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 .
Response to Amendment
The amendments filed 09/24/2025 have been entered. Claims 1 remain pending in the application.
Applicant’s amendments and arguments, with respect to claim rejections of claims 1-20 under
35 U.S.C 101 filed 07/08/2025 have been considered and are not persuasive. Therefore, the previous
rejections as set forth in the previous office action will be maintained.
The applicant argues that the amended claims are not directed to an abstract idea and instead recites patent-eligible subject matter. Applicant asserts that claim 1 has been amended to include “a kit comprising a set of predetermined food items” and further recites instructing the patient to consume a next food at a particular time, which applicant contends that it cannot be performed in the human mind, and therefore does not constitute a mental process. Applicant further argues that these limitations integrate any alleged abstract idea into a practical application by reciting real-world food items and a physical consumption schedule.
Applicant additionally argues that receiving an actual biomarker level from a glucose sensor connected to the patient after consumption of the food item further grounds the claim in a technological environment and ties the claimed process to a real-world medical context. Applicant contends that calibrating the model based on a comparison between estimated and actual biomarker levels improve the accuracy of patient-specific nutrition modeling and therefore constitutes a technological improvement.
With respect to claim 8 and 13, applicant argues that the amended limitation “automatically deliver insulin via the delivery device in accordance with the modified insulin therapy” integrates the claimed subject matter into a practical application by reciting a particular method of treatment. Applicant asserts that automatic insulin delivery based on model predictions and patient data transforms the claims into patent-eligible subject matter and overcomes the prior 101 rejections.
The examiner respectfully disagrees. As previously set forth in the previous Office Action, claim 1 recites determining an estimated biomarker level based on nutritional attributes of a food item, comparing the estimated biomarker level to an actual level, calibrating a model based on the comparison, and determining whether to repeat the training based on the comparison results. These steps constitute mental evaluations, comparisons and judgements that can be performed by a human mind using observation and reasoning and therefore recite a mental process. The amendments do not alter the fundamental character of the claim, which remains directed to the abstract idea under Step 2A, Prong one.
Applicant’s reliance on the recitation of include “a kit comprising a set of predetermined food items” is not persuasive. The kit merely supplies predetermined inputs to the abstract mental process and does not itself perform any technical operation or improve any underlying technology. The claim does not recite any non-conventional structure or function of the kit, nor it does not specify how the kit operates in a manner that improves food analysis, sensing or consumption. As such, the kit constitutes an insignificant extra solution activity that does not integrate the abstract idea into a practical application.
Similarly, instructing the patient to consume a next food item at a particular time merely schedules when data is collected. This limitation does not improve the functioning of a model, computer or sensor and does not impose meaningful limit on the abstract ideas. The step of instruction is an administrative or data-gathering activity that does not transform the claim into patent-eligible subject matter.
The limitation of receiving an actual biomarker level from a sensor likewise does not integrate the abstract idea into a practical application. As explained in the prior Office Action, this limitation merely confines the abstract idea into a particular technological environment and does not recite any improvement to the sensor technology or describe how the sensor operates in a non-conventional manner. The claim simply uses the sensor as a tool to collect data for the abstract evaluation and comparison process.
With respect to applicant’s argument regarding model calibration and training, the claim recites these steps at a high level of abstraction without specifying any particular algorithm, architecture, or technological improvement. The steps of training, calibrating, repeating training, and outputting a model are generic data processing operations that amount to “apply it” instructions and post-solution activity, as previously explained.
Regarding claim 8 and 13, applicant’s amendment to recites “automatically deliver insulin via the delivery device in accordance with the modified insulin therapy” does not render the claims patent-eligible. The claims do not recite any specific configuration or technical improvement of the delivery device, nor do they explain how the automatic delivery is achieved beyond applying the abstract determination of a modified therapy. The automatic delivery merely applies the abstract decision to a known medical treatment context and constitutes a field-of-use limitation and routine automation of a medical decision, which is insufficient to confer eligibility.
Accordingly, when considered individually or as an ordered combination, the additional elements of the amended claims do not amount to significantly more than the abstract idea itself. The amendments do not overcome the deficiencies identified in the prior Office Action and the claim rejections is therefore maintained.
Applicant’s amendments and arguments, with respect to claim rejections of claims 1-20 under
35 U.S.C 103 filed 07/08/2025 have been considered and are not persuasive. Therefore, the previous
rejections as set forth in the previous office action will be maintained.
The applicant argues that Hadley does not discloses or suggest the newly amended claim “a kit comprising a set of predetermined food items” as recited in claim 1. Applicant notes that Hadley relied on paragraph 25, which describes a nutrition database storing nutrition data extracted from nutrient data sources such as food or vitamins. Applicant contends that a nutrition database is not “a kit comprising a set of predetermined food items” and asserts that Hadley nowhere discloses or suggests such a kit.
Applicant further argues that because Hadley does not disclose a kit comprising predetermined edible food items, Hadley likewise does not discloses executing a training process to predict a patient nutrition state based on a predetermined food item of a set of predetermined food items include in the kit consumed by the patient within a time period. Applicant also asserts that Hadley does not discloses repeating the training process for one or more food items of the set by instructing the patient to consume the next food item at a particular time.
Applicant further argues that the rejection of independent claim 8 and 13 improperly relies on paragraph 73 of Hadley, which states that the nutrition twin module predicts a patient’s metabolic state based on patient data identifying food items consumed by the patient. Applicant asserts that Hadley therefore predicts metabolic or nutritional state based on food the patient has already consumed. Applicant contends that Hadley does not discloses or suggest applying a trained model to one or more food item profiles corresponding to a candidate food items for consumption by a patient to generate a predicted nutrition states for each food item profile, as recited in claims 8 and 13. According to applicant, Hadley analyze food that has already been consumed, whereas the claim require applying the trained model to food item profiles corresponding to candidate food items for consumption and selecting one or more food items to recommended based on predicted nutrition states.
Applicant further address that paragraph 88 of Hadley as allegedly teach “predicting future inputs”. Applicant argues that paragraph 88 merely states that the model may be used to predict a patient’s metabolic response to future input stimuli, and does not discloses applying the trained model to one or more food item profiles for consumption by a patient to generate predicted patient nutrition states for each food item profile.
The examiner respectfully disagrees. While Hadley alone may not expressly disclose a “kit” within the amended claim, Hadad expressly teaches a kit comprising a set of predetermined food items. Specifically, Hadad discloses a kit including pre-packaged meals containing known amounts of different foods and further instructions on when to consume each meal to generate a food baseline profile. Hadad teaches this at paragraph 69 “According to another aspect of the disclosure, a kit for generating a food baseline profile of a user is provided. The kit can comprise one or more pre-packaged meals containing known amounts of different foods. The kit can further comprise a set of instructions for instructing the user (1) on when to consume each of the one or more pre-packaged meals, and (2) on using one or more devices to monitor effects of the different foods on the user's body, to generate the food baseline profile of the user.”
Hadley then teaches training metabolic state models using patient data recorded throughout the day when the patient consumes food within a time period at paragraph 47 “In one implementation, each type of patient data 320 may be recorded instantaneously throughout the day when the patient consumes a food”, and further discloses the models trained to predicts a patient’s metabolic state given patient data as inputs at paragraph 50 “a first set of models trained to predict the patient's metabolic state given patient data as inputs”. When considered in view of Hadad, the food consumed by the patient corresponds to the predetermined food items from the kit that are consumed within a defined time period according to the provided instruction (Hadad at paragraph 69). Thus, the claimed training process to predict a patient nutrition state based on a predetermined food item of the set is met by training on patient data generated from controlled consumption of known food item supplied by the kit. The claim requires that the training be based on predetermined food items included in the kit consumed by the patient within a time period, which is expressly taught by the combination of Hadley and Hadad.
