DETAILED ACTION
1. Claims 1-4, 6-14, and 16-17 have been presented for examination.
Claims 5, 15, and 18-20 have been cancelled.
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 .
Response to Arguments
3. Applicant's arguments filed 9/24/25 have been fully considered but they are not persuasive.
i) The Terminal Disclaimer filed 9/24/25 has been approved and therefore the Double Patenting rejection has been rendered moot and is WITHDRAWN.
ii) Following Applicants arguments and amendments the previously presented 101 rejection is WITHDRAWN.
iii) Following Applicants arguments and amendments the previously presented 112 rejection is MAINTAINED. As noted in the previous office action the specification does not provide the structure, material or acts to support the claimed function of the “food processing unit” of claim 14. Applicants have merely pointed to nearly half of their specification to claim such a teaching and also to paragraphs 87-88 which do not recite the necessary structure, material or acts to support the claimed function of the “food processing unit” of claim 14. As such the rejection is MAINTAINED.
iv) Applicants argue that the prior art Korsunsky does not recite a “molecular profile of single-molecule ingredients.” Is it noted that Applicants have made no arguments to support this assertion and their arguments appear conclusory. As noted in the previous office action and maintained below the prior art recites this limitation in at least Column 6, Lines 51-61, “The flavor database 112 includes molecular information and flavor information for each ingredient in the ingredients database 108. In an embodiment, the molecular information for an ingredient may be represented as a binary vector indicating the presence of the different molecular features in the ingredient. In an embodiment, the flavor information may be a normalized sum of flavor features corresponding to the ingredient molecules. In an embodiment, the molecular information and the flavor information may be obtained from open-source databases.” As such the prior art rejection is MAINTAINED.
v) Applicants further argue that “the representations of ingredients in Korsunsky are an encoding of, for example, USDA information for an ingredient (see e.g., the "USDA vector" and "USDA feature matrix" of col. 13, In. 16 - col. 14, In. 34). These representations are not described as being "a latent vector representing a sensory response to each single- molecule ingredient."” As noted in the previous office action and maintained below the prior art recites this limitation in at least 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.” Column 6, Lines 40-43, “The properties may include human sensorial feedback such as taste (e.g., salt, sweet, bitter, sour, and umami), texture descriptors, acceptance, and the like, and a picture of the food dish.” As such the prior art rejection is MAINTAINED.
Claim Rejections - 35 USC § 112
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
4. This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: the “food processing unit” of claim 14.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
5. Claims 14, 16-17 are rejected under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, because the claim purports to invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, but fails to recite a combination of elements as required by that statutory provision and thus cannot rely on the specification to provide the structure, material or acts to support the claimed function. As such, the claim recites a function that has no limits and covers every conceivable means for achieving the stated function, while the specification discloses at most only those means known to the inventor. Accordingly, the disclosure is not commensurate with the scope of the claim. Specifically the specification does not provide the structure, material or acts to support the claimed function of the “food processing unit” of claim 14.
Appropriate correction is required.
All claims dependent upon a rejected base claim are rejected by virtue of their dependency.
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) 1-4 and 6-13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent No. 11164069, hereafter K in view of U.S. Patent Publication No. 20050109792, hereafter F.
Regarding Claim 1: The reference discloses A system comprising:
a molecular embedder configured to receive a molecular profile of each single-molecule ingredient out of a plurality of single-molecule ingredients and generate a representation of each single-molecule ingredient, the molecular embedder including a learned model, the molecular profile of each single-molecule ingredient including chemical and physical properties of each single-molecule ingredient, and the representation generated by the molecular embedder for the molecular profile of each single molecule ingredient including a latent vector representing a sensory response to each single-molecule ingredient; (K. Column 6, Lines 51-61, “The flavor database 112 includes molecular information and flavor information for each ingredient in the ingredients database 108. In an embodiment, the molecular information for an ingredient may be represented as a binary vector indicating the presence of the different molecular features in the ingredient. In an embodiment, the flavor information may be a normalized sum of flavor features corresponding to the ingredient molecules. In an embodiment, the molecular information and the flavor information may be obtained from open-source databases.”)
a preparation modeler coupled to the molecular embedder and configured to receive representations of single-molecule ingredients and preparation instructions, the molecular embedder further configured to generate a representation of prepared ingredients, the prepared ingredients including the single-molecule ingredients as prepared according to the preparation instructions; (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
a predictor coupled to the preparation modeler and configured to receive the representation of the prepared ingredients and generate predicted characteristics of the prepared ingredients; and (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
K recites sensorial information, as per Column 7, lines 44-53 above but does not explicitly recite one or more sensors configured to sense measurable physical and chemical characteristics of a preparation of the prepared ingredients.
