DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
The status of the claims as of the response filed 6/19/2026 is as follows: Claims 1-3 and 11-13 are currently amended. Claims 4-10 and 14-20 are original. Claims 1-20 are currently pending in the application and have been considered below.
Response to Amendment
Double Patenting Rejection
The amendments alter the scope of the claims to sufficiently distinguish over the claims of US patent 11935642 B2 such that the corresponding double patenting rejections are withdrawn.
Response to Arguments
Priority Determination
On page 1 of the response filed 6/19/2026 Applicant disagrees with Examiner’s priority determination outlined in paras. 1-2 of the non-final rejection mailed 12/19/2026. Applicant specifically asserts that support for “obtain a plurality of neonatal indicator elements by determining a plurality of patterns based on the infant measurements” is supported in at least Col3 L51 – Col4 L61 of issued US patent 11935642 B2 which corresponds to paras. [0010]-[0011] in the specification of parent application 17/187,970. Examiner respectfully disagrees that these paragraphs sufficiently support “determining a plurality of patterns based on the infant measurements” as part of the step for obtaining a plurality of neonatal indicator elements, because they merely list types of neonatal indicator element data that may be obtained from sensors and/or input by a user with no mention of determining patterns. Regardless, the instant claims introduce additional subject matter that is not supported by the parent ‘970 application (as outlined in the priority section below), such that the priority determination does not solely rely on the challenged subject matter.
Rejection Under 35 USC 101
On pages 3-6 Applicant argues that the amended independent claims do not recite an abstract idea in the form of a certain method of organizing human activity, at least because none of the limitations “recite a behavioral rule, a relationship-management scheme, an interaction between persons, or a requirement governing how any parent, caregiver, clinician, or other person must behave” and the claims do not include “a clinician’s judgment, a caregiver’s conduct, a communication between people, or a rule for directing personal behavior.” Applicant’s arguments are fully considered, but are moot, because the altered scope of the independent claims necessitated a new ground of rejection which characterizes the claims as reciting a mental process, not a certain method of organizing human activity (see below). Examiner respectfully disagrees that the claims do not include steps amounting to “a clinician’s judgment,” at least because they include clinical determinations and evaluations about the health status and treatment plan of an infant that a clinician would be capable of making, as explained in more detail below.
On pages 6-7 Applicant argues that “amended claim 1 recites a particular technological solution to a problem in neonatal nourishment-program generation: conventional edible suggestion systems do not account for the status of a newborn, resulting in inefficient nutrition plans and a lack of uniformity in nutritional plans.” Applicant submits that the claimed “sensor-based and machine-learning-based processing architecture” addresses this technical problem in a manner that “imposes meaningful technical constraints on both the input and the output of the alleged abstract idea” and “does not use machine learning as a generic tool for producing a recommendation.” Applicant’s arguments are fully considered, but are not persuasive. Examiner respectfully disagrees that generating nourishment programs / feeding regimens for babies is a technical field as Applicant asserts; the drawbacks to current diet recommendation procedures that Applicant outlines do not appear to be technical in nature, and instead show limitations about the amount and/or types of information/characteristics about a newborn that are considered when making nutritional recommendations, which is a drawback in the existing abstract business practice of providing pediatric guidance to a caregiver of a baby (see [0003] of the specification). Though the invention provides a technological solution in that it utilizes a gas sensor device coupled with machine learning processes, it is not solving a technical problem and therefore does not provide an improvement to a computer or other technical field. Rather, the invention seeks to apply existing sensor and machine learning technologies to the abstract field of health status evaluation and nutritional recommendation in an effort to automate and/or digitize these otherwise-abstract practices.
Examiner further notes that limiting or narrowing the types of data represented by the inputs, intermediate analysis results, and ultimate outputs of the otherwise-abstract diagnostic and nutritional recommendation workflow does not provide integration into a practical application. A narrow abstract idea is still an abstract idea; in the instant case, limiting the input data to gas sensor measurements associated with burping or digestion processes, the intermediate analysis results to classification of normal burping and irregular gas-pattern categories, and the output to a nourishment program comprising a formula for a bottle-feeding technique that will reduce effects of an identified aliment intolerance merely limits the abstract analysis and recommendation operation to evaluating and producing these specific types of data, which is still an abstract process that could be carried out by a human actor such as a clinician either mentally or with aid of pen and paper.
