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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 12/03/2025 has been entered.
Claim Status
Claims 1-20 are pending.
Claims 1-20 are rejected.
Claims 1 and 11 are independent.
No claims are canceled, withdrawn, or new.
Priority
The instant application was originally filed 04/02/2021. No priority is claimed to any previous application. The effective filing date of pending claims 1-20 is 04/02/2021.
Office Action Outline
Rejections applied
Abbreviations
112/b Indefiniteness
PHOSITA
"a Person Having Ordinary Skill In The Art before the effective filing date of the claimed invention"
112/b "Means for"
BRI
Broadest Reasonable Interpretation
112/a Enablement,
Written description
CRM
"Computer-Readable Media" and equivalent language
112 Other
IDS
Information Disclosure Statement
X
102, 103
JE
Judicial Exception
X
101 JE(s)
112/a
35 USC 112(a) and similarly for 112/b, etc.
101 Other
N:N
page:line
X
Double Patenting
MM/DD/YYYY
date format
Overview of Withdrawal/Revision of Objections/Rejections
In view of the amendment and remarks received 12/03/2025:
• The objection to the drawings is withdrawn.
• The objection to claims 1 and 11 is withdrawn.
• The 101 rejection is maintained with revision.
• The 103 rejections are withdrawn and new 103 rejections are asserted.
• New Double Patenting rejections are asserted.
Claim Interpretation
The term "binding element", recited in claims 6 and 16, is interpreted to be an element of data denoting a target receptor, cell, tissue, and/or organ that a xenobiotic binds to (Spec.[0027].)
The term "dosage vector", recited in claims 6 and 16, is interpreted to be included in a toxic response vector; as dosages may be denoted by a dose-response curve; and that dose response curve may be included in a toxic response vector (see last five lines of Spec.[0042].)
The term "edible", recited in claims 1, 8, 10, 11, 18, and 20, is interpreted to be a source of nourishment that may be consumed by a user such that the user may absorb the nutrients from the source. (Spec.[0035].)
The term "elimination element", recited in claims 10 and 20, is interpreted to be an element of data associated with the clearance of the xenobiotic from the individual's body. (Spec.[0054].)
The term "exposure input", recited in claims 1, 2, 11, and 12, is interpreted to be an element of datum representing the magnitude and/or length of exposure to a xenobiotic. (Spec.[0014].)
The term "exposure route", recited in claims 2 and 12, is interpreted to be a route and/or pathway by which a xenobiotic entered an individual's body. (Spec.[0015].)
The term "hormetic element", recited in claims 9 and 19, is interpreted to be an element of data denoting that a xenobiotic exhibits hormesis, wherein hormesis denotes a biphasic dose response to a xenobiotic such that a low dose results in a beneficial effect and a high dose and/or no dose results in toxicity and/or deficiency. (Spec.[0046].)
The term "medical guideline", recited in claims 6 and 16, is interpreted to be a medical resource that identifies and/or outlines one or more processes in the human body. (Spec.[0027].)
The term "nourishment machine learning model", recited in claims 1 and 11, is interpreted to be a machine-learning model to produce a nourishment program output given edibles and/or toxicological functional goals as inputs. (Spec.[0051].)
The term "nourishment program", recited in claims 1 and 11, is interpreted to be a program (i.e., a recommendation or a diet) consisting of one or more edibles that are to be consumed over a given time period, wherein a time period is a temporal measurement such as seconds, minutes, hours, days, weeks, months, years. (Spec.[0050]); and the program is generated by the machine learning model, and is interpreted to be a program only, not physical food. Note, the machine learning model itself cannot generate edibles or food, but generates a nourishment program (i.e., a recommendation or a diet, see Spec [0050]).
The term "nourishment training set", recited in claims 1 and 11, is interpreted to be a training set that correlates a toxicological functional goal to an edible. (Spec.[0052].)
The term "physiological impact", recited in claims 5, 6, 15, and 16, is interpreted to be an effect that a xenobiotic has on the health system of an individual. (Spec.[0027].)
The term "physiological machine learning model", recited in claims 6 and 16, is interpreted to be a machine-learning model to produce a physiological impact output given binding elements and toxicological indicators as inputs. (Spec.[0028].)
The term "probabilistic vector", recited in claims 3 and 13, is interpreted to be an element of data that represents one or more a quantitative values and/or measures probability associated with a health system modification. (Spec.[0023].)
The term "profile machine learning model", recited in claims 1 and 11, is interpreted to be a machine-learning model to produce a toxicological profile output given exposure inputs and toxicological indicators as inputs. (Spec.[0016].)
The term "profile training set", recited in claims 1 and 11, is interpreted to be a training set that correlates an exposure input and/or toxicological indicator to a toxicological profile. (Spec.[0017].)
The term "progression element", recited in claims 4 and 14, is interpreted to be an element of datum that denotes the progression of a xenobiotic in an individual's body. (Spec.[0031].)
The term "response machine learning model", recited in claims 9 and 19, is interpreted to be a machine-learning model to produce a toxic response vector output given toxicological profiles (and/or hormetic elements in [0046]) as inputs. (Spec.[0043], [0046].)
The term "toxic response vector", recited in claims 8, 9, 18, and 19, is interpreted to be a data structure that represents one or more a quantitative values and/or measures toxic responses in an individual's body. (Spec.[0042].)
The term "toxicity stage", recited in claims 4 and 14, is interpreted to be a stage and/or step the individual is experiencing associated with the xenobiotic. (Spec.[0031].)
The term "toxicological ailment", recited in claims 1, 7, 11, and 17, is interpreted to be an ailment and/or collection of ailments that impact an individual's health status. (Spec.[0032].)
The term "toxicological functional goal", recited in claims 1 and 11, is interpreted to be a goal that an edible may generate according to a predicted and/or purposeful plan. (Spec.[0050].)
The term "toxicological indicator", recited in claims 1 and 11, is interpreted to be an element of data associated with an individual's biological system that denotes a health status of the individual, wherein a health status is a measure of the relative level of physical, social and/or behavioral well-being. (Spec.[0010].)
