where f0f_{\theta}f0 is a function parameterized by model parameters 0\theta0, learned from a dataset.

[0085] Model choices:

[0086] 1. Naive Bayes Approximation (assumes feature independence):

[0087] P(Xobs|dj)=niGXobsP(Xi|dj)P(X_{\text{obs}} | dj) = \prod_{i\inX_{\text{obs}}} P(X i | dJ)P(Xobs|dj)=ieXobsnP(Xi|dj)

[0088] and thus:

[0089] P(dj|Xobs)ocP(dj)niGXobsP(Xi|dj)P(dJ | X_{\text{obs}}) \propto P(dJ) \prod_{i \in X_{\text{obs}}} P(X i | dJ)P(dj|Xobs)ocP(dj)iGXobsnP(Xi|dj).

[0090] 2. Neural Network Approximation:

[0091] A deep learning model (e.g., a fully connected neural network) learns a function:ABHI001

[0092] P(D|Xobs)=softmax(WXobs+b)P(D|X_{\text{obs}}) = \text{softmax}(W X_{\text{obs}} + b)P(D|Xobs)=softmax(WXobs+b)

[0093] where WWW and bbb are learned parameters.

[0094] 3. Decision Trees I Random Forests:

[0095] Decision trees can learn complex feature interactions, while ensemble models (random forests, gradient boosting) improve generalization.

[0096] 5. Interpretation of Results

[0097] For a given patient case, the model outputs a probability distribution over all possible diagnosis:

[0098] P(D|Xobs)={P(dl|Xobs),P(d2|Xobs),...,P(dm|Xobs)}P(D|X_{\text{obs}})= \{P(d_l|X_{\text{obs}}), P(d_2|X_{\text{obs}}), ..., P(d_m|X_{\text{obs}}) \} P(D |Xobs)={ P(d 1 IXobs), P(d2 IXobs), ... ,P(dm IXobs) }

[0099] A diagnosis is considered reasonable if P(dj|Xobs)P(dJ| X_{\text{obs}})P(dj IXobs) exceeds a predefined threshold r\taur (e.g., 90% confidence).

[0100] If multiple diagnosis have similar probabilities, further investigations are recommended to improve certainty.

[0101] Conclusion:

[0102] • The Bayesian framework provides a solid mathematical basis for probabilistic diagnosis.

[0103] • Al models can learn from historical data to approximate these probabilities efficiently.

[0104] • The approach allows for incomplete data handling and provides probabilistic reasoning instead of binary decisions.

[0105] D. SYSTEMIC IMPROVEMENTS IN HEALTHCARE DELIVERY PROCESS THROUGH AUTOMATION AND PROCESS REENGINEERING

[0106] Beyond improving safety, the digital transformation of healthcare through Al and data analytics is fundamentally reshaping how care is delivered. These technologies enable theABHI001

[0107] healthcare system to track, analyze, and respond to emerging trends in health conditions across various regions. By assessing where specific diseases are most prevalent or identifying areas in need of urgent healthcare attention, Al-driven analytics offer valuable insights. These insights empower both healthcare providers and policymakers to make informed decisions, allowing for better resource allocation and targeted healthcare interventions. This not only helps in addressing regional disparities but also enables businesses and governments to tailor their approaches and focus on the areas most in need of support. The present system is designed to provide a fully integrated healthcare experience, consolidating all essential services in one accessible platform. From patient registration and appointment booking to consultations, diagnostics, and medication dispensation, every step of the healthcare journey is made seamless and efficient. This integration reduces the need for patients to navigate multiple locations or systems, saving both time and effort. Additionally, the platform equips healthcare providers with Al-powered diagnostic tools that enhance their clinical decision-making process, enabling more accurate diagnosis and timely treatments. As a result, the quality of care is further elevated, benefiting both patients and healthcare professionals alike. In essence, this integrated healthcare platform is not only a tool for streamlining administrative tasks but also a comprehensive solution that supports both patients and healthcare providers in delivering optimal care. By leveraging technology and Al, it fosters a more equitable healthcare system, where every patient, regardless of their location, receives the necessary care and attention. With its ability to integrate care delivery, improve diagnostics, and provide actionable data, this platform is a significant step toward achieving a more efficient, effective, and inclusive healthcare ecosystem.

