DETAILED ACTION
This action is responsive to documents filed on 28 April 2025.
Claims 1-20 are pending for examination.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR
1.17(e), was filed in this application after final rejection. Since this application is eligible for continued
examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the
finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's
submission filed on 02/20/2025 has been entered
Response to Arguments
The arguments filed 04/28/2025 have been entered. Claims 1-20 remain pending in the application.
Applicant’s argument, with respect to claim rejections of claims 1-20 under 35 U.S.C 101 filed 04/28/2025have been considered and they are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintain.
The applicant argues that independent claim 1 has been amended to recite "receive an actual biomarker level of the patient from a glucose sensor connected to the patient after the patient consumes the food item within the time period." Even assuming arguendo, that claim 1 recites an abstract idea (which Applicant does not concede), at least this limitation integrates any alleged abstract idea into a practical application. In particular, the claim, as amended, recites a particular machine (i.e., a glucose sensor) that is used to receive the actual biomarker level and which is integral to the claim. (See MPEP 2106.05(b)).
Without conceding in the basis of the rejection, and solely to expedite prosecution, claims 8 and 13 have been amended to recite, inter alia, "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" and "delivering insulin in accordance with the modified insulin therapy." At least these amended limitations integrate any alleged abstract idea 9 into a practical application, because at least these limitations recite a particular method of treatment.
The examiner respectfully disagrees. While the claim has been amended to recites a particular machine, which ius a glucose sensor that is used to receive the actual biomarker level, this additional element does not contribute to an inventive concept or significantly more than the abstract idea of determining an estimated biomarker level, comparing the estimated and actual biomarker level to calibrate a model and determine whether to repeat the training process of the model. The amended limitation to recite a glucose sensor is considered to be an additional element of generally linking the use of a judicial exception to a particular technological environment or field of use as identified in MPEP 2106.05(h). Simply using a glucose sensor to measure the glucose level of a patient to be used in a training a machine learning model is not an inventive concept as there is no structural change or improvement toward the training phase of the machine learning model. The additional element simply recites how data is obtained by applying a field of use of the glucose device in which the machine learning model may use that data to train. However, simply training a machine learning model with collected data is not an inventive concept when it is claimed in a generic manner as recited in the limitation “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, wherein to execute the training process, ...”. This limitation simply recites an additional element of 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 simply recites execute the training of a machine learning model to perform a prediction without reciting how the model performs the training as an inventive concept that may integrate a different configuration of a machine learning model or some specific training steps that is considered as a novel idea.
Furthermore, regarding the amended 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" and "delivering insulin in accordance with the modified insulin therapy" in claim 8, these limitations simply recites an abstract idea of a mental process to determine a modification to insulin therapy for a patient, an additional element of a prediction generated using the trained model, and an additional element of delivering the modified insulin therapy to the patient. 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. Therefore, when considering the integration of a machine learning model to perform a prediction of the patient’s state after consuming a food item, it simply recites an additional element of 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. Without a machine learning model, the doctor may perform the prediction based alone on their intuition and expertise in the field. Therefore, the integration of a machine learning model does not contribute to the claim as a whole as an inventive concept as it does not recite any improvement. An argument may be made that the integration of the machine learning model provides a better accuracy in prediction in comparison with a doctor’s thought process. However, this argument is not corrected because the machine learning model needed to be trained to provide a prediction, wherein the training of a machine learning model is based on the data provided from an expert as the expert decides a set of rules for the machine learning model to learn and perform in accordance with the received data. In other words, the machine learning model behave in accordance with how the expert feed the data to the model. The expert need to rely on one or more doctors’ skills and expertise to devise these rules and algorithms for the machine learning model to learn. In other word, the output of a machine learning model heavily relies on a decision and knowledge of the doctor. Thus, the model cannot surpass the doctor in prediction or determination on a therapy for a patient. Finally, the claim does not recite any configurations or apparatus that may perform the process of delivering insulin therapy to a patient in such an inventive concept. Therefore, this limitation is considered as an additional element of 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, the rejection of claims 1-20 under 35 U.S.C 101 will be maintained and modified in accordance with the amended claims.
Applicant’s argument, with respect to claim rejections of claims 1-20 under 35 U.S.C 103 filed 04/28/2025 have been considered and they are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintain.
