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 .
Applicants’ arguments and amendments, filed June 2, 2026, have been fully considered and they are persuasive in-part. The following rejections are newly applied as necessitated by amendment. They constitute the complete set of rejections and/or objections presently being applied to the instant application.
Status of the Claims
Claims 1-19 are under examination.
Claim Rejections - 35 USC § 103
2. 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.
3. Claims 1-4, 6, 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (US 2019/0231253 A1) in view of Paßler et al. (“Food Intake Recognition Conception for Wearable Devices”, Mobile Health ’11: Proceedings of the First ACM MobilHoc Workshop on Pervasive Wireless Healthcare, Article No.: 7, pages 1-4 (May 2011)).
Regarding claim 1, Ahmed et al teach a method for detecting and quantifying liquid or food intake of a user wearing a hearing device (paragraph [0024]) which comprises at least one microphone (paragraphs [0026] and [0028]) where the method includes the steps of receiving an audio signal from the microphone or a sensor signal from a sensor (paragraphs [0043] and [0044]); collecting and analyzing the audio signal or sensor signal to detect each time the user drinks or takes medication or eats something (paragraph [0044]), where the drinking or medication is distinguished from eating and drinking is distinguished from medication intake (paragraphs [0044] and [0056]), to determine values indicative of how often this is detected or the amount of liquid, food, or medication (paragraph [0055]); where the step of analyzing includes applying a machine learning algorithm in the hearing device or hearing system or a remove server or cloud (paragraphs [0048] and [0063]), and storing the determined values in the hearing system and based on the stored values generating a predetermined type of output (i.e. notifications) (paragraphs [0029]-[0032] and [0055]).
However, Ahmed et al. does not teach detecting, for a given intake event of liquid, food, or medication intake, two more different phases, and based on the two or more different phases detect each time the user at one of drinks, takes medication, or eats something.
Paßler et al. teaches a hearing device that detects different phases of liquid, food, or medication intake and based on the different phases detect one of drinks, takes medication, or eats something (page 3, right column).
Regarding claim 2, Ahmed et al. teach where the machine learning algorithm is applied in its training phase (i.e., “learning stage”) to learn user-specific manners of drinking, eating, or medication intake and the manners are incorporated into future analysis (paragraph [0048]).
Regarding claim 3, Ahmed et al. teach analyzing two or more phases of drinking, eating or medication intake are distinguished in the course of detecting liquid, food, or medication intake (paragraphs [0046] and [0055]), where the analysis is based on different sensors or different machine learning algorithms (paragraphs [0043], [0048] and [0049]).
Regarding claims 4 and 6, Ahmed et al. teach detecting tilting the user’s head from a movement sensor for medication intake (paragraph [0056]).
Regarding claim 18, Ahmed et al. teach a computer readable medium with a program that is adapted to carry out the steps of the method (paragraphs [0030] and [0032]).
Regarding claim 19, Ahmed et al. teach a hearing device including a microphone, a processor, a sound output device and where the hearing device adapted for performing the method (paragraphs [0026] and [0028]).
It would have obvious for one of ordinary skill in the art, at the time of filing, to combine the teachings for Ahmed et al. and Paßler et al. Ahmed et al. discloses a device that extends the functionality of hearables. Paßler et al. teaches an additional function to be incorporated into hearables. One of ordinary skill in the art would have been motivated to incorporate the additional functionalities of Paßler et al. in the device of Ahmed et al. Furthermore, one of ordinary skill in the art would have had a reasonable expectation of success because the device of Paßler et al. a standard h earing device (page 2, left column).
Response to Arguments
4. Applicants have responded to the rejection made under 35 U.S.C. §102 with Ahmed et al. by amending the claims to include the limitation of “detecting, for a given intake event of liquid, food, or medication intake, two more different phases, and based on the two or more different phases detect each time the user at one of drinks, takes medication, or eats something” and stating that Ahmed et al. does not teach this limitation. The rejection has been rewritten as a rejection under 35 U.S.C. §103 with the reference by Paßler et al. to address the amendment.
