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 .
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1, 3-7, 9-13, 15 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (abstract idea) without significantly more.
Under Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, the claims are directed to a process (claim 1, a method) which is statutory category.
However, evaluating claim 1, under Step 2A, Prong One, the claim is directed
to the judicial exception of an abstract idea using the grouping of a mathematical relationship/mental process. The limitations include:
monitoring a frequency spectrum of vibrations measured from a platform by a vibration sensor.
This limitation constitutes mathematical concepts and mental processes, including analyzing signals (frequency spectrum analysis) to derive information.
Next, Step 2A, Prong Two evaluates whether additional elements of the claim “integrate the abstract idea into a practical application” in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The claim does not recite additional elements that integrate the judicial exception into a practical application.
The claim further recites “vibration measured from a platform” and “by a vibration sensor. However, these elements merely perform data gathering and recited at a high level of generality (generic “sensor” and “platform” and therefore: do not integrate the abstract idea into a practical application.
There is no recitation of:
A specific improvement to sensor technology.
A particular transformation of signals beyond general analysis.
A specific technical mechanism for extracting physiological signals.
At Step 2B, consideration is given to additional elements that may make the abstract idea significantly more. Under Step 2B, there are no additional elements that make the claim significantly more than the abstract idea.
The additional elements of “a vibration sensor” and “a platform” are well-understood, routine, and conventional components used for data acquisition. Thus, the claim does not amount to significantly more than the abstract idea.
Dependent claims 3-7, 9-13, 15 and 20 does not add anything which would
render the claimed invention a patent eligible application of the abstract idea. The claims recite additional limitations such as: extracting metrics from frequency spectrum (claims 3 and 20), calculating amplitude, mean/median frequency, or statistical distance (claim 4), applying machine learning model (claim 5), determining thresholds using adaptive filtering or Bayesian learning (claims 6 and 10), classifying spectra or comparing to templates (claims 4 and 7), removing or isolating frequency components (claims 9 and 10), combining signals from multiple sensors and evaluating amplitude/phase relationships (claims 11-13), and triggering actions based on detected patterns (claim 15).
These limitations constitute data analysis, mathematical operations, and classification techniques and operate on data collected from sensors without improving the sensor itself and therefore: represent mental processes and mathematical concepts that do not integrate the abstract idea into a practical application.
Additionally, the recited techniques:
“Machine learning” is considered performing mathematical calculation
which falls within the “mathematical concept” grouping of abstract ideas (see Example 47, in the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence).
Filtering, classification, and statistical comparison are well-understood, routine, and conventional in the field of data processing.
Claims 2, 8, 14 and 16-18 are considered eligible under 35 U.S.C. § 101.
Regarding claim 2, the additional element “utilizing influence of body mass on platform resonance and vibration damping; and utilizing influence of physiology on platform vibrations together with influence of body mass on the platform resonance and vibration damping, wherein physiology includes infant movement, breathing, and heartbeat” is not directed to an abstract idea, but rather contains how the signal is interpreted based on physical system dynamics. Therefore, claim 2 is directed to a practical application.
Regarding claim 8, the additional element “wherein detection of the absence or presence of the infant includes incorporation of physiological signals or detection of the presence of the infant is contingent upon detection of physiological signals associated with the infant, wherein the physiological signals are selected from two or more of breathing, heartbeat, or movement” ties the analysis to real-world biological phenomena rather than abstract data alone. For this reason, claim 8 is considered to be directed to a practical application.
Regarding claim 14, the additional element “leveraging amplitude and phase relationships between frequencies simultaneously detected by one or more external microphones and by the vibration sensor and identifying patterns of motion, sound, or both prior to or during crying and evaluating for likeness to known patterns for clustering utterances into types.” The recitation of associating low-frequency vibrations with movement and using changes over time as an activity metric, reflecting a specific physical interpretation of signal components ties the invention to physical system behavior, claim 14 is considered to be directed to a practical application.
