DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/30/2025 has been entered.
Response to Amendment
This action is in response to the amendments filed on 12/30/2025. Claims 1, 9, 11, and 19 have been amended. Claims 10 and 20 were previously canceled. Claims 1-9 and 11-19 are currently pending.
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 1-9 and 11-19 are rejected under 35 U.S.C. 101 because (additional limitations crossed out):
A system for identifying compatible meal options the system
receive,
receive,
iteratively train,
a plurality of user body measurements as input; and
a plurality a food tolerances score as output;
generate,
receive,
receive a user selection identifying a dietary preference from the plurality of dietary preferences;
filter the user selection identifying the dietary preference from the plurality of dietary preferences
generate a food tolerance instruction set using the at least a food tolerance score;
receive,
select,
generate,
The above limitations, as drafted, are processes that, under their broadest reasonable interpretation, covers following rules or instructions, as well as performance of limitations by the human mind or with pen and paper. That is, other than reciting the claims as being performed by a “processor”, “modules” operating on the processor, a “first machine-learning model”, and a “supervised machine-learning process”, nothing in the claims precludes the steps as being described as a mental process, or a human following instructions. The claims, as written describe instructions for analyzing data via a mathematical process (i.e., training of a model) to determine meal options. If a claim limitation, under its broadest reasonable interpretation, covers performance of activities that may be performed mentally or with pen and paper, then it falls within the “Mental Processes” grouping of abstract ideas. Further, if a claim limitation, under its broadest reasonable interpretation, covers following rules or instructions, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. The claim further recites “generate…a menu model that includes a polynomial regression algorithm…(i.e., a mathematical algorithm). Accordingly the claim features mathematical functions/relationships, and falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claims only recite the additional elements of a “processor”, “modules” operating on the processor, training a “first machine-learning model”, and a “supervised machine-learning process” to perform the claimed steps. The “processor” and “modules” are recited at a high level of generality (see at least Paras. [0085] and [0113]-0116]) such that it amounts to no more than mere instructions to apply the exception using generic computing components. In regards to the training of the “first machine-learning model” and the “supervised machine-learning process”, it is considered to be generic computer function and/or field-of-use/”general link” implementations and does not meaningfully limit the claim (See Accenture, 728 F.3d 1336, 108 U.S.P.Q.2d 1173 (Fed. Cir. 2013), citing Cf. Diamond v. Diehr, 450 U.S. 175, 191-192 (1981) ("[I]nsignificant post-solution activity will not transform an unpatentable principle in to a patentable process.”). Moreover, the functionality intended to be performed by the machine learning model and process appears to be based on very rudimentary constraints (e.g., biological markers, user body measurements). Without some prohibition in the claims regarding scalability, computation load, etc., the training of the first machine learning model, and the supervised machine-learning process could reasonably be considered an additional abstract idea in the “mental process” or “mathematical concepts” category, but for which is simply automated (i.e., “apply it”). The claims further recite “display on a graphical user interface located on the processor the output containing the plurality of menu options”. However, this is merely insignificant extra-solution activity. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are therefore still directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of a “processor” and “modules” to perform the claimed steps amounts to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Further, the limitation “display on a graphical user interface located on the processor the output containing the plurality of menu options” does not amount to significantly more than the judicial exception because it is a well-understood, routine and conventional function. The Examiner takes Official Notice that the display of data via a user interface is a well-understood, routine, and conventional function since it has been prevalent in the art for decades (i.e., since the advent of computers with monitors). Therefore, the claims are not found to be patent eligible.
Claim 11 features limitations similar to those of claim 1, and is also directed to the same abstract idea without significantly more.
Claims 2-9 are dependent on claim 1, and include all the limitations of claim 1. Claims 12-19 are dependent on claim 11, and include all the limitations of claim 11. Claims 9 and 19 feature a “second machine-learning process”, however similar to the “first machine-learning model” it merely equates to an automated mental process or mathematical function (i.e., “apply it”). The remaining dependent claims recite no further additional elements, and the previously identified additional elements do not integrate the abstract idea of the remaining dependent claims into a practical application, and thus they are also directed to an abstract idea. Therefore, the dependent claims are found to be directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-9 and 11-19 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
MPEP 2161.01 I. recites in part:
"... original claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. See MPEP §§ 2163.02 and 2181, subsection IV. .."
The following limitations define the invention in functional language, but the specification lacks the algorithm or steps/procedure for performing the functions or are not explained in sufficient detail:
Regarding claims 1 and 11, the limitation “receive, using a body analysis module operating on the processor, at least a user body measurement and a user biological marker wherein the user biological marker comprises at least an element of physiological state data” is not supported by the specification. The Examiner points to paragraph [0014] of the specification which states, in part, “Continuing to refer to FIG. 1, user biological marker 112 contains a plurality of user body measurements.” Based on this, a biological marker is the same as the user body measurement, and are not two separately received types of data.