[0108] Some of the key features of the simultaneous multi-variable data acquisition and analysis equipment forming subject of the present invention include:

[0109] 1. Integrated Clinical Terminal: The proposed solution combines loT-enabled sensors, location sensors, certified laboratory devices, and diagnostic tools for realtime data acquisition.

[0110] 2. Simultaneous Multiple Measurements: The solution accurately measures multiple variables at the same time to prevent noise from time dependent variables. It creates a more exact real time snap shot of the problem being considered and so removes time related noise.ABHI001

[0111] 3. Standardized Data Mapping: It further maps patient complaints and test results to ICD-11 and SNOMED CT classifications and HL7 standards for data compatibility with external systems for consistent diagnosis. Epidemiology is mapped by standard GPS coordinates.

[0112] 4. Integrated Complete Pharmacopeia: Ensuring all data required for safe management of disease are available to the clinician including choices of pharmacological compounds, pharmacokinetics, pharmacodynamics, drug interactions, contraindications, special precautions, stop use directions, parameters to watch for, brands and options available in the real world and allowing these to be iterated at the time of prescription in real time.

[0113] 5. Secure Communication: The proposed solution enables remote consultations via rich video conferencing while ensuring data confidentiality with encryption protocols.

[0114] 6. Prescription Generation: The subject invention automates the generation of electronic prescriptions accessible through secure QR codes.

[0115] 7. Compliance with Regulations: The solution also adheres to data protection standards, including HIPAA and GDPR, thereby ensuring patient privacy.

[0116] The present invention offers significant advantages over existing solutions by integrating diagnostics, analytics, and patient management into a single real time platform, thereby reducing time, cost, and medical errors.

[0117] In one aspect, an embodiment of the present invention provides a system for simultaneous multi-variable clinical data acquisition and analysis, including:

[0118] a clinical terminal equipped with:

[0119] - a personal identification system using facial analysis through biometrics;

[0120] - loT-enabled sensors, cameras and scanners with OCR for capturing vitals, anthropomorphic measurements, and physiological parameters;

[0121] - integrated medical devices including digital stethoscope, otoscope, laryngoscope, fetal doppler, digital ECG, and point-of-care lab testing devices including but not limited to biochemistry, hematology, immunological tests for real-time clinical diagnostics;

[0122] a processing unit comprising:

[0123] - an artificial intelligence engine configured to normalize and process patient data from multiple sources;ABHI001

[0124] - modules for mapping patient complaints into standardized medical classifications, including ICD-11 and SNOMED CT;

[0125] - decision-support logic utilizing probabilistic frameworks to suggest diagnostic and therapeutic options; with real time drug safety assessment including drug interactions, adverse drug reactions, contraindications and special precautions;

[0126] - knowledge tree based and Al supported nutritional and lifestyle recommendations for personalized and precision care;

[0127] a secure communication interface enabling video consultations and remote diagnostics; a prescription-generation module for creating electronic prescriptions, accessible via secure QR codes;

[0128] a data protection mechanism employing encryption protocols and compliance with HIPAA and GDPR standards;

[0129] wherein the system reduces errors in diagnostics and enhances treatment outcomes by providing an integrated platform for real-time clinical data acquisition, analysis, and decision support.

[0130] In an embodiment, the system includes a calibration module configured to cross-validate data from multiple devices to reduce measurement errors; and compute normalized averages for improved diagnostic reliability.

[0131] In an embodiment, the prescription-generation module includes:

[0132] - complete pharmacopeia integration including pharmacokinetics, pharmacodynamics, drug interactions, contraindications, special precautions, parameters to monitor and stop use directions for real time iteration of therapy decisions at the time of prescription;

[0133] - integration with local regulatory databases to ensure compliance with drug approval and labeling requirements and actual availability of the medicine; and

[0134] - options for telemedicine-based approval by authorized healthcare providers.

[0135] In an embodiment, the Al engine is trained on:

[0136] - validated medical datasets, including population-level health trends;

[0137] - historical patient outcomes for improving prediction accuracy over time.ABHI001

[0138] In another embodiment, the system includes a compliance monitoring module configured to log all data access and modifications for auditing purposes; and ensure adherence to regional medical and data protection regulations.

