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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. FR2013872, filed on 12/22/2020. All claims have been found to be supported by this document and thus examined using the effective filing date of 12/22/2020.
Information Disclosure Statement
Information Disclosure Statement filed 06/22/2023 has been considered by examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character is not mentioned in the description: 170. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 2, 8, and 9 are 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.
Regarding claims 2, 8, and 9, the phrase "for example" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
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 16 and 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because they are directed to software per se. Amending the claims so that they are directed to the non-transitory storage medium containing the computer program product should overcome these rejections.
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.
Claims 1-6, 8-9, 12, and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Hernandez Gomez (WO2013098435A1) in view of Banabilh (Banabilh, S.M., Suzina, A.H., Dinsuhaimi, S. et al. Craniofacial obesity in patients with obstructive sleep apnea. Sleep Breath 13, 19–24 (2009)) .
With respect to claim 1, Hernandez Gomez teaches a process for determining a patient's apnoea+hypopnoea index (“It is in this context of scarcity of resources and increasing demand for alternative diagnoses of SAHS where this invention is proposed, consisting of a system that, from at least two (front and profile), digital images of the patient that collect their face and neck (while awake) and through the use of image processing techniques, provide an accurate estimate of the apnea-hypopnea index (AHI).” Page 2 paragraph 4), comprising: supplying a data set relating to a patient (“at least two (front and profile), digital images of the patient that collect their face and neck (while awake)” page 2 paragraph 4), to a remote server (“A device with processing capacity 101, such as a computer, processes the captured Images” page 4 lines 14-15) or a computer program product (“from at least two (front and profile), digital images of the patient that collect their face and neck (while awake) and through the use of image processing techniques, provide an accurate estimate of the apnea-hypopnea index (AHI).” Page 2 paragraph 4 and “The present invention is further illustrated by an embodiment based on a method of detecting and extracting characteristics of the images, and using a Naive Bayes classifier.” Page 4 lines 9-10), the data set relating to the patient comprising, or making it possible to determine, characteristic data of the patient's at least maxillofacial morphology with respect to a reference morphology (“There are therefore sufficient grounds to state that the existence of specific anatomical features in SAHS patients should be reflected, in some way, in the images (both front and profile) that reflect the shape of their face and neck.” Page 3 paragraph 2 lines 11-13 and “at least two (front and profile), digital images of the patient that collect their face and neck (while awake)” page 2 paragraph 4),
introducing the data set relating to the patient ,comprising the characteristic data of the at least maxillofacial morphology of the patient, into a machine learning model trained to predict an apnoea+hypopnoea index (“A wide variety of pattern classification techniques can be used for this purpose, such as those based on Discriminant Analysis, Bayesian Classifiers, Neural Networks, Regression Techniques, etc.” page 5 paragraph 5 and Examiner figure 1), for said data set, from a plurality of data sets of a database relating to a set of separate patients (“A database processing module (step 302) obtains each of the images from database 301 and applies the same face and neck detection techniques (step 204) and discriminative features extraction (step 205) described previously for the image analysis procedure performed by the system (Figure 2). In this way, a database of characteristic vectors 303 is generated (with the measurements included in Table 1). Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database” page 6 paragraph 4 and Table 2 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), each data set of the database: comprising at least maxillofacial morphology data relating to a patient from the set, and being associated with an apnoea+hypopnoea index (“Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database. On the basis of feature vector data and using the information of which people do not suffer from SAHS and IAH indices of SAHS patients, a training process is carried out (step 304) that will generate patterns 207 of the Naive Bayes classifier, such and as detailed above. You could use other types of classification schemes of different patterns to the Naive Bayes classifier. In this case the pattern training process (step 304) would use the existing procedures corresponding to the pattern classification technique (Neural Networks, Regression Methods etc.) to be used (step 206) to obtain the SAHS score 102” page 6 paragraph 4 lines 4-12 and Table 2 and “A wide variety of pattern classification techniques can be used for this purpose, such as those based on Discriminant Analysis, Bayesian Classifiers, Neural Networks, Regression Techniques, etc.” page 5 paragraph 5 and Examiner figure 1 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), such that the learning model predicts an apnoea+hypopnoea index (“It is in this context of scarcity of resources and increasing demand for alternative diagnoses of SAHS where this invention is proposed, consisting of a system that, from at least two (front and profile), digital images of the patient that collect their face and neck (while awake) and through the use of image processing techniques, provide an accurate estimate of the apnea-hypopnea index (AHI).” Page 2 paragraph 4 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), for the data set relating to the patient.
