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 Objections
Claim 18 is objected to because of the following informalities:
Regarding claim 18, “prepartum data” in “and the method further comprises collecting the prepartum data” should be “postpartum data”.
Appropriate correction is required.
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.
Claims 11, 12 and 16-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Devroey (US 20130096368 A1).
Regarding claim 11, Devroey teaches a baby bed comprising:
A platform configured to support an infant (support member 60, shown in annotated Fig. 3A),;
A transducer coupled to the platform (speakers 48, shown in annotated Fig. 3A);
A mechanical actuator coupled to the platform (pillows 44 and 46, shown in annotated Fig. 3A); and
An electronic controller (“control unit 30 is also in communication with the speakers 48 located within the inner sound and motion activation unit 42”, paragraph [0061]; control unit 30, shown in annotated Fig. 3A) configured to simulate an intrauterine environment (“mimic the intrauterine conditions”, paragraph [0032]) for the infant by simulating a heartbeat using the transducer (“control unit will contain…separate sound tracks for heartbeat, respiration, mother’s voice, bowel”, paragraph [0034]) and simulating a walking gait using the mechanical actuator (“control unit 30 communicates and controls the function of the inner sound and motion activation unit 42. The Uterine Sound and Motion Simulation Devices 10 contains within it both the gait or body motion bladders or pillows 44”, paragraph [0059])
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Wherein one or more settings of the simulated intrauterine environment are determined based on female biometric information (“the sounds and movement may be customized to be exactly those of the mother or by the use of a standard program that is devised to match a mother with a particular set of physical characteristics and conditions”, paragraph [0031]).
Regarding claim 12, Devroey teaches the female biometric information comprising prepartum data or postpartum data collected from the mother of the infant (“FIG. 2B depicts a view of a pregnant woman or new mother 18 wearing the recording device 20, either before or after giving birth, in the area of the upper chest…near the heart…and to the abdomen”, paragraph [0058]; Fig. 2B).
Regarding claim 16, Devroey teaches a method for transitioning an infant after birth using a simulated intrauterine environment (incubator shown in Fig. 1), the method comprising:
Providing a simulated intrauterine environment by simulating a heartbeat using a transducer coupled to a platform configured to support an infant (“control unit will contain…separate sound tracks for heartbeat, respiration, mother’s voice, bowel”, paragraph [0034]; speakers 48 coupled to platform in Fig. 3A)
Simulating a walking gait by moving the platform using a mechanical actuator (“pillow 46 to simulate gait”, paragraph [0060]; pillows 44, 46 shown in Fig. 3A)
Determining and automatically setting one or more parameters of the simulated intrauterine environment based on biometric information of one or more mothers (“the sounds and movement may be customized to be exactly those of the mother or by the use of a standard program that is devised to match a mother with a particular set of physical characteristics and conditions”, paragraph [0031]).
Regarding claim 17, Devroey teaches simulating intrauterine audio by playing sounds through an audio speaker coupled to the platform (“one or more speakers will be located within the activation unit…producing the sounds heard in the uterus including the mother’s voice”, abstract; speakers 48 shown coupled to the platform in Fig. 3A).
Regarding claim 18, Devroey teaches the biometric information comprising prepartum data collected from the mother of the infant, and the method further comprises collecting the postpartum data from the mother of the infant (“FIG. 2B depicts a view of a pregnant woman or new mother 18 wearing the recording device 20, either before or after giving birth, in the area of the upper chest…near the heart…and to the abdomen”, paragraph [0058]; Fig. 2B).
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 1-10, 13-15, 19 and 20 are rejected under 35 U.S.C. 103 as being obvious over Devroey (US 20130096368 A1) in view of Monge Nunez et al. (US 20180078871 A1).
