DETAILED ACTION
1. Claims 1-20 have been presented for examination.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
PRIORITY
3. Acknowledgment is made that this application claims benefit as a CIP of 17/691,662 filed 03/10/2022.
Response to Arguments
4. Applicant's arguments filed 10/27/25 have been fully considered but they are not persuasive.
i) Applicants argue the previously presented 101 rejection by noting their amendment “a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to the mixture definition” which they argue is a “particular machine or manufacture that is integral to the claim.” However in view of the broadest reasonable interpretation of the claim limitation the Examiner notes that the amendment merely presents additional elements that merely use a computer as a tool to perform the abstract idea. (MPEP 2106.05(f)) The claims merely detail instructions on the collection of measurement data. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a mental process or a mathematical concept) does not integrate a judicial exception into a practical application. (MPEP 2106.05(f)(2)). As such the 101 rejection is MAINTAINED.
ii) In view of Applicants amendments an additional prior art has been presented in the 103 rejection below.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
i) In view of Step 1 of the analysis, claim(s) 1, 7, and 13 are directed to a statutory category as a machine and claim 18 is directed to a statutory category as a process, which each represent a statutory category of invention. Therefore, claims 1-20 are directed to patent eligible categories of invention.
ii) In view of Step 2A, Prong One, claims 1, 7, 13 and 18 recite the abstract idea of evaluating a set of ingredients based on various calculations or evaluations which constitutes an abstract idea based on Mental Processes based on concepts performed in the human mind, or with the aid of pencil and paper as well as and alternatively as Mathematical Concepts including mathematical formulas or equations as well as calculations.
As per claim 1, the limitations of "an encoder receiving inputs for base ingredients, each input of the inputs including a molecular structure, chemical properties, and physical properties of a base ingredient of the base ingredients, each base ingredient of the base ingredients including a mono-molecular tastant, the encoder configured to produce a plurality of corresponding representations for the inputs; a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representations and their relative proportions, and output a representation of the mixture definition; a decoder coupled to the composite modeler and configured to receive a representation of the mixture definition and output a list of features.” would be analogous to a person taking a set of ingredients and producing a set of mixtures of said ingredients based on provided proportions and thus fall under Mental Processes.
As per claim 7, the limitations of "process each training mixture definition of the plurality of training mixture definitions by an encoder, the encoder processing a list of base ingredients in each training mixture definition and producing a plurality of corresponding representations, each base ingredient of the list of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient, each base ingredient including a mono-molecular tastant; a composite modeler coupled to the encoder and configured to receive a plurality of corresponding representations and their relative proportions from each training mixture definition, and output a representation of each training mixture definition; and a decoder coupled to the composite modeler and configured to receive the representation of each training mixture definition and output a list of features for each training mixture definition;” would be analogous to a person taking a set of ingredients and producing a set of mixtures of said ingredients based on provided proportions and thus fall under Mental Processes.
As per claim 7, the limitations of “a loss function configured to receive the plurality of training mixture definitions and the representation of each training mixture definition of the plurality of training mixture definitions and a plurality of training pairwise comparisons, and produce a number based on the plurality of training pairwise comparisons; and an optimizer configured to adjust a plurality of parameters of the system to minimize a value of the loss function;” would be analogous to a person taking a set of ingredients and producing a set of mixtures of said ingredients based on provided proportions and optimizing the mixtures and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations as per the recitation of loss functions, pairwise comparisons, and optimization.
As per claim 13, the limitations of “an encoder receiving a plurality of base ingredients and producing a plurality of corresponding representations, each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient, each base ingredient including a mono-molecular tastant; a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representations and their relative proportions, and output a representation of the mixture definition; a decoder coupled to the composite modeler and configured to receive a representation of a mixture and output a list of features; a candidate mixture definition manager configured to receive a candidate mixture definition and produce a corresponding list of features;” would be analogous to a person taking a set of ingredients and producing a set of mixtures of said ingredients based on provided proportions and optimizing the mixtures and thus fall under Mental Processes.
