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 .
DETAILED ACTION
Response to Amendment
In the amendment dated 10/15/2025, the following occurred: Claims 1 and 11 have been amended.
Claims 1-20 are pending and have been examined.
Priority
Acknowledgement is made of applicant’s claim to priority under 35 U.S.C. 120, 121, 365(c), or 386(c) and 37 CFR 1.78 for a continuing application, which claims priority to U.S. Application 17/136,095 filed 12/29/2020.
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 a judicial exception (i.e., an abstract idea) without significantly more.
Claims 1 and 11 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Eligibility Analysis Step 1 (YES): Claims 1 and 11 fall into at least one of the statutory categories (i.e., process, machine).
Eligibility Analysis Step 2A1 (YES): The claims recite an abstract idea. The identified abstract idea is a method of dietary communications using intelligent systems regarding endocrinal measurements, as underlined (claim 11 being representative):
a wearable device, communicatively connected at least a processor, wherein the wearable device receives information pertaining to an individual input;
obtaining, by the processor, a first endocrinal measurement relating to a user from the wearable device;
comparing, by the processor, the first endocrinal measurement to an endocrinal system effect;
generating, by the processor, a body dysfunction label for the first endocrinal measurement as a function of the comparing to the endocrinal system effect, wherein the body dysfunction label further comprises:
a first comparison of a first endocrinal label and an endocrinal measurement for the user with a diagnosed endocrinal disorder; and
a second comparison of the first endocrinal label and the endocrinal measurement for the user without a diagnosed endocrinal disorder;
identifying, by the processor, a dietary communication as a function of the body dysfunction label and the first endocrinal measurement, wherein the dietary communication comprises a limitation of a plurality of limitations, wherein the limitation is configured to identify an optimal time of day when one or more ingredients is to be consumed, and wherein identifying further comprises:
training a first machine learning process as a function of a first training set relating inputs containing endocrinal measurements and body dysfunction labels to outputs containing dietary communications, wherein training the first machine learning process comprises: training the first machine learning process using training data as input; adjusting one or more connections and one or more weights between nodes in adjacent layers of the first machine learning process; and updating the first machine learning process as a function of the connections to produce the output layer of nodes; and
identifying the dietary communication as a function of the trained first machine learning process;
obtaining, by the processor, a second endocrinal measurement relating to the first endocrinal measurement;
updating, by the processor, the dietary communications as a function of the second endocrinal measurement;
adjusting, by the processor, the first machine learning process as a function of the updated dietary communications; and
presenting the dietary communication on the computing device.
The identified claim elements, as drafted, is a process that under the broadest reasonable interpretation (BRI) covers a method of organizing human activity but for the recitation of generic computer component language (discussed below in 2A2). That is, other than reciting the generic computer component language, the claimed invention amounts to a human following a series of rules or steps to receive/obtain, compare, generate, identify, relate, update and present data, which is a method of managing personal behavior or relationships or interactions between people. For example, but for the generic computer component language, the claims encompass a person identifying a dietary communication, i.e., a limitation of a plurality of limitations that identifies an optimal time of day when one or more ingredients is to be consumed, as a function of a body dysfunction label and a first endocrinal measurement. The Examiner notes that certain “method[s] of organizing human activity” includes a person’s interaction with a computer. MPEP § 2106.04(a)(2)(II). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or relationships or interactions between people but for the recitation of generic computer component language, then it falls within the “method of organizing human activity” grouping of abstract ideas. See additionally MPEP 2106. Accordingly, the claims recite an abstract idea.
The Examiner notes that the abstract idea could also be characterized as a mathematical process along with a mental process, reciting multiple abstract ideas falling into different abstract idea sub-groupings; however, this has been omitted for brevity.