Regarding claim 1, Devroey teaches a baby bed, comprising:
A platform configured to support an infant (support member 60, shown in annotated Fig. 3A),;
A transducer coupled to the platform (speakers 48, shown in annotated Fig. 3A);
A mechanical actuator coupled to the platform (pillows 44 and 46, shown in annotated Fig. 3A); and
An electronic controller (“control unit 30 is also in communication with the speakers 48 located within the inner sound and motion activation unit 42”, paragraph [0061]; control unit 30, shown in annotated Fig. 3A) configured to simulate an intrauterine environment (“mimic the intrauterine conditions”, paragraph [0032]) for the infant by simulating a heartbeat using the transducer (“control unit will contain…separate sound tracks for heartbeat, respiration, mother’s voice, bowel”, paragraph [0034]) and simulating a walking gait using the mechanical actuator (“control unit 30 communicates and controls the function of the inner sound and motion activation unit 42. The Uterine Sound and Motion Simulation Devices 10 contains within it both the gait or body motion bladders or pillows 44”, paragraph [0059])
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Devroey does not teach the electronic controller having one or more settings determined by a machine learning algorithm.
However, Monge Nunez et al. teaches a system with an electric controller configured for stimulation to an infant in a baby bed (“controlling a mobile having mobile elements controllable by a mobile stimulation pattern”, abstract).
Wherein one or more settings are determined by a machine learning algorithm (“stimulation pattern using a machine learning algorithm to learn the baby’s reactions”, paragraph [0004]) based on biometric information of the infant (“sensors include devices that can detect various actions of the baby such as motions, sounds, biometrics and heart rate”, paragraph [0014])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the baby bed of Devroey with the electronic controller of Monge Nunez et al. and integrate a system that gathers biometric information of the infant in order to produce an intrauterine simulation based off those parameters. It would also have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further configure the machine learning algorithm to be based off of biometric information of the mother rather than the infant, and then in turn simulate an intrauterine environment for the infant by simulating the heartbeat and walking gait of the mother.
Regarding claims 2 and 4, Devroey in view of Monge Nunez et al. teaches the biometric information comprising prepartum and postpartum data collected from the mother of the infant (“FIG. 2B depicts a view of a pregnant woman or new mother 18 wearing the recording device 20, either before or after giving birth, in the area of the upper chest…near the heart…and to the abdomen”, paragraph [0058]; Fig. 2B).
Regarding claim 3, Devroey in view of Monge Nunez et al. teaches the biometric information comprising heartrate data and walking gait information in relation to time of day (“sound and motion recorder device will be used to record the body sounds and movements of the mother including the mothers voice throughout a normal twenty four hour period”, paragraph [0033]).
Regarding claim 5, Devroey in view of Monge Nunez et al. teaches all the limitations of claim 4.
Furthermore, Monge Nunez et al. teaches the machine learning algorithm being further trained to estimate prepartum information based on the postpartum data (“the reaction analysis module 26 incorporates known machine learning algorithms to analyze the data from one or more of the above described or other exisiting approaches for learning the cognitive state of the baby and based on learning information”, paragraph [0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the baby bed of Devroey in view of Monge Nunez et al. to analyze postpartum information of the mother and estimate prepartum information of the mother based off of detected postpartum information, in order to simulate a familiar intrauterine environment for the infant.
Regarding claim 6, Devroey in view of Monge Nunez et al. teaches the machine learning algorithm being further trained to classify a prepartum walking gait of the mother of the infant into one category of a plurality of walking gait categories (“motion activation unit will consist of a durable supple material container, housing one or more gait, or body motion units using bladders or pillows activated”, paragraph [0030]), and to adjust the walking gait of the simulated intrauterine environment to match the one category (“control unit will contain computer programs…will have software and buttons for respiration and gait movement adjustments allowing time, interval and intensity control”, paragraph [0034]).
Regarding claim 7, Devroey in view of Monge Nunez et al. teaches all the limitations of claim 1.
Furthermore, Monge Nunez et al. teaches the electronic controller being in communication with one or more sensors configured to determine information relating to a real-time characteristic of the infant (“sensors detecting reactions to a first mobile stimulation pattern of a baby”, paragraph [0016]), and the electric controller is configured to automatically adjust the one or more settings based on the information relating to the real-time characteristic (“analyzing the sensor data and the audio/video data to determine the behavior of the baby in response to the first mobile stimulation pattern and step…comparing the determined behavior with expected behaviors of the baby”, paragraph [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the baby bed of Devroey in view of Monge Nunez et al. and utilize sensors to determine real-time information detected of the baby and adjusting the settings of the intrauterine environment based off of it, in order to provide an environment that is familiar to the baby, mimicking the mother’s womb and yielding favorable reactions from the baby.
