DETAILED CORRESPONDANCE
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
This final office action on merits is in response to the communication received on 05/05/2026. Claims 52 and 53 are cancelled. Amendments to claims 40, 43, 45-48, and 50 are acknowledged and have been carefully considered. Claims 40-51 and 54 are pending and considered below.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 3/24/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
In view of Applicant's amendments and arguments, the rejection under 35 U.S.C. 101 has been withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 40-51 and 54 are rejected under 35 U.S.C. 103 as being unpatentable over Kaplan et al. (U.S. Patent Publication 2012/0232801 A1), referred to hereinafter as Kaplan, in view of Lau et al. (U.S. Patent Publication 2019/0114942 A1), referred to hereinafter as Lau, and Falk et al. (U.S. Patent Publication 2017/0347917 A1), referred to hereinafter as Falk.
Regarding claim 40, Kaplan teaches a method executed by an engine on a computing device for quantitatively measuring infant feeding performance, the method comprising (Kaplan [0040] “Calculating of values for feeding parameters preferably involves executing a set of instructions that calculate a value the desired parameter based upon the received data. In certain preferred embodiments, the instructions are in the form of an executable computer program, i.e. software or firmware. For such embodiments, the computing step involves the use of a microprocessor or other automated computational device. The type of programming languages and/or paradigms that are useful with the present invention are not particularly limited, provided that the software can performed the desired functions on the chosen computer platform.”):
analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period (Kaplan [0051] “As shown in FIG. 3, in certain embodiments the digital data 14 can be received by the system 21 from an instrument 33 attached to a baby bottle 32 or other feeding device that directly measures the desired physical and/or chemical quantities, and converts such measurements into digital data. In such systems, the raw data is transferred 31 as a wireless RF or electronic signal and is received by the system via a data interface (not shown). Examples of such data interfaces include data ports, such as a USB port, or an RF receiver capable of detecting and converting an wireless signal into an electronic data.”);
determining a baseline pressure from the data received from the device (Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline.”, and
Kaplan [0035] “Digital data useful in the present invention includes, for example, collections of digitized outputs from a device that converts physical quantities into measured values. Examples of such physical quantities include pressure and time, such as the interior pressure of a baby bottle, while an infant is feeding from the bottle. Such data is typically characterized as per a feeding session.”);
determining, using a pressor sensor coupled to the bottle and based on the baseline pressure, pressure fluctuations in an oral cavity of the infant during the first time period (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”, and
Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”);
detecting sucks by comparing the pressure fluctuations to known characteristics (Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”);
detecting swallows by comparing the pressure fluctuations to known characteristics (Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”
Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”, and
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”);
coupled to the bottle (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”.);
calculating a second time period between the sucks or the swallows to detect bursts (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
calculating a third time period without the sucks and without the swallows (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
calculating at least one biomarker for the infant or the feeding instance based on the second time period and the third time period (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
analyzing the at least one biomarker to track maturation, neuro-development, or recovery of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”
[0041] “Generating a referenced infant feeding scare preferably involves associating a feeding parameter value for an individual infant to a corresponding feeding parameter metric, for example by matching the value of the feeding parameter of the individual under observation to a similar value, position, or form within the feeding parameter metric. Generation of a referenced risk score likewise involves associating a feeding parameter value for an individual infant to a corresponding risk outcome metric. By comparing the data collected on an individual infant to a standard value or range, or by specifying the relationship between the parameter value and a medical condition or risk for adverse outcomes, an objective, quantitative evaluation of the infant's feeding performance is obtained. In certain preferred embodiments, the corresponding step involves comparing the value of the feeding parameter to the closest value of the metric to determine a quantitative referenced feeding score for the individual infant.”
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”, and
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”); and
outputting the maturation, neuro-development, recovery, or feedinq competency of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”, and
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”)
Kaplan fails to explicitly teach receiving, from a user and via a graphical user interface (GUI) of the computing device, non-sensor data during the first time period; determining, using a temperature sensor coupled to the bottle, a temperature fluctuation caused by the infant exhaling over the temperature sensor during the first time period; detecting respiration by comparing the temperature fluctuation to known characteristics; and via the GUI to the user.
Lau teaches receiving, from a user and via a graphical user interface (GUI) of the computing device, non-sensor data during the first time period (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”, and Lau [0152] “FIG. 12 shows a second example of an algorithm for determining OFS levels. FIG. 12 follows the simplified algorithm defined in Table 1. Note: the parameter PRO(5) is defined as the % volume (ml) taken during the first 5 min divided by the total volume (ml) of liquid prescribed. The parameter RT(20) is defined as the overall (average) rate of milk transfer (ml/min) averaged over an entire 20 minute feeding session. The algorithm starts with inputting the gestational age (GA), weight (W), of the infant, and the number of feeding sessions in a day, N.”); and
via the GUI to the user (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”).
Falk teaches determining, using a temperature sensor, a temperature fluctuation caused by the infant exhaling over the temperature sensor during the first time period (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
detecting respiration by comparing the temperature fluctuation to known characteristics (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's infant feeding assessment system with the smart device application and user entered infant information taught by Lau and the temperature respiratory monitoring taught by Falk. Kaplan teaches evaluating infant feeding performance by measuring sucking pressure, deriving feeding parameters, calculating feeding scores, and correlating those scores with developmental and clinical outcomes. Lau teaches incorporating a smart device application that receives caregiver entered, non sensor information, such as gestational age and weight, for use in evaluating infant feeding, while Falk teaches using temperature measurements to determine inspired and expired gas and generate respiratory information. A person of ordinary skill in the art would have recognized that these teachings are directed to complementary aspects of infant physiological assessment and could be predictably combined to improve the infant feeding evaluation.
Furthermore, one of ordinary skill in the art would have been motivated to incorporate Falk's temperature respiratory sensing into Kaplan's bottle mounted feeding assessment system because Kaplan identifies respiration as a clinically relevant feeding factor during infant feeding, and does not limit the particular sensing modality used to obtain respiratory information. Also, incorporating Lau's smart device application for receiving caregiver entered infant information would have predictably enabled the feeding assessment system to consider both sensor derived physiological measurements and clinically relevant non sensor information when evaluating infant feeding performance. These modifications involve the predictable use of known sensing, data acquisition, and computing techniques according to their established functions to improve the assessment of infant feeding competency and clinically relevant outcomes, yielding no more than the expected and predictable results.
