DETAILED ACTION
Comments
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . 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 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.
Claims 1-20 are pending and examined in the instant Office action.
The claims are subject matter eligible because the core limitations recite use of neural networks, which is a limitation too complex to be conducted in the human mind. Even assuming (en arguendo) that the claims recite judicial exceptions, the claims recite the practical application of more computationally efficiently analysis of anatomical tree structure data than conventional techniques with analogous objectives.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
The following rejection is reiterated:
35 U.S.C. 103 Rejection #1:
Claims 1-2, 4-5, 8-12, 14-15, and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vunjak-Novakovic et al. [WO 2018/156460 A1] in view of Vion-Dury [US PGPUB 2013/0151565 A1].
Claim 1 is drawn to a method for an anatomical tree structure analysis. The method comprises receiving model inputs related to an anatomical tree structure for a set of positions in the anatomical tree structure. The anatomical tree structure includes at least one bifurcation point and a plurality of branches splitting from the at least one bifurcation point. The method comprises applying a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure. Each encoder is external to the neural network and extracts features from one of the model inputs at a corresponding position in the set of positions in the anatomical tree structure. The neural network has a plurality of nodes constructed according to the set of positions in anatomical tree structure and each node of the neural network is connected to one or more of the encoders and is configured to receive and process the extracted features from one or more of the encoders. The method comprises providing an output of the tree structured network as an analysis result of the anatomical tree structure analysis.
Claim 11 is drawn to similar subject matter as claim 1, except claim 11 is drawn to a system.
Claim 19 is drawn to similar subject matter as claim 1, except claim 19 is drawn to a non-transitory computer-readable medium.
Claims 2, 12, and 20 are further limiting wherein the anatomical tree structure comprises an airway.
Claims 5 and 15 are further limiting wherein the encoders comprise CNNs.
The document of Vunjak-Novakovic et al. studies a method and apparatus for computer-vision guided targeted delivery of small liquid volumes into selected lung regions [title]. Figure 1 of Vunjak-Novakovic et al. illustrates the anatomical tree-like lung structure wherein the encoders external to the human and the lungs extracts features from at least one of the positions of the anatomical tree like lung structure. Paragraph 138 of Vunjak-Novakovic et al. teaches application of CNNs to input training sets of airway tree bifurcation data to predict the bifurcations of a new tree structure of a lung that has yet to be classified. It is evident that a lung has a plurality of bifurcation points with a plurality of branches splitting from at least one bifurcation point. Each bifurcation point in a lung is interpreted to be a node. Figure 24 of Vunjak-Novakovic et al. teaches output of the tree structured network as an analysis result of the anatomical tree structure analysis.
Vunjak-Novakovic et al. does not teach that the plurality of nodes is constructed according to the set of positions in the anatomical tree structure.
The document of Vion-Dury studies arithmetic node encoding for tree structures [title]. Figure 1 of Vion-Dury illustrates an upside-down tree structure of nodes. Figure 1, the abstract, and paragraphs 6-9 of Vion-Dury teaches that each node encodes data (i.e. the nodes also act as encoders of data).
With regard to claims 4, 10, and 14, paragraph 138 of Vunjak-Novakovic et al. teaches receiving images of the lung as acquired by an image acquisition device, deriving model inputs at the set of positions in the anatomical tree structure, and using the thousands of images to jointly train a new model of the lung.
With regard to claims 8-9 and 17-18, Figure 24 of Vunjak-Novakovic et al. illustrates image patches and anatomical structure labeling.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the machine learning of anatomical tree structures of Vunjak-Novakovic et al. by use of the nodes encoding data of Vion-Dury wherein the motivation would have been that Vion-Dury gives additional mathematical tools to assist with the machine learning of Vunjak-Novakovic et al. [paragraph 6-9 of Vion-Dury]. There would have been a reasonable expectation of success in combining Vunjak-Novakovic et al. with Vion-Dury because both studies are analogously applicable to studying biological applications of trees of data structure.
Response to arguments:
Applicant's arguments filed 17 October 2025 have been fully considered but they are not persuasive.
Applicant’s central argument is that the amendments to the claims overcome the rejection. This argument is not persuasive because Figure 1 of Vunjak-Novakovic et al. illustrates the anatomical tree-like lung structure wherein the encoders external to the human and the lungs extracts features from at least one of the positions of the anatomical tree like lung structure.
The following rejection is reiterated:
35 U.S.C. 103 Rejection #2:
Claims 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vunjak-Novakovic et al. in view of Vion-Dury as applied to claims 1-2, 4-5, 8-12, 14-15, and 17-20 above, in further view of Tsukahara et al. [US PGPUB 2018/0255284 A1].
