DETAILED ACTION
Notices to Applicant
This communication is a final rejection. Claims 1-20, as filed 03/10/2026, are currently pending and have been considered below.
Priority is generally acknowledged as shown on the filing receipt with the earliest priority date being 09/25/2020.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon and the rationale supporting the rejection would be the same under either status.
Claim Objections
Claim 1 and 11 are objected to because of the following informalities. Claim 1 contains the following spelling error: “biological extractions corelated to exemplary nutrient programs”. Claims 1 and 11 both contain an ungrammatical “a” in the phrase “correlating a one or more of a plurality of biological extractions.”
Appropriate correction is required.
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 non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter 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
The claim(s) recite(s) subject matter within a statutory category as a process, machine, and/or article of manufacture which recite:
1. A system for generating a directional response using machine learning, the system comprising:
a computing device, wherein the computing device is configured to: (additional element – applying the abstract idea with a computer)
receive user data (additional element – insignificant extra-solution activity; mere data-gathering);
retrieve a biological extraction of a user (additional element – insignificant extra-solution activity; mere data-gathering);
generate a nutrient program as a function of the user data, wherein generating the nutrient program comprises: (abstract idea – mental process)
generating program training data, wherein the program training data comprises exemplary user data and exemplary biological extractions correlated to exemplary nutrient programs (abstract idea – mental process);
training a program machine-learning model using the program training data (abstract idea – mental process; specification as published [0018] describes ML models that can be done mentally or with pen and paper; mathematical processes); and
generating the nutrient program using the trained program machine-learning model (abstract idea – mental process; specification as published [0085] describes using linear regression);
generate a directional response as a function of the nutrient program wherein the directional response is generated using a machine learning process, wherein the machine learning process is trained using directional training data correlating a one or more of a plurality of biological extractions, outcome datums, and nutrient programs to a plurality of directions, and wherein the computing device is configured to output the directional response as a function of the biological extraction from the user and the machine learning process (abstract idea – mental process; specification as published in [0085] describes linear regression as a suitable ML model for the invention, and linear regression can be performed mentally or with pen and paper; a person can correlate biological data and outcomes to directions using a simple regression or lookup table); and
output the directional response (additional element – insignificant extra-solution activity and applying the abstract idea with a computer).
2. The system of claim 1, wherein the user data comprises information related to a family history of the user related to the biological extraction (additional element – insignificant extra-solution activity; mere data-gathering).
3. The system of claim 1, wherein retrieving the biological extraction comprises analyzing a food intake of the user to generate microbiome data of the biological extraction (abstract idea – mental process).
4. The system of claim 1, wherein the computing device is further configured to determine a stress level datum as a function of the biological extraction (abstract idea – mental process).
5. The system of claim 4, wherein determining the stress level datum comprises:
extracting at least a keyword from the biological extraction using a language processing module; and
determining the stress level datum as a function of the at least a keyword (abstract idea – mental process).
6. The system of claim 4, wherein determining the stress level datum comprises:
generating stress level training data, wherein the stress level training data comprises exemplary biological extractions correlated to exemplary stress level datums;
training a stress level machine-learning model using the stress level training data; and
determining the stress level using the trained stress level machine-learning mode (abstract idea – mental process).
7. The system of claim 4, wherein generating the nutrient program comprises generating the nutrient program as a function of the stress level datum (abstract idea – mental process).
8. The system of claim 4, wherein the computing device is further configured to pair a third-party with the user as a function of the stress level datum and user data comprising vocation data (abstract idea – mental process).
9. The system of claim 1, wherein the computing device is further configured to:
determine an outcome datum related to the nutrient program; and
generate the directional response as a function of the outcome datum (abstract idea – mental process).
10. The system of claim 1, wherein the computing device is further configured to:
generate a tendency model;
generate at least one priority value as a function of the directional response and the tendency model; and
remove a priority value of the at least one priority value as a function of a filter comprising a user-selected threshold value for the at least one priority value (abstract idea – mental process).
Claims 1-10 is presented as an exemplary claim but the same analysis applies to claim 11-20.
