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 .
Response to Arguments
Applicant's arguments, in the amendment filed 10/7/2025, with respect to the rejections of claims 16-30 under 35 U.S.C. 103(a)have been fully considered but are moot in view of the new ground(s) of rejection necessitated by the amendments. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wei et al (US 2015/0265752).
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.
Claim(s) 16-30 are rejected under 35 U.S.C. 103 as being unpatentable over Keustermans et al (high quality statistical shape modelling of the human nasal cavity and applications", vol. 5, no. 12, 1 December 2018 (2018-12-01), pages 181558, XP055799280) In view of Wei et al (US 2015/0265752)
As to claim 16, Keustermans et al teaches the computer-implemented model for describing a specific nasal cavity shape comprising: a generic nasal cavity shape model including an average nasal cavity shape and a set of nasal cavity shape eigenmodes (The first eigenmode applies a general scaling to average nasal shape. It is not a perfect isotropic scaling, but nonetheless there is a clear size increase/decrease in all three dimensions, section 3.1), a set of specific parameters such that the specific nasal cavity shape is modelled by a combination of the average nasal cavity shape and a linear combination of the set of specific parameters with the set of nasal cavity shape eigenmodes (PCA was applied, delivering an average nasal shape x (by averaging the corresponding landmarks) and an orthogonal set of shape variations (the so-called shape modes). Existing and new nasal shapes can then be described as the sum of the average nasal cavity and a specific linear combination of the shape modes; page 5 second paragraph; P contains the eigenvectors of the covariance matrix and b is a vector given by equation (2.4)), While Keustermans et al teaches the limitation above, Keustermans et al fails to teach” wherein the set of specific parameters is derived without tomographic imaging data and from measurement data of the nasal cavity, including from measurement data on quasi static and/or dynamic nasal pressure changes, wherein the measurement data on the quasi static nasal pressure changes includes rhinomanometry data and the measurement data on the dynamic nasal pressure changes includes acoustic rhinometry data.” However, Wei et al teaches wherein the set of specific parameters is derived without tomographic imaging data and from measurement data of the nasal cavity (The units on the ordinates and abscissa of the graphs are in cm, and represent the dimensions of nasal cavity determined by the acoustic rhinometry instrument, paragraph [0028][0043][0091]), including from measurement data on quasi static and/or dynamic nasal pressure changes, wherein the measurement data on the quasi static nasal pressure changes includes rhinomanometry data and the measurement data on the dynamic nasal pressure changes includes acoustic rhinometry data(In acoustic rhinometry (AR) a sound pulse enters the nasal cavity, where it is reflected due to changes in the local impedances. From the incident and reflected sound signal, an algorithm is used to calculate an area-distance relationship. This method for measuring nasal cavity volume has been validated in humans and animals using other techniques (e.g. CT-scanning, MR scanning and fluid displacement) and is now a standard tool in the clinic, paragraph[0091])” . It would have been obvious to one skilled in the art before filing of the claimed invention to use the pathological changes of Wei in order to relieve the discomforts of rhinitis and other nasal cavity discomforts. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 17, Keustermans et al teaches the computer-implemented model of claim 16, wherein the average nasal cavity shape and the set of nasal cavity shape eigenmodes are 3D surface representations of nasal cavities (From a mathematical standpoint, a three-dimensional shape with n landmarks can be represented as a vector x with length 3n, section 2.2).
As to claim 18, Keustermans et al teaches the computer-implemented method for modelling a specific nasal cavity shape, the method comprising the steps of:
obtaining measurement data of a nasal cavity nasal cavity, excluding tomographic imaging data including data on quasi static and/or dynamic nasal pressure changes( section 2.2); feeding said measurement data into a neural network, wherein the neural network is trained to output a set of specific parameters such that the specific nasal cavity shape is modelled by a combination of an average nasal cavity shape and a linear combination of the set of specific parameters with a set of nasal cavity shape eigenmodes(PCA was applied, delivering an average nasal shape x (by averaging the corresponding landmarks) and an orthogonal set of shape variations (the so-called shape modes). Existing and new nasal shapes can then be described as the sum of the average nasal cavity and a specific linear combination of the shape modes. M, page 5) .While Keustermas teaches the limitative above. Keustermans fails to teach” wherein the measurement data on the quasi static nasal pressure changes includes rhinomanometry data and the measurement data on the dynamic nasal pressure changes includes acoustic rhinometry data.” However, Wei et al teaches wherein the set of specific parameters is derived without tomographic imaging data and from measurement data of the nasal cavity (The units on the ordinates and abscissa of the graphs are in cm, and represent the dimensions of nasal cavity determined by the acoustic rhinometry instrument, paragraph [0028][0043][0091]).Additionally, Wei et al teaches in acoustic rhinometry (AR) a sound pulse enters the nasal cavity, where it is reflected due to changes in the local impedances. From the incident and reflected sound signal, an algorithm is used to calculate an area-distance relationship. This method for measuring nasal cavity volume has been validated in humans and animals using other techniques (e.g. CT-scanning, MR scanning and fluid displacement) and is now a standard tool in the clinic, paragraph[0091])” . It would have been obvious to one skilled in the art before filing of the claimed invention to use the pathological changes of Wei in order to relieve the discomforts of rhinitis and other nasal cavity discomforts. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 19, Keustermans et al teaches the computer-implemented method of claim 18, wherein the obtaining of measurement data includes cross-sectional surface area in function of a depth of at least one of a right nasal channel and a left nasal channel of a nasal cavity ( Their model and the shape instances it produces for different positions along the PC axes show artefacts, i.e. left and right nasal channels are fused at the top of the nasal cavity, page 2 2nd paragraph).