As per claim 13, the limitations of “a loss function configured to receive a target list of features and produce a number; and an optimizer coupled to the candidate mixture definition manager and configured to update the candidate mixture definition to minimize a value of the loss function;” would be analogous to a person taking a set of ingredients and producing a set of mixtures of said ingredients based on provided proportions and optimizing the mixtures and thus fall under Mental Processes. In addition, the steps would constitute Mathematical Concepts including mathematical formulas or equations as well as calculations as per the recitation of loss functions and the minimization of the loss function.
As per claim 18, the limitations “receiving ingredient data associated with a plurality of base ingredients, each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient of the plurality of base ingredients, each base ingredient of the plurality of base ingredients including a mono- molecular tastant; producing a plurality of representations corresponding to the plurality of base ingredients; receiving a mixture definition comprising the plurality of representations and their relative proportions; generating an output representation of the mixture definition; receiving a representation of a mixture; generating an output list of features of the mixture;” would be analogous to a person taking a set of ingredients and producing a set of mixtures of said ingredients based on provided proportions and thus fall under Mental Processes.
Dependent claims 2-6, 8-12, 14-17, and 19-20 further narrow the abstract ideas, identified in the independent claims.
iii) In view of Step 2A, Prong Two, the judicial exception is not integrated into a practical application. Claims 1, 7, 13, and 18 lack any recitation of the judicial exception integrated into a practical application. Further, the limitations in claim 1 of "an encoder receiving inputs for base ingredients, each input of the inputs including a molecular structure, chemical properties, and physical properties of a base ingredient of the base ingredients, each base ingredient of the base ingredients including a mono-molecular tastant, the encoder configured to produce a plurality of corresponding representations for the inputs; a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representations and their relative proportions, and output a representation of the mixture definition; a decoder coupled to the composite modeler and configured to receive a representation of the mixture definition and output a list of features” and “a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to the mixture definition” and similarly recited in claim 1 and in claim 7 “a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to each training mixture definition” and in claim 13, “an encoder receiving a plurality of base ingredients and producing a plurality of corresponding representations, each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient, each base ingredient including a mono-molecular tastant; a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representations and their relative proportions, and output a representation of the mixture definition; a decoder coupled to the composite modeler and configured to receive a representation of a mixture and output a list of features; a candidate mixture definition manager configured to receive a candidate mixture definition and produce a corresponding list of features;” and “a loss function configured to receive a target list of features and produce a number; and an optimizer coupled to the candidate mixture definition manager and configured to update the candidate mixture definition to minimize a value of the loss function;” and in claim 18 “receiving ingredient data associated with a plurality of base ingredients, each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient of the plurality of base ingredients, each base ingredient of the plurality of base ingredients including a mono- molecular tastant; producing a plurality of representations corresponding to the plurality of base ingredients; receiving a mixture definition comprising the plurality of representations and their relative proportions; generating an output representation of the mixture definition; receiving a representation of a mixture; generating an output list of features of the mixture;” and “monitoring mechanical and chemical properties of prepared food according to the mixture definition” alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the judicial exception is not integrated into a practical application.
Dependent claims 2-6, 8-12, 14-17, and 19-20 further narrow the abstract ideas, identified in the independent claims and do not introduce further additional elements for consideration beyond those addressed above.