Eligibility Analysis Step 2A2 (NO):
The judicial exception, the above-identified abstract idea, is not integrated into a practical application. In particular, the claims recite the additional elements of a processor/computing device and/or a memory (claims 1 and 11) that implement the identified abstract idea. The additional elements aforementioned are not described by the applicant and are recited at a high-level of generality (i.e., a generic computer or computer component performing a generic computer or computer component function that facilitates the identified abstract idea) such that these amount no more than mere instructions to apply the exception using a generic computer component (see Specification e.g., at para. 8: “Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) … system on a chip (SoC)… a mobile device such as a mobile telephone or smartphone”; and para. 43: “Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc… a magneto-optical disk, a read-only memory "ROM" device, a random-access memory "RAM" device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof”). See MPEP § 2106.04(d)(I). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
The claims further recite the additional element of a wearable device communicatively connected to at least a processor that transmits data. The additional element is recited at a high-level of generality (i.e., as a general means of collecting, transmitting or outputting data) and amounts to a location from which data is received or to which data is transmitted or outputted, each of which represents an extra-solution activity. See MPEP § 2106.04(d)(I). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims further recite the additional elements of training a first machine learning process (adjusting one or more connections and one or more weights between nodes in adjacent layers and updating the first machine learning process as a function of the connections to produce the output layer of nodes), using the trained first machine learning process, and adjusting the first machine learning process that implement the identified abstract idea. The additional element is not described by the Applicant, is recited at a high-level of generality and is merely invoked as a tool to perform an existing process (MPEP § 2106.05(f)(2), see case involving a commonplace business method or mathematical algorithm being applied on a general-purpose computer within the “Other examples”), such that this amounts no more than mere instructions to apply the abstract idea using a general-purpose computer (see Specification at para. 31: “Training set classifier 708 may include a “classifier”, which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith… Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression… As a non-limiting example, training set classifier 708 may classify elements of training data to specific endocrinal measurements and/or body dysfunction labels”) (emphasis added). See MPEP § 2106.04(d)(I); and Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014). Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea.
Eligibility Analysis Step 2B (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 into a practical application, the additional elements of a processor/computing device and/or a memory (claims 1 and 11) to perform the method (represented by claim 11) amount no more than mere instructions to apply the exception using a generic computer or generic computer component. Mere instructions to apply an exception using generic computer(s) and/or generic computer component(s) cannot provide an inventive concept (“significantly more”). See MPEP § 2106.05(f).
Also discussed above with respect to integration of the abstract idea into a practical application, the additional element of the wearable device (i.e., a device that collects, transmits or outputs data) is considered extra-solution activity. This has been re-evaluated under the “significantly more” analysis and determined to be well-understood, routine, conventional activity in the field. MPEP 2016.05(d)(II) indicates that receiving, transmitting and outputting data over a network, e.g. using the Internet to gather data, has been held by the courts to be well-understood, routine, conventional activity (citing Symantec, TLI Communications, OIP Techs., and buySAFE). See also MPEP 2106.05(g) (citing CyberSource, Mayo and OIP Techs.) Well-understood, routine, conventional activity cannot provide an inventive concept (“significantly more”). As such, the claims are not patent eligible.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of training a first machine learning process (adjusting one or more connections and one or more weights between nodes in adjacent layers and updating the first machine learning process as a function of the connections to produce the output layer of nodes), using the trained first machine learning process, and adjusting the first machine learning process to perform the method amounts no more than mere instructions to “apply it” with the exception by invoking an algorithm merely as a tool to perform an existing process (i.e., only recites the algorithm as a tool to apply data to an algorithm and report the results), in this case to receive input data and output output data. The use of a trained machine learning algorithm in its ordinary capacity to perform tasks in the identified abstract idea does not provide an inventive concept (“significantly more”). See MPEP § 2106.05(f). Accordingly, alone or in combination, the additional elements do not provide significantly more.
Dependent claims 2-10 and 12-20, when analyzed as a whole, are similarly rejected under 35 U.S.C. §101 because the additional limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. The claims, when considered alone or as an ordered combination, either (1) merely further define the abstract idea, (2) do not further limit the claim to a practical application, or (3) do not provide an inventive concept such that the claims are subject matter eligible.
Claim(s) 2-3, 7-9, 12-14 and 17-20 merely further describe(s) the abstract idea (e.g., the first endocrinal measurement, selecting the endocrinal system effect, the individual input, the body dysfunction label, the dietary communication, identifying the dietary communication). See analysis, supra.
Claim(s) 4, 6, 10 and 16 merely further describe(s) the additional element(s) of the processor (e.g., selecting/choosing, receiving/obtaining, identifying, or updating data). See analysis, supra.