Regarding claim 41, Kaplan, Lau, and Falk teach the invention in claim 40, as discussed above, and further teach further comprising: detecting characteristic shapes of a specific patient population, a medical condition, or a given biomarker; and/or adapting the baseline pressure to or within an actual reading (Kaplan [0086] “A number of changes in the sucking pattern over the course of individual test sessions were obtained, as assessed by 2-way [epoch within session X GA] ANOVA. Within-burst suck frequency did not vary across epochs, but significant changes over the 5 epochs of the sucking bout were obtained for: number of sucks, number of bursts, mean burst duration, total burst time as percent of epoch, mean maximum sucking pressure, and for total burst time as percent of epoch (pattern mirroring that for number of sucks). A significant main effect of GA was obtained for each parameter evaluated except for within-burst suck frequency; the results for these parameters at the epoch and whole-session levels were thereby in good agreement. There were no 2-factor interactions except for one parameter-mean burst duration. For this parameter, the epoch curves for each GA were similar to the group curve, but with a somewhat flatter profile over epochs 2 to 4 for some GAs than for others and with somewhat less of a rebound from epoch 4 to 5 for GA 37 weeks than for the other neonates.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”
Lau [0139] “Alternatively, the change in internal air pressure (increase in vacuum level) inside of a sealed bottle (i.e., with no anti-vacuum valve) can be measured with a sensitive pressure transducer placed at or near the top of the bottle. The removal of a bolus of liquid during a single suck creates an incremental change in the vacuum air pressure level (via the relationship Pressure×Volume=constant, at constant temperature), which can be measured, in real-time, by a pressure transducer. Once a particular bottle's geometry has been calibrated (and assuming a bottle with a constant cross-section along it's length), then the drop in internal air pressure measured by the pressure transducer, in real-time, will correlate directly to the volume of liquid removed, in real-time, from the nipple.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's infant feeding assessment method to incorporate the pressure calibration techniques taught by Lau. Kaplan teaches analyzing characteristic feeding patterns and epoch curves across different patient populations, including gestational age groups, to identify physiologically meaningful differences in feeding behavior and developmental status. Kaplan further teaches evaluating feeding events relative to a baseline, while Lau teaches calibrating the bottle pressure measurement system so that real time pressure measurements accurately correspond to actual bottle conditions. One of ordinary skill in the art would have recognized that incorporating Lau's known calibration technique into Kaplan's pressure feeding assessment system would improve the reliability of the baseline pressure measurements used to detect characteristic feeding patterns and physiological biomarkers across infant populations. This modification applies known pressure calibration techniques according to their established function to improve the accuracy and consistency of infant feeding analysis, yielding no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 42, Kaplan, Lau, and Falk teach the invention in claim 40, as discussed above, and further teach further comprising: utilizing the at least one biomarker to calculate tracking indicators; and comparing at least one biomarker or the tracking indicators for the infant to determine how they change over time; and/or comparing the at least one biomarker and the tracking indicators of the infant to a dataset to determine a normative comparison (Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to utilize the feeding parameters and physiological indicators generated by Kaplan as tracking indicators and to compare those indicators over time to evaluate changes in infant feeding performance. Kaplan teaches generating quantitative feeding parameters from measured feeding data and further teaches evaluating those parameters across successive observation epochs to identify changes in feeding behavior, recovery, and developmental progression. One of ordinary skill in the art would have recognized that using these generated physiological parameters as tracking indicators and comparing them over time is a predictable extension of Kaplan's disclosed analysis for monitoring infant feeding performance and developmental status. This modification applies Kaplan's known data analysis techniques according to their established function to monitor physiological changes over time, yielding no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 43, Kaplan, Lau, and Falk teach the invention in claim 40, as discussed above, and further teach wherein outputting the maturation, neuro-development, recovery, or feeding competency of the infant comprises outputting curves depicting at least one of feeding trace data, maturation progress data, duration of quality data, and recovery data, and/or wherein: the feeding trace data comprises at least one of: an average number of sucks/bursts in the first time period, an average length of time between bursts in the first time period, a frequency of sucks in a first two minutes in the first time period, a normal respiratory rate during the first time period, and an overall duration of feed during the first time period; and/or utilizing the maturation progress data for curve tracking, slope tracking, and to determine a speed of change; and/or the duration of quality data comprises a score for a quality of the feeding instance during a discrete portion of the feeding instance and a length of time the quality is maintained (Kaplan [0019] “In certain preferred embodiments of the invention, provided is a method for evaluating the feeding performance of an infant that involves receiving an input of digitized data corresponding to measurements of at least one feeding factor generated during at least one feeding session of an individual infant. The digitized data is converted into at least one value for at least one feeding parameter, which in turn is used to compute a referenced feeding score for the infant based upon a comparison with a corresponding feeding parameter metric, or a referenced risk outcome score for the infant based upon a comparison with a corresponding risk outcome metric. The referenced feeding score and/or referenced risk outcome score is then provided as a user-recognizable output.”;
Kaplan [0039] “Examples of feeding parameters for a particular feeding session include, but are not limited to, number of sucks, average pressure peaks for all suck events, average number of sucks per sucking burst, total burst time as a percentage of the observation interval, mean, variance and coefficient of variation of time intervals between sucking pressure peak and peak inspiration (i.e., inhalation), and breathing rate during sucking bursts.”;
Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”,
Kaplan [0079] “This example illustrates a method for determining abnormal feeding organization in preterm neonates and shows that different aspects of the sucking pattern mature at different GAs and are of relevance to neurobehavioral development.”,
Kaplan [0080] “One hundred and eighty-six neonates with GA between 33 and full-term (38-42 weeks) were studied. All infants were free from congenital anomalies, with birth weights within the normal for GA at birth. At the time of testing, the neonates were free of medical complications, breathing room air, in open cribs and medically stable. Neonates were assigned to the following groups; GA 33 weeks (N=40), GA 34 weeks (N=39), GA 35 weeks (N=40), GA 36 (N=16), GA 37 (N=21), and full-term (GA range=38-42 weeks, mean=39.49±1.01; n=30). The full-term neonates were designated as 40 weeks for analysis purposes. There were an equal number of males and females in all of the GA groups. There were no significant differences in Apgar scores at 1 or 5 minutes, or maternal age between groups. As expected with increasing GA at birth, there were highly significant differences (ANOVA: F=70.95; p<0.001) in birth weights, with significant pair-wise differences (p<0.001) except between 33-34 GA groups.”;
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”, and
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to present the feeding parameters, referenced feeding scores, developmental assessments, and recovery information generated by Kaplan as graphical curves depicting feeding and developmental trends. Kaplan teaches generating quantitative feeding parameters, referenced feeding and risk outcome scores, and evaluating changes in feeding performance, maturation, and recovery over successive observation periods for presentation to a user. One of ordinary skill in the art would have recognized that graphically presenting such longitudinal physiological data, including feeding trace information and maturation or recovery progress, as curves or trend lines would have been a well known and predictable visualization technique that facilitates interpretation of changes over time by caregivers and clinicians without altering the underlying analysis. This modification applies a known data presentation technique to Kaplan's existing physiological assessment system according to its established function, yielding no more than the predictable result of improving the readability and interpretation of the feeding assessment results.