iv) In view of Step 2B, claims 1, 7, 13, and 18 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The limitations in claim 1 of "an encoder receiving inputs for base ingredients, each input of the inputs including a molecular structure, chemical properties, and physical properties of a base ingredient of the base ingredients, each base ingredient of the base ingredients including a mono-molecular tastant, the encoder configured to produce a plurality of corresponding representations for the inputs; a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representations and their relative proportions, and output a representation of the mixture definition; a decoder coupled to the composite modeler and configured to receive a representation of the mixture definition and output a list of features” and “a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to the mixture definition” and similarly recited in claim 1 and in claim 7 “a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to each training mixture definition” and in claim 13, “an encoder receiving a plurality of base ingredients and producing a plurality of corresponding representations, each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient, each base ingredient including a mono-molecular tastant; a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representations and their relative proportions, and output a representation of the mixture definition; a decoder coupled to the composite modeler and configured to receive a representation of a mixture and output a list of features; a candidate mixture definition manager configured to receive a candidate mixture definition and produce a corresponding list of features;” and “a loss function configured to receive a target list of features and produce a number; and an optimizer coupled to the candidate mixture definition manager and configured to update the candidate mixture definition to minimize a value of the loss function;” and in claim 18 “receiving ingredient data associated with a plurality of base ingredients, each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient of the plurality of base ingredients, each base ingredient of the plurality of base ingredients including a mono- molecular tastant; producing a plurality of representations corresponding to the plurality of base ingredients; receiving a mixture definition comprising the plurality of representations and their relative proportions; generating an output representation of the mixture definition; receiving a representation of a mixture; generating an output list of features of the mixture;” and “monitoring mechanical and chemical properties of prepared food according to the mixture definition” alternatively can be viewed as insignificant extra-solution activity, specifically pertaining to mere data gathering/output necessary to perform the abstract idea (MPEP 2106.05(g)) and is not sufficient to integrate the judicial exception into a practical application. This is akin to selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, which has been identified as extra solution activity. Therefore, the claim as a whole does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, when considered alone or in combination, do not amount to significantly more than the judicial exception. As stated in Section I.B. of the December 16, 2014 101 Examination Guidelines, “[t]o be patent-eligible, a claim that is directed to a judicial exception must include additional features to ensure that the claim describes a process or product that applies the exception in a meaningful way, such that it is more than a drafting effort designed to monopolize the exception.”
The dependent claims include the same abstract ideas recited as recited in the independent claims, and merely incorporate additional details that narrow the abstract ideas and fail to add significantly more to the claims.
Dependent claim 2 further defines the features of the evaluation and calculation of claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 3 further defines the features of the evaluation and calculation of claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 4 further defines the features of the evaluation and calculation of claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 5 further defines the features of the evaluation and calculation of claim 1 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 6 further defines the features of the evaluation and calculation of claim 5 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 8 further defines the features of the evaluation and calculation of claim 7 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 9 further defines the features of the evaluation and calculation of claim 7 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 10 further defines the features of the evaluation and calculation of claim 7 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 11 further defines the features of the evaluation and calculation of claim 10 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 12 further defines the features of the evaluation and calculation of claim 7 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 14 further defines the features of the evaluation and calculation of claim 13 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 15 further defines the features of the evaluation and calculation of claim 13 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 16 further defines the features of the evaluation and calculation of claim 13 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 17 further defines the features of the evaluation and calculation of claim 13 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 19 further defines the features of the evaluation and calculation of claim 18 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
Dependent claim 20 further defines the features of the evaluation and calculation of claim 18 which merely narrows the abstract idea identified as a mental process and/or mathematical concepts.
v) Accordingly, claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without anything significantly more.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103(a) 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.
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.
6. Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 20190228855, hereafter T in view of U.S. Patent No. 11164069, hereafter K.
Regarding Claim 1: The reference discloses An apparatus comprising:
and physical properties of a base ingredient of the base ingredients. (T. [0019] Generating a recipe database preferably includes generating recipe data structures (e.g., a structure recipe, etc.) for recipes based on recipe characteristics (e.g., including recipe ingredients, amount))
the encoder configured to produce a plurality of corresponding representations for the inputs; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a composite modeler coupled to the encoder and configured to receive a mixture definition comprising a list of base ingredients and their relative proportions, and output a representation of the mixture definition; and (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a decoder coupled to the composite modeler and configured to receive a representation of a mixture and output a list of features. (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
T does not explicitly recite an encoder receiving inputs for base ingredients, each base ingredient of the base ingredients including a mono-molecular tastant, each input of the inputs including a molecular structure, chemical properties.
