# US 12,178,759 B2 · Relocation module and methods for surgical equipment

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- Number: US 12,178,759 B2
- Document ID: US12178759B2
- Status: Granted
- Kind: B2
- Application number: 18/598,110
- Filed: 2024-03-07
- Published: 2024-06-27
- Granted: 2024-12-31
- Earliest claimed: 2018-03-26
- Assignees: Augustine Biomedical + Design, LLC
- Applicants: Augustine Biomedical + Design, LLC
- Inventors: Scott D. Augustine; Susan D. Augustine; Garrett J. Augustine; Brent M. Augustine; Ryan S. Augustine; Randall C. Arnold
- Web page: https://patentgrep.com/patent/US12178759B2

## CPC

- A61G 13/108: {Means providing sterile air at a surgical operation table or area (treatment rooms A61G10/00; instruments or implements for sterile operation A61B90/00; sterilisation of air A61L9/00; purification of air in air-conditioning systems F24F3/16, F24F8/00)}
- A61B 50/13: Trolleys{, e.g. carts}
- A61B 50/15: Mayo stands; Tables
- A61B 90/94: coded with symbols, e.g. text
- A61B 90/96: using barcodes
- A61B 90/98: using electromagnetic means, e.g. transponders
- A61M 5/1414: {Hanging-up devices}
- A61M 5/16845: {by weight}
- A61M 5/16895: {by monitoring weight change, e.g. of infusion container}
- A61M 16/06: Respiratory or anaesthetic masks
- A61M 16/18: Vaporising devices for anaesthetic preparations
- G06T 7/70: Determining position or orientation of objects or cameras (camera calibration G06T7/80)
- G06V 10/00: Arrangements for image or video recognition or understanding (character recognition in images or video G06V30/10)
- G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis
- G16H 10/60: for patient-specific data, e.g. for electronic patient records
- G16H 20/40: relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
- G16H 30/20: for handling medical images, e.g. DICOM, HL7 or PACS
- G16H 40/63: for local operation
- G16H 40/67: for remote operation
- G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems
- G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients
- A61B 46/10: specially adapted for instruments{, e.g. microscopes}
- A61B 90/50: Supports for surgical instruments, e.g. articulated arms
- A61B 2050/155: {Mayo stands}
- A61M 13/00: Insufflators for therapeutic or disinfectant purposes {, i.e. devices for blowing a gas, powder or vapour into the body (hand-held units in which gas flow is produced by muscular energy at the moment of use B05B11/062)}
- A61M 16/00: Devices for influencing the respiratory system of patients by gas treatment, e.g. ventilators (iron lungs A61H31/02); Tracheal tubes
- A61M 16/01: specially adapted for anaesthetising {(A61M16/104, A61M16/18 take precedence)}
- A61M 16/04: Tracheal tubes (catheters in general A61M25/00)
- A61M 2016/003: {with a flowmeter}
- A61M 2202/0208: Oxygen
- A61M 2202/0241: Anaesthetics; Analgesics
- A61M 2202/0275: Nitric oxide [NO]
- A61M 2202/0283: Nitrous oxide (N2O)
- A61M 2202/048: Anaesthetics
- A61M 2205/18: with alarm
- A61M 2205/3306: Optical measuring means
- A61M 2205/3334: Measuring or controlling the flow rate
- A61M 2205/3553: remote, e.g. between patient's home and doctor's office
- A61M 2205/3561: local, e.g. within room or hospital
- A61M 2205/3592: using telemetric means, e.g. radio or optical transmission
- A61M 2205/505: Touch-screens; Virtual keyboard or keypads; Virtual buttons; Soft keys; Mouse touches
- A61M 2205/52: with memories providing a history of measured variating parameters of apparatus or patient
- A61M 2205/6054: Magnetic identification systems
- A61M 2205/6072: Bar codes
- A61M 2205/80: voice-operated command
- A61M 2209/084: Supporting bases, stands for equipment
- A61M 2210/04: Skin
- A61M 2210/0606: Face
- A61M 2230/04: Heartbeat characteristics, e.g. ECG, blood pressure modulation
- A61M 2230/06: Heartbeat rate only
- A61M 2230/10: Electroencephalographic signals
- A61M 2230/205: partial oxygen pressure (P-O2)
- A61M 2230/30: Blood pressure (A61M2230/04 takes precedence)
- A61M 2230/42: Rate
- A61M 2230/43: Composition of exhalation
- A61M 2230/46: Resistance or compliance of the lungs
- A61M 2230/50: Temperature
- B01D 46/0093: {against fire or explosion}
- G06F 16/10: File systems; File servers
- G06F 16/48: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06T 2207/20081: Training; Learning
- G06T 2207/30004: Biomedical image processing
- G06T 2210/41: Medical
- G16H 20/17: delivered via infusion or injection
- G16H 30/40: for processing medical images, e.g. editing
- A61G: TRANSPORT, PERSONAL CONVEYANCES OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS (appliances for aiding patients or disabled persons to walk A61H3/00); OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES (embalming corpses A01N1/00)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)}
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- B01D: SEPARATION (separating solids from solids by wet methods B03B, B03D; by pneumatic jigs or tables B03B; by other dry methods B07; magnetic or electrostatic separation of solid materials from solid materials or fluids, separation by high-voltage electric fields B03C; centrifuges, vortex apparatus B04; presses per se for squeezing-out liquid from liquid-containing material B30B9/02; treatment of water C02F, e.g. softening by ion-exchange C02F1/42; {arrangements of air intake cleaners in gas turbine plants F02C7/05} ; arrangements or mounting of filters in air-conditioning, air-humidification or ventilation F24F13/28)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Module for housing electronic and electromechanical medical equipment including a portable digital camera and processing circuitry with machine vision and machine learning software for automatically documenting healthcare events and healthcare equipment operations in the electronic health record.

