DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “ultrasound unit” in claims 22 and 29.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
Para 0033 teaches ultrasound unit, is an ultrasound system.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 18-37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-13 of U.S. Patent No. 11,791,049. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim diagnosing conditions predictive of intravascular device failure.
U. S. Application No. 19/044,764
U.S. Patent No. 11,791,049
18. (New) A method for diagnosing conditions predictive of intravascular device failure, comprising: capturing data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device; processing the captured data using an artificial intelligence model; and generating an output indicative of impending intravascular device failure based on the processed captured data.
19. (New) The method of claim 18, wherein the data characterizing the area of the subject's skin comprises at least one of an image or measurement of the area surrounding the insertion site of the intravascular device.
20. (New) The method of claim 18, wherein the data characterizing the area of the subject’s skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.
21. (New) The method of claim 18, wherein the intravascular device comprises a peripheral intravenous catheter, an arterial catheter, a peripherally inserted central catheter (PICC), a midline catheter, an extended dwell catheter, a central venous catheter (CVC), a hemodialysis catheter, an ECMO cannulation, a Reboa catheter, or an intra-aortic balloon pump.
28. (New) A system for diagnosing conditions predictive of intravascular device failure, comprising: an imaging device configured to capture data characterizing an area of a subject’s skin surrounding an insertion site of an intravascular device; and a computing device communicatively coupled to the imaging device, the computing device comprising a processor, a memory, and a machine learning-based computer program, wherein the machine learning-based computer program includes instructions to:process the data captured from the imaging device using an artificial intelligence model; andgenerate an output that provides an indication of impending device failure based on the processed data.
29. (New) The system of claim 28, wherein the imaging device is an ultrasound unit configured to apply ultrasonic energy.
1. A method for diagnosing conditions predictive of intravascular device failure, comprising: applying ultrasonic energy from an ultrasound unit to an area of a subject’s skin over an insertion site of an intravascular device; collecting and storing data characterizing the area underneath the subject’s skin, the data including at least one of an image or measurement taken by the ultrasound unit of the area underneath the subject’s skin surrounding the insertion site of the intravascular device; applying a trained machine learning computer implemented method to process the data collected and stored, wherein the trained machine learning computer-implemented method is configured to receive and develop knowledge of ultrasound training data, the ultrasound training data comprising at least one of images or measurements from a plurality of test subjects of an area underneath the subjects’ skin surrounding an insertion site of an intravascular device, and an indication, the indication comprising at least one of intravascular device failure or intravascular device success paired with the images or measurements received from the test subjects; and providing an indication to a user of one or more conditions underneath the subject’s skin that predict intravascular device failure.
2. The method of claim 1, wherein the data collected by the ultrasound unit comprises at least one or a combination of: images of the intravascular device; images of a distance from the intravascular device to a wall of vasculature in which it is inserted; measurements of a distance between the intravascular device and the wall of vasculature in which it is inserted; measurements of a ratio of intravascular device diameter to vascular diameter; measurements of a length of intravascular device that resides within the vasculature; images of an area inside the vasculature in which the intravascular device is inserted; images of an area surrounding the vasculature in which the intravascular device is inserted; images or measurements of an angle of insertion of the intravascular device; images or measurements of an angle of a distal tip of the intravascular device against the wall of vasculature; images or measurements of a thickness of the wall of vasculature; images or measurements of a distance of the distal tip to the wall; images or measurements of a degree of catheter kinking; images or measurements of thrombus formation; and images or measurements of subcutaneous edema formation.
3. The method of claim 1, wherein the intravascular device comprises at least one of: peripheral intravenous catheter, arterial catheter, peripherally inserted central catheter (PICC), midline catheter, extended dwell catheter, central venous catheter (CVC), hemodialysis catheter, ECMO cannulation, Reboa catheter, or intra-aortic balloon pump.
7. A method for diagnosing conditions predictive of intravascular device failure, comprising: applying ultrasonic energy from an ultrasound to an area of a subject's skin over an insertion site of an intravascular device; collecting and storing data characterizing the area underneath the subject's skin, the data including at least one of an image or measurement taken by the ultrasound of the area underneath the subject's skin surrounding the insertion site of the intravascular device; applying a trained machine learning computer-implemented method to process the data, wherein the trained machine learning computer-implemented method is configured to receive ultrasound training data, the ultrasound training data comprising at least one of images or measurements from a plurality of test subjects of an area underneath the subjects' skin surrounding an insertion site of an intravascular device; identifying the presence of one or more conditions underneath the subject's skin that predict intravascular device failure with the trained machine learning computer-implemented method; and providing an indication to a user of an impending intravascular device failure.
Claims 18-37 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-17 of U.S. Patent No. 12,217,870. Although the claims at issue are not identical, they are not patentably distinct from each other because both application and patent claim diagnosing conditions predictive of intravascular device failure.
U. S. Application No. 19/044,764
U.S. Patent No. 12,217,870
18. (New) A method for diagnosing conditions predictive of intravascular device failure, comprising: capturing data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device; processing the captured data using an artificial intelligence model; and generating an output indicative of impending intravascular device failure based on the processed captured data.