Step 2A Prong One
These steps of gathering data, analyzing the data, and outputting a resulting recommendation to a user, as drafted, under the broadest reasonable interpretation, includes mental processes. Other than reciting generic computer terms like a computing device, nothing in the claims precludes the italicized portions from practically being performed in the mind. The Examiner notes that the ML techniques such as generating program training data, training a model, and using the model to generate a result are all recited at a high enough level that they can practicably be performed in the mind or with pen and paper. For example, the model could be based on two patients. Alternatively, the ML techniques amount to mathematical concepts that are also abstract ideas.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims. For example, claim 3-10 and 13-20 recite particular aspects of how the data analysis is performed but for recitation of generic computer components.
Step 2A Prong Two
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements:
amount to mere instructions to apply an exception. For example, configuring a computer to perform the steps of claim 1 and outputting a directional response (i.e., displaying a message to the user) amounts to invoking computers as a tool to perform the abstract idea, see applicant’s specification as published [0060], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea. For example, receiving user data and a biological extraction (i.e., “any data indicative of a person’s physiological state” in [0023]) amounts to mere data gathering and selecting a particular data source or type of data to be manipulated, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. For example, claim 2 recites additional insignificant extra-solution activity, namely, mere data gathering. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields. For example, receiving user data and retrieving a biological extraction of a user amount to receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i), and electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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 1-4, 6-7, 11-14, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi (Zeevi, David et al., Personalized Nutrition by Prediction of Glycemic Responses; Cell, Volume 163, Issue 5, 1079 - 1094) in view of Borthakur (WO2019012471A1).
Regarding claim 1, Zeevi discloses: A system for generating a directional response using machine learning, the system comprising: a computing device, wherein the computing device is configured (“participants were instructed to log their activities in real-time, including food intake, exercise and sleep, using a smartphone-adjusted website (www.personalnutrition.org) that we developed (Figure S2A)”) to:
--receive user data; retrieve a biological extraction of a user (“a comprehensive profile was collected from each participant, including: food frequency, lifestyle, and medical background questionnaires; anthropometric measures (e.g., height, hip circumference); a panel of blood tests; and a single stool sample, used for microbiota profiling by both 16S rRNA and metagenomic sequencing,” page 1083; Figure 1);
--generate a nutrient program as a function of the user data (Personalized Nutrition Predictor in Graphical Abstract, reproduced below for convenience; personalized dietary interventions on page 1089),
PNG
media_image1.png
540
544
media_image1.png
Greyscale
--wherein generating the nutrient program comprises:
--generating program training data, wherein the program training data comprises exemplary user data and exemplary biological extractions correlated to exemplary nutrient programs (“To this end, we employed a two-phase approach. In the first, discovery phase, the algorithm was developed on the main cohort of 800 participants, and performance was evaluated using a standard leave-one-out cross validation scheme, whereby PPGRs of each participant were predicted using a model trained on the data of all other participants. In the second, validation phase, an independent cohort of 100 participants was recruited and profiled, and their PPGRs were predicted using the model trained only on the main cohort,” page 1084);
--training a program machine-learning model using the program training data (trained model and gradient boosting on page 1084); and
--generating the nutrient program using the trained program machine-learning model (“We then used these rankings to design two 1-week diets: (1) a diet composed of the meals predicted by the algorithm to have low PPGRs (the ‘‘good’’ diet); and (2) a diet composed of the meals with high predicted PPGRs (the ‘‘bad’’ diet). Every participant then followed each of the two diets for a full week, during which they were connected to a CGM and a daily stool sample was collected (if available),” page 1087);
--generate a directional response (interpreted as actionable guidance such as a meal plan) as a function of the nutrient program (design personalized diet to lower glycemic responses in Graphical Abstract);
--wherein the machine learning process is trained using directional training data correlating a one or more of a plurality of biological extractions, outcome datums, and nutrient programs to a plurality of directions (“To this end, we employed a two-phase approach. In the first, discovery phase, the algorithm was developed on the main cohort of 800 participants, and performance was evaluated using a standard leave-one-out cross validation scheme, whereby PPGRs of each participant were predicted using a model trained on the data of all other participants,” page 1084; “a comprehensive profile was collected from each participant, including: food frequency, lifestyle, and medical background questionnaires; anthropometric measures (e.g., height, hip circumference); a panel of blood tests; and a single stool sample, used for microbiota profiling by both 16S rRNA and metagenomic sequencing,” page 1083; “We then used these rankings to design two 1-week diets: (1) a diet composed of the meals predicted by the algorithm to have low PPGRs (the ‘‘good’’ diet); and (2) a diet composed of the meals with high predicted PPGRs (the ‘‘bad’’ diet),” page 1087).
