DETAILED ACTION
Notice of AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Withdrawal of Objections and Rejections
Applicant's response, filed 02/18/2026, has been fully considered.
In view of the amendment and remarks from 02/18/2026, the objection to claims 8 and 12 are withdrawn:
The following rejections and/or objections are either maintained or newly applied for claims 26 and 30-32. They constitute the complete set applied to the instant application. Herein, "the previous Office action" refers to the Non-Final Rejection of 11/18/2025.
Status of the Claims
Claims 1-17 are pending.
Claims 1 and 15-16 are objected to.
Claims 1-17 are rejected.
Priority
This US Application 17/634,080 (09/28/2021) is a 371 of PCT/EP2020/077721 (10/02/2020) which claims priority from Application 62/911,096 (10/04/2019) and Foreign Application EP19212031.9 (11/28/2019); as reflected in the filing receipt mailed on April. 12, 2022. The claims to the benefit of priority are acknowledged and the effective filing date of claims 1-17 is 10/04/2019.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/04/2026 were considered by the examiner.
Claim objections
Claims 1 and 15-16 are objected to because of the following informalities related to grammar/punctuation. Appropriate correction is required.
Claim 1 is missing an "and" between the 6th claim element and the last claim element.
In claims 15-16, the recited "means for retrieving …" should recite "a means for retrieving …" for writing consistency in the claims.
Claim Interpretation
Claims 1 and 15 recite “value representative of an odorant receptor activation threshold” which it is interpreted as the odorant receptor sensitivity value.
Claims 1, 3-4, 6, 10 and 14-16 recite “tonality”; which it is interpreted as one or the combination of the odorant properties, profile or characteristics that make up the organic aspect of a fragrance such as the odor character for each molecule.
The recited "olfactory receptor(s)" (claims 1 and 14-16) and "odorant receptor(s)" (claims 2-3, 5-8, 10-11, 14 and 16) are being interpreted as terms that can be used interchangeably. See [0095] of this instant specification.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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 having 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) 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):
(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” or the generic placeholder) 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). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) 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” or the generic placeholder) 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) 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” or the generic placeholder) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word "means" (or "step” or the generic placeholder) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Such claim limitations that use the term "means" being interpreted under 112(f) are:
a means of inputting a formula including a plurality of volatile molecule digital identifiers, said volatile molecule digital identifier being representative of fragrant volatile molecule. (claim 15)
a means of calculating a value representative of an activity level of each said molecule on an activity level of at least one olfactory receptor, as a function of impact of said volatile molecules represented in said formula. (claim 15)
means for retrieving from said second database a tonality digital identifier associated with activation of said at least one olfactory receptor. (claim 15)
a means of determining a value representative of an odorant receptor activation threshold, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold. (claim 15)
a means of inputting for at least one tonality digital identifier, upon a computer interface, a value representative of the tonality of a composition resulting from a formula to be determined, said formula including a plurality of volatile molecule. (claim 16)
a means of determining by a computing system, for the formula to be determined and as a function of at least one value representative of at least one tonality of said composition, a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor. (claim 16)
means for retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors. (claim 16)
a means of second determining a formula including a plurality of volatile molecule digital identifiers such that impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor. (claim 16)
Because these claim limitations are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. As it appears in the instant specification and claims, there is sufficient structure to describe the recited “means”. The specification must disclose structural components (computer/processor and algorithm) responsible for the performance of the claimed specific computer function, or else the claim is indefinite under 35 U.S.C. 112(b) (MPEP 2181 (II)(B)).
Claim 15 recites a means of inputting a formula including a plurality of volatile molecule digital identifiers, said volatile molecule digital identifier being representative of fragrant volatile molecule. The specification shows disclosure of a structure to perform such functions (pg. 28, line 4-9).
Claim 15 recites a means of calculating a value representative of an activity level of each said molecule on an activity level of at least one olfactory receptor, as a function of impact of said volatile molecules represented in said formula. The specification shows disclosure of a structure to perform such functions (pg. 28, line 9-12 and pg. 27).
Claim 15 recites a means for retrieving from said second database a tonality digital identifier associated with activation of said at least one olfactory receptor. The specification shows disclosure of a structure to perform such functions (pg. 17, line 20).
Claim 15 recites a means of determining a value representative of an odorant receptor activation threshold, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold. The specification shows disclosure of a structure to perform such functions (pg. 28, line 9-12 and pg. 27).
Claim 16 recites a means of inputting for at least one tonality digital identifier, upon a computer interface, a value representative of the tonality of a composition resulting from a formula to be determined, said formula including a plurality of volatile molecule. The specification shows disclosure of a structure to perform such functions (pg. 28, line 31-32).
