DETAILED ACTION
Summary
Claims 1 and 3-10 are pending in the application. Claims 1 and 3-10 are rejected under 35 USC 112(b). Claims 1 and 3-8 are rejected under 35 USC 101. Claims 1 and 3-10 are rejected under 35 USC 103.
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 Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, and 3-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “a risky action” in line 5. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 1 recites “a surgery image” in line 6. It is not clear if this is referring to the surgery image previously set forth, or if this is referring to a different surgery image. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 1 recites “a surgery image” in lines 10-11. It is not clear if this is referring to the surgery image previously set forth, or if this is referring to other surgery images. Clarification is required. For the purposes of examination, the latter definition will be used.
Claim 1 recites “a risky action” in line 11. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 1 recites “a risky action” in line 16. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 5 recites “a human body structure” in line 4. It is not clear if this referring to the human body previously set forth, or if this is setting forth a new human body. Clarification is required. For the purposes of examination, the former definition will be used.
The term “not clearly shown” in claim 5 is a relative term which renders the claim indefinite. The term “not clearly shown” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. For the purposes of examination, “not clearly shown” will be interpreted as a portion of the feature being obscured.
Claim 9 recites “a risky action” in line 5. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 9 recites “a surgery image” in line 6. It is not clear if this is referring to the surgery image previously set forth, or if this is referring to a different surgery image. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 9 recites “a surgery image” in lines 11-12. It is not clear if this is referring to the surgery image previously set forth, or if this is referring to other surgery images. Clarification is required. For the purposes of examination, the latter definition will be used.
Claim 9 recites “a risky action” in line 12. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 9 recites “a risky action” in line 17. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 10 recites “a risky action” in line 5. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 10 recites “a surgery image” in line 6. It is not clear if this is referring to the surgery image previously set forth, or if this is referring to a different surgery image. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 10 recites “a surgery image” in lines 10-11. It is not clear if this is referring to the surgery image previously set forth, or if this is referring to other surgery images. Clarification is required. For the purposes of examination, the latter definition will be used.
Claim 10 recites “a risky action” in line 11 It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
Claim 10 recites “a risky action” in line 16. It is not clear if this is referring to the same “risky action” as set forth in line 4, or if this is referring to any risky action. Clarification is required. For the purposes of examination, the former definition will be used.
All claims dependent from the above claims rejected under 35 USC 112(b) are also rejected, as the limitations of the dependent claims fail to cure the deficiencies identified above.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-8 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because they encompass signals per se.
Claims 1, and 3-8 are directed to “a computer readable medium”. By the broadest reasonable interpretation, a “computer readable medium” encompasses transitory signals, which are not one of the four categories of patent eligible subject matter (MPEP 2106(II)). The Examiner recommends amending the claims to make clear it is only encompassing non-transitory computer readable medium (e.g. memory) to make the claims statutory.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-4, 6-7, and 9-10 are rejected under 35 U.S.C. 103 as being unpatentable over Konno et al. (U.S PGPub 2021/0015432 A1) in view of Kobayashi et al. (U.S PGPub 2024/0049944 A1).
Regarding Claim 1, Konno teaches a computer readable medium storing a program that causes a computer to execute a process for surgery assistance (Fig. 4, 10) [0033]-[0034], the process comprising:
acquiring a surgery image in which a surgical site is captured [0034]+[0036];
detecting a risky action by inputting the acquired surgery image into a model [0034]+[0076] configured to output presence/absence of a risky action in response to an input of a surgery image (Fig. 4, 116) [0077] (dangerous condition is a risky action) to cause the model to output presence/absence of the risky action [0077]+[0084]; and
notifying that the risky action is detected [0084], wherein
the model is generated by training with training data in which an input is a surgery image [0076] and an output is presence/absence of a risky action [0077] and region information of a human body structure that is altered due to the risky action [0083] (position that the dangerous condition occurs).