The applicant argues that the Office Action asserted that Hadley discloses at paragraph 0061 "determin[ing] 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,". Paragraph [0061] recites "the platform 130 compares 440 the predicted metabolic state with the true metabolic state to determine whether the states match," and, if they do not match, "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." Hadley goes on to state "the platform 130 communicates the inconsistency and enabling the patient to correct the inconsistency" and "the platform 130 receives the updated patient data and determines 430 an updated predicted metabolic state based on the updated patient data."
First, Applicant respectfully submits that paragraph [0061] of Hadley has nothing to do with training a model. Instead, the steps described in paragraph [0061], associated with FIG. 4 of Hadley, are utilized with an already trained model. Accordingly, the correction of inconsistencies described in paragraph [0061] of Hadley have nothing to do with the training process for a model as recited in independent claim 1. The applicant has amended independent claim 1 to recite "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" and "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." Hadley makes no mention of determining whether to repeat a training process for one or more other food items of a set of predetermined food items based on the comparison of an estimated biomarker level to the actual biomarker level. Rather, Hadley describes correcting a particular inconsistency, not performing a calibration associated with a first food item, and then determining whether to repeat the training process on one or more other food items.
The applicant has amended independent claims 8 and 13 to recite "determining 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" and "delivering insulin in accordance with the modified insulin therapy." In discussing these amendments during the telephonic interview, the Examiner indicated that paragraph [0025] of Hadley is relevant to this claim. Paragraph [0025] states "stored data may include ... relationships between foods and metabolic responses (for example, an impact of a given food on insulin sensitivity)." An impact of a given food on insulin sensitivity is simply not "a modification to insulin therapy," as recited in amended claims 8 and 13. Nowhere does Hadley disclose or suggest "determining a modification to insulin therapy based on whether or not the patient consumed the recommended one or more food items" let alone determining such a modification based on "a prediction, generated using the trained model, of an effect of consumption of the one or more food items on the at least one biomarker" Even assuming arguendo that the impact of a given food on insulin sensitivity as described in paragraph [0025] of Hadley corresponds to an insulin therapy, Hadley merely states that the impact of a food on insulin sensitivity may be stored data, and makes no mention of determining a modification to the insulin sensitivity, let alone based on a prediction using a trained model of an effect of consumption of the food item on at least one biomarker.
The examiner respectfully disagrees. Hadley recites at 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)” and Paragraph 78 “As described above, the patient health management platform 130 applies machine-learning based artificial intelligence to generate a precision treatment recommendation for improving a patient's metabolic health by predicting their response to future input stimuli”. Hadley discloses a training process of the patient health management platform, wherein the patient health management platform utilizes the digital twin module to apply machine-learning based artificial intelligence to generate a precision treatment recommendation. The digital twin module implements one or more 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.
Then Hadley teaches these 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" and "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" 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”, 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.” The claim recites the training of a predetermined food item using estimated and actual biomarker level correlated with when the patient consumed the predetermined food item within a time period to obtain a trained model, which corresponding with a trained model recited at paragraph 61 by Hadley, wherein this trained model undergoes a comparison process between 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 data is not similar, the platform identifies and communicate the inconsistency to allow the patient to correct the recorded patient data, wherein the patient data may be nutrient data recorded by the patients as a list of foods have been consumed by the patient over a time period, which may then be updated by the patient as an updated recorded patient data to determine an updated predicted metabolic state. This updated metabolic state suggests the retraining of the prediction model by Hadley which is further explain in detailed at recitation of paragraph 84. Thus, Hadley still discloses a process of training a model and repeat the training of the model with updated patient data based on the comparison of the predicted and recorded metabolic state, which suggest the estimated and actual biomarker level, wherein the updated recorded patient data may include a list of foods which comprise of a different food item from a previous time period in which the patient consumed a previous food item that the model has received the data and is trained upon, such that an updated model is obtained through the retraining process.
Hadley also discloses at 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 as recited within the Office Action. Hadad discloses 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.
Therefore, the previous rejection based on the teaching of Hadley in view of Hadad will be maintained and modified in accordance with the amended claims.
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.
“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, 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.
“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”. 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.
“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 “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).
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).
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 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.
“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.
“deliver insulin in accordance with the modified insulin therapy” 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.
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 ...”, “deliver insulin in accordance with the modified insulin therapy” 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)
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.
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), 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 reject