Regarding claim 9, Devroey in view of Monge Nunez et al. teaches all the limitations of claim 1 and teaches the machine learning algorithm being trained on prepartum and postpartum data (“wearing the recording device 20, either before or after giving birth”, paragraph [0058]; Fig. 2B) collected from a plurality of mothers (“the sounds and movement may be customized to be exactly those of the mother or by the use of a standard program that is devised to match a mother with a particular set of physical characteristics and conditions”, paragraph [0031]).
Futhermore, Monge Nunez et al. teaches the machine learning algorithm is configured to estimate prepartum data of the mother of the infant based on the biometric information of the mother of the infant (“the reaction analysis module 26 incorporates known machine learning algorithms to analyze the data from one or more of the above described or other exisiting approaches for learning the cognitive state of the baby and based on learning information”, paragraph [0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the baby bed of Devroey in view of Monge Nunez et al. to analyze biometric information of the mother, and other mothers with the use of the sensors and estimate prepartum information of the mother based off of the detected biometric information, in order to simulate a familiar intrauterine environment for the infant.
Regarding claim 10, Devroey in view of Monge Nunez et al. teaches the biometric information comprising postpartum data collected from the mother of the infant (“recordings may occur…after birth of the new baby”, paragraph [0041]).
Regarding claim 13, Devroey teaches all the limitations of claim 11, but does not teach the biometric information comprising aggregated prepartum data.
However, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to configure the one or more settings of the device to be able to summarize or average the prepartum data of multiple mothers, as the device comprises of sensors to monitor the biometric information of more than one mother.
Regarding claim 14, Devroey teaches all the limitations of claim 11 and collecting prepartum and postpartum data (“wearing the recording device 20, either before or after giving birth”, paragraph [0058]; Fig. 2B) from a plurality of mothers (“the sounds and movement may be customized to be exactly those of the mother or by the use of a standard program that is devised to match a mother with a particular set of physical characteristics and conditions”, paragraph [0031]) but does not teach a processing logic including a machine learning algorithm.
However, Monge Nunez et al. teaches a machine learning algorithm trained to estimate prepartum data of the mother of the infant based on the biometric information of the mother of the infant (“the reaction analysis module 26 incorporates known machine learning algorithms to analyze the data from one or more of the above described or other existing approaches for learning the cognitive state of the baby and based on learning information”, paragraph [0028]) and determine the one or more settings based on the female biometric information and the estimated prepartum data (“system also analyzes the sensor data and the audio/video data to determine the behavior of the baby in response to the first mobile stimulation pattern and compares the determined behavior with expected behaviors of the baby with respect to the first mobile stimulation pattern using a machine learning algorithm to learn the baby’s reactions”, paragraph [0005]);
Wherein the machine learning algorithm is trained on prepartum and postpartum data collected from a plurality of mothers (“the cognitive mobile learns from interactions with the baby…based on learning, the mobile adjusts actions, such as movement, sounds and visuals…may learn based on cohorts of babies based on various classifications”, paragraph [0012]); and
Wherein the female biometric information comprises postpartum data collected from the mother of the infant (“the sensors include devices that can detect various actions of the baby such as motions, sounds, biometrics and heart rate”, paragraph [0014]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the baby bed of Devroey with the machine learning algorithm of Monge Nunez et al. in order to create a trained model that determines the one or more settings based on the female biometric information and estimated prepartum data in order to learn the prepartum and postpartum data, as well as the biometric information collected to replicate a mother’s intrauterine environment for a baby.
Regarding claim 15, Devroey teaches all the limitations of claim 11, but does not teach the electronic controller being in communication with one or more sensors to determine real-time information of an infant.