[0139] In another aspect, an embodiment of the present invention provides a computer-implemented method for real-time clinical diagnostics and decision-making, including:

[0140] a. acquiring patient-specific variables, including demographic data, medical history, and vitals, through loT devices and OCR-based digitization of historical records;

[0141] b. applying artificial intelligence algorithms to:

[0142] - map patient-reported symptoms to standardized nomenclatures;

[0143] - process multi-variable inputs for probabilistic diagnostic analysis;

[0144] c. retrieving drug safety data, dosage recommendations, and contraindications from public databases via APIs;

[0145] d. generating diagnostic and therapeutic recommendations for clinician validation using a decision-support interface;

[0146] e. providing the clinician with an integrated overview of diagnostic outputs, evidentiary tests, and potential treatment options;

[0147] wherein the method improves healthcare efficiency by consolidating diagnostic, analytic, and therapeutic processes into a single workflow.

[0148] In an embodiment, the Al algorithms employ natural language processing (NLP) to convert unstructured patient inputs into structured medical data for further analysis.

[0149] In an embodiment, the diagnostic options are prioritized based on:

[0150] - patient history and clinical test results;

[0151] - localized epidemiological patterns to suggest region-specific diagnostics and treatments.

[0152] In yet another aspect, an embodiment of the present invention provides a software system for Al-driven clinical decision support, including:

[0153] modules for acquiring and digitizing clinical data from loT-enabled devices and existing medical records;

[0154] an artificial intelligence engine configured to:

[0155] - normalize multi-source data inputs using probabilistic and statistical models;ABHI001

[0156] - map patient symptoms and test results to standardized diagnostic codes;

[0157] a decision-support engine for generating treatment suggestions, incorporating:

[0158] - drug interactions, adverse effects, and patient-specific contraindications;

[0159] - epidemiological data for tailoring diagnostic outputs based on geographic and demographic factors;

[0160] a clinician interface to display Al-derived insights, confirm diagnostic decisions, and approve therapeutic plans;

[0161] a secure storage module ensuring patient confidentiality via encryption and access control protocols;

[0162] wherein the software provides technical improvements in diagnostic accuracy, data integration, and real-time decision-making.

[0163] In an embodiment, the software is further configured to:

[0164] - perform visual Al analysis of uploaded diagnostic images for augmenting clinical decisionmaking;

[0165] - integrate laboratory test results in real time to update diagnostic outputs.

[0166] In an embodiment, the decision-support engine integrates public health datasets to identify emerging disease patterns; and predictive models for resource allocation and outbreak management.

[0167] In yet another aspect, an embodiment of the present invention provides a system for multivariable data acquisition and analysis, comprising:

[0168] an integrated technology kiosk equipped with:

[0169] - loT-enabled sensors for capturing data, measurements, and other parameters;

[0170] - integrated devices including microscopes, document scanner, cameras, weighing machines, length measurement machine for real-time assessment of a sample;

[0171] a processing unit comprising:

[0172] - an artificial intelligence engine connected through cloud configured to do pattern recognition, normalization and identification of a particular state of the sample from multiple sources;

[0173] - modules for mapping sample state into standardized classifications;

[0174] - decision-support logic utilizing probabilistic frameworks to assess quality and quantity ofABHI001

[0175] the sample and remediation of the sample, if possible;

[0176] a secure communication interface enabling video conference for remote expert advice; a report-generation module for creating electronic results, accessible via secure QR codes; a data protection mechanism employing encryption protocols and compliance with cybersecurity standards;

[0177] wherein the simultaneous parallel streams of (i) measurement of data, (ii) artificial intelligence, and (iii) expert evaluation leads to more accurate analysis and reduction of errors in the assessment and remediation of the sample.

[0178] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments construed in conjunction with the appended claims that follow.

[0179] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

[0180] It is to be expressly understood that ZoyeMed terminal and associated systems, as described herein and forming subject of the present invention, are not limited to applications solely within the healthcare domain, including diagnostics, clinical analysis, or patient monitoring. While the healthcare domain exemplifies a prominent implementation, the claimed invention is inherently versatile and adaptable for deployment across a variety of industries, sectors, and technology domains.

[0181] The architecture, methodology, and functionality of the claimed system enable its application in fields, such as (but not limited to):

[0182] • Industrial Automation: For predictive maintenance, process optimization, and realtime equipment monitoring.