On pages 7-8 Applicant argues that the instant claims are analogous to claim 2 of Example 49 which was found patent eligible “because using the patient-risk determination to provide the particular treatment constituted a practical application of the alleged judicial exception.” Applicant specifically asserts that “amended claim 1 similarly uses the claimed sensor-based pattern determination to identify a specific infant population” which is “used to generate a nourishment program comprising a formula for a bottle-feeding technique directed to reducing effects of the identified aliment intolerance” which “has more than a nominal relationship to the gas-pattern classification and neonatal-profile generation steps.” Applicant concludes that amended claim 1 is distinguished from ineligible claim 1 of Example 49 because it “does not leave the result at a generic instruction to apply the alleged abstract idea” and instead “meaningfully confines the claimed process to a particular neonatal nourishment-program application.” Applicant’s arguments are fully considered, but are not persuasive. First, Examiner notes that in contrast to the claims of Example 49, in the instant claims no actual treatment is positively or affirmatively recited as being administered to a patient; rather, a nourishment program recommendation is generated, but there is no indication that the program is positively provided to the neonate. Per MPEP 2106.04(d)(2), “in order to qualify as a ‘treatment’ or ‘prophylaxis’ limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition”; examples of such positively recited treatments can include “e.g., acupuncture, administration of medication, dialysis, organ transplants, phototherapy, physiotherapy, radiation therapy, surgery, and the like.” Second, even if the nourishment program were to be positively administered to the neonate, the determined program is not particular as understood by MPEP 2106.04(d)(2)(a); there is no one specific diet, nutrient, formula, etc. matched to treat one particular condition. Instead, any “formula” appropriate for “reducing effects” of any number of different types of aliment intolerances may be determined and recommended. Accordingly, the instant claims are not analogous to those found eligible under the particular treatment/prophylaxis consideration as in Example 49.
On pages 8-9 Applicant argues that the claims use “a particular sensor-derived input, a particular machine-learning classification operation, and a particular downstream nourishment-program generation sequence” that do not “merely apply a computer to a conventional practice of observing an infant and making a generalized nutritional recommendation.” Applicant asserts that “the gas sensor, pattern machine-learning model, gas-pattern classifications, gastrointestinal medical bundle, aliment-intolerance profile, and formula-based bottle-feeding technique operation together in a defined order to transform gas measurements associated with burping or digestion processes into a nourishment program directed to reducing effects of an identified aliment intolerance.” Applicant concludes that the prosecution record has not established that “this claimed sensor-based and machine-learning-based arrangement was well-understood, routine, or conventional.”
Rejection Under 35 USC 103
Applicant’s arguments with respect to the newly-introduced limitations of the independent claims have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Priority
This application’s status as a continuation-in-part of 17/187,970 is acknowledged. Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 17/187,970, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. Such limitations include:
wherein the plurality of infant measurements comprises infant sensor measurements generated by a gas sensor, and wherein the infant sensor measurements comprise gas measurements associated with burping or digestion processes of an infant as in claims 1 and 11;
Though paras. [0010], [0012], [0022] of the ‘970 specification discuss determining infant conditions related to the gastrointestinal system, feeding, digestion, etc. by evaluating data collected from monitoring devices / sensors (including devices capable of collecting chemical data as in [0010]), there is no disclosure of a specific gas sensor collecting gas measurements associated with burping or digestion processes of an infant.
obtaining a plurality of neonatal indicator elements by determining a plurality of patterns based on the infant measurements, wherein determining the plurality of patterns comprises applying a pattern machine-learning model to the gas measurements to classify gas patterns into at least a normal burping pattern and an irregular gas pattern as in claims 1 and 11;
Though paras. [0010]-[0011] of the ‘970 specification discuss obtaining various types of neonatal indicators like user inputs, sensor measurements, etc., there is no description of determining a plurality of patterns based on the infant measurements as part of obtaining the plurality of neonatal indicator elements. The only mention of “patterns” in the ‘970 specification is in para. [0048], which broadly discusses how unsupervised learning processes may be used to find interesting patterns and/or inferences between variables; however, there is no indication that the process of obtaining a plurality of neonatal indicator elements as in paras. [0010]-[0011] utilizes unsupervised (or supervised) machine learning methods. Additionally, there is no disclosure of the measurements specifically being gas measurements, nor of using the machine-learning model to classify gas patterns into at least a normal burping pattern and an irregular gas pattern.
wherein the neonatal bundle comprises a gastrointestinal medical bundle identified as a function of the irregular gas pattern as in claims 1 and 11;
Though para. [0012] of the ‘970 specification discusses identifying a neonatal bundle as a function of the neonatal indicator element (which can be related to digestion conditions/issues), there is no disclosure of a specific gastrointestinal medical bundle being identified as a function of the irregular gas pattern, because there is no mention of gas patterns at all.