Claims 5 & 15 further recite the additional elements of train(ing) a second machine learning process and using the trained second machine learning process, which is recited at a high-level of generality and is merely invoked as a tool to perform an existing process. See analysis, supra.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Wolf et al. (US 2019/0252058 A1; “Wolf” herein) in view of Neumann (US 2020/0315527 A1; effectively filed 04/02/2019) and Hadad et al. (US 2019/0244541 A1; “Hadad” herein).
Re. Claim 1, Wolf teaches an apparatus for dietary communications using intelligent systems regarding endocrinal measurements (Abstract, Fig. 1 teach generating personalized nutritional recommendations using the described operational environment 100), the apparatus comprising:
a wearable device, wherein the wearable device receives information pertaining to an individual input (The Specification at para. 19 describes a “wearable device” as an electronic device that may detect, analyze, and/or transmit information concerning a user… may not make contact and/or touch one or more body parts of a user, but rather may be in the vicinity and/or located adjacent to the user, such as a computer and/or mobile phone. Figs. 1-2 and [26] teach, utilizing computing devices (one being a wearable device) 102, a user may submit data to the nutritional environment 106. Alternately, [11] and Fig. 2 teach technology worn and/or utilized by an individual.);
at least a processor (704) (Fig. 1, [24] teach a nutritional environment 106 may include a collection of computing resources, in communication with one another, that are implemented by one or more physical computing devices. Fig. 7, [88] teach computer hardware architecture for a physical computing device utilized to execute software components for performing operations described. See also [89].) communicatively connected to the wearable device ([89] teaches a multitude of components or devices may be connected by way of a system bus or other electrical communication paths.); and
a memory (708, 710, 712) communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to (see previous citations. See also [91]. Also, Fig. 7, [93]-[94] teach a mass storage device 718 storing system programs, application programs, other program modules and data… connected… characterized as primary or secondary storage.):
obtain a first endocrinal measurement relating to a user [… from the wearable device…] (The Specification at para. 6 describes Fig. 5 to illustrate a table of endocrinal measurements and at para. 14 describes hemoglobin A1C as an endocrinal measurement and a particular range for a particular endocrinal system effect. [26], [43] teaches submitting data 108, e.g., health data, to the nutritional environment 106, which receives (obtains) the health data. [46] teaches blood data 206D may include blood tests relating to a variety of different biomarkers… associated with measuring blood sugar, insulin, triglycerides, IL-6 inflammation, ketone bodies, nutrient levels, allergy sensitivities, iron levels, blood count levels, HbAlc (a first endocrinal measurement), and the like. Fig. 2, [11] teach wearable data 206E obtained from worn technology.);
compare the first endocrinal measurement to an endocrinal system effect (The Specification at para. 14 describes an “endocrinal system effect” as a standard reference range. [16], [35] teaches the nutrition service can take objectives that specify specific target values and/or a range (endocrinal system effect) for one or more biomarkers and calculate target outcomes or ranges for two or more biomarkers. [39] teaches utilizing classifier 124A to classify a predicted score of each of the biomarkers (the endocrinal measurements) into a classification category indicating that the predicted value is within a healthy range or a non-healthy range (necessarily comparing).);
generate a body dysfunction label for the first endocrinal measurement as a function of the comparing to the endocrinal system effect (see Applicant’s disclosure at para. 15 and Figs. 6A-6B. [39] teaches utilizing classifier 124A to classify a predicted score of each of the biomarkers into a classification category (necessarily generated) indicating that the predicted value is within a healthy range or a non-healthy range (as a function of the comparing).), wherein the body dysfunction label further comprises:
a first comparison of a first endocrinal label and an endocrinal measurement for the user with a diagnosed endocrinal disorder ([39] teaches a classification category indicating that the predicted value is within a non-healthy range.); and
a second comparison of the first endocrinal label and the endocrinal measurement for the user without a diagnosed endocrinal disorder ([39] teaches a classification category indicating that the predicted value is within a healthy range);