Regarding claims 16-18, the additional element “wherein the platform comprises a vibration substrate comprising an expanse of material configured to receive and propagate vibrations caused by motion of the infant positioned on a mattress or pad positioned over the platform” anchor the method to a particular machine/structure. The recitation of a vibration substrate configured to propagate vibrations, define signal transduction mechanism rather than generic data collection step. Therefore, claims 16-18 are considered eligible under 35 U.S.C. § 101.
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.
Claims 1-4, 8-10, 14, 15 and 20 are rejected under 35 U.S.C. 103 as being
unpatentable over Higgins et al. (Patent No. US 5479932) (hereinafter Higgins) in view of Zhao et al. (NPL: “Accurate Estimation of Heart and Respiration Rates Based on an Optical Fiber Sensor Using Adaptive Regulations and Statistical Classifications Spectrum Analysis”, Frontiers 2021) (hereinafter Zhao).
As per claim 1, Higgins teaches a contact-free infant monitoring system,
vibrations measured from a platform by a vibration sensor (see col. 2, lines 46-55, col. 3, lines 56-67, and col. 4, lines 7-14, i.e., sensor positioned beneath a mattress/crib/support surface (contact-free infant monitoring system), detecting respiration, heartbeat, and movement) (the examiner notes that “passive sensor, formed from such material as PVDF” is vibration sensor).
However, Higgins fails to explicitly teach monitoring a frequency spectrum of the vibration signal.
Zhao, however, teaches a sensor integrated into a mattress/support platform, measurement of mechanical vibration signals (ballistocardiography), extraction of respiration and heart rate, use of spectrum-based analysis, statistical classification in frequency domain, and separation of physiological signals based on frequency characteristics (see page 2, METHODS, System Setup and pages 5-6, Compound Signal Separation). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify Higgins to incorporate the spectral analysis techniques of Zhao because Zhao teaches analyzing vibration signals in the frequency domain enables accurate separation and extraction of both respiration and cardiac components from platform-based measurements, thereby applying such known spectrum-based processing techniques to the system of Higgins would predictably improve the accuracy, robustness, and reliability of infant cardiopulmonary monitoring.
As per claim 2, the combination of Higgins and Zhao teaches the system as stated above.
Higgins fails to explicitly teach body mass influence on resonance/damping.
Zhao, however teaches that vibration signals measured through a mattress-based sensor vary with subject weight and body characteristics because the physiological vibrations are transmitted through the support surface (see page 10, RESULTS AND DISCUSSION, Verification of Individual Differences, Verification of Heart Rates Under Different Breathing Conditions, Table 1 and Fig. 11 ). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to because interaction between a body and a support surface affects the transmission characteristics of vibration signals (e.g., amplitude and frequency response, which may include effects such as resonance and damping), therefore incorporating such known physical effects would improve signal interpretation.
As per claims 3 and 20, the combination of Higgins and Zhao teaches the system as stated above.
Higgins fails to explicitly teach extracting spectral metrics over time windows.
Zhao, however teaches processing vibration signals over time intervals and analyzing their spectral characteristics to extract physiological information, including heart rate and respiration rate, based on features derived from the signal spectrum (see page 9, Optimization). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Higgins to process vibration signals over defined time intervals and analyze corresponding spectral characteristics as taught by Zhao because Zhao teaches that analyzing vibration signals in the frequency domain to extract physiological parameters, including heart rate and respiration rate, from platform-based measurements and processing signals over periods of time is a conventional technique in signal analysis to obtain stable and reliable measurements, thereby incorporating time-interval-based spectral processing into the system of Higgins would predictably improve the accuracy, stability, and reliability of cardiopulmonary signal detection.
As per claim 4, the combination of Higgins and Zhao teaches the system as stated above.