Hernandez Gomez does not teach a system that utilizes characteristic data being dependent on a positioning of at least four homologous points; on a 3D scan of a portion of the patient's head representing their at least maxillofacial morphology, or a status dependent on an apnoea+hypopnoea index, for said data set
Banabilh teaches characteristic data being dependent on a positioning of at least four homologous points (see figure 1); on a 3D scan of a portion of the patient's head representing their at least maxillofacial morphology (see figure 1), and a status dependent on an apnoea+hypopnoea index, for said data set (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13).
Banabilh is analogous art in the same field of endeavor as the claimed invention. Banabilh is directed toward finding connections between 3D facial morphology and AHI (“In the current study, the BMI was found to be significantly greater for the OSA group (32.3 kg/m2 ±7.4) when compared to the control group (24.8 kg/m2±6.5, p<0.001). In addition, the neck circumference was greater for the OSA group (42.7 cm± 2.5) compared to the control group (37.1 cm±2.2, p<0.001). The AHI was also significantly greater for the OSA group (40.0/h±30.3) when compared to the control group (2.0/h±2.0, p<0.001).” page 3 right hand side paragraph). A person of ordinary skill in the art before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Banabilh with the system of Hernandez Gomez by using the 3D scans of Banabilh as an input into its AHI determination process, with the expectation that doing so would lead to accurate predictions of OSA and the AHI due to the correlation of features found within the 3D scans and the presence of OSA and AHI (“In the current study, the BMI was found to be significantly greater for the OSA group (32.3 kg/m2 ±7.4) when compared to the control group (24.8 kg/m2±6.5, p<0.001). In addition, the neck circumference was greater for the OSA group (42.7 cm± 2.5) compared to the control group (37.1 cm±2.2, p<0.001). The AHI was also significantly greater for the OSA group (40.0/h±30.3) when compared to the control group (2.0/h±2.0, p<0.001).” page 3 right hand side paragraph).
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Examiner Figure 1: A translated version of Hernandez Gomez figure 2
With respect to claim 2, Hernandez Gomez and Banabilh teach the process according to claim 1. Hernandez Gomez teaches the predicted apnoea+hypopnoea index for the data set relating to the patient (“It is in this context of scarcity of resources and increasing demand for alternative diagnoses of SAHS where this invention is proposed, consisting of a system that, from at least two (front and profile), digital images of the patient that collect their face and neck (while awake) and through the use of image processing techniques, provide an accurate estimate of the apnea-hypopnea index (AHI).” Page 2 paragraph 4).
Banabilh further teaches wherein: when the apnoea+hypopnoea index is greater than 15, or the status having at least one value associated with a low risk of having a condition and at least one value associated with an established risk of having the condition, when the status has the at least one value associated with the established risk, the process comprises a diagnosis of the patient's condition, for example obstructive sleep apnoea-hypopnoea syndrome. (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13)
With respect to claim 3, Hernandez Gomez and Banabilh teach the process according to claim 1. Hernandez Gomez teaches the predicted apnoea+hypopnoea index for the data set relating to the patient (“It is in this context of scarcity of resources and increasing demand for alternative diagnoses of SAHS where this invention is proposed, consisting of a system that, from at least two (front and profile), digital images of the patient that collect their face and neck (while awake) and through the use of image processing techniques, provide an accurate estimate of the apnea-hypopnea index (AHI).” Page 2 paragraph 4).