[0183] • Agriculture: For environmental monitoring, crop health analysis, and precision farming applications.

[0184] • Defense and Security: For personnel tracking, threat detection, and secure communication protocols.ABHI001

[0185] • Retail and Logistics: For inventory management, customer insights, and supply chain optimization.

[0186] • Education: For real-time feedback systems, student performance analytics, and virtual learning environments.

[0187] It is further highlighted that the invention’s ability to integrate advanced loT sensors, AI-driven algorithms, and data analytics modules renders it adaptable for diverse purposes where multi-variable data collection, real-time analysis, and decision-making are required. The detailed embodiments described hereinbelow are intended merely as illustrative examples, and the scope of the invention should not be limited to these exemplary use cases but construed broadly to encompass equivalent applications across varied domains of technology and industry.

[0188] BRIEF DESCRIPTION OF FIGURES

[0189] Some embodiments of the invention are herein described, by way of example only, with reference to the accompanying drawings. With specific reference now to the drawings in detail, it is stressed that the particulars shown are by way of example and for purposes of illustrative discussion of embodiments of the invention. In this regard, the description taken with the drawings makes apparent to those skilled in the art how embodiments of the invention may be practiced.

[0190] Fig. 1 represents ZoyeMed VPC Server Architecture, in accordance with an embodiment of the subject invention.

[0191] Fig. 2 represents ZoyeMed Software Workflow Architecture, in accordance with an embodiment of the subject invention.

[0192] Fig. 3 represents a flowchart depicting workflow of ZoyeMed Multi-Variable Health Terminal, in accordance with an embodiment of the subject invention.

[0193] In the accompanying drawings, an underlined number is employed to represent an item over which the underlined number is positioned or an item to which the underlined number is adjacent. A non-underlined number relates to an item identified by a line linking the non-ABHI001

[0194] underlined number to the item. When a number is non-underlined and accompanied by an associated arrow, the non-underlined number is used to identify a general item at which the arrow is pointing.

[0195] DETAILED DESCRIPTION OF FIGURES

[0196] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0197] In the figures, similar components and / or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

[0198] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machineexecutable instructions, which may be used to cause a general-purpose or special- purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and / or by human operators.

[0199] Embodiments of the present invention may be provided as a computer program product, which may include a machine -readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media / machine-readable mediumABHI001

[0200] suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).

[0201] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.

[0202] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be readily apparent to persons skilled in the art.

[0203] The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed. For purpose of clarity, details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.ABHI001

[0204] The present invention provides a multi-faceted healthcare diagnostic system, referred to as “ZoyeMed”, which integrates the following:

[0205] 1. loT Health Terminal: It captures real-time vitals using sensors and communicates data through an Al-powered cloud infrastructure.

[0206] 2. AI-Driven Diagnostics: It leverages global medical standards (e.g., ICD-11, FDA protocols) and algorithms for automated preliminary diagnosis.

[0207] 3. Clinical Integration: Facilitating patient interaction, symptoms input, and expert consultation via user-friendly interfaces.

[0208] 4. Biochemistry Test Automation: It includes dry chemistry and rapid diagnostic tests (RDTs) with real-time data transfer to enhance accuracy.

[0209] 5. Decision Support System : It further assists doctors in clinical decision-making with algorithmic and evidence -based recommendations.

[0210] “ZoyeMed” is a hardware and software based multi-variable based clinical terminal for clinical data acquisition and patient management that achieves the following in one set up:

[0211] 1. Remote or physical medical consultation, 2. Clinical examination, 3. Anthropomorphic measurements, 4. Vitals measurement, 5. Clinical tests and, 6. Finally prints the prescription. All diagnostic decisions are made by the doctor or clinician using the system.

[0212] It integrates the use of (a) loT based sensors, (b) multiple medical devices like digital ECG, digital stethoscope, otoscope, rhinoscope, laryngoscope, fetal doppler, height, weight, SpO2 digital, (c) Certified laboratory equipments like dry biochemistry analyser, hemoglobinometer, urine analyser, spirometer, rapid diagnostic kits for testing within 15 minutes during a consultation, (d) Artificial Intelligence to combine these parameters for a, (e) Rich video conference for allowing remote doctors to do consultation and examination, (f) uses authoritative public databases like USFDA to check for drug safety , labelling information, dosages, drug interactions and also uses OCR to acquire data from existing medical records to help a human specialist to make the optimal decision, and generates a prescription and adds it to the Electronic Medical Records.ABHI001

[0213] The integrated terminal of the present invention uses face biometrics, proximity sensor and liveliness check to correctly identify the patient and uses HIPAA guidelines, security protocols and encryption to ensure patient confidentiality as per HIPAA guidelines.