identify a dietary communication as a function of the body dysfunction label and the first endocrinal measurement (The Specification at para. 16 describes a dietary communication as personalized nutritional information. [36], [39] teach that the nutrition service 130 utilizes the predictions (the body dysfunction label and the first endocrinal measurement) generated by the prediction service 120 classifier 124A, to generate nutritional recommendations. Also, Fig. 6, [84]-[85] teach generating (necessarily identifying) the nutritional recommendations using the predicted values of the biomarkers and/or food scores along with the other data, such as the objective data (having the endocrinal system effect) (step 614 = YES), subsequently providing recommendation data of foods/meals (at step 618). For examples, see [0016].), […] a limitation of a plurality of limitations (Fig. 4, [0064]), wherein the limitation is configured to identify […], and wherein identifying further comprises:
training a first machine learning process as a function of a first training set relating [… inputs containing…] endocrinal measurements (blood data 206D, e.g., HbA1c data) and body dysfunction labels (the classification categories) to [… outputs containing…] dietary communications (the recommendation data of foods/meals) ([15] teaches the prediction service can utilize training data to train one or more machine learning mechanisms that can be used by the prediction service and/or the nutrition service. [41] teaches “machine learning” may refer to one or more programs that learns to model the relationship between variables from the training data it receives.), […];
identifying the dietary communication as a function of the trained first machine learning process (see previous citations. [15], [41] teach the nutrition service uses the trained machine learning mechanism to identify the recommended foods for a particular user. [39] teaches classifying a food within a classification category / predicting, e.g., recommended/not-recommended.);
obtain a second endocrinal measurement relating to the first endocrinal measurement ([26], [43] teaches submitting data 108, e.g., health data, to the nutritional environment 106, which receives (obtains) the health data. [46] teaches blood data 206D may include blood tests (a first and second) relating to a variety of different biomarkers… associated with measuring blood sugar, insulin, triglycerides, IL-6 inflammation, ketone bodies, nutrient levels, allergy sensitivities, iron levels, blood count levels, HbAlc (a related second endocrinal measurement), and the like. Also, [56] teaches the training data 308 may expand to include further individual data points as individuals add data.);
update the dietary communications as a function of the second endocrinal measurement ([15] teaches the prediction service can utilize the (updated) training data to (re)train one or more machine learning mechanisms that can be used by the prediction service and/or the nutrition service. [56] teaches adding individual/user data, such that the training data can be updated to include the data (the second endocrinal measurement). Fig. 6, [84]-[85] teach generating the (updated) nutritional recommendations using the (updated) predicted values of the (updated) biomarkers (e.g., HbA1c), subsequently providing recommendation data of foods/meals (at step 618).);
adjust the first machine learning process [… as a function of…] the updated dietary communications ([15] teaches the prediction service can utilize (updated) training data to (re)train (adjust) one or more machine learning mechanisms that can be used by the prediction service and/or the nutrition service.); and
present the dietary communication on a computing device (Fig. 1, [66] teaches the nutrition service 130 of the nutritional environment 106 can provide the personalized nutritional recommendations 142D via the user interface 104 that can be presented on a display associated with a computing device 102. See also Fig. 6 step 618.)
Wolf does not explicitly teach obtain a first endocrinal measurement relating to a user (e.g., the taught blood data 206D) from the wearable device (e.g., the second physical computing device).
However, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to combine the noted features of Wolf with teachings thereof, since the combination is merely combining prior art elements according to known methods to yield predictable results (KSR rational A). It can be seen that each element claimed, i.e., data and data source, is present in Wolf. Providing data with a physical computing device or “wearable device” (as taught by Wolf) does not change or affect the normal data transmission-related functionality of the computing devices or the normal ingestion-related functionality of the computing environment of Wolf. Transmitting user-related information to the nutritional environment would be performed the same way even with the addition of blood data transmission technology. Since the functionalities of the elements in Wolf do not interfere with each other, the results of the combination would be predictable.