Zeevi does not expressly disclose that the personalized diet is outputted (such as with a display). Borthakur teaches: wherein the computing device is configured to output the directional response as a function of the biological extraction from the user and the machine learning process …output the directional response (“Alternatively, the output data may also be remotely accessible or the output unit 31 may reside outside the wearable device 28 such as smart watch, smart phone, smart kiosk, smart panels on cars, fridges, tabletops and electronic display boards,” page 15; “The combined data is utilized for providing the user with therapeutic recommendation. In step 107, the therapeutic recommendation, body score as well as information on mental health and stress levels and feedback on the current state of health of a user is provided as output to the user,” page 16).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi’s personalized dietary recommendations with Borthakur’s output because this would allow patients and clinicians to act on the recommendation and improve the patient’s health (see Borthakur page 4; timely interventions on page 15).
Regarding claim 2, Zeevi further discloses: wherein the user data comprises information related to a family history of the user related to the biological extraction (Questionnaires in Graphical Abstract).
Regarding claim 3, Zeevi further discloses: wherein retrieving the biological extraction comprises analyzing a food intake of the user to generate microbiome data of the biological extraction (Food Diary in Graphical Abstract).
Regarding claim 4, Zeevi does not expressly disclose but Borthakur teaches: wherein the computing device is further configured to determine a stress level datum as a function of the biological extraction (sensor for sweat glad activity on page 10; “the data received by the various body sensors 2 is provided to the extraction unit 15 which processes the sensor data to generate body data 3. The body data 3 is provided to the primary processing unit 4 which compares the body data 3 to reference values 7 for determining the state of mental health 5 of the user can the stress levels 6 which the user is undergoing,” page 16).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi’s personalized dietary recommendations with Borthakur’s stress calculation based on a biological sensor reading because detecting sweat would make the determination of the user’s health “much more accurate” (Borthakur page 10).
Regarding claim 6, Zeevi further discloses: wherein determining the stress level datum comprises:
--generating stress level training data, wherein the stress level training data comprises exemplary biological extractions correlated to exemplary stress level datums; training a stress level machine-learning model using the stress level training data (“the system has a Machine Learning and Artificial Intelligence processing unit adapted to receive and process the body data and the contextual data, adapted to identify one or more patterns of the human body based on processing of the body data and the contextual data and adapted to compare the patterns of the human body, and to determine risks related to stress level and mental health of a human body” page 7); and
--determining the stress level using the trained stress level machine-learning model (“Based on the algorithms, the mental health and/or stress levels of a user are determined. In step 103, the body data, body score, mental health and stress level data is provided to an IOT unit which is capable of storing the data on a remote database for further processing,” page 16).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi’s personalized dietary recommendations with Borthakur’s stress calculations because this would make the health recommendations “much more accurate” (Borthakur page 10).
Regarding claim 7, Zeevi discloses using a trained ML model to generate a personalized diet. Zeevi does not expressly disclose, but Borthakur teaches: further discloses: wherein generating the nutrient program comprises generating the nutrient program as a function of the stress level datum (“the therapeutic recommendation, body score as well as 20 information on mental health and stress levels and feedback on the current state of health of a user is provided as output to the user,” page 16).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi’s personalized dietary recommendations with Borthakur’s stress calculations because this would make the health recommendations “much more accurate” (Borthakur page 10).
Additionally, it can be seen that each element is taught by either Zeevi or Borthakur. The stress-based determinations do not affect the normal functioning of Zeevi’s using a trained ML model to generate a personalized diet. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Zeevi with the teachings of Borthakur since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claims 11-14 and 16-17 are substantially similar to claims 1-4 and 6-7 and is rejected with the same reasoning.
Claims 5 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi (Zeevi, David et al., Personalized Nutrition by Prediction of Glycemic Responses; Cell, Volume 163, Issue 5, 1079 - 1094) in view of Borthakur (WO2019012471A1) and Beauchamp (US20170323065A1).