wherein the updated neonatal profile comprises an aliment intolerance identified as a function of the irregular gas pattern as in claims 1 and 11;
Though para. [0013] of the ‘970 specification discusses identifying a neonatal profile as a function of the neonatal bundle (which can be related to gastrointestinal conditions/issues) and including aliment intolerances, there is no disclosure of the aliment intolerance being identified specifically as a function of the irregular gas pattern, because there is no mention of gas patterns at all.
wherein an infant measurement comprises an oxygen saturation level of the infant as in claims 2 and 12;
Though paras. [0010]-[0011] of the ‘970 specification discuss obtaining various types of neonatal indicators like sensor measurements, user inputs of medical assessments, etc., there is no description of specifically oxygen saturation level being one of the sensor measurements.
wherein obtaining the plurality of neonatal indicator elements comprises determining a neonatal indicator element comprising a sleep pattern of the infant using a baby monitor comprising a camera and sleep tracking functionality, wherein the baby monitor is configured to provide visual and quantitative insights into a baby’s sleep patterns as in claims 3 and 13;
Though para. [0010] of the ‘970 specification discusses use of a baby monitor with sleep monitoring capabilities to provide the neonatal indicator element, it does not disclose the baby monitor determining a sleep pattern of an infant, the baby monitor having a camera, or the baby monitor being configured to provide visual and quantitative insights into a baby’s sleep patterns.
wherein determining the neonatal disorder comprises: receiving the neonatal indicator element comprising a photograph of waste; training an image-based waste machine learning model with training data correlating a plurality of waste related photos to a plurality of neonatal disorders; and outputting, using the image-based waste machine learning model, the neonatal disorder as in claims 6 and 16; and
The only mentions of images in the ‘970 specification are in reference to magnetic resonance images or computed tomographic images as neonatal indicator elements in para. [0011], and the display of images at a display device in para. [0066]. There is no mention of photographs of waste, training an image-based waste machine learning model in the manner recited, or using the image-based waste machine learning model to output a neonatal disorder.
Accordingly, claims 1-3, 6, 11-13, and 16 are not entitled to the filing date of the ‘970 application, and will instead be afforded the filing date of the instant application: 1/29/2024. Note that claims 2-10 and 12-20 inherit the claim language of claims 1 and 11, respectively, due to their dependence on these claims, and are thus also afforded the filing date of the instant application: 1/29/2024.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-10 are directed to a system (i.e. a machine) and claims 11-20 are directed to a method (i.e. a process). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1 and 11 recite steps that, under their broadest reasonable interpretations, cover mental processes. Specifically, claim 1 (as representative) recites:
A system for generating a neonatal disorder nourishment program, the system comprising: a computing device, the computing device configured to:
receive a plurality of infant measurements, wherein the plurality of infant measurements comprises infant sensor measurements generated by a gas sensor, and wherein the infant sensor measurements comprise gas measurements associated with burping or digestion processes of an infant;
obtain a plurality of neonatal indicator elements by determining a plurality of patterns based on the infant measurements, wherein determining the plurality of patterns comprises applying a pattern machine-learning model to the gas measurements to classify gas patterns into at least a normal burping pattern and an irregular gas pattern;
identify a neonatal bundle as a function of the plurality of neonatal indicator elements, wherein the neonatal bundle comprises a gastrointestinal medical bundle identified as a function of the irregular gas pattern;
update a neonatal profile of the infant as a function of the neonatal bundle, wherein updating the neonatal profile further comprises:
receiving neonatal training data correlating a plurality of neonatal functional goals and a plurality of neonatal recommendations to the neonatal bundle and the neonatal profiles;
training a neonatal machine learning model using the neonatal training data;
inputting the plurality of neonatal functional goals and the plurality of neonatal recommendations to the trained neonatal machine learning model; and
outputting the updated neonatal profile from the trained neonatal machine learning model, wherein the updated neonatal profile comprises an aliment intolerance identified as a function of the irregular gas pattern;
determine an aliment as a function of the updated neonatal profile, wherein determining the aliment comprises determining a formula for a bottle-feeding technique to reduce effects of the aliment intolerance; and
generate a nourishment program as a function of the aliment, wherein the nourishment program comprises the formula for the bottle-feeding technique to be administered to the infant over a time period.
