INTELLIGENT BABY BASSINET

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

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THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT

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INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC OR AS A TEXT FILE VIA THE OFFICE ELECTRONIC FILING SYSTEM (EFS-WEB)

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STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT INVENTOR

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Field of the Invention

The present disclosure generally relates to an intelligent baby bassinet apparatus and system and, more specifically, to an AI-powered baby bassinet system for soothing babies based on the collected data.

Background

Currently, within the existing automatic cradle technology available on the market, the primary function is to control the initiation and cessation of the automatic cradle's motion, lacking the capacity for autonomous sensing. These products, designed for the comfort of children, particularly infants, encompass items like motorized swings and bouncers that offer amusement. However, these child-oriented products often lack the capability to monitor an infant's condition, thereby failing to respond to changes in the baby's state. For instance, a motorized swing might be incapable of gauging whether a baby continues to cry, consequently remaining ignorant of the need to adjust factors such as swing speed in response to the baby's distress.

When a baby cries, parents typically try new rocking motions in an attempt to soothe the baby, albeit without a clear comprehension of the ensuing effects of these adjustments. In many instances, parents need to undergo a learning curve to determine what methods effectively calm their specific child. Unfortunately, this knowledge is not documented and so readily accessible. Regrettably, no existing system possesses the requisite data and knowledge to devise cradle motions that swiftly sooth babies.

SUMMARY OF THE INVENTION

The following summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

The present invention provides an intelligent bassinet apparatus capable of detecting the intensity and type of baby crying and generating new soothing motions by intelligently mixing previously stored motion patterns. The apparatus learns and adapts to the preferences of individual babies, providing an effective and personalized solution for soothing infants.

In various implementations, an intelligent bassinet apparatus capable of detecting the intensity and type of baby crying, can generate new soothing motions with the assistance of an AI model, and customize motions based on various parameters, including feeding status. The apparatus learns and adapts to the preferences of individual babies while considering geographic locations, time of the day, baby's age, gender, and other relevant factors, providing an effective and personalized solution for soothing infants. The use of a centralized cloud server for data storage and analysis further enhances the system's capabilities in suggesting the most suitable motions for each baby.

According to an embodiment of the invention, a method automatically generates customized motion patterns for a baby bassinet apparatus through the application of an AI model. Initially, a global dataset is gathered, comprising motion data and infant crying data from multiple baby bassinets, alongside contextual information such as the baby's gender, age, and feeding times. Subsequently, this data is securely stored in a cloud server, encompassing various details like bassinet properties, infant images, sound identification records, geographic coordinates, and demographic data. The method involves training an AI model using this extensive dataset, allowing it to discern patterns and relationships within the information. The AI model may generate novel motion patterns by leveraging its understanding of contextual factors like age, gender, location, and time of day to generate soothing and tailored motions for individual babies in specific circumstances.

While specific embodiments of the invention have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the invention, which is to be given the full breadth of the appended claims and any equivalents thereof.

These and other features and advantages will be apparent from a reading of the following detailed description and a review of the appended drawings. It is to be understood that the foregoing summary, the following detailed description and the appended drawings are explanatory only and are not restrictive of various aspects as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an intelligent baby bassinet apparatus in accordance with the subject disclosure.

FIG. 2 depicts an example process of controlling a baby bassinet apparatus in accordance with the subject disclosure.

FIG. 3 illustrates an exemplary system for soothing babies in accordance with the subject disclosure.

FIG. 4 depicts an example process of automatically generating new motions for baby bassinet apparatuses in accordance with the subject disclosure.

DETAILED DESCRIPTION

The subject disclosure is directed to an intelligent baby bassinet system and, more specifically, to an AI powered baby bassinet system for soothing babies based on data collected from multiple babies.

The detailed description provided below in connection with the appended drawings is intended as a description of examples and is not intended to represent the only forms in which the present examples can be constructed or utilized. The description sets forth functions of the examples and sequences of steps for constructing and operating the examples. However, the same or equivalent functions and sequences can be accomplished by different examples.

References to “one embodiment,” “an embodiment,” “an example embodiment,” “one implementation,” “an implementation,” “one example,” “an example” and the like, indicate that the described embodiment, implementation or example can include a particular feature, structure or characteristic, but every embodiment, implementation or example can not necessarily include the particular feature, structure or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment, implementation or example. Further, when a particular feature, structure or characteristic is described in connection with an embodiment, implementation or example, it is to be appreciated that such feature, structure or characteristic can be implemented in connection with other embodiments, implementations or examples whether or not explicitly described.

