DETAILED ACTION
Applicant’s arguments, filed 12/23/2025, have been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Claims 1-20 and 22-23 are the current claims hereby under examination.
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 .
Allowable Subject Matter
The indicated allowability of claims 1-20 and 22-23 is withdrawn in view of the newly discovered reference to Soldner. Rejections based on the newly cited reference(s) follow.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 10, 18-19, and 23 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Soldner (US 20240277260).
Regarding claim 1, Solder teaches a headset, comprising:
a frame (Figs. 1A-B and 2A-B, frame 102);
a vibration sensor coupled to the frame, the vibration sensor configured to monitor vibration of a tissue of a user wearing the headset (Paragraph 0077, “The accelerometer may detect vibrations; the signal from an accelerometer may be processed to determine motion of, for example, the surface of the nose. This motion information may, for example, be interpreted to estimation respiration rate, whether the user is breathing in or out, and changes in respiration”; Paragraph 0111); and
a controller (Paragraph 0078), configured to:
receive a signal corresponding to the monitored vibration from the vibration sensor (Paragraph 0078; Paragraph 0077, wherein an accelerometer detects vibrations);
analyze the received signal to infer a sequence of states of the received signal (Paragraph 0077, “This motion information may, for example, be interpreted to estimation respiration rate, whether the user is breathing in or out, and changes in respiration”);
determine a value of a health metric based upon the inferred sequence of states (Paragraph 0103), and
perform an action using the determined value of the health metric (Paragraph 0106), wherein the vibration sensor is located proximal to the frame and is coupled to a spring that increases a compliance level of the vibration sensor (Paragraph 0040).
Regarding claim 2, Soldner further teaches wherein the vibration sensor is located within a nosepad of the frame (Paragraph 0039, “ As another example, nasal mount 104 may be a nosepiece of the frame 102. One or more biometric sensors 110 may be integrated in the nasal mount 104. The sensors 110 may include one or more motion sensors (e.g., accelerometers including but not limited to audio accelerometers)”).
Regarding claim 3, Soldner further teaches wherein the nosepad comprises an overmold configured to surround at least a portion of the vibration sensor (Figs. 6A-B, wherein the sensors are integrated in a diaphragm).
Regarding claim 4, Soldner further teaches wherein the overmold contains a slot or cavity configured to accommodate the vibration sensor (Figs. 6A-B, wherein the sensors are integrated in a diaphragm).
Regarding claim 5, Soldner further teaches wherein a flexible portion of the overmold comprises the spring (Paragraph 0040, “the diaphragm may be configured to provide a spring action to ensure contact of the sensor 110 with the surface of the nose 192”).
Regarding claim 6, Soldner further teaches wherein the sequence of states corresponds to respiratory states of the user (Paragraph 0077, “This motion information may, for example, be interpreted to estimation … whether the user is breathing in or out”).
Regarding claim 10, Soldner further teaches where the controller is further configured to:
determine a respiratory rate of the user based upon the sequence of states (Paragraph 0041, “Motion data from the user's face collected by these motion sensors 110 may be analyzed and used to estimate respiration rate”); and
determine the health metric based upon at least in part upon the respiratory rate, wherein the health metric indicates a physical or emotional condition of the user (Paragraph 0103, “Detecting respiration rate and changes in respiration may be used in many applications. Detecting respiration rate and changes may, for example, be used as a non-invasive method of tracking a user's psychological and emotional state, as well as their physiological state”).
Regarding claim 18, Soldner teaches a computer-implemented method, comprising:
receiving, from a vibration sensor coupled to a frame of a headset (Figs. 1A-B and 2A-B, frame 102), a signal corresponding to a monitored vibration of a tissue of a user wearing the headset (Paragraph 0077, “The accelerometer may detect vibrations; the signal from an accelerometer may be processed to determine motion of, for example, the surface of the nose. This motion information may, for example, be interpreted to estimation respiration rate, whether the user is breathing in or out, and changes in respiration”; Paragraph 0078; Paragraph 0111);
analyzing the received signal to infer a sequence of states of the received signal (Paragraph 0077, “This motion information may, for example, be interpreted to estimation respiration rate, whether the user is breathing in or out, and changes in respiration”);
determining a value of a health metric based upon the inferred sequence of states (Paragraph 0103), and
performing an action using the determined value of the health metric (Paragraph 0106), wherein the vibration sensor is located proximal to the frame of the headset and is coupled to a spring that improves the ability of the sensor to capture vibration data at lower frequencies (Paragraph 0040; Paragraph 0098).
