DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
In the amendment filed 12/09/2025 the following occurred: Claim 25 was added as new. Claims 1-25 are presented for examination.
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1-25 are drawn to methods, systems, and non-transitory computer readable storage mediums, which is/are statutory categories of invention (Step 1: YES).
Independent claim 1 recites accessing a record of food items previously recorded by the patient, the record of food items comprising a current classification of each of a plurality of food items describing an effect of the food item on a metabolic state of the patient; retrieving a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a current time period and 2) a current metabolic state of the patient determined for the current time period; encoding, into a vector representation, the record of food items, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each of a plurality of food items of the record of food items, determining an updated classification of the food item by inputting the vector representation to a patient-specific metabolic food model, wherein the patient-specific metabolic food model is iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient; and identifying a subset of the record of food items comprising food items for which the updated classification differs from the current classification; and generating, for display to the patient, a notification comprising a graphic representation of the identified subset and a recommendation for the patient to consume food items of the identified subset classified as improving the metabolic state of the patient.
Independent claim 11 recites accessing a record of activities previously recorded by a patient, each of a plurality of entries in the record of activities comprising 1) a duration of the activity and 2) biosignal measurements collected for the patient during the activity; retrieving a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a preceding time period and 2) a metabolic state of the patient determined for the preceding time period; encoding, into a vector representation, the record of activities, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each activity in the record of activities, determining an effect of the activity on the metabolic state of the patient by inputting the vector representation to a patient-specific metabolic activity model, wherein the metabolic model is iteratively trained to predict the effect of the activity based on a training dataset of previously recorded activities labeled with a corresponding effect on a metabolic state of the patient; and identifying a subset of activities that improve the metabolic state of the patient based on the effects determined by the patient-specific metabolic model; and generating, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to perform activities of the identified subset.
Independent claim 21 recites access a record of food items previously recorded by the patient, the record of food items comprising a current classification of each of a plurality of food items describing an effect of the food item on a metabolic state of the patient; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a current time period and 2) a current metabolic state of the patient determined for the current time period; encode, into a vector representation, the record of food items, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each of a plurality of food items of the record of food items, determine an updated classification of the food item by inputting the vector representation to a patient-specific metabolic food model, wherein the patient-specific metabolic food model is iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient; and identify a subset of the record of food items comprising food items for which the updated classification differs from the current classification; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to consume food items of the identified subset classified as improving the metabolic state of the patient.
Independent claim 22 recites a access a record of food items previously recorded by the patient, the record of food items comprising a current classification of each of a plurality of food items describing an effect of the food item on a metabolic state of the patient; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a current time period and 2) a current metabolic state of the patient determined for the current time period; encode, into a vector representation, the record of food items, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each of a plurality of food items of the record of food items, determine an updated classification of the food item by inputting the vector representation to a patient-specific metabolic food model, wherein the patient-specific metabolic food model is iteratively trained to classify the food item based on a training dataset of previously classified food items labeled with a corresponding effect on a metabolic state of a patient; and identify a subset of the record of food items comprising food items for which the updated classification differs from the current classification; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to consume food items of the identified subset classified as improving the metabolic state of the patient
Independent claim 23 recites access a record of activities previously recorded by a patient, each of a plurality of entries in the record of activities comprising 1) a duration of the activity and 2) biosignal measurements collected for the patient during the activity; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a preceding time period and 2) a metabolic state of the patient determined for the preceding time period; encode, into a vector representation, the record of activities, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each activity in the record of activities, determine an effect of the activity on the metabolic state of the patient by inputting the vector representation to a patient-specific metabolic activity model, wherein the metabolic model is iteratively trained to predict the effect of the activity based on a training dataset of previously recorded activities labeled with a corresponding effect on a metabolic state of the patient; and identify a subset of activities that improve the metabolic state of the patient based on the effects determined by the patient-specific metabolic model; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to perform activities of the identified subset
Independent claim 24 recites access a record of activities previously recorded by a patient, each of a plurality of entries in the record of activities comprising 1) a duration of the activity and 2) biosignal measurements collected for the patient during the activity; retrieve a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during a preceding time period and 2) a metabolic state of the patient determined for the preceding time period; encode, into a vector representation, the record of activities, the biosignal measurements of the current metabolic profile, and the current metabolic state of the patient; for each activity in the record of activities, determine an effect of the activity on the metabolic state of the patient by inputting the vector representation to a patient-specific metabolic activity model, wherein the metabolic model is iteratively trained to predict the effect of the activity based on a training dataset of previously recorded activities labeled with a corresponding effect on a metabolic state of the patient; and identify a subset of activities that improve the metabolic state of the patient based on the effects determined by the patient-specific metabolic model; and generate, for display to the patient , a notification comprising a graphic representation of the identified subset and a recommendation for the patient to perform activities of the identified subset.