The term "toxicological profile", recited in claims 1, 3-5, 7, 9, 11, 13-15, 17, and 19, is interpreted to be a profile and/or estimation of an individual's health status as pertaining to effects of exposure to at least a xenobiotic. (Spec.[0012].)
The term "xenobiotic", recited in claims 1 and 11, is interpreted to be a substance found within an organism that is not naturally produced or expected to be present within the organism. (Spec.[0012].)
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 one or more judicial exceptions without significantly more.
MPEP 2106 details the following framework to analyze Subject Matter Eligibility:
Step 1: Are the claims directed to a category of statutory subject matter (a process, machine, manufacture, or composition of matter)? (see MPEP § 2106.03);
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. an abstract idea, a law of nature, or a natural phenomenon? (see MPEP § 2106.04(a)). Note, the MPEP at 2106.04(a)(2) & 2106.04(b) further explains that abstract ideas and laws of nature are defined as:
mathematical concepts, (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
laws of nature and natural phenomena are naturally occurring principles/ relations that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature.
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application? (see MPEP § 2106.04(d)); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept? (see MPEP § 2106.05).
Step 1 Analysis:
Claims 1-10 are directed to a 101 machine or manufacture, here a system. Claims 11-20 are directed to a 101 process, here a method. As such, claims 1-20 are directed to a related system and method, which fall under categories of statutory subject matter. (See MPEP § 2106.03). (Step 1: Yes.)
Step 2A, Prong One Analysis:
The claims recite judicial exceptions (JEs) of mathematical concepts, mental processes, and a law of nature as follows:
Independent claim 1 recites a system for performing mental processes for:
• considering the information of the toxicologic indicator; identifying a toxicological profile as a function of the toxicological indicator;
• determining at least a xenobiotic as a function of the toxicological indicator; considering the information of the exposure input;
• identifying the toxicological profile as a function of the at least a xenobiotic and the exposure input;
• using a profile machine-learning model (also considered a mathematical concept); training the profile machine-learning model using a profile training set (also considered a mathematical concept); considering the information of the training profile set (examples of xenobiotics and exposure units as inputs correlated to toxicological profiles as outputs) (also considered a mathematical concept); updating the profile training set as a function of inputs and outputs of a previous iteration of the profile machine-learning model (also considered a mathematical concept); retraining the profile machine-learning model using the updated profile training set (also considered a mathematical concept);
• generating the toxicological profile using the trained profile machine-learning model (also considered a mathematical concept);
• identify a toxicological ailment;
• training an ailment machine learning model with an ailment training set (also considered a mathematical concept);
• determining an edible as function of the toxicological profile;
• generating a nourishment program as a function of the edible and a toxicological functional goal; considering the information of the toxicological functional goal comprises a treatment goal;
• using a nourishment machine-learning model (also considered a mathematical concept); training the nourishment machine-learning model using a nourishment training set (also considered a mathematical concept); considering the information of the (nourishment) training set (user-entered edible and toxicological functional goal data correlated to nourishment program data) (also considered a mathematical concept); updating the nourishment training set as a function of inputs and outputs of a previous iteration of the profile machine-learning model (also considered a mathematical concept); retraining the profile machine-learning model using the updated nourishment training set (also considered a mathematical concept); generating the toxicological profile using the trained nourishment machine-learning model (also considered a mathematical concept).
Independent claim 11 recites a method for performing mental processes and mathematical concepts as recited in claim 1.
Claims 2 and 12 recite mental processes of: considering the information of the exposure route.
Claims 3 and 13 recite mental processes of: (determining) a probabilistic vector (also considered a mathematical concept); and identifying the toxicological profile as a function of the probabilistic vector.
Claims 4 and 14 recite mental processes of: considering the information of the progression element; determining a toxicity stage as a function of the progression element; and identifying the toxicological profile as a function of the toxicity stage.
Claims 5 and 15 recite mental processes of: determining a physiological impact; and identifying the toxicological profile as a function of the physiological impact.
Claims 6 and 16 recite mental processes of: considering the information of the binding element from a medical guideline; and determining the physiological impact as a function of a dosage vector and the binding element using a physiological machine-learning model (also considered a mathematical concept);
Claims 7 and 17 recite mental processes of: determining a toxicological ailment; and producing the toxicological profile as a function of the toxicological ailment.
Claims 8 and 18 recite mental processes of: identifying a toxic response vector ; and determining the edible as a function of the toxic response vector (also considered a mathematical concepts).
Claims 9 and 19 recite mental processes of: determining a hormetic element; and identifying the toxic response vector as a function of the hormetic element and the toxicological profile using a response machine-learning model (also considered a mathematical concept).
Claims 10 and 20 recite mental processes of: considering the information of the elimination element; and determining the edible as a function of the elimination element (also considered a mathematical concept).
Additionally, claims 7 and 17 recite a law of nature in the naturally occurring correlation of the health status of an individual with an ailment of the individual; these aspects are represented by data which respectively pertains to: the toxicological indicator, and the toxicological profile/toxicological ailment.
Step 2A Prong One summary: The claims recite JEs, characterized as mental processes, mathematical concepts, and a law of nature. Considering the broadest reasonable interpretation (BRI) of the claims, the mental processes recited in independent claims 1 and 11 (e.g., "identifying a toxicological profile", "determining at least a xenobiotic as a function of the toxicological indicator", "identifying the toxicological profile as a function of the at least a xenobiotic and the exposure input using a profile machine-learning model", etc.) are directed to processes that may be performed in the human mind, or with pen and paper, as there are no particular limitations recited in claims 1 or 11 which would prevent the mental processes from being performed in the human mind or with pen and paper. Additionally, the limitations involving using and training the machine-learning models, inherently recite mathematical concepts such as those disclosed in Specification [0019-0024], [0028], [0038], [0041-0043], [0046], [0058-0063], [0065-0066]. Such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea. Finally, the law of nature in claims 7 and 17 correlates a naturally occurring health status of an individual (represented by the toxicological indicator) with an ailment of the individual (represented by the toxicological profile which includes the toxicological ailment).
