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 the Claims
Claims 1-5, 7-9, and 22-26 are currently pending. Claim 26 is newly added in the Claims filed on October 16, 2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 October 16, 2025 has been entered.
Claim Objections
Claims 1 and 26 are objected to due to the following informalities: Claims 1 and 26 recite “repeated measures ANOVA with Bonferroni-corrected two-tailed paired-samples t-tests,” but neither the Claims nor the Specification provides an explicit definition for the term “ANOVA.” In the interest of compact prosecution, and based on the context and disclosures of [0076] of the as-filed Specification, Examiner will interpret “ANOVA” as “Analysis of Variance,” which is a commonly used technique in statistics.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-9, and 22-25 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, Claim 1 recites “[receiving] human feedback data…through the API,” e.g. see element vii of Claim 1. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claims 2-5, 7-9, and 22-25 are also rejected under 35 U.S.C. 112(b) due to their dependence on Claim 1.
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 1-5, 7-9, and 22-26 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.
Step 1
Claims 1-5, 7-9, and 22-26 are within the four statutory categories. Claims 1-5, 7-9, and 22-26 are drawn to systems for reducing clinical symptoms, which is within the four statutory categories (i.e. machine).
Prong 1 of Step 2A
Claim 1 recites: A system to reduce clinical symptoms in a patient having an immune-mediated inflammatory disease or irritable bowel syndrome (IBS) comprising:
processing unit having a data repository and a knowledge module, wherein the knowledge module is in communication with the data repository containing collected data, wherein the processing unit comprises at least one of a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), or digital signal processor (DSP), and wherein the processing unit is configured to:
i. generate a machine learning algorithm programmed to execute multivariate statistical regression analysis on the processing unit;
ii. train the machine learning algorithm by feeding the collected data from the repository through the algorithm to establish baseline correlation weights between food categories and symptom severity scores;
iii. extract, by the machine learning algorithm executing statistical computations on the processing unit, new data from the collected data by algorithmically identifying temporal patterns between recorded food intake events and corresponding symptom severity measurements;
iv. analyze, by the machine learning algorithm executing statistical computations on the processing unit, the new data received from daily encrypted digital surveys including diet and symptom data by performing automated real-time statistical analysis to generate rapid computational feedback within 24 hours of survey data collection;
v. identify trigger foods by executing, via the machine learning algorithm on the processing unit, multivariate correlation analysis on the new data to computationally determine associations between specific food categories and symptom severity relevant to the immune-mediated inflammatory disease or IBS of the patient, wherein identifying comprises applying at least one statistical method selected from repeated measures ANOVA with Bonferroni-corrected two-tailed paired-samples t-tests or McNemar's Chi-square test with Yates' continuity correction;
vi. develop, by automated execution of the machine learning algorithm, at least one personalized diet for the patient by computationally processing the identified trigger foods through phased algorithmic cycles, wherein developing comprises:
algorithmically determining a subset of 3 to 5 high-probability trigger foods from the identified trigger foods for elimination;
computationally generating an elimination phase diet excluding the subset of trigger foods;
algorithmically scheduling sequential reintroduction of each eliminated food with computational timing determined by analyzing daily symptom severity data; and
computationally constructing a maintenance phase diet that excludes foods confirmed as triggers through the statistical analysis;
vii. receive human feedback data regarding symptom changes and dietary adherence through the API; and
viii. retrain the machine learning algorithm by feeding the human feedback, the collected data, and the new data from the data repository back through the algorithm to update correlation weights and refine statistical models, wherein the computational retraining improves accuracy of trigger food identification and further reduces the clinical systems symptoms associated with the patient; and
ix. continuously improve the machine learning algorithm by incorporating data from each additional patient participant into the data repository as an expanding database to refine trigger food identification accuracy through iterative statistical model updates;
b. an application program interface (API) in communication with the processing unit, and wherein the API is configured to communicate information with the patient through an interactive portal executing on a mobile device computing platform, wherein the API:
transmits encrypted daily digital surveys to the mobile device via secure data transmission;
receives encrypted survey responses including diet recall data and symptom severity measurements;
executes real-time computational visualization of collected data to generate graphical displays on a user interface of the mobile device; and
automatically generates and transmits computational notifications and recommended dietary actions to the mobile device user interface based on the real- time statistical analysis performed by the machine learning algorithm;