Regarding claim 5, Zeevi does not expressly disclose but Beauchamp teaches: wherein determining the stress level datum comprises: extracting at least a keyword from the biological extraction using a language processing module; and determining the stress level datum as a function of the at least a keyword (Table 5 shows keywords and associated sentiments; sentiment analysis of text in [0055]-[0056] gathered via natural language processing).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi and Borthakur’s personalized dietary recommendations based on stress calculations to include Beauchamp’s stress determination from sentiment analysis because stress level would help better tailor the outputted direction responses and more accurately track the user’s state of health (see Beauchamp [0071]).
Claim 15 is substantially similar to claim 5 and is rejected with the same reasoning.
Claims 8 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi (Zeevi, David et al., Personalized Nutrition by Prediction of Glycemic Responses; Cell, Volume 163, Issue 5, 1079 - 1094) in view of Borthakur (WO2019012471A1) and Bindler (US20030059750A1).
Regarding claim 8, Zeevi further discloses: wherein the computing device is further configured to pair a third-party with the user as a function of the stress level datum and user data comprising vocation data (assessing stress at work in [0155]; sensors for physiological levels at work including frustration in [0238])).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi and Borthakur’s personalized dietary recommendations based on stress calculations to include Bindler’s working stress features because stress level would help better tailor the outputted direction responses and enhance the therapy output that is provided to the user (see Bindler [0066]).
Claim 18 is substantially similar to claim 8 and is rejected with the same reasoning.
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi (Zeevi, David et al., Personalized Nutrition by Prediction of Glycemic Responses; Cell, Volume 163, Issue 5, 1079 - 1094) in view of Borthakur (WO2019012471A1) and Haddad (US20190145988A1).
Regarding claim 9, Zeevi does not expressly disclose but Haddad teaches: determine an outcome datum related to the nutrient program; and generate the directional response as a function of the outcome datum (iterative cycle of testing and offering recommendations as described in [0058] and FIG. 1).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi and Borthakur’s personalized dietary recommendations based on stress calculations to include Haddad’s iterative process because this would lead to improved therapy and recommendations over time (see Haddad [0018]).
Claim 19 is substantially similar to claim 9 and is rejected with the same reasoning.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zeevi (Zeevi, David et al., Personalized Nutrition by Prediction of Glycemic Responses; Cell, Volume 163, Issue 5, 1079 - 1094) in view of Borthakur (WO2019012471A1) and Horvitz (EP1287444B1).
Regarding claim 10, Zeevi further discloses: wherein the computing device is further configured to:
--generate a tendency model (“The classifier can consider data features such as the structural relationship between the user and the sender of the text, as well as the time of events referenced in the text,” [0016]);
--generate at least one priority value as a function of the directional response and the tendency mode (“The text 36 to be analyzed is input into the classifier 20, which outputs a scalar number 49, for example, measuring the likelihood that the text being analyzed is of high or low priority,” [0054]); and
--remove a priority value of the at least one priority value as a function of a filter comprising a user-selected threshold value for the at least one priority value The text 36 to be analyzed is input into the classifier 20, which outputs a scalar number 49, for example, measuring the likelihood that the text being analyzed is of high or low priority (filter out low priority items in [0087]; “user-set threshold in [0067]).
One of ordinary skill in the art before the effective filing date would have been motivated to expand Zeevi and Borthakur’s personalized dietary recommendations based on stress calculations to include Horvitz’s filtering because this would improve the usefulness of the recommendations by ensuring that only items likely to be helpful are output to the user (see Horvitz [0011]).
Additionally, it can be seen that each element is taught by either Zeevi, Borthakur, or Horvitz. The filtering of Horvitz does not affect the normal functioning of the elements of the claim which are taught by Zeevi and Borthakur. Because the elements do not affect the normal functioning of each other, the results of their combination would have been predictable. Therefore, before the effective filing date of the claimed invention, it would have been obvious to combine the teachings of Horvitz with the teachings of Zeevi and Borthakur since the result is merely a combination of old elements, and, since the elements do not affect the normal functioning of each other, the results of the combination would have been predictable.
Claim 20 is substantially similar to claim 10 and is rejected with the same reasoning.