However, Monge Nunez et al. teaches the electronic controller being in communication with one or more sensors configured to determine information relating to a real-time characteristic of the infant (“sensors detecting reactions to a first mobile stimulation pattern of a baby”, paragraph [0016]), and the electric controller is configured to automatically adjust the one or more settings based on the information relating to the real-time characteristic (“analyzing the sensor data and the audio/video data to determine the behavior of the baby in response to the first mobile stimulation pattern and step…comparing the determined behavior with expected behaviors of the baby”, paragraph [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the baby bed of Devroey with the electronic controller of Monge Nunez et al. and utilize sensors to determine real-time information detected of the baby and adjusting the settings of the intrauterine environment based off of it, in order to provide an environment that is familiar to the baby, mimicking the mother’s womb and yielding favorable reactions from the baby.
Regarding claim 19, Devroey teaches all the limitations of claim 16, including retrieving prepartum, postpartum, and biometric information of the mother but does not teach a machine learning algorithm.
However, Monge Nunez et al. teaches determining the one or more parameters of the simulated intrauterine environment includes using a machine learning algorithm trained to estimate prepartum data of the one or more mothers based on the biometric information of the one or more mothers (“the reaction analysis module 26 incorporates known machine learning algorithms to analyze the data from one or more of the above described or other existing approaches for learning the cognitive state of the baby and based on learning information”, paragraph [0028]) and determine the one or more parameters based on the biometric information and the estimated prepartum data (“system also analyzes the sensor data and the audio/video data to determine the behavior of the baby in response to the first mobile stimulation pattern and compares the determined behavior with expected behaviors of the baby with respect to the first mobile stimulation pattern using a machine learning algorithm to learn the baby’s reactions”, paragraph [0005]); and
Wherein the biometric information of the one or more mothers comprises postpartum data collected from the one or more mothers (“the sensors include devices that can detect various actions of the baby such as motions, sounds, biometrics and heart rate”, paragraph [0014])
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of estimating prepartum data based on biometric information of one or more mothers with the method of using a machine learning algorithm of Monge Nunez et al. in order to create a trained model that determines the one or more parameters based on the biometric information and estimated prepartum data in order to learn the mothers’ prepartum and postpartum state and their biometric information, as well as the baby’s reactions to it, in order to facilitate an accurate replication of the mothers’ intrauterine environment.
Regarding claim 20, Devroey teaches all the limitations of claim 16, but does not teach using sensors to determine real-time information of an infant.
However, Monge Nunez et al. teaches using one or more sensors to determine information relating to a real-time characteristic of the infant (“sensors detecting reactions to a first mobile stimulation pattern of a baby”, paragraph [0016]); and
Automatically adjusting the one or more parameters based on the information relating to the real-time characteristic (“analyzing the sensor data and the audio/video data to determine the behavior of the baby in response to the first mobile stimulation pattern and step…comparing the determined behavior with expected behaviors of the baby”, paragraph [0016]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the method of Devroey with the method of using sensors of Monge Nunez et al. and utilize sensors to determine real-time information detected of the baby and adjusting the settings of the intrauterine environment based off of it, in order to provide an environment that is familiar to the baby, mimicking the mother’s womb and yielding favorable reactions from the baby.
Claim 8 is rejected under 35 U.S.C. 103 as being obvious over Devroey in view of Monge Nunez et al. and further in view of Gatts et al. (US 5183457 A).
Regarding claim 8, Devroey in view of Monge Nunez et al. teaches the mechanical actuator comprising a linear-motion actuator (“the air/vacuum source is controlled directly by the control unit to send air (in the direction of the arrows shown), or pull a vacuum (in the direction of the arrows shown) to or from the gait or body motion bladders or pillows 44 and the respiration bladders or pillow 46 to simulate gait or respiration motions”, paragraph [0060]; Fig. 3A and Fig. 3B).
Devroey in view of Monge Nunez et al. does not teach the mechanical actuator comprising a rotational-motion actuator.
However, Gatts et al. teaches the mechanical actuator of a bed for an infant with a platform (shown in Fig. 2) that comprises a linear-motion actuator and rotational motion actuator (“linear and rotational motions using separate linear activators or motors”, Col. 5, lines 34-38)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the baby bed of Devroey in view of Monge Nunez et al. with the motion actuators of Gatts et al. in order to provide various tactile stimulation to the infant in order to accurately simulate the intrauterine environment of the mother.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LARA LINH TRAN whose telephone number is (571)272-3598. The examiner can normally be reached 7:30am-5:00pm M-F.
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/L.L.T./Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791