But for the recitation of generic computer components like a computing device and high-level machine learning, the italicized functions, when considered as a whole, describe a clinical analysis and nutritional recommendation operation that could be achieved by a human actor such as a clinician or other medical professional either mentally or with aid of pen and paper. For example, a clinician could use their medical expertise to determine normal burping and irregular gas patterns in gas sensor measurements of an infant and identify metrics related to a digestive/gastrointestinal system/condition (i.e. identify a gastrointestinal medical bundle as a function of irregular gas patterns). The clinician could also receive labeled training data correlating goals and recommendations to neonatal bundles and profiles (e.g. by looking at a pre-established table of data correlations, by organizing the data themselves, etc.) and use the training data as a basis for learning how the neonatal bundles and profiles correlate to goals and recommendations. After learning such correlations, the clinician could use their new expertise to think about functional goals and recommendations when determining a neonatal profile that includes an aliment intolerance that they have identified by evaluating the identified irregular gas pattern. The clinician could then use their medical expertise to determine a bottle-feeding formula and appropriate nourishment program over a span of time based on the profile intolerance (e.g. selecting a goat’s milk regimen for a baby who matches a profile of cow’s milk intolerance). Accordingly, claim 1 recites an abstract idea in the form of a mental process because it describes clinical evaluations and judgments that a clinician could achieve mentally or with aid of pen and paper. Claim 11 recites substantially similar subject matter as claim 1 and is found to recite an abstract idea under the same analysis.
Dependent claims 2-10 and 12-20 inherit the limitations that recite an abstract idea from their dependence on claims 1 and 11, respectively, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2-10 and 12-20 recite additional limitations that further describe the abstract idea identified in the independent claims.
Specifically, claims 2 and 12 specify that the infant measurement comprises an oxygen saturation level of the infant, which is a type of data that a clinician would be capable of observing from a sensor readout and mentally analyzing for patterns or indications of health issues.
Claims 3 and 13 recite determining a sleep pattern of the infant and providing visual and quantitative insights into a baby’s sleep patterns, which a clinician could achieve by observing the baby as they sleep and/or assessing collected indicators from the baby’s sleep periods to determine patterns and use their medical expertise to develop quantitative insights for visual representation (e.g. in the form of a written report noting how many hours the baby tends to sleep per night). This development of visual insights can also be considered a certain method of organizing human activity in the form of managing personal behavior or interactions between people, because it describes how a clinician might manage their personal behavior to visually communicate information to another person (e.g. the caregiver or parent of the baby).
Claims 4 and 14 recite determining a neonatal disorder and updating the neonatal profile as a function of the neonatal disorder, which a clinician could achieve by using their medical expertise to diagnose a disorder of the infant.
Claims 5 and 15 specify that determining the neonatal disorder includes receiving the neonatal bundle and outputting the neonatal disorder. A clinician could achieve these functions by thinking about the neonatal bundle when determining a disorder.
Claims 6 and 16 specify that determining the neonatal disorder comprises receiving a photograph of waste and outputting the neonatal disorder. A clinician could achieve these functions by looking at pictures of an infant’s waste and using their medical expertise about which disorders correlate to different visual characteristics of waste to determine a disorder for the infant.
Claims 7 and 17 recite determining a waste remedy as a function of the neonatal disorder, which a clinician could achieve by using their medical expertise to recommend a treatment to improve the infant’s digestion and waste processes.
Claims 8 and 18 recite classifying a user to a cohort of users with similar neonatal disorders, which a clinician could achieve by thinking about similar patients and grouping the users accordingly.
Claims 9 and 19 specify that determining the aliment comprises calculating a neonatal cognitive phase, which a clinician could achieve by thinking about what stage of development the infant is at (e.g. based on age, interaction with the infant, cognitive tests, etc.) when identifying an appropriate nutrient, food, supplement, etc. for the infant.
Claims 10 and 20 specify that generating the nourishment program further comprises receiving a neonatal outcome and generating the nourishment program as a function of the neonatal outcome, which a clinician could achieve by determining a desired outcome (e.g. waste output of a certain quantity and/or frequency, waste of a particular color and/or texture, etc.) and using their medical expertise to select a nourishment program that will help the infant achieve the desired outcome.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1 and 10 do not include additional elements that integrate the abstract idea into a practical application. The additional elements of claims 1 and 10 include a computing device to perform the steps, use of a pattern machine-learning model to classify the gas patterns, training and use of a neonatal machine-learning model to update the neonatal profile, as well as the step of receiving a plurality of infant measurements comprising infant sensor measurements comprising gas measurements associated with burping or digestion processes of an infant generated by a gas sensor. These additional elements, when considered in the context of each claim as a whole, merely serve to digitize and/or automate clinical evaluation and judgment steps that could be performed by a human actor mentally or with aid of pen and paper (as described above), and thus amount to instructions to “apply” the abstract idea using generic computer components (see MPEP 2106.05(f)), as well as provide insignificant extra-solution activity in the form of necessary data gathering (see MPEP 2106.05(g)).
