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 .
Examiner’s Remark
Please note Qingdao is dropped from this rejection. Applicant’s argument against Kim is persuasive and thus is presented with additional references including Lawlor, Kremer or Kahn. Applicant’s arguments against Hong and Lee are not persuasive. Therefore examiner will sustain the rejections. However, examiner will also address Hong and Lee with additional reference including Lawlor, Kremer or Kahn for the sake of argument that the applicant was correct.
The current rejection is focally emphasized on the independent claims and there is no change in examiner’s position regarding all the dependent claims.
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hong et al (US 2024/0081730) OR Lee et al (US 2025/0099028).
Claims 1, 11 and 20, Hong teaches an electronic device, a server and a method of performed a home appliance ([0095] For example, the environment adjustment unit 140 may be implemented as a smart air conditioner, a smart heater, a smart boiler, a smart window, a smart humidifier, a smart dehumidifier, and a smart lighting based on links through the Internet of Things. The specific description of the above-described environment adjustment unit is only an example, and the present invention is not limited thereto) comprising:
obtaining sleep information of a user (Fig. 7, [0177-0181]) and operation information of each of a plurality of home appliances ([0095]);
determining a noise source device which causes noise outside a deep sleep range ([0090] The one or more environment sensing modules may include, for example, at least one sensor module among a temperature sensor, an air flow sensor, a humidity sensor, a sound sensor, and an illuminance intensity sensor. However, they are not limited thereto, and may further include various sensors that may affect the user's sleep) from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, (0132] For example, as shown in FIG. 5, each spectrogram may be configured to have a frequency spectrum of different concentrations according to various sleep stages. That is, it may be difficult to predict at least one of an awake state, a REM sleep state, a light sleep state, and a deep sleep state by only changing the energy level of the sleep sound information, but by converting the sleep sound information into a spectrogram, each frequency change of a spectrum can be easily detected. Therefore, analyses corresponding to small sounds (e.g., breathing and body movements) can be implemented), [[wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed. Please see the examiner’s response below]]; and
controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range. ( [0171-0173] the processor 150 may determine to transmit external environment adjustment information to an environment adjustment unit. That is, the processor 150 may improve the quality of a user's sleep by generating external environment adjustment information that allows the user to easily fall asleep or wake up naturally based on the sleep plan information).
Response to Arguments
Applicant's arguments filed 6/16/26 have been fully considered but they are not persuasive.
Applicant respectfully submits that Kim, Hong, Qingdao, and Lee fail to disclose or render obvious, inter alia, "obtaining sleep information of a user and operation information of each of a plurality of home appliances; determining a noise source device which causes noise outside a deep sleep range, from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed; and controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range," as presently recited.
Applicant stated that Paragraphs [0171]-[0173] of Hong describes generating external environment adjustment information based on a user's sleep state information to allow the user to easily fall asleep or wake up naturally based on the sleep plan information. As indicated in paragraph [0173] of Hong, if a user enters a sleep state, the processor generates the external environment adjustment information.
Please note that applicant makes no counter-argument or analysis against the provided par. [0132] wherein examiner believes that Hong still teaches the amendment.
In general, Hong teaches “the steps of: acquiring sleep sound information related to a user's sleep, pre-processing the sleep sound information, and acquiring sleep state information by analyzing the pre-processed sleep sound information, wherein the sleep state information includes sleep stage information related to the user's sleep depth, [0014]”. Examiner reproduces for convenience par. [0132] “For example, as shown in FIG. 5, each spectrogram may be configured to have a frequency spectrum of different concentrations according to various sleep stages. That is, it may be difficult to predict at least one of an awake state, a REM sleep state, a light sleep state, and a deep sleep state by only changing the energy level of the sleep sound information, but by converting the sleep sound information into a spectrogram, each frequency change of a spectrum can be easily detected. Therefore, analyses corresponding to small sounds (e.g., breathing and body movements) can be implemented)”.
By definition, “Frequency and time period are inversely related: the frequency of a repeating event is the reciprocal of its time period, and vice versa. Precisely, Frequency (f) is the number of complete cycles or oscillations of a repeating event that occur per unit of time WHILE Time Period (T) is the duration required to complete one full cycle of a repeating event.
For that reason, examiner holds firm that Hong does teach the new amendment.