Hadley further teaches the repeating of the training process for one or more food items of the set by instructing the patient to consume the next food item at a particular time. Hadley discloses at paragraph 84 “The metabolic state model(s) are periodically re-trained based on the updated training dataset. This continuously improves the model and allows it to accurately predict future metabolic states for each patient based on their biosignal inputs. In comparison, the metabolic state model is trained or re-trained/modified on a training dataset comprising the information described above for a particular patient” which demonstrate that metabolic state models are periodically retrained based on updated training datasets comprising patient data. Hadley further discloses at paragraph 60 “Patient data includes nutrient data which is recorded by the patients as a list of foods which have been consumed by the patient over a time period” indicating that patient data includes recorded nutrition data identifying foods consumed by the patient over time. Importantly, Hadley discloses at paragraph 58 “One example of a food schedule may include a recommended food item or, more broadly, a category of food item and an amount of the food item to be consumed ... For example, a lifestyle adjustment may recommend a patient replace refined carbohydrates with wholegrain foods, while the food schedule includes a set of particular wholegrain foods” indicating generation of a food or meal schedule as a part of a recommendation, wherein the schedule includes food items and corresponding consumption parameters. A food schedule, by its ordinary meaning, constitutes an instruction identifying what food is to be consumed. Accordingly, when the patient consumes food items according to the schedule, newly recorded nutrition data is generated and incorporated into the updated training dataset, thereby repeating the training process following each instructed consumption according to the food schedule. Thus, Hadley teaches the repeating of the training process by instructing the patient to consume the next food item at a particular time, as claimed.
Furthermore, Hadley teaches the claimed process of applying a trained model to one or more food item profiles corresponding to a candidate food items for consumption by a patient to generate a predicted nutrition states for each food item profile. Hadley discloses at paragraph 81 “Additionally, the digital twin module 350 may implement one or more metabolic models to predict a patient's metabolic state that would result from the recommended nutrition, medication, or lifestyle changes included in a recommendation”, and paragraph 88 “Training both models in such a manner enables the patient health management platform 130 to predict a patient's metabolic response to future input stimuli (i.e., patient data 320 recorded by a patient in the future) for not just patients already included in the training dataset, but also new patients included in a holdout dataset ... Additionally, the model predicts a patient's response to input stimuli for each patient at different stages of his or her treatment because the platform maintains a history of a patient's changing metabolic condition. Finally, it allows for long-range precision prediction of the patient's metabolic state by using current and short-range predictions to inform longer-range predictions.” Hadley teaches applying one or more metabolic models to predict a patient’s metabolic state that would result from recommended nutrition included in a recommendation. Additionally, Hadley discloses that training the model enables prediction of a patient’s metabolic response to future input stimuli recorded by the patient. Under the broadest reasonable interpretation, “recommended nutrition” by Hadley encompasses recommended food items to be consumed by the patient and food schedule as expressly disclosed at paragraph 58, and corresponding to candidate food items as claimed. Accordingly, Hadley teaches the applying of the trained model to food items identified prior to consumption, which corresponds to the claimed process of applying a trained model to one or more food item profiles corresponding to a candidate food items for consumption by a patient to generate a predicted nutrition states for each food item profile.
Finally, Hadley discloses at paragraph 58 “the recommendation module 360 generates a recommendation for improving the patient's biosignals to more closely resemble those of the baseline metabolic state. The recommendation includes a set of objectives for a patient to complete to improve the patient's metabolic health. The set of objectives include a medication regimen or schedule, a food or meal schedule, ... One example of a food schedule may include a recommended food item or, more broadly, a category of food item and an amount of the food item to be consumed.” Hadley discloses a recommendation module that generated recommendation for improving a patient’s health. The recommendation includes objective such as a food item or meal schedule, and expressly includes recommended food items and corresponding amounts to be consumed. Because these recommendations are generated based on predicted metabolic state of nutrition state produced by the trained models, Hadley necessarily teaches selecting one or more food items to recommended based on predicted nutrition states, as claimed.
Therefore, Hadley and Hadad teaches the amended claim and the claim rejections is maintained.
Regarding amended claim 8 and 13, the examiner respectfully agrees that Hadley and Hadad does not teach the amended limitation “automatically deliver insulin via the delivery device in accordance with the modified insulin therapy”.
However, upon further consideration, new ground(s) of rejections have been raised (See Below.)
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.
Regarding claim 1,
Step 1:
Claim 1 recites a method, one of the four statutory categories of patentable subject matter.
Step 2A, Prong I:
Claim 1 further recites the limitations of:
“determine an estimated biomarker level based on a predetermined food item profile having a set of nutritional attributes for the predetermined food item and the model”. The determination of an estimated biomarker level based on nutrition attributes of food item is considered to be a mental process. A user can manually determine an estimated biomarker level based on looking at the nutrition attributes of the food item.
“calibrate the model based on comparing the estimated biomarker level to the actual biomarker level”. The calibration of a model based on comparing the estimated biomarker level to the actual level is considered to be a mental process. A user can manually compare the biomarker level and calibrate the model in accordance with such comparison result to maintain the accuracy.
“determine whether to repeat the training process for one or more other food items of a set of predetermined food item based on the comparison of the estimated biomarker level to the actual biomarker level, the one or more food items being different from the predetermined food item” The process of determining whether to repeat the training process based on a comparison result is considered a mental process. A user can manually compare the result of the estimated biomarker level with the actual biomarker level and mentally make the determination whether to perform the training of the model again.
Step 2A, Prong II:
Claim 1 recites the following additional elements:
“memory configured to store a model”, “processing circuitry communicatively coupled to the memory, wherein the processing circuitry is configured”. This additional element is high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
“a kit comprising a set of predetermined food items” This additional element recites an insignificant extra-solution of a well-known technique of mere data gathering as identified in MPEP 2106.05(g), and does not provide integration into a practical application.
“execute a training process for the model to predict a patient nutrition state of a patient based on a predetermined food item of the set of predetermined food item included in the kit consumed by the patient within a time period, wherein to execute the training process, the processing circuitry is configured to”. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The limitation recites the application of conventional machine learning training using a model to predict a result without provide any improvement over the conventional machine learning method, neural network configuration or improvement toward a computer hardware element.
“receive an actual biomarker level of the patient from a sensor connected to the patient after the patient consumes the food item within the time period” This additional element recites an insignificant extra-solution of a well-known technique of mere data gathering as identified in MPEP 2106.05(g), and does not provide integration into a practical application.
“responsive to determining the training process is to be repeated, repeat the training process for the one or more other food items of the set of predetermined food items included in the kit by instructing the patient to consume the next food item at a particular time”. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The limitation recites the conventional machine learning practice of repeatedly train a machine learning model by providing instruction to perform the retraining. The instructing of a patient is merely a condition to perform the retraining step but not an improvement toward a machine learning model, unconventional machine learning algorithm practice or improvement toward a computer hardware element.
“output the trained model” This additional element recites an insignificant extra-solution of a well-known technique as identified in MPEP 2106.05(g), and does not provide integration into a practical application.
Step 2B:
When considered individually or in combination, the additional limitations and elements of claim 1 does not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application. The additional elements of outlined in Step 2A performing functions as designed simply accomplishes execution of the abstract ideas.
The additional elements “memory configured to store a model”, “processing circuitry communicatively coupled to the memory, wherein the processing circuitry is configured” are high-level recitations of generic computer components used as a tool.
The additional element “a kit comprising a set of predetermined food items” further represents field of use and technological environment in which to apply a judicial exception as identified in MPEP 2106.05(h). The element recites the usage of a kit comprising a set of predetermined food items. The additional element merely limits the claims to the technological environment of a kit, without reciting how the kit works or functional implementation of the kit, therefore the claim does not amount to significantly more than the judicial exception itself, and cannot integrate a judicial exception into a practical application
The additional element “execute a training process for the model to predict a patient nutrition state of a patient based on a predetermined food item consumed by the patient within a time period” recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not amount to significantly more than the judicial exception for the same reasons discussed above.
The additional element “responsive to determining the training process is to be repeated, repeat the training process for the one or more other food items of the set of predetermined food items”. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not amount to significantly more than the judicial exception for the same reasons discussed above.
The additional elements “receive an actual biomarker level of the patient from a sensor connected to the patient after the patient consumes the food item within the time period” further recite a field of use and technological environment in which to apply a judicial exception as identified in MPEP 2106.05(h). The element recites the usage of a sensor connected to the patient to receive an actual biomarker level of the patient, thus merely limited the use of the exception to a particular technological environment which is using a sensor to measure biomarker level of a patient after the patient consumes the food item within the time period. The additional element merely limits the claims to the technological environment of sensor, without reciting how the sensor work, therefore the claim does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
The additional elements “output the trained model” further represents well-understood, routine, conventional activity as identified in MPEP 2106.05(d)(II)(3rd entry from the bottom: iv) indicate that a general purpose 'display' type step is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the ‘display’ type step is well-understood, routine, conventional activity is supported under Berkheimer option II.