wherein the execution of the-personalized diet reduces the clinical symptoms in a person the patient having an immune-mediated inflammatory disease or IBS.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a mathematical concept and/or a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations (in this case, the limitations of generating a machine learning algorithm in the form of a multivariate statistical regression analysis, training the machine learning algorithm by feeding it data to establish baseline correlation weights, extracting new data by the machine learning algorithm, analyzing the new data using statistical analysis, identifying trigger foods using multivariate correlation analysis and statistical methods, developing the personalized diet through algorithmic cycles, algorithmically determining a subset of high probability trigger foods, computationally generating an elimination phase diet, algorithmically scheduling sequential reintroduction of each eliminated food, computationally constructing a maintenance phase diet, retraining the machine learning algorithm by feeding received data through the algorithm, and continuously improving the machine learning algorithm are properly interpreted as at least mathematical relationships and/or calculations), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of receiving human feedback data regarding symptom changes and dietary adherence, communicating information with the patient, transmitting daily digital surveys, receiving survey responses, executing visualization of the collected data to generate a graphical display, generating and transmitting notifications and recommended dietary actions in order to reduce clinical symptoms in a patient are properly interpreted as rules or instructions for a patient to follow in order to reduce the patient’s clinical symptoms), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Dependent Claims 2-5, 7-9, and 22-25 include other limitations, for example Claim 2 recites types of data received from a patient, Claim 3 recites receiving data from various sources, Claim 4 recites enabling communication between various entities, Claim 5 recites managing access to the data, Claim 7 recites periodically advising the patient, Claims 8-9 recite various types of diseases, Claims 22-24 recite constructing a personalized diet for the patient, and Claim 25 recites steps to be performed by the machine learning algorithm, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-5, 7-9, and 22-25 are nonetheless directed towards fundamentally the same abstract idea as independent Claim 1.
Claim 26 recites: A machine learning system for computationally identifying trigger foods associated with immune-mediated inflammatory disease or irritable bowel syndrome (IBS) symptoms comprising:
a processing unit comprising at least one of a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), or digital signal processor (DSP);
b. a data repository in communication with the processing unit, the data repository storing an expanding database aggregating data from multiple patients including behavioral health data, clinical characteristics, and user engagement metrics;
c. a first machine learning algorithm executing on the processing unit, the first machine learning algorithm programmed to:
identify potential trigger foods associated with a patient's IBD, IBS, or IMID symptoms by computationally processing diagnosis type, demographic data, and symptom severity at intake against a database of trigger foods compiled from literature reviews and clinical practice guidelines to generate a patient-specific hypothesis of 20 to 25 high-potential trigger foods;
d. a second machine learning algorithm executing on the processing unit, the second machine learning algorithm programmed to:
analyze the patient's individual diet data and symptom data collected during an identification phase by performing multivariate statistical regression analysis;
computationally narrow the 20 to 25 high-potential trigger foods to a subset of 3 to 5 foods by executing correlation analysis that identifies associations between specific foods from the hypothesis and adverse symptoms reported during the identification phase; and
generate elimination phase instructions to remove the identified subset from the patient's diet;
e. a third machine learning algorithm executing on the processing unit, the third machine learning algorithm programmed to:
evaluate daily symptom severity data during a reintroduction phase by performing statistical comparisons between symptom severity during trigger food reintroduction and baseline symptom severity at end of elimination phase using at least one statistical method selected from t-tests or comparative statistics;
computationally determine, based on the statistical comparisons, whether symptoms have returned upon reintroduction of each food;
algorithmically identify foods for continued reintroduction by determining that symptom severity has not increased; and
generate maintenance phase dietary recommendations excluding confirmed trigger foods;
f. a continuous improvement module executing on the processing unit programmed to:
aggregate data collected from each patient participant into the expanding database;
retrain the first, second, and third machine learning algorithms by feeding the aggregated data through the algorithms to update statistical models and correlation weights;
improve trigger food identification accuracy through iterative refinement using the expanding dataset;
g. an application program interface (API) in communication with the processing unit programmed to:
transmit encrypted daily digital surveys to a mobile device computing platform;
receive encrypted responses including 24-hour food recall data and symptom severity measurements;
execute real-time statistical analysis on received data within 24 hours of collection; and
automatically generate and transmit computational notifications and phase transition instructions to the mobile device based on the real-time statistical analysis, wherein the machine learning system computationally reduces clinical symptoms by iteratively identifying and eliminating trigger foods through the coordinated execution of the first, second, and third machine learning algorithms.