While Konno teaches using a supervised learning model, Konno fails to explicitly teach the detecting the risky action includes reconstructing region information of the human body structure from a feature extracted from the acquired surgery image, and determining presence/absence of a risky action using the feature of the surgery image and a feature of the reconstructed region information of the human body structure.
Kobayashi teaches a system for using a machine learning model during surgery (Abstract). This system detects the risky action by reconstructing region information of the human body structure from a feature extracted from the acquired surgery image [0077] (decoding the feature map is reconstructing region information), and the system determining presence/absence of the risky action by using the feature of the surgery image and a feature of the reconstructed region information of the human body structure [0073]+[0078]+[0080] (the system uses the features of a surgery image (as input), and the features of the decoded image (as output) [0076] to determine the vessel probability).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Konno to use the detection method of Kobayashi to determine the risky action because this algorithm both increases the recognition accuracy and processing speed, as recognized by Kobayashi [0079]. One of ordinary skill would recognize that, as not highlighting the vessel may cause the vessel to start bleeding, identifying the vessel can be considered identifying a risky action [0067]-[0068].
Regarding Claim 3, the combination of references teaches the invention substantially as claimed. Konno further teaches the computer readable medium according to claim 1, the program stored on the computer readable medium causing the computer to: cause the detection of the risky action to be displayed on the acquired surgery image (Fig. 2) [0085]+[0090].
Regarding Claim 4, the combination of references teaches the invention substantially as claimed. Konno further teaches wherein the training data is created using information detected by a sensor [0037]-[0038]+[0055]+[0076] (surgical image is used for learning, and acquired by a sensor).
Regarding Claim 6, the combination of references teaches the invention substantially as claimed. Konno further teaches the program stored on the computer readable medium causing the computer to: detect the risky action further using information of a preoperative patient's state [0058]+[0067]+[0076].
Regarding Claim 7, the combination of references teaches the invention substantially as claimed. Konno further teaches the program stored on the computer readable medium causing the computer to: evaluate a procedure of a surgery of an operator based on the presence/absence of the risky action that is output by inputting the acquired surgery image to the model [0090]-[0091] (by alerting the user of the dangerous condition, the system is evaluating the procedure of the surgery).
Regarding Claim 9, Konno teaches a surgery assistance apparatus (Abstract), comprising:
a surgery image acquirer configured to acquire a surgery image in which a surgery site is captured [0034]+[0036];
a risky action detector (Fig. 4, 116) [0077] configured to detect a risky action by inputting the acquired surgery image into a model [0034]+[0076] configured to output presence/absence of a risky action in response to an input of a surgery image (Fig. 4, 116) [0077] (dangerous condition is a risky action) to cause the model to output presence/absence of the risky action [0077]+[0084]; and
a risky action notifier (Fig. 4, 117) configured to notify that the risky action is detected [0084], wherein
the model is generated by training with training data in which an input is a surgery image [0076] and an output is presence/absence of a risky action [0077] and region information of a human body structure that is altered due to the risky action [0083] (position that the dangerous condition occurs).
While Konno teaches using a supervised learning model, Konno fails to explicitly teach the detecting the risky action includes reconstructing region information of the human body structure from a feature extracted from the acquired surgery image, and determining presence/absence of a risky action using the feature of the surgery image and a feature of the reconstructed region information of the human body structure.
Kobayashi teaches a system for using a machine learning model during surgery (Abstract). This system detects the risky action by reconstructing region information of the human body structure from a feature extracted from the acquired surgery image [0077] (decoding the feature map is reconstructing region information), and the system determining presence/absence of the risky action by using the feature of the surgery image and a feature of the reconstructed region information of the human body structure [0073]+[0078]+[0080] (the system uses the features of a surgery image (as input), and the features of the decoded image (as output) [0076] to determine the vessel probability).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Konno to use the detection method of Kobayashi to determine the risky action because this algorithm both increases the recognition accuracy and processing speed, as recognized by Kobayashi [0079]. One of ordinary skill would recognize that, as not highlighting the vessel may cause the vessel to start bleeding, identifying the vessel can be considered identifying a risky action [0067]-[0068].