Banabilh further teaches wherein: when the apnoea+hypopnoea index is between 15 and 30, or the status having at least one value associated with a moderate established risk of having a condition and at least one value associated with a severe established risk of having a condition, when the predicted status has the at least one value associated with the moderate established risk, the patient is diagnosed with a moderate degree of obstructive sleep apnoea-hypopnoea syndrome (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13), and when the apnoea+hypopnoea index is greater than 30, or when the predicted status has the at least one value associated with the severe established risk, the patient is diagnosed with a severe degree of obstructive sleep apnoea-hypopnoea syndrome. (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13)
With respect to claim 4, Hernandez Gomez and Banabilh teach the process according to claim l. Hernandez Gomez teaches a process that comprises acquiring images of patients with respect to reference morphology (“There are therefore sufficient grounds to state that the existence of specific anatomical features in SAHS patients should be reflected, in some way, in the images (both front and profile) that reflect the shape of their face and neck.” Page 3 paragraph 2 lines 11-13 and “at least two (front and profile), digital images of the patient that collect their face and neck (while awake)” page 2 paragraph 4) prior to inputting them into the machine learning model (see Examiner figure 1).
Banabilh teaches wherein the supplied data set relating to the patient comprising a 3D scan of a portion of the patient's head representing the patient's at least maxillofacial morphology (see figure 1), the process comprises, positioning the at least one homologous points on the 3D scan (see figure 1), determining, from the homologous points positioned, characteristic data of the patient's at least maxillofacial morphology with respect to the reference morphology (see figure 1 and “In the current study, the BMI was found to be significantly greater for the OSA group (32.3 kg/m2 ±7.4) when compared to the control group (24.8 kg/m2±6.5, p<0.001). In addition, the neck circumference was greater for the OSA group (42.7 cm± 2.5) compared to the control group (37.1 cm±2.2, p<0.001). The AHI was also significantly greater for the OSA group (40.0/h±30.3) when compared to the control group (2.0/h±2.0, p<0.001).” page 3 right hand side paragraph).
With respect to claim 5, Hernandez Gomez and Banabilh teach the process according to claim 1. Hernandez Gomez teaches a process that comprises acquiring images of patients with respect to reference morphology (“There are therefore sufficient grounds to state that the existence of specific anatomical features in SAHS patients should be reflected, in some way, in the images (both front and profile) that reflect the shape of their face and neck.” Page 3 paragraph 2 lines 11-13 and “at least two (front and profile), digital images of the patient that collect their face and neck (while awake)” page 2 paragraph 4) prior to inputting them into the machine learning model (see Examiner figure 1).
Banabilh teaches wherein, the supplied data set relating to the patient comprising a 3D scan of a portion of the patient's head (see figure 1), whereon the at least four homologous points are positioned (see figure 1), the process comprises, determining, from the at least four homologous points positioned, characteristic data of the patient's at least maxillofacial morphology with respect to the reference morphology (see figure 1 and “In the current study, the BMI was found to be significantly greater for the OSA group (32.3 kg/m2 ±7.4) when compared to the control group (24.8 kg/m2±6.5, p<0.001). In addition, the neck circumference was greater for the OSA group (42.7 cm± 2.5) compared to the control group (37.1 cm±2.2, p<0.001). The AHI was also significantly greater for the OSA group (40.0/h±30.3) when compared to the control group (2.0/h±2.0, p<0.001).” page 3 right hand side paragraph).
With respect to claim 6, Hernandez Gomez and Banabilh teach the process according to claim 1. wherein the supplied data set relating to the patient comprises characteristic data of the patient's at least maxillofacial morphology with respect to a reference morphology (see figure 1 and “In the current study, the BMI was found to be significantly greater for the OSA group (32.3 kg/m2 ±7.4) when compared to the control group (24.8 kg/m2±6.5, p<0.001). In addition, the neck circumference was greater for the OSA group (42.7 cm± 2.5) compared to the control group (37.1 cm±2.2, p<0.001). The AHI was also significantly greater for the OSA group (40.0/h±30.3) when compared to the control group (2.0/h±2.0, p<0.001).” page 3 right hand side paragraph), as determined according to the positioning of the homologous points on the 3D scan (see figure 1).
With respect to claim 8, Hernandez Gomez and Banabilh teach the process according to claim 1. Banabilh further teaches wherein, the process comprising determining, from the homologous points positioned, characteristic data of the patient's at least maxillofacial morphology with respect to the reference morphology (see figure 1), determining the characteristic data of the patient's at least maxillofacial morphology with respect to the reference morphology comprises a reduction of the dimensionality of the data from positioning the at least four homologous points, for example by a principal component analysis (“For statistical testing, principal components analysis (PCA) was used to identify significant shape changes, and FEM was used to compare the mean 3-D OSA facial morphology with the mean 3-D control facial morphology” page 3 paragraph 2 lines 24-27).