Examiner holds firm that Lee does teach the new amendment.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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Claims 1, 11 and 20, Lee teaches an electronic device, a server and a method of controlling a home appliance (an air conditioner, an air purifier, a humidifier, a dehumidifier, a blind, a curtain, a light, a smart speaker, a smart bed, a smart diffuser, and a smart device on which a healthcare application is installed, [0033]), comprising:
obtaining sleep information of a user (Based on the sleep analysis model, sleep information regarding a user's sleep quality may be inferred, [0213, 0413, 0445, 0480, 0512, 0679]) and operation information of each of a plurality of home appliances (display information, e.g., air purifier, [1044]);
determining a noise source device (ambient noise, [0042]; air conditioning noise, [0176]; (de)humidifier operating noise, [0180]) which causes noise outside a deep sleep range (sleep stage information may refer to information about whether a user's sleep was light sleep, moderate sleep, deep sleep, or REM sleep at each point during a user's last eight hours of sleep, [0138], the user's circadian rhythm may be adjusted to a normal range (e.g., falling asleep around 12:00 p.m. and waking up around 7:00 a.m.), [0597]), from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, (the sleep stage information may be information about changing sleep stages during a user's sleep. For example, the sleep stage information may refer to information that the user's sleep has changed to light sleep, normal sleep, deep sleep, or REM sleep at each time point during the user's last eight hours of sleep, [0344]); [[wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed. Please see examiner’s response below]]; and
controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range. (it is possible to build an AI sleep stage analysis model by learning various ambient noises including noises that occur routinely in the surrounding space of a user's sleep environment, noises that occur abnormally or intermittently, etc, [0042]; adjusting air purifier or air conditioner operating noise,… if the environment adjustment device (30) is an air conditioner, the environment adjustment information may include adjusting temperature and humidity of a sleep space, adjusting blowing intensity, [0176]; adjusting dehumidification/humidification, adjusting blowing intensity, adjusting operation noise of the environment adjustment device (30), [0180]; The present invention may remove noise from sleep sound information, convert it into a spectrogram (Mel spectrogram), and generate a sleep analysis model by learning the spectrogram, [0275, 0278, 0346, 0349, 0529, 0711, 0712]).
Response to Arguments
Applicant's arguments filed 6/16/26 have been fully considered but they are not persuasive.
While applicant respectfully submits that Kim, Hong, Qingdao, and Lee fail to disclose or render obvious, inter alia, "obtaining sleep information of a user and operation information of each of a plurality of home appliances; determining a noise source device which causes noise outside a deep sleep range, from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed; and controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range," as presently recited.
Applicant simply states that “Lee describes providing a customized sleep environment based on an analysis of a report of AI-trained sleep analysis results.
However, while the applied references describe similar features to the claims, none of the cited references discloses the features of determining an appliance making noise louder than a deep sleep range at a time when a sleep phase of the user included in the sleep information changes in which the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed, as presently recited.
Lee on the other hand teaches “Paper analyzing the relationship between temperature and/or humidity and sleep shows differences in the frequency of awakening during sleep and the percentage of deep sleep, adversely affecting the user's work efficiency of the next day, [0851]. For example, studies show that sleeping in an environment with 80% of relative humidity and a temperature of 32 degrees Celsius increases the frequency of awakening during sleep and decreases the percentage of deep sleep when compared to sleeping in an environment with 50% of relative humidity and a temperature of 26 degrees Celsius, [0669].
By definition, “Frequency and time period are inversely related: the frequency of a repeating event is the reciprocal of its time period, and vice versa. Precisely, Frequency (f) is the number of complete cycles or oscillations of a repeating event that occur per unit of time WHILE Time Period (T) is the duration required to complete one full cycle of a repeating event.
For that reason, examiner holds firm that Lee does teach the new amendment.
Conclusion
THIS ACTION IS MADE FINAL. 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.
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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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(10 as anticipated by or, in the alternative, under 35 U.S.C. 103 as obvious over Kim, Hong OR Lee in view of Lawlor et al (US 2022/0133221), Kremer et al (US 10,991,355) or Kahn et al US 10,791,986).