In conclusions from above for the elements considered as a mental process, elements reciting an insignificant extra-solution of a well-known technique in MPEP 2106.05(g), elements as a well-understood, routine, conventional activity in MPEP 2106.05(d), elements reciting a field of use and technological environment in which to apply a judicial exception as identified in MPEP 2106.05(h), elements reciting generic computer components or elements used as a tool, and elements reciting a mere instruction to apply an exception in MPEP 2106.05(f) are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claim is ineligible.
Therefore, additional limitations of claim 1 do not amount to significantly more than the judicial exception.
Thus, claim 1 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Therefore, claim 1 is not patent eligible.
Regarding claim 2 depends on claim 1, thus the rejection of claim 1 is incorporated.
Claim 2 recites the element “wherein to calibrate the model, wherein the processing circuitry is further configured to register a confirmation that the patient consumed the predetermined food item within the time period” which further specifies the mental process of calibrating the model by configure the circuitry to register a confirmation that the patient consumed the food item within a time period. A user can manually observe a register that the patient consumed a predetermined food item within a time period. Therefore, claim 2 is not patent eligible.
Regarding claim 3 depends on claim 1, thus the rejection of claim 1 is incorporated.
Claim 3 recites the element “processing circuitry configured to apply the model or the trained model to a food item having a known food type and unknown nutrition attributes” which further specifies the additional element of claim 1. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. Therefore, claim 3 is not patent eligible.
Regarding claim 4 depends on claim 1, thus the rejection of claim 1 is incorporated.
Claim 4 recites the element “wherein to repeat the training process for the one or more food items, the processing circuitry is further configured to repeat the training process until an accuracy metric for the model is within a threshold value” which further specifies the additional element of claim 1. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. Therefore, claim 4 is not patent eligible.
Regarding claim 5 depends on claim 1, thus the rejection of claim 1 is incorporated.
Claim 5 recites the element “processing circuitry configured to output, for displaying, instructions for the patient, the instructions comprising a schedule for consuming each of the set of predetermined food items” which further specifies the additional element of claim 1. This additional element merely recites an insignificant extra-solution of a well-known technique as identified in MPEP 2106.05(g). This additional element also represents well-understood, routine, conventional activity as identified in MPEP 2106.05(d)(II)(3rd) entry from the bottom: iv) indicate that a general purpose 'display' type step is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the ‘display’ type step is well-understood, routine, conventional activity is supported under Berkheimer option II. Therefore, claim 5 is not patent eligible.
Regarding claim 6 depends on claim 1, thus the rejection of claim 1 is incorporated.
Claim 6 recites following additional elements:
“determine a first predicted patient nutrition state of the patient based in part on applying the trained model to a first food item profile corresponding to a first food item, wherein the corresponding food item profile comprises a set of nutritional attributes for the first food item and wherein the trained model determines, for the set of nutritional attributes, at least one estimated biomarker level expected to be within at least one desired range if the patient consumes the first food item”. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application.
“select the first food item to recommend for the patient based on the first predicted patient nutrition state”. The process of selecting the first food item to recommend for the patient based on the predicted patient nutrition state is considered to be a mental process. A user can select and recommend the food item to consume to a patient based on the predicted nutrition state of the patient.
“generate, for display, output data indicating the selected food item” This additional element merely recites an insignificant extra-solution of a well-known technique as identified in MPEP 2106.05(g). This additional element also represents well-understood, routine, conventional activity as identified in MPEP 2106.05(d)(II)(3rd entry from the bottom: iv) indicate that a general purpose 'display' type step is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the ‘display’ type step is well-understood, routine, conventional activity is supported under Berkheimer option II. Therefore, claim 5 is not patent eligible.
Therefore, claim 6 is not patent eligible.
Regarding claim 7 depends on claim 1, thus the rejection of claim 1 is incorporated.
Claim 7 recites following additional elements:
“apply the model to each food item profile of a second set of food item profiles to determine a predicted patient nutrition state for each food item of a second set of food items, wherein each predicted patient nutrition state of the patient comprises at least one estimated biomarker level based on a delivery device directing therapy delivery and the patient consuming a particular food item within the time period”. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application.
“identify, from amongst the second set of food items, a food item to recommend to the patient based on a comparison between a corresponding predicted patient nutrition state and a desired patient nutrition state of the patient”. The process of identify the food item to recommend for the patient based on the comparison of precited patient nutrition state and a desired nutrition state is considered to be a mental process. A user can identify and recommend the food item to consume to a patient based on the comparison result between a predicted and desired nutrition state of the patient.
Therefore, claim 7 is not patent eligible.
Regarding claim 8,
Step 1:
Claim 8 recites a system, one of the four statutory categories of patentable subject matter.
Step 2A, Prong I:
Claim 8 further recites the limitations of:
“select, without patient input, one or more food items associated with the one or more food item profiles to recommend for consumption by the patient based at least on predicted patient nutrition states of the patient corresponding to the one or more food item profiles, wherein the predicted patient nutrition state comprises at least one estimated biomarker level expected to be within at least one desired range if the patient consumes the selected food item”. The process of selecting one or more food items to recommend for the patient based on the predicted patient nutrition state correspond to food item profiles with an estimated biomarker levels is considered to be a mental process. A user can select and recommend the food item to consume to a patient based on the predicted nutrition state of the patient, and the estimated biomarker levels.
“determine whether the patient consumed the recommended one or more food items” The process of determining whether the patient consumed the recommended one or more food items is considered as a mental process. A user may manually perform verbal communication to determine whether the patient consumed the recommended food item.
“determine a modification to insulin therapy for the patient based on whether or not the patient consumed the recommended one or more food items and a prediction, ..., of an effect of consumption of the one or more food items on the at least one biomarker;” The process of determining determine a modification to insulin therapy information is considered as a mental process. A person can mentally determine a modification to insulin therapy for a patient as the determination to modify insulin therapy is merely a thought concept. For example, a doctor may determine that a patient have consumed a lot of sugary food items and the doctor, based on his knowledge and expertise in the field, may adjust the insulin therapy for the patient. The doctor does not require a machine to make up this determination but may devise it mentally based on their knowledges and expertise in the field.
Step 2A, Prong II:
“memory configured to store a trained model”, “processing circuitry communicatively coupled to the delivery device, wherein the processing circuitry is configured to”. This additional element is high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
“apply the trained model to one or more food item profiles corresponding to candidate food items for consumption by a patient to generate a predicted patient nutrition state for each of the one or more food item profiles, wherein the trained model generates each predicted patient nutrition state of a patient based on a set of nutritional attributes for a corresponding food item”. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The claim merely recites the application of convention machine learning practice, which is applying a trained model to perform a prediction on unseen data, without providing any improvements toward the machine learning algorithm, unconventional machine learning configuration or improvements toward computer hardware element.
“generate, for display, output data indicating the selected one or more food items”. This additional element recites an insignificant extra-solution of a well-known technique as identified in MPEP 2106.05(g), and does not provide integration into a practical application.
“... prediction, generated using the trained model ...” This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application. The limitation merely recites the conventional practice of machine learning model, which is applying a trained model to generate a prediction, without providing any improvements toward the machine learning algorithm, unconventional machine learning configuration or improvements toward computer hardware element.
“automatically deliver insulin via the delivery device in accordance with the modified insulin therapy” This additional element recites an insignificant extra-solution of a well-known technique as identified in MPEP 2106.05(g), and does not provide integration into a practical application.
Step 2B:
When considered individually or in combination, the additional limitations and elements of claim 8 does not amount to significantly more than the judicial exception for the same reasons discussed above as to why the additional limitations do not integrate the abstract idea into a practical application. The additional elements of outlined in Step 2A performing functions as designed simply accomplishes execution of the abstract ideas.
The additional elements “memory configured to store a trained model”, “processing circuitry communicatively coupled to the delivery device, wherein the processing circuitry is configured to” are high-level recitations of generic computer components used as a tool.