The respective dependent claims 2-10, 12-20, and 25, but for the inclusion of the additional elements specifically addressed below, provide recitations further limiting the invention of the independent claim(s).
The recited limitations, as drafted, under their broadest reasonable interpretation, cover certain methods of organizing human activity, as reflected in the specification, which states that the invention is to “monitoring a patient's metabolic health, for performing analytics on metabolic health data recorded for the patient, and for generating a patient-specific recommendation for treating any metabolic health-related concerns” (see: specification paragraph 28). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The present claims cover certain methods of organizing human activity because they address a problem where “[c]onventional disease management platforms or techniques either ignore or fail to fully understand important markers…these platforms are designed to treat symptoms as they arise rather than treating the root cause of the disease - the deterioration of a patient's metabolic health…conventional disease management platforms are incapable of generating patient-specific recommendations for improving metabolic health by consuming foods or participating in activities” (see: specification paragraph 3-4), a problem which the invention address by providing, for example, “a personalized metabolic state program that provides tailored nutrition and exercise recommendations aligned with individual preferences” (see: specification paragraph 10). Note that the limitations of “the patient-specific metabolic food model is iteratively trained…” (claim 1)/“the metabolic model is iteratively trained…” (claim 11)/“ the patient-specific metabolic food model is iteratively trained…”(claim 21)/“ the patient-specific metabolic food model is iteratively trained…” (claim 22)/“ the metabolic model is iteratively trained…” (claim 23)/“the metabolic model is iteratively trained…” (claim 24)/“training the patient-specific metabolic food model with…” (claim 25) have been included as part of the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, or mathematical formulas or equations, or mathematical calculations, then it falls within the “Mathematical Concepts” grouping of abstract ideas. The claimed limitations to aspects of training a model cover mathematical concepts because the model employed covers a broad spectrum of machine learning: “As described herein, the term “model” refers to the result of a machine learning training process” (see: specification paragraph 73). Hence, all claimed limitations to aspects of training a model are directed to mathematical concepts, while the claims as a whole are directed toward certain methods of organizing human activity. Accordingly, the claims recite an abstract idea(s) (Step 2A Prong One: YES).
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including an “by one or more wearable sensors worn by the patient…on a computing device…” (claim 1), “on the computing device” (claim 2), “via the computing device…” (claim 9), “by one or more wearable sensors worn by the patient…on a computing device…” (claim 11), “on the computing device” (claim 12), “one or more computer processors; and one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to:…by one or more wearable sensors worn by the patient…on a computing device…” (claim 21), “non-transitory computer readable storage medium comprising stored instructions that when executed by one or more processors of one or more computing devices, cause the one or more computing devices to:…by one or more wearable sensors worn by the patient…on a computing device…” (claim 22), “one or more computer processors; and one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to:…by one or more wearable sensors worn by the patient…on a computing device…” (claim 23), and “a non-transitory computer readable storage medium comprising stored instructions that when executed by one or more processors of one or more computing devices, cause the one or more computing devices to:…by one or more wearable sensors worn by the patient…on a computing device…” (claim 24), which are additional elements that are recited at a high level of generality (e.g., “one or more wearable sensors worn by the patient” is configured though no more than a statement than that data is collected “by” said sensor(s); the “computing device” is configured through no more than a statement than that data is displayed and recorded “on” said computing device; the “one or more computer processors; and one or more computer-readable mediums” are configured though no more than a statement than that instructions “stor[ed]” on said computer-readable medium(s) are “executed by” said processor(s); and the “non-transitory computer readable storage medium” is configured though no more than a statement than that instructions “stored” on said non-transitory computer-readable medium are to be “executed by” one or more processors of one or more computing devices) such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea(s) into a practical application because they do not impose any meaningful limits on practicing the abstract idea(s). Accordingly, the claims are directed to an abstract idea(s) (Step 2A Prong Two: NO).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea(s) into a practical application, using the additional elements to perform the abstract idea(s) amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using generic components cannot provide an inventive concept. See MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea(s). The originally filed specification supports this conclusion:
Paragraph 37, where “FIG. 2 is a high-level block diagram illustrating physical components of an example computer 200 that may be used as part of a client device (e.g., devices 110, 120, 150), application server 130, and/or database server 140 from FIG. 1, according to one embodiment. Illustrated is a chipset 210 coupled to at least one processor 205. Coupled to the chipset 210 is volatile memory 215, a network adapter 220, an input/output (1/0) device(s) 225, a storage device 230 representing a non-volatile memory, and a display 235. In one embodiment, the functionality of the chipset 210 is provided by a memory controller 211 and an 1/0 controller 212. In another embodiment, the memory 215 is coupled directly to the processor 205 instead of the chipset 210. In some embodiments, memory 215 includes high-speed random access memory (RAM), such as DRAM, SRAM, DDR RAM or other random access solid state memory devices.”