However K discloses an encoder receiving inputs for base ingredients, each base ingredient of the base ingredients including a mono-molecular tastant, each input of the inputs including a molecular structure, chemical properties. (K. Column 16, Line 64 – Column 17, Lines 1, “Each ingredient in the ingredients database may be associated with a USDA ingredient vector, which may be a list of values relating to chemical, nutritional, and/or molecular descriptors or features.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the properties of K for the ingredients in T in order to “to improve food-related personalization for a user, such as through generating food plans tailored to user food preferences (e.g., personalized food plans) and/or food substitution parameters, while satisfying various constraints (e.g., fulfillment restrictions, availability to the user due to he or she already possessing food items or ingredients, cost restrictions, etc.).” (T [0009])
Regarding Claim 2: The reference discloses The apparatus of claim 1, wherein each feature in the list of features includes a numerical value labeled by a feature name. (T. “[0013] Additionally or alternatively, parameters, metrics, inputs, outputs, and/or other suitable data can be associated with value types including: scores (e.g., substitutability scores for ingredients or ingredient substitution; user food preference scores indicating a strength of the preference(s) by the user; similarity scores between types of ingredient entities; recipe matching scores indicating the degree to which user food preferences are satisfied by a given recipe; etc.), binary values (e.g., available versus unavailable in relation to food item availability; etc.), classifications (e.g., ingredient entity types; preparation levels; dietary preferences; etc.), confidence levels (e.g., associated with food item availability, substitutability, etc.), values along a spectrum, and/or any other suitable types of values.”)
Regarding Claim 3: The reference discloses The apparatus of claim 2, wherein the list of features is a vector having dimensions that are annotated by the feature names. (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
Regarding Claim 4: The reference discloses The apparatus of claim 1, wherein the list of features includes at least one of a taste, a smell, a texture, or a nutritional value. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).” [0019] “the ingredient parameter set can additionally or alternatively include any other suitable ingredient-related characteristics (e.g., seasonality parameters, diet-related parameters such as nutrition information, origin parameters such as indications of organic, place of origin, sustainability parameters, etc.).” “[0038] User food preferences can include any one or more of: grocery item preferences (e.g., organic versus non-organic, brands, current food items already owned by the user, etc.) and/or preferences for minimizing leftover ingredients (e.g., in relation to quantity purchased vs. quantity needed, in relation to minimizing cost per unit of an item, etc.), dietary preferences (e.g., vegan, keto, gluten-free, allergies, caloric preferences, macronutrient preferences, micronutrient preferences, etc.), taste preferences (e.g., types of cuisines, texture, types of tastes, preferences for sweetness, sourness, saltiness, bitterness, umami, etc.)…”)
Regarding Claim 5: The reference discloses The apparatus of claim 1 wherein the mixture definition is a first mixture definition, the apparatus further comprising a pairwise comparator coupled to the decoder and configured to receive a pair of lists of features from the decoder for the first mixture definition and a second mixture definition and produce a list of pairwise comparisons. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”)
Regarding Claim 6: The reference discloses The apparatus of claim 5, wherein the pairwise comparator is further configured to determine if one of the first mixture definition and the second mixture definition has a stronger presence of the feature than the other. (T. “[0038] User food preferences can include any one or more of: grocery item preferences (e.g., organic versus non-organic, brands, current food items already owned by the user, etc.) and/or preferences for minimizing leftover ingredients (e.g., in relation to quantity purchased vs. quantity needed, in relation to minimizing cost per unit of an item, etc.), dietary preferences (e.g., vegan, keto, gluten-free, allergies, caloric preferences, macronutrient preferences, micronutrient preferences, etc.), taste preferences (e.g., types of cuisines, texture, types of tastes, preferences for sweetness, sourness, saltiness, bitterness, umami, etc.)…”) Examiner Notes: The taste values read on the stronger presence of flavors such as the ones recited.)
Regarding Claim 7: The reference discloses An apparatus comprising:
a system configured to, for each mixture definition of a plurality of training mixture definitions: process each training mixture definition of the plurality of training mixture definitions by an encoder, the encoder processing a list of base ingredients in each training mixture definition and producing a plurality of corresponding representations, physical properties of each base ingredient, (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…” [0019] Generating a recipe database preferably includes generating recipe data structures (e.g., a structure recipe, etc.) for recipes based on recipe characteristics (e.g., including recipe ingredients, amount))
a composite modeler coupled to the encoder and configured to receive a plurality of corresponding representations and their relative proportions from each training mixture definition, and output a representation of each training mixture definition; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a decoder coupled to the composite modeler and configured to receive the representation of each training mixture definition and output a list of features for each training mixture definition; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a loss function configured to receive the plurality of training mixture definitions and the representation of each training mixture definition of the plurality of training mixture definitions and a plurality of training pairwise comparisons, and produce a number based on the plurality of training pairwise comparisons; (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).” Examiner Notes the pairwise comparison and optimization read on the claimed loss function)
an optimizer configured to adjust a plurality of parameters of the system to minimize a value of the loss function; (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
T does not explicitly recite and a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to each training mixture definition, and each base ingredient of the list of base ingredients including a molecular structure, chemical properties each base ingredient including a mono-molecular tastant.