Claims 1, 11 and 20, Kim teaches an electronic device, a server and a method performed a home appliance (See Abstract) comprising:
obtaining sleep information of a user and operation information of each of a plurality of home appliances (a variety of apparatus used at home, IoT apparatus such as air conditioning, an air cleaner, a furnace, a refrigerator, lighting, an automated blind, a robotic vacuum, [0002]; a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like, [0046]);
determining a noise source device which causes noise outside a deep sleep range (a variety of apparatus used at home, IoT apparatus such as air conditioning, an air cleaner, a furnace, a refrigerator, lighting, an automated blind, a robotic vacuum, [0002]; a washing machine, a refrigerator, a desktop computer, a digital signage, a robot, a vehicle, and the like, [0046]) from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes (Thereafter, the processor 180 may determine a sleep state of the user by regarding the sound signals from which the noises are separated and removed in the process of S430 as the input value of the AI model (S440). The process above will be described in detail in FIGS. 5 and 6, [0145-0147]), [[wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed = X1]]; and
controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range. (Kim: the home appliance provided with a personalized AI apparatus by controlling the peripheral home appliances to improve an actual sleep environment of the user, and re-learning the AI model when the output value of the AI model is different from the state of the actual user or is unclear, can be supplied to the user, [0010]).
Claims 1, 11 and 20, Hong teaches an electronic device, a server and a method of performed a home appliance ([0095] For example, the environment adjustment unit 140 may be implemented as a smart air conditioner, a smart heater, a smart boiler, a smart window, a smart humidifier, a smart dehumidifier, and a smart lighting based on links through the Internet of Things. The specific description of the above-described environment adjustment unit is only an example, and the present invention is not limited thereto) comprising:
obtaining sleep information of a user (Fig. 7, [0177-0181]) and operation information of each of a plurality of home appliances ([0095]);
determining a noise source device which causes noise outside a deep sleep range ([0090] The one or more environment sensing modules may include, for example, at least one sensor module among a temperature sensor, an air flow sensor, a humidity sensor, a sound sensor, and an illuminance intensity sensor. However, they are not limited thereto, and may further include various sensors that may affect the user's sleep) from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, (0132] For example, as shown in FIG. 5, each spectrogram may be configured to have a frequency spectrum of different concentrations according to various sleep stages. That is, it may be difficult to predict at least one of an awake state, a REM sleep state, a light sleep state, and a deep sleep state by only changing the energy level of the sleep sound information, but by converting the sleep sound information into a spectrogram, each frequency change of a spectrum can be easily detected. Therefore, analyses corresponding to small sounds (e.g., breathing and body movements) can be implemented), [[wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed = X1]]; and
controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range. ( [0171-0173] the processor 150 may determine to transmit external environment adjustment information to an environment adjustment unit. That is, the processor 150 may improve the quality of a user's sleep by generating external environment adjustment information that allows the user to easily fall asleep or wake up naturally based on the sleep plan information).
Claims 1, 11 and 20, Lee teaches an electronic device, a server and a method of controlling a home appliance (an air conditioner, an air purifier, a humidifier, a dehumidifier, a blind, a curtain, a light, a smart speaker, a smart bed, a smart diffuser, and a smart device on which a healthcare application is installed, [0033]), comprising:
obtaining sleep information of a user (Based on the sleep analysis model, sleep information regarding a user's sleep quality may be inferred, [0213, 0413, 0445, 0480, 0512, 0679]) and operation information of each of a plurality of home appliances (display information, e.g., air purifier, [1044]);
determining a noise source device (ambient noise, [0042]; air conditioning noise, [0176]; (de)humidifier operating noise, [0180]) which causes noise outside a deep sleep range (sleep stage information may refer to information about whether a user's sleep was light sleep, moderate sleep, deep sleep, or REM sleep at each point during a user's last eight hours of sleep, [0138], the user's circadian rhythm may be adjusted to a normal range (e.g., falling asleep around 12:00 p.m. and waking up around 7:00 a.m.), [0597]), from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, (the sleep stage information may be information about changing sleep stages during a user's sleep. For example, the sleep stage information may refer to information that the user's sleep has changed to light sleep, normal sleep, deep sleep, or REM sleep at each time point during the user's last eight hours of sleep, [0344]); [[wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed = X1]]; and
controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range. (it is possible to build an AI sleep stage analysis model by learning various ambient noises including noises that occur routinely in the surrounding space of a user's sleep environment, noises that occur abnormally or intermittently, etc, [0042]; adjusting air purifier or air conditioner operating noise,… if the environment adjustment device (30) is an air conditioner, the environment adjustment information may include adjusting temperature and humidity of a sleep space, adjusting blowing intensity, [0176]; adjusting dehumidification/humidification, adjusting blowing intensity, adjusting operation noise of the environment adjustment device (30), [0180]; The present invention may remove noise from sleep sound information, convert it into a spectrogram (Mel spectrogram), and generate a sleep analysis model by learning the spectrogram, [0275, 0278, 0346, 0349, 0529, 0711, 0712]).