References to a “module”, “a software module”, and the like, indicate a software component or part of a program, an application, and/or an app that contains one or more routines. One or more independent modules can comprise a program, an application, and/or an app.

References to an “app”, an “application”, and a “software application” shall refer to a computer program or group of programs. The terms shall encompass partial programs, standalone programs, low-level software layers, thin client applications, thick client applications, web-based applications, web programs such as a browser, and other similar applications.

Numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments of the described subject matter. It is to be appreciated, however, that such embodiments can be practiced without these specific details.

Various features of the subject disclosure are now described in more detail with reference to the drawings, wherein like numerals generally refer to like or corresponding elements throughout. The drawings and detailed description are not intended to limit the claimed subject matter to the particular form described. Rather, the intention is to cover all modifications, equivalents and alternatives falling within the spirit and scope of the claimed subject matter.

In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.

The following below are more detailed descriptions of various concepts related to, and implementations of, methods, apparatuses, and systems of determining a credibility status of an image a person. The various concepts introduced above and discussed in greater detail below may be implemented in any of numerous ways.

The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.

The present invention relates to a method and system of providing an intelligent baby bassinet. Now referring to the drawings and particularly to FIG. 1 to FIG. 4, various features of the subject disclosure are now described in more detail with respect to the intelligent baby bassinet apparatus which uses artificial intelligence and machine learning techniques to control the device motions and calm down the baby.

FIG. 1 illustrates an intelligent baby bassinet device 100 for soothing babies using various motions generated by artificial intelligence models. The baby bassinet 100 comprises a cradle 101, a base structure 102 to support the cradle and hold one or more sensors and controllers. The cradle 101 may be substantially parallel to a floor. In some configurations, the cradle 101 may be rectangular or oval in shape. The baby bassinet 100 comprises one or more processors 103 or controllers to control the operations and motion of the bassinet 100. The baby bassinet 100 may include one or more memory devices 104 that may store data, such as computer programs, sample sound data of baby crying etc. Furthermore, the baby bassinet 100 may include one or more oscillation and vibration modules to provide various sets of motions to the cradle 101. In some embodiments, the baby bassinet 100 may also include speakers, lights, microphones, cameras, an air quality sensor, a pressure sensor, an infrared sensor, temperature and humidity sensors, etc. Each sensor in the bassinet 100 may generate data that may be stored on a memory 104 or a remote storage medium (e.g., a cloud service or the client device 110).

In the preferred embodiment, the baby bassinet apparatus 100 includes one or more microphones 107 to capture sound data from the baby's crying. Additionally, the baby bassinet may include an audio processing module. In a preferred embodiment of the invention, the processor(s) 103 may receive sound data from the baby's crying and can control bassinet device settings such as controlling the oscillation and vibration motors by adjusting the amplitude. The audio processing module processes the audio data to determine if the baby is crying and the intensity of the sound. The baby bassinet apparatus 101 may also include one or more batteries or power sources 108 to provide sufficient power for the operation of the apparatus. In some embodiments, some of these components might be physically connected to the baby bassinet 100, while in alternative embodiments, certain components may be distributed and/or maintain communication with the baby bassinet through wired or wireless means. The oscillation module 105 may include a swing motor designed to induce motion, such as back-and-forth, side-to-side, elliptical, random, or circular movements, for instance, to a baby seat or other infant carrier. A vibration module 106 may be housed under a seat, play surface, sleeping surface, or other child-contacting surface on the baby bassinet apparatus 100. The vibration module 106 can be configured to produce vibrations, and notably, in the preferred embodiments of this invention, it allows for the adjustment of vibration intensity.

A communication device 109 may be included and/or physically integrated into the bassinet 100. The communication device 109 may be a wired connection capability such as Ethernet and/or a wireless connection such as Wi-Fi, Bluetooth (including low-energy Bluetooth), near field communication (“NFC”), a radio antenna, or the like. The communication device 109 may be configured to communicate with one or more client devices, a controller, and/or a remote cloud system. A client device 110 may refer to a smartphone, tablet, personal computer (laptop, desktop), etc. The client device 110 may contain a display, microphone, and one or more speakers.