Claim 16 recites a means of determining by a computing system, for the formula to be determined and as a function of at least one value representative of at least one tonality of said composition, a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor. The specification shows disclosure of a structure to perform such functions (pg. 28, line 31-32).
Claim 16 recites a means for retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors. The specification shows disclosure of a structure to perform such functions (pg. 17, line 20).
Claim 16 recites a means of second determining a formula including a plurality of volatile molecule digital identifiers such that impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor. The specification shows disclosure of a structure to perform such functions (pg. 28, line 31-32).
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitations to avoid them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid them being interpreted under 35 U.S.C. 112(f).
Response to applicant's remarks in regard to Claim Interpretation
The Remarks of 02/18/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 10 para. 6:
It should be noted that the specification as filed defines this value in page 17, lines 14 to 20 as "Preferably, an OR activation level threshold is used in order to determine the value representative of a tonality such as disclosed above. In such variants, the activation threshold may be common for all ORs or determined on a OR by OR basis, said threshold being observed and recorded empirically. Such a threshold value is, for example, 20% of the average maximum activation level of ORs. Such threshold values are, for example, stored in an activation threshold database used during the step of determining 115." Hence, the applicant asserts that the interpretation of the term "threshold" as a sensitivity value is inconsistent with the description of the present patent application as filed. In order to more clearly define this term, the claims are amended to define an olfactory receptor tonality activation threshold. In addition, the Examiner states that the term "tonality" is interpreted as the combination of the odorant properties, profile, or characteristics that make up the fragrance. Such an interpretation does not correspond to the definition clearly given in the patent application as filed, on page 10 lines 8 to 11: "As used herein, "olfactory tonality" or "tonality" means an organoleptic property of a volatile molecule initiated by the activation of an OR, which activity is routed through the olfactory bulb and processed by the central nervous system to produce a specific experience in the subject
It is respectfully submitted that this is not persuasive because the instant specification is correctly applied and supports the interpretation of the claim language. The disclosed "OR activation level threshold is used in order to determine the value representative of a tonality" pg. 17 lines 30-31 indeed allows the recited "value representative of an odorant receptor activation threshold” to be interpreted as odorant receptor sensitivity under broadest reasonable interpretation consistent with the specification – since sensitivity is indeed directly related to the level of activation of an OR (see Zarzo pg. 458 col. 2 para. 2).
Similarly, pg. 10 lines 20-21 allows the interpretation of "olfactory tonality" since the combination of properties that defines an odorant comprises organic properties such as the smell characteristics (See Frater ("Fragrance chemistry." Tetrahedron 54.27 (1998): 7633-7703) in pg. 7665 para. 4) – includes the dominant property or an "organoleptic property that is the dominant note" as described by the Applicant to define tonality.
Please note: 1. the described specification section in pg. 17 lines 14-20 in the examined (i.e. most recently submitted) specification dated 08/04/2022 does not describe the argued "activation threshold"; which is actually disclosed in pg. 17 lines 30-31; and 2. the described specification section in pg. 10 lines 8-11 in the examined (i.e. most recently submitted) specification dated 08/04/2022 does not describe the argued "tonality"; which is actually disclosed in pg. 10 lines 20-21.
The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005) expressly recognized that the USPTO employs the "broadest reasonable interpretation" standard: "The Patent and Trademark Office ("PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art."
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 9-10, 14 and 16 are rejected under 35 U.S.C. 112(b)as being indefinite for failing to particularly point out and distinctly claim the subject matter the invention. Dependent claims are rejected similarly, unless otherwise noted below. Any newly recited portions are necessitated by claim amendment. The following issues cause the respective claims to be rejected under 112(b) as indefinite:
Claim 9 recites "The method according to claim 9" which lacks antecedent basis. Claim 9 is missing an annotation regarding the change from "The method according to claim 8" to the instant "The method according to claim 9."
Claim 10 recites "at least … volatile molecule identifier" (1st to 9th claim elements) and "minimal number of volatile molecule identifiers" (1st claim element), "corresponding volatile molecule identifiers" (2nd claim element), "negative impact volatile molecule identifiers" (3rd claim element), "the volatile molecule identifiers" (4th claim element)' which presents an unclear relationship among the recited terms. It is unclear if said terms represent the same volatile molecule identifiers or different ones.