Examiner agrees that Kim does not teach X1, and in regarding to Hong and Lee, as examiner demonstrated that Hong and Lee did teach X1 (new amendment) and for the sake of argument that the applicant was correct. Examiner respectfully provides:
Lawlor: [0119] For example, if the user's sleep data shows a disruption event at 3:35 AM where she is awakened from a light stage of sleep, and data collected from her sleep partner shows sudden movement at exactly 3:35 AM, then a correlation between her disruption and his movement at 3:35 AM may be assumed. If this type of disturbance occurs regularly, then corrective action may be necessary to ensure that the user's sleep quality is at least maintained or even improved.
Kremer: “The method may further comprise receiving, from the subject, at least one known disturbing noise to mask prior to outputting the masking sound. The method may further comprise monitoring the at least one biosignal parameter of the subject over a time period to gather historical sleep data of the subject. Predicting when the noise will disturb the subject may be further based on the historical sleep data. The method may further comprise monitoring a sleeping environment of the subject for a potentially disturbing noise over a time period via a microphone. (Col. 2, lines 38-47).
Kahn teaches, via Fig. 5, If there is no change in sleep phase, the process continues to block 560, to continue recording sleep noises with the periodicity indicated. In one embodiment, any time a sleep sound is detected, the process of verifying that the noise does not require urgent response is applied. If the sleep phase changes, at block 560, in one embodiment the process resets the recording timer, at block 565. The process then continues to block 520, to determine whether the user is awake. Col. 6, lines 37-52.
It would have been obvious to the ordinary artisan to incorporate the teaching of Lawlor, Kremer or Kahn into the teaching of Kim, Hong or Lee for the purpose of provide better management and improvement of sleeping pattern, quality and also for the accuracy of prediction on disturbing noise and the concurrence of time of the disturbing time for the accuracy of an event.
Claims 2 and 12. The method of claim 1, wherein the obtaining of the sleep information and the operation information comprises obtaining noise information caused from each of the plurality of home appliances in an operation mode of each of the plurality of home appliances. (Lee: See Fig. 23; Hong, [0165]; Kim, [0090]).
Claims 3 and 13. The method of claim 1, wherein the obtaining of the sleep information and the operation information comprises excluding data about environmental factors which affect sleep of the user, except for noise. (Lee: ambient noise, [0042, 0458-0459]. Hong: [0001]; Kim, [0005-0010]).
Claims 4 and 14. The method of claim 1, wherein the determining of the noise source device comprises: identifying identification information and operation mode information of the noise source device which changes the sleep phase; and determining the deep sleep range of the user based on at least one of critical noise loudness or critical noise frequency of the user. (Lee: FIG. 52 is a table describing exemplary operations by a location where each environment adjustment device is placed, an activation status according to sleep state information for each specific device, a sleep mode, and a wake-up mode, [0184]. Here examiner maps identification to location. Kim: The object identification information may include a name, a type, a distance, and a position. [0106]. Here examiner maps identification to position).
Claims 5 and 15. The method of claim 1, wherein the controlling of the noise source device comprises controlling the noise source device to perform, in advance, at least part of a performance-preferred mode in which noise outside the deep sleep range occurs in a first sleep phase. (Lee: For example, the processor (130) may determine the wake-up induction time to be 30 minutes before the time the user wishes to wake up. As a specific example, if the user-set desired time to wake up (i.e., the wake-up prediction time) is 7:00 a.m., the processor (130) may determine 6:30 a.m. as the wake-up induction time, [0539]. Beside Lee, See respective other independent claims).