Wolf may not teach
training… as a function of a first training set relating inputs containing data… and labels to outputs containing data, wherein training the first machine learning process comprises: training the first machine learning process using training data as input; adjusting the first machine learning process as a function of the updated dietary recommendations; and updating the first machine learning process as a function of the connections to produce the output layer of nodes; or
adjust the first machine learning process as a function of the updated dietary communications
Neumann teaches
compare the first endocrinal measurement to an endocrinal system effect (The Specification at para. 14 describes an “endocrinal system effect” as a standard reference range. [0094] teaches ameliorative label may be generated as a function of severity and/or progression of prognostic output. For example, a prognostic label that includes a diagnosis of hypothyroidism as evidenced by a thyroid stimulating level (TSH) of 6.0 (normal range is 1.4-5.5) (necessarily compared) may generate an ameliorative label that includes 150 mcg per day of iodine supplementation to lower TSH within normal limits due to mild TSH elevation and/or mild progression of hypothyroidism.);
training a first machine learning process as a function of a first training set relating inputs containing endocrinal measurements and body dysfunction labels to outputs containing dietary communications (see Applicant’s disclosure at para. 0033. [0083] teaches “training” the neural network, in which elements (inputs) from a training dataset are applied to the input nodes, a suitable training algorithm is then used to adjust… to produce the desired values (outputs) at the output nodes. Abstract, [0029], [0033] teach a first training set including a plurality of first data entries, each first data entry including at least an element of physiological state data, e.g., measures of hemoglobin A1-C (HbA1c), and at least a correlated first prognostic label. Abstract, [0051], [0069], [0096] teach generating a diagnostic output including at least a prognostic label and at least an ameliorative process label, e.g., dietary choices chosen to alleviate conditions associated with prognostic labels in the past, i.e., dietary or nutritional recommendations.), wherein training the first machine learning process comprises:
training the first machine learning process using training data as input ([0083] teaches “training” the neural network, in which elements from a training dataset are applied to the input nodes.);
adjusting one or more connections and one or more weights between nodes in adjacent layers of the first machine learning process; and updating the first machine learning process as a function of the connections to produce the output layer of nodes ([0083] teaches a suitable training algorithm is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes); and
adjusting the first machine learning process as a function of… (See Applicant’s disclosure at para. 0010 and 0033. [0083] teaches a suitable training algorithm is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. [0029] teaches diagnostic engine 108 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the techniques for generating personalized nutritional recommendations using predicted values of biomarkers of Wolf to analyze diagnostic measurements with reference to normal ranges, to perform a “training” process, and/or to iterate the “training” process and to use this information as part of methods and systems for utilizing diagnostics for informed vibrant constitutional guidance as taught by Neumann (effectively filed 4/2/2019), with the motivation of improving patient diagnostics and treatment and improving accuracy of machine-learning processes (see Neumann at para. 0002-0003, 0051, 0084).
Wolf/Neumann may not teach wherein the dietary communication comprises a limitation of a plurality of limitations, wherein the limitation is configured to identify an optimal time of day when one or more ingredients is to be consumed.
Hadad teaches
wherein the dietary communication comprises a limitation of a plurality of limitations, wherein the limitation is configured to identify an optimal time of day when one or more ingredients is to be consumed (Fig. 2 and [0048] teach a recommended menu (the dietary communication) created for personalized nutrition schedules. Between 7-9am, it is recommended that the user eat 1 egg, 2 slices whole bread, 1 tomato, 1 orange between 7-9am. See Applicant’s disclosure at para. 0016.)
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date to have modified the techniques for generating personalized nutritional recommendations using predicted values of biomarkers of Wolf/Neumann to generate personalized nutritional recommendation data that includes a recommended menu and to use this information as part of systems and methods for generating personalized nutritional recommendations as taught by Hadad, with the motivation of improving user health and wellness based on preferences, habits, medical and activity profiles, and constraints (see Hadad at Abstract and para. 0002, 0016).
Re. Claim 2, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the first endocrinal measurement identifies a current endocrinal disorder (Wolf [46] teaches blood data including blood tests relating to / associated with measuring HbA1c. Wolf [39] teaches classifying the predicted score for a biomarker (the first endocrinal measurement) into a classification category indicating (identifying), e.g., a non-healthy range.)
Re. Claim 3, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the first endocrinal measurement identifies a probable endocrinal disorder (see claim 1 prior art rejection. Wolf [39], [59] teaches classifying the predicted score for a biomarker (the first endocrinal measurement) into a classification category indicating (identifies), e.g., a healthy range, a non-healthy range (probable endocrinal disorder); or some other category, e.g., very low, low, average, high, very high (probable endocrinal disorder).)
