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 Amendment
In light of the amendments, the previous 112(b) rejections have been withdrawn.
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the previous 35 U.S.C. 103 rejections have been withdrawn.
Notice to Applicant
In the amendment dated 06/08/2026, the following has occurred: claims 1, 3, 5, 7, 9, 14, 15, 16, and 23 have been amended; claims 8, 19-20, 22, 24-27, and 29-30 have been canceled; claims 2, 4, 6, 10-13, 17-18, 21, and 28 remain unchanged; and no new claims have been added.
Claims 1-7, 9-18, 21, 23, and 28 are pending.
Effective Filing Date: 09/17/2024
Response to Arguments
35 U.S.C. 112(b) Rejections:
Applicant has overcome the 112(b) issue with claim 9 via amendment. Examiner withdraws this previous rejection.
35 U.S.C. 101 Rejections:
Applicant argues that the amended claims should overcome the 101. Examiner however respectfully disagrees.
Step 2A, Prong One:
Applicant argues that the abstract idea categorization of certain methods of organizing human activity is incorrect. Examiner however respectfully disagrees. Claims 1, 15, and 16 all recite this grouping in the updated 101 rejection section, though claim 16 also include mathematical concepts. Image processing and data extraction are not inherently limited to non-human activities.
Step 2A, Prong Two:
Applicant argues certain aspects of the abstract idea as being additional elements which are not insignificant, extra-solution activities. Examiner however respectfully disagrees. The listed features that Applicant states are additional elements are directed towards the abstract idea in the updated 101 rejection section. The transmission limitation also adds insignificant extra-solution activity.
Step 2B:
Applicant argues that the claims are adding significantly more than the abstract idea. Examiner however respectfully disagrees. Based on the above, Examiner’s classification of limitations as part of the abstract idea and additional elements are different than what Applicant has classified.
35 U.S.C. 103 Rejections:
Examiner withdraws these rejections in view of the amendments requiring an unreasonable combination of references in order reject these claims.
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 1-7, 9-18, 21, 23, and 28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Claims 1-7, 9-13, 16-18, 21, 23, and 28 are drawn to methods, claim 14 is drawn to a media, and claim 15 is drawn to a system, each of which is within the four statutory categories. Claims 1-7, 9-18, 21, 23, and 28 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a method comprising:
1) capturing a first set of images depicting at least a portion of an oral cavity region of a subject body;
2) detecting a set of image features associated with the oral cavity region within the first set of images, wherein detecting the set of image features comprises detecting a subregion of the oral cavity region of the subject body, the subregion including at least one of an upper teeth region, a lower teeth region, a back of a mouth region, or a tongue region;
3) accessing one or more stored feature thresholds comprising an axis of symmetry feature threshold defining a threshold degree of symmetry within one or more subregions of the oral cavity region of the subject body relative to a vertical axis;
4) extracting, from the first set of images and using the stored feature thresholds, a second set of images that satisfy one or more of the stored feature thresholds, wherein extracting the second set of images comprises extracting one or more images that include a subregion within a threshold percentage of vertical alignment;
5) upon extracting the second set of images, detecting a set of regions within the second set of images that represent one or more target anatomical structures within the oral cavity region of the subject body;
6) generating one or more anatomical features for at least one region of the set of regions that represent the target anatomical structures of the subject body;
7) accessing one or more stored obstructive sleep apnea features that indicate obstructive sleep apnea;
8) generating a prediction indicating a likelihood that the subject body suffers from obstructive sleep apnea using (i) the anatomical features for the at least one region of the oral cavity of the subject body and (ii) the stored obstructive sleep apnea features;
9) generating clinical decision support information based on the prediction, wherein the clinical decision support information comprises one or more identified anatomical abnormalities and one or more anatomical measurements contributing to the prediction; and
10) causing transmission of the clinical decision support information to a) a healthcare provider device.
Claim 1 recites, in part, performing the steps of 1) capturing a first set of images depicting at least a portion of an oral cavity region of a subject body, 2) detecting a set of image features associated with the oral cavity region within the first set of images, wherein detecting the set of image features comprises detecting a subregion of the oral cavity region of the subject body, the subregion including at least one of an upper teeth region, a lower teeth region, a back of a mouth region, or a tongue region, 3) accessing one or more stored feature thresholds comprising an axis of symmetry feature threshold defining a threshold degree of symmetry within one or more subregions of the oral cavity region of the subject body relative to a vertical axis, 4) extracting, from the first set of images and using the stored feature thresholds, a second set of images that satisfy one or more of the stored feature thresholds, wherein extracting the second set of images comprises extracting one or more images that include a subregion within a threshold percentage of vertical alignment, 5) upon extracting the second set of images, detecting a set of regions within the second set of images that represent one or more target anatomical structures within the oral cavity region of the subject body, 6) generating one or more anatomical features for at least one region of the set of regions that represent the target anatomical structures of the subject body, 7) accessing one or more stored obstructive sleep apnea features that indicate obstructive sleep apnea, 8) generating a prediction indicating a likelihood that the subject body suffers from obstructive sleep apnea using (i) the anatomical features for the at least one region of the oral cavity of the subject body and (ii) the stored obstructive sleep apnea features, 9) generating clinical decision support information based on the prediction, wherein the clinical decision support information comprises one or more identified anatomical abnormalities and one or more anatomical measurements contributing to the prediction; and 10) causing transmission of the clinical decision support information to a healthcare provider device. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claims recite how to analyze image data to determine a prediction of a condition.