With respect to claim 9, Hernandez Gomez and Banabilh teach the process according to claim 1. Hernandez Gomez teaches wherein the data set relating to the patient furthermore comprises additional clinical information data on the patient (“Thus, for example, the score offered by the system object of the present invention could be combined with both measures of the patient's clinical history, such as his Body Mass Index (BMI) or Cervical Perimeter, as well as other scores obtained through procedures and systems. similar to the one presented in this invention that work with other biometric signals, such as the patient's voice signal” page 3 paragraph 3 lines 5-8), for example chosen from a patient body mass index (“Thus, for example, the score offered by the system object of the present invention could be combined with both measures of the patient's clinical history, such as his Body Mass Index (BMI) or Cervical Perimeter, as well as other scores obtained through procedures and systems. similar to the one presented in this invention that work with other biometric signals, such as the patient's voice signal” page 3 paragraph 3 lines 5-8), clinical examination upper airway obstruction indexes (“Thus, for example, the score offered by the system object of the present invention could be combined with both measures of the patient's clinical history, such as his Body Mass Index (BMI) or Cervical Perimeter, as well as other scores obtained through procedures and systems. similar to the one presented in this invention that work with other biometric signals, such as the patient's voice signal” page 3 paragraph 3 lines 5-8), the machine learning model being trained to predict the apnoea+hypopnoea index (“Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database. On the basis of feature vector data and using the information of which people do not suffer from SAHS and IAH indices of SAHS patients, a training process is carried out (step 304) that will generate patterns 207 of the Naive Bayes classifier, such and as detailed above. You could use other types of classification schemes of different patterns to the Naive Bayes classifier. In this case the pattern training process (step 304) would use the existing procedures corresponding to the pattern classification technique (Neural Networks, Regression Methods etc.) to be used (step 206) to obtain the SAHS score 102” page 6 paragraph 4 lines 4-12 and Table 2 and “A wide variety of pattern classification techniques can be used for this purpose, such as those based on Discriminant Analysis, Bayesian Classifiers, Neural Networks, Regression Techniques, etc.” page 5 paragraph 5 and Examiner figure 1), for the data set relating to the patient (“A database processing module (step 302) obtains each of the images from database 301 and applies the same face and neck detection techniques (step 204) and discriminative features extraction (step 205) described previously for the image analysis procedure performed by the system (Figure 2). In this way, a database of characteristic vectors 303 is generated (with the measurements included in Table 1). Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database” page 6 paragraph 4 and Table 2 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17, each person), from the plurality of data sets of the database (“A database processing module (step 302) obtains each of the images from database 301 and applies the same face and neck detection techniques (step 204) and discriminative features extraction (step 205) described previously for the image analysis procedure performed by the system (Figure 2). In this way, a database of characteristic vectors 303 is generated (with the measurements included in Table 1). Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database” page 6 paragraph 4 and Table 2 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), each data set of the database comprising said additional clinical information data relating to a patient of the set (see Examiner figure 1 element 208).
Banabilh further teaches the status dependent on an apnoea+hypopnoea index (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13), for the data set relating to the patient (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13) and additional patient information being recorded for all patients (“In addition, individual nurses, medical/dental students, and university staff were also asked to seek volunteers for the study. Each patient’s age, sex, height, and weight were recorded. The body mass index (BMI) was calculated from the patient’s height and weight in standard units (kg/m2), and the neck circumference (NC) was measured at the level of the thyroid cartilage.” Page 2 right hand side paragraph 1 lines 19-25).
With respect to claim 12, Hernandez Gomez and Banabilh teach the process according to claim 1. Hernandez Gomez teaches wherein, when the data set relating to the patient is supplied to the remote server (“A device with processing capacity 101, such as a computer, processes the captured Images” page 4 lines 14-15), the process comprises returning, from the remote server to a computer program product, the apnoea+hypopnoea index, predicted for the data set relating to the patient (see Examiner Figure 1).
Banabilh teaches a status based on the AHI (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13).