Regarding Claim 10, Konno teaches a method performed by a surgery assistance apparatus (Abstract), the method comprising:
a step of acquiring a surgery image in which a surgical site is captured [0034]+[0036];
a step of detecting a risky action by inputting the acquired surgery image into a model [0034]+[0076] configured to output presence/absence of a risky action in response to an input of a surgery image (Fig. 4, 116) [0077] (dangerous condition is a risky action) to cause the model output presence/absence of the risky action [0077]+[0084]; and
a step of notifying that the risky action is detected [0084]; wherein
the model is generated by training with training data in which an input is a surgery image [0076] and an output is presence/absence of a risky action [0077] and region information of a human body structure that is altered due to the risky action [0083] (position that the dangerous condition occurs).
While Konno teaches using a supervised learning model, Konno fails to explicitly teach the detecting the risky action includes reconstructing region information of the human body structure from a feature extracted from the acquired surgery image, and determining presence/absence of a risky action using the feature of the surgery image and a feature of the reconstructed region information of the human body structure.
Kobayashi teaches a system for using a machine learning model during surgery (Abstract). This system detects the risky action by reconstructing region information of the human body structure from a feature extracted from the acquired surgery image [0077] (decoding the feature map is reconstructing region information), and the system determining presence/absence of the risky action by using the feature of the surgery image and a feature of the reconstructed region information of the human body structure [0073]+[0078]+[0080] (the system uses the features of a surgery image (as input), and the features of the decoded image (as output) [0076] to determine the vessel probability).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the system of Konno to use the detection method of Kobayashi to determine the risky action because this algorithm both increases the recognition accuracy and processing speed, as recognized by Kobayashi [0079]. One of ordinary skill would recognize that, as not highlighting the vessel may cause the vessel to start bleeding, identifying the vessel can be considered identifying a risky action [0067]-[0068].
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Konno in view of Kobayashi as applied to claim 1 above, and further in view of Piskun et al. (U.S PGPub 2022/0093236 A1).
Regarding Claim 5, the combination of references teaches the invention substantially as claimed. The combination fails to explicitly teach wherein training data further includes information of a human body structure not clearly shown in the surgery image.
Piskun teaches a system for tracking obscured anatomical structures using machine learning (Abstract). This system uses training data which includes information of human body structures [0021], which are not clearly shown in the surgery image [0029]+[0043].
It would have been obvious to one of ordinary skill in the art to include information of a human body structure not clearly shown in the surgery image, as taught by Piskun, because this aids in surgeons identifying/tracking structures that are difficult to see, thereby reducing the chance of complications from the surgery, as recognized by Piskun [0007]+[0029].
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Konno in view of Kobayashi as applied to claim 1 above, and further in view of Sato et al. (Sato, Kazuma, et al. "Real-time detection of the recurrent laryngeal nerve in thoracoscopic esophagectomy using artificial intelligence." Surgical Endoscopy 36.7 (2022): 5531-5539.).
Regarding Claim 8, the combination of references teaches the invention substantially as claimed. Konno fails to explicitly teach the risky action is an action that causes recurrent nerve paralysis.
Sato teaches an artificial intelligence system which identifies recurrent nerves to reduce neve paralysis (Abstract). This system shows identifying the recurrent nerve can reduce the incidence of paralysis caused by surgery (Pg. 5538, Col 1, ¶ 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date to modify the combined system to identify a risky action that causes recurrent nerve paralysis, as taught by Sato, because identifying the recurrent nerve reduces the incidence of nerve paralysis, thereby reducing the morbidity from surgery, as recognized by Sato (Abstract) (Pg. 5538, Col 1, ¶ 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Owen et al. (U.S PGPub 2025/0143806 A1), which teaches a method of preventing complications during surgery.
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/SEAN D MATTSON/ Primary Examiner, Art Unit 3798