Additional elements “... prediction, generated using the trained model ...”, and “apply the trained model to one or more food item profiles to generate a predicted patient nutrition state for each of the one or more food item profiles, wherein the trained model generates each predicted patient nutrition state of a patient based on a set of nutritional attributes for a corresponding food item” recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and do not amount to significantly more than the judicial exception for the same reasons discussed above.
The additional element “generate, for display, output data indicating the selected one or more food items” further represents well-understood, routine, conventional activity. The Symantec, TLI, and OIP Techs. court decisions cited in MPEP 2106.05(d)(II)(3rd) entry from the bottom: iv) indicate that a general purpose 'display' type step is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the ‘display’ type step is well-understood, routine, conventional activity is supported under Berkheimer option II.
The additional element “automatically deliver insulin via the delivery device in accordance with the modified insulin therapy” further recite a field of use and technological environment in which to apply a judicial exception as identified in MPEP 2106.05(h). The element recites the usage of the delivery device to automatically deliver insulin, thus merely limited the use of the exception to a particular technological environment which is using a delivery device to automatically deliver insulin with the modified insulin therapy. The additional element merely limits the claims to the technological environment of the delivery device, without reciting how the delivery device work, or the automated mechanism to allow the delivery device to deliver insulin. Therefore, the claim does not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application
In conclusions from above for the elements considered as a mental process, elements reciting generic computer components or elements used as a tool, elements reciting an insignificant extra-solution of a well-known technique in MPEP 2106.05(g), elements as a well-understood, routine, conventional activity in MPEP 2106.05(d), elements reciting a field of use and technological environment in which to apply a judicial exception as identified in MPEP 2106.05(h), and elements reciting a mere instruction to apply an exception in MPEP 2106.05(f) are carried over and do not provide significantly more than the abstract idea. Looking at the limitations in combination and the claims as a whole does not change this conclusion and the claim is ineligible.
Therefore, additional limitations of claim 8 do not amount to significantly more than the judicial exception.
Thus, claim 8 recites abstract ideas with additional elements rendered at a high level of generality resulting in claims that do not integrate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Therefore, claim 8 is not patent eligible.
Regarding claim 9 depends on claim 8, thus the rejection of claim 8 is incorporated.
Claim 9 recites the element “wherein to select the food item, the processing circuitry is further configured to: identify, from amongst the set of food items, the selected one or more food items to recommend based on a comparison between the predicted patient nutrition state and a desired patient nutrition state.” which further specifies mental process of claim 8. The selection of food item by identifying from amongst the set of food items to recommend to patient is considered to be a mental process. A user can manually identify and select a food item to recommend to a patient. The usage of the processing circuitry is a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application. Therefore, claim 9 is not patent eligible.
Regarding claim 10 depends on claim 8, thus the rejection of claim 8 is incorporated.
Claim 10 recites the element:
“train the trained model by, for a baseline food item of a set of predetermined baseline food items, applying a model to a corresponding baseline food item profile having a predetermined set of nutritional attributes for that baseline food item” which further specifies the additional element of claim 8. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application.
“wherein the processing circuitry is further configured to train the model for each baseline food item in the set of predetermined baseline food items or until an accuracy metric is within a threshold value” which further specifies the additional element of claim 8. This additional element recites a mere instruction to apply an exception with a recitation of the words "apply it" (or an equivalent) as identified in MPEP 2106.05(f), and does not provide integration into a practical application.
Therefore, claim 10 is not patent eligible.
Regarding claim 11 depends on claim 10, thus the rejection of claim 10 is incorporated.
Claim 11 recites the element:
“determine an estimated biomarker level based on the patient consuming a particular baseline food item of the set of predetermined baseline food items” which further specifies the additional element of claim 10. The determination of an estimated biomarker level based on the patient consuming a food item is considered to be a mental process. A user can manually determine an estimated biomarker level based on the patient consuming a food item
“calibrate the model based on comparing the estimated biomarker level to an actual biomarker level after the patient consumes the particular baseline food item within a time period”. The process of calibrating the model by comparing the biomarker level after the patient consumes the food item within a time period is considered to be a mental process A user can manually compare the estimated biomarker level to an actual biomarker level after the patient consumes the food item, thus calibrate the model according to the comparison result.
Therefore, claim 11 is not patent eligible.
Regarding claim 12 depends on claim 8, thus the rejection of claim 8 is incorporated.
Claim 12 recites the element
“a delivery device configured to execute therapy information for directing therapy delivery for the patient” which further specifies the additional element of claim 8. This additional element is a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
“wherein the processing circuitry is configured to select, without the patient input, the one or more food items to recommend for the consumption by the patient based on one or more predicted patient nutrition states of the patient corresponding to one or more food item profiles, wherein the predicted patient nutrition state comprises at least one estimated biomarker level expected to be within at least one desired range if the delivery device executes the therapy information and the patient consumes the selected food item”. The process of selecting one or more food items to recommend for the patient based on the predicted patient nutrition state correspond to food item profiles with an estimated biomarker levels is considered to be a mental process. A user can select and recommend the food item to consume to a patient based on the predicted nutrition state of the patient, and the estimated biomarker levels. The usage of the processing circuitry is a high-level recitation of generic computer components used as a tool, and does not provide integration into a practical application.
Therefore, claim 12 is not patent eligible.
Regarding claim 13 recites a method, one of the four statutory categories of patentable subject matter. Claim 13 is further similarly rejected based on the same rationale as claim 8 because claim 13 recites limitation similar to claim 8 above.
Regarding claim 14 depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 12 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 15, depends on claim 14, thus the rejection of claim 14 is incorporated. The applicant is further directed to the rejections of claim 13 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 16, depends on claim 14, thus the rejection of claim 14 is incorporated. The applicant is further directed to the rejections of claim 13 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 17, depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 7 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 18, depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 1 and 4 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 19, depends on claim 18, thus the rejection of claim 18 is incorporated. The applicant is further directed to the rejections of claim 1 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 20, depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 1 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Claim Rejections - 35 USC § 103
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.
Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable by Hadley et.al (US 20220061710 A1) in view of Hadad et.al (US 20190295440 A1).
Regarding claim 1,
Hadley teaches the limitation “memory configured to store a model” (paragraph 0029 “The memory 215 holds instructions and data used by the processor 205”. Hadley discloses a patient health management platform that implement a machine-learned metabolic model to generate a prediction of a patient's biomarker information wherein the model is stored by a memory.)
Hadley teaches the limitation “processing circuitry communicatively coupled to the memory, wherein the processing circuitry is configured to” (paragraph 0028 “...Coupled to the chipset 210 is volatile memory...” and paragraph 0032 “In one embodiment, program modules are stored on the storage device 230, loaded into the memory 215, and executed by the processor 205”. Hadley discloses within the model, the memory is coupled to the chipset and program modules are stored on the storage device loaded into memory and then executed by the processor, thus make the embodiment become a processing circuitry to execute the program.)
Hadley teaches the training to provide prediction aspect within the limitation “execute a training process for the model to predict a patient nutrition state of a patient based on a predetermined food of the set of predetermined food items included in the kit item consumed by the patient within a time period, wherein to execute the training process, the processing circuitry is further configured to” (paragraph 47 “In one implementation, each type of patient data 320 may be recorded instantaneously throughout the day when the patient consumes a food”, paragraph 50 “a first set of models trained to predict the patient's metabolic state given patient data as inputs”, and paragraph 51 “Based on nutrition data, medication data, symptom data, lifestyle data, and supplemental nutrition information retrieved by the nutrient data module 340, the digital twin module 350 generates a prediction of the patient's metabolic state (herein referred to as a patient's “predicted metabolic state”). The digital twin module 350 implements one or more machine-learned, metabolic models to analyze the patient data 320 recorded over a given time period to generate a prediction of the patient's metabolic state for that time period. Accordingly, the prediction of the patient's metabolic state is a function of a large number of metabolic factors recorded in the patient data 320 (e.g., fasting blood glucose, sleep, and exercise) and a nutrition profile (e.g., macronutrients, micronutrients, biota nutrients)” Hadley discloses a training process comprise training metabolic state models using patient data recorded throughout the day when the patient consumes food within a time period and further discloses the models are trained to predicts a patient’s metabolic state given patient data as inputs. The training is handled by the patient health management platform, which utilizes machine-learned, metabolic models to analyze the patient data recorded over a given time period to generate a prediction of the patient's metabolic state for that time period, thus the teaching by Hadley recites the training process for a model. Accordingly, when Hadley is considered in view of Hadad, the food consumption by the patient that is used to train the metabolic model may comprises consumption of predetermined food items from the kit disclosed by Hadad. The motivation to combine the teachings is below.)