However K recites and a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to each training mixture definition, and each base ingredient of the list of base ingredients including a molecular structure, chemical properties each base ingredient including a mono-molecular tastant. (K. Column 16, Line 64 – Column 17, Lines 1, “Each ingredient in the ingredients database may be associated with a USDA ingredient vector, which may be a list of values relating to chemical, nutritional, and/or molecular descriptors or features.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the properties of K for the ingredients in T in order to “to improve food-related personalization for a user, such as through generating food plans tailored to user food preferences (e.g., personalized food plans) and/or food substitution parameters, while satisfying various constraints (e.g., fulfillment restrictions, availability to the user due to he or she already possessing food items or ingredients, cost restrictions, etc.).” (T [0009])
Regarding Claim 8: The reference discloses The apparatus of claim 7, further configured to:
receive the plurality of training mixture definitions; (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
output a corresponding plurality of pairwise comparisons to the loss function based on the plurality of training mixture definitions; and (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
quantify, using the loss function, agreement of said plurality of pairwise comparisons to the corresponding training pairwise comparisons. (T. “[0049] In a variation, Block S150 can include determining any suitable number and/or type of personalized food plans (e.g., including any combination of personalized food plan components, etc.). In an example, Block S150 can include determining a plurality of personalized purchase plans satisfying parameters (e.g., user food preferences, fulfillment parameters, etc.); ranking the personalized purchase plans (e.g., based on satisfaction of optimization parameters, such as optimization parameters selected by and/or inferred for a user; etc.); and presenting the ranked personalized purchase plans to a user.”)
Regarding Claim 9: The reference discloses The apparatus of claim 7, wherein the number produced based on the plurality of training pairwise comparisons predicts whether a particular feature is stronger in one of a pair of the plurality of training mixture definitions. (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
Regarding Claim 10: The reference discloses The apparatus of claim 7, wherein the loss function is further configured to receive ground truth information associated with the plurality of pairwise comparisons. (T. “[0025] Vector representations of food related data can enable constraints to be applied in the vector space in which the food-related data is represented. For example, in determining a personalized food plan (e.g., as in Block 150), recipe vector representations can be compared to the constraints (e.g., elementwise constraints on the magnitude of components of the vector, norm-based constraints on the combined vector magnitude of the vector representation, etc.) to determine whether the recipe represented by the recipe vector satisfies the constraints of the personalized food plan. In another example, in determining substitution parameters (e.g., as in Block 120), ingredient vector representations can be compared to constraints (e.g., elementwise constraints on the magnitude of vector components, relative constraints on the distance of the vector from other ingredient vectors in one or more dimensions, etc.) to determine whether an ingredient is a suitable substitute for another ingredient.” [0036] “user inputs (e.g., user feedback, such as regarding how a user rates a recipe that included one or more food substitutions; user purchases, such as purchases for ingredient entities other than those presented in a personalized food plans, where such purchases can indicate a user-determined food substitution; etc.)” Examiner Notes: The user input represents the ground truth since flavor and taste are to be regarded as subjective values.)