[0214] The terminal comes with a host of physical security features such as, a steel body, manual and electronic locking system and power, network, and scanning and printing solutions.

[0215] The invention aims to solve the problem of time and cost of delivery of primary healthcare and reduce medical errors. The fragmentation of clinic and lab and asynchronous data availability leads to medical errors and increased cost, time and creates inconvenience. The multi-variable real time and simultaneous data acquisition, converting analogue data to digital data, old medical records processing by OCR to machine readable text to glean relevant data using Al, human language inputs to discrete medical data by mapping tools using NLM to standardized classifications like ICD 11 / SNOMED CT, and processing of data using a decision tree framework, large language models based artificial intelligence systems enable a muti-variate analysis. This ensures that randomized errors in data acquisition and artifacts cancel out on multiple data acquisition from multiple devices. It is a qualitatively more scientific process than Fermi’s estimate model.

[0216] Here, instead of estimates, actual data measurement is done and there are multiple variables flowing into each question. This can be estimated like this:

[0217] Dx (Medical Diagnosis ) = 1. Personal Variables (Age, Gender at birth, Height, Weight, Family History, Past Medical History) X Weightage + Local Variables (Epidemiology based on place, travel History) X Weightage + Immediate measurement (Vitals BP, Temp, SpO2, Respiratory Rate etc ) X Weightage + Complaints - converted using NLM and mapped to Symptomatology and linked to ICD11 nomenclatures + Investigation results of two types -confirmatory and evidentiary.

[0218] Rx (Medical Treatment Plan) = Drug choices X Contraindications X Special Precautions X Adverse Drug Reactions X Drug Interactions X dosage based on weight, age and other conditions like renal impairment, liver disease, systemic diseases, etc.ABHI001

[0219] Both the above decision processes need a humungous amount of data and it is generally never available at the same time and the process is done serially. The lack of a complete EMR and real time data leads to estimation errors and clinical biases due to clinicians’ knowledge, skills and past experience and the fact that test results come much later than the interaction and need re -consultation when same are available. This integrated terminal creates a baseline at the time of consultation itself by the following three steps:

[0220] • Multiple data measurements of each variable electronically using loT devices, thereby cancelling measurement errors and normalizing to an average

[0221] • Multiple variables recorded real time for the same fundamental query and real time cross checking with personal, family medical history to prepare a comprehensive snapshot of concerns in real time, thereby cancelling out biases

[0222] • Using Point-of-Care devices to do clinical testing during the consultation

[0223] • Using decision trees, NUM and EEM and visual Al to record these simultaneously into a probabilistic output

[0224] • Clinician makes all the decision but in this case with the benefit of a comprehensive real time snapshot of multiple variables

[0225] 1. Using API’s from real time authoritative databases like USFDA which is the single largest repository of approved medicines and known adverse drug reactions, Al is able to pull out relevant and up to date choices for the clinician and can give dosage, route drug safety information in a single step to allow the clinician to make a better decision.

[0226] 2. As this data is all recorded electronically and is available through QR code from anywhere based on the patients consent and authorization, it allows life of care and personalized patient centric care.

[0227] 3. By integrating everything into one terminal, it reduces time and cost, thereby lowering cost of delivery of healthcare.ABHI001

[0228] The multi-faceted healthcare diagnostic system “ZoyeMed” is operationalized in the following steps:

[0229] Step 1: Data Acquisition:

[0230] • Patient identification is performed using face biometrics and liveliness detection.

[0231] • loT devices capture vitals and anthropomorphic measurements.

[0232] • Historical medical records are digitized using OCR and mapped to machine-readable formats.

[0233] Step 2: AI-Based Analysis:

[0234] • Al algorithms normalize and analyze multi-variable data inputs.

[0235] • Probabilistic models correlate patient complaints, test results, and historical data to generate diagnostic probabilities.

