Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This non-final office action on merits is in response to the Patent Application filed on 12/20/2024.
Status of claims
Claims 1-39 are cancelled. Claims 40-54 are pending and considered below. This application is a 371 of PCT/US22/34215 filed on 06/21/2022.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 40-54 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
Under step 1, the analysis is based on MPEP 2106.03, and claims 40-44, and 46-47 are drawn to a method, claim 45 is drawn to a computer system, claims 48-54 are drawn to a device. Thus, each claim, on its face, is directed to one of the statutory categories (i.e., useful process, machine, manufacture, or composition of matter) of 35 U.S.C. §101.
Step 2A Prong One
Claim 40, 45, 46, and 48 recite the limitations of analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period; determining a baseline pressure from the data received from the device; detecting sucks by comparing pressure fluctuations to known characteristics; detecting swallows by comparing pressure fluctuations to known characteristics; detecting respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics; calculating a second time period between the sucks or the swallows to detect bursts; calculating a third time period without the sucks and without the swallows; calculating at least one biomarker for the infant or the feeding instance; analyzing the at least one biomarker to track maturation, neuro-development, or recovery of the infant (claim 40), analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period; determining a baseline pressure from the data received from the device; adapting the baseline pressure to an actual reading; detecting sucks by comparing pressure fluctuations to known characteristics; detecting swallows by comparing pressure fluctuations to known characteristics; detecting respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics; calculating a second time period between the sucks or the swallows to detect bursts; calculating a third time period without the sucks and without the swallows; calculating at least one biomarker for the infant or the feeding instance; analyzing the at least one biomarker to track maturation and neuro-development of the infant (claim 45), analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period; determining a baseline pressure from the data received from the device; adapting the baseline pressure to an actual reading; detecting respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics; calculating at least one biomarker for the infant or the feeding instance; analyzing the at least one biomarker to track maturation and neuro-development of the infant (claim 46); and calculate a baseline pressure from the data; detect sucks by comparing pressure fluctuations to known characteristics; detect swallows by comparing pressure fluctuations to known characteristics; detect respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics; calculate a second time period between the sucks and the swallows to detect bursts; calculate a third time period without the sucks and without the swallows; calculate at least one biomarker for the infant or the feeding instance; analyze the at least one biomarker to track maturation and neuro-development of the infant (claim 48). These limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitations in the mind or by using a pen and paper. But for the “a graphical user interface (GUI) of the computing device” or “one or more processors” language, the claims encompass a user simply observing, comparing, calculating and evaluating feeding related data in their mind or by using a pen and paper. The mere nominal recitation of a graphical user interface (GUI) of the computing device or one or more processors does not take the claim limitations out of the mental processes grouping.
Under Step 2A Prong Two
The claimed limitations, as per claim 40, include:
analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period;
receiving, from a user and via a graphical user interface (GUI) of the computing device, non-sensor data during the first time period;
determining a baseline pressure from the data received from the device;
detecting sucks by comparing pressure fluctuations to known characteristics;
detecting swallows by comparing pressure fluctuations to known characteristics;
detecting respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics;
calculating a second time period between the sucks or the swallows to detect bursts;
calculating a third time period without the sucks and without the swallows;
calculating at least one biomarker for the infant or the feeding instance;
analyzing the at least one biomarker to track maturation, neuro-development, or recovery of the infant; and
outputting results of the feeding instance via the GUI to the user.
The claimed limitations, as per claim 45, include:
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:
analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period;
receiving, from a user and via a graphical user interface (GUI), non-sensor data during the first time period;
determining a baseline pressure from the data received from the device; adapting the baseline pressure to an actual reading;
detecting sucks by comparing pressure fluctuations to known characteristics;
detecting swallows by comparing pressure fluctuations to known characteristics;
detecting respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics;
calculating a second time period between the sucks or the swallows to detect bursts;
calculating a third time period without the sucks and without the swallows;
calculating at least one biomarker for the infant or the feeding instance;
analyzing the at least one biomarker to track maturation and neuro-development of the infant; and
outputting results of the feeding instance via the GUI to the user.
The claimed limitations, as per claim 46, include:
analyzing data received from a device affixed to a bottle and associated with a feeding instance of an infant during a first time period;
receiving, from a user and via a graphical user interface (GUI), non-sensor data during the first time period;
determining a baseline pressure from the data received from the device;
adapting the baseline pressure to an actual reading;
detecting respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics;
calculating at least one biomarker for the infant or the feeding instance;
analyzing the at least one biomarker to track maturation and neuro-development of the infant; and
outputting results of the feeding instance via the GUI to the user.
The claimed limitations, as per claim 48, include:
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:
calculate a baseline pressure from the data;
detect sucks by comparing pressure fluctuations to known characteristics;
detect swallows by comparing pressure fluctuations to known characteristics;
detect respiration by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics;
calculate a second time period between the sucks and the swallows to detect bursts;
calculate a third time period without the sucks and without the swallows;
calculate at least one biomarker for the infant or the feeding instance;
analyze the at least one biomarker to track maturation and neuro-development of the infant; and
transfer results of the feeding instance to 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, wherein the results of the feeding instance comprise curves depicting at least one of feeding trace data, maturation progress data, duration of quality data, and recovery data.