Paragraph 38, where “The storage device 230 is any non-transitory computer-readable storage medium, such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory 215 holds instructions and data used by the processor 205. The 1/0 device 225 may be a touch input surface (capacitive or otherwise), a mouse, track ball, or other type of pointing device, a keyboard, or another form of input device. The display 235 displays images and other information for the computer 200. The network adapter 220 couples the computer 200 to the network 150.”
Paragraph 39, “As is known in the art, a computer 200 can have different and/or other components than those shown in FIG. 2. In addition, the computer 200 can lack certain illustrated components. In one embodiment, a computer 200 acting as server 140 may lack a dedicated 1/0 device 225, and/or display 218. Moreover, the storage device 230 can be local and/or remote from the computer 200 (such as embodied within a storage area network (SAN)), and, in one embodiment, the storage device 230 is not a CD-ROM device or a DVD device.”
Paragraph 40, where “Generally, the exact physical components used in a client device 110 will vary in size, power requirements, and performance from those used in the application server 130 and the database server 140. For example, client devices 110, which will often be home computers, tablet computers, laptop computers, or smart phones, will include relatively small storage capacities and processing power, but will include input devices and displays. These components are suitable for user input of data and receipt, display, and interaction with notifications provided by the application server 130. In contrast, the application server 130 may include many physically separate, locally networked computers each having a significant amount of processing power for carrying out the asthma risk analyses introduced above. In one embodiment, the processing power of the application server 130 provided by a service such as Amazon Web Services™. Also, in contrast, the database server 140 may include many, physically separate computers each having a significant amount of persistent storage capacity for storing the data associated with the application server.”
Paragraph 41, where “As is known in the art, the computer 200 is adapted to execute computer program modules for providing functionality described herein. A module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device 230, loaded into the memory 215, and executed by the processor 205.”
Paragraph 44, where “…The platform determines a current metabolic state of a human body by analyzing a unique combination of continuous biosignals received from various sources including, but not limited to, near-real-time data from wearable sensors (e.g. continuous blood glucose, heart rate, etc.)…”
Paragraph 49, where “A patient using the metabolic health manager is outfitted with one or more wearable sensors configured to continuously record biosignals, herein referred to as wearable sensor data. Wearable sensor data includes, but is not limited to, biosignals describing a patient's heart rate, record of exercise (e.g., steps, average number of active minutes), quality of sleep (e.g., sleep duration, sleep stages), a blood glucose measurement, a ketone measurement, systolic and diastolic blood pressure measurements, weight, BMI, percentage of fat, percentage of muscle, bone mass measurement, and percent composition of water. A wearable sensor may be a sensor that is periodically removable by a patient (e.g., a piece of jewelry worn in contact with a patient's skin to record such biosignals) or a non-removable device/sensor embedded into a patient's skin (e.g., a glucose patch)…”
Paragraph 150, where “Any of the steps, operations, or processes described herein may be performed or implemented with one or more hardware or software modules, alone or in combination with other devices. In one embodiment, a software module is implemented with a computer program product including a computer-readable non-transitory medium containing computer program code, which can be executed by a computer processor for performing any or all of the steps, operations, or processes described.”
Paragraph 151, where “Embodiments of the invention may also relate to a product that is produced by a computing process described herein. Such a product may include information resulting from a computing process, where the information is stored on a non-transitory, tangible computer readable storage medium and may include any embodiment of a computer program product or other data combination described herein.”
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment.
Further, the concepts of receiving or transmitting data over a network, such as using the Internet to gather data, and storing and retrieving information in memory have been identified by the courts as well-understood, routine, and conventional activities. See: MPEP 2106.05(d)(II).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea(s) with routine, conventional activity specified at a high level of generality in a particular technological environment. Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea(s) (Step 2B: NO).
Dependent claim(s) 2-10, 12-20, and 25, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea(s) without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
Response to Arguments
Applicant’s arguments from the response filed on 12/09/2025 have been fully considered and will be addressed below in the order in which they appeared.