This rejection is necessitated by amendment.
5. Claims 5 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (US 2019/0231253 A1) in view Paßler et al. (“Food Intake Recognition Conception for Wearable Devices”, Mobile Health ’11: Proceedings of the First ACM MobilHoc Workshop on Pervasive Wireless Healthcare, Article No.: 7, pages 1-4 (May 2011)) as applied to claims 1-4, 6, 18 and 19 above, and further in view of Connor (US 2015/0379238 A1).
Ahmed et al. and Paßler et al. are applied as above.
While Ahmed et al. teaches distinguishing between different activities (paragraphs [0044] and [0056]), Ahmed et al. and Paßler et al. do not teach detecting the phase of medication intake of bringing a medication in contact with the mouth or inserting medication into the mouth.
Regarding claim 5, Connor teaches a method that includes detecting bringing food to the mouth, inserting food into the mouth, chewing or swallowing the food, and lowering the hand (paragraph [0240]).
It would have been obvious for one of ordinary skill in the art, at the time of filing, to combine the teachings of Ahmed et al., Paßler et al., and Connor. Ahmed et al., Paßler et al. and Connor are drawn to detecting food intake (Ahmed et al., paragraph [0055]; Paßler et al., abstract; Connor, abstract). Connor offers the advantage of being able to detect the physical motions of eating (paragraph [0240]). Thus, one of ordinary skill in the art would have been motivated to incorporate the teachings of Connor into the teachings of Ahmed et al. and Paßler et al. in order to better monitor food intake. In addition, one of ordinary skill in the art would have had a reasonable expectation of success since a food intake monitoring system incorporate multiple sensors such as the ones taught of Ahmed et al., Paßler et al. and Connor.
Response to Arguments
6. Applicants have responded to this rejection by relying on their arguments regarding Ahmed et al. See above for the Examiner’s response.
7. Claims 7-10 and 12-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (US 2019/0231253 A1) in view of Paßler et al. (“Food Intake Recognition Conception for Wearable Devices”, Mobile Health ’11: Proceedings of the First ACM MobilHoc Workshop on Pervasive Wireless Healthcare, Article No.: 7, pages 1-4 (May 2011)) as applied to claims 1-4, 6, 18 and 19 above, and further in view of Shalon et al. (US 2006/0064037 A1).
Ahmed et al. and Paßler et al. are applied as above.
However, Ahmed et al. and Paßler et al. do not teach using the physiological property to determine the liquid intake.
Regarding claims 7, Shalon et al. teach where the sensor signals comprise physiological signals indicative of a physiological property determines which kind of liquid the user is taking (paragraph [0329]).
Regarding claim 8, Shalon et al. teach where the physiological signal is indicative a cardiovascular property, body fluid analyte level and body temperature (paragraph [0329]).
Regarding claim 9, Shalon et al. teach the amount of water ingested is based on the physiological property (paragraph [0329]).
Regarding claim 10, Shalon et al. teach where the machine learning algorithm is an artificial neural network (paragraph [0256]), where the input data set is sensor data collected over a predetermined period of time (paragraphs [0212], [0256], [0258] and [02061]-[0263]), where the output data set includes the frequency or number of detected liquid or food intakes as well as the duration (paragraph [0261]), where the learning phase is implemented by supervised learning using input sensor data (paragraph [0256]).
Regarding claim 12, Shalon et al. teach where the machine learning method is a Hidden Markov Model (paragraph [0256]).
Regarding claim 13, Shalon et al. teach where the dehydration risk is estimated on the determined values of the amount and frequency of the user’s liquid intake (paragraph [0329]) and the generated output that counsels the user to ingest a lacking amount of liquid (paragraph [0329]).
Regarding claim 14, Shalon et al. teach where the interactive user interface is provided in the hearing system (paragraph [0302]), and where the interface allows the user to input additional information (paragraph [0311]).