Examiner Note: underlined elements indicate additional elements of the claimed invention identified as performing the steps of the claimed invention.
The judicial exception expressed in claims 40, 45, 46, and 48 are not integrated into a practical application. The claims as a whole merely describes how to generally “apply” the concept of analyzing, comparing, calculating, and evaluating physiological feeding data to access infant feeding performance and development in a computer environment. The claimed computer components (i.e., from a user and via a graphical user interface (GUI) of the computing device (claims 40, 45, and 46), via the GUI to the user (claims 40, 45, and 46), 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 (claim 45), and a computerized data processing system (claim 48)) are recited at a high level of generality and are merely invoked as tools to perform an existing process of observing, interpreting, and accessing feeding related information. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claim 48 is not integrated into a practical application. The abstract idea is merely carried out in a technical environment or field (i.e., infant feeding monitoring using a bottle affixed device with physiological sensors), however fails to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment (see MPEP 2106.05(h)). The additional element that is carried out in a technical environment include at least one sensor. Accordingly, alone and in combination, these additional elements do not integrate the abstract idea into a practical application.
The judicial exception expressed in claims 40, 45, 46, and 48 is not integrated into a practical application. The claims recite the additional element of receiving non-sensor data during the first time period (claims 40, 45, and 46); to capture data associated with a feeding instance of an infant during a first time period (claim 48); outputting results of the feeding instance (claims 40, 45, and 46); and transfer results of the feeding instance to 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, wherein the results of the feeding instance comprise curves depicting at least one of feeding trace data, maturation progress data, duration of quality data, and recovery data (claim 48). These limitations are recited at a high level of generality (i.e., as a general means of receiving and displaying information), and amounts to merely gathering, presenting, and reporting results, which is a form of insignificant extra-solution activity. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
Therefore, under step 2A, the claims are directed to the abstract idea, and require further analysis under Step 2B.
Under step 2B
Claims 40, 45, 46, and 48 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the claims as a whole merely describes how to generally “apply” the concept of analyzing, comparing, calculating, and evaluating physiological feeding data to access infant feeding performance and development in a computer environment. Thus, even when viewed as a whole, nothing in the claims add significantly more (i.e., an inventive concept) to the abstract idea.
Claim 48 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to Step 2A, the abstract idea is merely carried out in a technical environment or field, however fails to contain meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. Thus, even when viewed as a whole, nothing in the claim adds significantly more (i.e., an inventive concept) to the abstract idea.
For claims 40, 45, 46, and 48, under step 2B, the additional elements of receiving non-sensor data during the first time period (claims 40, 45, and 46); to capture data associated with a feeding instance of an infant during a first time period (claim 48); outputting results of the feeding instance (claims 40, 45, and 46); and transfer results of the feeding instance to 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, wherein the results of the feeding instance comprise curves depicting at least one of feeding trace data, maturation progress data, duration of quality data, and recovery data (claim 48) have been evaluated. The method, system, and device perform a general function of receiving patient data for subsequent processing, which represents a well-understood, routine, and conventional activity in the field of computer implemented medical data analysis and health monitoring. The specification discloses that the processor is used in its ordinary capacity as a data input device and does not describe any improvement to the computer itself or to the functioning of the overall computer system (see [0138]). Also noted in Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016), merely collecting information for analysis and displaying results without a technological improvement does not add significantly more to an abstract idea. The use of the method, system, and device is no more than collecting information before analysis and evaluating and does not integrate the abstract idea into a practical application. Additionally, as noted in In re Brown, 645 Fed. App'x 1014, 1016-1017 (Fed. Cir. 2016), merely transmitting or displaying results is an insignificant application of the underlying mental process, as the transmission simply communicates the result of the determination and does not impose any meaningful limitation or add any technological improvement. Therefore, the claims do not recite an inventive concept and is not patent eligible.
Claims 41-43, and 47 recite no further additional elements, and only further narrow the abstract idea. The previously identified additional elements, individually and as a combination, do not integrate the narrowed abstract idea into a practical application for reasons similar to those explained above, and do not amount to significantly more than the narrowed abstract idea for reasons similar to those explained above.
Claims 44 and 49-54 recite the additional element of receiving another set of data from a third-party (claim 44), the computerized data processing system (claims 49-50 and 54), a pyroelectric detector or a pyroelectric film (claim 50), at least one sensor (claims 51-53) . However, these additional elements amount to implementing an abstract idea on a generic computing device, mere linking to a particular environment, or mere data gathering (i.e., an insignificant extra-solution activity)). As such, these additional elements, when considered individually or in combination with the prior devices, do not integrate the abstract idea into a practical application or amount to significantly more than the abstract idea.
Thus, as the dependent claims remain directed to a judicial exception, and as the additional elements of the claims do not amount to significantly more, the dependent claims are not patent eligible.