Higgins fails to explicitly teach utilizing metrics of signal amplitude and change over time to monitor the presence and physiology of the infant, wherein the metrics of measured vibrations include one or more of amplitude of one or more frequency bands change in amplitude of one or more frequency bands relative to a recent history, a mean or median frequency of raw or ranked spectrum over one or more frequency bands, a statistical distance of the spectrum from pre-defined exemplars, classification of spectra via correlation with templates or based on signal features identified via machine learning, or combination thereof.
Zhao further teaches performing frequency-domain analysis, the vibration signal is represented as a set of frequency components, each associated with an amplitude. Accordingly, Zhao’s analysis inherently involves evaluating amplitudes of frequency components (i.e., frequency bands) corresponding to physiological signals (pages 5-6). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to utilize amplitude of one or more frequency bands as a metric because Zhao teaches analyzing vibration signals in frequency domain and considering amplitude characteristics of the signal in extracting physiological parameters, and frequency-domain representations inherently associate amplitude values with frequency components, thereby incorporating amplitude-based spectral metrics into the system of Higgins would predictably improve the accuracy and robustness of detecting infant physiological states.
As per claim 8, the combination of Higgins and Zhao teaches the system as stated above.
Higgins discloses monitoring an infant using physiological signals including respiration, heartbeat, and movement, and detecting the presence or absence of the infant based on those signals, such that the detection of the infant is contingent upon the detection of physiological activity (see Abstract). Zhao discloses monitoring vibration signals in the frequency domain (see pages 2-6 . Therefore, the combination teaches monitoring a frequency spectrum of vibrations and detecting presence or absence of the infant based on physiological signals.
As per claim 9, the combination of Higgins and Zhao teaches the system as stated above.
Higgins teaches contact-free infant monitoring system that senses physiological signals (e.g., respiration, heartbeat, and movement) via vibrations transmitted through a mattress or platform (see col. 2, lines 46-55, col. 3, lines 56-67, and col. 4, lines 7-14 ), but fails to explicitly teach removing or using as reference external frequency components.
Zhao discloses analyzing vibration signals in frequency-domain and extracting physiological signals based on their spectral characteristics (see pages 6-10, see also section: “Statistical Classification Spectrum Analysis). It is well known in signal processing that signals measured from a platform may include both desired physiological components and undesired components (such as environmental vibrations or acoustic sources), and that frequency-domain analysis enables differentiation of signal components based on their frequency characteristics, thereby facilitating isolation of desired physiological signals from undesired components. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Higgins to remove or account for external frequency components using the frequency-domain of Zhao because Zhao teaches spectrum-based analysis for extracting physiological signals and it is well known that such analysis can be used to differentiate and mitigate undesired signal components, therefore applying these techniques to the system of Higgins would predictably improve the accuracy and reliability of detecting infant physiological signals in the presence of external vibrations or sounds.
As per claim 10, the combination of Higgins and Zhao teaches the system as stated above.
Higgins teaches contact-free infant monitoring system that senses physiological signals (e.g., respiration, heartbeat, and movement) via vibrations transmitted through a mattress or platform (see col. 2, lines 46-55, col. 3, lines 56-67, and col. 4, lines 7-14 ), but fails to explicitly teach using external frequency components as a reference.
Zhao discloses analyzing vibration signals in the frequency domain and extracting physiological signals based on their spectral characteristics (see page 4, section “Weak Signal Processing”, page 9, section “Statistical Classifications Spectrum Analysis”, and page 10 section “Results and Discussion”). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Higgins to use identified non-physiological components to aid in isolating external signal components from infant-generated signals because Zhao discloses analyzing vibration signals in the frequency domain and extracting physiological signals based on their spectral characteristics, and it is well known in signal processing that signals measured from platform may include both desired physiological components and undesired components, and that frequency-domain analysis enables differentiation of signal components based on their frequency characteristics, thereby facilitating isolation of desired signals from undesired components, therefore incorporating such separation techniques into the system of Higgins would predictably improve the accuracy and robustness of physiological signal detection.