[0236] Step 3 : Decision Support:

[0237] • The system retrieves real-time drug safety and interaction data from regulatory databases.

[0238] • Clinicians receive diagnostic outputs, evidentiary test results, and treatment recommendations in an integrated view.

[0239] Step 4: Prescription Generation:

[0240] • Based on clinician approval, the system generates an electronic prescription, accessible via a secure QR code.

[0241] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:

[0242] Referring Fig. 1, it illustrates an architecture of ZoyeMed server deployed in an AWS private VPC (Virtual Private Cloud) environment, showcasing its structural and functional elements thereto.ABHI001

[0243] The architecture is deployed within a ZoyeMed AWS Account with a dedicated VPC for private networking. The Internet Gateway provides external connectivity for components requiring public internet access such as, updates or integrations. A router manages traffic between public and private subnets within the VPC.

[0244] The Public Subnet hosts externally accessible resources, including ZoyeMed API Gateway which facilitates communication between external applications and internal APIs; elastic network interfaces that enable network-level connectivity for critical workloads; and workspaces 1 and 2 that represent remote desktop environments for administrative or operational access.

[0245] The Private Subnet hosts core ZoyeMed components, such as, ZoyeMed API that is a backend service processing requests and enabling integration with external and internal components; ZoyeMed Video that handles video data related to patient diagnostics and interactions; ZoyeMed Al Module that implements advanced machine learning and artificial intelligence algorithms for health diagnostics and predictions; and ZoyeMed Database that stores patient data, medical records, and analytical results in compliance with privacy standards.

[0246] Further, the architecture integrates monitoring tools like CloudWatch Alarms and configuration management using AWS Config. SageMaker is utilized for developing, training, and deploying Al models; and Rekognition offers advanced image and video analysis for patient identification and diagnostics.

[0247] As far as data lifecycle management is concerned, patient logs and diagnostic data are archived in an S3 bucket with lifecycle policies to transition older data to Glacier for longterm storage.

[0248] The ZoyeMed architecture ensures seamless communication between public and private resources, leveraging Al-powered diagnostics, robust data security, and efficient resource allocation.

[0249] Referring Fig. 2, it illustrates a software architecture and workflow underlying ZoyeMed system, divided into React and Flutter architectures.ABHI001

[0250] The React Frontend Framework handles the web-based UI, enabling user interaction with ZoyeMed applications. The key components include a router which manages page navigation and routing; component that is modular UI elements facilitating scalability and reusability; and API Client that bridges the frontend and backend, thereby enabling data exchange.

[0251] Furthermore, the Flutter Architecture used for mobile applications ensures a consistent experience across platforms. The key components include a UI that displays information and accepts user input; Bloc that manages state and event handling; and Repository that encapsulates data access logic and integrates with local storage or remote APIs.

[0252] In addition, Al Integration is done using Python-based Al modules analyzing patient data and providing recommendations based on the diagnostic algorithms. However, the system is not limited to use any specific kind of a model and can well be equipped and suitably employed with any alternative Al model, as can be comprehended by a person skilled in the art.

[0253] Further, according to the architecture, Spring Boot APIs provide microservices for authentication, data access, and secure interactions. It includes Security Uayer that uses JWT tokens for secure communication; Business Uogic Uayer that processes diagnostic rules and treatment protocols; and Data Access Uayer that interfaces with the database for efficient query handling.

[0254] The software architecture enables secure, scalable, and user-friendly interactions across multiple platforms. Al modules enhance decision-making, while robust backend services support real-time analytics and diagnostics.

[0255] Referring Fig. 3, it illustrates a flowchart representing the workflow for patient diagnostics and treatment using ZoyeMed’ s terminal and application, the critical steps of which are as shown under:

[0256] Patient Identification: Patients are identified using facial recognition and ID numbers, ensuring accurate record matching.

[0257] Data Collection:ABHI001

[0258] - Health variables such as age, gender, location are collected.

[0259] - Vitals: Data such as height, weight, temperature, SpCh. and BPM are gathered using loT sensors integrated into the ZoyeMed terminal.

[0260] and Medical History Analysis:

[0261] - Symptoms are captured using voice / text inputs and matched against ICD or SNOMED CT databases.

[0262] - Past medical and family history is retrieved from digital records.