Claims 6 and 16. The method of claim 1, wherein the obtaining of the sleep information and the operation information comprises: monitoring the sleep information and the operation information in real time; and determining a time when the sleep phase of the user is changed based on the sleep information monitored in real time. (Lee: it is possible to monitor a user's physical activity states in real time for 24 hours using a smart home appliance and/or a smartphone, [0046]; The user may acquire monitoring information regarding his or her own sleep through the user terminal (10). For example, monitoring information related to a sleep may include sleep state information related to a time a user went to sleep, a length of time a user slept, and a time a user woke up, or sleep stage information related to changes in sleep stages during a sleep, [0138]. Hong: since the sleep sound information 210 is time-sequential data acquired time-sequentially during the user's sleep, [0150]. Kim: Specifically, the processor 180 may control the sound signal of the input interface 120 to be received during the sleep detection mode execution time set by the user, [0139]).
Claims 7 and 17. The method of claim 1, further comprising: obtaining, in real time, operation information of at least one device located within a critical distance likely to affect sleep of the user from the user among the plurality of home appliances; determining the deep sleep range based on the obtained operation information; and controlling operation of the at least one device located within the critical distance based on the determined deep sleep range. (Lee: at least one smart home appliance that is located at a distance around the user and simultaneously collects the sleep sound information and transmits it to the smartphone, [0020]; The user terminal (10) may generate a discrete waveform (respiration information) corresponding to the user's breathing by processing the user's movement and distance measured through the radar sensor, [0166]; the environment sensing information may include movement and distance information related to the user's movements during sleep, and breathing information generated based on the movement, [0186, 0374-0375]. Kim: The object identification information may include a name, a type, a distance, and a position, [0106]. Hong: [0138]).
Claims 8 and 18. The method of claim 1, further comprising: registering type and location information of each of a plurality of devices; identifying the at least one of the plurality of devices which is operating at the time when sleep phase of the user changes; and determining the noise source device among the identified at least one of the plurality of devices and controlling operation of the noise source device. (Lee: monitoring information related to a sleep may include sleep state information related to a time a user went to sleep, a length of time a user slept, and a time a user woke up, or sleep stage information related to changes in sleep stages during a sleep, [0138, 0178, 0223, 0287] Wherein FIG. 3 depicts the probability of belonging to one of the four classes (Wake, Light, Deep, REM) at 30-second intervals when predicting sleep stages based on the user's sound information. The four classes represent wakefulness, a light sleep, a deep sleep and a REM sleep, respectively, [0225]). Beside Lee, See respective other independent claims).
Claims 9 and 19. The method of claim 1, further comprising outputting at least one notification user interface (UI) which indicates control of the noise source device. (Lee: user interfaces (e.g., PUI, VUI, and/or GUI), [0514] OR see Figs. 23a, b, c and d with displaying unit to control various units, [0967] for controlling air purifier, [1057]. Beside Lee, See respective other independent claims)
Claim 10. The method of claim 1, further comprising outputting at least one proposal user interface UI which includes guide information relating to an operation or placement of a home appliance to prevent sleep disturbance in relation to the noise source device. (Lee: FIG. 41 is a table describing exemplary operations of a bedtime preparation stage among specific scenarios of a plurality of smart home appliances chronologically operating according to a user's sleep stages using a sleep analysis method according to the present invention, [0102]. Beside Lee, See respective other independent claims, specifically the last limitation).
Response to Arguments
Applicant’s arguments with respect to claim(s) 6/16/26 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argues that Kim, Hong, Qingdao, and Lee fail to disclose or render obvious, inter alia, "obtaining sleep information of a user and operation information of each of a plurality of home appliances; determining a noise source device which causes noise outside a deep sleep range, from among the plurality of home appliances, at a time when a sleep phase of the user included in the sleep information changes, wherein the time when the sleep phase of the user changes corresponds to a time at which sleep of the user is disturbed; and controlling operation of the noise source device so that noise caused from the noise source device is generated within the deep sleep range," as presently recited.
Examiner respectfully disagrees as examiner has produced the additional references (Lawlor, Kremer or Kahn) to address the applicant’s concern.
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 PHUNG-HOANG J. NGUYEN whose telephone number is (571)270-1949. The examiner can normally be reached Reg. Sched. 6:00-3:00.
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, Duc Nguyen can be reached at 571-272-7503. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHUNG-HOANG J NGUYEN/Primary Examiner, Art Unit 2691
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