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 .
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:
obtaining a plurality of neonatal indicator elements by determining a plurality of patterns based on the infant measurements 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.
wherein an infant measurement comprises an infant sensor measurement comprising 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 an 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.
Status of the Claims
Claims 1-20 are currently pending and have been considered below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/30/2024 is in accordance with the provisions of 37 CFR 1.97 and is considered by the Examiner.
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 certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. 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;
obtain a plurality of neonatal indicator elements by determining a plurality of patterns based on the infant measurements;
identify a neonatal bundle as a function of the plurality of neonatal indicator elements;
update a neonatal profile of an 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;
determine an aliment as a function of the updated neonatal profile; and
generate a nourishment program as a function of the aliment.
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 managing their personal behavior and/or interactions with others. For example, a clinician could receive a plurality of infant measurements (e.g. by directly observing the infant and noting measurements like height, weight, temperament, color, temperature, or others, by reading measurement information from an infant’s chart, by speaking with a parent of the infant, etc.) and use their medical expertise to determine patterns in the measurements and identify metrics related to a given disorder, organ system, or other health category (i.e. identify a neonatal bundle as a function of neonatal indicator elements). The clinician could also receive labeled training data correlating goals and recommendations to neonatal bundles and profiles (e.g. by organizing the data themselves, collaborating with a colleague to obtain the data, etc.) and use the training data to fit/train a model (e.g. a regression equation, decision tree, or other simple predictive model). The clinician could then test out the model by inputting goals and recommendations and receiving an appropriate neonatal profile as an output, and use their medical expertise to determine a food/aliment and appropriate nourishment program based on the profile (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 certain method of organizing human activity. 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 infant sensor measurement comprising 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 analyzing for patterns or indications of health issues.
Claims 3 and 13 recite determining a sleep pattern of an 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 communication to another user (e.g. in the form of a written report noting how many hours the baby tends to sleep per night).
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, training a disorder model with a disorder training set correlating at least a neonatal enumeration and an infant organ system effect to a neonatal disorder, and outputting the neonatal disorder. A clinician could achieve these functions by generating a training set and fitting/training a diagnosis model with the training set, then using the diagnosis model to assist them in determining a disorder.
Claims 6 and 16 specify that determining the neonatal disorder comprises receiving a photograph of waste, training an image-based waste model with training data correlating a plurality of waste related photos to a plurality of neonatal disorders, and outputting the neonatal disorder with the image-based waste model. A clinician could achieve these functions by gathering a set of photographs representative of different disorders, fitting/training an image-based waste model like a decision tree with the photographs, and traversing the decision tree to determine a disorder for a given 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 using a nourishment model, 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 selecting using a treatment selection model to help 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 and specifying that the neonatal model is a neonatal machine learning model. These additional elements, when considered in the context of each claim as a whole, merely serve to automate interactions that could occur by and among human actors (as described above), and thus amount to instructions to “apply” the abstract idea using generic computer components (see MPEP 2106.05(f)). For example, a clinician could collect and analyze data about an infant via fitting and using a predictive model, and the use of a computing device to perform these steps as well as specifying that the fitted model is an unspecified machine learning model merely digitizes and/or automates these otherwise-abstract functions such that they occur automatically in a digital environment. 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 infant sensor measurement comprising 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 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 that the disorder model and image-based waste model are machine-learning models, which amounts to instructions to “apply” the exception as explained for the similar machine learning model of the independent claims above because it merely digitizes/automates the otherwise-abstract fitting and use of diagnostic models.
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. 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 models, such that one of ordinary skill in the art would understand that any generic computing device and machine learning modelling method could be used to implement the invention.
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 Caflisch (Reference U on the accompanying PTO-892) 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 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 certain method of organizing human activity. 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 1-2, 4-5, 7-12, 14-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann (US 20220277827 A1) in view of Bradley et al. (US 20240423535 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 (Neumann [0018]-[0019], noting the system may receive inputs from sensors and/or users related to measurements of an infant like biological samples, sensor data, observations about skin tint, fussiness, movements, body temperature, etc.);
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 (Neumann [0020]);
update a neonatal profile of an 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 (Neumann [0023]-[0024], noting the trained neonatal machine learning model outputs a neonatal profile; 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 (Neuman [0033]); and
generate a nourishment program as a function of the aliment (Neumann [0041]).
In summary, Neumann teaches a computerized system for obtaining infant data, identifying a neonatal bundle associated with the infant data, producing a neonatal profile based on the bundle, and determining an aliment and associated nourishment program based on the profile. However, Neumann fails to explicitly disclose determining a plurality of patterns based on the infant measurements as in the instant claim. However, Bradley teaches an analogous system for monitoring infant data 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 determining a plurality of patterns in the monitored infant data as a basis for the intervention recommendations (Bradley [0032]-[0033], noting the system performs pattern identification on collected infant measurements related to different physiological processes like breathing and feeding to use as a basis for generating recommended feeding interventions; see also [0043], noting many other types of physiological data patterns that are informed by the monitored infant measurements). 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 analysis of infant measurements for the purpose of determining an appropriate nourishment program as in Neumann such that a plurality of patterns are determined based on the infant measurements to guide selection of an appropriate intervention as in Bradley in order to consider the adaptability or rigidity of the measurements over sustained periods of time rather than just instantaneous measurements, thereby improving the accuracy of the analysis and resulting in better intervention recommendations corresponding to the infant’s true health status measured over time (as suggested by Bradley [0078]-[0079]).
Claim 11 recites substantially similar subject matter as claim 1, and is also rejected as above.