For example, a clinician could collect and analyze gas/digestion data about an infant to classify normal and irregular burping/gas patterns, and the use of a computing device executing a pattern machine-learning model to perform these steps merely digitizes and/or automates these otherwise-abstract functions such that they occur automatically in a digital environment. Examiner notes that the pattern machine-learning model is used to generally apply the abstract idea without placing any limits on how the model functions; this limitation only recites the desired functional outcome of ‘classifying gas patterns into at least a normal burping pattern and an irregular gas pattern’ without including any details about how the ‘classifying’ is accomplished.
Similarly, the high-level computerized training and use of a neonatal machine-learning model to update the neonatal profile merely digitizes and/or automates the otherwise-abstract mental process of learning correlations between different data types and using that learned knowledge to output a certain neonatal profile when considering certain goals and recommendations as inputs. Though the claim recites training the neonatal machine learning model using the neonatal training data, this merely describes the high-level, ordinary operation of a machine learning model (which requires some kind of training data to learn correlations between desired inputs and outputs), with no details about how the model is specifically trained, or how the trained model operates to achieve the desired functional outcome of updating a neonatal profile. Accordingly, the steps for training and using this model are merely invoked as a means to generally “apply” the otherwise-abstract neonatal profile updating operation in an automated/digitized fashion.
The use of a gas sensor to obtain the gas measurements of the infant amounts to insignificant pre-solution data gathering because it merely utilizes the gas sensor as a means for obtaining the infant measurements necessary for the main clinical data analysis steps of the invention. Accordingly, claims 1 and 11 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-10 and 12-20 is also not integrated into a practical application under a similar analysis as above. Claims 4, 7-9, 14, and 17-19 are performed with the same additional elements as the independent claims, without introducing any new additional elements of their own, such that they do not provide integration into a practical application.
Claims 2 and 12 specify that the infant measurement is an oxygen saturation level of the infant, which merely defines the type of data that is analyzed and is thus considered part of the abstract idea, as indicated above, and does not positively recite an oxygen saturation sensor as part of the claim. However, even if an oxygen saturation sensor were positively recited as obtaining or providing the measurement, such an element would amount to insignificant extra-solution activity in the form of data gathering because the sensor would merely be invoked as a means of obtaining the measurement data for the main clinical data analysis steps of the invention (see MPEP 2106.05(g)).
Claims 3 and 13 include the additional element of a baby monitor with a camera and sleep tracking functionality for determining a sleep pattern of the infant and providing visual and quantitative insights into the baby’s sleep patterns. The camera-based baby monitor merely acts as a means of obtaining the sleep pattern data and visually providing the determined sleep insights such that it amounts to insignificant extra-solution activity in the form of data gathering and outputting (see MPEP 2106.05(g)).
Claims 5-6 and 15-16 specify training and using a disorder machine-learning model and image-based waste machine-learning model to perform the neonatal disorder determination, which amount to instructions to “apply” the exception as explained for the similar high-level training and use of machine learning models as in the independent claims above. The high-level computerized training and use of machine-learning models to determine a neonatal disorder merely digitizes and/or automates the otherwise-abstract mental process of performing medical diagnosis based on patient data and known or learned correlations between different input data types (e.g. enumerations and infant organ systems, or photographs of waste) and neonatal disorders. Though each claim recites training the machine learning models using training data, this merely describes the high-level, ordinary operation of a machine learning model (which requires some kind of training data to learn correlations between desired inputs and outputs), with no details about how the models are specifically trained, or how the trained models operate to achieve the desired functional outcome of determining a neonatal disorder. Accordingly, the steps for training and using these models are merely invoked as a means to generally “apply” the otherwise-abstract neonatal disorder determination operation in an automated/digitized fashion.
Claims 10 and 20 specify that the nourishment program is generated using a nourishment machine-learning model, which again amounts to instructions to “apply” the exception because it merely invokes a high-level “machine-learning” model as a tool with which to digitize/automate the otherwise-abstract function of generating a nourishment program for an infant based on a desired neonatal outcome.
Accordingly, the additional elements of claims 1-20 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-20 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a computing device and machine learning models used for performing the receiving, obtaining, identifying, updating, training, inputting, outputting, determining, generating, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes paras. [0113]-[0116] of Applicant’s specification, disclosing generic examples of computing devices like “an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof.” See also paras. [0024]-[0028], noting various examples of existing machine learning models that may be utilized by the system such as regression, K-nearest neighbor, SVMs, decision tree, clustering, etc., as well as paras. [0076]-[0099], where high-level overviews of existing machine learning training methods and model architectures are provided. These disclosures do not indicate that the elements of the invention are particular machines, and instead provide generic examples of computer hardware and machine learning model architectures and training methods, such that one of ordinary skill in the art would understand that any generic computing device and machine learning modelling and training method could be used to implement the invention.