19. (New) The method of claim 18, wherein the data characterizing the area of the subject's skin comprises at least one of an image or measurement of the area surrounding the insertion site of the intravascular device.
20. (New) The method of claim 18, wherein the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.
21. (New) The method of claim 18, wherein the intravascular device comprises a peripheral intravenous catheter, an arterial catheter, a peripherally inserted central catheter (PICC), a midline catheter, an extended dwell catheter, a central venous catheter (CVC), a hemodialysis catheter, an ECMO cannulation, a Reboa catheter, or an intra-aortic balloon pump.
22. (New) The method of claim 18, wherein the data is captured by applying ultrasonic energy from an ultrasound unit to the area of the subject's skin.
26. (New) The method of claim 24, wherein the training data are received from a plurality of test subjects that experience intravascular device failure and from a plurality of test subjects that experience successful intravascular device operation.
27. (New) The method of claim 23, wherein the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers.
28. (New) A system for diagnosing conditions predictive of intravascular device failure, comprising: an imaging device configured to capture data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device; and a computing device communicatively coupled to the imaging device, the computing device comprising a processor, a memory, and a machine learning-based computer program, wherein the machine learning-based computer program includes instructions to: process the data captured from the imaging device using an artificial intelligence model; and generate an output that provides an indication of impending device failure based on the processed data.
29. (New) The system of claim 28, wherein the imaging device is an ultrasound unit configured to apply ultrasonic energy.
30. (New) The system of claim 28, wherein the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.
31. (New) The system of claim 28, further comprising a display device configured to display the output to a user.
32. (New) The system of claim 28, wherein the artificial intelligence model is a trained machine learning computer-implemented method.
33. (New) The system of claim 32, wherein the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers.
34. (New) The system of claim 32, wherein the trained machine learning computer-implemented method is configured to receive and to develop knowledge of ultrasound training data.
35. (New) The system of claim 34, wherein the ultrasound training data comprises at least one of images or measurements of the area underneath the subject's skin surrounding the insertion site of the intravascular device and an indication comprising at least one of intravascular device failure or intravascular device success paired with images or measurements received from test subjects.
36. (New) The system of claim 34, wherein the ultrasound training data are received from a plurality of subjects that experience intravascular device failure and from a plurality of subjects that experience successful intravascular device operation.
37. (New) The system of claim 34, wherein the knowledge developed by the trained machine learning computer-implemented method comprises at least one of information permitting classification of types of alterations underneath the subject's skin that lead to intravascular device failure, information permitting classification of optimal placement of the intravascular device underneath the subject's skin, or information permitting classification of an optimal rotation or angle of the intravascular device underneath the subject's skin.
1. A method for diagnosing conditions predictive of intravascular device failure, comprising: acquiring data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device; applying a trained machine learning computer implemented method to process the acquired data, wherein the trained machine learning computer implemental method is configured to develop knowledge of training data, the training data comprising at least one of an image or measurement from a plurality of test subjects of an area underneath the test subjects' skin surrounding an insertion site of an intravascular device and an indication comprising intravascular device failure or intravascular device success paired with the image or measurement received from the test subjects; and providing an indication to a user whether the acquired data indicates impending intravascular device failure.
2. The method of claim 1, wherein the data characterizing the area of the subject's skin comprises at least one of an image or measurement of the area surrounding the insertion site of the intravascular device.
3. The method of claim 2, wherein the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.
4. The method of claim 1, wherein the intravascular device comprises a peripheral intravenous catheter, an arterial catheter, a peripherally inserted central catheter (PICC), a midline catheter, an extended dwell catheter, a central venous catheter (CVC), a hemodialysis catheter, an ECMO cannulation, a Reboa catheter, or an intra-aortic balloon pump.
5. The method of claim 1, wherein the data is acquired by applying ultrasonic energy from an ultrasound unit to the area of the subject's skin.
6. The method of claim 1, wherein the training data are received from a plurality of test subjects that experience intravascular device failure and from a plurality of test subjects that experience successful intravascular device operation.
7. The method of claim 1, wherein the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers.
8. A system for diagnosing conditions predictive of intravascular device failure, comprising: an imaging device configured to capture data characterizing an area of a subject's skin surrounding an insertion site of an intravascular device; a computing device communicatively coupled to the imaging device, the computing device comprising a processor, a memory, and a computer program stored in the memory, the computer program including instructions configured to, when executed by the processor, apply artificial intelligence to process at least one image or measurement taken by the imaging device; and provide an indication to a user of the system whether the at least one image or measurement indicates impending intravascular device failure.
9. The system of claim 8, wherein the imaging device is an ultrasound unit configured to apply ultrasonic energy.
10. The system of claim 8, wherein the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation.
11. The system of claim 8, further comprising a display device configured to display the indication to the user.
12. The method of claim 8, wherein the artificial intelligence is a trained machine learning computer implemented method.
13. The method of claim 12, wherein the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers.
14. The method of claim 12, wherein the trained machine learning computer-implemented method is configured to receive and to develop knowledge of ultrasound training data.