Regarding claim 44, Kaplan, Lau, and Falk teach the invention in claim 40, as discussed above, and further teach further comprising: receiving another set of data from a third-party; and incorporating the other set of data into the analysis of the infant feeding instance, and/or wherein the other set of data comprises at least one of: a component of an oral feeding evaluation, a clinician evaluation of feeding quality, and an anthropometric evaluation completed prior to discharge and post-discharge (Kaplan [0014] “According to yet another aspect of the invention, provided is a method for producing a metric database comprising: receiving an input of digitized data corresponding to measurements of at least one feeding factor obtained from each infant in a population-based sample of infants; calculating a value for said feeding parameter for each said infant from said digitized data; receiving an input of medical history data for each said infant; receiving an input of medical outcome data from each said infant; structuring said values for said feeding parameter and said medical history data and said medical outcome data into a collection of electronic records; deriving distribution statistics for said values and data from said sample of infants; deriving statistical relationships between a feeding parameter and medical outcome data; referencing feeding parameter values or value ranges to an estimated risk of a medical outcome; structuring results of said deriving and referencing operations into a collection of electronic records; and storing all said electronic records in a computerized database system.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to incorporate additional clinical information received from third parties into Kaplan's infant feeding assessment method. Kaplan teaches receiving medical history data and medical outcome data for individual infants and combining those data with measured feeding parameters to derive statistical relationships and estimate medically relevant outcomes. One of ordinary skill in the art would have recognized that incorporating externally obtained clinical information, such as clinician evaluations or other medical assessment data, into the feeding analysis would predictably provide a more comprehensive evaluation of infant feeding performance and developmental status. This modification merely applies known clinical data integration techniques according to their established function to improve infant assessment and would have yielded no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 45, Kaplan teaches a computer system comprising: one or more processors; one or more memories; and one or more computer-readable hardware storage devices, the one or more computer- readable hardware storage devices containing program code executable by the one or more processors via the one or more memories to implement a method for quantitatively measuring infant feeding performance, the method comprising (Kaplan [0049] “The system 10 of FIG. 1 comprises a data interface 12 for receiving digital data 14 corresponding to measurements of at least one feeding factor generated during at least one feeding session of an individual infant; a microprocessor 11 programmed with instructions 13 to compute (i) at least one value for at least one feeding parameter from said raw digitized data 14 and (ii) rendering a referenced infant feeding score for said individual infant, wherein said rendering involves a comparison of said feeding parameter value to a feeding parameter metric 15; and at least one output device 16 selected from the group consisting of visual display, printer, output data port, RF transmitter, and digital medium storage device. Preferably, the microprocessor is integrated into printed circuit board which may also include memory, input/output lines, and ancillary processors and components. In certain preferred embodiments, the system also comprises an input device 17, such as a push-button, keyboard, mouse, touch screen, microphone, or the like.”,
Kaplan [0050] “As shown in FIG. 2, in certain embodiments the digital data 14 can be received by the system 21 from a digital data storage medium, such as semiconductor memory 23. In such systems, the data interface is preferably a data port (not shown), such as a USB port, wireless port, etc., through which data can be electronically transferred 22.”, and
Kaplan [0053] “Turning to FIG. 4, shown is a flow diagram of a method according to a preferred embodiment of the invention wherein the method is performed using a measuring device 100, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, a flash memory chip, and an electronic display; a computer system 200 comprising a microprocessor, data interface, an electronic, display; and a computerized database 300. The steps of FIG. 4 are provided in Table A.”):
analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period (Kaplan [0051] “As shown in FIG. 3, in certain embodiments the digital data 14 can be received by the system 21 from an instrument 33 attached to a baby bottle 32 or other feeding device that directly measures the desired physical and/or chemical quantities, and converts such measurements into digital data. In such systems, the raw data is transferred 31 as a wireless RF or electronic signal and is received by the system via a data interface (not shown). Examples of such data interfaces include data ports, such as a USB port, or an RF receiver capable of detecting and converting an wireless signal into an electronic data.”);
determining a baseline pressure from the data received from the device (Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.” and
Kaplan [0035] “Digital data useful in the present invention includes, for example, collections of digitized outputs from a device that converts physical quantities into measured values. Examples of such physical quantities include pressure and time, such as the interior pressure of a baby bottle, while an infant is feeding from the bottle. Such data is typically characterized as per a feeding session.”);
the baseline (Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”);
determining, using a pressor sensor coupled to the bottle and based on the baseline pressure, pressure fluctuations in an oral cavity of the infant during the first time period (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”, and
Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”):
detecting sucks by comparing the pressure fluctuations to known characteristics (Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”);
detecting swallows by comparing the pressure fluctuations to known characteristics (Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”
Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”, and
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”);
coupled to the bottle (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”);
calculating a second time period between the sucks or the swallows to detect bursts (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
calculating a third time period without the sucks and without the swallows (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
calculating at least one biomarker for the infant or the feeding instance based on the respiration, the second time period, and the third time period (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
analyzing the at least one biomarker to track maturation and neuro-development of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”
Kaplan [0041] “Generating a referenced infant feeding scare preferably involves associating a feeding parameter value for an individual infant to a corresponding feeding parameter metric, for example by matching the value of the feeding parameter of the individual under observation to a similar value, position, or form within the feeding parameter metric. Generation of a referenced risk score likewise involves associating a feeding parameter value for an individual infant to a corresponding risk outcome metric. By comparing the data collected on an individual infant to a standard value or range, or by specifying the relationship between the parameter value and a medical condition or risk for adverse outcomes, an objective, quantitative evaluation of the infant's feeding performance is obtained. In certain preferred embodiments, the corresponding step involves comparing the value of the feeding parameter to the closest value of the metric to determine a quantitative referenced feeding score for the individual infant.”
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”, and
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”); and
outputting the maturation, neuro-development, recovery, or feeding competency of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”, and
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome score generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”).
Kaplan fails to explicitly teach receiving, from a user and via a graphical user interface (GUI), non-sensor data during the first time period; adapting the pressure to an actual reading; determining, using a temperature sensor, a temperature fluctuation caused by the infant exhaling over the temperature sensor during the first time period; detecting respiration by comparing the temperature fluctuation to known characteristics; and via the GUI to the user.
Lau teaches receiving, from a user and via a graphical user interface (GUI), non-sensor data during the first time period (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”, and Lau [0152] “FIG. 12 shows a second example of an algorithm for determining OFS levels. FIG. 12 follows the simplified algorithm defined in Table 1. Note: the parameter PRO(5) is defined as the % volume (ml) taken during the first 5 min divided by the total volume (ml) of liquid prescribed. The parameter RT(20) is defined as the overall (average) rate of milk transfer (ml/min) averaged over an entire 20 minute feeding session. The algorithm starts with inputting the gestational age (GA), weight (W), of the infant, and the number of feeding sessions in a day, N.”);
adapting the pressure to an actual reading (Lau [0139] “Alternatively, the change in internal air pressure (increase in vacuum level) inside of a sealed bottle (i.e., with no anti-vacuum valve) can be measured with a sensitive pressure transducer placed at or near the top of the bottle. The removal of a bolus of liquid during a single suck creates an incremental change in the vacuum air pressure level (via the relationship Pressure×Volume=constant, at constant temperature), which can be measured, in real-time, by a pressure transducer. Once a particular bottle's geometry has been calibrated (and assuming a bottle with a constant cross-section along it's length), then the drop in internal air pressure measured by the pressure transducer, in real-time, will correlate directly to the volume of liquid removed, in real-time, from the nipple.”);
via the GUI to the user (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”).
