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 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 § 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 and, prior to an identification phase, determine a patient-specific hypothesis of 20 to 25 high-potential trigger foods based on diagnosis type, demographic data, symptom severity at intake, the patient's unique biological, clinical, and socioeconomic characteristics, and data gathered from other users and third-party sources;
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 from the patient-specific hypothesis 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 from the daily encrypted digital surveys; 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 machine learning algorithm learns and adapts without following explicit instructions by using the updated correlation weights and refined statistical models to analyze and draw inferences from patterns in the collected data, the new data, and the human feedback, and 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 comprising behavioral health data, clinical characteristics, user engagement metrics, and other participant-specific metrics to refine trigger food identification accuracy through iterative statistical model updates using iterative updates to the correlation weights and refined statistical models;
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 using updated correlation weights, and continuously improving the machine learning algorithm are steps that recite 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 steps that recite 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 based on the patient’s unique biological, clinical, and socioeconomic characteristics and data gathered from other users and third-party sources;
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, wherein the first, second and third machine learning algorithms learn and adapt without following explicit instructions by using the updated statistical models and correlation weights to analyze and draw inferences from patients in the expanding dataset;
improve trigger food identification accuracy through iterative refinement using the expanding dataset, wherein the expanding dataset includes behavioral health data, clinical characteristics, user engagement metrics, and other participant-specific metrics to improve the statistical analysis performed by the first, second, and third machine learning algorithms;
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 steps that recite 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 steps that recite 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 May 4, 2026, 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. The rejections of Claims 1-5, 7-9, and 22-25 under 35 U.S.C. 112(b) have been withdrawn.
Applicant’s arguments, see Remarks, filed May 4, 2026, with respect to the rejections of Claims 1-5, 7-9, and 22-26 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant first alleges that the grounds of rejection under 35 U.S.C. 101 are not proper because Examiner has not met the evidentiary requirements of taking Examiner’s Official Notice, e.g. see pgs. 12-13 of Remarks – Examiner disagrees.
As an initial matter, Examiner notes that, as shown above, official notice has not been relied upon for any grounds of rejection. Additionally, regarding the evidentiary requirements for showing that an additional element is well-understood, routine, and conventional, the evidentiary requirements may be met by providing one or more of (a) a citation to an express statement in the Specification that demonstrates the well-understood, routine, conventional nature of the additional elements, (b) a citation to one or more of the court decisions discussed in MPEP 2106.05(d), and/or (c) a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s), e.g. see MPEP 2106.07(a)(III). As shown above, Examiner has provided citations from the as-filed Specification disclosing that the structural additional elements (i.e. 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 components, and further has also provided citations to cases relevant to various claim limitations to show the well-understood, routine, and conventional nature of the additional elements. Hence, Examiner has met the evidentiary burden to show the well-understood, routine, and conventional nature of the additional elements.
Applicant additionally alleges that the claimed invention is patent eligible because the Claims do not recite the abstract ideas of mathematical calculations or a certain method of organizing human activities, e.g. see pgs. 14-15 of Remarks – Examiner disagrees.
Claims 1 and 26 explicitly recite that the machine learning algorithms, among other operations, “execute multivariate statistical regression analysis,” are trained using “baseline correlation weights between food categories and symptom severity scores,” perform “statistical computations,” and are retrained using “updated correlation weights and refined statistical models.” Examiner asserts that these limitations, in addition to those identified above in the grounds of rejection under 35 U.S.C. 101, are directed towards at least mathematical calculations. Additionally, the limitations identified above in the grounds of rejection under 35 U.S.C. 101 also recite steps for following rules or instructions for a patient to follow in order to reduce the patient’s clinical symptoms because the claimed invention is directed towards a series of data processing steps to ultimately provide patients with guidance regarding foods for the patient to avoid. Hence, the claimed invention is directed towards an abstract idea.
Applicant further alleges that the claimed invention is patent eligible because it integrates any abstract idea into a practical application, specifically because it applies machine-learning operations to a disease-specific dietary treatment protocol for patients, e.g. see pgs. 15-18 of Remarks – Examiner disagrees.
The Claims do not recite a particular treatment or prophylaxis because the claim limitations do not recite providing a “treatment.” A “treatment” or “prophylaxis” is defined as “an action that effects a particular treatment or prophylaxis for a disease or medical condition,” for example “administering a course of plasmapheresis to a patient,” e.g. see MPEP 2106.04(d)(2). Additionally, Examples 43 and 49 of the USPTO-issued Subject Matter Eligibility Examples disclose inventions that administer various substances to patients with various medical conditions. In contrast, the claimed invention only provides a “treatment” insofar as it ultimately displays data to a patient in the form of notifications regarding recommended dietary actions. Even assuming that the claims identified a narrow pathway including eliminating a subset of foods, reintroducing foods, and constructing a maintenance diet, none of these steps are actually administered – the aforementioned limitations merely describe the determined recommendations, without actually performing or administering the aforementioned described actions. That is, the claimed invention does not effect a particular treatment or prophylaxis for a patient because the mere display of recommended dietary data is not properly analogized to the administration of a medication to a patient.
Applicant also alleges that the claimed invention is patent eligible because it recites significantly more than any abstract idea, specifically because of the newly amended features pertaining to the retraining and updating of the models, e.g. see pgs. 18-19 of Remarks – Examiner disagrees.
As shown above, the newly amended claim limitations are not significantly more than the abstract idea because they are part of the abstract idea, and at most narrow the abstract idea. For example, the claim amendment language of determining a patient-specific hypothesis of 20 to 25 high-potential trigger foods based on various data recites a rule or instruction that ultimately goes into constructing the patient personalized diet, which corresponds to the abstract idea of a certain method of organizing human activities. Additionally, the claim amendment language of the machine learning algorithm learning and adapting by using the updated correlation weights and refined statistical models to analyze and draw inferences from various data recite mathematical calculations, which is corresponds to the abstract idea of a mathematical concept. Similarly, the newly amended language incorporated into Claim 26 merely describes types of data used as a basis for generating the patient-specific hypothesis of 20 to 25 potential trigger foods, and the data used to improve the machine learning algorithms, and hence at most merely narrows the abstract idea rather than providing any additional elements that could amount to significantly more than the abstract idea.
For the aforementioned reasons, Claims 1-5, 7-9, and 22-26 are rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Francois (US 2017/0262604) – teaches making various suggestions and recommendations for a patient via a website, wherein the patient may be suffering from various types of health states including IBD such as Crohn’s disease and/or ulcerative colitis.
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 JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H CHOI can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN P GO/Primary Examiner, Art Unit 3681
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