Response to arguments
Applicant's arguments filed 03/10/2026 have been fully considered but are not found persuasive for the reasons set forth below.
Regarding the subject matter ineligibility rejections, Applicant argues that the claimed invention is not directed to a mental process (Step 2A Prong One) because the ML training and inference operations cannot be practicably performed in the human mind. Remarks pages 2-4. As described in the specification at [0085], the ML models encompass linear regressions. A linear regression model can be trained on a small number of patients (e.g., two patients) and used to generate outputs by hand with pen and paper. The claims do not recite technical ML detail such as model architecture, dataset size, or computational requirements that would preclude this mental or pen-and-paper implementation. Applicant’s argument that the claims require “computational training over a dataset to generate and store model parameters” is not required by the broadest reasonable interpretation of the claims. While the claimed invention now includes a second ML process for generating the directional response, this second process is also recited at a high level of abstraction and could also be a linear regression with a small number of data points.
Applicant argues that the claimed invention is not directed to mathematical processes (Step 2A Prong One). Remarks pages 4-5. The Examiner disagrees because the specification at [0085] confirms that the models can be implemented with mathematical techniques like linear regression. The fact that the claims use words instead of symbols does not remove them from the mathematical concept category. See MPEP 2106.04(a)(2)(I)(A) (“A mathematical relationship may be expressed in words or using mathematical symbols.”).
Applicant argues that the claimed invention integrates any abstract idea into a practical application (Step 2A Prong Two) because the claimed invention is analogous to Examples 47 and 48 and constitute a “technical improvement over traditional, manual or purely rule-based nutrition guidance systems”. Remarks pages 5-7. This is not persuasive. Example 47 (Claim 3) recited an ANN that detected malicious packets in real time and automatically took remedial actions such as blocking source addresses (i.e., reconfiguring the firewall). The claim was eligible because it achieved a concrete technical improvement to the functioning of the network security system itself, not merely to the quality of the information presented to humans. Example 48 (Claim 2) recited a deep neural network that separated speech waveforms from mixed audio signal and thus also produced a concrete technical improvement in speech separation technology.
The instant claims are distinguishable from both examples. The output of the claimed invention is a “directional response” (i.e., a dietary recommendation) that is displayed to a user. This is a human-readable recommendation rather than a technical output like a change to a firewall setting or a reconfigured waveform file. The claims do not purport to improve the ML process. The claimed improvement is that the dietary recommendations are generated automatically rather than mentally by a dietician, but automating an otherwise manual process or improving a mental process is not a technical improvement.
Applicant argues that the claimed invention amounts to significantly more than any abstract idea (Step 2B) because the claimed invention is a “non-conventional and specific arrangement of steps.” Remarks pages 7-8. This arguments conflates eligibility and novelty/nonobviousness. As the Supreme Court emphasizes: “[t]he ‘novelty' of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.” Diehr, 450 U.S. at 188-89 (emphasis added). The Federal Circuit further guides that “[eligibility and novelty are separate inquiries.” Two-Way Media Ltd. v. Comcast Cable Commc' ns, LLC, 874 F.3d 1329, 1340 (Fed. Cir. 2017). The analysis in Berkheimer does not result in eligibility of all nonconventional data processing steps. Here, the claimed invention trains a first model and uses the first model as output into a second model. Since both of these models could be linear regressions calculated by hand, this process remains part of the abstract idea. Accordingly, the Examiner maintains the 101 rejections.
Regarding the prior art rejections, Applicant generally asserts that the amended claims have features that are not taught by Zeevi and Borthakur without engaging with the references. Remarks pages 9-10. The Examiner disagrees for the reasons cited above. In brief, Zeevi discloses a gradient boosting model trained on 800 participants that uses biological extractions such as blood tests in the training data and PPGR (postprandial glycemic response) in the outcome data. The term “directional input” lacks a particular definition in the specification and is thus interpreted under the BRI to include Zeevi’s recommending of meals based on predicted glycemic response.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 JOSHUA BLANCHETTE whose telephone number is (571)272-2299. The examiner can normally be reached on Monday - Thursday 7:30AM - 6:00PM, EST.
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, Shahid Merchant, can be reached on (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JOSHUA B BLANCHETTE/ Primary Examiner, Art Unit 3624