However F recites one or more sensors configured to sense measurable physical and chemical characteristics of a preparation of the prepared ingredients. (F. “[0117] From the above it can be inferred that by measuring the relevant chemical-physical quantity or quantities of the mixture getting out through a suitable sensor, and by measuring through sensors the same quantity or quantities of the two ingredients getting in, it is possible to adjust the volumes of the latter by acting upon the corresponding adjustment valves, so as to obtain a mixture having the desired value of the relevant quantity or quantities.”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to utilize the sensors of F for the values of K in order to allow for proper measurements, ingredient mixing, accuracy, and ingredient supply as per F in at least “[0113] After the request for a drink, the control system SC checks the relevant property of water and syrup getting in by means of the sensors 31A and 31B (steps ii and iii); on the basis of the respective values as measured the control system SC calculates the theoretical flow rates of water and syrup required for obtaining an optimal drink, i.e. whose value of the chemical-physical property corresponds to the reference value; water and syrup flow rates are then adjusted and the two ingredients are mixed so as to obtain the drink, which is then supplied (step v).
[0114] The value of the relevant quantity of the supplied mixture is measured by means of the sensor 29 (step vi) and compared with the reference value for the desired drink (step vii); said measuring and comparing step is further necessary in order to compensate possible tolerances of the supply system (step viii), for instance positioning tolerances of the shutters 11A, 11B, in order to ensure the highest level of accuracy as possible.”
Regarding Claim 2: The reference discloses The system of claim 1, wherein the molecular profile includes chemical and structural information of each single molecule ingredient. (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.” Examiner Note: The molecular descriptors or features read on the broadest reasonable interpretation of the claimed structural information.)
Regarding Claim 3: The reference discloses The system of claim 1, wherein the predicted characteristics include at least one of a flavor profile, a texture, a cost, or a nutritional profile. (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.” Column 6, Lines 40-43, “The properties may include human sensorial feedback such as taste (e.g., salt, sweet, bitter, sour, and umami), texture descriptors, acceptance, and the like, and a picture of the food dish.” Examiner Note: Taste reads on the broadest reasonable interpretation of flavor profile.)
Regarding Claim 4: The reference discloses The system of claim 3, wherein the flavor profile includes at least one of a taste, a smell, or a texture. (K. Column 6, Lines 40-43, “The properties may include human sensorial feedback such as taste (e.g., salt, sweet, bitter, sour, and umami), texture descriptors, acceptance, and the like, and a picture of the food dish.”)
Regarding Claim 6: The reference discloses The system of claim 1, wherein the preparation modeler further comprises a mixture modeler configured to receive molecular profiles of the plurality of single-molecule ingredients, wherein the mixture modeler is further configured to generate a representation of a mixture of the single-molecule ingredients. (K. Column 4, Lines 60-66, “In an embodiment, the quantity solver 120 is programmed to receive a target food item and a candidate set of ingredients generated by the prediction model 118 and to determine a quantity, amount or proportion for each of the ingredients in the candidate set, based on the target food item, resulting in a candidate formula (set of ingredients and proportions).”)
Regarding Claim 7: The reference discloses The system of claim 1, wherein the preparation instructions further include at least one processing step applied to the plurality of single-molecule ingredients. (K. Column 14, Lines 5-9, “In an embodiment, an optimal formula may be transmitted downstream to a recipe generator for further processing such as to determine a new recipe including a set of cooking directions or instructions for that formula.”)
Regarding Claim 8: The reference discloses The system of claim 7, wherein the at least one processing step includes at least one of heating, cooling, agitating, or stirring. (K. Column 14, Lines 5-9, “In an embodiment, an optimal formula may be transmitted downstream to a recipe generator for further processing such as to determine a new recipe including a set of cooking directions or instructions for that formula.” Examiner Note: The recited cooking directions would read on the broadest reasonable interpretation of heating, cooling, agitating, or stirring.)