Hadley teaches a part of the limitation “... a predetermined food item profile having a set of nutritional attributes for the predetermined food item and the model” (paragraph 0060 “Patient data includes nutrient data which is recorded by the patients as a list of foods which have been consumed by the patient over a time period. While the impact of a food item by itself on a patient's metabolic state may not be known, the impact of particular macronutrients, micronutrients, and biota nutrients associated with the food item on a patient's metabolic state is known”. Hadley discloses the patient data includes a list of foods which have been consumed by the patient over a time period, as well as the nutrition data associated with the food and their impact on a patient’s metabolic state is known. This list of food of patient data suggests a predetermined food item profile.)
Hadley teaches the limitation “receive an actual biomarker level of the patient from a sensor connected to the patient after the patient consumes the food item within the time period” (paragraph 0024 “... biological data recorded from a plurality of sources including ... food or diet-related data recorded by the patient”, and paragraph 0052 “... generate a true representation of a patient's metabolic state (herein referred to as a “true metabolic state”) based on the biological data 310 recorded for a time period... Accordingly, given biological data 310 as an input, the metabolic model is further trained to output a patient's actual biological response (e.g., a measured insulin sensitivity or change in glucose in response to consuming a food or taking a medication))”, and paragraph 0059 “Biological data describes data manually recorded by wearable sensors or measured based on lab tests before being communicated to the platform 130”. Hadley discloses the system of the model to utilize biological data recorded from a plurality of sources including food consumption data within a time period recorded by a patient, thus based on such information, the system can generate a true representation of a patient's metabolic state (herein referred to as a “true metabolic state”). Accordingly, the model is trained to output a patient's biological response (e.g., a measured insulin sensitivity or change in glucose in response to consuming a food or taking a medication). These biological responses, which represent a metabolic state of the patient, comprise responses such as change in glucose level, which corresponding to the actual biomarker level within the claim, thus suggest the system is configured to be able to determine the true biomarker level representing by the true metabolic state after inputting food consumption data within a time period recorded by a patient. Biological data may be data manually recorded by wearable sensors.) Hadley teaches the limitation “calibrate the model based on comparing the estimated biomarker level to the actual biomarker level” (paragraph 0053 “In some embodiments, digital twin module 350 communicates both the predicted metabolic state and the true metabolic state to the timeliness, accuracy, and completeness (TAC) manager 370. The TAC manager 370 compares the predicted metabolic state and the true metabolic state to determine whether the two states are within a threshold level of similarity to each other”, and paragraph 0054 “... Based on the inconsistency, or inconsistencies, between the true metabolic state and the predicted metabolic state, the TAC manager 370 identifies one or more potential errors in the recorded patient data which may have contributed to the one or more inconsistencies and generates notifications to the patient device 110 for presentation to the patient”. Hadley discloses the digital twin module to compare the predicted state and the true metabolic state to the timeliness, accuracy, and completeness manager component (TAC), wherein the manager component determines if the two states are similar and if inconsistencies are found, an error of recorded data is identified and the patient is notified of the error, thus allowing them to correct the data to calibrate the model. While Hadley does not explicitly teach the estimated biomarker level, Hadley discloses the predicted metabolic state, which under the broadest reasonable interpretation, encompass the outputs by Hadad that corresponding to the estimated biomarker level within the claim, since Hadley defines the predicted metabolic state as a model output representing a patient’s physiological condition derived from biosignals, which necessarily includes biomarker values predicted by a model, such as the neural network output as taught by Hadad below. The motivation to combine the teaching is below.)
Hadley teaches the limitation "determine whether to repeat the training process for one or more other food items of a set of predetermined food items based on the comparison of the estimated biomarker level to the actual biomarker level, the one or more other food items being different from the predetermined food item" (paragraph 60 “Patient data includes nutrient data which is recorded by the patients as a list of foods which have been consumed by the patient over a time period”, Paragraph 61 “The platform 130 compares 440 the predicted metabolic state with the true metabolic state to determine whether the two states match, or are within a threshold level of similarity. If the two metabolic states are not within the threshold level of similarity, the platform 130 determines 450 one or more inconsistencies in the patient's recording of their patient data, which may have caused the predicted metabolic state to differ from the true metabolic state. The platform 130 communicates the inconsistency back to a patient device (i.e., patient device 110). Upon receiving the inconsistency, the patient device 110 presents a user interface notifying the patient of the inconsistency and enabling the patient to correct the inconsistency. The platform 130 receives the updated patient data and determines 430 an updated predicted metabolic state based on the updated patient data.”, and Paragraph 84 “For example, after a metabolic state model determines an aspect of a patient's true metabolic state for a time period, the digital twin module 350 may update a training dataset with the determined true metabolic state and a plurality of biosignals recorded during the time period that contributed to the true metabolic state. The metabolic state model(s) are periodically re-trained based on the updated training dataset. This continuously improves the model and allows it to accurately predict future metabolic states for each patient based on their biosignal inputs. In comparison, the metabolic state model is trained or re-trained/modified on a training dataset comprising the information described above for a particular patient.” Hadley discloses the platform compare the predicted metabolic state (under the broadest reasonable interpretation, Hadley’s predicted metabolic state constitutes a model-derived representation of a patient’s physiological condition and encompasses biomarker level value as output by the neural network taught by Hadad below) with the true metabolic state to determine whether the two states match, or are within a threshold level of similarity to detect the inconsistency, which is then communicate back to the patient device and enabling the patient to correct the inconsistency, thus obtain updated patient data. Upon receive updated patient data, the digital twin module may update its training dataset to periodically re-train the metabolic state model(s). Thus, a comparison process is performed to obtain updated patient data for retraining the model. Under the broadest reasonable interpretation and as understood by one of ordinary skilled in the art, the retraining my be performed upon the patient updates the patient data with correct nutrition data from consuming a food, thus imply that the patient may consume another food item within the list of food, thus the model can be retrained to perform the metabolic state prediction on that food. Accordingly, Hadley’s retraining process is based on the comparison which corresponds to the repeating of the training process based on comparison within the claim.)
Hadley teaches the limitation "responsive to determining that the training process is to be repeated, repeat the training process for the one or more other food items of the set of predetermined food items included in the kit by instructing the patient to consume the next food item at a particular time" (paragraph 84 “The metabolic state model(s) are periodically re-trained based on the updated training dataset. This continuously improves the model and allows it to accurately predict future metabolic states for each patient based on their biosignal inputs. In comparison, the metabolic state model is trained or re-trained/modified on a training dataset comprising the information described above for a particular patient” Hadley teaches that metabolic state models are periodically retrained based on updated training datasets comprising patient data. Hadley further discloses at paragraph 60 “Patient data includes nutrient data which is recorded by the patients as a list of foods which have been consumed by the patient over a time period” indicating that patient data includes recorded nutrition data identifying foods consumed by the patient over time. Hadley also discloses at paragraph 58 “One example of a food schedule may include a recommended food item or, more broadly, a category of food item and an amount of the food item to be consumed ... For example, a lifestyle adjustment may recommend a patient replace refined carbohydrates with wholegrain foods, while the food schedule includes a set of particular wholegrain foods” indicating generation of a food or meal schedule as a part of a recommendation, wherein the schedule includes food items and corresponding consumption parameters. A food schedule, by its ordinary meaning, constitutes an instruction identifying what food is to be consumed. Accordingly, when the patient consumes food items according to the schedule, newly recorded nutrition data is generated and incorporated into the updated training dataset, thereby repeating the training process following each instructed consumption according to the food schedule. The food schedule may be configured with the food kit as disclosed by Hadad below. Thus, Hadley teaches the repeating of the training process by instructing the patient to consume the next food item at a particular time, as claimed.)