Falk teaches determining, using a temperature sensor, a temperature fluctuation caused by the infant exhaling over the temperature sensor during the first time period (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”, and
Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”.):
detecting respiration by comparing the temperature fluctuation to known characteristics (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's infant feeding assessment system to incorporate the smart device application and caregiver-entered information taught by Lau, as well as the temperature respiratory sensing and respiratory parameter processing taught by Falk. Kaplan teaches a computerized bottle system that measures infant feeding characteristics, derives feeding parameters, and evaluates developmental outcomes based on those parameters. Lau teaches utilizing a smart device application through which a caregiver provides clinically relevant, non sensor information, such as gestational age and weight, for use during the feeding assessment, while Falk teaches processing temperature respiration measurements to generate respiratory information, including respiration rate and other respiratory parameters. One of ordinary skill in the art would have recognized that these references address complementary aspects of infant physiological monitoring and feeding assessment and that their combination would have predictably enhanced the comprehensiveness of the infant feeding evaluation.
Furthermore, it would have been obvious to incorporate Lau's pressure sensor calibration techniques into Kaplan's bottle pressure sensing system to improve the correspondence between measured pressure values and actual bottle conditions, which improve the reliability of the pressure data used to derive feeding parameters. Also, because Kaplan recognizes respiration as a clinically relevant feeding factor and derives developmental assessments from physiological feeding parameters, a person of ordinary skill in the art would have been motivated to incorporate Falk's known temperature respiratory sensing and respiratory parameter calculations into Kaplan's assessment framework so that respiration derived physiological information could be evaluated together with feeding parameters. The proposed combination applies known sensing, calibration, data processing, and user interface techniques according to their established functions to improve infant feeding assessment and developmental evaluation, yielding no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 46, Kaplan teaches a method executed by an engine on a computing device for quantitatively measuring infant feeding performance, the method comprising (Kaplan [0040] “Calculating of values for feeding parameters preferably involves executing a set of instructions that calculate a value the desired parameter based upon the received data. In certain preferred embodiments, the instructions are in the form of an executable computer program, i.e. software or firmware. For such embodiments, the computing step involves the use of a microprocessor or other automated computational device. The type of programming languages and/or paradigms that are useful with the present invention are not particularly limited, provided that the software can performed the desired functions on the chosen computer platform.”):
analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period (Kaplan [0051] “As shown in FIG. 3, in certain embodiments the digital data 14 can be received by the system 21 from an instrument 33 attached to a baby bottle 32 or other feeding device that directly measures the desired physical and/or chemical quantities, and converts such measurements into digital data. In such systems, the raw data is transferred 31 as a wireless RF or electronic signal and is received by the system via a data interface (not shown). Examples of such data interfaces include data ports, such as a USB port, or an RF receiver capable of detecting and converting an wireless signal into an electronic data.”);
determining a baseline pressure from the data received from the device (Kaplan [0025]
“As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline.”, and
Kaplan [0035] “Digital data useful in the present invention includes, for example, collections of digitized outputs from a device that converts physical quantities into measured values. Examples of such physical quantities include pressure and time, such as the interior pressure of a baby bottle, while an infant is feeding from the bottle. Such data is typically characterized as per a feeding session.”);
the baseline (Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”);
coupled to the bottle (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”.);
calculating at least one biomarker for the infant or the feeding instance ((Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”, and
Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”);
analyzing the at least one biomarker to track maturation and neuro-development of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”
Kaplan [0041] “Generating a referenced infant feeding scare preferably involves associating a feeding parameter value for an individual infant to a corresponding feeding parameter metric, for example by matching the value of the feeding parameter of the individual under observation to a similar value, position, or form within the feeding parameter metric. Generation of a referenced risk score likewise involves associating a feeding parameter value for an individual infant to a corresponding risk outcome metric. By comparing the data collected on an individual infant to a standard value or range, or by specifying the relationship between the parameter value and a medical condition or risk for adverse outcomes, an objective, quantitative evaluation of the infant's feeding performance is obtained. In certain preferred embodiments, the corresponding step involves comparing the value of the feeding parameter to the closest value of the metric to determine a quantitative referenced feeding score for the individual infant.”
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”, and
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”); and
outputting the maturation, neuro-development, recovery, or feeding competency of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”, and
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”).
Kaplan fails to explicitly teach receiving, from a user and via a graphical user interface (GUI), non-sensor data during the first time period; adapting the baseline pressure to an actual reading; determining, using a temperature sensor, a temperature fluctuation caused by the infant exhaling over the temperature sensor durinq the first time period; detecting respiration by comparing the temperature fluctuation to known characteristics; based on the respiration; and via the GUI to the user.
Lau teaches receiving, from a user and via a graphical user interface (GUI), non-sensor data during the first time period (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”, and Lau [0152] “FIG. 12 shows a second example of an algorithm for determining OFS levels. FIG. 12 follows the simplified algorithm defined in Table 1. Note: the parameter PRO(5) is defined as the % volume (ml) taken during the first 5 min divided by the total volume (ml) of liquid prescribed. The parameter RT(20) is defined as the overall (average) rate of milk transfer (ml/min) averaged over an entire 20 minute feeding session. The algorithm starts with inputting the gestational age (GA), weight (W), of the infant, and the number of feeding sessions in a day, N.”);
adapting the baseline pressure to an actual reading (Lau [0139] “Alternatively, the change in internal air pressure (increase in vacuum level) inside of a sealed bottle (i.e., with no anti-vacuum valve) can be measured with a sensitive pressure transducer placed at or near the top of the bottle. The removal of a bolus of liquid during a single suck creates an incremental change in the vacuum air pressure level (via the relationship Pressure×Volume=constant, at constant temperature), which can be measured, in real-time, by a pressure transducer. Once a particular bottle's geometry has been calibrated (and assuming a bottle with a constant cross-section along it's length), then the drop in internal air pressure measured by the pressure transducer, in real-time, will correlate directly to the volume of liquid removed, in real-time, from the nipple.”);
via the GUI to the user (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”).
Falk teaches determining, using a temperature sensor, a temperature fluctuation caused by the infant exhaling over the temperature sensor durinq the first time period ((Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
detecting respiration by comparing the temperature fluctuation to known characteristics (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
based on the respiration (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's computerized infant feeding assessment method to incorporate the smart device application and caregiver-entered information taught by Lau, as well as the temperature respiratory sensing and respiratory parameter processing taught by Falk. Kaplan teaches obtaining pressure measurements from a bottle mounted sensor, deriving physiological feeding parameters, and evaluating infant developmental status using those parameters. Lau teaches receiving clinically relevant, non sensor information, such as gestational age and weight, through a smart device application, while Falk teaches utilizing a temperature sensor to obtain respiration measurements and processing those measurements to generate respiratory information, including respiration rate and other respiratory parameters. One of ordinary skill in the art would have recognized that these references are directed to complementary aspects of infant physiological monitoring and that combining their teachings would predictably provide a more comprehensive assessment of infant feeding performance.