Therefore, the claims here fail to contain any additional element(s) or combination of additional elements that can be considered as significantly more and the claim is rejected under 35 U.S.C. 101 for lacking eligible subject matter.
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 40-54 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.
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 [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.”);
detecting sucks by comparing 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.” And [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 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.” and 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”);
detecting respiration ((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.”);
calculating a second time period between the sucks or the swallows to detect bursts (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.”);
calculating a third time period without the sucks and without the swallows (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.”);
calculating at least one biomarker for the infant or the feeding instance (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).”);
analyzing the at least one biomarker to track maturation, neuro-development, or recovery of the infant (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 results of the feeding instance (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; by comparing temperature fluctuations caused by inhalation and exhalation by the infant 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.”);
by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics (Lau [0129] “The sensor module 14 can have sensors that measure pressure (mm Hg), and also the fluid flow rate (ml/min) through the nipple, among other parameters (temperature, etc.). The module's means for measuring an instantaneous fluid flow rate (“flow rate sensor”) can utilize or comprise any of a wide variety of methods, devices, and structures that measure/respond to a variety of physical properties of a moving fluid (e.g., velocity, and, hence, volumetric or mass flow rate; pressure; density; etc.), including, but not limited to: airflow sensor, pressure differential or pressure drop across a flow discontinuity or restriction (e.g., a Venturi section, calibrated orifice plate), ultrasonic techniques, thermal properties technique (e.g., Resistance Temperature Detectors (RTD) thermistor, hot-wire technique, thermal flow sensor), MEMS micro flow sensor, electrochemical techniques (electrolytes, electrical admittance, “Lab-on-a-Chip”), MEMS Coriolis-effect flowmeter (resonant tube), semiconductor field effect, Particle Image Velocimetry (PIV), ultrasonic flow detectors, and flow-based laser or optical techniques, as described below. The volume of liquid (bolus, in ml) passing through the flow sensor as a function of time can be calculated by integrating over time the instantaneous measured flow rate (ml/s). The time period for integration can equal, for example, the duration of the single suck; or it can be a longer fixed duration (e.g., a 1 minute or 5 minute time period).”);
via the GUI to the user (Lau [0019] “The chain of technologies that make this work starts with a notification display on the bottle (and/or on a remote device) that is akin to a smartphone screen and/or vibration or auditory sound (e.g., a chime sound) when it receives a signal. This is the immediate feedback method to the caregiver and is based on the real time OFS level used by the infant during a feeding ( OFS scale levels 1, 2, 3, 4). Signals may comprise information/message such as: “Feeding is Adequate”, or “Stop Feeding”, or “Feeding is Inadequate” (e.g., see “yellow light” in FIG. 14). The display screen is controlled by a micro-computer processing data from one or more sensors located in the feeding bottle or bottle nipple. The logic driving the display notifications comprises innovative algorithms that are unique to babies.”).
One of ordinary skill in the art would have been motivated to incorporate Lau’s GUI user input of non sensor data and its teachings regarding additional physiological sensing techniques into Kaplan’s feeding analysis system to improve the medical relevance of infant feeding assessments. Kaplan teaches the importance of evaluating multiple feeding factors and presenting analyzed results to caregivers and clinicians for decision making. Incorporating user provided contextual information such as gestational age, weight, and feeding protocols, as taught by Lau, into Kaplan’s system would have been a predictable and routine enhancement to improve feeding analysis accuracy and personalization. Further, Kaplan identifies respiration as a feeding factor, and Lau teaches temperature and thermal sensing techniques applicable to physiological monitoring. Applying Lau’s known temperature sensing techniques to Kaplan’s system to detect respiration during feeding would have been an obvious use of a known sensor technology to monitor an identified physiological parameter recognized as relevant in the art.
Additionally, Kaplan discloses calculating feeding parameters over time, detecting feeding bursts, and analyzing changes in feeding behavior across feeding epochs, which provides a basis for analyzing derived parameters to assess infant maturation, development, or recovery. Combining these teachings with Lau’s disclosure of caregiver input, algorithmic evaluation, and result presentation represents the use of known methods and technologies in the same field to achieve predictable results. The proposed combination applies well-understood data collection, analysis, and user interface techniques to a known infant feeding analysis system and yields no unexpected results.
Regarding claim 41, Kaplan and Lau 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 (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.”); and/or
adapting the baseline pressure to or within an actual reading (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.”, 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. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the method to detect characteristic shapes associated with a specific patient population, medical condition, or biomarker and to adapt the baseline pressure to or within an actual reading, as taught or suggested by Kaplan. Kaplan discloses analyzing feeding behavior patterns across different gestational age populations and identifying statistically significant differences in feeding parameter curves over feeding epochs, which involves detecting characteristic waveform shapes associated with particular patient populations or developmental conditions. Kaplan further teaches baseline relative pressure analysis by defining feeding event features as excursions from a baseline and processing pressure transducer measurements to determine peak values, durations, and areas under the curve, which necessarily requires adapting or referencing the baseline pressure relative to actual sensor readings. A person of ordinary skill in the art would have found it obvious to recite these steps as part of Kaplan’s disclosed feeding analysis, as they represent routine and predictable signa -processing and pattern recognition techniques commonly used in clinical physiological monitoring systems.