Therefore, the claims recite elements that constitute a judicial exception in the form of an abstract idea and a law of nature. (Step 2A, Prong One: Yes.)
Step 2A, Prong Two analysis:
23. In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs). Here at Step 2A, Prong Two, any remaining steps and/or elements not identified as JEs are therefore in addition to the identified JE(s), and are considered additional elements. Because the claims have been interpreted as being directed to judicial exceptions (abstract ideas in this instance) then Step 2A, Prong Two provides that the claims be examined further to determine whether the judicial exception is integrated into a practical application [see MPEP § 2106.04(d)]. A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
MPEP § 2106.04(d)(I) lists the following five example considerations for evaluating whether a judicial exception is integrated into a practical application:
(1) An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a).
(2) Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2).
(3) Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b).
(4) Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c).
(5) Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
53. Additional elements of data gathering: Claims 1, 2, 4, 6, 10-12, 14, 16, and 20 recite the additional elements of receiving or obtaining data. Data gathering steps are additional elements which perform functions of inputting, collecting, and outputting the data needed to carry out the abstract idea. These steps are considered insignificant extra-solution activity, and are not sufficient to integrate an abstract idea into a practical application as they do not impose any meaningful limitation on the abstract idea or how it is performed, nor do they provide an improvement to technology [see MPEP § 2106.04(d)(I)].
Additional elements of computer components: Claims 1 and 11 recite an additional element of a computing device. The claims require only generic computer components, which do not improve computer technology, and do not integrate the recited judicial exception into a practical application (see MPEP § 2106.04(d)(1) and MPEP § 2106.05(f)).
Step 2A Prong Two summary: Claims 1-20 have been further analyzed with respect to Step 2A, Prong Two, and no additional elements have been found, alone or in combination, that would integrate the judicial exception into a practical application. (Step 2A, Prong Two: No).
Step 2B analysis:
Because the additional claim elements do not integrate the abstract idea into a practical application, the claims are further examined under Step 2B, which evaluates whether the additional elements, individually and in combination, amount to significantly more than the judicial exception itself by providing an inventive concept. An inventive concept is furnished by an element or combination of elements that is recited in the claim in addition to the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself (see MPEP § 2106.05).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that are well-understood, routine, and conventional. Those additional elements are as follows:
Additional elements of data gathering: The additional elements of receiving or obtaining data in claims 1, 2, 4, 6, 10-12, 14, 16, and 20, do not cause the claims to rise to the level of significantly more than the judicial exception. The courts have recognized receiving or transmitting data over a network; storing and retrieving information in memory; determining the level of a biomarker in blood by any means; using polymerase chain reaction to amplify and detect DNA; detecting DNA or enzymes in a sample; and analyzing DNA to provide sequence information or detect allelic variants, [see MPEP§2106.05(d)(II)], as well-understood, routine, conventional activity when they are claimed in a merely generic manner (e.g., at a high level of generality) or as extra-solution activity. As a result, the additional elements of data gathering do not provide an inventive concept needed to amount to significantly more than the judicial exception.
Additional elements of computer components: The additional element of a computing device is recited in claims 1 and 11. This is a conventional computer component.
Further regarding the conventionality of additional elements, the MPEP at 2106.05(b) and 2106.05(d) presents several points relevant to conventional computers and data gathering steps in regard to Step 2A Prong 2 and Step 2B, including:
• A general purpose computer that applies a judicial exception, such as an abstract idea, by use of conventional computer functions, does not qualify as a particular machine (see 2106.05(b)(I)), as in the case of claims 1 and 11, which recite a conventional computer component.
• Integral use of a machine to achieve performance of a method may integrate the recited judicial exception into a practical application or provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not integrate the exception into a practical application or provide significantly more (see 2106.05(b)(II). In the instant claims, the recited computing device is used in the method of obtaining data and analyzing the data, and as such, the computing device acts only as a tool to perform the steps of data analysis, and do not integrate the exception into a practical application or provide significantly more.
• Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not integrate a judicial exception or provide significantly more (see 2106.05(b)(III). The computing device of claims 1 and 11 used in performing data analysis does not impose meaningful limitations on the claims.
• The courts have recognized "receiving or transmitting data over a network", "performing repetitive calculations", and "storing and retrieving information in memory", as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity (see MPEP 2106.05(d)(II)). The obtaining and receiving data is recited in a generic manner.
Step 2B Summary: All the limitations of claims 1-20 have been analyzed with respect to Step 2B. Considering these elements both alone and in combination, the additional elements do not provide an inventive concept that transforms the judicial exception into a patent eligible application of the exception; as such, the claims do not amount to significantly more than the judicial exception itself. (Step2B: No.)
Therefore, claims 1-20, when the limitations are considered individually and as a whole, are rejected under 35 U.S.C. § 101 as being directed to non patent-eligible subject matter.
Response to Applicant Arguments - 35 U.S.C. § 101
Applicant's arguments filed 12/03/2025 regarding the 101 rejection (p.10-18) have been fully considered but they are not yet persuasive.
Regarding Step 2A Prong One arguments:
Applicant asserts regarding mental processes, on p.11-13:
• "…the amended claims do not recite a mental process or a mathematical concept" (near end of p.11).
• "The MPEP provides…Synopsys, Inc. v. Mentor Graphics Corp., 839 F. 3D 1138, 1148 (Fed. Cir. 2016) as an example of a limitation that cannot be practically performed in the human mind" (p.12, para. 1).
• "…humans do not operate trained machine-learning models cognitively" (p.12, para. 3).
•"The amended claim is not directed to a mental process…the recited operations cannot be practically performed in the human mind" (p.12, para. 2).
• "A human mind does not update a dataset …and does not retrain a model repeatedly" (p.12, para. 3).
• "Humans do not chain trained inference engines together…" (p.13, para. 1).