Furthermore, it would have been obvious to incorporate Lau's pressure sensor calibration techniques into Kaplan's bottle pressure sensing system to improve the correspondence between measured pressure values and actual bottle conditions, thereby improving the reliability of the pressure measurements used during feeding assessment. Also, because Kaplan identifies respiration as a clinically relevant feeding factor and evaluates developmental status using physiological feeding parameters, one of ordinary skill in the art would have been motivated to incorporate Falk's known temperature respiratory sensing and respiration derived physiological parameters into Kaplan's assessment methodology so that respiration derived biomarkers could be analyzed together with feeding information to evaluate infant maturation, neuro development, and feeding competency. The proposed combination applies known sensing, calibration, physiological monitoring, and computerized processing techniques according to their established functions to improve infant feeding assessment and developmental evaluation, yielding no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 47, Kaplan, Lau, and Falk teach the invention in claim 46, as discussed above, and further teach wherein the temperature sensor does not physically contact the infant during the first time period (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method of Kaplan to employ the non contact temperature sensing arrangement taught by Falk. Falk teaches determining respiratory information by measuring the temperature of inspired and expired gas within the breathing circuit, rather than by physically contacting the infant, and one of ordinary skill in the art would have recognized that incorporating this known sensing technique into Kaplan's infant feeding assessment system would predictably permit respiration temperature measurements to be obtained while minimizing interference with the infant during feeding. This modification applies a known temperature sensing technique according to its established function to improve infant physiological monitoring and would have yielded no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 48, Kaplan teaches a device affixed between a nipple and a bottle and comprising embedded electronic components, the embedded electronic components comprising at least one sensor and a computerized data processing system, the at least one sensor being configured to capture data associated with a feeding instance of an infant during a first time period; and the computerized data processing system being configured to (Kaplan [0059] “The apparatus for measuring feeding factors for these examples comprises a baby bottle, a nipple disposed on one end of the bottle, a pressure transducer in operative communication with the nipple to measure negative pressures applied by the neonate inside the bottle; and a data processing device to process said measurements.”
Kaplan [0053] “Turning to FIG. 4, shown is a flow diagram of a method according to a preferred embodiment of the invention wherein the method is performed using a measuring device 100, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, a flash memory chip, and an electronic display; a computer system 200 comprising a microprocessor, data interface, an electronic, display; and a computerized database 300. The steps of FIG. 4 are provided in Table A.”,
Kaplan [0040] “Calculating of values for feeding parameters preferably involves executing a set of instructions that calculate a value the desired parameter based upon the received data. In certain preferred embodiments, the instructions are in the form of an executable computer program, i.e. software or firmware. For such embodiments, the computing step involves the use of a microprocessor or other automated computational device. The type of programming languages and/or paradigms that are useful with the present invention are not particularly limited, provided that the software can performed the desired functions on the chosen computer platform.”,
Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”):
calculate a baseline pressure from the data (Kaplan [0035] “Digital data useful in the present invention includes, for example, collections of digitized outputs from a device that converts physical quantities into measured values. Examples of such physical quantities include pressure and time, such as the interior pressure of a baby bottle, while an infant is feeding from the bottle. Such data is typically characterized as per a feeding session.”
Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”, and
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline.”);
determine, using a pressor sensor coupled to the bottle and based on the baseline pressure, pressure fluctuations in an oral cavity of the infant during the first time period (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”, and
Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”);
detect sucks by comparing the pressure fluctuations to known characteristics (Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”);
detect swallows by comparing the pressure fluctuations to known characteristics (Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”
Kaplan [0025] “As used herein, the term “event” with respect to feeding factor means a detected change in values associated with a feeding factor, such the pressure changes measuring during a suck cycle.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”, and
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”);
coupled to the bottle (Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”.);
calculate a second time period between the sucks and the swallows to detect bursts (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
calculate a third time period without the sucks and without the swallows (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
calculate at least one biomarker for the infant or the feeding instance, the second time period, and the third time period (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”,
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
analyze the at least one biomarker to track maturation and neuro-development of the infant (Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”
Kaplan [0041] “Generating a referenced infant feeding scare preferably involves associating a feeding parameter value for an individual infant to a corresponding feeding parameter metric, for example by matching the value of the feeding parameter of the individual under observation to a similar value, position, or form within the feeding parameter metric. Generation of a referenced risk score likewise involves associating a feeding parameter value for an individual infant to a corresponding risk outcome metric. By comparing the data collected on an individual infant to a standard value or range, or by specifying the relationship between the parameter value and a medical condition or risk for adverse outcomes, an objective, quantitative evaluation of the infant's feeding performance is obtained. In certain preferred embodiments, the corresponding step involves comparing the value of the feeding parameter to the closest value of the metric to determine a quantitative referenced feeding score for the individual infant.”
Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”, and
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”); and
transfer the maturation, neuro-development, recovery, or feeding competency of the infant, wherein transferring the maturation, neuro- development, recovery, or feeding competency of the infant comprises transferring curves depicting at least one of feeding trace data, maturation progress data, duration of quality data, and recovery data (Kaplan [0046] “The referenced feeding parameter scores and the referenced risk outcome scores generated for an infant of interest can be used as a diagnostic and/or prognostic aide. For example, feeding parameter scores can be used by a physician or other professional to determine the feasibility of discharging an infant from a hospital after birth. In certain embodiments, a single score is determinative of a medically relevant condition, while in other embodiments, the cumulative result of a plurality of scores are determinative.”, and
Kaplan [0093] “Significant changes from the first to fifth epochs (i.e., about the first and fifth minute of the five-minute test) of the sucking bout were obtained for number of sucks, number of bursts, burst duration and Pmax. Of the five parameters evaluated over epochs, only within-burst suck frequency did not change as the session progressed. Values declined over the first 4 epochs for number of sucks, burst duration and Pmax. A recovery from epoch 4 to 5 was observed for sucks, burst duration and number of bursts, with values for these measures approaching or exceeding those obtained during the first epoch. Episodic increases in feeding vigor at later stages of a neonate feed have been noted in studies of breast feeding. It would have been expected that such periods of increased sucking would have appeared at random points from feeding onset. It is surprising, therefore, to see reliable increases at a consistent point from feeding onset that, moreover, held up across GA.”).
Kaplan fails to explicitly teach determine, using a temperature sensor, a temperature fluctuation caused by the infant exhalinq over the temperature sensor durinq the first time period; detect respiration by comparing the temperature fluctuation to known characteristics; based on the respiration; at least one of an engine executable on a computing device, a cloud, a graphical user interface (GUI) of the computing device, or a telehealth interface of the computing device.