In a preferred embodiment of the invention, the controller 103 can provide multiple different motions to the cradle of the baby bassinet 100. These motions can be, for example, moving the cradle horizontally, vertically, waving, vibrating, side-to-side, back and forth, elliptical, circular etc. In some embodiments, these motions are controlled by an algorithm. In some embodiments, the controller 103 may simulate a car's motion such as rotation on the baby cradle, like moving back and forth, moving sideways or up and down. In some embodiments, various different motions can be pre-programmed in the memory 104 of the baby bassinet 100. In another embodiment, the controller 103 can generate new motions based on the collected historical data for the baby. The controller 103 can detect the baby crying activity using the microphone 107. After detecting the crying activity, the controller 103 can generate motions and feed to the cradle 101. The controller 103 may keep on changing the motions until the baby stops crying or reduces the intensity of crying.

In some embodiments of the invention, the baby bassinet 100 may include a method of detecting the intensity and type of baby crying. The apparatus may comprise a microphone or other suitable audio sensor for capturing the baby's cries. The captured audio data is processed to determine the intensity and type of crying, which can include different patterns such as hunger cries, discomfort cries, or general fussiness cries. In one embodiment of the invention, the baby bassinet 100 may fetch motion patterns data from the memory 104 that the baby liked in the past and got calmed. In some situations, the pre-stored motion patterns may not effectively calm the baby. This can occur when the baby has unique preferences that differ from the pre-stored motion patterns. The method may generate new motion patterns by intelligently mixing the previously stored motions from the memory 104. The new motion pattern generation process can be based on the detected type of crying and the historical effectiveness of various motions for the baby. The generated motions are then applied to the bassinet to soothe the baby. If the baby responds positively to the newly generated motions, for example, reduces the intensity, amount and/or volume of crying or if the baby stops crying, the bassinet 100 may learn this information and store the new motion data in the memory 104 for future use. Additionally, if the generated motions are ineffective in soothing the baby, the method learns this information and stores the new motion data in the memory 104 for future use. This allows the bassinet apparatus to learn and adapt to the specific preferences of the baby over time by recognizing patterns that are effective in calming/soothing the baby and motion patterns that are not effective in soothing the baby. Furthermore, the bassinet 100 alters the applied motion patterns to the cradle based on the changes in one or more of the volume or intensity of a baby's crying or a pattern of the cry.

In some embodiments of the invention, the bassinet apparatus 101 may include an AI model for generating new motion patterns. The AI model can be integrated into the apparatus's control system and operates in conjunction with the cry detection and motion mixing functionalities. The AI model utilizes data collected from the motions applied to the baby bassinet 100 for a baby and the baby's reactions to these motions. This data is used to train the AI model, allowing it to better understand the baby's specific preferences and comfort levels. The bassinet apparatus and/or the AI model can use the learned effective and ineffective motion patterns for the baby to determine a preferred motion pattern for the baby. In another embodiment, the bassinet apparatus and/or the AI model can use the learned effective and ineffective motion patterns for the baby to generate a motion pattern for soothing the baby. In the preferred embodiments, the AI model not only suggests new motion patterns but also customizes the motion patterns based on various factors. The AI model may combine different motion patterns and evaluate the responses of babies to the suggested motion patterns. The AI model can create motions tailored to the baby's individual needs and preferences. Furthermore, the AI model's customization extends to different scenarios, including recognizing that babies may prefer distinct types of motions before and after feeding. For example, it may generate gentler motions before feeding to calm a hungry baby and more vigorous motions after feeding to help with digestion and comfort.

Additionally, the AI model may consider the geographic location of the baby using the bassinet apparatus. The parameters like time of the day, baby's age, gender, geographic location etc. can be used to fine-tune the motion suggestions while ensuring that the generated motions are appropriate for the baby's specific circumstances. It should be understood that other physical and behavioral parameters, including but not limited to baby's height, weight, age, and type of crying, can be used to determine a soothing motion pattern for the baby. In additional embodiments, the baby bassinet 100 and the AI model can use feeding information such as time of the baby's feeding, a time when the baby was last fed and/or other inputs from the baby's mother to determine a soothing motion pattern for the baby. The bassinet apparatus and/or the AI model may use one or more of learned historical/past effective and ineffective patterns for the baby and baby feeding data to determine a soothing motion pattern for the baby. The soothing motion pattern may be a repetitive motion pattern.

In some embodiments of the invention, parents or caregivers may be provided with a mobile application “app”. The mobile app may provide critical updates, status, notifications and alarms about the baby's condition while nestled within the cradle of the baby bassinet. The mobile app may generate analytics based on current sensor monitoring data and the data stored on the cloud server. The analytics may include insights related to the infant's sleep schedule, sleep quality, and the overall duration of their sleep patterns. Additionally, the parents or caregivers may control certain features and settings of the baby bassinet using the mobile app.

