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 .
Response to Amendment
The Office Action is responsive to the Amendment filed 23 April 2026. Claims 1, 4-20 are now pending. The Examiner acknowledges the amendments to Claims 1, 9-12, 14, 16, 18, and 19.
Claim Objections
Claim 1 is objected to because of the following informalities:
Regarding claim 1, replace the period after “and walking gait data of a mother of the infant” with a semicolon.
Appropriate correction is required.
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, 4-7 and 9-20 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) (hereon referred as Monge Nunez).
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]), wherein the electronic controller is configured to:
Receive female biometric information comprising at least one of prepartum data, heartrate data, and walking gait data of a mother of the infant (“recorder to be carried by the mother during the twenty four hour recording period recording movements, sounds, inclusive heart beat…environment”, paragraph [0034]),
Determine one or more settings of the transducer and the mechanical actuator of the simulated intrauterine environment based on the biometric information of the mother of the infant (“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]).
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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 teaches a system with an electric controller (mobile control module 16) configured for stimulation to an infant in a baby bed ("controlling a mobile having mobile elements controllable by a mobile stimulation pattern…stimulation pattern using a machine learning algorithm", paragraph [0004]), wherein the electronic controller is capable of be configured to:
Receive biometric information comprising at least one of prepartum data, heartrate data, and walking gait data of a mother of the infant (“sensors include devices that can detect various actions of the baby such as motions, sounds, biometrics and heart rate”, paragraph [0014]); and
Wherein the 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]), and adjust the one or more settings.
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 and integrate a machine learning algorithm that receives biometric information and in turn determines the one or more settings of the transducer and mechanical actuator. 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 and simulate an intrauterine environment for the infant by simulating the heartbeat and walking gait of the mother.
Regarding claim 4, Devroey in view of Monge Nunez teaches the biometric information comprising 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 5, Devroey in view of Monge Nunez teaches all the limitations of claim 4.
Furthermore, Monge Nunez teaches the machine learning algorithm capable of 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]). The machine learning algorithm can also learn the postpartum data from the mother in order to estimate prepartum information.
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 to analyze postpartum information of the mother and estimate prepartum information of the mother using the machine learning algorithm in order to accurately simulate a familiar intrauterine environment for the infant.
Regarding claim 6, Devroey in view of Monge Nunez 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 teaches all the limitations of claim 1.
Furthermore, Monge Nunez 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 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 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 teaches all the limitations of claim 1 and teaches the machine learning algorithm being trained on the prepartum data 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 teaches the machine learning algorithm capable of being 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 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 teaches the biometric information comprising the postpartum data collected from the mother of the infant ("recordings may occur...after birth of the new baby", paragraph [0041]).
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]), wherein the electronic controller is configured to:
Receive female biometric information comprising at least one of prepartum data, heartrate data, and walking gait data of a mother of the infant (“recorder to be carried by the mother during the twenty four hour recording period recording movements, sounds, inclusive heart beat…environment”, paragraph [0034]),
Determine one or more settings of the transducer and the mechanical actuator of the simulated intrauterine environment based on the 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]).
Devroey does not teach the electronic controller having one or more settings determined by a machine learning algorithm.
However, Monge Nunez teaches a system with an electric controller (mobile control module 16) configured for stimulation to an infant in a baby bed ("controlling a mobile having mobile elements controllable by a mobile stimulation pattern…stimulation pattern using a machine learning algorithm", paragraph [0004]), wherein the electronic controller is capable of be configured to:
Receive biometric information comprising at least one of prepartum data, heartrate data, and walking gait data of a mother of the infant (“sensors include devices that can detect various actions of the baby such as motions, sounds, biometrics and heart rate”, paragraph [0014]); and
Wherein the 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]), and adjust the one or more settings.
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 and integrate a machine learning algorithm that receives biometric information and in turn determines the one or more settings of the transducer and mechanical actuator. 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 and simulate an intrauterine environment for the infant by simulating the heartbeat and walking gait of the mother.