• "A human cannot run correlation training across large volumes of toxicological data., nor can they carry out automated inference. These operations require machine processing" (p.13, para. 1).
• "…the amended claim does not recite a mental process under Step 2A, Prong 1. It is directed to a technical system that applies trained models and computational feedback loops to perform toxicological analysis, and therefore is not directed to an abstract idea." (p.13, para. 1).
The arguments (regarding mental processes at Step 2A Prong One) not persuasive because claims 1 and 11 do not recite details which would prevent mental performance of the steps, and at least the steps involving machine learning or training have also been characterized as reciting mathematical concepts, as put forth in the 101 rejection above.
Regarding updating datasets, chaining trained inferences together, running correlation on large volumes of data (note, a large volume of data is not recited in the claims), etc., such analysis performed mentally, or with paper and pencil, may take considerable time and effort, and although a general-purpose computer can perform these calculations at a rate and accuracy that can far exceed the mental performance of a skilled artisan, the nature of the activity is essentially the same, and therefore constitutes an abstract idea, such that claims can recite a mental process even if they are claimed as being performed on a computer (see MPEP 2106.04(a)(2)(III)(C)).
Regarding Synopsys, the fact pattern differs between Synopsys, Inc. v. Mentor Graphics Corp. (a specific data encryption method for computer communication involving a several-step manipulation of data) and the claimed invention (for using machine learning to generate a nourishment program). The judicial exceptions identified at Step 2A, Prong One are not integrated into a practical application at Step 2A, Prong Two, and further, the claims do not recite additional elements that amount to significantly more than the judicial exceptions at Step 2B when those additional elements are evaluated individually and in combination to determine whether they contribute an inventive concept.
Applicant asserts regarding mathematical concepts, on p.13-15:
• "Example 47 'shows limitations that recite an abstract idea.'…The claim
language in Example 39 reciting "training the neural network in a first stage using the first training set" was held not to recite a judicial exception, even though that limitation may rely on mathematical concepts" (p.14, para. 2).
•"…the present claim recites training…, updating…, retraining… and generating... These limitations do not set forth or describe mathematical relationships…do not identify any equations, algorithms, numerical formulas, or calculation steps. They describe training data flow and system behavior, not mathematics itself. As such, they merely involve mathematics rather than recite it" (p.14, para. 3).
•"… the limitations of claim 1… do not recite a judicial exception. At most, they involve
mathematical concepts inherent in machine-learning technology… The claim therefore aligns with Example 39 and is readily distinguishable from Example 47" (p.15, para. 1).
The argument is not persuasive because the machine learning models, the training and retraining of those models, and the integration of the two models, inherently recite statistical and mathematical concepts; these concepts and algorithms are disclosed at least in Specification paragraphs [0019-0023,0043, 0046, 0051, and 0066].
Regarding Examples 39 and 47, this argument is not persuasive, not least because the fact pattern differs between Example 39 (method for training a neural network for facial detection) and the instant application (training machine learning model to generate a nourishment program), and further because Example 39 and 47 are examples that are teaching tools using certain fact-specific scenarios, but are not the same as the guidance provided in the MPEP. Furthermore, as noted above, and at Eligibility Step 2A: Prong One, the machine learning models, the training and retraining, etc. of the claims have been identified as reciting mathematical concepts.
Regarding Step 2A Prong Two arguments:
Applicant asserts, p.15-16:
• (referring to July 2024 Subject Matter Eligibility Example 47): "…similar parallels can be drawn…The amended claims include training… updating… and retraining the model,… like the training and optimization of the ANN in Example 47,… the present claims improve automated toxicological assessment by enabling machine-based identification of toxicological profiles and ailments based on exposure inputs…The direct application of machine learning…aligns with the principles established in Example 47" (bridging p.15-16).
• "…present claims improve upon traditional toxicology analysis by integrating
trained machine-learning models into a technical pipeline for identifying toxicological profiles and ailments" (p.16, para. 3).
• "This is a clear technical improvement over conventional toxicology processes that rely on static lookup tables or manual expert analysis, and instead enables automated classification and predictive toxicity modeling across complex exposure data" (p.16, para. 3).
The arguments regarding an improvement at Step 2A Prong Two are not persuasive because there has not yet been an improvement to technology shown in the claims. A mere assertion of improvement without the detail necessary to be apparent to a person of ordinary skill in the art is not sufficient to show an improvement to technology.
Regarding showing an improvement to technology, a detailed explanation of a technical improvement may help to overcome a 101 rejection, (see MPEP 2106.04(d) and (d)(1), regarding the first consideration, showing an improvement to technology, at Step 2A Prong Two of the 101 analysis, as well as MPEP 2106.05(a)). The explanation might include a concise statement of the improvement, including improvement over the previous state of the technology field; identification of the technology field; explanation of how the claims deliver the improvement and that reasonably all embodiments within the claim scope also will result in the asserted improvement, and extension of the explanation to persuasively demonstrate the nexus of integration of the judicial exceptions into a practical application. As further examples, argument may clearly and adequately explain cause and effect leading to improvement or, for example when such cause and effect explanation is not possible, then may include evidence (e.g. experimental data) comparing a claimed result to conventional results. Also, arguments and evidence may be extrinsic to the original disclosure, including references available after the priority date, as long as it is clear that an argument applies to all embodiments of a properly supported claim.
An interview may be requested for discussion and clarification of this issue and others in this action. The examiner's contact information and instructions on how to submit an Automated Interview Request (AIR) appear at the end of this communication.
Regarding Example 47, besides reciting different fact patterns, briefly, Example 47 provides a practical application in improved network security by reflecting the improvement in both the background and in claim 3 steps (d)-(f); this integrates the judicial exception into a practical application. At present, there are no additional elements in the instant application which reflect or recite an improvement.
Regarding Step 2B arguments:
Applicant asserts, p.17-18:
• "This non-conventional and ordered combination…supplies a concrete technical architecture rather than an abstract result…The claim therefore recites significantly more than routine data analysis and instead provides a technical improvement in automated toxicology modeling, in alignment with the principles set forth in Berkheimer" (p.17, para. 2).