Regarding claim 42, Kaplan and Lau teach the invention in claim 40, as discussed above, and further teach further comprising:
utilizing the at least one biomarker to calculate tracking indicators (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).”); and
comparing at least one biomarker or the tracking indicators for the infant to determine how they change over time (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).”); 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).”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further utilize the at least one biomarker calculated in the method of Kaplan to calculate tracking indicators and to compare the biomarker and/or tracking indicators over time and against a dataset to determine changes and normative comparisons. Kaplan teaches generating feeding parameters and statistical descriptors, such as rates, mean values, and variability metrics, for one or more observation intervals during a feeding session, which involves tracking those parameters across time intervals and evaluating changes in feeding behavior. A person of ordinary skill in the art would have understood these derived parameters to function as tracking indicators and would have found it obvious to compare such indicators across successive observation intervals and against reference datasets or statistical distributions to assess feeding performance and development, as this is a routine and predictable application of known statistical analysis techniques in clinical feeding assessment systems.
Regarding claim 43, Kaplan and Lau teach the invention in claim 40, as discussed above, and further teach wherein the results comprise curves depicting at least one of feeding trace data, maturation progress data, duration of quality data, and recovery data, and/or wherein (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 [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.”):
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 (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.”); and/or
utilizing the maturation progress data for curve tracking, slope tracking, and to determine a speed of change (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.”); 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.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further modify the method such that the results comprise curves depicting feeding trace data, maturation progress data, duration of quality data, and/or recovery data. Kaplan teaches generating quantitative feeding parameters (number of sucks, sucks per burst, burst duration, respiratory rate, and feeding session duration) and analyzing these parameters over discrete time intervals and feeding epochs, including observing declines and subsequent recovery of feeding vigor within a feeding session and across gestational ages. Kaplan further discloses evaluating feeding parameters as diagnostic and prognostic aids and comparing parameter values across infants and developmental stages, which inherently involves tracking changes over time and presenting trends indicative of maturation or recovery. Presenting these known time based feeding parameters and developmental trends as curves constitutes a predictable and routine visualization technique for physiological data and would have been an obvious design choice to facilitate interpretation by caregivers and clinicians.
Regarding claim 44, Kaplan and Lau teach the invention in claim 40, as discussed above, and further teach further comprising:
receiving another set of data from a third-party (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.”); 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 further modify the method to receive another set of data from a third party and incorporate that data into the analysis of an infant feeding instance, as taught by Kaplan. Kaplan discloses receiving additional non-device data, including medical history data and medical outcome data, and integrating that data with feeding parameter measurements to derive statistical relationships and assess medical risk. Such medical history and outcome data reasonably encompass clinician evaluations, oral feeding assessments, and anthropometric measurements obtained prior to discharge or post discharge. Incorporating third party clinical data into feeding analysis represents a predictable and routine extension of Kaplan’s system to improve clinical interpretation and risk assessment, consistent with common neonatal care practices. The claimed modification applies known data integration techniques in the same field to achieve predictable results.
Regarding claim 45, Kaplan teaches a computer system comprising (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.”):
one or more processors (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.” ;
one or more memories (Kaplan [0049] “Preferably, the microprocessor is integrated into printed circuit board which may also include memory, input/output lines, and ancillary processors and components.”); 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.”, and 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.”):
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 [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.”);
adapting the baseline pressure to an actual reading (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.”, 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. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”);
detecting sucks by comparing 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.” And [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 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.” and 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”);
detecting respiration (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.”);
calculating a second time period between the sucks or the swallows to detect bursts (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.”);
calculating a third time period without the sucks and without the swallows (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.”);
calculating at least one biomarker for the infant or the feeding instance (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).”);
analyzing the at least one biomarker to track maturation and neuro-development of the infant (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 results of the feeding instance (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; by comparing temperature fluctuations caused by inhalation and exhalation by the infant 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.”);
by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics (Lau [0129] “The sensor module 14 can have sensors that measure pressure (mm Hg), and also the fluid flow rate (ml/min) through the nipple, among other parameters (temperature, etc.). The module's means for measuring an instantaneous fluid flow rate (“flow rate sensor”) can utilize or comprise any of a wide variety of methods, devices, and structures that measure/respond to a variety of physical properties of a moving fluid (e.g., velocity, and, hence, volumetric or mass flow rate; pressure; density; etc.), including, but not limited to: airflow sensor, pressure differential or pressure drop across a flow discontinuity or restriction (e.g., a Venturi section, calibrated orifice plate), ultrasonic techniques, thermal properties technique (e.g., Resistance Temperature Detectors (RTD) thermistor, hot-wire technique, thermal flow sensor), MEMS micro flow sensor, electrochemical techniques (electrolytes, electrical admittance, “Lab-on-a-Chip”), MEMS Coriolis-effect flowmeter (resonant tube), semiconductor field effect, Particle Image Velocimetry (PIV), ultrasonic flow detectors, and flow-based laser or optical techniques, as described below. The volume of liquid (bolus, in ml) passing through the flow sensor as a function of time can be calculated by integrating over time the instantaneous measured flow rate (ml/s). The time period for integration can equal, for example, the duration of the single suck; or it can be a longer fixed duration (e.g., a 1 minute or 5 minute time period).”);
via the GUI to the user (Lau [0019] “The chain of technologies that make this work starts with a notification display on the bottle (and/or on a remote device) that is akin to a smartphone screen and/or vibration or auditory sound (e.g., a chime sound) when it receives a signal. This is the immediate feedback method to the caregiver and is based on the real time OFS level used by the infant during a feeding ( OFS scale levels 1, 2, 3, 4). Signals may comprise information/message such as: “Feeding is Adequate”, or “Stop Feeding”, or “Feeding is Inadequate” (e.g., see “yellow light” in FIG. 14). The display screen is controlled by a micro-computer processing data from one or more sensors located in the feeding bottle or bottle nipple. The logic driving the display notifications comprises innovative algorithms that are unique to babies.”).