The underlined limitations as shown above, given the broadest reasonable interpretation, cover the abstract idea of a mathematical concept and/or a certain method of organizing human activity because they recite mathematical relationships, formulas, equations, and/or mathematical calculations (in this case, the limitations of the first machine learning algorithm comprising computationally identifying potential trigger foods, the second machine learning algorithm comprising computationally narrowing the high-potential trigger foods and generating elimination phase instructions to remove the narrowed subset of high-potential trigger foods, the third machine learning algorithm comprising evaluating the severity data using at least one statistical method, computationally determining whether symptoms have returned upon reintroduction of each food, algorithmically identifying foods for continued reintroduction, generating a maintenance phase dietary recommendations excluding confirmed trigger foods, aggregating data from each patient, retraining the first, second, and third machine learning algorithms using the aggregated patient data, and executing real-time statistical analysis on received data are properly interpreted as at least mathematical relationships and/or calculations), and/or managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of storing patient behavioral health data, clinical characteristics, and user engagement metrics, aggregating the patient data, transmitting daily digital surveys, receiving responses, and generating and transmitting the computational notifications and phase transition instructions are properly interpreted as rules or instructions for a patient to follow in order to reduce the patient’s clinical symptoms), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Prong 2 of Step 2A
Claims 1 and 26 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the structural limitations defining the processing unit, the repository, the fact that the digital surveys are encrypted, the API, the interactive portal, the mobile device computing platform, and the user interface of the mobile device) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the structural limitations defining the processing unit, the repository, the API, the interactive portal, the mobile device computing platform, and the user interface of the mobile device, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see [0061] and [0063] of the present Specification, see MPEP 2106.05(f); and/or
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language reciting that the daily surveys regarding the symptoms are encrypted, which amounts to limiting the abstract idea to the field of healthcare and encryption, see MPEP 2106.05(h).