Regarding Claim 9: The reference discloses The system of claim 1, wherein the preparation instructions are represented as a processing graph and the preparation modeler is configured to receive the processing graph. (K. Column 22, Lines 10-14, “The instructions may be organized as a presentation layer, application layer and data storage layer such as a relational database system using structured query language (SQL) or no SQL, an object store, a graph database, a flat file system or other data storage.”)
Regarding Claim 10: The reference discloses The system of claim 1, further comprising:
defining an objective that receives predicted characteristics and generates a numerical value; (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
a plurality of constraints that receive the representation of the prepared ingredients and the preparation instructions, wherein the plurality of constraints further generate a Boolean value; and (K. Column 14, Lines 53-63, “In an embodiment, the importance value is any non-negative value. The higher the value, the more important it is to match the corresponding feature. An importance value of zero (0) means that there is no importance of matching that corresponding feature. In an embodiment, scaling it by a non-negative factor does not affect the output of the control. Importance values for all features may be set to a default value (e.g., 1). When the importance values for all features are set to one (1), then it is as if this control is not defined.” Examiner Note: The 0 and 1 values read on the claimed Boolean value.)
an optimizer configured to find the plurality of single-molecule ingredients and the preparation instructions that optimize the objective and satisfy the plurality of constraints. (K. Column 11, Lines 49-63, which recites an optimization technique and constraint values)
wherein the predicted characteristics include a predicted flavor profile, a predicted nutritional profile, and a predicted cost. (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.” Column 6, Lines 40-43, “The properties may include human sensorial feedback such as taste (e.g., salt, sweet, bitter, sour, and umami), texture descriptors, acceptance, and the like, and a picture of the food dish.” Examiner Note: Taste reads on the broadest reasonable interpretation of flavor profile.)
Regarding Claim 11: The reference discloses The system of claim 10, wherein the objective includes at least one of a similarity of the predicted flavor profile to a target flavor profile, a similarity of the predicted nutritional profile to a target nutritional profile, or a similarity of the predicted cost to a target cost. (K. Column 14, Lines 18-24, “For example, some target food items may not have correct or desired values. It may be desirable to modify original target values of a target food item. Or, it may be desirable to encode additional information, such as flavor, for the target food item. The different controls allow a user (e.g., a chef) to manipulate the formula generation to produce better candidate formulas.” Column 11, Lines 43-47, “Each vector includes a list of values relating to chemical, nutritional, and/or molecular descriptors or features in the USDA feature space.”)
Regarding Claim 12: The reference discloses The system of claim 10, wherein the plurality of constraints includes at least one of similarity of the predicted flavor profile to a target flavor profile within a pre-determined flavor threshold, the similarity of the predicted nutritional profile to a target nutritional profile within a pre-determined nutritional threshold, the predicted cost within a target range, or a number of ingredients within a target range. (K. Column 14, Lines 18-24, “For example, some target food items may not have correct or desired values. It may be desirable to modify original target values of a target food item. Or, it may be desirable to encode additional information, such as flavor, for the target food item. The different controls allow a user (e.g., a chef) to manipulate the formula generation to produce better candidate formulas.” Column 11, Lines 43-47, “Each vector includes a list of values relating to chemical, nutritional, and/or molecular descriptors or features in the USDA feature space.” Column 16, Lines 17-24, “(115) A desired flavor profile may defined using a GUI for the flavors control. For example, a desired flavor profile may indicate sweetness as having an intensity value of 3.5 and/or saltiness as having an intensity value of 1.5. Intensity values for other flavor descriptors (e.g., umami, sourness, bitterness, etc.) may also be indicated. In an embodiment, the range of intensity values for a flavor descriptor is from one (1) to five (5).”)