FIG. 2 depicts a process flow 200 of a method controlling the motions of the baby bassinet apparatus to calm a baby in accordance with one or more embodiments of the invention. The baby bassinet device may be equipped with a camera to capture one or more images of the baby inside the cradle. Baby images may include a complete image of the infant, or an image of the face, limbs, etc. of the infant, which may be obtained by a camera. At step 201, baby can be identified based one or more images captured by the camera.

The baby bassinet apparatus may be configured to detect and process sound in order to recognize a baby's cry and generate and implement one or more motions to the baby bassinet apparatus to calm the baby. At step 202, the baby bassinet apparatus may be configured to detect sound. For example, the baby bassinet apparatus may include a microphone configured to capture ambient noise and generate an audio signal.

At step 203, the apparatus may process the captured sound to detect baby's cry. Various noise filtering options can be used to eliminate the background noise effects. By processing audio signals and assessing crying intensity, the apparatus can determine if the infant is crying.

Upon determining whether the baby is crying, at step 204, the baby bassinet apparatus may implement one or more actions. The actions may include, for example, one or more adjustments to the speed or direction of the apparatus, and/or an adjustment to the motion patterns of the device. The motion and vibration of the baby bassinet apparatus may be adjusted by the oscillation and vibration modules. An AI model, integrated into the apparatus's control system, can operate in conjunction with the cry detection and motion mixing functionalities. The AI model can use the learned effective and ineffective motion patterns for the baby to determine a preferred motion pattern for the baby. The AI model not only suggests new motion patterns but also customizes the motion patterns based on various factors. The AI model may combine different motion patterns and evaluate the responses of babies to the suggested motion patterns. The AI model can create motions tailored to the baby's individual needs and preferences. The AI may also consider preferred distinct types of motions before and after feeding and according to the geographic location of the baby using the bassinet apparatus. The parameters like time of the day, baby's age, gender, geographic location etc. can be used to fine-tune the motion suggestions while ensuring that the generated motions are appropriate for the baby's specific circumstances. The bassinet apparatus and/or the AI model may use one or more of learned historical/past effective and ineffective patterns for the baby and baby feeding data to determine a soothing motion pattern for the baby. The soothing motion pattern may be a repetitive motion pattern.

The microphone can continuously monitor the sound near the baby bassinet apparatus. At determination step 205, based on the sound data collected by the microphone a determination is made as to whether the baby is crying. If it is determined that the baby is still crying, the process may return to step 203 to determine the baby's crying intensity. Conversely, if the baby stops crying or crying intensity is significantly reduced, the process advances to step 206 and oscillation and vibration settings are stored in a database. The reduction in crying intensity, frequency and/or volume may be considered as positive feedback for the applied oscillation and vibration motion pattern. The database may also contain one or more baby images, sound identification data, the geographic location of the baby, the age and gender of the baby etc.

According to another embodiment of the invention, the baby bassinet 100 can detect the movement of the baby inside the bassinet and adjust the motion patterns of the bassinet based on the detected movements of the baby. As a baby is placed in the bassinet cradle 101 and some motion patterns are applied to the bassinet to soothe the baby, the baby may start moving in a particular pattern inside the cradle. The baby bassinet 100 can detect the movement of the baby inside the bassinet cradle 101 and start collecting the baby's feedback data. The bassinet 100 can learn from the behaviour of one or more babies against applied motion patterns. Certain types of babies may prefer some specific type of motion patterns applied to the cradle and may like to be moved in a specific way. Therefore, the process controller 103 of the baby bassinet 100 may send the instructions to the oscillation module 105 and the vibration module 106 to change the applied motion patterns based on the feedback received from the baby.

FIG. 3 illustrates a system 300 for soothing babies. The system 300 may include one or more intelligent baby bassinets, 301a-c. The one or more intelligent baby bassinets 301a-c may include one or more processors or controllers that control one or more aspects of the device 301.

According to some embodiments of the invention, one or more baby bassinet apparatuses 301a-c can be connected to a cloud server 310 to collect data. The cloud server can process the data collected from the baby bassinet apparatuses 301a-c and automatically control the oscillation and vibration of the device. In some embodiments, an AI algorithm can be trained on the data collected from various devices from multiple geographic locations. The AI algorithm may generate new motions and recommend the generated motions to oscillation and vibration modules of the baby bassinet apparatuses to calm down the baby.