Regarding claim 19, Soldner further teaches wherein the sequence of states corresponds to respiratory states of the user (Paragraph 0077, “This motion information may, for example, be interpreted to estimation … whether the user is breathing in or out”).
Regarding claim 23, Soldner further teaches wherein the lower frequencies comprise a range of frequencies between 300 and 1200 Hertz (Hz) (Paragraph 0098).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over as applied to claim 6 and 19 above, and further in view of Tanriover (US 20190038179 – cited by Applicant), hereinafter “Tanriover ‘179”.
Regarding claim 7, while Soldner discloses that the controller may use a multi-core neural engine (Paragraph 0133) to identify respiration parameters, Soldner fails to explicitly disclose using two models.
However, Tanriover ‘179 teaches an apparatus for identifying breathing patterns wherein one or more machine learning algorithms are used (Paragraph 0060) and wherein a breathing analyzer detects breathing phases and then to identify breathing pattern, breathing pattern metrics, and breathing activities based on the previously identified breathing phases (Paragraph 0022).
Tanriover ‘179 discusses this is useful to identify changes in subject activity (Paragraph 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soldner to incorporate the teachings of Tanriover ‘179 to identify changes in subject activity.
Regarding claim 20, while Soldner discloses that the controller may use a multi-core neural engine (Paragraph 0133) to identify respiration parameters, Soldner fails to explicitly disclose using two models.
However, Tanriover ‘179 teaches an apparatus for identifying breathing patterns wherein one or more machine learning algorithms are used (Paragraph 0060) and wherein a breathing analyzer detects breathing phases and then to identify breathing pattern, breathing pattern metrics, and breathing activities based on the previously identified breathing phases (Paragraph 0022).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Soldner and Tanriover ‘179 as applied to claim 7 above, and further in view of Odame (US 20220167856 – previously cited).
Regarding claim 8, while Soldner as modified discloses a neural engine and machine learning algorithms as above, Soldner as modified fails to explicitly disclose a kNN model.
However, Odame teaches a breathing monitor that uses a kNN model to classify respiratory phases (Paragraph 0023), and the substitution of a neural engine for a kNN model yields predictable results of classifying respiratory states to one of ordinary skill in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soldner and Tanriover ‘179 to incorporate the teachings of Odame to yield the predictable results of classifying respiratory states.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Soldner and Tanriover ‘179 as applied to claim 7 above, and further in view of Tran (US 20210106281 – previously cited).
Regarding claim 9, while Soldner as modified discloses a neural engine and machine learning algorithms as above, Soldner as modified fails to explicitly disclose a HSMM model.
However, Tran teaches a system for monitoring signals from sensors on a subject that uses a hidden semi-Markov model to segment signals to assess the medical well-being of the user (Paragraph 0199), and a person of ordinary skill in the art would have been motivated to combine the prior art to achieve the claimed invention so as to automatically segment signals into its features and there would have been a reasonable expectation of success in doing so. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soldner and Tanriover ‘179 to incorporate the teachings of Tran to automatically segment signals into its features.
Claims 11-16 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Soldner as applied to claim 1 and 18 above, and further in view of Tanriover (US 20190038186 – cited by Applicant).
Regarding claim 11, while Soldner discloses measuring respiration parameters and various states of the user as above, Soldner fails to disclose the limitations of the claim.
However, Tanriover teaches an apparatus for detecting vibrational signal data from a nasal bridge of a user (Abstract), wherein the controller is further configured to:
monitor the received signal to detect a predetermined characteristic within the received signal (Paragraph 0031, “may serve to process the vibration data generated by the sensor(s) 106 to identify chewing and/or drinking activities”);
responsive to detecting the predetermined characteristic:
identify a portion of the vibration signal corresponding to an event associated with the predetermined characteristic (Paragraph 0033, “analyze the vibration data from the user 104 to extract features associated with chewing food and/or drinking beverages”);
analyze the identified portion of the vibration signal to classify the identified event (Paragraph 0033, “use the features to identify categories of food chewed and/or beverages drank”); and
perform the action based upon a type of the identified event (Paragraph 0034, generate one or more outputs based on the user’s food chewing and/or beverage drinking activities”).