## Claims

### Claim 1

An automated data consolidation module including a system to receive and record data produced by electronic and electromechanical medical equipment, the automated data consolidation module comprising:
a module;
at least one handheld digital camera configured to produce digital data; and
processing circuitry in wired or wireless electrical communication with the at least one handheld digital camera to receive the digital data, wherein the digital data is automatically delivered to the processing circuitry and software, and wherein the processing circuitry and software is configured to interpret the digital data by:
performing at least one of machine learning (ML) and artificial intelligence (AI) analysis to:
identify specific visual elements of an image of the digital data by matching a subject image of the digital data to known images stored in an image library, to create matched identified specific visual elements of the image that show an operating parameter of a medical item or device performing a dose event or the specific visual elements of an image that show a measurement of a response event;
add time stamps or other indicators of time to the identified specific visual elements so that the digital data can be temporally correlated during subsequent big data or clinical decision support analysis; and
automatically save information provided by the matched identified specific visual elements of the image or the image itself to an electronic record or database;
wherein the at least one handheld digital camera, processing circuitry and software are configured to photograph, analyze, and record at least one of a quick response (QR) code, a barcode, or other identifier that is attached to the medical item or device, and wherein the QR code, barcode, or other identifier identifies at least one of a type of item or device, a manufacturer of the item or device, and a model of the item or device, narrowing the ML or AI analysis by directing the ML or AI analysis to a folder containing images of a specific type, manufacturer, or model of the item or device associated with the identifier.

### Claim 2 (depends on 1)

The automated data consolidation module of claim 1, wherein the at least one handheld digital camera includes a pistol grip that is user graspable to aim the at least one handheld digital camera at a subject item or device.

### Claim 3 (depends on 1)

The automated data consolidation module of claim 1, wherein the at least one handheld digital camera includes a laser pointer to assist in aiming the at least one handheld digital camera at a subject item or device.

### Claim 4 (depends on 1)

The automated data consolidation module of claim 1, wherein the at least one handheld digital camera includes a liquid crystal display (LCD) projector, a laser projector, or a cluster of lasers configured to project an indicator image onto the medical item or device after an image of the medical item or device is successfully acquired by the at least one handheld digital camera.

### Claim 5 (depends on 1)

The automated data consolidation module of claim 1, wherein the QR code, barcode, or other identifier is attached to the medical item or device in a prescribed location on the medical item or device relative to the specific visual elements to be identified and the prescribed location of the QR code, barcode, or other identifier becomes a target for a laser pointer to standardize a scene and to orient the at least one handheld digital camera to match similarly structured scenes and camera orientations of the known images stored in an image library.

### Claim 6 (depends on 1)

The automated data consolidation module of claim 1, wherein the QR code, barcode, or other identifier identifies an area or areas of interest within a scene so that the ML or AI analysis can focus on the area or areas of interest on the medical item or device that show desired information.

