DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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.
Information Disclosure Statement
2. The Information Disclosure Statement submitted on 27 December 2024 has been considered by the Examiner.
Priority
3. Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Kingdom of Denmark on 31 May 2022. It is noted, however, that applicant has not filed a certified copy of the application (PA 2022 70286) as required by 37 CFR 1.55.
Claim Rejections - 35 USC § 102
4. 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.
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.
5. Claims 1-7 and 17-22 are rejected under 35 U.S.C. 102 (a) (1) and (a) (2) as being anticipated by Vairavan et al. (US 2020/0327882 A1).
Regarding claims 1 and 22, Vairavan teaches a hearing system and a method (the hearing device 500 an audio input arrangement 506 which consist of a microphone for capturing vocalized speech of a subject [abstract, 0034]), comprising:
an electronic device (the hearing device 500 comprises one or more electronic devices [0034, 0040]. For example, one of the electronic devices may be a display 512 for generating a visual output to a user [0034]. Meanwhile, the hearing device 500 may communicate with another electronic device (e.g., mobile computing device, smart phone, computing device, or tablet) [0034, 0040]); and
a hearing device comprising one or more sensors (the hearing device 500 comprises a sensor or audio input arrangement 506 which consist of a microphone for capturing vocalized speech of a subject [0034]);
wherein the hearing system is configured to:
obtain external sensor data from the one or more sensors (the hearing device 500 includes a processor 502 that is configure the receive the audio data from the audio input arrangement 506 (e.g., microphone) [0034]);
determine, based on the external sensor data, a health parameter indicative of a cognitive state of a user of the hearing device (the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the audio recordings from the audio input arrangement 506 (e.g., microphone) to determine health parameters (e.g., verbal memory, verbal episodic memory, or rate of learning) which are associated with a cognitive state or neurodegeneration [0020, 0034-0037]);
determine whether the health parameter satisfies a first criterion indicative of a cognitive decline (the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the audio recordings from the audio input arrangement 506 (e.g., microphone) to determine health parameters (e.g., verbal memory, recognition memory, verbal episodic memory, or rate of learning) which are associated with a cognitive state or neurodegeneration [0020, 0034-0037]. For example, the ensemble classifier 516 may indicate that patient is suffering from a cognitive decline if the health parameters are abnormal (e.g., decline in short-term verbal memory or decline in recognition memory) [0037]); and
if the health parameter satisfies the first criterion, output a health representation associated with the cognitive state (the hearing device 500 is configured to generate an output of information to the clinician which represents the patient’s cognitive decline and the associated health parameters (e.g., decline in short-term verbal memory or decline in recognition memory) [0037]. Specifically, hearing device 500 may output the results on the display 512 for the clinician or user [0034, 0036-0037]).
Regarding claim 2, Vairavan teaches wherein the hearing system is configured to output the health representation using a display of the electronic device (the hearing device 500 is configured to generate an output of information to the clinician which represents the patient’s cognitive decline and the associated health parameters (e.g., decline in short-term verbal memory or decline in recognition memory) [0037]. Specifically, hearing device 500 may output the results on the display 512 for the clinician or user [0034, 0036-0037]).
Regarding claim 3, Vairavan teaches wherein if the first criterion is satisfied, the hearing system is configured to perform a cognitive test scheme via an interface of the electronic device (as stated previously above, the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the audio recordings from the audio input arrangement 506 (e.g., microphone) to determine health parameters (e.g., verbal memory, recognition memory, verbal episodic memory, or rate of learning) which are associated with a cognitive decline [0020, 0034-0037]. Furthermore, the output generated from the ensemble classifier 516 of the hearing device 500 may assist a clinician in directing at-risk patients (e.g., cognitive decline) to further cognitive test so as to confirm the ensemble output [0037])
Regarding claim 4, Vairavan teaches wherein the external sensor data comprises microphone input data, and wherein the health parameter is based on the microphone input data (the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the audio recordings from the audio input arrangement 506 (e.g., microphone) to determine health parameters (e.g., verbal memory, verbal episodic memory, or rate of learning) which are associated with a cognitive state or neurodegeneration [0020, 0034-0037]).
Regarding claim 5, Vairavan teaches wherein the hearing system is configured to determine the health parameter by determining, based on the microphone input data, a first voice biomarker (the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the audio recordings (e.g., voice biomarker) from the audio input arrangement 506 (e.g., microphone) to determine health parameters (e.g., verbal memory, verbal episodic memory, or rate of learning) which are associated with a cognitive state or neurodegeneration [0020, 0034-0037]).
Regarding claim 6, Vairavan teaches wherein the hearing system is configured to determine the first voice biomarker by determining one or more of:
a verbal fluency parameter or a speech rhythm parameter (the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the speech or audio recordings (e.g., biomarkers) from the audio input arrangement 506 (e.g., microphone) to determine if the patient is suffering from a cognitive decline or neurodegeneration [0020, 0034-0037]. For example, the biomarkers of the audio recordings may include a decline short-term verbal memory, a decline in delayed verbal memory, or decline in speech recall performance [0037, 0042]).