In the remarks, Applicant argues in substance that (1) the 35 U.S.C. 101 rejections should be withdrawn because “[t]he claimed system transforms continuous physiological measurements into personalized medical insights through specialized machine learning models continuously re-trained on individual patient data. This represents a concrete technological solution to the problem of providing individualized metabolic health guidance that adapts to each patient's unique physiological responses over time. More robustly, the claims recite a specific technological process that ingests wearable biosignals, determines a current metabolic state, encodes a time-synchronized feature vector, applies a patient-specific model, and generates targeted notifications. Claim 1 requires "accessing a record of food items previously recorded by the patient" with a "current classification of [ each of the food items] describing an effect of the food item on a metabolic state of the patient." The system then retrieves "a current metabolic profile of the patient" that includes "biosignal measurements collected during a current time period by ... wearable sensors worn by the patient and a current metabolic state of the patient determined for the current time period." The system encodes this data "into a vector representation" and applies it to "a patient-specific metabolic food model" that is "iteratively trained to classify the food item based on a training dataset of previously classified food items" according to "a corresponding effect on a metabolic state of a patient." The specification anchors this in disclosed continuous glucose monitoring, heart rate, and blood pressure collection systems that enable real-time metabolic state computation. The claimed invention does not organize human activity, constitute a mental process, or recite mathematical concepts. Individualized physiologic modeling on time-series biosignals cannot be performed in the human mind due to the complexity of processing continuous multiparameter sensor data and applying machine learning algorithms to generate patient-specific classifications. The claims do not manage social or economic behavior but instead process objective physiological measurements to determine biological responses to specific foods. The claims do not recite mathematical formulas or equations but rather apply machine learning models as tools to real-world sensor data to classify physiological effects. (Under MPEP 2106.04(a)(2), the machine learning model serves as an applied tool for processing sensor data rather than a claim to mathematics itself.)”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
The argued “problem of providing individualized metabolic health guidance that adapts to each patient's unique physiological responses over time” is not a technical problem, it is an abstract problem for “providing individualized” “guidance” to a “patient[]”, which covers at least managing personal behavior for said patient, where the process of individualizing the guidance may be read as rules or instructions to be followed.
It is argued that “the claims recite a specific technological process that ingests wearable biosignals, determines a current metabolic state, encodes a time-synchronized feature vector, applies a patient-specific model, and generates targeted notifications”, but similarly constructed ideas have been found abstract by the courts, including for example 1) collecting and analyzing information to detect misuse and notifying a user when misuse is detected (FairWarning) and 2) collecting information, analyzing it, and displaying certain results of the collection and analysis (Electric Power Group).
Certain argued elements are decidedly not abstract such as the “wearable sensors worn by the patient”, but these were not treated as abstract elements and instead were treated as additional elements. The “one or more wearable sensors worn by the patient” is configured though no more than a statement than that data is collected “by” said sensor(s), and as such, is an additional element recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer elements.
As per enabling “real-time metabolic state computation”, it is noted that the features upon which the argument relies (i.e., the “real-time” aspect) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
It is argued that “[i]ndividualized physiologic modeling on time-series biosignals cannot be performed in the human mind due to the complexity of processing continuous multiparameter sensor data and applying machine learning algorithms to generate patient-specific classifications”, but as shown in the rejection, the claims are representative of certain methods of organizing human activity, and so whether they are performable “in the human mind”, as argued, is not relevant to the rejection. Even if considered an additional element, the machine learning algorithms, as argued, are merely being “appl[ied]” to the collected data at a high level to generate a desired output such that said additional elements would amount to no more than mere instruction to apply the exception using generic computer elements.
It is argued that, “[u]nder MPEP 2106.04(a)(2), the machine learning model serves as an applied tool for processing sensor data rather than a claim to mathematics itself”, but left uncited in the arguments from this section of the MPEP is that “i. performing a resampled statistical analysis to generate a resampled distribution, SAP America, Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65, 127 USPQ2d 1597, 1598-1600 (Fed. Cir. 2018), modifying SAP America, Inc. v. InvestPic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018)”. As demonstrated in the rejection, at the level claimed, such mathematical calculations are no less similar than what is claimed, such that “the patient-specific metabolic food model is iteratively trained…” (claim 1), for example, are representative of a mathematical concept. The specification supports this conclusion because the model employed covers a broad spectrum of machine learning: “As described herein, the term “model” refers to the result of a machine learning training process” (see: specification paragraph 73). Hence, all claimed limitations to aspects of training a model are directed to mathematical concepts, while the claims as a whole are directed toward certain methods of organizing human activity. There are other court provided examples in the MPEP that support this interpretation, such as “ii. a conversion between binary coded decimal and pure binary, Benson, 409 U.S. at 64, 175 USPQ at 674”, where respondents' method for converting numerical information from binary-coded decimal numbers into pure binary numbers, for use in programming conventional general purpose digital computers, is merely a series of mathematical calculations or mental steps, and does not constitute a patentable "process".