With respect to claim 14, Hernandez Gomez and Banabilh teaches the process according to claim 1. Hernandez Gomez teaches a remote server capable of communicating with computer program product, for the implementation of the process according to claim 1 (“A device with processing capacity 101, such as a computer, processes the captured Images” page 4 lines 14-15), comprising: a database relating to a set of distinct patients comprising a plurality of data sets (“A database processing module (step 302) obtains each of the images from database 301 and applies the same face and neck detection techniques (step 204) and discriminative features extraction (step 205) described previously for the image analysis procedure performed by the system (Figure 2). In this way, a database of characteristic vectors 303 is generated (with the measurements included in Table 1). Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database” page 6 paragraph 4 and Table 2 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17, each person), each data set of the database: comprising at least maxillofacial morphology data relating to a patient from the set, being associated with an apnoea+hypopnoea index (“Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database. On the basis of feature vector data and using the information of which people do not suffer from SAHS and IAH indices of SAHS patients, a training process is carried out (step 304) that will generate patterns 207 of the Naive Bayes classifier, such and as detailed above. You could use other types of classification schemes of different patterns to the Naive Bayes classifier. In this case the pattern training process (step 304) would use the existing procedures corresponding to the pattern classification technique (Neural Networks, Regression Methods etc.) to be used (step 206) to obtain the SAHS score 102” page 6 paragraph 4 lines 4-12 and Table 2 and “A wide variety of pattern classification techniques can be used for this purpose, such as those based on Discriminant Analysis, Bayesian Classifiers, Neural Networks, Regression Techniques, etc.” page 5 paragraph 5 and Examiner figure 1 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), and a machine learning model trained to predict an apnoea+hypopnoea index, for said data set, from the database (“Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database. On the basis of feature vector data and using the information of which people do not suffer from SAHS and IAH indices of SAHS patients, a training process is carried out (step 304) that will generate patterns 207 of the Naive Bayes classifier, such and as detailed above. You could use other types of classification schemes of different patterns to the Naive Bayes classifier. In this case the pattern training process (step 304) would use the existing procedures corresponding to the pattern classification technique (Neural Networks, Regression Methods etc.) to be used (step 206) to obtain the SAHS score 102” page 6 paragraph 4 lines 4-12 and Table 2 and “A wide variety of pattern classification techniques can be used for this purpose, such as those based on Discriminant Analysis, Bayesian Classifiers, Neural Networks, Regression Techniques, etc.” page 5 paragraph 5 and Examiner figure 1 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), the remote server being configured to: receive the data set relating to the patient comprising (“A device with processing capacity 101, such as a computer, processes the captured Images” page 4 lines 14-15 and Examiner figure 1), or making it possible to determine, characteristic data of the patient's at least maxillofacial morphology with respect to a reference morphology (“There are therefore sufficient grounds to state that the existence of specific anatomical features in SAHS patients should be reflected, in some way, in the images (both front and profile) that reflect the shape of their face and neck.” Page 3 paragraph 2 lines 11-13 and “at least two (front and profile), digital images of the patient that collect their face and neck (while awake)” page 2 paragraph 4), introduce the data set relating to the patient, comprising the characteristics of the patient's at least maxillofacial morphology, in the trained machine learning model (“Therefore, there will be a vector of characteristics for each pair of images (front and profile) associated with each person represented in the database. On the basis of feature vector data and using the information of which people do not suffer from SAHS and IAH indices of SAHS patients, a training process is carried out (step 304) that will generate patterns 207 of the Naive Bayes classifier, such and as detailed above. You could use other types of classification schemes of different patterns to the Naive Bayes classifier. In this case the pattern training process (step 304) would use the existing procedures corresponding to the pattern classification technique (Neural Networks, Regression Methods etc.) to be used (step 206) to obtain the SAHS score 102” page 6 paragraph 4 lines 4-12 and Table 2 and “A wide variety of pattern classification techniques can be used for this purpose, such as those based on Discriminant Analysis, Bayesian Classifiers, Neural Networks, Regression Techniques, etc.” page 5 paragraph 5 and Examiner figure 1 and “It should be noted, however, that the image capture device could be excluded from the procedure since, for example, the system could work on previously captured images accessible through some multimedia storage system or the Internet.” Page 4 lines 15-17), such that the learning model predicts an apnoea+hypopnoea index, for the data set relating to the patient (see examiner figure 1).