Hadley teaches the limitation “output the trained model” (paragraph 0083 “Outputs, for example, include the actual biological data 310, which represents biosignals characterizing a patient's metabolic health (i.e., blood glucose level, blood pressure, and cholesterol)”. Hadley discloses the output of the trained machine learning model to include biological data that characterizing a patient’s metabolic health.)
Hadley does not teach the limitation “a kit comprising a set of predetermined food items”. However, Hadad teaches this limitation (paragraph 69 “According to another aspect of the disclosure, a kit for generating a food baseline profile of a user is provided. The kit can comprise one or more pre-packaged meals containing known amounts of different foods. The kit can further comprise a set of instructions for instructing the user (1) on when to consume each of the one or more pre-packaged meals, and (2) on using one or more devices to monitor effects of the different foods on the user's body, to generate the food baseline profile of the user.” Hadad discloses a kit for generating a food baseline profile of a user is provided. The kit can comprise one or more pre-packaged meals containing known amounts of different foods. The kit can further comprise a set of instructions for instructing the user on when to consume each of the one or more pre-packaged meal and on using one or more devices to monitor effects of the different foods on the user's body.)
Hadley does not teach part of the limitation “determine an estimated biomarker level based on a predetermined food item profile ...”. However, Hadad teaches this part of limitation (paragraph 0061 “According to an aspect of the disclosure, a method for generating a food baseline profile of a user is provided. The method for generating a food baseline profile of a user can comprise monitoring effects of different foods on the user's body as the user consumes one or more pre-packaged meals containing known amounts of the foods over a time period”, paragraph 0259 “... The ANN can be an adaptive system that is configured to change its structure (e.g., the connections among the units) based on external or internal information that flows through the network during the learning phase. The ANN can be used to model complex relationships between inputs and outputs or to find patterns in data, where the dependency between the inputs and the outputs cannot be easily attained. In some examples, the complex relationships can include how foods affect the user's body in a multitude of biomarkers”, and paragraph 261 “The insights and recommendation engine 230 can use data collected and analyzed by the food analysis system 210 and the device/data hub 220 to generate at least one decision tree learning algorithm ... At least one decision tree learning algorithm can also be used to predict how foods that the user has never consumed or other lifestyle events that may affect the user's biomarkers (e.g. glucose level)” Hadad discloses a system for providing personalized food and health management recommendations, wherein a food baseline profile of a user for effects of foods on a user is provided, and providing a neural network to model complex relationships between inputs and outputs that demonstrate how foods affect the user's body in a multitude of biomarkers, wherein the consumed food may be one or more pre-packaged meals containing known amounts of the foods over a time period. The neural network may be a decision tree model to predict how foods that the user has never consumed affect the user's biomarkers. The output of the neural network model by Hadad corresponds the estimated biomarker level within the claim.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teaching of A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's information by Hadley with the teaching of kit of predetermined food and using neural network model to obtain estimated biomarker level by Hadad. The motivation to do so is referred to in Hadad’s disclosure (paragraph 0029 “the method for mapping foods can further comprise utilizing the food ontology for one or more of the following purposes: ... (1) estimate nutritional values for recipes and/or restaurant dishes; (2) provide food and health recommendations to a user, and to gain an understanding of the user's taste profile; (3) construct food logs; (4) generate missing elementary foods from existing packaged foods; (5) generate more accurate labels of food characteristics; (6) analysis of food costs; (7) model effects of cooking on nutritional values and estimate degree of food processing; (8) improved image classification or computer vision classification of foods...”, paragraph 0195 “The analyzer's learning algorithm can find one or more patterns in the training set between the input data and the target, and generate an improved machine learning algorithm that can capture one or more patterns. The analyzer can be trained with more than one training sets, respectively, to generate more than one improved machine learning algorithms for an identical characteristic”, and paragraph 305 “A calibration kit can be used to optimize the platform 200 to a user's physiological responsiveness to different foods... As users can respond differently to the same food, and a wearable and/or medical device can have different compatibility to different users, the calibration kit may be used to set a food baseline for all users”. Hadad expressly teaches using neural network-based modeling to estimate nutritional and biomarker effect of predetermined foods in order to provide personalized food and health recommendation and improve understanding of a user’s physiological profile. Hadad further teaches that its learning algorithms identify pattens between food inputs and physiological targets to generate improved predictive models, which would have predictably improved the accuracy of personalization of Hadley’s metabolic state prediction. Hadad also teaches the benefit of the food kit, which helps create a food baseline for all users despite each user can have different compatibility to the same food or device. A person of ordinary skilled in the art would have been motivated to incorporate Hadad’s kit of predetermined food item and neural-network-based food and biomarker modeling into Hadley’s metabolic state prediction platform because Hadley relies on patient food consumption and biosignal data to retrain and refine its metabolic models, and Hadad expressly teaches using predetermined food inputs from the kit to reduce variability in food inputs as well as using learned food-biomarker relationships to improve personalization, calibration, and predictive accuracy across users, thereby predictably improving Hadley’s metabolic state predictions given restricted input and improved neural network model generalization.
Regarding claim 2 depends on claim 1, thus the rejection of claim 1 is incorporated.
Hadley teaches the limitation “The system of claim 1, wherein to calibrate the model, wherein the processing circuitry is further configured to register a confirmation that the patient consumed the predetermined food item within the time period” (paragraph 0060 “Patient data includes nutrient data which is recorded by the patients as a list of foods which have been consumed by the patient over a time period”. Hadley discloses the system to utilize patient data, which includes nutrient within food items data consumption within a time period data recorded by the patient.)
Regarding claim 3 depends on claim 1, thus the rejection of claim 1 is incorporated.
Hadad teaches the limitation “The system of claim 1 further comprising processing circuitry configured to apply the model or the trained model to a food item having a known food type and unknown nutrition attributes” (paragraph 0229 “The food analysis system 210 can estimate unknown nutrients from at least one consumer packaged food, at least one restaurant menu item, or at least one food recipe”. Hadad discloses the food analysis system can estimate unknown nutrients from at least one consumer packaged food or food recipe, which contain known food type.)
Regarding claim 4 depends on claim 1, thus the rejection of claim 1 is incorporated.
Hadley teaches the limitation “The system of claim 1, wherein to repeat the training process for the one or more food items, the processing circuitry is further configured to repeat the training process until an accuracy metric for the model is within a threshold value” (paragraph 0061 “... If the two metabolic states are not within the threshold level of similarity, the platform 130 determines 450 one or more inconsistencies in the patient's recording of their patient data, which may have caused the predicted metabolic state to differ from the true metabolic state ... The platform 130 receives the updated patient data and determines 430 an updated predicted metabolic state based on the updated patient data”, and paragraph 0078 “The digital twin module 350 implements a combination of machine-learned models that are iteratively trained to predict a response of the human body based on each patient's current metabolic state and a set of inputs (e.g., recorded patient data, sensor data, and biological data”. Hadley discloses the comparison of the two metabolic states between a predicted state and the true state to determine if they are within the threshold level of similarity. If there are consistencies, the patient data is updated and an updated predicted metabolic state is obtained for another comparison with the true state, whereas the module with the implementation of the machine learning module is iteratively trained, thus the comparison can be occurred iteratively until the threshold level of similarity is met.)
Regarding claim 5 depends on claim 1, thus the rejection of claim 1 is incorporated.
Hadley teaches the limitation “The system of claim 1 further comprising processing circuitry configured to output, for displaying, instructions for the patient, the instructions comprising a schedule for consuming each of the set of predetermined food items” (paragraph 0021 “ Application 115 provides a user interface (herein referred to as a “patient dashboard”) that is displayed on a screen”, and paragraph 0058 “The recommendation includes a set of objectives for a patient to complete to improve the patient's metabolic health. The set of objectives include a medication regimen or schedule, a food or meal schedule, micronutrient and biota nutrient supplements, one or more lifestyle adjustments, or a combination thereof”. Hadley discloses the application provides a user interface that is displayed on a screen. The system also provides the recommendation, which includes a set of objectives for a patient to complete such as a food or meal schedule, wherein this food or meal suggest the set of predetermined food items within the claim.)
Regarding claim 6 depends on claim 1, thus the rejection of claim 1 is incorporated.