Additionally, dependent Claims 2-5, 7-9, and 22-25 include other limitations, but these limitations also amount to no more than mere instructions to apply an exception (e.g. the software/computer programs recited in dependent Claims 4 and 5), generally linking the abstract idea to a particular technological environment or field of use (e.g. the various types of data recited in dependent Claims 8-9, and the steps for the personalized diet recited in dependent Claims 22-24), and/or adding insignificant extra-solution activity to the abstract idea (e.g. the internal and external data sources recited in dependent Claim 3), and/or do not include any additional elements beyond those already recited in independent Claim 1, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence Claims 1-5, 7-9, and 22-26 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1 and 26 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the structural limitations defining the processing unit, the repository, the fact that the digital surveys are encrypted, the API, the interactive portal, the mobile device computing platform, and the user interface of the mobile device), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, and/or generally link the abstract idea to a particular technological environment or field of use, wherein the additional elements comprise limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
[0061] and [0063] of the Specification disclose that the additional elements (i.e. the structural limitations defining the processing unit, the repository, the API, the interactive portal, the mobile device computing platform, and the user interface of the mobile device) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of data on a data repository;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing data in a data repository, and retrieving the data from storage in order to ultimately generate recommendations for the patient to reduce symptoms;
A web browser’s back and forward button functionality, e.g. see Internet Patent Corp. v. Active Network, Inc. – similarly, the current invention recites providing an interactive portal that enables communications with the patient;
Dependent Claims 2-5, 7-9, and 22-25 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to mere instructions to apply the exception (e.g. the software/computer programs recited in dependent Claims 4 and 5), generally link the abstract idea to a particular technological environment or field of use (e.g. the various types of data recited in dependent Claims 8-9, and the steps for the personalized diet recited in dependent Claims 22-24), storing and retrieving information in memory (e.g. the internal and external data sources recited in dependent Claim 3), and/or the limitations recited by the dependent claims do not recite any additional elements not already recited in independent Claim 1, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-5, 7-9, and 22-26 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-5, 7-9, and 22-26 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1-5, 7-9, and 22-26 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 101. The following represents Examiner’s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Breitenstein (US 2011/0077973) teaches a platform for patient analytics that extracts data from a plurality of disparate sources, and includes API that enables the communication of data between various entities. However, Breitenstein does not teach any type of machine learning algorithm and/or the training of the machine learning algorithm. Furthermore, although the invention of Breitenstein teaches that it may obtain patient data from a survey, it does not teach that the surveys are provided daily and encrypted. Additionally, Breitenstein does not teach obtaining any food, meal, and/or severity data for the patient. Furthermore, Breitenstein does not teach utilizing any of repeated measures ANOVA with Bonferroni-corrected two-tailed paired-samples t-tests or McNemar’s Chi-square test with Yates’ continuity correction.
Shrager (US 2020/0411199) teaches utilizing machine learning algorithms to generate models to predict one or more treatment options for a patient. Furthermore, Shrager teaches utilizing known data to train and retrain the machine learning models, wherein the data used to train the machine learning models includes clinical survey data, and wherein the machine learning models dynamically generate and test novel personalized treatment hypotheses. However, Shrager does not teach utilizing food, meal, and/or severity data to train the machine learning models, and further does not teach determining potential trigger foods utilizing repeated measures ANOVA with Benferroni-corrected two-tailed paired samples t-test or McNemar’s Chi-square test with Yates’ continuity correction. Additionally, Shrager does not teach determining a personalized diet and the specific steps for the generation of the personalized diet. Furthermore, Shrager does not teach that the surveys are encrypted and/or provided daily.
Shea (US 2011/0150776) teaches identifying an initial solution for a patient based on the patient’s submitted medical and dietary data, wherein the initial solution includes a plurality of components/ingredients, and further teaches splitting the initial solution into a plurality of components, wherein the system determines which of the plurality of components should be eliminated and which should be administered to the patient. Furthermore, Shea teaches determining the patient’s condition after administering the solution. However, Shea does not teach utilizing any type of machine learning to execute the aforementioned functions, or training and retraining of a machine learning algorithm, and further does not teach that the determining of the potential trigger foods is performed utilizing repeated measures ANOVA with Benferroni-corrected two-tailed paired samples t-test or McNemar’s Chi-square test with Yates’ continuity correction. Furthermore, Shea does not teach that the patient submitted data is obtained from a daily encrypted survey.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claims 1-5, 7-9, and 22-26 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed October 16, 2025, with respect to the interpretation of Claims 1-5, 7-9, and 22-26 under 35 U.S.C. 112(f) have been fully considered and, in combination with the claim amendments, are persuasive. Claims 1-5, 7-9, and 22-26 are not interpreted under 35 U.S.C. 112(f) because Claim 1 recites sufficient structure (i.e. the processing unit comprising at least one of the hardware processor types now recited in Claims 1 and 26 and in [0063] of the as-filed Specification) to perform the recited functions of the claim.
