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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Restriction election
Applicant’s election without traverse of Group I (claim 8) in the reply filed on 06/05/2025 is acknowledged.
Claim 7 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected invention.
Claim Status
Claims 1-12 are pending.
Claim 7 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a non-elected species, as described above.
Claims 1-6 and 8-12 are under examination.
Claims 1-6 and 8-12 are rejected.
Priority
Applicant's claim for the benefit as it is a 371 of PCT/EP2021/053289 02/11/2021, filed 02/11/2021, is acknowledged.
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d) to App. No. FR2001364, filed 02/11/2020. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 08/10/2022 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The Drawings submitted 08/10/2022 are accepted.
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 1-6 and 8-12 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 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to method and system, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas 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).
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Independent claim 1:
selecting an individual
applying a set of predefined rules by a calculator, comprising: at least one first subset of rules aimed at generating at least one phenotypical index starting from a calculation of a score of a quantification of a phenotypical descriptor, said index being normalized; at least one second subset of rules aimed at generating at least one genotypical index starting from a calculation of a score of a quantification of a genotypical descriptor, said index being normalized
generating a set of personalized phenotypical and genotypical indices for an individual;
calculating target values for daily intakes of a plurality of nutrients from the application of an inference engine configured from: a knowledge base comprising a repository of predefined values of phenotypical and/or genotypical indices and at least one set of conditional rules applied to said predefined values of phenotypical indices and genotypical indices and a facts base comprising all phenotypical and genotypical indices of said individual calculated from the data acquired,
determining a composite nutritional index comprising an operation of associating a selection of target values of daily intakes of the set of nutrients with at least one metabolic function.
Dependent claim 2:
generating at least one differential indicator representing an individual reference value and a target value for daily intake of said nutrient, for each nutrient.
Dependent claim 9:
a phenotypical descriptor is calculated from a sum of scores, each score quantifying a physiological condition of the individual.
Dependent claim 10:
generating a list of recipes, said list of recipes being extracted from a recipe database comprising a set of recipes each containing a list of ingredients, each ingredient being associated with a list of macronutrients and micronutrients, each of said nutrients being quantified for a recipe according to a value and at least one time data quantifying a time period during which the nutrients are present in the body, said extraction operation correlating target values of daily intakes of nutrients with the recipe base in order to produce a list of recipes for a plurality of days.
Dependent claim 11:
filtered from a selection of predetermined ingredients, said recipes generated in the list not comprising the filtered ingredients.
Dependent claims 3-6 and 8 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claim 3 further limits the individual reference values of claim 1, claim 4 further limits the composite nutritional index of claim 1, claim 5 further limits the nutrients of claim 1, claim 6 further limits the macronutrient of claim 5, and claim 8 further limits the reference value of daily intake of a nutrient of claim 2.
Under the BRI, the instant claims recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mental process”, such as procedures for evaluating, analyzing or organizing information, and forming judgement or an opinion. The instant claims further recite judicial exceptions that are an abstract idea of the type that is in the grouping of a “mathematical concept”, such as mathematical relationships and mathematical equations.
The claims recite selecting. The human mind is capable of selecting an individual to for generating a nutritional index. The claims recite mathematical concepts of a calculation of a score, generating a set of personalized phenotypical and genotypical indices, calculating target values, determining a composite nutritional index, and generating at least one differential indicator.
Therefore, claims 1 and 12 and those claims dependent therefrom recite an abstract idea [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (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. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claim 1:
acquiring a first set of phenotypical data of said individual (-U4) characterizing phenotypical descriptors, said data comprising at least one age data, one gender data and at least one set of data characterizing physiological signs of said individual;
acquiring a second set of data of a genotype characterizing genotypical descriptors of said individual, said data comprising information characterizing mutations and/or variations of at least one gene
Dependent claim 2:
receiving a plurality of individual reference values of a daily intake of a plurality of nutrients
Independent claim 12:
to store a repository comprising at least predefined data for thresholds, ranges of values, scale of values, and predefined calculation rules, the system also comprising a data acquisition interface for a first and a second data set for at least one individual and a memory for storing said data, the system comprising a calculator to execute a set of rules and an inference engine to produce a composite nutritional index using the method according to claim 1.
displaying said composite nutritional index
The claims also include non-abstract computing elements. For example, independent claim 12 includes a system, memory, and display.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “acquiring” and “receiving”, and to data outputting, such as “store” and “display”, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract elements of “system, memory, and display” do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)).
