IP Library Granted Patent US 12,658,310
Granted Patent B2
US 12,658,310 · App. 18/165,767 · Granted Jun 16, 2026

Method and system for automatically tracking and managing inventory of surgical tools in operating rooms

Inventor: Jagadish Venkataraman (Menlo Park, CA)
Assignee: Auris Health, Inc.
G16H40/20G06Q10/08G16H40/40
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Quick Facts
Patent No.
US 12,658,310
App. No.
18/165,767
Granted
Jun 16, 2026
Kind
B2
Abstract

Embodiments described herein provide various examples of automatically processing surgical videos to detect surgical tools and tool-related events, and extract surgical-tool usage information. In one aspect, a process for automatically tracking usages of robotic surgery tools is disclosed. This process can begin by receiving a surgical video captured during a robotic surgery. The process then processes the surgical video to detect a surgical tool in the surgical video. Next, the process determines whether the detected surgical tool has been engaged in the robotic surgery. If so, the process further determines whether the detected surgical tool is engaged for a first time in the robotic surgery. If the detected surgical tool is engaged for the first time, the process subsequently increments a total-engagement count of the detected surgical tool. Otherwise, the process continues monitoring the detected surgical tool in the surgical video.

Claims (63)

1 . A computer-implemented method for automatically tracking usages of surgical tools, the method comprising:

operating a robotic arm using a limited-use surgical tool;

receiving a surgical video captured during a surgery;

processing the surgical video to detect the limited-use surgical tool in the surgical video;

in response to detecting the limited-use surgical tool, activating a tool-usage counting process to track a counted number of uses of the limited-use surgical tool as used by the robotic arm during a tool engagement, wherein activating the tool-usage counting process to track the counted number of uses of the limited-use surgical tool includes activating a machine-learning model trained to detect an event in the surgical video corresponding to a single use of the limited-use surgical tool;

retrieving a stored number of total uses of the limited-use surgical tool accumulated from previous engagements of the limited-use surgical tool;

determining whether a combined number of uses of the counted number of uses during the tool engagement and the stored number of total uses has reached a predetermined number of uses determined based on a maximum number of safe uses of the limited-use surgical tool or a statistical number of effective uses of the limited-use surgical tool before the limited-use surgical tool becomes unstable; and

if so, generating a tool-expiration warning to surgical staff.

2 . The computer-implemented method of claim 1 , wherein the limited-use surgical tool is a surgical stapler, wherein a single use of the surgical stapler is a single firing of the surgical stapler, and wherein the machine-learning model is trained to detect a single firing of the surgical stapler by detecting an event when a firing knob of the surgical stapler is pushed from a first position to a second position.

3 . The computer-implemented method of claim 2 , further comprising using the counted number of uses of the surgical stapler during the tool engagement to infer a length of a tissue traversed by the surgical stapler.

4 . The computer-implemented method of claim 1 , wherein the limited-use surgical tool is an electrocautery tool, wherein a single use of the electrocautery tool is a single firing of the electrocautery tool, and wherein the machine-learning model is trained to detect a single firing of the electrocautery tool by detecting each occurrence of a plume of surgical smoke.

5 . The computer-implemented method of claim 1 , wherein the surgery is a robotic surgery, and wherein the limited-use surgical tool is a robotic surgery tool comprising a tool controller and a tool memory.

6 . The computer-implemented method of claim 5 ,

wherein the stored number of total uses of the limited-use surgical tool is retrieved from the tool memory by the tool controller, and

the combined number of uses of the limited-use surgical tool is written back into the tool memory by the tool controller to replace the stored number of total uses of the limited-use surgical tool.

7 . The computer-implemented method of claim 1 , further comprising:

in response to determining that the combined number of uses has not reached the predetermined number of uses, updating the stored number of total uses with the combined number of uses, and continuing monitoring the limited-use surgical tool during the surgery.

8 . The computer-implemented method of claim 1 , further comprising, in response to determining that the combined number of uses has reached the predetermined number of uses, marking the limited-use surgical tool as expired.

9 . The computer-implemented method of claim 1 , further comprising:

prior to activating a tool-usage counting process, determining whether the limited-use surgical tool has been engaged in the surgery by determining if the limited-use surgical tool continues to present in the surgical video for at least a predetermined minimum time period.

10 . The computer-implemented method of claim 1 , further comprising using the counted number of uses of the limited-use surgical tool during the tool engagement for post-operation analysis.