Applicant’s arguments, see Remarks, filed October 16, 2025, with respect to the rejections of Claims 2 and 7 under 35 U.S.C. 112(a) have been fully considered and, in combination with the claim amendments, are persuasive. The rejections of Claims 2 and 7 under 35 U.S.C. 112(a) have been withdrawn.
Applicant’s arguments, see Remarks, filed October 16, 2025, with respect to the rejections of Claims 1-5, 7-9, and 22-25 under 35 U.S.C. 112(b) have been fully considered and, in combination with the claim amendments, are persuasive. However, for the reasons shown above, Claims 1-5, 7-9, and 22-25 are rejected under 35 U.S.C. 112(b) due to the newly amended language in Claim 1.
Applicant’s arguments, see Remarks, filed October 16, 2025, with respect to the rejections of Claims 1-5, 7-9, and 22-25 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the claimed invention is patent eligible because the claims do not recite an abstract idea, but instead recite “specific computational operations performed by machine learning algorithms executing on specifically structure processing units to process data in a manner that improves the functioning of the computer system itself,” e.g. see pg. 12 of Remarks – Examiner disagrees.
Examiner asserts that the as-filed Specification does not disclose an improvement to the functioning of the computer itself. [0046], [0058], and [0064] of the as-filed Specification disclose that the present invention improves the quality of life for a patient suffering from a disease or condition. That is, the as-filed Specification discloses that the claimed invention is directed towards the improvement of a patient condition, rather than an improvement to the functioning of the computer itself. For example, unlike the invention recited in McRO or Enfish, the present invention does not enable a function not previously capable of being performed by a computer (i.e. objectively and accurately setting morph weights for facial expressions for animated character speech) or recite a data structure that results in increased flexibility, faster search times, and smaller memory requirements when compared to a conventional database respectively.
Applicants further allege that the claimed invention is patent eligible because it integrates any abstract idea into a practical application, specifically because it recites “specific processor architectures,” “specific statistical methods executed by the machine learning algorithms,” and improves the computer’s ability to process data because the claim language now recites processing data within 24 hours of collection, e.g. see pgs. 12-14 of Remarks – Examiner disagrees.
Regarding the “specificity” of the processors and the statistical techniques, Examiner notes that the Claims being narrowly claimed is not dispositive in determining the eligibility of the Claims. The Court has held that a claim may not preempt abstract ideas, laws of nature, or natural phenomena, even if the judicial exception is narrow, e.g. see MPEP 2106.04. That is, a claim reciting a narrow abstract idea nonetheless recites an abstract idea.
Additionally, none of the specific processors are properly considered a “particular machine” because the claimed types of processors are properly considered “generic” for the purposes of 35 U.S.C. 101. The claimed types of processors are existing, conventional types of processors, and any combination of the processors may be used to execute the claimed statistical analysis techniques. That is, Applicant does not purport to have invented any of the aforementioned types of processors, and [0063] of the Specification discloses that the aforementioned types of processors are merely examples of types of processors the processing unit may be, which indicates that the processors are merely recited as a well-understood, routine, and conventional tool to execute the functions of the processing unit.
Similarly, the recited statistical techniques are existing, conventional statistical techniques used to compare data, and Applicants do not purport to have invented these statistical techniques. Rather, the present claim language merely claims the types of data (i.e. food categories and symptom severity) processed by the statistical techniques, and/or a particular usage of the statistical techniques (i.e. to identify trigger foods, and ultimately to generate a personalized diet for the patient).