Lau teaches at least one of an engine executable on a computing device, a cloud, a graphical user interface (GUI) of the computing device, or a telehealth interface of the computing device (Lau [0090] “FIG. 11 shows a first example of a high-level methodology for using the smart baby bottle with a smart device and embedded application (“APP”). In step 100, the feeding protocol is defined by the caregiver and smart device application, based on the infant's weight (Step 60) and gestational age (Step 50), for a range of nutritional requirements.”).
Falk teaches determine, using a temperature sensor, a temperature fluctuation caused by the infant exhalinq over the temperature sensor durinq the first time period (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
detect respiration by comparing the temperature fluctuation to known characteristics (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”);
based on the respiration (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”,);
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's bottle mounted infant feeding assessment device to incorporate the temperature respiratory sensing taught by Falk. Kaplan teaches a bottle mounted device including pressure sensing, computerized data processing, and the derivation of feeding parameters and developmental assessments from measured feeding data. Falk teaches using a temperature sensor to obtain respiration measurements and processing those measurements to generate respiratory information, including respiration rate and other respiration derived physiological parameters. Because Kaplan recognizes respiration as a clinically relevant feeding factor, one of ordinary skill in the art would have recognized that incorporating Falk's known respiratory sensing technique into Kaplan's device would predictably provide additional physiological information relevant to evaluating infant feeding performance and developmental status.
Furthermore, it would have been obvious to incorporate the pressure measurement and calibration techniques taught by Lau into Kaplan's bottle sensing system to improve the reliability of the pressure measurements from which feeding parameters are derived. Also, incorporating Lau's smart device application for presenting feeding assessment results would have predictably enabled the developmental and feeding competency information generated by Kaplan, including the maturation and recovery information enhanced through Falk's respiration derived physiological parameters, to be communicated to caregivers through a graphical user interface. The proposed combination combines known sensors, calibration techniques, computerized processing, and user interface technologies according to their established functions to improve the reliability and usability of infant feeding assessment, yielding no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 49, Kaplan, Lau, and Falk teach the invention in claim 48, as discussed above, and further teach wherein the computerized data processing system comprises one or more algorithms that are configured to: smooth the data and separate the sucks from the swallows, select single and multiple suck signals in a suck/swallow sequence, and detect a correlation between the suck/swallow sequence and respirations, and/or wherein, subsequent to establishing the baseline pressure from the data, the computerized data processing system is further configured to (Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”
Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”
Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”);
Kaplan [0039] “Examples of feeding parameters for a particular feeding session include, but are not limited to, number of sucks, average pressure peaks for all suck events, average number of sucks per sucking burst, total burst time as a percentage of the observation interval, mean, variance and coefficient of variation of time intervals between sucking pressure peak and peak inspiration (i.e., inhalation), and breathing rate during sucking bursts.”
Lau [0014] “Results [23]: Lau's hypotheses were confirmed. OFS levels were: (a) positively correlated with an infant's feeding performance; i.e., the better the OFS levels, the greater the OT and the shorter the feeding duration; (b) positively correlated with GA strata, i.e., the less premature the infant, the more mature his/her skills; and (c) inversely associated with days from SOF to IOF, i.e., the better the skills, the faster the attainment of independent oral feeding. In summary, OFS levels were correlated with GA, OT, PRO5; and days from SOF-IOF were associated with OFS and GA; whereas RT20 was only with OFS levels. The correlations of OT and PRO5 with GA can be explained by the greater proportion of infants at the older GA strata, who being more developmentally mature, naturally demonstrated more mature OFS levels. The observation that RT20 was associated with OFS, but not GA, suggests that rate of milk transfer is primarily regulated by an infant's feeding aptitude, e.g., sucking skills, swallowing skills, suck-swallow-respiration coordination, and/or endurance.”;
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”;
Lau [0139] “Alternatively, the change in internal air pressure (increase in vacuum level) inside of a sealed bottle (i.e., with no anti-vacuum valve) can be measured with a sensitive pressure transducer placed at or near the top of the bottle. The removal of a bolus of liquid during a single suck creates an incremental change in the vacuum air pressure level (via the relationship Pressure×Volume=constant, at constant temperature), which can be measured, in real-time, by a pressure transducer. Once a particular bottle's geometry has been calibrated (and assuming a bottle with a constant cross-section along it's length), then the drop in internal air pressure measured by the pressure transducer, in real-time, will correlate directly to the volume of liquid removed, in real-time, from the nipple.”);
determine an amplitude of suck, swallow and respiration curves; and detect a difference in a shape of a suck curve as compared to a swallow curve; and/or wherein the baseline pressure is calculated from the data by taking a measurement at a beginning and an end of the feeding instance or by taking a measurement at the beginning and at the end of every burst during the feeding instance (Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”,
Kaplan [0038] “ One or more feeding parameters are rendered from the received digitized data, in part or in its entirety, so as to bring out the meaning of the data as it relates to an infant's feeding performance during a feeding session. More particularly, feeding parameters are obtained by detecting an event, computing the features of the event, aggregating the event and its features, and then performing a composing function on the aggregation to render a feeding parameter. Feeding parameters include both primary parameters as well as composites of two or more parameters.”,
Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”,
Kaplan [0039] “Examples of feeding parameters for a particular feeding session include, but are not limited to, number of sucks, average pressure peaks for all suck events, average number of sucks per sucking burst, total burst time as a percentage of the observation interval, mean, variance and coefficient of variation of time intervals between sucking pressure peak and peak inspiration (i.e., inhalation), and breathing rate during sucking bursts.”,
Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”,
Kaplan [0026] “As used herein, the term “features” with respect to event means a quantitative descriptor of the excursion of the event from a baseline. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”, and
Lau [0139] “Alternatively, the change in internal air pressure (increase in vacuum level) inside of a sealed bottle (i.e., with no anti-vacuum valve) can be measured with a sensitive pressure transducer placed at or near the top of the bottle. The removal of a bolus of liquid during a single suck creates an incremental change in the vacuum air pressure level (via the relationship Pressure×Volume=constant, at constant temperature), which can be measured, in real-time, by a pressure transducer. Once a particular bottle's geometry has been calibrated (and assuming a bottle with a constant cross-section along it's length), then the drop in internal air pressure measured by the pressure transducer, in real-time, will correlate directly to the volume of liquid removed, in real-time, from the nipple.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the infant feeding assessment system of Kaplan in view of Lau and Falk to further process acquired feeding signals by detecting feeding events, extracting signal features, selecting individual and grouped suck events, correlating suck, swallow, and respiratory events, determining waveform characteristics, and calculating baseline pressure measurements. Kaplan teaches processing digitized feeding data by detecting events, computing event features, aggregating those features into feeding parameters, and analyzing pressure signals to derive parameters including suck amplitude, pressure peaks, burst characteristics, durations, rhythmicity, and relationships between sucking and respiration. Lau further teaches that effective infant feeding performance depends upon coordination among sucking, swallowing, and respiration, and teaches calibration of bottle pressure measurements to correlate measured pressure with actual feeding conditions. Falk also teaches computerized processing of respiratory measurements to generate respiratory parameters and waveform derived physiological information.