Banabilh teaches a status dependent on an apnoea+hypopnoea index (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13) and data being dependent on the positioning of at least four homologous points (see figure 1), on a 3D scan of a portion of the patient's head representing their at least maxillofacial morphology (see figure 1)
Claims 7, 10-11,13 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Hernandez Gomez and Banabilh as applied to claim 1 above, and further in view of Cistulli (WO 2009043080 A1).
With respect to claim 7, Hernandez Gomez and Banabilh teach the process according to claim 1. Cistulli additionally teaches wherein a data set relating to a patient comprises, or makes it possible to furthermore determine, characteristic data of the patient's submandibular morphology “The at least one calculated measurement may comprise at least one areal measurement selected from the group of thyromental space area (sag), cricomental space area (sag), anterior neck space area (sag), submandibular soft tissue area (sag)…” paragraph 0019 lines 1-3) with respect to the reference morphology (“The craniofacial landmarks identified may be selected from the group of Tragion (L), Tragion (R), Gordon (L), Gonion (R), Euryon (L), Euryon (R), Exocanthion (L), Exocanthion (R), Endocanthion (L), Endocanthion (R), Alare (L), Alare (R), Neck (L), Neck (R), Tragion, Exocanthion, Supraorbital ridge, Glabella, Nasion, Subnasion, Stomion, Sublabiale, Gnathion, Mentum, Cervical point, Thyroid, Cricoid, Neck plane, Sternal notch, Gonion, Ramus, Opisthocranion, Vertex, Anterior neck, Posterior neck, Columella of nose, Labiale superius, Labiale inferius, Cheilion (L) or Cheilion (R). The landmarks may be automatically identified with the use of a facial recognition software package.” Paragraph 0015), said characteristic data being dependent on a positioning of at least three supplementary homologous points (“The craniofacial landmarks identified may be selected from the group of Tragion (L), Tragion (R), Gordon (L), Gonion (R), Euryon (L), Euryon (R), Exocanthion (L), Exocanthion (R), Endocanthion (L), Endocanthion (R), Alare (L), Alare (R), Neck (L), Neck (R), Tragion, Exocanthion, Supraorbital ridge, Glabella, Nasion, Subnasion, Stomion, Sublabiale, Gnathion, Mentum, Cervical point, Thyroid, Cricoid, Neck plane, Sternal notch, Gonion, Ramus, Opisthocranion, Vertex, Anterior neck, Posterior neck, Columella of nose, Labiale superius, Labiale inferius, Cheilion (L) or Cheilion (R). The landmarks may be automatically identified with the use of a facial recognition software package.” Paragraph 0015 and “The at least one calculated measurement may comprise at least one areal measurement selected from the group of thyromental space area (sag), cricomental space area (sag), anterior neck space area (sag), submandibular soft tissue area (sag)…” paragraph 0019 lines 1-3), on the 3D scan of a portion of the patient's head representing their at least maxillofacial and submandibular morphology (“Subjects were recruited from a population referred to a sleep laboratory for assessment of OSA. Craniofacial assessment was performed using three standardised techniques: (1) standardised photographic technique (SPT); (2) laser-calibrated digicam (LCD); (3) 3D digitisation (3DD). These techniques, in combination with computer software analyses, provided detailed quantification of craniofacial geometry. Test-retest reliability and agreement between the techniques were assessed.” Paragraph 0140).