According to a preferred embodiment, the AI model can use data collected from multiple bassinet devices across different locations to train and refine its motion generation capabilities. Therefore, the bassinet apparatus and AI model can learn effective patterns for the first baby from historical/past motion data for the first baby, such as patterns that have soothed the baby in the past and patterns that were ineffective in soothing the baby, as well as motion data from other babies from the same or different geographical locations. The bassinet apparatus and/or the AI model can use the learned effective and ineffective motion patterns for the baby to determine a preferred motion pattern for the baby. In another embodiment, the bassinet apparatus and/or the AI model can use the learned effective and ineffective motion patterns for the baby to generate a motion pattern for soothing the baby. In the preferred embodiments, the AI model not only suggests new motion patterns but also customizes the motion patterns based on various factors. The AI model may combine different motion patterns and evaluate the responses of babies to the suggested motion patterns. The AI model can create motions that are tailored to each baby's individual needs and preferences. Furthermore, the AI model's customization extends to different scenarios, including recognizing that babies may prefer distinct types of motions before and after feeding. For example, it may generate gentler motions before feeding to calm a hungry baby and more vigorous motions after feeding to help with digestion and comfort.

Additionally, the AI model may take into account the geographic locations of babies using the bassinet apparatus, the time of the day, baby's age, gender, and other relevant parameters to fine-tune its suggestions to ensure that the generated motions are appropriate for the baby's specific circumstances. In a preferred embodiment of the invention, the system can collect data from multiple babies and store this data on a centralized cloud server. This data may relate to all the different motions that babies prefer and patterns that do not help in soothing babies, and it may be categorized by geographic location, time, baby's age, gender, and other factors. The AI model can then access this data to suggest the motion patterns that a particular baby would likely find the most soothing pattern based on their individual profile and current conditions. In preferred embodiments of the invention, in addition to learned effective and ineffective motion patterns for the baby, and motion patterns for other babies, the bassinet apparatus and/or the AI model can use parameters such as the baby's height, weight, age, and type of crying to determine a soothing motion pattern for the baby. It should be understood that other physical and behavioural parameters, including but not limited to the parameters described above, can be used to determine a soothing motion pattern for the baby. In additional embodiments, the bassinet apparatus and or the AI model can use feeding information such as time of the baby's feeding, a time when the baby was last fed and/or other inputs from the baby's mother to determine a soothing motion pattern for the baby. The bassinet apparatus and/or the AI model may use one or more of learned historical/past effective and ineffective patterns for the baby, effective and/or ineffective patterns for other babies, physical and behavioural parameters for the baby and/or other babies, and feeding related information of the baby to determine a soothing motion pattern for the baby. The soothing motion pattern may be a repetitive motion pattern.

FIG. 4 illustrates an exemplary process flow 400 for automatically generating new motion patterns for the baby bassinet apparatus using an AI model in accordance with one or more embodiments of the invention.

At step 401, the system may collect baby bassinet motion data and corresponding baby crying data from multiple baby bassinets located worldwide. Additionally, various contextual data points are gathered, including information about the baby's gender, the time of day, the baby's age, and whether the data was collected before or after feeding, among others.

At step 402, the collected data may be stored in a cloud server for subsequent processing. The collected data may include diverse information, such as the oscillation and vibration properties of the baby bassinet apparatus, images of infants, sound identification data, geographic coordinates reflecting the baby's location, and demographic details such as the baby's age and gender.

At step 403, an AI model is trained using the comprehensive dataset acquired during data collection. During training, the AI model learns to recognize patterns and relationships within the data. In various embodiments of the invention, the system may use any suitable artificial intelligence and machine learning algorithm without limitation.

At step 404, the trained AI model leverages its learned knowledge to generate novel motion patterns for the baby bassinet apparatus. These motions are formulated based on the patterns and insights gleaned from the training data. Essentially, the AI model employs its understanding of how various factors, such as the baby's age, gender, location, time of day, and other contextual information, correlate with effective soothing motions. The AI model may generate new motion patterns that are likely to be soothing and comforting to a specific baby in a given context using this knowledge.

The wordings such as “include”, “including”, “comprise” and “comprising” do not exclude elements or steps which are present but not listed in the description and the claims.

The detailed description provided above in connection with the appended drawings is intended as a description of examples and is not intended to represent the only forms in which the present examples can be constructed or utilized. It is to be understood that the configurations and/or approaches described herein are exemplary in nature, and that the described embodiments, implementations and/or examples are not to be considered in a limiting sense, because numerous variations are possible.