Regarding claim 7, Vairavan teaches wherein the first voice biomarker is based on one or more of:
a verbal fluency parameter or a speech rhythm parameter (the hearing device 500 comprises an ensemble classifier 516 that is configured to analyze the speech or audio recordings (e.g., biomarkers) from the audio input arrangement 506 (e.g., microphone) to determine if the patient is suffering from a cognitive decline or neurodegeneration [0020, 0034-0037]. For example, the biomarkers of the audio recordings may include a decline short-term verbal memory, a decline in delayed verbal memory, or decline in speech recall performance [0037, 0042]).
Regarding claim 17, Vairavan teaches wherein the hearing system comprises machine learning circuitry configured to operate according to a machine learning model, wherein the hearing system is configured to determine the health parameter using the machine learning model (the hearing device 500 comprises an ensemble classifier 516 (e.g. machine learning classifier) that is configured to analyze the audio recordings from the audio input arrangement 506 (e.g., microphone) to determine health parameters (e.g., verbal memory, recognition memory, verbal episodic memory, or rate of learning) which are associated with a cognitive state or neurodegeneration [abstract, 0020, 0034-0037]. For example, the ensemble classifier 516 may indicate that patient is suffering from a cognitive decline if the health parameters are abnormal (e.g., decline in short-term verbal memory or decline in recognition memory) [0037]).
Regarding claim 18, Vairavan teaches wherein one of the electronic device or the hearing device comprises a Bluetooth interface (as stated previously in claim 1, the hearing device 500 may communicate with another electronic device (e.g., mobile computing device, smart phone, computing device, or tablet) [0019, 0034, 0040]. The Examiner respectfully submits that a mobile computing devices, tablets, or smart phones (e.g., iPhone) are known to include Bluetooth technology for communicating with other devices [0040]).
Regarding claim 19, Vairavan teaches wherein the electronic device is a server device (as stated previously in claim 1, the hearing device 500 may communicate with another electronic device (e.g., mobile computing device, smart phone, computing device, or tablet) [0019, 0034, 0040]. The Examiner respectfully submits that the computing device may store data in a database server which is connected to a communications network [0019, 0034, 0040]).
Regarding claim 20, Vairavan teaches wherein the server device is configured to train a machine learning model to obtain an updated machine learning model, and to transmit to the electronic device and/or the hearing device, the updated machine learning model (the hearing device 500 communicates with a computing device or an external user device which stores data in a database server which is connected to a communications network [0019, 0033-0034, 0040]. For example, the external user devices or computing devices may store a trained ensemble classifier 516 (e.g., machine learning model) in the database which can be transmitted or communicated to the hearing device 500 [0019, 0033-0034, 0040]).
Regarding claim 21, Vairavan teaches wherein the electronic device is an accessory device (the hearing device 500 comprises one or more electronic devices [0034, 0040]. For example, one of the electronic devices may be a display 512 for generating a visual output to a user [0034]. Meanwhile, the hearing device 500 may communicate with another electronic device (e.g., mobile computing device, smart phone, computing device, or tablet) [0034, 0040]. The Examiner respectfully submits that electronic devices such as a smart phone is considered to be an accessory device [0034, 0040]).
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 8-16 are rejected under 35 U.S.C. 103 as being unpatentable over Vairavan et al. in view of Chen (WO 2021/007394 A1, with citations to the corresponding US Publication No. 2022/0273227 A1).
Regarding claim 8, Vairavan suggests the hearing system according to claim 1. Vairavan does not explicitly teach wherein the external sensor data comprises physiological data, and wherein the health parameter is based on the physiological data.
The prior art by Chen is analogous to Vairavan, as they both teach system that utilizes measured data (e.g., biomarkers) from a sensor to determine if the patient is experiencing cognitive decline ([abstract, 0033-0034, 0084]).