As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 12 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (see MPEP 2106.06(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [p. 5, lines 27-p. 6, lines 11]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
A. Claim(s) 1-6 and 8-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cioara et al. (Cioara, Tudor, et al. "Expert system for nutrition care process of older adults." Future Generation Computer Systems 80 (2018): 368-383, newly cited) in view of Ryan et al. (Ryan, N. M., et al. "New tools for personalised nutrition: the Food4Me project." Nutrition Bulletin 40.2 (2015): 134-139, newly cited).
Claim 1 is directed to a method for generating a composite nutritional index comprising:
Cioara discloses expert system provides personalized intervention plans covering nutrition education, diet prescription and food ordering adapted to the older adult’s specific nutritional needs, health conditions and food preferences. [abstract].
selecting an individual; acquiring a first set of phenotypical data of said individual characterizing phenotypical descriptors, said data comprising at least one age data, one gender data and at least one set of data characterizing physiological signs of said individual;
Cioara discloses data describing the older adult self-feeding behavior such as personal information (such as age and gender), measured weight and height, and blood chemistry results [p. 371, col. 1, par. 1-3].
acquiring a second set of data of a genotype characterizing genotypical descriptors of said individua, said data comprising information characterizing mutations and/or variations of at least one gene;
Cioara is silent on the use of genotype.
However, Ryan discloses personalized nutrition can offer three levels of individualized nutrition advice based on dietary data alone (level 1), dietary and phenotypic data (level 2) and dietary, phenotypic and genetic data (level 3) [p. 134, col. 1, par. 1].
applying a set of predefined rules by a calculator, comprising: at least one first subset of rules aimed at generating at least one phenotypical index starting from a calculation of a score of a quantification of a phenotypical descriptor, said index being normalized; at least one second subset of rules aimed at generating at least one genotypical index starting from a calculation of a score of a quantification of a genotypical descriptor, said index being normalized; generating a set of personalized phenotypical and genotypical indices for an individual;
Cioara discloses the proposed system uses a reasoning process based on SWRL (Semantic Web Rule Language) rules upon nutritional and user profile ontologies to generate recommendations through semantic similarity measures [p. 370, col. 1, par. 1]. Cioara further discloses defined rules for assessing short term and long term unhealthy behaviors which may lead to the development and instauration of malnutrition [p. 373, col. 1, par. 1 and table 1].
calculating target values for daily intakes of a plurality of nutrients from the application of an inference engine configured from: a knowledge base comprising a repository of predefined values of phenotypical and/or genotypical indices and at least one set of conditional rules applied to said predefined values of phenotypical indices and genotypical indices and; a facts base comprising all phenotypical and genotypical indices of said individual calculated from the data acquired, and
Cioara discloses our evaluation process is based on laboratory validation, which measures the usefulness and quality of the system, aiming to expose problems regarding the defined nutrition knowledge base and inference system [p. 376, col. 2, par. 1]. Cioara further discloses data resulting from Miguel’s screening and nutrition monitoring as well as his profile are inserted as individuals in the Nutrition Care Process ontology and where this information, is input into the inference engine of the prototype is able to calculate parameters regarding Miguel’s condition [p. 377, col. 2, par. 4]. Cioara also discloses the system is based on predefined rules for foods and dishes employing genetic algorithms to calculate the fitness of candidate solutions using personalized target values of various nutrients [p. 370, col. 1, par. 1]. Cioara further discloses a knowledge base was developed consisting of predefined foods grouped according to the proportions of carbohydrate, protein and fat [p. 370, col. 1, par. 1]. The inference engine uses if–then production rules while linear programming is applied to select foods from the knowledge base and construct the overall personalized diet [p. 370, col. 1, par. 1].
determining a composite nutritional index comprising an operation of associating a selection of target values of daily intakes of the set of nutrients with at least one metabolic function.