[0263] AI-Based Analysis Al algorithms calculate probable diagnosis, analyze patient inputs, and generate treatment recommendations.

[0264] Clinical Decision-

[0265]

[0266] - Diagnostic results are reviewed by healthcare professionals for clinical decisions.

[0267] - Final decisions include prescribed treatments, drug safety checks, and algorithmically generated dosage protocols.

[0268] The ZoyeMed terminal integrates loT, Al, and software algorithms to automate diagnostics, improving accuracy and reducing reliance on manual data interpretation. The system offers a blend of hardware-based, software -based, and expert-based decision-making for comprehensive care.

[0269] The multi-faceted healthcare diagnostic system “ZoyeMed” is industrially applicable across several healthcare scenarios, including:

[0270] 1. Primary Healthcare Centers: Provides a cost-effective solution for diagnostic services.

[0271] 2. Hospitals and Emergency Rooms: Enables rapid decision-making in critical care situations.

[0272] 3. Telemedicine and Remote Clinics: Ensures healthcare accessibility in rural and underserved areas.ABHI001

[0273] 4. Preventive Healthcare Programs: Promotes early detection and management of chronic diseases.

[0274] 5. Clinical Research: Offers standardized data collection and analysis for studies.

[0275] The following are some of the key advantages offered by the multi-variable data acquisition and analysis equipment of the type described hereinabove by addressing several gaps in the current market:

[0276] • Accuracy: The proposed solution reduces diagnostic errors through multi-variable data analysis and normalization.

[0277] Efficiency: It helps consolidate diagnostics, analytics, and consultation into a single workflow.

[0278] • Accessibility: It further enables remote consultations and real-time decision-making.

[0279] Security: It ensures patient confidentiality through robust encryption and compliance with regulations.

[0280] Although the proposed system has been elaborated as above to include all the main modules, it is completely possible that actual implementations may include only a part of the proposed modules or a combination of those or a division of those into sub-modules in various combinations across multiple devices that can be operatively coupled with each other, including in the cloud. Further the modules can be configured in any sequence to achieve objectives elaborated. Also, it can be appreciated that proposed system can be configured in a computing device or across a plurality of computing devices operatively connected with each other, wherein the computing devices can be any of a computer, a laptop, a smartphone, an Internet enabled mobile device and the like. All such modifications and embodiments are completely within the scope of the present disclosure.

[0281] As used herein, and unless the context dictates otherwise, the term “coupled to” is intended to include both direct coupling (in which two elements that are coupled to each other or in contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are usedABHI001

[0282] synonymously. Within the context of this document terms “coupled to” and “coupled with” are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C ....and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

[0283] While some embodiments of the present disclosure have been illustrated and described, those are completely exemplary in nature. The disclosure is not limited to the embodiments as elaborated herein only and it would be apparent to those skilled in the art that numerous modifications besides those already described are possible without departing from the inventive concepts herein. All such modifications, changes, variations, substitutions, and equivalents are completely within the scope of the present disclosure. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims.

Claims

ABHI001We Claim:

1. A system for simultaneous multi-variable clinical data acquisition and analysis, comprising:a. a clinical terminal equipped with:i. a personal identification system using facial analysis through biometrics;ii. loT-enabled sensors, cameras and scanners with OCR for capturing vitals, anthropomorphic measurements, and physiological parameters;iii. integrated medical devices including digital stethoscope, otoscope, laryngoscope, fetal doppler, digital ECG, and point-of-care lab testing devices for real-time clinical diagnostics;b. a processing unit comprising:i. an artificial intelligence engine configured to normalize and process patient data from multiple sources;ii. modules for mapping patient complaints into standardized medical classifications, including ICD-11 and SNOMED CT;iii. decision-support logic utilizing probabilistic frameworks to suggest diagnostic and therapeutic options; with real time drug safety assessment including drug interactions, adverse drug reactions, contraindications and special precautions; iv. knowledge tree based and Al supported nutritional and lifestyle recommendations for personalized and precision care;c. a secure communication interface enabling video consultations and remote diagnostics;d. a prescription-generation module for creating electronic prescriptions, accessible via secure QR codes;e. a data protection mechanism employing encryption protocols and compliance with HIPAA and GDPR standards;wherein the system reduces errors in diagnostics and enhances treatment outcomes by providing an integrated platform for real-time clinical data acquisition, analysis, and decision support.