Regarding any purported improvements to the computer itself, Examiner asserts that the recitation of conventional structural limitations (i.e. the claimed types of processors) executing conventional statistical techniques does not improve the computer itself, but instead at most improves a computer for the specific purpose of generating a personalized diet. That is, rather than achieving the technological improvement of, for example, enabling a function not previously capable of being performed by a computer, or a data structure that results in increased flexibility, faster search times, and smaller memory requirements when compared to a conventional database respectively, the present invention improves the process of generating a personalized diet for a patient, which, as discussed above, is an improvement to the abstract idea rather than a technological improvement.
Additionally, Examiner cites [0061] and [0063] of the as-filed Specification to support Examiner’s position that the Specification discloses that the hardware limitations (i.e. the claimed types of processors and the repository) are conventional types of hardware. For example, [0061] of the as-filed Specification discloses that “the data repository may be a magnetic storage unit, optical storage unit, solid state storage unit, or similar storage unit,” and [0063] discloses that the processing unit “may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets.” That is, the Specification discloses that the processing unit and the data repository may be any combination of the aforementioned existing, conventional types of hardware.
Applicants further allege that the claimed invention is patent eligible because it recites a plurality of machine learning algorithms that distributes computational tasks across specialized algorithms, and because the iterative process of training and retraining the machine learning algorithms improve the computational accuracy of the algorithms, e.g. see pgs. 14-15 of Remarks – Examiner disagrees.
Examiner initially notes that Claim 1 does not claim multiple machine learning algorithms, so the aforementioned arguments are not applicable to Claim 1. Regarding Claim 26, Claim 26 recites first, second, and third machine learning algorithms that are configured to perform particular functions, wherein each of the machine learning algorithms are executed on the processing unit. That is, the processing unit executes different program modules (i.e. each machine learning algorithm) that are configured to perform different processing operations, but this is not equivalent to distributed computing tasks among a plurality of specialized hardware/structural elements. At most, the aforementioned claim language of Claim 26 recites a plurality of mathematical models being used to perform different calculations. Furthermore, even assuming, arguendo, that the plurality of machine learning algorithms were remotely located from each other and hence part of a distributed computing system, merely generally configuring a system to perform certain functions remotely and others locally to save on computing resources is not, in itself, evidence of a technological improvement because distributed computing is a well-known computational technique.
Applicants additionally allege that the claimed invention is patent eligible because its limitations are not properly analogized to Alice Corp v. CLS Bank and/or Parker v. Flook, e.g. see pg. 17 of Remarks – Examiner disagrees.
Regarding electronic recordkeeping, an “expanding” database is not in itself grounds for distinguishing the claimed data repository limitation from the invention of Alice Corp v. CLS Bank because a database being capable of receiving additional data is how a conventional database operates. Furthermore, the use of the data stored in the repository for the purpose of training and retraining machine learning algorithms does not change the fact that the data repository itself is merely storing data.
Regarding the repetitive calculations aspect, as shown above, the claim limitations are no longer analogized to Parker v. Flook, and hence any arguments pertaining to Parker v. Flook are moot.
Applicants also allege that the claimed invention is patent eligible because it recites significantly more than an abstract idea when considered as an ordered combination, e.g. see pg. 18 of Remarks – Examiner disagrees.
The claimed elements, whether considered individually or as an ordered combination, do not represent significantly more than the aforementioned abstract idea because each element merely performs functions they are expected to be capable of performing. That is, the processing unit processes received data, the data repository stores data, and the API enables the communication of data between different components – these functions are not changed and/or do not achieve technological improvements as a result of them being in an ordered combination with one another, and hence are not properly considered as reciting “significantly more” than the abstract idea.
For the aforementioned reasons, Claims 1-5, 7-9, and 22-25 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed October 16, 2025, regarding the rejections of Claims 1-5, 7-9, and 22-25 under 35 U.S.C. 103 have been considered and are persuasive for the reasons disclosed above. For the aforementioned reasons, the rejections of Claims 1-5, 7-9, and 22-26 under 35 U.S.C. 103 have been withdrawn
Conclusion
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/JOHN P GO/Examiner, Art Unit 3681