Re. Claim 4, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the at least a processor is further configured to select the endocrinal system effect as a function of a user attribute (Wolf [16], [35], [80] teaches the nutrition service can take objectives that specify (select) specific target values and/or a range (endocrinal system effect) for one or more biomarkers and calculate target outcomes or ranges for two or more biomarkers… for example, a cardiovascular health objective may be mapped to targeting the biomarker triglyceride to remain below a certain level. Neumann [0057], [0068] teaches data associating a person (from whom a physiological sample was extracted) with one or more cohorts, including demographic groupings such as ethnicity, sex, age, income… entries retrieved from the biological extraction database 200 and prognostic label database 212 may be selected via query to match one or more additional elements of information, so as to retrieve a first training set 112 including data belonging to a given cohort, so as to generate outputs that are tailored to a person (as a function of a user attribute) with regard to whom the system 100 classifies physiological samples to prognostic labels.)
Re. Claim 5, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the at least a processor is further configured to:
train a second machine learning process (a second classifier) as a function of a second training set (training data) relating inputs containing endocrinal system effects (objective ranges for one or more biomarkers) to outputs containing body dysfunction labels (classification categories) (see claim 1 prior art rejection citing Wolf at para. 15, 39, 41, 46. Neumann [0083] teaches “training” the neural network, in which elements (inputs) from a training dataset are applied to the input nodes, a suitable training algorithm is then used to adjust… to produce the desired values (outputs) at the output nodes.); and
generate the body dysfunction label as a function of the trained second machine learning process, wherein the body dysfunction label is an output of the trained second machine learning process (The Specification at para. 15 describes a body dysfunction label may indicate if a first endocrinal measurement is within normal limits or outside normal limits. See claim 1 prior art rejection. Wolf [39], [59] teaches utilizing classifier 124B (the second classifier) to classify the predicted score for a biomarker into a classification category (necessarily generated) indicating, e.g., a healthy range, a non-healthy range; or some other category, e.g., very low, low, average, high, very high (body dysfunction labels).)
Re. Claim 6, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the at least a processor is further configured to:
choose an individual input as a function of the body dysfunction label (The specification at para. 18 describes “individual input” as additional information requested to be known about a user. Wolf Fig. 1, [27] teaches stored user data 140A (individual input). Neumann [0057], [0068] teaches retrieval of one or more records from prognostic label database (as a function of the body dysfunction label)… entries retrieved… may be filtered and selected via query (choose the individual input) to match one or more additional elements of information, so as to retrieve data associated with a person belonging to a given cohort.);
receive an entry relating to the individual input from the user (Wolf [54] teaches the prediction service 120 receives user data 140A. Also, Wolf Fig. 6, [81] teaches accessing preference data for an individual.); and
identify the dietary communications as a function of the individual input (Wolf Fig. 1, [32] teaches the nutrition service 130 utilizes predicted values of the biomarkers generated by the prediction manager 122 of the prediction service 122, along with the user data 140A, other data 142B, and/or nutritional data 142C, to generate personalized nutritional recommendations for the individual (necessarily identified). Also, Wolf [36] teaches the nutrition service 130 utilizes the predictions generated by the prediction service 120 to generate nutritional recommendations (necessarily identified) based on the objectives for the biomarkers, preferences from the user, as well as other data.)
Re. Claim 7, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the individual input describes a user’s fitness patterns (Wolf Fig. 2, [27], [54] teaches the prediction service receives user data 140A, e.g., wearable data 206E (individual input) stored in data store 140. Wolf [47] teaches wearable data 206E can include any data received from a fitness device associated with the user, e.g., motion, heart rate, sleep, calories burned, activities performed, blood pressure, body temperature, blood glucose levels.)
Re. Claim 8, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the body dysfunction label indicates if the first endocrinal measurement is within normal limits (see claim 1 prior art rejection. Wolf [39], [59] teaches classifying the predicted score for a biomarker (e.g., HbA1c) into a classification category (body dysfunction label), the categories having labels, e.g., “healthy range” (indicates the biomarker is within normal limits), “non-healthy range”.)
Re. Claim 9, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the dietary communication comprises personalized nutritional information (see claim 1 prior art rejection. For example, Wolf at Abstract teaches generating and providing the personalized nutritional recommendations.)