As to claim 20, Keustermans teaches the computer-implemented method of training a neural network to output a set of specific parameters derived without tomographic imaging data and from measurement data of a nasal cavity such that a specific nasal cavity shape is modelled by a combination of an average nasal cavity shape and a linear combination of the set of specific parameters with a set of nasal cavity shape eigenmodes, the training including the steps of: randomly generating sets of specific parameters simulating measurement data of nasal cavities, the measurement data including quasi static and/or dynamic nasal pressure changes; generating specific nasal cavity shape models, said models including a combination of an average nasal cavity shape and a linear combination of said simulated sets of specific parameters with a set of nasal cavity shape eigenmodes (PCA was applied, delivering an average nasal shape x (by averaging the corresponding landmarks) and an orthogonal set of shape variations (the so-called shape modes). Existing and new nasal shapes can then be described as the sum of the average nasal cavity and a specific linear combination of the shape modes. M, page 5, section 2.2) ; determining a cross-sectional surface area of at least one of the nasal channels of the specific nasal cavity shape models provided by said simulated sets of specific parameters (the volume of the respiratory region was calculated by subtracting the volume of the total nasal shape from that of the other three previously mentioned. The ten first shape modes are consecutively applied to the average shape .The effect of each shape mode on the surface area and on the volume of the different anatomical regions is captured and compared; figure 5, page 8; note that Keustermans et al teaches to increase the amount of morphological variation captured by the statistical shape model, mirrored versions of all nasal cavities are generated. PARAVIEW v. 5.4.0 was used to generate these mirrored shapes, hereby doubling the size of the training data for the statistical shape model. The average shape obtained from principal component analysis (PCA) will be symmetrical, section 2.2).
While Keustermans et al teaches the limitation above, Keustermans et al fails to teach” feeding the determined cross-sectional surface area of at least one of the nasal channels into the neural network; training the neural network to output the sets of specific parameters wherein the measurement data on the quasi static nasal pressure changes includes rhinomanometry data and the measurement data on the dynamic nasal pressure changes includes acoustic rhinometry data.”., However, Wei et al teaches wherein the set of specific parameters is derived without tomographic imaging data and from measurement data of the nasal cavity (The units on the ordinates and abscissa of the graphs are in cm, and represent the dimensions of nasal cavity determined by the acoustic rhinometry instrument, paragraph [0028][0043][0091]), including from measurement data on quasi static and/or dynamic nasal pressure changes, wherein the measurement data on the quasi static nasal pressure changes includes rhinomanometry data and the measurement data on the dynamic nasal pressure changes includes acoustic rhinometry data(In acoustic rhinometry (AR) a sound pulse enters the nasal cavity, where it is reflected due to changes in the local impedances. From the incident and reflected sound signal, an algorithm is used to calculate an area-distance relationship. This method for measuring nasal cavity volume has been validated in humans and animals using other techniques (e.g. CT-scanning, MR scanning and fluid displacement) and is now a standard tool in the clinic, paragraph[0091])” . It would have been obvious to one skilled in the art before filing of the claimed invention to use the pathological changes of Wei in order to relieve the discomforts of rhinitis and other nasal cavity discomforts. Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
As to claim 21, Keustermans teaches the method of claim 20, method of claim 20, wherein the generating specific nasal cavity shape models includes obtaining a generic nasal cavity shape model, the obtaining comprising the steps of: generating 3D surface representations of a plurality of nasal cavities, wherein each 3D surface representation includes a same number of points; finding corresponding points between said 3D surface representation, wherein said corresponding points are located on a same anatomic position; generating an average nasal cavity shape based on average values of said corresponding points; extract from said 3D surface representations a set of nasal cavity shape eigenmodes; wherein the generic nasal cavity shape model includes the average nasal cavity shape and the set of nasal cavity shape eigenmodes (abstract, section 2.2, section 2.3).
As to claim 22, Keustermans teaches the method according to claim 21, wherein the generating of 3D surface representations is based on tomographic images of the plurality of nasal cavities The entire statistical analysis starts with tomographic data from patients, collected at the hospital, section 4.1).
As to claim 23, Keustermans teaches the method according to claim 21, wherein the generating of 3D surface representations includes mirroring said 3D surface representations ( section 2.2 and 2.3).
As to claim 24, Keustermans teaches the method according to claim 21, wherein the finding of corresponding points includes applying a cylindrical parametrization technique for mapping tubular surfaces (A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded nasal cavities resulted in an average nasal geometry and geometrical variations, known as principal components, present in the population with a high precision, abstract, page 1).
As to claim 25, Keustermans teaches the method according to claim 21, wherein the generating of the average nasal cavity shape and the extracting the set of nasal cavity shape eigenmodes is done by applying a principal component analysis (A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded nasal cavities resulted in an average nasal geometry and geometrical variations, known as principal components, present in the population with a high precision. The analysis led to 46 principal components, which account for 95% of the total geometrical variation captured. These variations are first discussed qualitatively, and the effect on the average nasal shape of the first five principal components is visualized. Hereafter, by using this statistical shape model, two application examples that lead to quantitative data are shown: nasal shape in function of age and gender, and a morphometric analysis of different anatomical regions. Shape models, as the one presented here, can help to get a better understanding of nasal shape and variation, and their relationship with demographic data, abstract, page 1).
The limitation of claim 26-30 has been addressed above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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NANCY . BITAR
Examiner
Art Unit 2664
/NANCY BITAR/ Primary Examiner, Art Unit 2664