Tanriover discusses this method is useful to manage user habits of eating and drinking (Paragraph 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soldner to incorporate the teachings of Tanriover to manage user eating and drinking habits.
Regarding claim 12, Tanriover further discloses wherein the controller monitors the received signal to detect the predetermined characteristic In parallel with analyzing the received signal to infer a sequence of states of the received signal (Paragraph 0033, “analyzer 122 may analyze the vibration data 112 from the user 104 to determine breathing of the user, and analyze the vibration data 112 for food chewed and/or beverages drank during the non-breathing data intervals”).
Regarding claim 13, Tanriover further discloses wherein the event corresponds to an eating or drinking action by the user (Paragraph 0033, “analyze the vibration data 112 for food chewed and/or beverages drank”).
Regarding claim 14, Tanriover further discloses wherein performing the action comprises notifying the user of a level of food or fluid consumption of the user, or of a food type consumed by the user (Paragraph 0076, “the accumulated data may be analyzed to determine the category of food the user has consumed over a particular time period”).
Regarding claim 15, Tanriover further discloses wherein the controller is further configured to monitor identified events over time to determine a habit of the user (Paragraph 0034, “The outputs can, for example, be used to monitor the user’s eating and/or drinking, and be accumulated to determine the user’s eating and/or drinking habits”).
Regarding claim 16, Tanriover further discloses wherein the controller is further configured to cross-reference the value of the health metric determined based upon the inferred sequence of states with data associated with the identified event to determine a physical or emotional condition of the user (Paragraph 0101).
Regarding claim 22, while Soldner discloses measuring respiration parameters and various states of the user as above, Soldner fails to disclose the limitations of the claim.
However, Tanriover teaches an apparatus for detecting vibrational signal data from a nasal bridge of a user (Abstract), wherein the controller is further configured to:
monitor the received signal to detect a predetermined characteristic within the received signal (Paragraph 0031, “may serve to process the vibration data generated by the sensor(s) 106 to identify chewing and/or drinking activities”);
responsive to detecting the predetermined characteristic:
identify a portion of the vibration signal corresponding to an event associated with the predetermined characteristic (Paragraph 0033, “analyze the vibration data from the user 104 to extract features associated with chewing food and/or drinking beverages”);
analyze the identified portion of the vibration signal to classify the identified event (Paragraph 0033, “use the features to identify categories of food chewed and/or beverages drank”); and
perform the action based upon a type of the identified event (Paragraph 0034, generate one or more outputs based on the user’s food chewing and/or beverage drinking activities”).
Tanriover discusses this method is useful to manage user habits of eating and drinking (Paragraph 0002). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soldner to incorporate the teachings of Tanriover to manage user eating and drinking habits.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Soldner as applied to claim 1 above, and further in view of Shalon (US 20060064037 – previously cited).
Regarding claim 17, while Soldner discusses determining respiration parameters, Soldner fails to explicitly disclose measuring a tidal volume or a respiratory flow rate.
However, Shalon teaches a worn sensor system the uses vibration detectors/accelerometers (Paragraph 0141) and breathing sounds can be used to determine respiration rate and tidal volume (Paragraph 0299). Shalon indicates measuring these parameters is useful to determine physical activity of the user (Paragraph 0299). Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Soldner to incorporate the teachings of Shalon to determine the physical activity of the user.
Response to Arguments
Applicant’s arguments, see page 1, filed 12/23/2025, with respect to the 35 U.S.C. §112(b) rejections have been fully considered and are persuasive. Applicant has clarified the relationship between the overmold and the spring. The rejection of the claims has been withdrawn.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Abreu (US 20070106172) teaches a support structure with sensors positioned on a nosepad with a spring (Figs. 7A-D).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH MICHAEL HEALY whose telephone number is (703)756-5534. The examiner can normally be reached Monday - Friday 8:30am - 5:30pm ET.
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, Jason Sims can be reached at (571)272-7540. 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.
/NOAH M HEALY/Examiner, Art Unit 3791
/RENE T TOWA/Primary Examiner, Art Unit 3791