Re. Claim 10, Wolf/Neumann/Hadad teaches the apparatus of claim 1, wherein the at least a processor is further configured to:
obtain a second endocrinal measurement relating to the first endocrinal measurement (The Specification at para. 6 describes Fig. 5 to illustrate a table of endocrinal measurements and at para. 14 describes hemoglobin A1C as an endocrinal measurement and a particular range for a particular endocrinal system effect. Wolf [26], [43] teaches submitting data 108, e.g., health data, to the nutritional environment 106, which receives (obtains) the health data. [46] teaches blood data 206D may include blood tests (a first and second) relating to a variety of different biomarkers… associated with measuring blood sugar, insulin, triglycerides, IL-6 inflammation, ketone bodies, nutrient levels, allergy sensitivities, iron levels, blood count levels, HbAlc, and the like (a second endocrinal measurement). Wolf [56] teaches adding individual/user data, such that the training data can be updated to include the data.); and
update the dietary communications as a function of the second endocrinal measurement (Wolf [56] teaches that the training data 308 may expand to include further individual data points… For example, as individuals add data, the training data 308 can be updated automatically and/or manually to include the data (the second endocrinal measurement). See claim 1 prior art rejection.)
Re. Claim 11, the subject matter of Claim 11 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 1. Since Claim 11 is analogous to Claim 1, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 1.
Re. Claim 12, the subject matter of Claim 12 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 2. Since Claim 12 is analogous to Claim 2, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 2.
Re. Claim 13, the subject matter of Claim 13 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 3. Since Claim 13 is analogous to Claim 3, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 3.
Re. Claim 14, the subject matter of Claim 14 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 4. Since Claim 14 is analogous to Claim 4, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 4.
Re. Claim 15, the subject matter of Claim 15 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 5. Since Claim 15 is analogous to Claim 5, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 5.
Re. Claim 16, the subject matter of Claim 16 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 6. Since Claim 16 is analogous to Claim 6, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 6.
Re. Claim 17, the subject matter of Claim 17 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 7. Since Claim 17 is analogous to Claim 7, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 7.
Re. Claim 18, the subject matter of Claim 18 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 8. Since Claim 18 is analogous to Claim 8, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 8.
Re. Claim 19, the subject matter of Claim 19 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 9. Since Claim 19 is analogous to Claim 9, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 9.
Re. Claim 20, the subject matter of Claim 20 is essentially defined in terms of a method, which is technically corresponding to the apparatus of Claim 10. Since Claim 20 is analogous to Claim 10, it is similarly analyzed and rejected in a manner consistent with the rejection of Claim 10.
Response to Arguments
Claim Objections
Regarding the objection(s), the Applicant has amended the claims to overcome the claim objection(s). The objection(s) are withdrawn.
Rejections under 35 U.S.C. §112
Regarding the rejection(s) under 112(a), the Applicant has amended the claims to obviate the rejection for lacking adequate written description. The new matter has been removed, and the rejection is withdrawn.
Regarding the rejection(s) under 112(b), the Applicant has amended the claims to obviate the rejection for indefiniteness. The rejection is withdrawn.
Rejections under 35 U.S.C. §101
Regarding the rejection of Claims 1-20, the Examiner has considered the Applicant’s arguments but does not find them persuasive for at least the following reasons. Applicant argues:
A1. “Applicant respectfully submits that the amended claims do not recite a mental process or a mathematical concept” (Remarks, pg. 10-15).
Re. argument A1: The Examiner respectfully submits the basis of rejection as necessitated by amendment. The claims are rejected as being directed to an abstract idea of the CMOHA subgrouping without significantly more. Applicant has not presented arguments as to the contrary.
A2. “Claim 1, as amended, includes limitations such as "training a first machine learning process as a function of a first training set relating inputs containing endocrinal measurements and body dysfunction labels to outputs containing dietary communications,” “adjusting one or more connections and one or more weights between nodes in adjacent layers of the first machine learning process,” and “updating the first machine learning process as a function of the connections to produce the output layer of nodes.” These steps mirror the technological specificity of Example 47… the claim sets forth an iterative machine learning framework that improves the functionality of the underlying system by enabling the automatic identification of optimal dietary recommendations based on real-time endocrinal data… This represents a technical improvement over conventional systems that rely on static nutritional databases or manual interpretation of health measurements” (remarks, pg. 16).