Chen teaches wherein the external sensor data comprises physiological data, and wherein the health parameter is based on the physiological data (the sensors 202 are configured to obtain the sensed physiological data 216 (e.g. heart rate, blood pressure, or activity parameters) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the Vairavan’s hearing system to include external sensors for measuring physiological data, as taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the physiological data (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 9, Vairavan in view of Chen suggests the hearing system according to claim 9. Chen teaches wherein the hearing system is configured to determine the health parameter by determining, based on the physiological data, a first physiological biomarker (the sensors 202 are configured to obtain the sensed physiological data 216 (e.g. heart rate, blood pressure, or activity parameters) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the hearing system suggested by Vairavan in view of Chen to determine a first biomarker based on the physiological data, as further taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the physiological data (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 10, Vairavan in view of Chen suggests the hearing system according to claim 9. Chen teaches wherein the hearing system is configured to determine the first physiological biomarker by determining one or more of:
a blood pressure parameter, a heart rate parameter, or a temperature parameter (the sensors 202 are configured to obtain the sensed physiological data 216 (e.g. heart rate, blood pressure, or temperature data) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the hearing system suggested by Vairavan in view of Chen to determine the first biomarker based on a blood pressure parameter, a heart rate parameter, or a temperature parameter, as further taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the physiological data (e.g., blood pressure, heart rate, or temperature) (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 11, Vairavan in view of Chen suggests the hearing system according to claim 9. Chen teaches wherein the first physiological biomarker is based on one or more of:
the blood pressure parameter, the heart rate parameter, or the temperature parameter (the sensors 202 are configured to obtain the sensed physiological data 216 (e.g. heart rate, blood pressure, or temperature data) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the hearing system suggested by Vairavan in view of Chen to determine the first biomarker based on a blood pressure parameter, a heart rate parameter, or a temperature parameter, as further taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the physiological data (e.g., blood pressure, heart rate, or temperature) (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 12, Vairavan taches the hearing system of claim 1. Vairavan does not explicitly teach wherein the external sensor data comprises biokinetic data, and wherein the health parameter is based on the biokinetic data.
However, Chen teaches wherein the external sensor data comprises biokinetic data, and wherein the health parameter is based on the biokinetic data (the sensors 202 are configured to obtain the sensed biokinetic data 216 (e.g. motion activity or accelerometer data) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the Vairavan’s hearing system to include external sensors for measuring biokinetic data, as taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the biokinetic data (e.g., motion activity or accelerometer data) (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 13, Vairavan in view of Chen suggests the hearing system according to claim 12. Chen teaches wherein the hearing system is configured to determine the health parameter by determining, based on the biokinetic data, a first biokinetic biomarker (the sensors 202 are configured to obtain the sensed biokinetic data 216 (e.g. motion activity or accelerometer data) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the hearing system suggested by Vairavan in view of Chen to determine a biokinetic biomarker based on the biokinetic data, as taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the biokinetic data (e.g., motion activity or accelerometer data) (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 14, Vairavan in view of Chen suggests the hearing system according to claim 13. Chen teaches wherein the hearing system is configured to determine the first biokinetic biomarker by determining one or more of:
a motion parameter (the sensors 202 are configured to obtain the sensed biokinetic data 216 (e.g. motion activity or accelerometer data) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the hearing system suggested by Vairavan in view of Chen to determine a biokinetic biomarker based on a motion parameter, as taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the biokinetic data (e.g., motion activity or accelerometer data) (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 15, Vairavan in view of Chen suggests the hearing system according to claim 14. Chen teaches wherein the first biokinetic biomarker is based on one or more of:
a motion parameter (the sensors 202 are configured to obtain the sensed biokinetic data 216 (e.g. motion activity or accelerometer data) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify the hearing system suggested by Vairavan in view of Chen to determine a biokinetic biomarker based on a motion parameter, as taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the biokinetic data (e.g., motion activity or accelerometer data) (see paragraphs [0032-0034, 0084] by Chen).
Regarding claim 16, Vairavan teaches the haring system according to claim 1. Vairavan does not explicitly teach wherein the electronic device comprises one or more sensors, and wherein the hearing system is configured to obtain internal sensor data from the one or more sensors of the electronic device, and wherein the health parameter is based on the internal sensor data.
However, Chen teaches wherein the electronic device comprises one or more sensors (the one or more mobile devices 102 (e.g., electronic device) includes one or more sensors 202 [0032]), and wherein the hearing system is configured to obtain internal sensor data from the one or more sensors of the electronic device, and wherein the health parameter is based on the internal sensor data (the one or more mobile devices 102 comprises sensors 202 that are configured to obtain the internal sensor data 216 (e.g. heart rate, metabolic level, blood pressure, or activity parameters) which is further transmitted and stored in the collection component 204 as collected data 218 [0032-0034, 0084]. Specifically, the collected data 218 is used to generate one or more digital biomarkers that are analyzed to detect a cognitive decline of the subject 104 [0034, 0084]).
Therefore, it would have been obvious to a person having ordinary skill in the art at the time the application was effectively filed to modify Vairavan’s electronic device to include sensors for obtaining internal sensor data, as taught by Chen. The advantage of such modification will allow for determining if the patient is suffering from a cognitive decline based on the biomarkers which are extracted from the internal sensor data of the electronic device (e.g., mobile device) (see paragraphs [0032-0034, 0084] by Chen).
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA BRENDON SOLOMON whose telephone number is (571)270-7208. The examiner can normally be reached on 7:30am -4:30pm.
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/JOSHUA BRENDON SOLOMON/Examiner, Art Unit 3792
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