Regarding Claim 13: The reference discloses The system of claim 10, wherein the optimizer is further configured to perform an iterative process that includes:
selecting a candidate plurality of single-molecule ingredients and preparation instructions as current candidate plurality of single-molecule ingredients and preparation instructions; (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
evaluating the objective on the current candidate plurality of single-molecule ingredients and preparation instructions; (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
calculating an update of the current candidate plurality of single-molecule ingredients and preparation instructions and generating a second candidate plurality of single-molecule ingredients and preparation instructions; and (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
repeating previous steps until a converge condition is satisfied. (K. Column 11, Lines 49-63, which recites an optimization technique and constraint values)
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
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.
7. Claims 14 and 16-17 are rejected under 35 U.S.C. 102(a)(2) as being clearly anticipated by U.S. Patent No. 11164069, hereafter K
Regarding Claim 14: The reference discloses A method comprising:
identifying, by a food processing unit, a subjective flavor measurement associated with a target food; (K. Column 6, Lines 40-43, “The properties may include human sensorial feedback such as taste (e.g., salt, sweet, bitter, sour, and umami), texture descriptors, acceptance, and the like, and a picture of the food dish.” Examiner Note: Taste reads on the broadest reasonable interpretation of flavor profile.)
identifying, by the food processing unit, an objective flavor measurement associated with the target food by measuring physical and chemical information of a plurality of single-molecule ingredients of the target food and processing the physical and chemical information of the plurality of single-molecule ingredients; (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.”)
determining, by the food processing unit, a target flavor profile based on the subjective flavor measurement and the objective flavor measurement; and (K. Column 4, Lines 60-66, “In an embodiment, the quantity solver 120 is programmed to receive a target food item and a candidate set of ingredients generated by the prediction model 118 and to determine a quantity, amount or proportion for each of the ingredients in the candidate set, based on the target food item, resulting in a candidate formula (set of ingredients and proportions).”)
proposing, by the food processing unit, candidate preparation instructions having a predicted candidate flavor profile based on the target flavor profile; and (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
preparing the candidate preparation instructions and measuring actual flavor profiles; (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
wherein processing the physical and chemical information of the plurality of single-molecule ingredients comprises:
receiving a molecular profile of each single-molecule ingredient of the plurality of single-molecule ingredients, the molecular profile including the physical and chemical information for each single-molecule ingredient; (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.”)
generate a representation of each single-molecule ingredient by processing the molecular profile of each single-molecule ingredient using a molecular embedder, the molecular embedder including a learned model, the representation of each single molecule ingredient including a latent vector representing a sensory response to each single-molecule ingredient; (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
processing the molecular profiles of the plurality of single-molecule ingredients using a preparation modeler to generate a representation of the target food; and (K. Column 7, Lines 44-53, “In an embodiment, the representation model 116, the prediction model 118, the quantity solver 120, the formula searcher 122, and the optimizer 124 interoperate programmatically in an unconventional manner to generate one or more candidate formulas, for a target food item, with specific sensorial properties and/or desired characteristics, and to select an optimal formula from the candidate formulas. Each candidate formula may identify one or more ingredients and their respective proportions (e.g., percentage or quota).”)
processing the representation of the target food using a predictor to generate the objective flavor measurement. (K. Column 4, Lines 60-66, “In an embodiment, the quantity solver 120 is programmed to receive a target food item and a candidate set of ingredients generated by the prediction model 118 and to determine a quantity, amount or proportion for each of the ingredients in the candidate set, based on the target food item, resulting in a candidate formula (set of ingredients and proportions).”)
Regarding Claim 16: The reference discloses The method of claim 14, further comprising determining whether the predicted candidate flavor profile is substantially similar to the target flavor profile. (K. Column 14, Lines 18-24, "For example, some target food items may not have correct or desired values. It may be desirable to modify original target values of a target food item. Or, it may be desirable to encode additional information, such as vor, for the target food item. The different controls allow a user (e.g., a chef) to manipulate the formula generation to produce better candidate formulas.")
Regarding Claim 17: The reference discloses The method of claim 16, further comprising, responsive to determining that the predicted candidate flavor profile is not substantially similar to the target flavor profile, updating the predicted candidate flavor profile based on the actual flavor profiles. (K. Column 11, Lines 49-63, which recites an optimization technique and constraint values)
Conclusion
8. 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.
9. All Claims are rejected.
10. 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.
11. 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.
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SAA
/SAIF A ALHIJA/Primary Examiner, Art Unit 2188