11 . The computer-implemented method of claim 1 , wherein processing the surgical video includes using computer-vision to detect the limited-use surgical tool in the surgical video.

12 . The computer-implemented method of claim 1 , wherein the tool-expiration warning provides an expiration alert communicated to surgical staff.

13 . An apparatus for automatically tracking usages of surgical tools, the apparatus comprising:

one or more processors; and

a memory coupled to the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

operate a robotic arm using a limited-use surgical tool;

receive a surgical video captured during a surgery;

process the surgical video to detect the limited-use surgical tool in the surgical video;

in response to detecting the limited-use surgical tool, activate a tool-usage counting process to track a counted number of uses of the limited-use surgical tool as used by the robotic arm during a tool engagement;

retrieve a stored number of total uses of the limited-use surgical tool accumulated from previous engagements of the limited-use surgical tool; and

determine whether a combined number of uses of the counted number of uses during the tool engagement and the stored number of total uses has reached a predetermined number of uses determined based on a maximum number of safe uses of the limited-use surgical tool or a statistical number of effective uses of the limited-use surgical tool before the limited-use surgical tool becomes unstable, and if so then generate a tool-expiration warning to surgical staff,

wherein the surgery is a robotic surgery, the limited-use surgical tool is a robotic surgery tool comprising a tool controller and a tool memory, the stored number of total uses of the limited-use surgical tool is retrieved from the tool memory by the tool controller, and the combined number of uses of the limited-use surgical tool is written back into the tool memory by the tool controller to replace the stored number of total uses of the limited-use surgical tool.

14 . The apparatus of claim 13 , wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to activate the tool-usage counting process by activating a machine-learning model trained to detect an event in the surgical video corresponding to a single use of the limited-use surgical tool.

15 . The apparatus of claim 13 , wherein in response to determining that the combined number of uses has not reached the predetermined number of uses, the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

update the stored number of total uses with the combined number of uses; and

continue monitoring the limited-use surgical tool during the surgery.

16 . The apparatus of claim 13 , wherein the surgery is a robotic surgery and the limited-use surgical tool is a robotic surgery tool comprising a tool memory, and wherein the memory further stores instructions that, when executed by the one or more processors, cause the apparatus to:

retrieve the stored number of total uses of the limited-use surgical tool from the tool memory; and

write back the combined number of uses of the limited-use surgical tool into the tool memory to replace the stored number of total uses.

17 . An apparatus for automatically tracking usages of surgical tools, the apparatus comprising:

one or more processors; and

a memory coupled to the one or more processors, the memory storing instructions that, when executed by the one or more processors, cause the apparatus to:

operate a robotic arm using a limited-use surgical tool;

receive a surgical video captured during a surgery;

process the surgical video to detect the limited-use surgical tool in the surgical video;

in response to detecting the limited-use surgical tool, activate a tool-usage counting process to track a counted number of uses of the limited-use surgical tool as used by the robotic arm during a tool engagement, wherein activating the tool-usage counting process includes activating a machine-learning model trained to detect an event in the surgical video corresponding to a single use of the limited-use surgical tool;

retrieve a stored number of total uses of the limited-use surgical tool accumulated from previous engagements of the limited-use surgical tool;

determine whether a combined number of uses of the counted number of uses during the tool engagement and the stored number of total uses has reached a predetermined number of uses determined based on a maximum number of safe uses of the limited-use surgical tool or a statistical number of effective uses of the limited-use surgical tool before the limited-use surgical tool becomes unstable; and

if so, generate a tool-expiration warning to surgical staff.

18 . The apparatus of claim 17 , wherein the limited-use surgical tool is a surgical stapler, wherein a single use of the surgical stapler is a single firing of the surgical stapler, and wherein the machine-learning model is trained to detect a single firing of the surgical stapler by detecting an event when a firing knob of the surgical stapler is pushed from a first position to a second position.

19 . The apparatus of claim 17 , wherein the limited-use surgical tool is an electrocautery tool, wherein a single use of the electrocautery tool is a single firing of the electrocautery tool, and wherein the machine-learning model is trained to detect a single firing of the electrocautery tool by detecting each occurrence of a plume of surgical smoke.