As per claim 14, the combination of Higgins and Zhao teaches the system as stated above. Higgins fails to explicitly teach frequency-domain characterization or leveraging amplitude and phase relationships across sensors. Zhao discloses analyzing vibration signals in the frequency-domain and extracting physiological information based on spectral characteristics (see page 8, section “Statistical Classification Spectrum Analysis”), wherein frequency-domain representations inherently include amplitude and phase information for signal components. It is well known in signal processing that movement of a body on a support surface produces low-frequency vibration components and that the amplitude of such component varies with activity level over time. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to analyze the vibration signals of Higgins in the frequency-domain of as taught by Zhao and to leverage amplitude and phase relationships of the resulting frequency components, because Zhao teaches frequency frequency-domain analysis of vibration signals and it is well known that such representations provide amplitude and phase information useful for signal characterization, therefore applying such analysis to the system of Higgins would predictably improve interpretation of vibration signals. Furthermore, it would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to associate movement with low-frequency vibration components and to use changes in amplitude of those components overtime as a metric of overall activity because Higgins teaches detecting movement via platform vibrations and it is well known that movement includes low-frequency vibrations whose amplitude correlates with activity level, therefore tracking amplitude variations of low-frequency components overtime would predictably provide a reliable measure of infant activity.
As per claim 15, the combination of Higgins and Zhao teaches the system as stated above. Higgins discloses a contact-free infant monitoring system that senses vibrations of a support platform to detect infant physiological conditions and triggers an alarm when monitored conditions indicates an adverse state (e.g., lack of movement/respiration) (see col. 2, line 46 through col. 3, line 2), but fails to explicitly teach initiating soothing actions or using frequency-domain activity metrics.
Zhao discloses analyzing vibration signals in the frequency domain and extracting physiological information based on spectral characteristics (see page 6, section “Heart Rate Extraction” and page 9, section “Experimental Design, Procedure”). It is well known that an infant activity level (e.g., increased movement reflected in vibration amplitude over time) correlates with discomfort or impending crying, and that caregiver or device responses can be initiated based on detected infant state. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to modify the system of Higgins to analyze activity levels using frequency-domain characteristics as taught by Zhao and trigger a responsive action when activity exceeds a predetermined level because Higgins already teaches initiating a response (i.e., alarm) upon detection of a condition derived from sensed signals and Zhao teaches extracting signal characteristics indicative of physiological state, and it is a predictable design choice to substitute or supplement an alarm with an automated response action when a threshold indicative of discomfort is exceeded, therefore triggering proactive soothing actions based on increased activity levels would predictably improve infant comfort and system responsiveness.
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over
Higgins in view of Zhao and further in view of Rajput et al. (NPL: “Automated Detection of Hypertension Using Continuous Wavelet Transform and a Deep Neural Network with
Ballistocardiography Signals” MDPI Environmental Research and Public Health March 28th , 2022) (hereinafter Rajput).
As per claim 5, the combination of Higgins and Zhao teaches the system as stated above. The combination of Higgins and Zhao fails to explicitly teach using machine learning models in classification of spectra.
Rajput, however, discloses processing ballistocardiography (BCG) signals obtained from a noncontact sensing platform and applying a conventional neural network (CNN) to automatically extract features and classify physiological conditions (see Abstract and page 4, section 2.5. BCG Signal Related Literature, third paragraph). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to implement the classification of Zhao using a neural network as taught by Rajput because Rajput demonstrates that conventional neural network can be applied to ballistocardiography signals to automatically extract features and classify physiological conditions with improved accuracy, therefore incorporating such machine learning models into the system of Higgins and Zhao would predictably improve the accuracy and robustness of physiological signal classification.
As per claim 7, the combination of Higgins and Zhao teaches the system as stated above. Higgins discloses a contact-free infant monitoring system using a sensor associated with a support platform (e.g., crib or bassinet environment) to detect infant presence and physiological signals via vibrations (i.e., detecting infant movement using sensor associated with a support surface, and such movement inherently produces mechanical vibrations that propagate through the support surface and are detectable by a sensor (see col. 2, line 46 through col. 3, line 2) and Zhao discloses analyzing vibration signals in the frequency domain and extracting physiological information based on spectral characteristics (see page 6, section “Heart Rate Extraction”). However, the combination of Higgins and Zhao fails to explicitly teach classifying a frequency spectrum with high confidence using a pre-trained neural network or decision tree.