One of ordinary skill in the art would have been motivated to incorporate Lau’s GUI based caregiver input of non sensor data into Kaplan’s feeding analysis system to improve the medical relevance of infant feeding assessments, as Kaplan emphasizes evaluating multiple feeding factors and presenting analyzed results to caregivers and clinicians. Incorporating known contextual data such as gestational age and weight, as taught by Lau, into Kaplan’s system would have been a predictable and routine enhancement to improve personalization and interpretation of feeding performance. Further, because Kaplan identifies respiration as a feeding factor and Lau teaches known temperature physiological sensing techniques applicable to feeding devices, it would have been obvious to apply Lau’s temperature sensing techniques to Kaplan’s system to detect respiration during feeding, representing the predictable use of known sensor technologies to monitor a physiological parameter recognized as relevant in the art.
Additionally, Kaplan discloses calculating feeding parameters over time, detecting feeding bursts, and analyzing changes in feeding behavior across feeding epochs, which provides a clear basis for analyzing derived parameters to assess infant maturation, development, or recovery. Combining these teachings with Lau’s disclosures regarding caregiver input and result presentation constitutes the use of known methods and technologies in the same field to achieve predictable results. The combination merely applies well understood data collection, analysis, sensing, and user interface techniques to a known infant feeding analysis system and yields no unexpected results.
Claim 46 is analogous to claims 40 and 45, thus claim 46 is similarly analyzed and rejected in a manner consistent with the rejection of claims 40 and 45.
Regarding claim 47, Kaplan and Lau teach the invention in claim 46, as discussed above, and further teach wherein a respiratory measurement component is incorporated into the device and/or wherein the respiratory measurement component fails to come into physical contact with the infant (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.” and 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.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the method to incorporate a respiratory measurement component into the device, as taught by Kaplan. Kaplan identifies respiration as a feeding factor relevant to infant feeding performance and discloses a system configured to receive and process measurements of feeding factors using a microprocessor to generate feeding parameters and scores. The claimed modification represents the predictable use of known physiological measurements in an infant feeding analysis system.
Regarding claim 48, Kaplan teaches a device affixed between a nipple and a bottle and comprising embedded electronic components, the embedded electronic components comprising (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.”):
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 (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.”, 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
the computerized data processing system being configured to (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.”):
calculate a baseline pressure from the data (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.”);
detect sucks by comparing 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.” And [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 pressure fluctuations to known characteristics 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.” and 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”);
detect respiration (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.”);
calculate a second time period between the sucks and the swallows to detect bursts (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.”);
calculate a third time period without the sucks and without the swallows (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.”);
calculate at least one biomarker for the infant or the feeding instance (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).”);
analyze the at least one biomarker to track maturation and neuro-development of the infant (Kaplan [0092] “The transition for two other within-burst parameters, however, appeared to occur at an earlier GA. Both concern moment to-moment variability in sucking behavior. Despite the relative stability in the mean sucking frequency, the variability about the mean (re: coefficient of variation of within-burst inter-suck interval distribution) decreased with GA, with the transition occurring between 34-35 weeks. Similarly, despite the relative stability of mean sucking pressure over this GA time frame, the variability about that mean significantly decreased between 34 and 35 weeks, with no significant change thereafter. However, the postnatal testing point for neonates at GA 33 and 34 weeks is later than for those at GA 35 weeks and greater. This disparity is not regarded as a potential confound for our suggestion of the early transition in these measures of variability of the sucking pattern. The greater age and feeding experience in the most premature neonates is expected to reduce response variability, thereby muting the differences between these (33/34 GA) and neonates of greater GA. Sucking response variability, then, appears to represent a valid correlate of an early transition in the organization of the sucking pattern.”); and
transfer results of the feeding instance to 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, wherein the results of the feeding instance comprise 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.”, 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 by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics; and a graphical user interface (GUI) of the computing device.