Claims 2 and 12
Neumann in view of Bradley teaches the system of claim 1, and the combination further teaches wherein an infant measurement comprises an infant sensor measurement comprising an oxygen saturation level of the infant (Neumann [0018], noting the measurements can be obtained from a baby monitor or other device with sensing capabilities; see also Bradley [0010], [0043], noting pulse oximeters that measure oxygen saturation level).
Claim 12 recites substantially similar subject matter as claim 2, and is also rejected as above.
Claims 4 and 14
Neumann in view of Bradley 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 Bradley 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 Bradley 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 Bradley 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 Bradley 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 Bradley 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 3 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Neumann and Bradley as applied to claims 1 and 11 above, and further in view of Caflisch (Reference U on the accompanying PTO-892).
Claims 3 and 13
Neumann in view of Bradley 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 Bradley 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 Bradley 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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-2, 4-5, 7-12, 14-15, and 17-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-6, 10-11, 14-16, and 20 of U.S. Patent No. 11935642 B2 in view of Bradley et al. (US 20240423535 A1).
Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of the ‘642 patent recite every limitation of the independent claims, apart from receiving a plurality of infant measurements, obtaining a plurality of neonatal indicator elements by determining a plurality of patterns based on the infant measurements, identifying a neonatal bundle as a function of the plurality of neonatal indicator elements, and updating the neonatal profile rather than merely producing it. However, the disclosure of the ’642 patent shows that the neonatal indicator elements can comprise a plurality of received infant measurements (Col3 L51 – Col4 L61), as well as that any step of the method may be iteratively repeated (Col3 L24-50). 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 independent claims of the ‘642 patent to include receiving a plurality of infant measurements and a plurality of neonatal indicator elements for use as a basis of identifying a neonatal bundle as well as updating the neonatal profile in order to consider multiple infant parameters so that a more complete picture of the infant’s health can be established (as suggested by Col3 L51 – Col4 L61) as well as to update the neonatal profile as the system repeats certain processes with new information (as suggested by Col3 L24-50).
Additionally, Bradley teaches an analogous system for monitoring infant data 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 determining a plurality of patterns in the monitored infant data as a basis for the intervention recommendations (Bradley [0032]-[0033], noting the system performs pattern identification on collected infant measurements related to different physiological processes like breathing and feeding to use as a basis for generating recommended feeding interventions; see also [0043], noting many other types of physiological data patterns that are informed by the monitored infant measurements). 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 analysis of infant measurements for the purpose of determining an appropriate nourishment program as in the ‘642 patent such that a plurality of patterns are determined based on the infant measurements to guide selection of an appropriate intervention as in Bradley in order to consider the adaptability or rigidity of the measurements over sustained periods of time rather than just instantaneous measurements, thereby improving the accuracy of the analysis and resulting in better intervention recommendations corresponding to the infant’s true health status measured over time (as suggested by Bradley [0078]-[0079]).
Dependent claims 4-5, 8-10, 14-15, and 18-20 of the instant application recite subject matter that is patentably indistinct from that of dependent claims 4-6, 8, 10, 14-16, 18, and 20 of the ‘642 patent.
Claims 2 and 12 of the instant application further specify that an infant measurement comprises an infant sensor measurement comprising an oxygen saturation level of the infant, which is rendered obvious by the combination of the ‘962 patent and Bradley. Though the claims of the ‘962 patent do not specify an oxygen saturation level of the infant as a type of data received and analyzed by the system, the disclosure does note that measurements can be obtained from a baby monitor or other device with many types of sensing capabilities (Col4 L9-24), while Bradley establishes that monitored biosignals include oxygen saturation level ([0043]), rendering this subject matter obvious in view of the combination explained above.
Claims 7 and 17 of the instant application further specify determining a waste remedy as a function of the neonatal disorder, which is rendered obvious by the combination of the ‘962 patent and Bradley. Though the claims of the ‘962 patent do not specify determining a waste remedy as a function of the neonatal disorder, the disclosure does note in Col18 L26 – Col19 L5 that nourishment program recommendations can include things like 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 Col9 L60-65), considered equivalent to a waste remedy because digestive difficulties or intolerances are understood to be related to waste produced by a person. It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to specify that the nourishment program of the ‘642 claims could include a waste remedy based on a neonatal disorder as in the disclosure of the patent so that nourishment programs specific to digestive intolerances or issues can be recommended.
Claims 3 and 13 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 11 of U.S. Patent No. 11935642 B2 in view of Bradley and Caflisch (Reference U on the accompanying PTO-892).
Though the claims of the ‘962 patent do not specify that the neonatal indicator elements comprise a sleep pattern of an infant determined using a baby monitor comprising a camera and sleep tracking functionality and configured to provide visual and quantitative insights into a baby’s sleep patterns, the disclosure does note that measurements can be obtained from a baby monitor with sleep tracking capabilities (Col4 L14-24), showing that it would be obvious to specify that the neonatal indicator element of the independent claims of ‘642 comprises sleep indicators determined using a baby monitor with sleep tracking functionality. 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 further modify the baby monitor of the ‘642 patent 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).
Claims 6 and 16 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4, 11, and 14 of U.S. Patent No. 11935642 B2 in view of Bradley and Hong Jeong (KR 20230065179 A).
Though the claims of the ‘962 patent do not specify that 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, 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 ‘642 claims 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. De Oliveira Correa et al. (US 20230352146 A1) and Greer et al. (US 20250087359 A1) describe using machine learning models to evaluate infant-related measurement data and provide corresponding nourishment program or other care intervention recommendations.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid Merchant can be reached at 571-270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KAREN A HRANEK/ Primary Examiner, Art Unit 3684