Dependent claims are rejected as well because they inherit the limitations of the independent claims.
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 1-9 and 11-19 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 claims 1 and 11, the limitation “receive, using a body analysis module operating on the processor, at least a user body measurement and a user biological marker wherein the user biological marker comprises at least an element of physiological state data” is not supported by the specification. The Examiner points to paragraph [0014] of the specification which states, in part, “Continuing to refer to FIG. 1, user biological marker 112 contains a plurality of user body measurements.” Based on this, a biological marker is the same as the user body measurement. It is unclear how the biological marker and user body measurement are classified as separate data by the claim. The Examiner further notes that if the claim is indicating that the biological marker contains data other than the user body measurements, it does not appear to be supported by the specification.
Dependent claims are rejected as well since they inherit the limitations of the independent claims.
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.
Claim(s) 1-4, and 10-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimmer (US 2018/0240542) in view of Apte (US 2018/0122510).
Regarding claim 1, Grimmer discloses A system for identifying compatible meal options the system comprising a processor wherein the processor is further configured to:
receive, using a body analysis module operating on the processor, at least a user body measurement and a user biological marker, wherein the user biological marker comprises at least an element of physiological state data.
receive, at a food analysis module operating on the processor, the at least a user body measurement;
Grimmer discloses receiving data about a user’s vitals, genotype and phenotype (see at least Para. [0069]).
Grimmer does not explicitly disclose:
iteratively train, using the food analysis module, a first machine-learning model using a training set comprising:
a plurality of user body measurements as input; and
a plurality of food tolerance score as output;
generate, using the first machine learning model, at least a food tolerance score based on the at least a user body measurements.
generate a food tolerance instruction set using the at least a food tolerance score;
See Apte, at least Para. [0061] – “In another variation, Block S130 can include processing ( e.g., generating, training, updating, executing, storing, etc.) one or more characterization models (e.g., diet related condition characterization models, etc.) for one or more diet-related conditions. The characterization models preferably leverage microbiome features as inputs, and preferably output diet-related characterizations and/or any suitable components thereof; but characterization models can use and suitable inputs to generate any suitable outputs. In an example, Block S130 can include transforming the supplementary data, the microbiome composition diversity features, and the microbiome functional diversity features into a characterization model (e.g., training a diet-related characterization model based on the supplementary data and microbiome features; etc.) for the diet-related condition.” The Examiner notes that, per the Applicant’s specification, “food tolerance instruction set” appears to equate to the “food tolerance score” (see Para. [0053] – “Food tolerance scores may include a numerical score that may indicate tolerance to a particular food item as reflected in a score between zero to one hundred, whereby a score with a higher value and closer to one hundred may reflect higher tolerance.”, and Para. [0055] – “For instance and without limitation, food tolerance instruction set may include a numerical score on a scale from 1 to 100, with 1 being the least tolerable and 100 being the most tolerable.”). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grimmer to utilize the teachings of Apte since Grimmer and Apte are in the same field of endeavor (i.e., prediction of a person’s response to certain food types) and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention
Grimmer discloses:
receive, from the food analysis module, a plurality of dietary preferences;
receive a user selection identifying a dietary preference from the plurality of dietary preferences
(See Para. [0067] – “The user may also in some embodiments provide information about the food preferences, for example food religion (e.g., vegan, kosher, gluten free), or a list of foods that the user prefers or does not like. This information may be elicited through a browser interface with questions or lists of questions with dropdown predetermined choices according to some embodiments.”)