Lau teaches by comparing temperature fluctuations caused by inhalation and exhalation by the infant to known characteristics (Lau [0129] “The sensor module 14 can have sensors that measure pressure (mm Hg), and also the fluid flow rate (ml/min) through the nipple, among other parameters (temperature, etc.). The module's means for measuring an instantaneous fluid flow rate (“flow rate sensor”) can utilize or comprise any of a wide variety of methods, devices, and structures that measure/respond to a variety of physical properties of a moving fluid (e.g., velocity, and, hence, volumetric or mass flow rate; pressure; density; etc.), including, but not limited to: airflow sensor, pressure differential or pressure drop across a flow discontinuity or restriction (e.g., a Venturi section, calibrated orifice plate), ultrasonic techniques, thermal properties technique (e.g., Resistance Temperature Detectors (RTD) thermistor, hot-wire technique, thermal flow sensor), MEMS micro flow sensor, electrochemical techniques (electrolytes, electrical admittance, “Lab-on-a-Chip”), MEMS Coriolis-effect flowmeter (resonant tube), semiconductor field effect, Particle Image Velocimetry (PIV), ultrasonic flow detectors, and flow-based laser or optical techniques, as described below. The volume of liquid (bolus, in ml) passing through the flow sensor as a function of time can be calculated by integrating over time the instantaneous measured flow rate (ml/s). The time period for integration can equal, for example, the duration of the single suck; or it can be a longer fixed duration (e.g., a 1 minute or 5 minute time period).”); and
a graphical user interface (GUI) of the computing device (Lau [0019] “The chain of technologies that make this work starts with a notification display on the bottle (and/or on a remote device) that is akin to a smartphone screen and/or vibration or auditory sound (e.g., a chime sound) when it receives a signal. This is the immediate feedback method to the caregiver and is based on the real time OFS level used by the infant during a feeding ( OFS scale levels 1, 2, 3, 4). Signals may comprise information/message such as: “Feeding is Adequate”, or “Stop Feeding”, or “Feeding is Inadequate” (e.g., see “yellow light” in FIG. 14). The display screen is controlled by a micro-computer processing data from one or more sensors located in the feeding bottle or bottle nipple. The logic driving the display notifications comprises innovative algorithms that are unique to babies.”).
One of ordinary skill in the art would have been motivated to incorporate Lau’s known temperature based sensing techniques into Kaplan’s feeding device to detect respiration during feeding, as Kaplan identifies respiration as a relevant feeding factor. Applying Lau’s temperature based sensing to Kaplan’s device represents the predictable use of known sensor technologies to monitor an identified physiological parameter and would have involved no more than routine skill in the art. Additionally, combining Kaplan’s feeding analysis device with Lau’s known result presentation techniques to transfer feeding results to a display, graphical interface, or remote system would have been an obvious design choice to improve usability and clinical relevance. The combination of Kaplan and Lau applies known sensing, data processing, and result presentation techniques in the same field to achieve predictable results.
Regarding claim 49, Kaplan and Lau 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 (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.”), 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 [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 [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 [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.”):
determine an amplitude of suck, swallow and respiration curves (Kaplan [0060] “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
detect a difference in a shape of a suck curve as compared to a swallow curve (Kaplan [0060] “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 [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.”);
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 [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. Examples of features include time of the event, peak value of the event, duration of the excursion, area under the curve, and the like.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to configure the computerized data processing system of the claim to include one or more algorithms for smoothing feeding data, separating sucks from swallows, selecting single and multiple suck signals within a suck–swallow sequence, detecting correlations between suck/swallow activity and respiration, determining amplitudes of suck, swallow, and respiration curves, detecting differences in signal shape between suck and swallow events, and calculating baseline pressure using measurements taken at the beginning and end of a feeding instance or feeding bursts, because Kaplan teaches analyzing pressure derived feeding signals to extract feeding parameters such as number of sucks, burst structure, inter-suck intervals, breathing rate during sucking bursts, and timing relationships between sucking pressure peaks and inspiration, as well as deriving signal features including peak amplitude, duration, area under the curve, and deviation from a baseline. These disclosures require routine signal processing operations such as smoothing, segmentation, waveform comparison, and baseline normalization, which were well known and commonly used in physiological data analysis, and Lau further confirms that correlating suck–swallow–respiration patterns is a known and clinically relevant analysis in infant feeding systems, such that the claimed algorithms represent the predictable application of known analytical techniques to known feeding data.