Hadley teaches a part of the limitation “determine a first predicted patient nutrition state of the patient based in part on applying the trained model to a first food item profile corresponding to a first food item, ...” (paragraph 81 “Additionally, the digital twin module 350 may implement one or more metabolic models to predict a patient's metabolic state that would result from the recommended nutrition, medication, or lifestyle changes included in a recommendation”, and paragraph 88 “Training both models in such a manner enables the patient health management platform 130 to predict a patient's metabolic response to future input stimuli (i.e., patient data 320 recorded by a patient in the future) for not just patients already included in the training dataset, but also new patients included in a holdout dataset ... Additionally, the model predicts a patient's response to input stimuli for each patient at different stages of his or her treatment because the platform maintains a history of a patient's changing metabolic condition. Finally, it allows for long-range precision prediction of the patient's metabolic state by using current and short-range predictions to inform longer-range predictions.” Hadley teaches applying one or more metabolic models to predict a patient’s metabolic state that would result from recommended nutrition included in a recommendation. Additionally, Hadley discloses that training the model enables prediction of a patient’s metabolic response to future input stimuli recorded by the patient. Under the broadest reasonable interpretation, “recommended nutrition” by Hadley encompasses recommended food items to be consumed by the patient and food schedule, and corresponding to a first food item as claimed. Accordingly, Hadley teaches the applying of the trained model to food items identified prior to consumption, which corresponds to the claimed process of applying a trained model to one or more food item profiles corresponding to a candidate food items for consumption by a patient to generate a predicted nutrition states for each food item profile.
Hadad teaches the part of the limitation “wherein the corresponding food item profile comprises a set of nutritional attributes for the first food item and wherein the trained model determines, for the set of nutritional attributes, at least one estimated biomarker level expected to be within at least one desired range if the patient consumes the first food item” (paragraph 61 “a method for generating a food baseline profile of a user is provided. The method ... comprise monitoring effects of different foods on the user's body as the user consumes ... known amounts of the foods over a time period”, and paragraph 257 “... A personalized digital signature can be an algorithm to estimate the response of a specific biomarker of the user to consumption of a specific food item...”. Hadad discloses a food baseline profile is obtained to monitor the effect of foods on the user’s body, and the estimation of the response of a specific biomarker of the user after consume the food item. Thus, as a combination with Hadley, the user obtains a system and module that utilize machine learning model to determine a predicted patient’s metabolic state based on the data from a food baseline profile of the patient which contain food item information and nutrition attributes corresponding to such food item. The trained model can further include the algorithm to estimate the response of a specific biomarker data of the user if the user consumes the food item within a time period based on the recommendation.)
Hadley teaches the limitation “select the first food item to recommend for the patient based on the first predicted patient nutrition state” (paragraph 0101 “Based on the determined adjustments, the recommendation module 360 generates a recommendation for improving the patient's biosignals to more closely resemble those of the baseline metabolic state. The recommendation includes a set of objectives for a patient to complete to improve the patient's metabolic health. The set of objectives include a medication regimen or schedule, a food or meal schedule”. Hadley discloses after obtaining the comparison result of the patient predicted metabolic state and true state, the recommendation module generates a recommendation for patient, which include includes a set of objectives for a patient to complete such as a food schedule containing recommended food item.)
Hadley teaches the limitation “generate, for display, output data indicating the selected food item” (paragraph 0058 “One example of a food schedule may include a recommended food item or, more broadly, a category of food item and an amount of the food item to be consumed”. Hadley discloses the recommendation may be a food schedule include a recommended food item. The recommendation can be displayed through an interface as disclosed above.)
Regarding claim 7 depends on claim 1, thus the rejection of claim 1 is incorporated.
Hadley teaches a part of the limitation “apply the model to each food item profile of a second set of food item profiles to determine a predicted patient nutrition state for each food item of a second set of food items ...” (paragraph 81 “Additionally, the digital twin module 350 may implement one or more metabolic models to predict a patient's metabolic state that would result from the recommended nutrition, medication, or lifestyle changes included in a recommendation”, and paragraph 88 “Training both models in such a manner enables the patient health management platform 130 to predict a patient's metabolic response to future input stimuli (i.e., patient data 320 recorded by a patient in the future) for not just patients already included in the training dataset, but also new patients included in a holdout dataset ... Additionally, the model predicts a patient's response to input stimuli for each patient at different stages of his or her treatment because the platform maintains a history of a patient's changing metabolic condition. Finally, it allows for long-range precision prediction of the patient's metabolic state by using current and short-range predictions to inform longer-range predictions.” Hadley teaches applying one or more metabolic models to predict a patient’s metabolic state that would result from recommended nutrition included in a recommendation. Additionally, Hadley discloses that training the model enables prediction of a patient’s metabolic response to future input stimuli recorded by the patient. Under the broadest reasonable interpretation, “recommended nutrition” by Hadley encompasses recommended food items to be consumed by the patient and food schedule, and corresponding to a second set of food items as claimed. Accordingly, Hadley teaches the applying of the trained model to food items identified prior to consumption, which corresponds to the claimed process of applying a trained model to one or more food item profiles corresponding to a candidate food items for consumption by a patient to generate a predicted nutrition states for each food item profile.
Hadad teaches the part of the limitation “... wherein each predicted patient nutrition state of the patient comprises at least one estimated biomarker level based on a delivery device directing therapy delivery and the patient consuming a particular food item within the time period” (paragraph 61 “a method for generating a food baseline profile of a user is provided. The method ... comprise monitoring effects of different foods on the user's body as the user consumes ... known amounts of the foods over a time period”, paragraph 257 “the insights and recommendation engine 230 can generate one or more personalized digital signatures unique for the user. A personalized digital signature can be an algorithm to estimate the response of a specific biomarker of the user to consumption of a specific food item...”, and paragraph 267 “the insights and recommendation engine 230 can (1) monitor a user's food intake, blood glucose levels (either continuously from a CGM device or in a discrete manner using a conventional glucose meter), as well as insulin levels for users using the insulin injection therapy”. Hadad discloses a food baseline profile is obtained to monitor the effect of foods on the user’s body, and the insights and recommendation engine to provide an estimation of the response of a specific biomarker of the user after consuming the food item as well as insulin levels for users using the insulin injection therapy.)
Hadley teaches the limitation “identify, from amongst the second set of food items, a food item to recommend to the patient based on a comparison between a corresponding predicted patient nutrition state and a desired patient nutrition state of the patient” (paragraph 0061 “The platform 130 compares 440 the predicted metabolic state with the true metabolic state to determine whether the two states match” and paragraph 0062 “If the two metabolic states are within a threshold level of similarity, ... Based on the assigned category, the platform 130 generates 460 a patient-specific recommendation ... In particular, the recommendation may outline objectives for consuming food”. Hadley discloses the platform to compare the predicted metabolic state with the true metabolic state to determine whether the two states match and generate a patient-specific recommendation such as a food item recommendation.)
Claims 8-20 are rejected under 35 U.S.C. 103 as being unpatentable by Hadley et.al (US 20220061710 A1) in view of Hadad et.al (US 20190295440 A1), further in view of Mazlish et.al (US 20230125668 A1)
Regarding claim 8,
The applicant is further directed to the rejections of claim 1, 5 and 6 set forth above, as they are rejected based on the same rationale, because the claim recites similar limitations to claims 1, 5 and 6 above.
The motivation to combine the teaching of Hadley with Hadad is similar with the motivation as recited in claim 1 above because claim 8 recites similar limitations to claim 1 in combination with claim 5 and 6.
Hadley teaches the limitation “determine whether the patient consumed the recommended one or more food items” (paragraph 0063 “Following the receipt of the recommendation, a patient continues to record patient data and wearable sensors continue to record biological data, both of which are representative of a metabolic state for a subsequent time period. As patient data and biological data continue to be recorded, the patient health management platform 130 tracks 470 patient health over a time period to monitor changes in the patient's metabolic state. Based on the monitored changes, the platform 130 is able to confirm whether or not a patient is adhering to the recommendation generated by the platform 130.”. Hadley discloses using the platform to monitor and track the patient’s metabolic state to confirm whether or not a patient is adhering to the recommendation, wherein the recommendation comprise of food recommendation as recited in paragraph 0058 “The recommendation includes a set of objectives for a patient to complete to improve the patient's metabolic health. The set of objectives include a medication regimen or schedule, a food or meal schedule, micronutrient and biota nutrient supplements, one or more lifestyle adjustments, or a combination thereof.”)