### Claim 7 (depends on 1)

The automated data consolidation module of claim 1, wherein the at least one handheld digital camera, processing circuitry, and software includes machine vision capabilities for one or more of:
pill identification, pill counting, or observation of and verification of a patient taking pills.

### Claim 8

An automated data consolidation module including a handheld digital camera comprising:
a module;
at least one handheld digital camera; and
processing circuitry in wired or wireless electrical communication with the at least one handheld digital camera to receive and record digital data produced by the at least one handheld digital camera, wherein the at least one handheld digital camera and the processing circuitry and software are configured to photograph, analyze, and record at least one of a QR code, a barcode or other identifier that is attached to a medical item or device, the QR code, barcode, or other identifier identifying at least one of a type of the medical item or device, a manufacturer of the medical item or device, and a model of the medical item or device;
wherein the digital data from the at least one handheld digital camera includes an image of the medical item or device that is automatically delivered to the processing circuitry and software, and wherein the processing circuitry and software are configured to perform at least one of machine learning (ML) and artificial intelligence (AI) analysis to:
identify specific visual elements of the image by matching the image to known images stored in an image library to form matched specific visual elements of the image that show an operating parameter of the medical item or device performing a dose event or the specific visual elements of an image that show a measurement of a response event; and
automatically save information provided by the matched specific visual elements of the image or the image itself to an electronic record or database; and
wherein the QR code, barcode, or other identifier that is attached to the medical item or device narrows the ML and AI image analysis by directing a search of the analysis to a folder containing images of the type, manufacturer, or model of the medical item or device.

### Claim 9 (depends on 8)

The automated data consolidation module of claim 8, wherein the at least one handheld digital camera includes a pistol grip that is user graspable to aim the at least one handheld digital camera at a subject item or device.

### Claim 10 (depends on 8)

The automated data consolidation module of claim 8, wherein the at least one handheld digital camera includes a laser pointer to assist in aiming the at least one handheld digital camera at a subject item or device.

### Claim 11 (depends on 8)

The automated data consolidation module of claim 8, wherein the at least one handheld digital camera includes a liquid crystal display (LCD) projector, a laser projector, or a cluster of lasers configured to project an acquisition image onto an item or device after an image of the item or device is successfully acquired by the at least one handheld digital camera.

### Claim 12 (depends on 8)

The automated data consolidation module of claim 8, wherein the QR code, the barcode, or the other identifier is connected to the medical item or device in a prescribed location on the medical item or device relative to the specific visual elements to be identified and wherein the prescribed location of the QR code, the barcode, or the other identifier becomes a target for a laser pointer so that a scene is standardized and the at least one handheld digital camera is oriented to match similarly structured scenes and camera orientations of the known images stored in the image library.

### Claim 13 (depends on 8)

The automated data consolidation module of claim 8, wherein the QR code, the barcode, or the other identifier is configured to identifies an area or areas of interest within a scene so that the ML or AI analysis can focus on the area or areas of interest on the medical item or device that show desired information.

### Claim 14 (depends on 8)

The automated data consolidation module of claim 8, wherein the identified specific visual elements of the image constitute dose events or response events and wherein the processing circuitry and software are configured to add time stamps or other indicators of time to the image so that data can be temporally correlated during subsequent big data or clinical decision support analysis.

### Claim 15 (depends on 8)

The automated data consolidation module of claim 8, wherein the at least one handheld digital camera, processing circuitry and software include machine vision capabilities for one or more of:
pill identification, pill counting, or observation of and verification of a patient taking pills.

### Claim 16

An automated data consolidation module including a system to receive and record data produced by electronic and electromechanical medical equipment, the automated data consolidation module comprising:
a module;
at least one handheld digital camera configured to produce digital data, the at least one handheld digital camera including a laser pointer to assist in aiming the at least one handheld digital camera at a subject item or device; and
processing circuitry in wired or wireless electrical communication with the at least one handheld digital camera to receive the digital data, wherein the digital data is automatically delivered to the processing circuitry and software, and wherein the processing circuitry and software is configured to interpret the digital data by:
performing at least one of machine learning (ML) and artificial intelligence (AI) analysis to:
identify specific visual elements of an image of the digital data by matching a subject image of the digital data to known images stored in an image library, to create matched identified specific visual elements of the image that show an operating parameter of a medical item or device performing a dose event or the specific visual elements of an image that show a measurement of a response event;
add time stamps or other indicators of time to the identified specific visual elements so that the digital data can be temporally correlated during subsequent big data or clinical decision support analysis; and
automatically save information provided by the matched identified specific visual elements of the image or the image itself to an electronic record or database;
wherein the at least one handheld digital camera, processing circuitry and software are configured to photograph, analyze, and record at least one of a quick response (QR) code, a barcode, or other identifier that is attached to a medical item or device, and wherein the QR code, barcode, or other identifier identifies at least one of a type of item or device, a manufacturer of the item or device, and a model of the item or device, narrowing the ML or AI analysis by directing the ML or AI analysis to a folder containing images of a specific type, manufacturer, or model of the item or device associated with the identifier.