A person of ordinary skill in the art would have been motivated to incorporate Lau's coordination analysis and calibration techniques, together with Falk's respiratory signal processing, into Kaplan's feeding parameter extraction framework because each reference addresses complementary aspects of evaluating infant feeding performance from physiological sensor data. Combining these teachings would have predictably improved the accuracy and clinical usefulness of the generated feeding parameters by enabling more reliable identification of feeding events, comparison of waveform characteristics, correlation of suck-swallow-respiration activity, determination of waveform amplitudes, and establishment of calibrated baseline pressure measurements throughout a feeding session. The combination applies known signal processing, physiological monitoring, and calibration techniques according to their established functions to achieve the predictable result of a more comprehensive and accurate computerized infant feeding assessment.
Regarding claim 50, Kaplan, Lau, and Falk teach the invention in claim 48, as discussed above, and further teach wherein the temperature sensor is further configured to collect a respiration measurement and/or wherein the computerized data processing system is further configured to: analyze the respiration measurement; compare the respiration measurement to measurements taken during the feeding instance when the infant is engaging in sucking and swallowing actions; and determine a frequency or an absence during the sucking and swallowing actions, a quality of a shape, and a comparison of the frequency and a strength during a burst and during a rest period (Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”,
Kaplan [0022] “As used herein, the term “feeding factor” means one or more physical, physiological, and/or behavioral responses produced or exhibited by an infant while orally feeding or attempting to orally feed. Examples of feeding factors include sucking pressure, expression pressure, oxygen saturation level, swallowing, respiration, and the like.”,
Kaplan [0039] “Examples of feeding parameters for a particular feeding session include, but are not limited to, number of sucks, average pressure peaks for all suck events, average number of sucks per sucking burst, total burst time as a percentage of the observation interval, mean, variance and coefficient of variation of time intervals between sucking pressure peak and peak inspiration (i.e., inhalation), and breathing rate during sucking bursts.”,
Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”;
Kaplan [0062] “Data may be further parsed in relation to the succession of bursts and pauses between bursts within the observation interval. For most applications, ISI≧2 seconds is given as the criterion that defines the end of one sucking burst and the beginning of the next. A burst, then, may be defined as a continuous succession of suck cycles with ISI's all <2 seconds. Other parameters that may be derived include number of bursts, number of pauses, mean pause duration, mean burst duration, mean number of sucks per burst, statistical distribution parameters for ISI within bursts including mean ISI (and its reciprocal, the within-burst suck frequency), and statistical distribution parameters for Pmax within bursts, including mean, SD and CV.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's infant feeding assessment device to incorporate the respiration measurement and respiratory analysis techniques taught by Falk. Kaplan teaches analyzing sucking, swallowing, respiration, and feeding parameters during infant feeding, including evaluating breathing rate, sucking bursts, pauses, and temporal feeding characteristics, while Falk teaches collecting respiration measurements using a temperature sensor and processing those measurements to generate respiratory information, including respiration rate and other respiration-related parameters. One of ordinary skill in the art would have recognized that incorporating Falk's known temperature respiratory sensing into Kaplan's feeding assessment system would predictably enable respiration measurements to be analyzed together with sucking and swallowing activity to evaluate respiratory frequency and feeding coordination during bursts and rest periods. This modification merely combines known physiological sensing and signal processing techniques according to their established functions to provide a more comprehensive assessment of infant feeding performance and would have yielded no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 51, Kaplan, Lau, and Falk teach the invention in claim 48, as discussed above, and further teach wherein the at least one biomarker is associated with oral cavity pressure changes or respiratory changes by temperature variations, and/or wherein each sensor of the at least one sensor is selected from the group consisting of: a pressure sensor and a respiratory sensor (Kaplan [0060] “The feeding parameters in these examples are primarily derived from a measurement of fluid flow through, and/or pressure on one side of, and/or pressure differential across, a nipple or some other feeding device during a time interval or as a function of time over a time interval, wherein the flow, pressure, or pressure differential is generated by a neonate's mouth when feeding himself or herself or attempting to feed himself or herself. Data derived from pressure differential variations that are produced by a sucking neonate can be recorded as, for example, sucking pressure, duration of a suck cycle, rhythmicity of a series of sucks, intermittent bursts of sucks, pauses between sucks, and the like. More specifically, data may be derived from a continuous electrical signal produced by a pressure transducer that records the vacuum applied by a neonate against an artificial nipple during a series of suck cycles (i.e., individual sucks made by the neonate against the nipple). Fluctuations in the signal over various periods of time can be analyzed, for example by a computer running on-line or off-line, to generate parameters such as time of occurrence of the pressure peak for each suck cycle; amplitude of the pressure peak (“Pmax’) for each suck cycle; area under the curve (trough-to-trough) for each suck cycle; and duration of each suck cycle (trough-to-trough).”, and
Kaplan [0061] “From one or more of the above, the following parameters or parameter categories may be generated for one or more observation intervals of interest within a given feeding test session (e.g., a 5 minute feeding session): total number of sucks and suck rate (=number of sucks/observation interval duration); mean maximum sucking pressure; statistical distribution parameters for Pmax including the standard deviation (SD) and coefficient of variation (CV=SD/mean); statistical distribution parameters for inter-suck-intervals (ISI) (i.e., the time (sec) between one sucking peak and the next peak).”