Claims 3 and 13 are further limiting wherein the set of positions include the at least one bifurcation point and at least one point in each branch.
The documents of Vunjak-Novakovic et al. and Vion-Dury study using machine learning to analyze the anatomical tree structure of the lung, as discussed above.
Vunjak-Novakovic et al. and Vion-Dury do not teach that the set of positions include the at least one bifurcation point and at least one point in each branch.
The document of Tsukahara et al. studies an information processing apparatus, information processing method, and program [title]. Figure 4 of Tsukahara et al. illustrates a tree structure wherein the set of positions include the at least one bifurcation point and at least one point in each branch.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the machine learning of anatomical tree structures of Vunjak-Novakovic et al. and the nodes encoding data of Vion-Dury by use of the tree structure of Tsukahara et al. because it is obvious to combine known elements in the prior art to yield a predictable result. In this instance, the tree structure of Tsukahara et al. is an alternative to the lungs of Vunjak-Novakovic et al. There would have been a reasonable expectation of success in combining Vunjak-Novakovic et al., Vion-Dury, and Tsukahara et al. because all three studies are analogously applicable to analysis of tree structures.
Response to arguments:
Applicant's arguments filed 17 October 2025 have been fully considered but they are not persuasive.
Applicant argues that Tsukahara et al. does not overcome the alleged deficiencies of the initial obviousness prior art rejection. This argument is not persuasive because the initial obviousness prior art rejection is not deficient.
The following rejection is reiterated:
35 U.S.C. 103 Rejection #3:
Claims 6-7 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Vunjak-Novakovic et al. in view of Vion-Dury as applied to claims 1-2, 4-5, 8-12, 14-15, and 17-20 above, in further view of Sundaram [US Patent 9,972,339 B1].
Claims 6 and 16 are further limiting comprising using RNN. Claim 7 is further limiting wherein the RNN units are selected from LSTM and GRU.
The documents of Vunjak-Novakovic et al. and Vion-Dury study using machine learning to analyze the anatomical tree structure of the lung, as discussed above.
Vunjak-Novakovic et al. and Vion-Dury do not teach the tools and features recited in the rejected claims.
The document of Sundaram studies neural network based beam selection [title]. Column 18, line 41 to column 19, line 13 of Sundaram teaches RNN, LSTM, GRU, bi-directionality between nodes, and single directionality between nodes.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the machine learning of anatomical tree structures of Vunjak-Novakovic et al. and the nodes encoding data of Vion-Dury by use of the RNN, LSTM, GRU, bi-directionality between nodes, and single directionality between nodes of Sundaram wherein the motivation would have been that Sundaram teaches additional tools and features that facilitates analysis of a graph [column 18, line 41 to column 19, line 13 of Sundaram]. There would have been a reasonable expectation of success in combining Vunjak-Novakovic et al., Vion-Dury, and Sundaram because all three studies are analogously applicable to using analysis of graphs and/or tree-like structures.
Response to arguments:
Applicant's arguments filed 17 October 2025 have been fully considered but they are not persuasive.
Applicant argues that Sundaram does not overcome the alleged deficiencies of the initial obviousness prior art rejection. This argument is not persuasive because the initial obviousness prior art rejection is not deficient.
The following rejection is newly applied:
35 U.S.C. 103 Rejection #4:
Claims 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. [CN 107977709 A] in view of Vion-Dury [US PGPUB 2013/0151565 A1]. An English machine translation of Wang et al. is cited in the instant Office action.
Claim 1 is drawn to a method for an anatomical tree structure analysis. The method comprises receiving model inputs related to an anatomical tree structure for a set of positions in the anatomical tree structure. The anatomical tree structure includes at least one bifurcation point and a plurality of branches splitting from the at least one bifurcation point. The method comprises applying a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure. Each encoder extracts features from one of the model inputs at a corresponding position in the set of positions in the anatomical tree structure. The neural network has a plurality of nodes constructed according to the set of positions in anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method comprises providing an output of the tree structured network as an analysis result of the anatomical tree structure analysis.
Claim 11 is drawn to similar subject matter as claim 1, except claim 11 is drawn to a system.
Claim 19 is drawn to similar subject matter as claim 1, except claim 19 is drawn to a non-transitory computer-readable medium.
Claims 2, 12, and 20 are further limiting wherein the anatomical tree structure comprises an airway.
Claims 3 and 13 are further limiting wherein each branch has a bifurcation point.