15. The method of claim 14, wherein the ultrasound training data comprises at least one of images or measurements of the area underneath the subject's skin surrounding the insertion site of the intravascular device and an indication comprising at least one of intravascular device failure or intravascular device success paired with images or measurements received from test subjects.
16. The method of claim 14, wherein the ultrasound training data are received from a plurality of subjects that experience intravascular device failure and from a plurality of subjects that experience successful intravascular device operation.
17. The method of claim 14, wherein the knowledge developed by the trained machine learning computer-implemented method comprises at least one of information permitting classification of types of alterations underneath the subject's skin that lead to intravascular device failure, information permitting classification of optimal placement of the intravascular device underneath the subject's skin, or information permitting classification of an optimal rotation or angle of the intravascular device underneath the subject's skin.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 18-37 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL titled "Development of an algorithm using ultrasonography- assisted peripheral intravenous catheter placement for reducing catheter failure" by Kanno et al. (from IDS 17/980067) in view of U.S. Publication No. 2021/0369246 to Baba.
Regarding Claims 18 and 28 Kanno teaches a method for diagnosing conditions predictive of intravascular device failure, comprising: applying ultrasonic energy from an ultrasound unit to an area of a subject's skin over an insertion site of an intravascular device (abstract; pages 028-033 applying ultrasound energy) collecting and storing data characterizing the area underneath the subject's skin, the data including at least one of images or measurements taken by the ultrasound unit of the area underneath the subject's skin surrounding the insertion site of the intravascular device (abstract; figs.1 and 2; pages 028-033) applying artificial intelligence to process the data collected and stored; and providing an indication to a user of one or more conditions underneath the subject's skin that predict intravascular device failure (abstract; figs.1 and 2; pages 028-033 teaches catheter failure detection using ultrasound and machine learning).
Kanno teaches all of the above claimed limitations but does not expressly teach artificial intelligence for analyzing the ultrasonic images.
Baba teaches artificial intelligence for analyzing the ultrasonic images (para 065).
It would be obvious to one of ordinary skill in the art at the time of filing to modify Kanno with a setup to analyze images with artificial images as taught by Baba since such a setup would result in faster processing of ultrasonic images.
Regarding Claims 19, 29, and 31, Kanno teaches that the data characterizing the area of the subject's skin comprises at least one of an image or measurement of the area surrounding the insertion site of the intravascular device the data characterizing the area of the subject's skin comprises at least one of an image or measurement of the area surrounding the insertion site of the intravascular device (abstract; figs.1 and 2; pages 028-033 teaches catheter failure detection by characterizing the skin).
Regarding Claims 20, 28, 30, and 37, Kanno teaches that the data characterizing the area of the subject's skin comprises at least one or a combination of: an image of the intravascular device; an image of a distance from the intravascular device to a wall of vasculature in which it is inserted; a measurement of a distance between the intravascular device and the wall of vasculature in which it is inserted; a measurement of a ratio of intravascular device diameter to vascular diameter; a measurement of a length of the intravascular device that resides within the vasculature; an image of an area inside the vasculature in which the intravascular device is inserted; an image of an area surrounding the vasculature in which the intravascular device is inserted; an image or measurement of an angle of insertion of the intravascular device; an image or measurement of an angle of a distal tip of the intravascular device against the wall of vasculature; an image or measurement of a thickness of the wall of vasculature; an image or measurement of a distance of the distal tip to the wall; an image or measurement of a degree of catheter kinking; an image or measurement of thrombus formation; or an image or measurement of subcutaneous edema formation (page 2 and 3 teaches image of the blood vessel insertion area).
Regarding claim 21, Kanno teaches that the intravascular device comprises a peripheral intravenous catheter, an arterial catheter, a peripherally inserted central catheter (PICC), a midline catheter, an extended dwell catheter, a central venous catheter (CVC), a hemodialysis catheter, an ECMO cannulation, a Reboa catheter, or an intra-aortic balloon pump (page 1 and 2 teaches that the device is a catheter).
Regarding claim 22, Kanno teaches that the data is captured by applying ultrasonic energy from an ultrasound unit to the area of the subject's skin (abstract; figs.1 and 2; pages 028-033 teaches applying ultrasound to the skin).
Regarding Claims 23 and 32 Baba teaches that the artificial intelligence is a trained machine learning computer implemented method (para 0065 teaches AI trained machine learning).
Regarding Claims 24-26 and 34-36 Baba teaches that the trained machine learning computer- implemented method is configured to receive and to develop knowledge of ultrasound training data (para 0065).
Regarding Claims 27 and 33, Baba teaches that the trained machine learning computer-implemented method comprises at least one of a deep learning network or a convolutional neural network that includes a plurality of convolutional layers (para 0071 teaches a plurality of convolution layers).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SANJAY CATTUNGAL whose telephone number is (571)272-1306. The examiner can normally be reached M-F 9-5 EST.
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/SANJAY CATTUNGAL/Primary Examiner, Art Unit 3798
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