The Step 2B argument is not persuasive because the process steps represent abstract ideas, while the identified additional elements of data gathering and a conventional computer do not provide significantly more needed to result in an inventive concept, even when considering each claim as a whole.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
103-1:
Claims 1, 2, 7, 11, 12 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Kane, (US Patent No. US 8000982, published 16 August 2011; cited on the 03/29/2024 form PTO-892), Gil, (Journal of Applied Toxicology: An International Journal, vol. 21(4), pages 245-255 (2001); cited on the 03/29/2024 form PTO-892), and Cang, (International journal for numerical methods in biomedical engineering, vol. 34(2):e2914, pages 1-17, (2017); cited on the 03/29/2024 form PTO-892).
Note, the abbreviations used below are not recited in the claims, but appear throughout the 103 rejection with compact prosecution in mind.
New portions added in revising the instant 103 rejections are underlined below.
Independent claims 1 and 11 are respectively drawn to a system and method for generating a nourishment program by obtaining toxicological indicator (TI) data and exposure input (EI) data; training/retraining a profile machine learning model with a profile training set, and using the profile machine learning model to generate a toxicological profile (TP) and to determine an edible; identifying a toxicological ailment (TA) as a function of an ailment machine learning model trained with an ailment training set, configured to correlate the TI to the TA; further generating a nourishment program from the determined edible, and from a toxicological functional goal which comprises a treatment goal, by using a nourishment machine learning model, which is trained/retrained using a nourishment training set.
Dependent claims 2 and 12 further recite the EI data includes an exposure route (ER) data (i.e., route/pathway by which a xenobiotic entered an individual's body) related to the EI.
Dependent claims 7 and 17 further recite identifying the TP includes determining a toxicological ailment (TA) (i.e., ailment(s) that impact an individual's health status) related to the TP.
Kane shows a CIS (central integration site) comprising the necessary operational elements (e.g., computers, central databases, service processors, central integration sites (CPUs), etc.) (col.8, line 8-10). (showing the computing device of claims 1 and 11).
Kane shows clinical test results obtained from an individual (col. 7, lines 5-22); obtained by blood, body fluid, or tissue testing, including serum and urine tests (showing the toxicological indicator of claims 1 and 11).
Kane shows comparing their clinical test results with biochemical marker data stored in the databases (col. 34, claim 1, lines 2-9); and a status report with pictorial and descriptive data given as indicated (i.e. for the individual) for assessment of a list of different diseases and symptoms (col.18, line 21-35) (showing the toxicological profile of claims 1 and 11).
Kane shows the network system of the invention generates a status report that indicates specific nutritional needs of the consumer. The network of the invention studies and analyses a consumer's current clinical test results.… against the vast body of medical knowledge stored in the databases of the invention in a detailed and informative fashion…The disease pattern matching system (i.e., identifying an ailment correlated with clinical test results) of the invention enables an asymptomatic consumer to seek medical assistance for prevention and/or treatment of a diseases or disorders which were not previously identified or identifiable through classical clinical blood tests or genetic testing results (col.17, line 5-17) (showing identifying of an ailment which correlates the indicator (e.g., clinical test results such as biomarker results) to ailment of claims 1 and 11; and a toxicological ailment of claims 7 and 17).
Kane shows a network system which includes a first database of biochemical marker data information, and a second database of nutritional data for at least one nutrient (i.e., a profile training set and a nourishment training set) (col.3, lines 28-42). Further, Kane shows the status report is generated by the network system (col.17, line 5-6), and the status report suggests appropriate nutrient intervention as digestive Support, nutritional Support, nutrients recommended, and/or nutrients to avoid (col.17, line 35-37.) Kane shows analyzing the association and effects of nutrients with biochemical markers specific to the individual consumer (col. 16, lines 4-12), determining nutrients or edibles needed by the consumer based on their toxicological profile and clinical test results. Different forms of nutrients are also indicated in the status report, with the main concept reflected in the status report being the balance of nutrients (i.e., a treatment goal) (column 17, lines 5-11, 24-31 and 41-46), and generation of a status report that indicates specific nutritional needs of the consumer (col. 17, line 5-7; col. 34, claim 1, line 15-22) (showing the edible determination, functional goal which comprises a treatment goal, nourishment program generation of claims 1 and 11).
Kane shows a network system which includes a first database of biochemical marker data information, and a second database of nutritional data for at least one nutrient (col.3, lines 28-42). While Kane does not teach the machine learning model or training of the model, it would be obvious to use the marker and nutritional databases as 1st and 2nd training sets, when combining Kane with Gil and Cang Further, Kane shows the status report is generated by the network system (col.17, line 5-6), and the status report suggests appropriate nutrient intervention as digestive Support, nutritional Support, nutrients recommended, and/or nutrients to avoid (col.17, line 35-37); this shows generating a nourishment program of claims 1 and 11.
Kane does not specifically show determining a xenobiotic or exposure input of claims 1 and 11. Kane does not show the machine learning model including updating and training the machine learning model of claims 1 and 11. Kane does not show an exposure route of claims 2 and 12.
Gil shows using biomarkers as biological indicators to identify xenobiotic exposure (p.245, abstract), and classification of biomarkers of exposure as indicators of internal dose by analyzing toxic compounds or metabolites in body fluids or excreta (p.246, column 2, "biomarkers of exposure") (showing a xenobiotic, and exposure input of claims 1 and 11).
Gil shows various biological systems in xenobiotic exposure, including the respiratory system, blood system, immune system, and nervous system (p.248-250), each of which involve different routes or pathways for exposure and response. Gil discusses lung toxicity and the use of biomarkers like the Clara cell protein (CC16) to detect acute or chronic disruptions in the bronchoalveolar/blood barrier integrity (p.248, col. 1-2, "Respiratory system"); this involves pathways related to respiratory exposure routes. Gil delves into heme synthesis as biomarker for lead exposure, which pertains to the blood system (p.248, col. 2, "Blood system"), and touches on immune responses and neurotoxin effects, both of which involve specific pathways and exposure routes (p.249, col. 1, "Nervous system"; and p.250, col. 1, "Immune system") This shows an exposure route of claims 2 and 12.