Claims 5 and 15 are further limiting wherein the encoders comprise CNNs.
The document of Wang et al. studies a method and system to predict the deep learning model and system of the flow characteristic on vascular tree blood flow paths [title]. The abstract of Wang et al. teaches a neural network performed at nodes on the blood flow paths in order to extract features and images of the blood flow paths. Figures 1 and 2 of Wang et al. illustrate the anatomical tree-like lung structure wherein the encoders external to the anatomic tree and extract features from at least one of the positions of the anatomical tree like lung structure. Figures 1 and 2 of Wang et al. illustrate the neural network set-up on the blood flow path wherein the blood flow path has a bifurcation point (with a corresponding node/neural network). Figures 1 and 2 of Wang et al. teaches nodes and neural networks associates with points on the tree-like bifurcated structure. The description of Figure 2 on page 4 of Wang et al. teaches use of CNNs.
Wang et al. does not teach encoders associated with the nodes.
The document of Vion-Dury studies arithmetic node encoding for tree structures [title]. Figure 1 of Vion-Dury illustrates an upside-down tree structure of nodes. Figure 1, the abstract, and paragraphs 6-9 of Vion-Dury teaches that each node encodes data (i.e. the nodes also act as encoders of data).
With regard to claims 4 and 14, the abstract of Wang et al. teaches acquiring an image feature at each point. Figures 1 and 2 of Wang et al. teach model inputs at a set of positions in the anatomical tree structure.
With regard to claims 6-7 and 16, the abstract of Wang et al. teaches use of RNNs. The description of Figure 2 on page 4 of Wang et al. teaches that the RNNs are selected from LSTM and GRU.
With regard to claims 8-10 and 17-18, page 4 of Wang et al. teaches image features as inputs. Figures 1 and 2 of Wang et al. teach anatomical structure labeling. Figures 1 and 2 of Wang et al. teach joint training of neural networks.
It would have been obvious to someone of ordinary skill in the art at the time of the effective filing date of the instant application to modify the machine learning of anatomical tree structures of Wang et al. by use of the nodes encoding data of Vion-Dury wherein the motivation would have been that Vion-Dury gives additional mathematical tools to assist with the machine learning of Wang et al. [paragraph 6-9 of Vion-Dury]. There would have been a reasonable expectation of success in combining Wang et al. with Vion-Dury because both studies are analogously applicable to studying biological applications of trees of data structure.
Response to arguments:
Applicant's arguments filed 17 October 2025 have been fully considered but they are not persuasive.
Applicant’s central argument is that the amendments to the claims overcome the rejection. This argument is not persuasive because Figures 1 and 2 of Wang et al. illustrate the anatomical tree-like lung structure wherein the encoders external to the anatomic tree and extract features from at least one of the positions of the anatomical tree like lung structure.
Related Prior Art
The prior art of Kalchbrenner et al. [arXiv:1404.2188v1; 8 April 2014, 11 pages; on IDS] teaches applying CNNs to tree structures representing sentences and not blood vessels or airways.
E-mail Communications Authorization
Per updated USPTO Internet usage policies, Applicant and/or applicant’s representative is encouraged to authorize the USPTO examiner to discuss any subject matter concerning the above application via Internet e-mail communications. See MPEP 502.03. To approve such communications, Applicant must provide written authorization for e-mail communication by submitting the following statement via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300):
Recognizing that Internet communications are not secure, I hereby authorize the USPTO to communicate with the undersigned and practitioners in accordance with 37 CFR 1.33 and 37 CFR 1.34 concerning any subject matter of this application by video conferencing, instant messaging, or electronic mail. I understand that a copy of these communications will be made of record in the application file.
Written authorizations submitted to the Examiner via e-mail are NOT proper. Written authorizations must be submitted via EFS-Web (using PTO/SB/439) or Central Fax (571-273-8300). A paper copy of e-mail correspondence will be placed in the patent application when appropriate. E-mails from the USPTO are for the sole use of the intended recipient, and may contain information subject to the confidentiality requirement set forth in 35 USC § 122. See also MPEP 502.03.
Conclusion
No claim is allowed.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to Russell Negin, whose telephone number is (571) 272-1083. This Examiner can normally be reached from Monday through Thursday from 8 am to 3 pm and variable hours on Fridays.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s Supervisor, Larry Riggs, Supervisory Patent Examiner, can be reached at (571) 270-3062.
/RUSSELL S NEGIN/ Primary Examiner, Art Unit 1686 29 October 2025
Prosecution Insights