Regarding the gas sensor as in claims 1 and 11, as noted above, this element amounts to insignificant extra-solution activity in the form of mere data gathering. Additionally, Examiner notes that it is well-understood, routine, and conventional to utilize a gas sensor in combination with computerized algorithm / machine learning processes to make clinical determinations about gastrointestinal activity/ conditions, as evidenced by at least Agelides (US 20200051693 A1) abstract, Figs. 1-2, [0025], [0030]-[0034], & [0055]; Hall et al. (US 20240268749 A1) abstract, Fig. 5, [0080], [0104]-[0107]; Hannula et al. (US 20220167930 A1) [0027], [0029], [0081], [0088]; and Sherwood et al. (US 20180336970 A1) abstract, [0044], [0051], [0136]-[0137].
Regarding the baby monitor as in claims 3 and 13, as noted above, this element amounts to insignificant extra-solution activity in the form of data gathering and outputting. Further, it is well-understood, routine, and conventional to utilize a baby monitor to monitor patterns of a baby’s sleep and provide visual insights related to sleep patterns, as evidenced by at least Caflisch (Reference U on the PTO-892 mailed 12/19/2025) Pgs 2-4.
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the gas sensor, computer implementation, and high-level machine learning models in combination is to digitize and/or automate a clinical analysis and nutritional recommendation operation that could otherwise be achieved as a mental process. Thus, when considered as a whole and in combination, claims 1-20 are not patent eligible.
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 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 1, 4-5, 7-11, 14-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20220277827 A1) in view of Hall et al. (US 20240268749 A1).
Claims 1 and 11
Neumann teaches a system for generating a neonatal disorder nourishment program, the system comprising: a computing device, the computing device configured to (Neumann abstract):
receive a plurality of infant measurements, wherein the plurality of infant measurements comprises infant sensor measurements (Neumann [0018], noting the system may receive inputs from sensors related to measurements of an infant, including from devices that “collect, store, and/or calculate one or more lights, voltages, currents, sounds, chemicals, pressures, and the like thereof that may be capable of monitoring an infant’s health status”);
obtain a plurality of neonatal indicator elements (Neumann [0018]-[0019], noting the system obtains neonatal indicator elements comprising the input data, i.e. based on the infant measurements);
identify a neonatal bundle as a function of the plurality of neonatal indicator elements, wherein the neonatal bundle comprises a gastrointestinal medical bundle identified as a function of (Neumann [0020]);
update a neonatal profile of the infant as a function of the neonatal bundle (Neumann [0021], noting the system produces a neonatal profile as a function of the neonatal bundle; see also [0017], noting that any method or step of the invention can be performed iteratively with any degree of repetition, indicating that the step of producing a neonatal profile may be repeated iteratively such that subsequent performances of the step serve to update the neonatal profile), wherein updating the neonatal profile further comprises:
receiving neonatal training data correlating a plurality of neonatal functional goals and a plurality of neonatal recommendations to the neonatal bundle and the neonatal profiles (Neumann [0023]-[0024]);
training a neonatal machine learning model using the neonatal training data (Neumann [0024]);
inputting the plurality of neonatal functional goals and the plurality of neonatal recommendations to the trained neonatal machine learning model (Neumann [0023]-[0024]); and
outputting the updated neonatal profile from the trained neonatal machine learning model, wherein the updated neonatal profile comprises an aliment intolerance identified as a function of (Neumann [0023]-[0024], noting the trained neonatal machine learning model outputs a neonatal profile, which per [0021] can include an aliment intolerance identified based on the analyzed infant data; see also [0017], noting that any method or step of the invention can be performed iteratively with any degree of repetition, indicating that this outputting step producing a neonatal profile may be repeated iteratively such that subsequent performances of the step serve to output an updated neonatal profile);
determine an aliment as a function of the updated neonatal profile, wherein determining the aliment comprises determining a formula for a bottle-feeding technique to reduce effects of the aliment intolerance (Neuman [0033], [0048]); and
generate a nourishment program as a function of the aliment, wherein the nourishment program comprises the formula for the bottle-feeding technique to be administered to the infant over a time period (Neumann [0041]).
In summary, Neumann teaches a computerized system for obtaining infant sensor data, identifying a gastrointestinal bundle associated with the infant data, producing a neonatal profile including an aliment intolerance based on the bundle, and determining an aliment and associated nourishment program to mitigate the intolerance based on the profile. However, though [0018] of Neumann shows that sensor data may include chemical measurements and data from “devices that collect, store, and/or calculate one or more lights, voltages, currents, sounds, chemicals, pressures, and the like thereof that may be capable of monitoring an infant’s health status,” Neumann fails to explicitly disclose that the infant sensor measurements comprise gas measurements associated with burping or digestion processes of an infant that are generated by a gas sensor; determining a plurality of patterns by applying a pattern machine-learning model to the gas measurements to classify gas patterns into at least a normal burping pattern and an irregular gas pattern; and using the irregular gas pattern as a basis for identifying the gastrointestinal bundle and aliment intolerance.