Cang shows machine learning methods uncover hidden patterns in data sets, and use of machine learning to elucidate molecular mechanism in protein-ligand binding and predict binding affinities (p.2/17). Cang shows training/testing data sets, fivefold cross validation, and repeating each experiment 50 times (p.10/17, Section 2.6, Machine learning and validation) (showing training, updating, and retraining machine learning models of claims 1 and 11).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine the interactive framework including biomarker and nutritional databases for personalized nutritional information through a central network of Kane, with the xenobiotic biomarkers and exposure pathways of Gil, with the relevant machine learning method of Cang, to yield a predictable result of a machine learning method for analyzing xenobiotic biomarker and exposure measurements in order to create a nourishment program. While Kane does not teach the machine learning model or training of the model, it would be obvious to use the marker and nutritional databases as 1st and 2nd training sets, when combining Kane with Gil and Cang. The system of Kane involves obtaining clinical test results from individuals and identifying their health profiles, including toxicological, by comparing the results with stored biochemical marker data and nutritional data. This process is fundamental in toxicology and used to assess an individual's health status and possible exposure to xenobiotics. Additionally, the work of Gil discusses biomarkers and exposure routes in various biological systems. These discussions provide valuable insight into how different pathways can be analyzed to determine exposure routes and toxicological impacts. Further, the machine learning method of Cang, while applied to data of protein-ligand interactions and binding affinities (binding being esp. relevant to claim 6), is a knowledge-based method which analyzes large data sets to uncover hidden patterns in the data; and as such, the machine learning methodology and techniques of Cang are relevant to understanding molecular interactions and their physiological consequences. By combining the teachings of Kane, with Gil, and Cang, a person of ordinary skill in the art would have reasonably expected to achieve predictable results in identifying and assessing toxicological profiles based on exposure routes and biomarker data to determine a nourishment program.
103-2:
Claims 3-6, 8, 10, 13-16, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kane, in view of Gil, in view of Cang, as applied to claims 1, 2, 7, 11, 12 and 17 above, and further in view of Schulz, (Critical Care, vol. 24, pages 1-4 (2020); cited on the 03/29/2024 form PTO-892).
Dependent claims 3 and 13 further recite identifying the TP includes determining a probabilistic vector (PV) (i.e., data representing quantitative values/ measures of probability associated with a health system modification) related to the TP.
Dependent claims 4 and 14 further recite identifying the TP includes receiving a progression element (PE) (i.e., data of progression of a xenobiotic in and through an individual's body), determining a toxicity stage (TS) (e.g., coughing stage associated with the xenobiotic COVID-19, wherein the next stage is fever and/or pneumonia) related to the PE, and identifying the TP related to the TS.
Dependent claims 5 and 15 further recite identifying the TP includes determining a related physiological impact (Pl) (i.e., an effect that a xenobiotic has on the health system of an individual, e.g., coughing, seizures, etc.).
Dependent claims 6 and 16 further recite determining the PI includes receiving a binding element (BE) (i.e., data denoting a target receptor, cell, tissue, and/or organ that the xenobiotic binds to) from a medical guideline; determining the PI related to the BE using a "physiological machine learning model".
Dependent claims 8 and 18 further recite determining the edible includes identifying a toxic response vector (TRV) (i.e., a data structure representing quantitative values/ measures of toxic responses in an individual's body) related to the edible.
Dependent claims 10 and 20 further recite determining the edible includes obtaining an elimination element (EE) (i.e., data associated with clearance of the xenobiotic from the individual's body) related to the edible.
Kane does not show a binding element from a medical guideline and a physiological impact as a function of the dosage vector and the binding element using a machine learning model of claims 6 and 16.
Gil shows use of different binding proteins (retinal binding protein and adenosine deaminase binding protein) (i.e., binding elements) and renal antigens as biomarkers for detecting physiological impacts caused by xenobiotics (p.249, col. 2, "Urinary biomarkers"); additionally, the broadest reasonable interpretation (BRI) of the Gil document reads on a medical guide. This shows a binding element from a medical guideline of claims 6 and 16.
Cang shows the integration of persistent homology and machine learning to elucidate molecular mechanism in protein-ligand binding and to predict binding affinities (p.1/17, abstract). While Cang focuses on protein-ligand interactions and binding affinities, the methodology and techniques discussed are relevant to understanding molecular interactions and their physiological consequences in the context of determining physiological impacts based on binding elements and dosage vectors. A BRI of the recited "dosage vector" reads on the modeled "ligand" and "molar concentration of ligand" of Cang (p.10/17, §2.5). (Showing physiological impacts based on binding elements and dosage vectors using machine learning of claims 6 and 16.)
Kane, in view of Gil, in view of Cang, do not show a toxic range of claims 3 and 13; nor a progression element and toxicity stage of claims 4 and 14; nor a physiological impact of claims 5 and 15; nor a toxic response vector of claims 8 and 18; nor an elimination element of claims 10 and 20.
Schulz shows a compilation and summary of therapeutic and toxic plasma concentration ranges and half-lives of more than 1100 drugs and other xenobiotics (p.1 of 4, Abstract) (showing identifying a toxic range of claims 3 and 13).
Schulz shows categorization of analytical data such as the organizing of data into distinct categories (therapeutic, normal, toxic, and comatose-fatal concentrations) of clinical effects based on plasma/blood concentrations of various xenobiotics (p.2 of 4, col.2; and p.9-92 of the 216 page document). This categorization provides a structured framework for understanding different toxicity stages associated with these substances. For example, in Schulz, the therapeutic category represents concentrations with minimal side effects, which can be considered as an initial stage where the substance is well-tolerated by the body, while the toxic category encompasses concentrations that produce clinically relevant adverse effects, representing a toxicity stage where the substance's concentration leads to harmful effects on the individual's health, including, e.g., severe outcomes like coma or death. These different physiological impacts provide a foundation for the system to identify and understand the impact of xenobiotics on physiological functions. Additionally, this compilation of data related to toxic, comatose-fatal, and therapeutic levels of substances can be seen as contributing to the determination of toxicological profiles and ailments. (This shows a progression element and determining a toxicity stage of claims 4 and 14; and the determining a physiological impact of claims 5 and 15.)