Cistulli is analogous art in the same field of endeavor as the current invention. Cistulli is directed towards craniofacial scans and their relation to OSA (“a need exists for a clinically practical, inexpensive and non-invasive method of quantitative craniofacial assessment of a patient's risk of a condition such as OSA or others where the condition is related to the patient's craniofacial morphology.” Paragraph 0011 And “In a first aspect, there is provided a method for assessing the presence of a condition in a patient or the patient's susceptibility to the condition, comprising the steps of: obtaining at least one photograph or image of the patient's craniofacial features; identifying a plurality of craniofacial landmarks derived from the photograph; calculating at least one measurement of the patient's craniofacial morphology from at least two of the landmarks; comparing the at least one measurement with craniofacial morphology measurements identified as being indicative of the presence of or susceptibility to the condition; and on the basis of the comparing, providing an assessment of the presence of the condition in the patient or the patient's susceptibility to the condition.” Paragraph 0012). A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine the system of Hernandez Gomez and Banabilh with the submandibular 3D scan teachings of Cistulli by utilizing the additionally taught landmark markings in conjunction with the previously taught points and additionally necessitated system structure, expecting an expansion in the systems diagnostic capabilities, due to the increase in relevant points being analyzed. (In a first aspect, there is provided a method for assessing the presence of a condition in a patient or the patient's susceptibility to the condition, comprising the steps of: obtaining at least one photograph or image of the patient's craniofacial features; identifying a plurality of craniofacial landmarks derived from the photograph; calculating at least one measurement of the patient's craniofacial morphology from at least two of the landmarks; comparing the at least one measurement with craniofacial morphology measurements identified as being indicative of the presence of or susceptibility to the condition; and on the basis of the comparing, providing an assessment of the presence of the condition in the patient or the patient's susceptibility to the condition.” Paragraph 0012).
With respect to claim 10, Hernandez Gomez and Banabilh teach the process according to claim 1. Cistulli further teaches wherein the data set relating to the patient furthermore comprises, or makes it possible to furthermore determine, supplementary characteristic data of the patient's maxillofacial morphology in at least one position from a prognathic position and a retrognathic position (see paragraphs 0017 and 0018 for various measurements taken including, angle, depth and length of the various areas).
Cistulli is analogous art in the same field of endeavor as the current invention. Cistulli is directed towards craniofacial scans and their relation to OSA (“a need exists for a clinically practical, inexpensive and non-invasive method of quantitative craniofacial assessment of a patient's risk of a condition such as OSA or others where the condition is related to the patient's craniofacial morphology.” Paragraph 0011 And “In a first aspect, there is provided a method for assessing the presence of a condition in a patient or the patient's susceptibility to the condition, comprising the steps of: obtaining at least one photograph or image of the patient's craniofacial features; identifying a plurality of craniofacial landmarks derived from the photograph; calculating at least one measurement of the patient's craniofacial morphology from at least two of the landmarks; comparing the at least one measurement with craniofacial morphology measurements identified as being indicative of the presence of or susceptibility to the condition; and on the basis of the comparing, providing an assessment of the presence of the condition in the patient or the patient's susceptibility to the condition.” Paragraph 0012). A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine the system of Hernandez Gomez and Banabilh with the submandibular 3D scan teachings of Cistulli by utilizing the additionally taught landmark markings in conjunction with the previously taught points and additionally necessitated system structure, expecting an expansion in the systems diagnostic capabilities, due to the increase in relevant points being analyzed. (In a first aspect, there is provided a method for assessing the presence of a condition in a patient or the patient's susceptibility to the condition, comprising the steps of: obtaining at least one photograph or image of the patient's craniofacial features; identifying a plurality of craniofacial landmarks derived from the photograph; calculating at least one measurement of the patient's craniofacial morphology from at least two of the landmarks; comparing the at least one measurement with craniofacial morphology measurements identified as being indicative of the presence of or susceptibility to the condition; and on the basis of the comparing, providing an assessment of the presence of the condition in the patient or the patient's susceptibility to the condition.” Paragraph 0012).