However, Hall teaches an analogous system for detecting, managing, and informing the treatment of gastrointestinal conditions (Hall [0002]) that includes using a gas sensor to obtain gas measurements associated with digestion processes of a user (Hall Fig. 5, [0057]-[0060], [0064], noting gas sensors detect gas concentrations in a user’s flatulence), applying a machine learning model to the gas measurements to determine normal or irregular indications/patterns in the gas measurements that result in a diagnosis of healthy or disordered (Hall [0104]-[0107], noting gas sensor data may be used as inputs to a machine learning network to diagnose presence or absence of gastrointestinal disorders, i.e. indicate normal or irregular patterns in the data), and using these insights as a basis for implementing precision nutrition strategies for the user (Hall [0098]). It therefore would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the analysis of infant sensor measurements for the purpose of determining an appropriate nourishment program as in Neumann such that the sensor measurements are gas measurements analyzed via a machine learning model to guide appropriate gastrointestinal intervention strategies as in Hall in order to incorporate evaluation of a specific type of sensor data known to be indicative of gastrointestinal health so that improved gastrointestinal diagnosis informed by prior clinical data is achieved (as suggested by Hall [0057] & [0106]).
Claim 11 recites substantially similar subject matter as claim 1, and is also rejected as above.
Claims 4 and 14
Neumann in view of Hall teaches the system of claim 1, and the combination further teaches wherein updating the neonatal profile includes determining a neonatal disorder and updating the neonatal profile as a function of the neonatal disorder (Neumann [0030], noting identifying a neonatal profile incudes identifying a neonatal disorder; see also [0017], noting that any method or step of the invention can be performed iteratively with any degree of repetition, indicating that the step of identifying a neonatal disorder when producing a neonatal profile may be repeated iteratively such that subsequent performances of the step serve to update the neonatal profile).
Claim 14 recites substantially similar subject matter as claim 4, and is also rejected as above.
Claims 5 and 15
Neumann in view of Hall teaches the system of claim 4, and the combination further teaches wherein determining the neonatal disorder comprises: receiving the neonatal bundle; training a disorder machine-learning model with a disorder training set correlating at least a neonatal enumeration and an infant organ system effect to a neonatal disorder; and outputting, using the disorder machine-learning model, the neonatal disorder (Neumann [0030]-[0031]).
Claim 15 recites substantially similar subject matter as claim 5, and is also rejected as above.
Claims 7 and 17
Neumann in view of Hall teaches the system of claim 4, and the combination further teaches wherein the computing device is further configured to determine a waste remedy as a function of the neonatal disorder (Neumann [0048], noting the system provides a nutritional recommendation such as a formula for bottle-feeding to reduce the effects of an aliment intolerance such as difficulty digesting a particular aliment (i.e. a neonatal disorder as listed in [0030]), equivalent to a waste remedy because digestive difficulties or intolerances are understood to be related to waste produced by a person).
Claim 17 recites substantially similar subject matter as claim 7, and is also rejected as above.
Claims 8 and 18
Neumann in view of Hall teaches the system of claim 4, and the combination further teaches wherein the computing device is further configured to classify a user to a cohort of users with similar neonatal disorders (Neumann [0030], noting identifying a neonatal profile can be achieved by performing classification techniques like K-nearest neighbors, K-means clustering, etc.; see also [0026]-[0029], [0052], noting descriptions of example classifiers like K-nearest neighbors that involve clustering similar data entries together. Taken together, these disclosures are considered to show that the system can classify a user into a disorder cluster/category (i.e. cohort) that includes other users with similar disorders).
Claim 18 recites substantially similar subject matter as claim 8, and is also rejected as above.
Claims 9 and 19
Neumann in view of Hall teaches the system of claim 1, and the combination further teaches wherein determining the aliment further comprises calculating a neonatal phase, wherein the neonatal phase comprises a cognitive phase (Neumann [0038], [0046]).
Claim 19 recites substantially similar subject matter as claim 9, and is also rejected as above.
Claims 10 and 20
Neumann in view of Hall teaches the system of claim 1, and the combination further teaches wherein generating the nourishment program further comprises: receiving a neonatal outcome; and generating the nourishment program as a function of the neonatal outcome using a nourishment machine-learning model (Neumann [0042]-[0043], claim 10).