By showing categorization and data compilation related to toxicological impacts (p.2 of 4, col.2; and p.9-92 of the 216 page document), Schulz provides a comprehensive framework for understanding and quantifying toxic responses in an individual's body, and categorizes substances based on their effects, including therapeutic, normal, toxic, and comatose fatal concentrations, which can be interpreted as quantitative values representing different levels of toxic response. This categorization system aligns with the concept of a toxic response vector, which is a data structure that represents one or more a quantitative values and/or measures toxic responses in an individual's body. Additionally, the data of Schulz on physiological impacts and adverse effects (p.2 of 4, col.2; and p.9-92 of the 216 page document) further contributes to identifying and understanding toxic response vectors within the context of toxicological assessments and clinical evaluations. (In this way, Schulz shows a toxic response vector of claims 8 and 18.)
Schulz teaches half-lives of more than 1100 drugs and other xenobiotics (mentioned on p.2 of 4, col.2; and shown on p.9-92 of the 216 page document), (showing an elimination element of claims 10 and 20).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Kane, Gil, Cang, and Schulz to provide a comprehensive framework using machine learning for integrating computational analysis, biomarker classification, toxicological data in order to assess and manage xenobiotic exposure, identify toxicological profiles and tailor nutritional programs for individual consumers. Schulz's comprehensive data on therapeutic and toxic plasma concentration ranges, categorization of substances based on physiological effects and understanding of toxic response vectors further strengthens the combination of Kane, Gil, Cang, because Schulz's data provides the necessary context for interpreting clinical test results, identifying toxicological ailments and determining appropriate nourishment programs based on individual toxicological profiles. Additionally, the machine learning method of Cang would add to the foundation by enhancing toxicological assessments, therapeutic interventions, and personalized healthcare strategies through understanding of xenobiotic exposure routes in determining physiological impacts and binding affinities. By combining Kane's computational methods, Gil's biomarker classifications, the machine learning methods of Cang, with exposure routes and Schulz's data on therapeutic and toxic concentrations, one skilled in the art would have a comprehensive array of resources for assessing and managing xenobiotic exposure, toxicological profiles, and nutritional needs.
103-3:
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kane, in view of Gil, in view of Cang, as applied to claims 1, 2, 7, 11, 12 and 17 above, in view of Schulz as applied to claims 3-6, 8, 10, 13-16, 18 and 20, and further in view of Schmidt-Heck, (Alcoholism: Clinical and Experimental Research, vol. 41(5), pages 883-894 (2017); cited on the 03/29/2024 form PTO-892).
Dependent claims 9 and 19 further recite identifying a toxic response vector (TRV) includes determining a hermetic element (HE) (i.e., data denoting that a xenobiotic exhibits hormesis, which denotes a biphasic dose response), and using a "response machine-learning model" in relating the TRV to the HE and the toxicological profile (TP).
Kane, in view of Gil, in view of Cang, as applied to claims 1, 2, 11, and 12 above, in view of Schulz as applied to claims 3-8, 10, 13-18 and 20, show the machine learning model and the TRV of claims 9 and 19.
Kane, in view of Gil, in view of Cang, as applied to claims 1, 2, 11, and 12 above, in view of Schulz as applied to claims 3-8, 10, 13-18 and 20, do not show the hormetic element of claims 9 and 19.
Schulz shows how substances at low concentrations can have therapeutic benefits with minimal side effects, while higher concentrations may result in toxic effects and/or death, which reflects the concept of hormesis of claims 9 and 19, where different doses lead to varying effects ranging from beneficial to toxic (p.2 of 4, col.2; and p.9-92 of the 216 page document).
Schmidt-Heck shows a study on response to varying ethanol concentrations in human liver cells, which revealed dose response relationships as well as hormetic behavior (p.885, col.2 thru p.890, col.1; also figs.3,4; tables 3,4). While Cang specifically shows training and updating of a machine learning model as shown above, Schmidt-Heck shows use of machine learning techniques in gene expression profile analysis, which provided insights into how ethanol stress impacts the toxicological profile of liver cells. This study's findings are particularly significant in the realm of toxicology risk assessment, shedding light on the complexities of cellular responses to xenobiotics and highlighting the potential of machine learning models in deciphering biological mechanisms; in this way, Schmidt-Heck reads on identifying the toxic response vector as a function of the hormetic element and toxicological profile using a response machine-learning model of claims 9 and 19.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to combine Kane, Gil, Cang, Schulz, and Schmidt-Heck to yield the predictable result of integrating diverse biomarker categorization techniques, toxicological profiling methodologies, and response machine learning models, specifically for identifying a toxic response vector based on hormetic elements. By leveraging the interactive network and clinical test result analysis of Kane, the biomarker utilization and physiological impact discussions of Gil, the trained machine learning model of Cang, the categorization of substances and hormetic element concepts of Schulz, and the study of ethanol-induced hormesis and gene expression profiling of Schmidt-Heck, one could establish a comprehensive framework for understanding hormetic elements, determining toxic response vectors and utilizing machine learning model for toxicological assessments. This combined approach aligns with established practices in the field of toxicology and healthcare, providing a robust foundation for enhancing toxicological risk assessments, therapeutic intervention, and predictive modeling in healthcare.
Response to Applicant Arguments - 35 USC § 103
The Applicant's arguments filed 24 January 2025 have been fully and respectfully considered but they are not persuasive.