In the remarks, Applicant argues in substance that (2) the 35 U.S.C. 101 rejections should be withdrawn because “The continuous biosignal collection from wearable sensors creates a technological framework that transforms raw physiological data into actionable medical insights tailored to individual patients. The patient-specific metabolic food model generates classifications based on each patient's unique physiological responses rather than generic population-based recommendations. The specification describes how the system "determines a current metabolic state of a human body by analyzing a unique combination of continuous biosignals received from various sources including ... near-real-time data from wearable sensors (e.g. continuous blood glucose, heart rate, etc.)" and performs "analysis continuously to establish a time series of metabolic states." Specification at, i 0044. This integration of sensor technology with personalized machine learning creates a practical application that addresses specific technological problems in medical monitoring. The system solves the technical challenge that "conventional disease management platforms are incapable of generating patient-specific recommendations for improving metabolic health" because "every person is unique in their metabolic health" and "the same foods or activities may have different effects on the metabolic health of different patients." To expand on this, new dependent claim 25 adds time-of-day optimization for food recommendations by using consumption timestamps with biosignals to determine food-induced metabolic states, training the patient-specific model to predict time-dependent effects, and presenting consumption times in the notification. New claim 25 aligns with the same sensordriven workflow established in Part I while adding temporal synchronization capabilities that enhance the system's precision to account for differences in patient specific metabolic responses. The new claim requires "accessing temporal data indicating times when each food item was previously consumed by the patient" and "determining metabolic states induced by each food item when consumed by the patient," then "training the patient-specific metabolic food model with the retrieved temporal data and the determined metabolic effect of the food items consumed by the patient to predict metabolic effects of food consumption at different times of day."”
The Examiner respectfully disagrees. Applicant’s arguments are not persuasive.
Again, the “wearable sensors worn by the patient” were not treated as abstract elements and instead were treated as additional elements. The claims are unconcerned with how the data collected “transforms raw physiological data”, and instead are concerned with the analysis of the collected data. The “one or more wearable sensors worn by the patient” is configured though no more than a statement than that data is collected “by” said sensor(s), and as such, is an additional element recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer elements. The claims are to “retrieving a current metabolic profile of the patient, the current metabolic profile comprising 1) biosignal measurements collected during…” – there is no claiming to a transformation of data. As argued, the “specification describes….analyzing a unique combination of continuations biosignals received…”, where the analysis has been shown to be abstract.
It is argued that the claims integrate sensor technology with personalized machine learning such that the “system solves the technical challenge that "conventional disease management platforms are incapable of generating patient-specific recommendations for improving metabolic health" because "every person is unique in their metabolic health" and "the same foods or activities may have different effects on the metabolic health of different patients."” But the sensors technology argued is claimed at such a high level that they encompass any broad “sensor”, where the senor itself is not described and is configured though no more than a statement than that data is collected “by” said sensor(s). The functions argued of “generating patient-specific recommendations…” are representative of the abstract idea. The claims here are not directed to a specific improvement to computer functionality that amount to a practical application. Rather, they are directed to the use of conventional or generic technology in a well-known environment, without any claim that the invention reflects an inventive solution to a technical problem presented by combining the two. In the present case, the claims fail to recite any elements that individually or as an ordered combination transform the identified abstract idea(s) in the rejection into a patent-eligible application of that idea. The expansion upon this abstract idea in claim 25 merely expands upon elements already indicated as abstract without expanding upon any technology that could integrate such ideas into a practical application.
However, the attempt to provide for integration into a practical application by “adding temporal synchronization capabilities” in claim 25 is appreciated and may advance prosecution if elaborated upon in terms of the technology arrangement with regard to how biosignals are collected and how and when the training is performed beyond a broad “training” “with” the desired data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found on the attached PTO-892 form, including:
U.S. Patent Application Publication 2019/0320976 to Roslin (para 31, 55, and 62-64).
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 ROBERT A SOREY whose telephone number is (571)270-3606. The examiner can normally be reached Monday through Friday, 8am to 5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Fonya Long can be reached at (571) 270-5096. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ROBERT A SOREY/Primary Examiner, Art Unit 3682