Claim 20 recites substantially similar subject matter as claim 10, and is also rejected as above.
Claims 2 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann as applied to claims 1 and 11 above, and further in view of Bradley et al. (US 20240423535 A1).
Claims 2 and 12
Neumann in view of Hall teaches the system of claim 1, but the combination fails to explicitly disclose wherein an infant measurement comprises an oxygen saturation level of the infant. However, Bradley teaches an analogous system for monitoring infant sensor measurements and selecting appropriate interventions related to nourishment (Bradley abstract, noting monitoring infant biosignals and recommending an intervention to improve oral feeding of the infant) that includes monitoring oxygen saturation level of the infant (Bradley [0010], [0043], noting pulse oximeters that measure oxygen saturation level). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the non-limited sensor data types of the combination (e.g. as outlined in Neumann [0018]) to include oxygen saturation as in Bradley in order to incorporate data from an additional sensor type known to be useful in generating a recommended treatment/intervention regimen for an infant (as suggested by Bradley [0077]).
Claim 12 recites substantially similar subject matter as claim 2, and is also rejected as above.
Claims 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann and Hall as applied to claims 1 and 11 above, and further in view of Caflisch (Reference U on the PTO-892 mailed 12/19/2025).
Claims 3 and 13
Neumann in view of Hall teaches the system of claim 1, and the combination further teaches wherein obtaining the plurality of neonatal indicator elements comprises determining a neonatal indicator element comprising a sleep (Neumann [0018], noting the measurements can be obtained from a baby monitor with sleep tracking capabilities).
Though the present combination discloses obtaining the neonatal indicator elements via a baby monitor with sleep tracking capabilities, it fails to explicitly disclose determining a sleep pattern of the infant, and that the baby monitor includes a camera as well as capabilities for providing visual and quantitative insights into a baby’s sleep patterns. However, Caflisch teaches that common features of baby monitors include cameras and capabilities for providing visual and quantitative insights into a baby’s sleep patterns (Caflisch Pgs 3-4, noting “smart baby monitors often come equipped with high-definition cameras that provide live video feeds of the baby’s room” and “certain smart baby monitors may offer sleep tracking features that monitor the child’s sleep patterns and provide insights into their sleep quality and duration”; see also Pg 10, showing an example commercially available Safety 1st baby monitor that includes video and 24-hour history timeline). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the baby monitor of the combination to specifically include a camera and sleep pattern determination and display capabilities as in Caflisch because Caflisch shows that such features are common in commercially-available baby monitors and allow parents to have live video feeds of their baby as well as valuable sleep pattern information that facilitate optimizing the baby’s sleep routine (as suggested by Caflisch Pgs 2-4).
Claim 13 recites substantially similar subject matter as claim 3, and is also rejected as above.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann and Hall as applied to claims 1, 4, 11, and 14 above, and further in view of Hong Jeong (KR 20230065179 A).
Claims 6 and 16
Neumann in view of Hall teaches the system of claim 4, but the present combination fails to explicitly disclose wherein determining the neonatal disorder comprises: receiving the neonatal indicator element comprising a photograph of waste; training an image-based waste machine learning model with training data correlating a plurality of waste related photos to a plurality of neonatal disorders; outputting, using the image-based waste machine learning model, the neonatal disorder. However, Hong Jeong teaches that a method of diagnosing a neonatal disorder of an infant includes training an image-based waste machine learning model with training data correlating a plurality of waste related photos to a plurality of neonatal disorders (Hong Jeong top of Pgs 5-7, noting artificial intelligence models like a deep learning network are trained using labeled urine and feces images defined for different baby characteristics like disease (i.e. waste images correlated to disorders)) and then receiving a photograph of waste and inputting it to the trained image-based waste machine learning model to output a neonatal disorder (Hong Jeong abstract, Pgs 4-5, noting a user acquires a urine and feces (i.e. waste) image of a baby and inputs the obtained image into the pretrained AI model). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the AI-based neonatal disorder diagnosis process of the combination to specifically include training and using a waste image machine learning model to diagnose a neonatal disorder based on waste images as in Hong Jeong in order to expand the diagnostic capabilities of the system to include image analysis of waste products of the infant, which are important measures for understanding the health and developmental risks of the infant (as suggested by Hong Jeong Pg 2) and would thus provide improved insights into the infant’s potential disorders.
Claim 16 recites substantially similar subject matter as claim 6, and is also rejected as above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. MacDonald et al. (US 20120150153 A1) describes monitoring and managing nutritional uptake of an individual (including an infant) by monitoring gas sensor data and using it as a basis for adjusting a nourishment program.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684
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