Rajput, however, discloses processing ballistocardiography (BCG) vibration signals obtained from a support surface and applying a deep learning model (e.g., CNN) to automatically extract features and classify physiological conditions (see page 4, “BCG is a contact-based approach, as it requires the subjects to stay on suspended beds or electronic weightscales” and page 5 section “Methodology”). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to classify the frequency-domain signals of Zhao using a pre-trained neural network as taught by Rajput because Rajput demonstrates that neural network models can be applied to vibration-based physiological signals to improve classification accuracy and robustness, therefore incorporating such machine learning classification into the system of Higgins and Zhao would predictably improve detection of infant presence based on vibration signals.
Claim 6 and 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over
Higgins in view of in view of Zhao and further in view of Childs (WO 2001012064).
As per claims 6 and 19, the combination of Higgins and Zhao teaches the system as stated above except for determining thresholds for infant presence, breathing, or both, wherein determining thresholds for infant presence or breathing is data driven via one or more of adaptive filtering, moving averages, or Bayesian learning.
Childs, however, teaches processing physiological signals obtained from a sensor positioned beneath a support surface, including filtering the signals and detecting respiration based on threshold criteria applied to processed signal (see paragraph 10, i.e., “a piezo based movement sensor mat (1) connecting to a low pass filter (2) in order to reject non- breathing frequencies such as heart beat and other unwanted vibration... is fed into a microprocessor (6), which determines movement of the infant by way of detecting frequency change from the VCO (4). If no movement has been detected for a pre-selected time (usually 20 seconds) an alarm (5) will sound”) (the examiner notes that evaluating the processed signal over a predefined time period and triggering an alarm when the signal fails to meet expected criteria (e.g., absence of movement for a specific duration corresponds to applying condition-based detection which is analogous to thresholding). It would have ben obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to implement threshold determination using adaptive filtering or moving averages because such techniques were well known for improving signal detection under varying conditions, therefore incorporating adaptive thresholding into the system of Higgins would predictably improve the robustness and reliability of detecting respiration and presence.
As per claims 16-18, the combination of Higgins and Zhao teaches the system as stated above except that the platform comprises a vibration substrate comprising an expanse of material configured to receive and propagate vibrations caused by motion of the infant positioned on a mattress or pad positioned over the platform.
Childs, however, discloses a sensor mat positioned beneath a mattress that receives vibrations transmitted from an infant through the mattress and propagates those vibrations across the mat to a sensing element, thereby functioning as a vibration-transmitting substrate (see Description 4th paragraph). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to implement the platform of Higgins as a vibration substrate as taught by Childs because Childs teaches that a mat positioned beneath a mattress efficiently receives and propagates vibrations caused by infant motion to enable reliable sensing, therefore incorporating such a vibration substrate into the system of Higgins as analyzed by Zhao would predictably improve transmission and detection of vibration signals generated by the infant.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over
Higgins in view of in view of Zhao and further in view of Liang et al. (NPL: Deep Learning for Infant Cry Recognition” (MDPI Environmental Research and Public Health (May 23rd 2022)) (hereinafter Liang).
As per claim 11, the combination of Higgins and Zhao teaches the system as stated above except for leveraging amplitude and phase relationships between frequencies simultaneously detected by one or more external microphones and by the vibration sensor to improve detection of internal vs. external sounds.