With respect to claim 11, Hernandez Gomez and Banabilh teach the process according to claim 1. Cistulli teaches the process comprising the acquisition of the 3D scan of a portion of the patient's head representing their at least maxillofacial morphology (“Subjects were recruited from a population referred to a sleep laboratory for assessment of OSA. Craniofacial assessment was performed using three standardised techniques: (1) standardised photographic technique (SPT); (2) laser-calibrated digicam (LCD); (3) 3D digitisation (3DD). These techniques, in combination with computer software analyses, provided detailed quantification of craniofacial geometry. Test-retest reliability and agreement between the techniques were assessed.” Paragraph 0140), the acquisition of the 3D scan comprising a verification of the alignment of the patient's head (“The subject was aligned next to the true vertical pole in the standing position with their head in the neutral head posture. This was achieved by asking the subject to rock his or her head back and forth, aided by looking into a mirror until a comfortable head posture was assumed. The operator ensured the subject landmark nasion is aligned along the pole-to-wall plane. …The operator also aligned the head so that the frontal view was symmetrical by ensuring both ears are equally seen. …The frontal photograph was then taken. The patient was asked to remain stationary after the photograph was taken and a head clip with a mounted laser pointer device was place over the subject's head. The operator aligned the laser pointer so it pointed at the centre of the camera lens whilst the subject remained stationary.” Paragraph 0081 and “The patient was then asked to turn his or her body 90 degrees to face the lateral wall for the profile photograph. The operator ensured the subject's mid-sagittal plane was aligned with the pole-to- wall plane. The subject was then asked to adjust his or her head position so the laser pointer is aligned with a vertical line on the wall to ensure a precise profile view of the subject's head, i.e. exactly half of the subject's face is on view. The neutral head posture was assumed, again aided by looking into a handheld mirror.” Paragraph 0082), by an alignment device (see paragraph 0081 and 0082 for alignment process and devices (laser, mirror, pole)).
Cistulli is analogous art in the same field of endeavor as the current invention. Cistulli is directed towards craniofacial scans and their relation to OSA (“a need exists for a clinically practical, inexpensive and non-invasive method of quantitative craniofacial assessment of a patient's risk of a condition such as OSA or others where the condition is related to the patient's craniofacial morphology.” Paragraph 0011 And “In a first aspect, there is provided a method for assessing the presence of a condition in a patient or the patient's susceptibility to the condition, comprising the steps of: obtaining at least one photograph or image of the patient's craniofacial features; identifying a plurality of craniofacial landmarks derived from the photograph; calculating at least one measurement of the patient's craniofacial morphology from at least two of the landmarks; comparing the at least one measurement with craniofacial morphology measurements identified as being indicative of the presence of or susceptibility to the condition; and on the basis of the comparing, providing an assessment of the presence of the condition in the patient or the patient's susceptibility to the condition.” Paragraph 0012). A person of ordinary skill in the art, before the effective filing date of the claimed invention, would have found it obvious to combine the system of Hernandez Gomez and Banabilh with the submandibular 3D scan teachings of Cistulli by utilizing the additionally taught landmark markings in conjunction with the previously taught points and additionally necessitated system structure, expecting an expansion in the systems diagnostic capabilities, due to the increase in relevant points being analyzed. (In a first aspect, there is provided a method for assessing the presence of a condition in a patient or the patient's susceptibility to the condition, comprising the steps of: obtaining at least one photograph or image of the patient's craniofacial features; identifying a plurality of craniofacial landmarks derived from the photograph; calculating at least one measurement of the patient's craniofacial morphology from at least two of the landmarks; comparing the at least one measurement with craniofacial morphology measurements identified as being indicative of the presence of or susceptibility to the condition; and on the basis of the comparing, providing an assessment of the presence of the condition in the patient or the patient's susceptibility to the condition.” Paragraph 0012).
With respect to claim 13, Hernandez Gomez and Banabilh teach the process according to claim 1. Hernandez Gomez teaches wherein, when the data set relating to the patient is supplied to the computer program product or when a computer program product receives the apnoea+hypopnoea index, returned from the remote server (“A device with processing capacity 101, such as a computer, processes the captured Images” page 4 lines 14-15) and predicting the the apnoea+hypopnoea index, for the data set relating to the patient (“It is in this context of scarcity of resources and increasing demand for alternative diagnoses of SAHS where this invention is proposed, consisting of a system that, from at least two (front and profile), digital images of the patient that collect their face and neck (while awake) and through the use of image processing techniques, provide an accurate estimate of the apnea-hypopnea index (AHI).” Page 2 paragraph 4).
Banabilh teaches a status based on the AHI (“For the OSA group, patients with mild OSA had an AHI of 5–15/h. Patients with moderate OSA had an AHI of 15–30/h, and patients with severe OSA had an AHI >30/h. The control group included subjects whose AHI ranged from 0–4/h” page 3 lines 9-13).
Cistulli similarly teaches a remote server (see figure 14) and a process comprising displaying information, by the computer program product