Regarding Claim 11: The reference discloses The apparatus of claim 10, wherein the ground truth information is generated based on at least one of human tasting or mechanical properties. (T. “[0025] Vector representations of food related data can enable constraints to be applied in the vector space in which the food-related data is represented. For example, in determining a personalized food plan (e.g., as in Block 150), recipe vector representations can be compared to the constraints (e.g., elementwise constraints on the magnitude of components of the vector, norm-based constraints on the combined vector magnitude of the vector representation, etc.) to determine whether the recipe represented by the recipe vector satisfies the constraints of the personalized food plan. In another example, in determining substitution parameters (e.g., as in Block 120), ingredient vector representations can be compared to constraints (e.g., elementwise constraints on the magnitude of vector components, relative constraints on the distance of the vector from other ingredient vectors in one or more dimensions, etc.) to determine whether an ingredient is a suitable substitute for another ingredient.” [0036] “user inputs (e.g., user feedback, such as regarding how a user rates a recipe that included one or more food substitutions; user purchases, such as purchases for ingredient entities other than those presented in a personalized food plans, where such purchases can indicate a user-determined food substitution; etc.)” Examiner Notes: The user input represents the ground truth since flavor and taste are to be regarded as subjective values.)
Regarding Claim 12: The reference discloses The apparatus of claim 7, wherein the optimizer is further configured to provide the adjusted parameters to the encoder. (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
Regarding Claim 13: The reference discloses An apparatus comprising:
an encoder receiving a plurality of base ingredients and producing a plurality of corresponding representations, each base ingredient of the plurality of base ingredients including physical properties of each base ingredient, (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…” [0019] Generating a recipe database preferably includes generating recipe data structures (e.g., a structure recipe, etc.) for recipes based on recipe characteristics (e.g., including recipe ingredients, amount))
a composite modeler coupled to the encoder and configured to receive a mixture definition comprising the plurality of corresponding representation and their relative proportions, and output a representation of the mixture definition; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a decoder coupled to the composite modeler and configured to receive a representation of a mixture and output a list of features; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a candidate mixture definition manager configured to receive a candidate mixture definition and produce a corresponding list of features; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
a loss function configured to receive a target list of features and produce a number; (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).” Examiner Notes the pairwise comparison and optimization read on the claimed loss function)
an optimizer coupled to the candidate mixture definition manager and configured to update the candidate mixture definition to minimize a value of the loss function. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
T does not explicitly recite each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient, each base ingredient including a mono-molecular tastant; and a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to the mixture definition.
However K discloses each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient, each base ingredient including a mono-molecular tastant; and a preparation instruction manager configured to monitor mechanical and chemical properties of prepared food according to the mixture definition. (K. Column 16, Line 64 – Column 17, Lines 1, “Each ingredient in the ingredients database may be associated with a USDA ingredient vector, which may be a list of values relating to chemical, nutritional, and/or molecular descriptors or features.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the properties of K for the ingredients in T in order to “to improve food-related personalization for a user, such as through generating food plans tailored to user food preferences (e.g., personalized food plans) and/or food substitution parameters, while satisfying various constraints (e.g., fulfillment restrictions, availability to the user due to he or she already possessing food items or ingredients, cost restrictions, etc.).” (T [0009])
Regarding Claim 14: The reference discloses The apparatus of claim 13, wherein the loss function is configured to quantify an agreement of the list of features produced based on the target list of features. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
Regarding Claim 15: The reference discloses The apparatus of claim 13, wherein the loss function is further configured to produce the number based on a similarity between the target list of features and the candidate mixture definition. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
Regarding Claim 16: The reference discloses The apparatus of claim 13, wherein the loss function includes a pairwise comparator configured to compare a predicted feature to the target list of features. (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
Regarding Claim 17: The reference discloses The apparatus of claim 13, wherein the optimizer is further configured to provide the updated candidate mixture definition to the encoder. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
Regarding Claim 18: The reference discloses A method comprising:
receiving ingredient data associated with a plurality of base ingredients, each base ingredient of the plurality of base ingredients including physical properties of each base ingredient; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
producing a plurality of representations corresponding to the plurality of base ingredients; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
receiving a mixture definition comprising the plurality of representations and their relative proportions; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
generating an output representation of the mixture definition; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
receiving a representation of a mixture; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
generating an output list of features of the mixture; and (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
T does not explicitly recite each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient of the plurality of base ingredients, each base ingredient of the plurality of base ingredients including a mono- molecular tastant; monitoring mechanical and chemical properties of prepared food according to the mixture definition.