The specific processes or methods described herein can represent one or more of any number of processing strategies. As such, various operations illustrated and/or described can be performed in the sequence illustrated and/or described, in other sequences, in parallel, or omitted. Likewise, the order of the above-described processes can be changed. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are presented as example forms of implementing the claims.

In an aspect of the present disclosure, a method of altering motion patterns of a baby bassinet is provided, which comprises: applying an initial motion pattern to the baby bassinet; and altering the initial motion pattern based on changes in one or more of the volume or intensity of a baby's crying or a pattern of the cry.

In another aspect of the present disclosure, a method of soothing a baby on a bassinet is provided, which comprises: collecting baby behaviour data related to the baby's response to motion patterns applied to the bassinet; learning from the past responses of the baby to the applied motion patterns; and applying new motion patterns on the bassinet based on the learned responses from the baby.

In a further aspect of the present disclosure, a method of soothing a baby on a bassinet is provided, which comprises: collecting behavioural data for one or more babies in response to one or more motion patterns applied to one or more bassinets; learning soothing motion patterns from the collected behavioural data; and applying one or more learned motion patterns to the bassinet based on positive feedback from the baby, wherein the feedback is determined from the sound of the baby's crying.

In another aspect of the present disclosure, a method of soothing a baby on a bassinet is provided, which comprises: obtaining learned motion patterns for soothing babies; determining a time of day; determining the baby's feeding status and a time of feed; selecting one or more motion patterns to be applied to the bassinet based on the time of the day, baby's feed status and said learned motion patterns; applying the selected one or more motion patterns to the bassinet for soothing the baby.

In a further aspect of the present disclosure, a method of soothing a baby on a bassinet is provided, which comprises: collecting physical and/or behavioural data for babies comprising location, height, weight, age, type of crying and/or intensity of crying; categorizing the collected data based on location and age of babies; selecting motion patterns for the bassinet based on a location and age of the baby; applying the said motion patterns to the bassinet to soothe the baby.

In an aspect of the present disclosure, a method of soothing a baby on a bassinet is provided, which comprises: applying an initial motion pattern to the bassinet; collecting feedback for the applied motion pattern by collecting data on the baby's motion in the bassinet in response to the applied motion pattern; identifying if the baby likes the applied motion pattern; and changing the motion patterns of the bassinet based on the feedback.

In another aspect of the present disclosure, a baby bassinet apparatus is provided, which comprises: a motion control mechanism for providing a repetitive motion to the baby bassinet apparatus; a memory unit for storing motion pattern data; a controller to determine a motion pattern for the baby bassinet apparatus wherein the motion pattern is determined based on one or more effective past motion patterns and/or one or more ineffective past motion patterns for soothing a baby.

In an embodiment of this aspect, the motion pattern for the baby bassinet apparatus is determined based on one or more physical and/or behavioural parameters of the baby.

In another embodiment of this aspect, the physical and/or behavioural parameters comprise height, weight, age, type of crying and/or intensity of crying.

In a further embodiment of this aspect, the motion pattern for the baby bassinet apparatus is determined based on additional parameters such as time of day, feeding information for the baby and/or caregiver input data for the baby.

In another embodiment of this aspect, the motion pattern for the baby bassinet apparatus is determined based on motion pattern data collected from one or more other baby bassinet apparatuses in one or more geographic locations.

In another embodiment of this aspect, the motion pattern data for other baby bassinet apparatuses includes effective and/or ineffective motion patterns, physical and/or behavioural parameters of one or more babies.

In another aspect of the present disclosure, a system for soothing baby is provided, which comprises: one or more baby bassinet apparatuses for collecting data on babies and their soothing status; a cloud server for securely storing collected data, including infant images, sound identification data, geographic location, age, gender, and motion properties of the baby bassinet apparatus in a database; an AI model trained on the stored data, enabling the AI model to generate customized motion patterns for soothing the baby; one or more sensors for detecting the baby's reactions to the adjusted motion patterns; and one or more controllers for controlling the motion patterns of the baby bassinet apparatuses.

In a further aspect of the present disclosure, a method for automatically generating new motion patterns for a baby bassinet apparatus is provided, which comprises: collecting baby bassinet motion data and corresponding baby crying data from multiple baby bassinets; storing the collected data in a cloud server; and generating the new motion patterns for the baby bassinet apparatus using the collected data.

In an embodiment of this aspect, images and sound data can be continuously captured to assess the effectiveness of the implemented motion patterns while soothing the baby.

INTELLIGENT BABY BASSINET

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