Cioara discloses for diabetes, the system’s dietary plan is based on a reduction in calorie intake in those who are overweight, coupled with an improvement in physical activity to improve the action of insulin [p. 378, col. 2, par. 5]. Cioara further discloses for diabetes patients requiring insulin as Miguel’s case, the system recommends eating carbohydrate containing foods at regular intervals and/or matched to their insulin dose [p. 378, col. 2, par. 5].
Claim 2 is directed to the method for generating a composite nutritional index according to claim 1, further comprising: - receiving a plurality of individual reference values of a daily intake of a plurality of nutrients , and- generating at least one differential indicator representing an individual reference value and a target value for daily intake of said nutrient, for each nutrient.
Cioara discloses system which analyses dietary logs to assess the diet of an individual and to construct a personalized menu is developed. The system is based on predefined rules for foods and dishes employing genetic algorithms to calculate the fitness of candidate solutions using personalized target values of various nutrients [p. 370, col. 1, par. 1]. Cioara further discloses inferred recommended daily nutrient intake [p. 379, table 7 and 8].
Claim 3 is directed to the method for generating a composite nutritional index according to claim 2, wherein the individual reference values of daily intakes of said nutrients are: directly extracted from a knowledge base referencing predefined daily intakes of nutrients, and/or calculated from reference rules automatically calculating daily intakes of nutrients from phenotypical data for said individual and predefined values referenced in a knowledge base.
Cioara discloses our evaluation process is based on laboratory validation, which measures the usefulness and quality of the system, aiming to expose problems regarding the defined nutrition knowledge base and inference system [p. 376, col. 2, par. 1]. Cioara further discloses data resulting from Miguel’s screening and nutrition monitoring as well as his profile are inserted as individuals in the Nutrition Care Process ontology and where this information, is input into the inference engine of the prototype is able to calculate parameters regarding Miguel’s condition [p. 377, col. 2, par. 4]. Cioara also discloses the system is based on predefined rules for foods and dishes employing genetic algorithms to calculate the fitness of candidate solutions using personalized target values of various nutrients [p. 370, col. 1, par. 1]. Cioara further discloses the nutrient values for each type of food are extracted from the McCance and Widdowson’s food composition tables [p. 372, col. 2, par. 3].
Claim 4 is directed to the method for generating a composite nutritional index according to claim 1 wherein determining a composite nutritional index includes a plurality of groupings of target values of daily nutrient intakes, each grouping contributing to improving a given metabolic function of said individual.
Cioara discloses for diabetes patients requiring insulin as Miguel’s case, the system recommends eating carbohydrate containing foods at regular intervals and/or matched to their insulin dose [p. 378, col. 2, par. 5]. Cioara further discloses main classes of diets and their variations such as Basal Diet, Diabetic diet, weight control diet, anticoagulant diet and special diets with specific variations in nutrients [p. 381, table 10].
Claim 5 is directed to the method according to claim 1 wherein the nutrients are macronutrients or micronutrients, wherein said macronutrients are associated with a metabolic function quantifying an energy intake of said individual.
Cioara discloses main classes of diets and their variations such as Basal Diet, Diabetic diet, weight control diet, anticoagulant diet and special diets with specific variations in nutrients [p. 381, table 10] the weight control diet and basal diet read on metabolic function of energy intake.
Claim 6 is directed to the method according to claim 5, wherein: at least one reference value of a daily intake of a global energy quantity of at least one macronutrient is calculated for an individual starting from a first set of phenotypical descriptors comprising an age, a gender, and at least one target value of a daily intake of a global energy quantity of said macronutrient is calculated for said individual starting from a first set of phenotypical descriptors comprising an age, a gender and a second set of phenotypical and/or genotypical descriptors.
Cioara discloses data describing the older adult self-feeding behavior such as personal information (such as age and gender), measured weight and height, and blood chemistry results [p. 371, col. 1, par. 1-3]. Cioara further discloses the SWRL rules for nutrition assessment include older adults( which reads on age) and gender and intake nutrient values such as fat [p. 374, table 2].