Kaplan [0036] “In certain preferred embodiments, receiving an input of digitized data involves downloading data from a measuring device in real time or approximate real time during an infant feeding session. In certain other preferred embodiments, receiving an input of digitized data involves downloading stored data that is compiled for one or more feeding sessions. Before being downloaded, the compiled data preferably resides in an electronic data storage medium, such as a flash memory chip. In certain preferred embodiments, the data is produced by a hand-held instrument, such as a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle, analog-digital convertor, a microcontroller, and a flash memory chip. The device is provided to an infant during a feeding session. As the infant feeds, a signal from the sensor is converted into a digital signal at a predetermined sample rate. A microcontroller converts this digital signal into digital data which is preferably stored on a memory chip until it is downloaded.”, and
Kaplan [0056] “The following examples demonstrate certain preferred methods for quantitatively assessing developmental risks of a neonate based upon objective observations of the neonate's sucking behavior and a statistical correlation between this observed behavior and a standard. The methods in these examples involve providing a neonate with a baby bottle; measuring negative sucking pressure, and optionally flow volume, produced by the neonate's sucking behavior, (e.g., the pressure differential generated across a nipple and changes in the pressure differential as a function of time); using the measurements to derive feeding parameters; compiling these feeding parameters and/or referenced feeding scores derived therefrom, into a composite feeding score for the neonate; and corresponding the neonate's individual composite feeding score pattern to one or more medically relevant outcome metrics. In practicing this method, the comparison of the neonate's composite feeding score with a metric will suggest a risk of abnormal development if the neonate's individual score pattern deviates more than a predefined range from the standard score pattern.”, and
Falk [0034] “Referring to FIGS. 2 and 3, the computing system 100 may include a software module stored in memory and executable on a processor 106 within the computing system 100, a resuscitation module 72, configured to process one or more of the respiration parameter measurements 90-95 to generate respiratory information 96 regarding the respiratory status of the infant 2. For example, the resuscitation module 72 may determine respiratory information 96 including an inspired O2 indicator, such as fraction of inspired oxygen (FiO2). Alternatively or additionally, respiratory information 96 determined by the resuscitation module 72 may include an end tidal CO2 (etCO2) based on the CO2 measurements 91. Likewise, resuscitation module 72 may calculate tidal volume based on the flow measurements 92, such as by calculating volume as an integral of the flow curve and/or sum of the flow measurements 92 during the inspiratory cycle, and/or intake air pressure based on the pressure measurements 93. Alternatively or additionally, the resuscitation module 72 may utilize the temperature measurements 94 to determine the temperature of the inspired gas and/or the expired gas. Such temperature measurements 94 may be used to regulate the temperature of the gas provided to the infant 2 and/or to determine information about the temperature of the infant 2. The resuscitation module 72 may utilize the humidity measurements 95 to determine a humidity of the gas being provided to the patient, and such information may be used to control the same. Any one of the aforementioned values may be included in the respiratory information 96, which may also include any number of alternative or additional parameters (e.g., respiration rate) outputted by the resuscitation module 72, and such respiratory information 96 may be transmitted to a hub device 68 and/or a host network 76 for storage in the patient's medical record in database 78. Alternatively or additionally, some or all of the respiratory information 96 may be displayed on the digital display 46.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify Kaplan's infant feeding assessment device to incorporate the respiration monitoring techniques taught by Falk. Kaplan teaches deriving physiological biomarkers from pressure measurements generated by an infant's oral feeding activity using a pressure sensor incorporated into a bottle feeding system, while Falk teaches utilizing temperature measurements to determine respiration-related information and generate respiratory parameters. One of ordinary skill in the art would have recognized that incorporating Falk's known temperature respiratory sensing into Kaplan's pressure feeding assessment system would predictably enable the generation of biomarkers associated with both oral cavity pressure changes and respiratory changes, thereby providing a more comprehensive assessment of infant feeding performance and physiological status. This modification merely combines known sensing technologies according to their established functions to improve infant physiological monitoring and would have yielded no more than the predictable results expected by one of ordinary skill in the art.
Regarding claim 54, Kaplan, Lau, and Falk teach the invention in claim 48, as discussed above, and further teach wherein the computerized data processing system is further configured to differentiate each of the sucks as a nutritive suck or a non-nutritive suck and/or to assess a ratio of suck to swallow in both count and strength, clarity of signal, and coordination between suck/swallow and respiration (Kaplan [0090]“The organization of sucking within bursts presented a more complex developmental profile. One parameter, the within-burst suck frequency, did not vary with GA, suggesting that this basic aspect of patterned sucking behavior was in already place in the most premature neonates. This result may be contrasted with that of a longitudinal study of non-nutritive sucking in premature neonates. The contrasting results may relate to methodological differences [e.g., nutritive vs. non-nutritive fluids] or to different degrees of postnatal feeding experience before testing.”, and
Kaplan [0082] “The tests involved the use of a Kron nutritive sucking apparatus similar to that described above. Customized software generated a set of sucking parameters including: number of sucks per session, sucking duration (interval from first to last suck in session), number of bursts in session (a two-second pause defined separation of bursts), mean burst duration, total burst time as percent of session, within-burst suck frequency, mean maximum sucking pressure (Pmax), the coefficient of variation (sd/mean) of the within-burst inter-suck interval distribution, and the coefficient of variation of the Pmax distribution for all sucks in the session. In addition, changes in the sucking pattern over time within the session were characterized. For this purpose, the sucking bout was divided into five parts (epochs).”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to configure Kaplan's computerized data processing system to differentiate nutritive sucking from non nutritive sucking when analyzing infant feeding performance. Kaplan teaches a computerized nutritive sucking apparatus that generates quantitative sucking parameters for evaluating infant feeding behavior and further recognizes the distinction between nutritive and non nutritive sucking in assessing developmental characteristics. One of ordinary skill in the art would have recognized that distinguishing between these two recognized categories of sucking behavior would improve the clinical evaluation of infant feeding performance and developmental status. This modification applies known computerized analysis techniques to differentiate recognized types of sucking behavior according to their established functions, yielding no more than the predictable results expected by one of ordinary skill in the art.
Response to Arguments
Applicant’s arguments and amendments, see Remarks/Amendments submitted on 05/05/2026 with respect to the rejection of the claims have been carefully considered and is addressed below.
Claim Rejections - 35 USC § 101
Applicant's arguments have been fully considered and are persuasive in view of the amendments to the independent claims. Specifically, Applicant amended independent claims 40, 45, 46, and 48 to recite determining, using a pressure sensor coupled to the bottle, pressure fluctuations in an oral cavity of the infant and determining, using a temperature sensor coupled to the bottle, temperature fluctuations caused by the infant exhaling over the temperature sensor. The amended claims therefore recite a specific physiological monitoring system utilizing physical sensors associated with an infant feeding device to obtain real time physiological measurements during a feeding instance, rather than merely analyzing existing information. The amendments further tie the claimed analysis to the operation of the recited physical sensors and the infant feeding device.
Applicant additionally states that the amended claims are directed to a physical system that provides a technical advantage by using pressure and temperature sensing to monitor infant feeding and respiration. Upon further consideration of the amended claim language and Applicant's arguments, the Examiner agrees that the amended claims, when considered as a whole, integrate the recited data analysis into a practical application through the use of the recited pressure and temperature sensors coupled to the bottle for obtaining physiological measurements during infant feeding. Accordingly, the rejection under 35 U.S.C. 101 has been withdrawn.
Claim Rejections - 35 USC § 103
Applicant’s arguments traversing the prior art rejection in the previous Office Action have been fully considered. However, those arguments are rendered moot because the present rejection under 35 U.S.C. §103 relies on a different set of prior art references (Kaplan, Lau, and Falk), which teach or suggest the limitations of the claims. Accordingly, Applicant’s prior arguments are not responsive to the current grounds of rejection. The rejection of claims 40-51 and 54 under 35 U.S.C. §103 is therefore maintained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
Aron et al. (U.S. Patent Publication 2014/0134585 A1) teaches a therapeutic system that assess a patient’s oral feeding activity using collected data and a computing device to predict underdeveloped neurological muscle activity and generate a therapy treatment to improve oral feeding.
Tarcan et al. (International Publication No. WO2014081401 A1) teaches a device that can objectively monitor feeding maturity in premature babies using swallowing patterns.
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.
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/K.R.L./Examiner, Art Unit 3685
/KAMBIZ ABDI/Supervisory Patent Examiner, Art Unit 3685
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