Grimmer also discloses filter, the user selection identifying the dietary preference from the plurality of dietary preferences using the food analysis module. Grimmer discloses a filtering engine filtering meals and recipes based on at least a user’s food preferences. (Para. [0086])
Grimmer also discloses:
Receive, using a menu generator module operating on the processor, a the food tolerance instruction set from the food analysis module;
select, using the menu generator module, a menu training set from a menu database as a function of the food tolerance instruction set, wherein the menu training set correlates food tolerance scores to menu options;
generate, using a supervised machine-learning process, a menu model which includes a polynomial regression algorithm that receives the food tolerance instruction set as an input and produces an output containing a plurality of menu options utilizing the menu training set; and
See at least Paras. [0087]-[0090] – “ The meal ranker engine 130 receives the available meals as well the user's macronutrient 109 classifications or diet type, and micronutrient 110 classifications. The meal ranker engine may also receive the following information from the databases 105 and 106: [0088] Data on calories, macronutrients and micronutrients for each meal, recipe, food or supplement [0089] Data on diet type, macronutrient and micronutrient recommendations for each user [0090] Goals and user preference information”, Para. [0091] – “The meal ranker algorithm outputs recommendations for one or more users. The meal ranker algorithm may rank meals, recipes, supplements, hero foods, snacks or other information. The meal ranker algorithm may take into account other user meals in a day or supplements that the user regularly takes. It may also take into account the activity level of the user, in addition to macronutrient and micronutrients.”, and Para. [0260] – “Relevant algorithms for decision rule include, but are not limited to: discriminant analysis including linear, logistic, and more flexible discrimination techniques (see, e.g., Gnanadesikan, 1977, Methods for Statistical Data Analysis of Multivariate Observations, New York: Wiley 1977; tree-based algorithms such as classification and regression trees (CART) and variants (see, e.g., Breiman, 1984, Classification and Regression Trees, Belmont, Calif.: Wadsworth International Group; generalized additive models (see, e.g., Tibshirani, 1990, Generalized Additive Models, London: Chapman and Hall; neural networks (see, e.g., Neal, 1996, Bayesian Learning for Neural Networks, New York: Springer-Verlag; and Insua, 1998, Feedforward neural networks for nonparametric regression In: Practical Nonparametric and Semiparametric Bayesian Statistics, pp. 181-194, New York: Springer, the entire contents of each of which are hereby incorporated by reference herein. Other suitable data analysis algorithms for decision rules include, but are not limited to, logistic regression, or a nonparametric algorithm that detects differences in the distribution of feature values (e.g., a Wilcoxon Signed Rank Test (unadjusted and adjusted)).” The Examiner notes that, per the Applicant’s specification, “food tolerance instruction set” appears to equate to the “food tolerance score” (see Para. [0053] – “Food tolerance scores may include a numerical score that may indicate tolerance to a particular food item as reflected in a score between zero to one hundred, whereby a score with a higher value and closer to one hundred may reflect higher tolerance.”, and Para. [0055] – “For instance and without limitation, food tolerance instruction set may include a numerical score on a scale from 1 to 100, with 1 being the least tolerable and 100 being the most tolerable.”), while the “menu training set” appears to equates to “menu options” (See Para. [0059] – “A "menu option" as used in this disclosure, includes a list of available dish choices that may be selected for a particular meal.”, and “Menu generator module 160 selects a menu training set 164 from menu database 168 as a function of food tolerance instruction set 148. For instance and without limitation, menu generator module 160 may select a menu training set 164 that includes a data entry containing a food tolerance for a vegetable such as spinach correlated to a menu option that includes a spinach salad if a food tolerance instruction set 148 contains spinach having a tolerance score of enjoy where a user is able to consume spinach without any resulting gastrointestinal symptoms.” Based on this, the Examiner has interpreted the limitations as merely selecting a menu option based upon a food tolerance score.
Grimmer also discloses display on a graphical user interface located on the processor the output containing the plurality of menu options (See Para. [0255] – “In 2920, the meals recommended for each user are presented to the corresponding user through email, messaging or the user logging in to the system and being presented with them there.”
Regarding claim 2, Grimmer discloses the system of claim 1, wherein the user selection identifies a diagnosis. Grimmer discloses a user providing information regarding their general wellbeing (Para. [0067]).
Regarding claim 3, Grimmer discloses the system of claim 1, wherein the user selection identifies a food item and a symptomatic complaint. Grimmer discloses a user providing a list of foods to which they are allergic (Para. [0103]).
Regarding claim 4, Grimmer discloses the system of claim 1, wherein the meal option contains an ingredient and wherein the ingredient configured to conform to a dietary requirement. Grimmer discloses filtering out meals with ingredients that are not allowed or desired for a user (Para. [0107]).
Claim 11 features limitations similar to those of claim 1, and is therefore rejected using the same rationale.
Claim 12 features limitations similar to those of claim 2, and is therefore rejected using the same rationale.
Claim 13 features limitations similar to those of claim 3, and is therefore rejected using the same rationale.
Claim 14 features limitations similar to those of claim 4, and is therefore rejected using the same rationale.
Claim(s) 5-7 and 15-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimmer and Apte, and in further view of Reenan (US 2003/0082647).
Regarding claim 5, Grimmer and Apte do not disclose the system of claim 1, wherein the biological marker further comprises a marker of mitochondrial function. Reenan teaches determining biological age based in part on mitochondrial function (Para. [0160]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grimmer and Apte to utilize the teachings of Reenan since at least Grimmer and Reenan are in the same field of endeavor (i.e., analysis of biological markers) and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 6, Grimmer and Apte do not disclose the system of claim 1, wherein the biological marker further comprises an indicator of a stress response. Reenan teaches determining biological age based in part on resistance to oxidative stress (Para. [0160]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grimmer and Apte to utilize the teachings of Reenan since at least Grimmer and Reenan are in the same field of endeavor (i.e., analysis of biological markers) and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Regarding claim 7, Grimmer and Apte do not disclose the system of claim 1, wherein the biological marker further comprises a marker of cellular energy. Reenan teaches determining biological age based in part on ATP levels (Para. [0160]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grimmer and Apte to utilize the teachings of Reenan since at least Grimmer and Reenan are in the same field of endeavor (i.e., analysis of biological markers) and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claim 15 features limitations similar to those of claim 5, and is therefore rejected using the same rationale.