20 . A computer-implemented method for automatically tracking usages of surgical tools, the method comprising:

operating a robotic arm using a limited-use surgical tool;

receiving a surgical video captured during a surgery;

processing the surgical video to detect the limited-use surgical tool in the surgical video;

in response to detecting the limited-use surgical tool, activating a tool-usage counting process to track a counted number of uses of the limited-use surgical tool as used by the robotic arm during a tool engagement;

retrieving a stored number of total uses of the limited-use surgical tool accumulated from previous engagements of the limited-use surgical tool; and

determining whether a combined number of uses of the counted number of uses during the tool engagement and the stored number of total uses has reached a predetermined number of uses determined based on a maximum number of safe uses of the limited-use surgical tool or a statistical number of effective uses of the limited-use surgical tool before the limited-use surgical tool becomes unstable, and if so then generate a tool-expiration warning to surgical staff,

wherein the surgery is a robotic surgery, the limited-use surgical tool is a robotic surgery tool comprising a tool controller and a tool memory, the stored number of total uses of the limited-use surgical tool is retrieved from the tool memory by the tool controller, and the combined number of uses of the limited-use surgical tool is written back into the tool memory by the tool controller to replace the stored number of total uses of the limited-use surgical tool.

21 . The computer-implemented method of claim 20 , wherein activating the tool-usage counting process to track the counted number of uses of the limited-use surgical tool includes activating a machine-learning model trained to detect an event in the surgical video corresponding to a single use of the limited-use surgical tool.

22 . The computer-implemented method of claim 20 , further comprising:

in response to determining that the combined number of uses has not reached the predetermined number of uses, updating the stored number of total uses with the combined number of uses, and continuing monitoring the limited-use surgical tool during the surgery.