Regarding claim 50, Kaplan and Lau teach the invention in claim 48, as discussed above, and further teach wherein the device further comprises a pyroelectric detector or a pyroelectric film, and wherein the pyroelectric detector or the pyroelectric film is further configured to collect a respiration measurement and/or wherein the computerized data processing system is further configured to (Lau [0129] “The sensor module 14 can have sensors that measure pressure (mm Hg), and also the fluid flow rate (ml/min) through the nipple, among other parameters (temperature, etc.). The module's means for measuring an instantaneous fluid flow rate (“flow rate sensor”) can utilize or comprise any of a wide variety of methods, devices, and structures that measure/respond to a variety of physical properties of a moving fluid (e.g., velocity, and, hence, volumetric or mass flow rate; pressure; density; etc.), including, but not limited to: airflow sensor, pressure differential or pressure drop across a flow discontinuity or restriction (e.g., a Venturi section, calibrated orifice plate), ultrasonic techniques, thermal properties technique (e.g., Resistance Temperature Detectors (RTD) thermistor, hot-wire technique, thermal flow sensor), MEMS micro flow sensor, electrochemical techniques (electrolytes, electrical admittance, “Lab-on-a-Chip”), MEMS Coriolis-effect flowmeter (resonant tube), semiconductor field effect, Particle Image Velocimetry (PIV), ultrasonic flow detectors, and flow-based laser or optical techniques, as described below. The volume of liquid (bolus, in ml) passing through the flow sensor as a function of time can be calculated by integrating over time the instantaneous measured flow rate (ml/s). The time period for integration can equal, for example, the duration of the single suck; or it can be a longer fixed duration (e.g., a 1 minute or 5 minute time period).” And Lau [0149] “Alternatively, the volumetric rate of incoming air flow can be determined by measuring the change in temperature of an electrically resistively-heated strip or thin film of deposited metal or wire suspended in the stream of air (or, by measuring the power required to maintain a constant temperature in such a heated strip or wire of metal). This is called a micro thermal flow rate sensor, and it has an output signal that changes linearly with corresponding changes in the velocity of fluid flowing through/over the thermal flow sensor. Because the metal strip/film is very thin (typically it is manufactured using MEMS-based technology), it has a very fast thermal response rate (e.g., 1-10 ms response time), and can record very fast changes in fluid velocity (air or liquid) over time. This would allow detailed measurement of the flow velocities as a function of time during an single, individual suck by the infant (ml/sec), assuming that the data collection rate is sufficiently fast to catch the transient velocity pulse (e.g., greater than 10 Hz collection rate).”):
analyze the respiration measurement (Lau [0129] “The sensor module 14 can have sensors that measure pressure (mm Hg), and also the fluid flow rate (ml/min) through the nipple, among other parameters (temperature, etc.). The module's means for measuring an instantaneous fluid flow rate (“flow rate sensor”) can utilize or comprise any of a wide variety of methods, devices, and structures that measure/respond to a variety of physical properties of a moving fluid (e.g., velocity, and, hence, volumetric or mass flow rate; pressure; density; etc.), including, but not limited to: airflow sensor, pressure differential or pressure drop across a flow discontinuity or restriction (e.g., a Venturi section, calibrated orifice plate), ultrasonic techniques, thermal properties technique (e.g., Resistance Temperature Detectors (RTD) thermistor, hot-wire technique, thermal flow sensor), MEMS micro flow sensor, electrochemical techniques (electrolytes, electrical admittance, “Lab-on-a-Chip”), MEMS Coriolis-effect flowmeter (resonant tube), semiconductor field effect, Particle Image Velocimetry (PIV), ultrasonic flow detectors, and flow-based laser or optical techniques, as described below. The volume of liquid (bolus, in ml) passing through the flow sensor as a function of time can be calculated by integrating over time the instantaneous measured flow rate (ml/s). The time period for integration can equal, for example, the duration of the single suck; or it can be a longer fixed duration (e.g., a 1 minute or 5 minute time period).” And Lau [0149] “Alternatively, the volumetric rate of incoming air flow can be determined by measuring the change in temperature of an electrically resistively-heated strip or thin film of deposited metal or wire suspended in the stream of air (or, by measuring the power required to maintain a constant temperature in such a heated strip or wire of metal). This is called a micro thermal flow rate sensor, and it has an output signal that changes linearly with corresponding changes in the velocity of fluid flowing through/over the thermal flow sensor. Because the metal strip/film is very thin (typically it is manufactured using MEMS-based technology), it has a very fast thermal response rate (e.g., 1-10 ms response time), and can record very fast changes in fluid velocity (air or liquid) over time. This would allow detailed measurement of the flow velocities as a function of time during an single, individual suck by the infant (ml/sec), assuming that the data collection rate is sufficiently fast to catch the transient velocity pulse (e.g., greater than 10 Hz collection rate).”);
compare the respiration measurement to measurements taken during the feeding instance when the infant is engaging in sucking and swallowing actions (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).”); 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 (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.” and 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 the device to further comprise a pyroelectric detector or a pyroelectric film configured to collect a respiration measurement, and/or to configure the computerized data processing system to analyze and compare respiration measurements during sucking and swallowing action. This is because Lau teaches known temperature based and thermal sensing techniques, including thin-film and MEMS-based sensors, for detecting airflow and temperature variations associated with inhalation and exhalation with sufficiently fast response times for infant feeding applications, and Kaplan identifies respiration as a feeding factor and teaches analyzing the temporal coordination between sucking, swallowing, and breathing, including deriving breathing rate, burst structure, pauses, and relative timing. Combining these teachings represents the predictable use of known sensor technologies to monitor a recognized physiological parameter within an infant feeding analysis system.
