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Application
2380.2.01
US-20150012794-A1
US-20150205664-A1
US-20100023800-A1
US-8737141-A1
US-10157004-B2
US10007433A1
US-9159419-B2
US-10114589-A1
US-10134728-A1
US-20200065270-A1
US-10637533-B2
US-9927986-A1
US-8380915-A1
US-9159419-A1
US-9208071-A1
US-20200098728-A1
US-10643676-A1
US-10468073-B2
US-10283200-A1
US-10461965-B1
US-20130279232-A1
US-8892980-B2
US9632727A1
US10558561A1
US20100023800A1
US7230213A1
OPT-9
FLO-2
FLO-5PROV
ONSO3175(B) - Onsemi378
ONSO3305US - Onsemi346
GTS-3DES
FLO-4
US8762658B2
US8533406B2
US9632727B2
KMN-1PROV
PAT-2
PER-8 PROV
PER-9 PROV
INS-4PROV
HAR-1
CES-16
NXT-5PROV NXT-5, 6, 7, 8
IPP-0051-US14 cross roads
FLO-7PROV
IMI-5PROV
IPP-0050-US35 nextremity
VIL-12
OPT-13
TOY-1
US10998041B1
FSP1845
US6559866B2
Placeholder App
PER-10
KBR-1 1400.2.623
PER-13PROV
PAT-3
US20030023453
RMS-1DES
SMG-1DES
FLO-5
US10318495
US10133662B2
PER-11
US20140066758
VIL-17
PER-17
JBR-1
PER-12
US11056880
US11302645
US20210407565
US11081191
PON-1PROV, 2PROV, 3PROV
PER-33
RMT-1PROV
PER-32
PER-34
MCC-1
FLO-10
PER-14
PER-19
PER-22
PER-18
PER-24
TMC-PAT-1
DAR-2
PER-23
TMC-PAT-4
PER-16
PER-4 DIV1
PER-20
PER-21
BRT-PAT-1
TMC-PAT-5
TMC-PAT-6PROV
BRT-PAT-2-PROV
71212.157.USU1
FPR-PAT-1-PROV
71212.158.USP1
RMT-1
DAR-1PROV
DAR-2PROV
PON-1PROV
PON-2PROV
PON-3PROV
PER-18PROV
TMC-1PROV
TMC-2PROV
PER-13PCT
PER-13
PER-16PROV
PER-14PROV
PER-34PROV
TMC-4PROV
TMC-3
PAS-1PROV
VEH-1
PER-29DES
TEST.001
E2E-TEST.001
TEST-001
TEST-002
TEST-003
TEST-004
ZED006
FSP1011
GAV-PAT-1-PROV
Matter Number
Paragraph Number
176
Application Number
Reference Case 1
Reference Case 2
Content
The selection module 1830 may be configured to automatically select a template cutting guide model 1832 and/or provide an automatic template cutting guide model 1832 recommendation that can be changed by a user such as a surgeon. For example, in one embodiment, the selection module 1830 includes an artificial intelligence or machine learning module. The artificial intelligence or machine learning module is configured to implement one or more of a variety of artificial intelligence modules that may be trained for selecting a template cutting guide model 1832 based on anatomic data 1812 and/or other input parameters. In one embodiment, the artificial intelligence or machine learning module may be trained using a large data set of anatomic data 1812 for suitable template cutting guide models 1832 identified and labeled in the dataset by professionals for use to treat a particular deformity 1826. The artificial intelligence or machine learning module may implement, or use, a neural network configured according to the training such that as the artificial intelligence or machine learning module is able to select a suitable template cutting guide model 1832. The template cutting guide model 1832 selected by the selection module 1830 is referred to as a selected template cutting guide model 1834.
Notes
Added by DJM 7 2021
Raw Data
<w:p w14:paraId="39E2343C" w14:textId="193F6284" w:rsidR="00A91D2B" w:rsidRPr="006135A5" w:rsidRDefault="00A91D2B" w:rsidP="003D02A3"><w:pPr><w:pStyle w:val="NumberedParagraph"/><w:tabs><w:tab w:val="clear" w:pos="360"/><w:tab w:val="num" w:pos="1080"/></w:tabs><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr></w:pPr><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>The selection module 1830 may be configured to automatic</w:t></w:r><w:r w:rsidR="005723FB" w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">ally select a </w:t></w:r><w:r w:rsidR="00B65D43" w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>template cutting guide model 1832 and/or provide an automatic template cutting guide model 1832 recommendation that can be changed by a user such as a surgeon</w:t></w:r><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">. For example, in one embodiment, the </w:t></w:r><w:r w:rsidR="007A2A9C" w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">selection module 1830 </w:t></w:r><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">includes an artificial intelligence or machine learning module. The artificial intelligence or machine learning module is configured to implement one or more of a variety of artificial intelligence modules that may be trained for </w:t></w:r><w:r w:rsidR="006135A5" w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">selecting a template cutting guide model 1832 </w:t></w:r><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>based on anatomic data 1812</w:t></w:r><w:r w:rsidR="006135A5" w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve"> and/or other input parameters</w:t></w:r><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">. In one embodiment, the artificial intelligence or machine learning module may be trained using a large data set of anatomic data 1812 for </w:t></w:r><w:r w:rsidR="00D07B86"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">suitable template cutting guide models 1832 </w:t></w:r><w:r w:rsidR="00866D2E"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">identified </w:t></w:r><w:r w:rsidR="00801E9C" w:rsidRPr="00801E9C"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">and labeled in the dataset </w:t></w:r><w:r w:rsidR="00866D2E"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>by professionals for use to treat a particular deformity 1826</w:t></w:r><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve">. The artificial intelligence or machine learning module may implement, or use, a neural network configured according to the training such that as the artificial intelligence or machine learning module is able to </w:t></w:r><w:r w:rsidR="00B0423E"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>select a suitable template cutting guide model 1832</w:t></w:r><w:r w:rsidRPr="006135A5"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>.</w:t></w:r><w:r w:rsidR="00D251DC"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t xml:space="preserve"> The template cutting guide model 1832 selected by the selection module 1830 is referred to as a selected template cutting guide model </w:t></w:r><w:r w:rsidR="00B15675"><w:rPr><w:rFonts w:asciiTheme="minorHAnsi" w:hAnsiTheme="minorHAnsi"/></w:rPr><w:t>1834.</w:t></w:r></w:p>
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