Claim 15 recites a system comprising:
b) one or more computers and c) one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations:
11) capturing a first set of images depicting at least a portion of an oral cavity region of a subject body,
wherein capturing the first set of images comprises 11a) generating an estimated pose of the subject body within the first set of images, 11b) providing one or more prompts indicating an adjustment for the subject body based on the estimated pose, 11c) receiving one or more adjusted images responsive to the one or more prompts, and 11d) iteratively updating the estimated pose based on the one or more adjusted images until the oral cavity region satisfies one or more capture criteria,
wherein the one or more prompts comprise at least one of a prompt to position a face within a capture region, a prompt to open a mouth, or a prompt to extend a tongue outward from the mouth, and
wherein the one or more prompts comprise one or more visual prompts, audio prompts, haptic prompts, or combinations thereof;
12) detecting a set of anatomically labeled image features associated with the oral cavity region within the first set of images;
13) accessing one or more stored feature thresholds;
14) extracting, from the first set of images and using the stored feature thresholds, a second set of images that satisfy one or more of the stored feature thresholds;
15) upon extracting the second set of images, detecting, using the anatomically labeled image features, a set of regions within the second set of images that represent one or more target anatomical structures within the oral cavity region of the subject body;
16) generating one or more anatomical features for at least one region of the set of regions that represent the target anatomical structures of the subject body;
17) accessing one or more stored obstructive sleep apnea features that indicate obstructive sleep apnea;
18) generating a prediction indicating a likelihood that the subject body suffers from obstructive sleep apnea using (i) the anatomical features for the at least one region of the oral cavity of the subject body and (ii) the stored obstructive sleep apnea features; and
19) transmitting a signal indicating the generated prediction of obstructive sleep apnea to b) a connected device.
Claim 15 recites, in part, performing the steps of 11) capturing a first set of images depicting at least a portion of an oral cavity region of a subject body, wherein capturing the first set of images comprises 11a) generating an estimated pose of the subject body within the first set of images, 11b) providing one or more prompts indicating an adjustment for the subject body based on the estimated pose, 11c) receiving one or more adjusted images responsive to the one or more prompts, and 11d) iteratively updating the estimated pose based on the one or more adjusted images until the oral cavity region satisfies one or more capture criteria, wherein the one or more prompts comprise at least one of a prompt to position a face within a capture region, a prompt to open a mouth, or a prompt to extend a tongue outward from the mouth, and wherein the one or more prompts comprise one or more visual prompts, audio prompts, haptic prompts, or combinations thereof, 12) detecting a set of anatomically labeled image features associated with the oral cavity region within the first set of images, 13) accessing one or more stored feature thresholds, 14) extracting, from the first set of images and using the stored feature thresholds, a second set of images that satisfy one or more of the stored feature thresholds, 15) upon extracting the second set of images, detecting, using the anatomically labeled image features, a set of regions within the second set of images that represent one or more target anatomical structures within the oral cavity region of the subject body, 16) generating one or more anatomical features for at least one region of the set of regions that represent the target anatomical structures of the subject body, 17) accessing one or more stored obstructive sleep apnea features that indicate obstructive sleep apnea, and 18) generating a prediction indicating a likelihood that the subject body suffers from obstructive sleep apnea using (i) the anatomical features for the at least one region of the oral cavity of the subject body and (ii) the stored obstructive sleep apnea features. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claims recite how to analyze image data to determine a prediction of a condition.
Claim 16 recites a method comprising:
20) capturing a first set of images depicting at least a portion of a subject body;
21) detecting a set of image features within the first set of images;
22) accessing one or more stored feature thresholds;
23) extracting, from the first set of images and using the stored feature thresholds, a second set of images that satisfy one or more criteria corresponding to the stored feature thresholds;
24) upon extracting the second set of images, detecting a set of regions within the second set of images that represent one or more target anatomical structures of the subject body;
25) generating one or more anatomical features for at least one region of the set of regions that represent the target anatomical structures of the subject body;
26) accessing one or more stored medical condition features associated with one or more medical conditions;
27) processing, using e) one or more machine learning models, a first four-channel input tensor corresponding to a tongue-in view and a second four-channel input tensor corresponding to a tongue-out view to generate a predicted medical condition,
wherein each four-channel input tensor comprises a three-channel image of a portion of the subject body and a one-channel anatomical model comprising a segmentation mask corresponding to the portion of the subject body including at least one of a tongue region and a pharyngeal wall region, and
28) wherein the second set of images are filtered based on geometric alignment criteria including a threshold for vertical symmetry using one or more segmentation mask boundaries associated with the segmentation mask;
29) generating the predicted medical condition of the subject body, from among the one or more medical conditions, using the anatomical features and the stored medical condition features; and
30) responsive to the generated predicted medical condition satisfying a threshold, automatically scheduling an appointment with a healthcare provider.