Claim 16 features limitations similar to those of claim 6, and is therefore rejected using the same rationale.
Claim 17 features limitations similar to those of claim 7, and is therefore rejected using the same rationale.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimmer and Apte, and in further view of Gudkov (US 2015/0285823)
Regarding claim 8, Grimmer and Apte do not disclose the system of claim 1, wherein the biological marker further comprises a toxicity measurement. Gudkov teaches the estimation of physiological age based in part on a quantitative measurement of cumulative genotoxicity experienced by an organism (Para. 0005]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grimmer and Apte to utilize the teachings of Gudkov since at least Grimmer and Gudkov are in the same field of endeavor (i.e., analysis of biological markers) and all of the claimed elements were known in the prior art and one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention.
Claim(s) 9 and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Grimmer in view of Apte, and in further view of Wolf (US 2021/0065873)
Regarding claim 9, Grimmer and Apte do not disclose the system of claim 1, wherein determining the food tolerance score further comprises:
Generating a second machine-learning process, wherein the second machine-learning process is trained within training data correlating a plurality of body data elements to a plurality of numerical food tolerance scores; and
Determining the numerical food tolerance score as a function of the second machine-learning process
Wolf teaches using a machine learning algorithm to predict biomarkers (i.e., tolerance score) (see at least Para. [0030], page 2 of provisional 62/893057). Wolf further teaches utilizing expert dietary input with biomarker predictions to define personalized weighting/scores of foods (see at least Para. [0057] of PGPub). It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grimmer to utilize the teachings of Wolf since the use of a machine learning algorithm may serve to provide continuous improvement of the system as more data is provided.
Claim 19 features limitations similar to those of claim 9, and is therefore rejected using the same rationale.
Response to Arguments
Applicant's arguments regarding claims rejected under 35 U.S.C. 101 have been fully considered but they are not persuasive. Applicant argues with substance:
Applicant argues that the claims are not directed to an abstract idea. This is not persuasive as this is merely a conclusive statement.
Applicant’s argues regarding the benefits of “ensemble learning” are not persuasive. First, the Examiner notes that the specification is silent in regards to any of the alleged benefits. Further, the Applicant has not invented ensemble learning, and merely introducing a well-known machine learning tactic in the claims does not present an improvement in the art.
Applicant’s arguments regarding the limitation “iteratively train, using the food analysis module, a first machine-learning model using a training set comprising: a plurality of user body measurements as input; and a plurality of food tolerance score as output” is not persuasive. The training of a machine learning model using a particular data set is not sufficient for patent eligibility.
Applicant’s arguments concerning Berkheimer are not persuasive. Of the limitations listed by the Applicant, all are part of the abstract idea itself, but for the limitation “display on a graphical user interface located on the processor the output containing the plurality of menu options”. The display of data via a GUI is insignificant extra-solution activity as explained in the body of the 101 rejection above.
The Examiner notes that the claims are analogous to Recentive Analytics, Inc. v. Fox Corp. in that the claims merely apply conventional machine learning to new data environments, without specific, non-generic improvements to the technology itself. In other words, the invention merely relies on the use of generic machine learning technology in carrying out the steps of determining food tolerance scores, and providing menu options based in part on the food tolerance score. The steps of training the claimed models do not represent a technological improvement as they merely describe the well-known routine functions of machine learning (i.e., training a model with particular input to receive a particular output), but within a particular environment (i.e., food selection).
There is nothing in the claims, whether considered individually or in their ordered combination, that would transform the invention into something “significantly more” than the abstract idea of determining food tolerance scores, and providing menu options through the application of machine learning.
Applicant's arguments regarding claims rejected under 35 U.S.C. 103 have been fully considered but they are not moot due to the application of additional prior art. Newly applied reference Apte was found to teach the limitations regarding the training of the first machine learning model, while previously cited Grimmer was found to disclose the remaining amendments pertaining to the output of menu options.
Based on this, the 103 rejection is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KYLE G ROBINSON whose telephone number is (571)272-9261. The examiner can normally be reached Monday - Thursday, 7:00 - 4:30 EST; Friday 7:00-11:00 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, Kambiz Abdi can be reached on 571-272-6702. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/KYLE G ROBINSON/Examiner, Art Unit 3626
/KAMBIZ ABDI/ Supervisory Patent Examiner, Art Unit 3685