### Claim 17 (depends on 16)

A The automated data consolidation module of claim 16, wherein the identified specific visual elements of the image constitute dose events or response events and the processing circuitry is configured to add time stamps or other indicators of time so that unrelated data can be temporally correlated during subsequent “big data” analysis.

### Claim 18 (depends on 16)

A The automated data consolidation module of claim 16, wherein the processing circuitry includes artificial intelligence or machine learning algorithms that compare the response event to the dose event and provide immediate feedback via a display or alert device.

### Claim 19 (depends on 16)

A The automated data consolidation module of claim 16, wherein the electronic record or database is configured to be accessed by artificial intelligence or machine learning algorithms to retrieve one or more temporally correlated dose-response events and compare the one or more temporally correlated dose-response events to other temporally correlated dose-response events across a population of patients and output big data correlation insights to at least one storage device or display.

### Claim 20 (depends on 16)

A The automated data consolidation module of claim 16, wherein the processing circuitry and software is configured to temporally correlate the dose events and response events and artificial intelligence (AI) analytics is configured to identify expected and unexpected physiologic responses to a given dose event.

### Claim 21

An automated data consolidation module including a handheld digital camera comprising:
a module;
at least one handheld digital camera; and
processing circuitry in wired or wireless electrical communication with the at least one handheld digital camera to receive and record digital data produced by the at least one handheld digital camera, wherein the at least one handheld digital camera and the processing circuitry and software are configured to photograph, analyze, and record at least one of a QR code, a barcode or other identifier that is attached to a medical item or device, the QR code, barcode, or other identifier identifying at least one of a type of the medical item or device, a manufacturer of the medical item or device, and a model of the medical item or device;
wherein the digital data from the at least one handheld digital camera includes an image of the medical item or device that is automatically delivered to the processing circuitry and software, and wherein the processing circuitry and software are configured to perform at least one of machine learning (ML) and artificial intelligence (AI) analysis to:
identify specific visual elements of the image by matching the image to known images stored in an image library to form matched specific visual elements of the image that show an operating parameter of the medical item or device performing a dose event or the specific visual elements of an image that show a measurement of a response event; and
automatically save information provided by the matched specific visual elements of the image or the image itself to an electronic record or database; and
wherein the QR code, barcode, or other identifier that is attached to the medical item or device narrows the ML and AI image analysis by directing a search of the analysis to a folder containing images of the type, manufacturer, or model of the medical item or device; and
wherein the QR code, barcode, or other identifier is attached to the medical item or device in a prescribed location on the medical item or device relative to the specific visual elements to be identified and the prescribed location of the QR code, barcode, or other identifier becomes a target for a laser pointer to standardize a scene and to orient the at least one handheld digital camera to match similarly structured scenes and camera orientations of the known images stored in an image library.

### Claim 22 (depends on 21)

The automated data consolidation module of claim 21, wherein the identified specific visual elements of the image constitute dose events or response events and the processing circuitry is configured to add time stamps or other indicators of time so that unrelated data can be temporally correlated during subsequent “big data” analysis.

### Claim 23 (depends on 21)

The automated data consolidation module of claim 21, wherein the processing circuitry includes artificial intelligence or machine learning algorithms that compare the response event to the dose event and provide immediate feedback via a display or alert device.

### Claim 24 (depends on 21)

The automated data consolidation module of claim 21, wherein the electronic record or database is configured to be accessed by artificial intelligence or machine learning algorithms to retrieve one or more temporally correlated dose-response event and compare the one or more temporally correlated dose-response event to other temporally correlated dose-response events across a population of patients and output big data correlation insights to at least one storage device or display.

### Claim 25 (depends on 21)

The automated data consolidation module of claim 21, wherein the processing circuitry and software is configured to temporally correlate the dose events and response events and artificial intelligence (AI) analytics is configured to identify expected and unexpected physiologic responses to a given dose event.