2. The system of claim 1, further comprising a calibration module configured to: simultaneous cross-validation of data from multiple devices to reduce measurementABHI001errors and to remove time dependent noise ; and compute normalized averages for improved diagnostic reliability.

3. The system of claim 1, wherein the prescription-generation module includes:i. complete pharmacopeia integration including pharmacokinetics, pharmacodynamics, drug interactions, contraindications, special precautions, parameters to monitor and stop use directions for real time iteration of therapy decisions at the time of prescription;ii. integration with local regulatory databases to ensure compliance with drug approval and labeling requirements; andiii. options for telemedicine-based approval by authorized healthcare providers.

4. The system of claim 1, wherein the Al engine is trained on:i. validated medical datasets, including population-level health trends;ii. historical patient outcomes for improving prediction accuracy overtime.

5. The system of claim 1, further comprising a compliance monitoring module configured to log all data access and modifications for auditing purposes; and ensure adherence to regional medical and data protection regulations.

6. A computer-implemented method for real-time clinical diagnostics and decisionmaking, comprising:a. acquiring patient-specific variables, including demographic data, medical history, and vitals, through loT devices and OCR-based digitization of historical records; b. applying artificial intelligence algorithms to:i. map patient-reported symptoms to standardized nomenclatures;ii. process multi-variable inputs for probabilistic diagnostic analysis;c. retrieving drug safety data, dosage recommendations, and contraindications from public databases via APIs;d. generating diagnostic and therapeutic recommendations for clinician validation using a decision-support interface;e. providing the clinician with an integrated overview of diagnostic outputs, evidentiary tests, and potential treatment options;ABHI001wherein the method improves healthcare efficiency by consolidating diagnostic, analytic, and therapeutic processes into a single workflow.

7. The method of claim 6, wherein the Al algorithms employ natural language processing (NLP) to convert unstructured patient inputs into structured medical data for further analysis.

8. The method of claim 6, wherein the system prioritizes diagnostic options based on:i. patient history and clinical test results;ii. localized epidemiological patterns to suggest region-specific diagnostics and treatments.

9. A software system for Al-driven clinical decision support, comprising:a. modules for acquiring and digitizing clinical data from loT-enabled devices and existing medical records;b. an artificial intelligence engine configured to:i. normalize multi-source data inputs using probabilistic and statistical models; ii. map patient symptoms and test results to standardized diagnostic codes;c. a decision-support engine for generating treatment suggestions, incorporating: i. drug interactions, adverse effects, and patient-specific contraindications;ii. epidemiological data for tailoring diagnostic outputs based on geographic and demographic factors;d. a clinician interface to display Al-derived insights, confirm diagnostic decisions, and approve therapeutic plans;e. a secure storage module ensuring patient confidentiality via encryption and access control protocols;wherein the software provides technical improvements in diagnostic accuracy, data integration, and real-time decision-making.

10. The software of claim 9, further configured to:i. perform visual Al analysis of uploaded diagnostic images for augmenting clinical decision-making;ii. integrate laboratory test results in real time to update diagnostic outputs.ABHI00111. The software of claim 9, wherein the decision-support engine integrates public health datasets to identify emerging disease patterns; and predictive models for resource allocation and outbreak management.

12. A system for multi-variable data acquisition and analysis, comprising:a. an integrated technology kiosk equipped with:i. loT-enabled sensors for capturing data, measurements, and other parameters; ii. integrated devices including microscopes, document scanner, cameras, weighing machines, length measurement machine for real-time assessment of a sample; b. a processing unit comprising:i. an artificial intelligence engine connected through cloud configured to do pattern recognition, normalization and identification of a particular state of the sample from multiple sources;ii. modules for mapping sample state into standardized classifications;iii. decision-support logic utilizing probabilistic frameworks to assess quality and quantity of the sample and remediation of the sample, if possible;c. a secure communication interface enabling video conference for remote expert advice;d. a report-generation module for creating electronic results, accessible via secure QR codes;e. a data protection mechanism employing encryption protocols and compliance with cybersecurity standards;wherein the simultaneous parallel streams of (i) measurement of data, (ii) artificial intelligence, and (iii) expert evaluation leads to more accurate analysis and reduction of errors in the assessment and remediation of the sample.

Images