Assignments (1)
MERGER Recorded Jan 27, 2026
From: VERB SURGICAL INC.
To: AURIS HEALTH, INC.
Reel/Frame 073601/0736 →
Continuity (3)
Continuation 16894018 · Jun 5, 2020
Continuation 16129607 · Sep 12, 2018
Related Publication 20230260637A1 · Aug 17, 2023
References Cited (82)
US 3706184A · Tucker · 1972 [cited by applicant]
US 6920347B2 · Simon · 2005 [cited by applicant]
US 7068842B2 · Liang et al. · 2006 [cited by applicant]
US 7317955B2 · McGreevy · 2008 [cited by applicant]
US 7379790B2 · Toth et al. · 2008 [cited by applicant]
US 7840042B2 · Kriveshko et al. · 2010 [cited by applicant]
US 7853305B2 · Simon et al. · 2010 [cited by applicant]
US 8086008B2 · Coste-Maniere et al. · 2011 [cited by applicant]
US 8108072B2 · Zhao et al. · 2012 [cited by applicant]
US 8131031B2 · Lloyd · 2012 [cited by applicant]
US 8147503B2 · Zhao et al. · 2012 [cited by applicant]
US 8398541B2 · DiMaio et al. · 2013 [cited by applicant]
US 8504136B1 · Sun et al. · 2013 [cited by applicant]
US 8527094B2 · Kumar et al. · 2013 [cited by applicant]
US 8600551B2 · Itkowitz et al. · 2013 [cited by applicant]
US 8706184B2 · Mohr et al. · 2014 [cited by applicant]
US 9014453B2 · Steinberg et al. · 2015 [cited by applicant]
US 9026247B2 · White et al. · 2015 [cited by applicant]
US 9171477B2 · Luo et al. · 2015 [cited by applicant]
US 9215293B2 · Miller · 2015 [cited by applicant]
US 9320428B2 · Taylor et al. · 2016 [cited by applicant]
US 9413976B2 · DiCarlo · 2016 [cited by applicant]
US 9767554B2 · Chou et al. · 2017 [cited by applicant]
US 9861446B2 · Lang · 2018 [cited by applicant]
US 10105149B2 · Haider et al. · 2018 [cited by applicant]
US 10107913B2 · Boillot et al. · 2018 [cited by applicant]
US 10126219B2 · Shabram · 2018 [cited by applicant]
US 10140508B2 · Zhang · 2018 [cited by applicant]
US 10226178B2 · Cohen et al. · 2019 [cited by applicant]
US 10250809B2 · Kuchnio et al. · 2019 [cited by applicant]
US 10383694B1 · Venkataraman et al. · 2019 [cited by applicant]
US 10522055B2 · Boulware et al. · 2019 [cited by applicant]
US 10529052B2 · Newman et al. · 2020 [cited by applicant]
US 10588699B2 · Richmond et al. · 2020 [cited by applicant]
US 10803320B2 · Calmus · 2020 [cited by applicant]
US 11081229B2 · Alvi et al. · 2021 [cited by applicant]
US 11176945B2 · Paul et al. · 2021 [cited by applicant]
US 11189379B2 · Giataganas et al. · 2021 [cited by applicant]
US 11202676B2 · Lightcap et al. · 2021 [cited by applicant]
US 11205508B2 · Venkataraman et al. · 2021 [cited by applicant]
US 20020032451A1 · Tierney et al. · 2002 [cited by applicant]
US 20030208196A1 · Stone · 2003 [cited by applicant]
US 20050251156A1 · Toth et al. · 2005 [cited by applicant]
US 20100285438A1 · Kesvadas et al. · 2010 [cited by applicant]
US 20110301447A1 · Park et al. · 2011 [cited by applicant]
US 20120253360A1 · White et al. · 2012 [cited by applicant]
US 20140005684A1 · Kim et al. · 2014 [cited by applicant]
US 20140171787A1 · Garbey et al. · 2014 [cited by applicant]
US 20140286533A1 · Luo et al. · 2014 [cited by applicant]
US 20150005622A1 · Zhao et al. · 2015 [cited by applicant]
US 20150230875A1 · Shademan et al. · 2015 [cited by applicant]
US 20160022374A1 · Haider · 2016 [cited by examiner]
US 20160140875A1 · Kumar et al. · 2016 [cited by applicant]
US 20160166345A1 · Kumar et al. · 2016 [cited by applicant]
US 20170049517A1 · Felder et al. · 2017 [cited by applicant]
US 20170105713A1 · Frimer et al. · 2017 [cited by applicant]
US 20170143366A1 · Groene et al. · 2017 [cited by applicant]
US 20180357514A1 · Zisimopoulos et al. · 2018 [cited by applicant]
US 20180360550A1 · Nakanishi · 2018 [cited by applicant]
US 20190006047A1 · Gorek · 2019 [cited by examiner]
US 20190150925A1 · Marczyk et al. · 2019 [cited by applicant]
US 20210000461A1 · Charles et al. · 2021 [cited by applicant]
US 20210290317A1 · Sen · 2021 [cited by examiner]
CN 203970548U · 2014 [cited by applicant]
CN 104582624A · 2015 [cited by applicant]
CN 106890014A · 2017 [cited by applicant]
CN 108289716A · 2018 [cited by applicant]
CN 108430339A · 2018 [cited by applicant]
CN 108472058A · 2018 [cited by applicant]
EP 2420197 · 2012 [cited by applicant]
KR 102008000162 · 2008 [cited by applicant]
KR 20140126322 · 2014 [cited by applicant]
WO WO2017075541 · 2017 [cited by applicant]
Sendhlhofer, Gerald et al. Implementation of a Surgical Safety Checklist: Interventions to Optimize the Process and Hints to Increase Compliance. PubMed Central, 2015. (Year: 2015). [cited by examiner]
International Search Report and Written Opinion for International Application No. PCT/US2018/051562 mailed Jun. 3, 2019, 11 pages. [cited by applicant]
International Preliminary Report on Patentability for International Application No. PCT/US2018/051562 mailed Mar. 25, 2021, 7 pages. [cited by applicant]
Extended European Search Report for European Application No. 18920011.6 mailed Feb. 3, 2022, 10 pages. [cited by applicant]
Lin, Henry C., et al., “Towards automatic skill evaluation: Detection and segmentation of robot-assisted surgical motions,” Computer Aided Surgery, vol. 11, No. 5, Dec. 31, 2006, pp. 220-230. [cited by applicant]
Padoy, Nicolas, “Workflow and Activity Modeling for Monitoring Surgical Procedures,” HAL archvies-ouvertes,fr, retrieved from the Internet <http://www.these.fr/2010NAN10025/document,> Apr. 14, 2010, 166 pages. [cited by applicant]
Loukas, C., “Video content analysis of surgical procedures,” Surg. Endosc. 32, 554-568 (2018). https://doi.org/10.1007/s00464-017-5878-1, Accepted: Sep. 7, 2017 / Published online: Oct. 26, 2017 (c) Springer Science + B… [cited by applicant]
Extended European Search Report for European Application No. 18933279.4 mailed May 17, 2022, 9 pages. [cited by applicant]
Office Action received for Chinese Patent Application No. 201880097496.5, mailed on Nov. 27, 2023, 28 pages (17 pages of English Translation and 11 pages of Original Document). [cited by applicant]