Re. argument A2: The Examiner respectfully submits that the additional elements are above-considered. The way the claims are drafted, these additional elements do not provide a practical application or significantly more. The recited training, adjusting and updating steps are unrelated to the other steps of the claim (claim 1 being representative). Taking the abstract idea and attaching it to a machine learning model training process amounts no more than mere instructions to apply the judicial exception using a machine learning model. The way that the additional elements are recited in the claim amount no more than apply it because they, alone or in combination, do not provide an integration into the claimed process or impose meaningful limits on practicing the claimed process. However, amending to recite “adjusting, by the processor, the updated first machine learning process as a function of the updated dietary communications” will provide an integration. Note: To standardize terms, Applicant would also amend the identifying step to recite “identifying the dietary communication as a function of the [[trained]] updated first machine learning process”.
Example 47 claim 1 is considered a mock eligible claim because it falls within a statutory category and does not recite a judicial exception (unlike the claims of the instant Application). Example 47 claim 3 is considered eligible because the claim as a whole integrates the abstract idea into a practical application by improving network security (i.e., providing a technical solution to a technical problem). The Examiner has read the Applicant’s disclosure but does not see how the claim limitations might be a technological improvement. “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification… If the specification does not set forth or explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the Examiner should not determine the claim improves technology.” MPEP 2106.05(a). Please provide evidence to advance prosecution or provide the suggested amendment to the independent claims.
A3. “The claim recites "obtain a second endocrinal measurement relating to the first endocrinal measurement," "update the dietary communications as a function of the second endocrinal measurement," and "adjust the first machine learning process as a function of the updated dietary communications." These limitations demonstrate that the system continuously adapts its outputs through retraining and feedback, producing a personalized and dynamically optimized dietary recommendation. This iterative improvement process constitutes a practical application of machine learning to a physiological problem and enhances both the accuracy and responsiveness of dietary communication systems” (remarks, pg. 16-17).
Re. argument A3: The Examiner respectfully submits that unlike Example 48, the claimed invention does not recite a technical solution to a technical problem. The Examiner did not find a part of the disclosure that set forth a technology improved with these argued benefits to accuracy and responsiveness. The mere application of machine learning technology to a physiological problem is not a technical solution to a technical problem.
The “obtain a second endocrinal measurement…” step is an instruction implemented by the processor. The relationship between the first and second endocrinal measurements is not recited. The “update the dietary communications…” step is an instruction implemented by the processor. The way in which the processor uses the second endocrinal measurement to update the dietary communications is not recited. The “adjusting, by the processor, the first machine learning process” additional element reads as if the first machine learning process is going to be trained in a second instance to produce a second trained machine learning process by applying new data to the algorithm. The claim does not read as re-training the trained first machine learning process as a function of the updated dietary communications. The Examiner suggests amending to recite “adjusting, by the processor, the updated first machine learning process as a function of the updated dietary communications” will provide an integration. Support is provided at least by the claimed “identifying…” sub-step of “updating the first machine learning process as a function of the connections to produce the output layer of nodes”. Note: To standardize terms, Applicant would also amend the identifying step to recite “identifying the dietary communication as a function of the [[trained]] updated first machine learning process”.
A4. “Similarly, claim 1 as amended recites… This non-conventional and specific arrangement of steps provides a technological improvement by enabling adaptive model retraining based on physiological feedback, resulting in real-time, personalized dietary recommendations that improve system accuracy and responsiveness” (remarks, pg. 17-18).
Re. argument A4: The Examiner respectfully asserts that the record does not yet clarify that the “adjusting, by the processor, the first machine learning process” is a retraining step for the updated first machine learning process. Applicant’s remarks (pg. 17, last para.) discuss multiple limitations prior to the assertion that the non-conventional and specific arrangement of steps provides a technological improvement by enabling adaptive model retraining based on physiological feedback. The Examiner cannot agree with this assertion because the relationship between the first and second endocrinal measurements is not recited in the claims; only the processor means is provided for updating the dietary communications; an