Claim 16 recites, in part, performing the steps of 20) capturing a first set of images depicting at least a portion of a subject body, 21) detecting a set of image features within the first set of images, 22) accessing one or more stored feature thresholds, 23) extracting, from the first set of images and using the stored feature thresholds, a second set of images that satisfy one or more criteria corresponding to the stored feature thresholds, 24) upon extracting the second set of images, detecting a set of regions within the second set of images that represent one or more target anatomical structures of the subject body, 25) generating one or more anatomical features for at least one region of the set of regions that represent the target anatomical structures of the subject body, 26) accessing one or more stored medical condition features associated with one or more medical conditions, 27) processing, using one or more models, a first four-channel input tensor corresponding to a tongue-in view and a second four-channel input tensor corresponding to a tongue-out view to generate a predicted medical condition, 28) wherein each four-channel input tensor comprises a three-channel image of a portion of the subject body and a one-channel anatomical model comprising a segmentation mask corresponding to the portion of the subject body including at least one of a tongue region and a pharyngeal wall region, 29) wherein the second set of images are filtered based on geometric alignment criteria including a threshold for vertical symmetry using one or more segmentation mask boundaries associated with the segmentation mask, 30) generating the predicted medical condition of the subject body, from among the one or more medical conditions, using the anatomical features and the stored medical condition features, and 31) responsive to the generated predicted medical condition satisfying a threshold, automatically scheduling an appointment with a healthcare provider. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claims recite how to analyze image data to determine a prediction of a condition.
Claim 16 also recites, in part, the step of 27) processing, using e) one or more machine learning models, a first four-channel input tensor corresponding to a tongue-in view and a second four-channel input tensor corresponding to a tongue-out view to generate a predicted medical condition, wherein each four-channel input tensor comprises a three-channel image of a portion of the subject body and a one-channel anatomical model comprising a segmentation mask corresponding to the portion of the subject body including at least one of a tongue region and a pharyngeal wall region. These steps correspond to Mathematical Concepts.
Going forward, the above abstract ideas will be considered as a singular abstract concept for further analysis.
Depending claims 2-7, 9-14, 17-18, 21, 23, and 28 include all of the limitations of claims 1 and 16, and therefore likewise incorporate the above described abstract idea. Depending claims 2-7, 9-10, 12-13, 17-18, 21, and 23 add additional functional steps. Additionally, the limitations of depending claims 11 and 28 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 2-7, 9-14, 17-18, 21, 23, and 28 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 16 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a healthcare provider device, b) one or more computers, c) one or more storage devices, d) a connected device (from claims 15 and 23), e) one or more machine learning models (from claims 16 and 3), and f) a handheld image capture device to perform the claimed steps.
Claim 15 also adds the additional element step of and 19) “transmitting a signal indicating the generated prediction of obstructive sleep apnea to a connected device”.
The a) healthcare provider device, d) connected device, and f) handheld image capture adds insignificant extra-solution activity to the abstract idea which amounts to insignificant application (a) and d)) and data gathering (f)), see MPEP 2106.05(g).
Furthermore, the b) one or more computers, c) one or more storage devices, and e) one or more machine learning models in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0198] where there is such as general-purpose microprocessors, also there is a lack of description for anything but what may be considered as generic computing components for these elements, see MPEP 2106.05(f)).
Lastly, the additional element step of 19) “transmitting a signal indicating the generated prediction of obstructive sleep apnea to a connected device” adds insignificant extra-solution activity to the abstract idea which amounts to insignificant application, see MPEP 2106.05(g).
Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of using a) a healthcare provider device, b) one or more computers, c) one or more storage devices, d) a connected device, e) one or more machine learning models to perform the claimed steps and the additional element step of 19) “transmitting a signal indicating the generated prediction of obstructive sleep apnea to a connected device” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates a prediction utilizing b) one or more computers, c) one or more storage devices, and e) one or more machine learning models, thus these components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Furthermore, the a) healthcare provider device, d) connected device, and f) handheld image capture in the claims adds insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives image data, and transmits prediction data to a connected device or provider device over a network, for example the Internet.
Lastly, the additional step of 19) “transmitting a signal indicating the generated prediction of obstructive sleep apnea to a connected device” in the claims adds insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives prediction data, and transmits the data to a connected device over a network, for example the Internet.
Mere instructions to apply an exception using generic computer components or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-7, 9-18, 21, 23, and 28 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
No Art Rejections
Claims 1-7, 9-18, 21, 23, and 28 do not have art rejections as any reference or combination of references would be unreasonable.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
Prosecution Insights