However K discloses each base ingredient of the plurality of base ingredients including a molecular structure, chemical properties, and physical properties of each base ingredient of the plurality of base ingredients, each base ingredient of the plurality of base ingredients including a mono- molecular tastant; monitoring mechanical and chemical properties of prepared food according to the mixture definition. (K. Column 16, Line 64 – Column 17, Lines 1, “Each ingredient in the ingredients database may be associated with a USDA ingredient vector, which may be a list of values relating to chemical, nutritional, and/or molecular descriptors or features.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the properties of K for the ingredients in T in order to “to improve food-related personalization for a user, such as through generating food plans tailored to user food preferences (e.g., personalized food plans) and/or food substitution parameters, while satisfying various constraints (e.g., fulfillment restrictions, availability to the user due to he or she already possessing food items or ingredients, cost restrictions, etc.).” (T [0009])
Regarding Claim 19: The reference discloses The method of claim 18, further comprising:
receiving a plurality of training mixture definitions; (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
receiving a plurality of training pairwise comparisons; (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
generating a number based on the plurality of training pairwise comparisons; and (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
adjusting a plurality of parameters to minimize a value of a loss function. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
Regarding Claim 20: The reference discloses The method of claim 18, further comprising:
receiving a candidate mixture definition; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
generating a corresponding list of features associated with the candidate mixture definition; (T. [0008] “generating a recipe database including at least one of recipe data structures (e.g., including preparation parameters, ingredient entities, associated characteristics, etc.) and recipe-related representations (e.g., vector representations of recipe data structures, of ingredients and/or ingredient entities, other non-vector abstractions or representations, etc.) S110; determining food substitution parameters based on the recipe database S120; determining user food preferences associated with the food-related personalization S130; determining fulfillment parameters for grocery items associated with the food-related personalization S140; and determining one or more personalized food plans for a user based on at least one of user food preferences, fulfillment parameters, food substitution parameters, recipe-related representations, and recipe data structures…”)
receiving a target list of features; (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
generating a number based on the target list of features; and (T. “[0048] For example, Block S150 can include applying a food plan model to determine pairwise rankings of grocery items matching against recipe ingredients (e.g., components of recipe data structures and/or recipe-related representations), such as for determining fulfillment capability for different recipes, recipe ingredients, and/or other suitable food-related components. In another example, Block S150 can include applying a semi-supervised training approach for generating a food plan model (e.g., wherein supervision is manually performed by a human entity, automatically performed using a bot or machine learning technique, etc.), where health-related professionals and/or other suitable entities (e.g., users, etc.) can provide selections of personalized food plans (e.g., recipe recommendations) in the context of different user food preferences, fulfillment parameters, and/or food substitution parameters.”)
updating the candidate mixture definition to minimize a value of a loss function. (T. “[0035] In a variation, Block S120 can include determining different food substitution parameters based on optimization parameters. For example, different pairwise rankings can be determined based on different optimization parameters applied to the determination process (e.g., optimizing for similar taste to the original recipe, optimizing for ensuring dietary preferences are satisfied, optimizing for maintaining a final cost within a threshold of an initial estimated cost for the pre-substitution recipe; etc.).”
Conclusion
7. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
8. All Claims are rejected.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
i) U.S. Patent No. 10915818
ii) Bi, Kexin, et al. "GC-MS fingerprints profiling using machine learning models for food flavor prediction." Processes 8.1 (2020): 23.
iii) Varshney, Lav R., et al. "A big data approach to computational creativity: The curious case of Chef Watson." IBM Journal of Research and Development 63.1 (2019): 7-1.
iv) Muteki, Koji, John F. MacGregor, and Toshihiro Ueda. "Mixture designs and models for the simultaneous selection of ingredients and their ratios." Chemometrics and Intelligent Laboratory Systems 86.1 (2007): 17-25.
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Saif A. Alhija whose telephone number is (571) 272-8635. The examiner can normally be reached on M-F, 10:00-6:00.
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, Renee Chavez, can be reached at (571) 270-1104. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300. Informal or draft communication, please label PROPOSED or DRAFT, can be additionally sent to the Examiners fax phone number, (571) 273-8635.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2188