Regarding claim 12, Devroey in view of Monge Nunez teaches the female biometric information comprising 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 13, Devroey in view of Monge Nunez 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 in view of Monge Nunez 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 teaches a machine learning algorithm capable of being trained to estimate prepartum data of the mother of the infant based on the female 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 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 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 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 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)
Receiving biometric information comprising at least one of prepartum data, heartrate data, and walking gait data of one or more mothers (“recorder to be carried by the mother during the twenty four hour recording period recording movements, sounds, inclusive heart beat…environment”, paragraph [0034]); and
Determining and automatically setting one or more parameters of the transducer and the mechanical actuator of the simulated intrauterine environment based on biometric information of the 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]).
Devroey does not teach the one or more settings being determined by a machine learning algorithm.
However, Monge Nunez teaches a method for providing an infant after birth a simulated environment ("controlling a mobile having mobile elements controllable by a mobile stimulation pattern…stimulation pattern using a machine learning algorithm", paragraph [0004]), the method comprising:
Receiving biometric information (“sensors include devices that can detect various actions of the baby such as motions, sounds, biometrics and heart rate”, paragraph [0014]); and
Wherein the 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]), and adjust the one or more settings.
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 method of Devroey with the method of using a machine learning algorithm of Monge Nunez and receive biometric information and determine the one or more settings of the transducer and mechanical actuator and simulate an intrauterine environment familiar of the mother for the infant.
Regarding claim 17, Devroey in view of Monge Nunez 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 method further comprising the prepartum 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).
Regarding claim 19, Devroey in view of Monge Nunez teaches all the limitations of claim 16.
Furthermore, Monge Nunez teaches determining the one or more parameters of the simulated intrauterine environment including using the machine learning algorithm capable of being 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 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 in view of Monge Nunez teaches all the limitations of claim 16.
Furthermore, Monge Nunez 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 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 and further in view of Gatts et al. (US 5183457 A) (hereon referred as Gatts).
Regarding claim 8, Devroey in view of Monge Nunez 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 does not teach the mechanical actuator comprising a rotational-motion actuator.
However, Gatts 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 with the motion actuators of Gatts in order to provide various tactile stimulation to the infant in order to accurately simulate the intrauterine environment of the mother.
Response to Arguments
Applicant’s arguments, filed 23 April 2026, with respect to the claim objections have been fully considered and are persuasive. The claim objections have been withdrawn.
Applicant’s arguments with respect to claims 11, 12, and 16-18 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. The rejection of claims 11, 12, and 16-18 under 35 U.S.C. 102(a)(1) have been updated accordingly.
Applicant’s arguments, filed 23 April 2026, with respect to 35 U.S.C. 103 have been fully considered but they are not persuasive.
In regards to claim 1, the Applicant states that Devroey in view of Monge Nunez does not teach applying machine learning to a mother’s biometric information or using machine learning to determine settings for a simulated intrauterine environment based on biometric information. However, the Examiner respectfully disagrees. Devroey does teach using a mother’s biometric information to simulate an intrauterine environment (“recorder to be carried by the mother during the twenty four hour recording period recording movements, sounds, inclusive heart beat…environment”, paragraph [0034], “mimic the intrauterine conditions", paragraph [0032], Devroey). By combining Devroey with the machine learning algorithm that provides stimulation for the infant of Monge Nunez ("controlling a mobile having mobile elements controllable by a mobile stimulation pattern…stimulation pattern using a machine learning algorithm", paragraph [0004], Monge Nunez), it would yield a system that is capable of carrying out the process of the claimed invention, which is using biometric information of a mother and/or using machine learning to determine the settings of the intrauterine environment based on the biometric information.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). Furthermore, it is not impermissible hindsight, as the combined references does teach using a mother’s biometric information and/or machine learning in order to determine further settings to simulate/mimic an intrauterine environment for the mother’s infant, as explained above.
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 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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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Valvis can be reached at 5712724233. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/L.L.T./Examiner, Art Unit 3791 /ALEX M VALVIS/Supervisory Patent Examiner, Art Unit 3791
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