The Applicant asserts, p. 18-22 of 22, that neither Kane, Gil, and Cang, nor Kane, Gil, Cang, and Schulz, nor Kane, Gil, Cang, Schulz, and Schmidt-Heck, alone or in combination, teach or suggest or motivate "identifying a toxicological ailment as a function of one or more ailment machine-learning models, wherein the one or more ailment machine-learning models are trained using an ailment training set, wherein the ailment training set is configured to correlate the toxicological indicator to the toxicological ailment."
The arguments are not persuasive because they depend on the 12/03/2025 Amendment, which has necessitated revised rejections under 35 U.S.C. 103 as put forth above. But briefly, Gil shows the toxicological agent (throughout Gil). Kane shows the network system of the invention generates a status report that indicates specific nutritional needs of the consumer. The network of the invention studies and analyses a consumer's current clinical test results… against the vast body of medical knowledge stored in the databases of the invention in a detailed and informative fashion.. The disease pattern matching system (i.e., identifying an ailment correlated with clinical test results) of the invention enables an asymptomatic consumer to seek medical assistance for prevention and/or treatment of a diseases or disorders which were not previously identified or identifiable through classical clinical blood tests or genetic testing results (Kane, col.17, line 5-17) (showing identifying of an ailment which correlates the indicator (e.g., clinical test results such as biomarker results) to ailment).
Regarding machine learning and the toxicological ailment of claims 1 and 11, as stated in the previous 103 rejections, the machine learning method of Cang, while applied to data of protein-ligand interactions and binding affinities (binding being esp. relevant to claim 6), is a knowledge-based method which analyzes large data sets to uncover hidden patterns in the data; and as such, the machine learning methodology and techniques of Cang are relevant to understanding molecular interactions (e.g., of xenobiotics) and their physiological consequences.
As such, the combination of Kane, Gil, and Cang teach "identifying a toxicological ailment as a function of one or more ailment machine-learning models, wherein the one or more ailment machine-learning models are trained using an ailment training set, wherein the ailment training set is configured to correlate the toxicological indicator to the toxicological ailment."
Double Patenting
Note, regarding the comment at p.32, para. 125, of the 06/03/2025 final Office action (which states why a double patenting rejection was not being asserted at that time), the comment is withdrawn and a double patenting rejection is asserted below. This is because after further consideration regarding double patenting, and in view of the current record, including the presently-asserted obviousness (i.e., 103) rejections of the instant application for obtaining and/or training with toxicological indicator data, toxicological profile data, xenobiotic exposure data, and toxicological ailment data, this narrowing versus the listed reference claims is now interpreted as obvious. Therefore, double patenting rejections have been applied.
The nonstatutory double patenting (NSDP) rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Double patenting rejections of instant claims 1-20:
NSDP-1:
Instant claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over:
one or more claims in reference patents 11600374 (from application 17/136,078); 11145400 (from application 17/136,087); 11763928 (from application 17/136,090); 11158417 (from application 17/136,109); 11367521 (from application 17/136,166); 11211159 (from application 17/136,179); 11355229 (from application 17/136,192); 11049603 (from application 17/136,199); 11688507 (from application 17/136,224); 12424310 (from application 17/164,385); 12555669 (from application 17/164,412); 11742069 (from application 17/164,631); 11935642 (from application 17/187,970); 11854685 (from application 17/187,983); 12322491 (from application 17/187,989); 11694787 (from application 17/187,997); 12068066 (from application 17/188,008); 12142362 (from application 17/221,442); 12512221 (from application 17/734,418); 12046352 (from application 18/200,635); and 12340893 (from application 18/522,039);
in view of Kane, Gil, Cang, Schulz, and Schmidt-Heck (as cited on the 03/29/2024 form PTO-892).
Although the reference claims are not identical to the instant claims, in a BRI they also are not patentably distinct from the instant claims: either (i) because the instant claims recite obviously equivalent or broader limitations in comparison to the reference claims or (ii) because the instant claims recite limitations which are obvious over the cited art. It is not clear that the instant claims recite limitations which are narrower than limitations in the reference claims.
Each reference patent recites method and system claims which involve training machine learning models using data related to one or more physiological conditions, resulting in the generation of a nourishment program. Although the instant application recites limitations using toxicological (indicator, profile, and ailment) data which includes xenobiotic exposure data, this narrowing versus the listed reference claims is now interpreted as obvious, such that it would have been prima facie obvious to modify the reference claims to arrive at the instant claims in view of the cited art.
NSDP-2:
Instant claims 1-20 are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over:
one or more claims in reference applications 17/164,558; 17/216,153; 17/216,190; 17/216,228; 17/243,662; 17/712,509; 18/197,162; 18/426,154; 18/766,090; 18/938,957; 19/332,295; and 19/424,283;
in view of Kane, Gil, Cang, Schulz, and Schmidt-Heck (as cited on the 03/29/2024 form PTO-892).
Although the reference claims are not identical to the instant claims, in a BRI they also are not patentably distinct from the instant claims: either (i) because the instant claims recite obviously equivalent or broader limitations in comparison to the reference claims or (ii) because the instant claims recite limitations which are obvious over the cited art. It is not clear that the instant claims recite limitations which are narrower than limitations in the reference claims.
Each reference application recites method and system claims which involve training machine learning models using data related to one or more physiological conditions, resulting in the generation of a nourishment program. Although the instant application recites limitations using toxicological (indicator, profile, and ailment) data which includes xenobiotic exposure data, this narrowing versus the listed reference claims is now interpreted as obvious, such that it would have been prima facie obvious to modify the reference claims to arrive at the instant claims in view of the cited art.
This is a provisional nonstatutory double patenting rejection.
Conclusion
No claims are allowed. This is a Non-Final Office action. A shortened statutory period for reply is set to expire THREE MONTHS from the mailing date of this action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Meredith A Vassell whose telephone number is (571)272-1771. The examiner can normally be reached 8:30 - 4:30.
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, KARLHEINZ SKOWRONEK can be reached at (571)272-9047.
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.
/M.A.V./Examiner, Art Unit 1687
/G. STEVEN VANNI/Primary patents examiner, Art Unit 1686