Regarding claim 51, Kaplan and Lau 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).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).”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device such that the at least one biomarker is associated with oral cavity pressure changes and/or that each sensor is selected from a pressure sensor or a respiratory sensor. Kaplan teaches generating feeding parameters and statistical measures derived from sucking behavior, including total number of sucks, suck rate, mean maximum sucking pressure (Pmax), and variability of inter-suck intervals, all of which are directly based on oral cavity pressure measurements obtained during a feeding session. Kaplan further discloses the use of a pressure transducer integrated with a bottle and nipple assembly to obtain such measurements. The claimed subject matter represents the predictable use of known sensors and pressure derived biomarkers in infant feeding analysis systems.
Regarding claim 52, Kaplan and Lau teach the invention in claim 51, as discussed above, and further teach wherein the at least one sensor comprises a respiratory sensor, wherein the respiratory sensor is located between the embedded electronic components and a personal computer connection, and wherein the respiratory sensor is configured to measure changes in temperature in exhalation (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.”, and Lau [0129] “The sensor module 14 can have sensors that measure pressure (mm Hg), and also the fluid flow rate (ml/min) through the nipple, among other parameters (temperature, etc.). The module's means for measuring an instantaneous fluid flow rate (“flow rate sensor”) can utilize or comprise any of a wide variety of methods, devices, and structures that measure/respond to a variety of physical properties of a moving fluid (e.g., velocity, and, hence, volumetric or mass flow rate; pressure; density; etc.), including, but not limited to: airflow sensor, pressure differential or pressure drop across a flow discontinuity or restriction (e.g., a Venturi section, calibrated orifice plate), ultrasonic techniques, thermal properties technique (e.g., Resistance Temperature Detectors (RTD) thermistor, hot-wire technique, thermal flow sensor), MEMS micro flow sensor, electrochemical techniques (electrolytes, electrical admittance, “Lab-on-a-Chip”), MEMS Coriolis-effect flowmeter (resonant tube), semiconductor field effect, Particle Image Velocimetry (PIV), ultrasonic flow detectors, and flow-based laser or optical techniques, as described below. The volume of liquid (bolus, in ml) passing through the flow sensor as a function of time can be calculated by integrating over time the instantaneous measured flow rate (ml/s). The time period for integration can equal, for example, the duration of the single suck; or it can be a longer fixed duration (e.g., a 1 minute or 5 minute time period).”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device to include a respiratory sensor configured to measure changes in temperature in exhalation and integrated within the device architecture. Kaplan teaches an infant feeding analysis system that includes embedded electronic components, sensors for measuring feeding factors including respiration, a microprocessor, and data interfaces for transmitting sensor data to computing devices or output interfaces. Lau teaches sensor modules for feeding devices that include temperature and thermal sensing techniques (RTDs, thermistors, thermal flow sensors) suitable for detecting physiological flow and temperature variations associated with respiration. A person of ordinary skill in the art would have been motivated to incorporate Lau’s known thermal respiration sensing techniques into Kaplan’s embedded feeding device to monitor respiration, a recognized feeding factor, using predictable and well understood sensor technologies. Further, positioning the respiratory sensor within the signal path between embedded electronic components and a computer interface represents a routine hardware implementation choice dictated by system architecture and does not impart patentable distinction. The combination applies known sensing technologies within a known infant feeding analysis system to achieve predictable results.
Regarding claim 53, Kaplan and Lau teach the invention in claim 51, as discussed above, and further teach wherein the at least one sensor comprises a pressure sensor, wherein the pressure sensor is located inside of the bottle, and wherein the pressure sensor is configured to measure an oral cavity pressure of the infant (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 [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.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to modify the device to include a pressure sensor located inside the bottle and configured to measure oral cavity pressure of the infant. Kaplan teaches a baby bottle equipped with a pressure sensor for monitoring pressure inside the bottle during an infant feeding session and processing those pressure measurements to derive feeding parameters such as suck counts, pressure peaks, and burst characteristics. A person of ordinary skill in the art would have understood that pressure changes inside the bottle during feeding directly correspond to oral cavity pressure applied by the infant through the nipple. Incorporating such a pressure sensor within the bottle represents the predictable use of a known sensor technology in a known infant feeding device to measure a recognized feeding factor.
Regarding claim 54, Kaplan and Lau 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 [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 [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).”, and 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.”).
It would have been obvious to one of ordinary skill in the art at the time of the invention to further configure the device to differentiate sucking events as nutritive or non-nutritive 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 teaches detailed quantitative analysis of sucking behavior during infant feeding, including measuring number of sucks, sucking pressure peaks, burst structure, within-burst suck frequency, and temporal relationships between sucking pressure peaks and respiration. Kaplan further distinguishes nutritive sucking from non-nutritive sucking and analyzes differences in sucking patterns and developmental profiles associated with each. Once sucking and swallowing events are detected and quantified, a person of ordinary skill in the art would have found it obvious to classify sucking events as nutritive or non-nutritive and to calculate ratios and coordination metrics between sucking, swallowing, and respiration as routine signal-processing and data analysis operations used to characterize feeding performance. The claimed modification organizes and evaluates known feeding parameters using predictable analytical techniques.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Tarcan et al. (International Publication No. WO2014081401 A1) teaches a device that can objectively monitor feeding maturity in premature babies using swallowing patterns.
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/K.R.L./Examiner, Art Unit 3685