Regarding claim 15, Shalon et al. teach where the information to take a medication is stored in the system (paragraph [0326]), where when fluid intake is detected, generating an output based on questioning the user whether he has taken the medication (paragraphs [0213] and [0326]), and transmitting the information to a health care professional (paragraph [0326]).
Regarding claim 16, Shalon et al. teach generating an output based on the frequency and amount of liquid ingested by the user, an output to enhance the user’s desire to drink by an augmented reality means (virtual coach) in the hearing system (paragraphs [0206] and [0329]).
Regarding claim 17, Shalon et al. teach when detecting that the user is drinking, to generate an output to enhance the user’s experience of drinking by augmented reality means (providing feedback in real-time) (paragraphs [0206] and [0326]).
It would have been obvious for one of ordinary skill in the art, at the time of filing, to combine the teachings of Shalon et al., Paßler et al. and Ahmed et al. Shalon et al., Paßler et al. and Ahmed et al. teach using hearables to monitor food intake (Ahmed et al., paragraph [0055]; Paßler et al. abstract; and Shalon et al., paragraph [0102]). Shalon et al. offers the benefit of virtual coach that encourages better behavior (paragraph [0329]). Thus, one of ordinary skill in the art, would have been motivated to incorporate the teachings of Shalon et al. into the teachings of Ahmed et al. and Paßler et al. to gain the benefit of coaching the user for better health. Furthermore, one of ordinary skill in the art would have had a reasonable expectation of success, because the virtual coach may be readily implemented as software for the system of Ahmed et al. and Paßler et al.
Response to Arguments
8. Applicants have responded to this rejection by relying on their arguments regarding Ahmed et al. See above for the Examiner’s response.
9. Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Ahmed et al. (US 2019/0231253 A1) in view of Paßler et al. (“Food Intake Recognition Conception for Wearable Devices”, Mobile Health ’11: Proceedings of the First ACM MobilHoc Workshop on Pervasive Wireless Healthcare, Article No.: 7, pages 1-4 (May 2011)) as applied to claims 1-4, 6, 18 and 19 above, and further in view of Pedersen et al. (US 2019/0394586 A1).
Ahmed et al. and Paßler et al. are applied as above.
However, Ahmed et al. and Paßler et al. do not teach where the neural network has a hidden layer.
Pedersen et al. teach a hearing device (abstract), which may be used to detect a user’s food intake (paragraph [0001]), utilizes a deep neural network with a hidden layer (paragraphs [0145] and [0147]).
It would have been obvious for one of ordinary skill in the art, at the time of filing, to combine the teachings of Pedersen et al., Paßler et al. and Ahmed et al. Pedersen et al., Paßler et al. and Ahmed et al. teach using hearables to monitor food intake (Ahmed et al., paragraph [0055]; Paßler et al., abstract; and Pedersen et al., paragraph [0001]). Pedersen et al. offers the benefit of distinguishing acoustic events to detect an activity (paragraph [0001]). Thus, one of ordinary skill in the art, would have been motivated to incorporate the teachings of Pedersen et al. into the teachings of Ahmed et al. and Paßler et al. to gain the benefit of being able to better distinguish acoustic events. Furthermore, one of ordinary skill in the art would have had a reasonable expectation of success, because the analysis taught by Pedersen et al. may be readily implemented into the system of Ahmed et al. and Paßler et al.
Response to Arguments
10. Applicants have responded to this rejection by relying on their arguments regarding Ahmed et al. See above for the Examiner’s response.
Withdrawn Rejections
11. Applicant’s arguments and amendments, filed June 2, 2026, with respect to the rejection made under 35 U.S.C. §101 have been fully considered and are persuasive. The amendments recite a particular device that integrates the judicial exception into a practical application. This rejection has been withdrawn.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JERRY LIN whose telephone number is (571)272-2561. The examiner can normally be reached T-F 7am-5pm.
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, Olivia Wise can be reached at (571) 272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JERRY LIN/Primary Examiner, Art Unit 1685
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