Hadley in combination with Hadad teaches the limitations “determine a modification to insulin therapy for the patient based on whether or not the patient consumed the recommended one or more food items and a prediction, generated using the trained model, of an effect of consumption of the one or more food items on the at least biomarker” (paragraph 62 “If the two metabolic states are within a threshold level of similarity, the platform 130 categorizes the predicted metabolic state as representative of poor metabolic health, ... Based on the assigned category, the platform 130 generates 460 a patient-specific recommendation outlining objectives for improving the patient's metabolic state. In particular, the recommendation may outline objectives for consuming food, taking medication” and paragraph 63 “Following the receipt of the recommendation, a patient continues to record patient data and wearable sensors continue to record biological data, both of which are representative of a metabolic state for a subsequent time period. As patient data and biological data continue to be recorded, the patient health management platform 130 tracks 470 patient health over a time period to monitor changes in the patient's metabolic state. Based on the monitored changes, the platform 130 is able to confirm whether or not a patient is adhering to the recommendation generated by the platform 130 ... If the platform is not improving the patient's metabolic health, the platform 130 is able to dynamically revise the recommendation to correct the deficiencies of the initial recommendation.” Hayley discloses a recommendation process of the patient health management platform, which provide recommendation and confirm whether or not a patient is adhering to the recommendation based on the prediction model that provide predicted metabolic state and recorded patient data, wherein the recorded patient data comprises of consumed food items, to provide true metabolic state for comparison between true and predicted metabolic state, in order to determine health condition of the patient to provide the recommendation. Hadley further discloses revising the recommendation to correct the deficiencies of the initial recommendation suggesting a modification to a recommendation for a treatment of the patient. While Hadley only recites monitoring an impact of food on insulin sensitivity and may not recommend a treatment relate to insulin therapy, the teaching by Hadad recites a recommendation relating to insulin rejection, which may be incorporated into one of the recommendations by the system by Hadley based on the teaching combination above. Hadad recites at paragraph 267 “The insights and recommendation engine 230 can be useful for individuals with type 1 diabetes or type 2 diabetes. An individual's blood glucose level can be affected by foods consumed and the individual's lifestyle (e.g., physical activity, sleep, stress, etc.) ... Individuals with the type 1 or type 2 diabetes can rely on insulin injections to control their blood glucose levels. Thus, the insights and recommendation engine 230 can (1) monitor a user's food intake, blood glucose levels ... as well as insulin levels for users using the insulin injection therapy” and Paragraph 269 “... If the user has a wearable insulin delivery device, the insight on the window may also inform the user about different bolus options available in the wearable insulin delivery device.” Hadad discloses insulin injections that a patient may receive as a recommendation as the patient’s blood glucose level can be affected by foods consumed which suggest the insulin therapy within the claim. Thus, the recommendation system by Hadley when incorporating with the recommendation by Hadad may include an option to provide insulin injection to patient based on the prediction model and the recorded patient data as explained above. A revision to the recommendation may be a revision to the insulin injection to improve the patient's metabolic health.)
Hadley/Hadad does not teach the limitation “automatically deliver insulin via the delivery device in accordance with the modified insulin therapy”. However, Mazlish teaches this limitation (paragraph 14 “systems and methods are provided to assist a user of insulin therapy in using semi-automated and automated insulin delivery systems.”, paragraph 19 “The computer-based control unit may also be configured to calculate and display a predicted change in glucose levels based on the function of the relative insulin on board value. In some embodiments, the computer-based control unit is configured to calculate a future insulin delivery schedule based in part on the relative insulin on board value calculated by the computer-based processor”, and paragraph 22 “an insulin delivery device is provided with an insulin delivery mechanism and a computer-based control unit. The insulin delivery mechanism is configured to deliver insulin to a user of the device. The computer-based control unit is coupled to the insulin delivery mechanism to automatically deliver insulin to the user.” Mazlish discloses a system and method to assist a user of insulin therapy in using automated insulin delivery systems. The system comprises a computer-based control unit that can calculate predicted change in glucose levels based on the insulin and thereby calculate a future insulin delivery schedule. The insulin delivery may be performed via the insulin delivery device coupled with the computer-based control unit to automatically deliver future insulin according to the calculated schedule based on the change of glucose level. Accordingly, the system, method and device for automatically insulin delivery by Mazlish corresponds to the claimed process of automatically deliver insulin via the delivery device in accordance with the modified insulin therapy.)
Before the effective filing date of the invention, it would have been obvious to one of ordinary skill in the art to combine the teaching of a patient health management platform that implements a machine-learned metabolic model to generate a prediction of a patient's information by Hadley, the teaching of kit of predetermined food and using neural network model to obtain estimated biomarker level by Hadad, with the teaching of the automatically insulin delivery device, method and system for future insulin intake based on predicted change by Mazlish. The motivation to do so is referred to in Mazlish’s disclosure (paragraph 13 “It has been contemplated for many years that it should be entirely feasible to couple a continuous glucose monitoring system with an insulin delivery device to provide some level of automation to the management of insulin delivery to people with diabetes. The effort in this domain has ranged from semi-automated systems to fully automated delivery systems; however, most systems have at least some level of user interaction. Further, in any of these systems, the user's role changes from direct actor to supervisor of the automated system. As such, the user requires new and different tools for overseeing such automation. The present disclosure includes novel systems and methods that assist the user in understanding and visualizing what actions automated insulin delivery systems are taking in an effort to maintain the patient's glycemic control”, and paragraph 18 “The computer-based control unit is configured with a mode to automatically control insulin delivery to the user through the insulin delivery device, thereby forming an automatic feedback control loop” Mazlish discloses an insulin delivery device that can deliver insulin therapy in an automated manner. The system, method and device by Mazlish provides improvement over conventional automatic insulin delivery system and device by providing assistance to the user in understanding and visualizing what actions automated insulin delivery systems are taking in an effort to maintain the patient's glycemic control. More particularly, the system can automatically calculate the change in user’s physiological level such as glucose and thereby automatically schedule and deliver insulin therapy in the most appropriate way. The teaching by Hadley in view of Hadad while providing a system to monitor patient’s metabolic state, predict the state and provide recommendation using machine learning and digital twin technology but does not provide a physical device to automatically deliver insulin in accordance with the predicted and recommendation result. Therefore, one of ordinary skilled in the art may incorporate the automatic insulin delivery device and system by Mazlish since the teaching by Mazlish address the same clinical problem of insulin therapy and provides an automated delivery mechanism that predictably improves execution of insulin therapy adjustments generated by Hadley and Hadad.)
Regarding claim 9 depends on claim 8, thus the rejection of claim 8 is incorporated. The applicant is further directed to the rejections of claim 7 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 10 depends on claim 8, thus the rejection of claim 8 is incorporated. The applicant is further directed to the rejections of claim 1, 3, and 4 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 11 depends on claim 10, thus the rejection of claim 10 is incorporated. The applicant is further directed to the rejections of claim 1 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 12 depends on claim 8, thus the rejection of claim 8 is incorporated. The applicant is further directed to the rejections of claim 7 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 13, the applicant is further directed to the rejections of claim 8 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 14 depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 7 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 15 depends on claim 14, thus the rejection of claim 14 is incorporated. The applicant is further directed to the rejections of claim 13 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 16 depends on claim 14, thus the rejection of claim 14 is incorporated. The applicant is further directed to the rejections of claim 13 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 17 depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 7 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 18 depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 1 and 4 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 19 depends on claim 18, thus the rejection of claim 18 is incorporated. The applicant is further directed to the rejections of claim 1 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Regarding claim 20 depends on claim 13, thus the rejection of claim 13 is incorporated. The applicant is further directed to the rejections of claim 1 set forth above, because the claim recites similar limitations, thus they are rejected based on the same rationale.
Conclusion
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DUY TU DIEP whose telephone number is (703)756-1738. The examiner can normally be reached M-F 8-4:30.
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/DUY T DIEP/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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