Liang, however, discloses capturing infant cry signals using microphones and extracting spectral features (e.g., MFCCs) from audio signals for analysis and classification (see pages 2-3 and 9). It would have been obvious to one having ordinary skill in the rat before the effective filing date of the claimed invention to incorporate one or more external microphones and to analyze signals from both the vibration sensor and the microphone in frequency domain, including comparing their spectral characteristics (which include the amplitude and phase information inherent to frequency-domain representations), because Liang teaches microphone-based spectral analysis of infant sounds and Zhao teaches spectral analysis of vibration signals, and it is well known that comparing frequency-domain characteristics across sensors improves discrimination between internally generated signals and externally sourced sounds, therefore combining these techniques with the system of Higgins would predictably improve detection of internal versus external sounds.
As per claim 12, the combination of Higgins and Zhao teaches the system as stated above except for designation of a sound as internal includes an absolute or relative amplitude threshold to be simultaneously exceeded by both the external microphone and the vibration sensor.
Liang, however, discloses analyzing audio signals using extracted spectral features and applying criteria (e.g., feature magnitudes) in classification of infant sounds (see page 3 , section 2.3. Feature Extraction and page 6, section Results and discussion). It is well known in signal processing to apply amplitude thresholds for detection and require corroboration across multiple sensors to reduce false positives. It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to designate a sound as internal when corresponding signals from both a vibration sensor and an external microphone exceed an absolute or relative amplitude threshold because Liang teaches evaluating signal features derived from audio signals and it is well known that thresholding and multi-sensor corroboration improve detection reliability, therefore applying simultaneous amplitude threshold criteria across the sensors in the system of Higgins as informed by Zhao would predictably improve robustness in distinguishing internal versus external sounds.
As per claim 13, the combination of Higgins and Zhao teaches the system as stated above except for leveraging amplitude and phase relationships between frequencies simultaneously detected by one or more external microphones and by the vibration sensor and identifying patterns of motion, sound, or both prior to or during crying and evaluating for likeness to known patterns for clustering utterances into types.
Liang, however, discloses extracting spectral features from infant cry audio signals and applying machine learning models (e.g., CNN/LSTM) to classify cries into categories corresponding to infant needs, thereby recognizing patterns in sound signals and grouping them into types (see pages 7-8, section 3.3. Experimental Results). It would have been obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to analyze combined vibration and audio signals to identify patterns of motion or sound and evaluate them for similarity to known patterns, including clustering or classification into types, because Liang teaches pattern recognition and classification of infant sounds using spectral features and Zhao teaches frequency-domain analysis of vibration signals, therefore integrating such pattern recognition techniques into the system of Higgins would predictably improve identification and classification of infant states, including pre-cry or cry-related patterns.
Prior art
The prior art made record and not relied upon is considered pertinent to applicant’s
disclosure:
Thompson [‘350] discloses a method, apparatus, and system for remote baby monitoring. The invention provides for non-audio alert when sound information associated with a baby exceeds a particular threshold. The invention provides for adjusting the level of sound that triggers the non-audio alert. This adjustment can be made on the remote unit. The invention further provides for adjustment of the intensity of the vibration on the remote unit.
Shvarzman et al. [‘466] discloses a system includes a memory, an optical subsystem, an audio system, a plurality of sensors outputting sensor data, a communication circuitry and a processor. The processor is configured to input to a baby-specific behavioral state detection machine learning model, image data, audio signal data, sensor data, and baby-specific personal data associated with the baby, to receive an output from the baby-specific behavioral state detection machine learning model that the baby is agitated and/or about to wake up, to transmit instructions based on the output that cause the audio system and/or the optical subsystem to perform at least one of (i) generate a soothing sound when the baby is agitated, (ii) generate a sleep-enhancing sound when the baby is about to wake up, or (iii) project a relaxing image to be viewed by the baby when the baby is agitated.
Contact information
Any inquiry concerning this communication or earlier communications from the
examiner should be directed to MOHAMED CHARIOUI whose telephone number is (571)272-2213. The examiner can normally be reached Monday through Friday, from 9 am to 6 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached on (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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Mohamed Charioui
/MOHAMED CHARIOUI/Primary Examiner, Art Unit 2857