Claim 8 is directed to the method according to claim 2 wherein: - at least one reference rule includes at least one operation considering a first set of quantifications of phenotypical descriptors, for the calculation of a reference value of a daily intake of a nutrient and at least one target rule includes at least one operation aimed at defining a fixed value of a daily intake of said nutrient based on at least one threshold value reached by at least one quantification of a phenotypical and/or genotypical descriptor, for the calculation of a target value of a daily intake of the nutrient.
Cioara discloses once the intervention plan is developed for Miguel’s individual conditions, the system prototype will continuously evaluate the nutritional adequacy of Miguel’s daily food intake using the SWRL rules [p. 378, col. 2, par. 6]. Cioara further discloses different simulated situations of Miguel’s daily intake, for which the prototype was used to evaluate the intake adequacy and the degree of violation of Miguel’s dietary goals [p. 378, col. 2, par. 6]. The assessment uses a reference value and adjusts the recommendation based on a target rule for example when salt exceeds recommendation then the system provides a recommendation to correct the imbalance as seen in table 8 [p. 379].
Claim 9 is directed to the method wherein a phenotypical descriptor is calculated from a sum of scores, each score quantifying a physiological condition of the individual.
Cioara discloses to assess the adherence of a person to the Mediterranean diet, the nutritionist uses the Mediterranean Diet Adherence Score questionnaire [p. 373, col. 2, par. 2] which reads on a sum of scores related to eating habits.
Claim 10 is directed to the method according to claim 1 further comprising generating a list of recipes, said list of recipes being extracted from a recipe database comprising a set of recipes each containing a list of ingredients, each ingredient being associated with a list of macronutrients and micronutrients, each of said nutrients being quantified for a recipe according to a value and at least one time data quantifying a time period during which the nutrients are present in the body, said extraction operation correlating target values of daily intakes of nutrients with the recipe base in order to produce a list of recipes for a plurality of days.
Cioara discloses considering Miguel’s screening, profile and the above assessment, to infer the Recommended Daily Intake values for different nutrients personalized for Miguel’s condition [p. 378, col. 1, par 1]. Cioara further discloses a generic nutrition intervention plan for an older adult incorporating minimum nutritional standards with no chronic condition [p. 375, col. 1, par. 5]. Cioara also discloses the basic food taxonomy classifies food items according to the PIPS (Personalized Information Platform for Health and Life Services) food ontology [p. 371, col. 2, par 6]. Cioara further discloses the nutrient values for each type of food are extracted from the McCance and Widdowson’s food composition tables, which provide the description of around 3400 food items. Based on these nutritional values, the ontology individuals that represent specific food items are created and classified in the food ontology which is turned into a recipe concept represents a collection of basic foods in different proportions or quantities and each recipe is associated with a combined food [p. 372, col. 1, par. 2-4].
Claim 11 is directed to the method according to claim 10, wherein the recipe base is filtered from a selection of predetermined ingredients, said recipes generated in the list not comprising the filtered ingredients.
Cioara discloses dietary and food preference information collected includes likes and dislikes of individual, unsuitable foods (due to texture modification needs, allergies, intolerances, personal or religious and cultural exclusions) and details of texture modification prescriptions (e.g. soft foods, pureed food, thickened drinks) [p. 371, col. 2, par. 3]. Cioara further discloses this information ensures tailoring to the individual in the planning and implementation of the intervention [p. 371, col. 2, par. 3].
In regards to claim(s) 1-6 and 8-12, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Cioara with Ryan as they both disclose personalized nutrition using machine learning. The motivation would have been to include the data analysis of Cioara to with the dietary, phenotypic and genetic data analysis of Ryan to determine the most effective combination of assessments and advice, focusing on nutrient intake, meal patterns, phenotyping/metabotyping and genetic data to improve dietary behaviors as disclosed by Ryan [p. 138, col. 1, par. 3].
One could have therefore combined the elements as claimed by the known methods of Cioara and Ryan, and that in combination, each element merely would have performed the same function as it did separately for a predictable result.
Conclusion
No claims are allowed.
Inquiries
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Dawn M. Bickham whose telephone number is (703)756-1817. The examiner can normally be reached M-Th 7:30 - 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, Olivia Wise can be reached at 571-272-2249. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/D.M.B./Examiner, Art Unit 1685
/Soren Harward/Primary Examiner, TC 1600
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