Claims 14 and 16 recite "said odorant receptor" which lacks antecedent basis because there is no previous recitation of "an odorant receptor" in the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 USC § 101 because the claimed inventions are directed to one or more Judicial Exceptions (JEs) without significantly more. Regarding JEs, "Claims directed to nothing more than abstract ideas..., natural phenomena, and laws of nature are not eligible for patent protection" (MPEP 2106.04 §I). Abstract ideas include mathematical concepts and procedures for evaluating, analyzing or organizing information, which are a type of mental process (MPEP 2106.04(a)(2)). Any newly recited portions are necessitated by claim amendment.
101 background
MPEP 2106 organizes JE analysis into Steps 1, 2A (Prong One & Prong Two), and 2B as analyzed below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter (MPEP 2106.03)?
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
Analysis of instant claims
Step 1: Are the claims directed to a 101 process, machine, manufacture, or composition of matter (MPEP 2106.03)?
The instant claims are directed a method (claims 1-14 and 17), and a system (claims 15-16), each of which falls within one of the categories of statutory subject matter.
[Step 1: claims 1-17: Yes]
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e., a law of nature, a natural phenomenon, or an abstract idea (MPEP 2106.04(a-c))?
Background
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. MPEP § 2106.04(a)(2) further explains that abstract ideas are defined as:
• mathematical concepts (mathematical formulas or equations, mathematical relationships
and mathematical calculations) (MPEP 2106.04(a)(2)(I));
• certain methods of organizing human activity (fundamental economic principles or practices, managing personal behavior or relationships or interactions between people) (MPEP 2106.04(a)(2)(II)); and/or
• mental processes (concepts practically performed in the human mind, including observations, evaluations, judgments, and opinions) (MPEP 2106.04(a)(2)(III)).
Analysis of instant claims
With respect to the instant claims, under the Step 2A, Prong One evaluation, the claims are found to recite abstract ideas that fall into the grouping of mental processes (in particular procedures for observing, analyzing and organizing information) and mathematical concepts (in particular mathematical relationships and formulas) are as follows:
Mathematical concepts (in particular mathematical relationships and formulas) include:
"a step of calculating …, a value representative of an activity level of at least one olfactory receptor as a function of impact of said volatile molecules represented in said formula" (independent claim 1);
• "a step of computing by the computing system, for at least one odorant receptor, a total activity level as a function of at least one impact on an activity level calculated" (claim 2);
• "a step of modifying, upon the computer interface, a value representative of a fragrance tonality of the composition" (claim 3);
• "a step of second calculating, by the computing system, a modification of total activity level of at least one odorant receptor" (claim 3);
• "the step of modifying the value representative of the fragrance of said selected tonality identifiers of the composition being configured to reduce the value of said selected tonality identifiers to a reduced value" (claim 4);
• "step of calculating, by the computing system, for at least one volatile molecule digital identifier of the formula, a value representative of an impact on an activity level of an odorant receptor" (claim 11); and
• "a means of calculating a value representative of an activity level of each said molecule on an activity level of at least one olfactory receptor, as a function of impact of said volatile molecules represented in said formula" (independent claim 15).
The abstract ideas recited in the claims are evaluated under the Broadest Reasonable Interpretation (BRI) and determined to each cover performance either in the mind and/or by mathematical operation. Without further detail as to the methodology involved in the "calculating and determining" steps above, under the BRI, one may simply, for example, use pen and paper to perform mathematical steps to arrive at the recited values. Further support for the mathematical techniques used in the claims is provided in the specification at pg. 28 lines 10-13, which describes mathematical algorithm used for said "calculating and determining" steps. Thus, the recited terms correspond to verbal equivalents of mathematical concepts because they constitute actions executed by a group of mathematical steps in a form of a mathematical algorithm; thus mathematical concepts (MPEP 2106.04(a)(2)). A mathematical concept need not be expressed in mathematical symbols, because "words used in a claim operating on data to solve a problem can serve the same purpose as a formula." In re Grams, 888 F.2d 835, 837 and n.1, 12 USPQ2d 1824, 1826 and n.1 (Fed. Cir. 1989). MPEP 2106.04(a)(2) pertains.
Mental processes, defined as concepts or steps practically performed in the human mind such as steps of observations, evaluations, judgments, analysis, opinions or organizing information include:
• "a step of determining by the computing system, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold association" (independent claim 1);
• "a step of second determining, by the computing system, a set of at least one volatile molecule digital identifier presenting a total activity level impact value equal to the total activity level modification calculated" (claim 3);
• "a step of … tonality selection as a function of the result of comparing the tonality digital identifiers of the composition to a set of at least one predetermined tonality digital identifier" (claim 4);
• "a step of selecting a volatile molecule digital identifier of the formula, upon the computer interface" (claim 7);
• "a step of third determining, …, at least a set of at least one volatile molecule digital identifier presenting a value representative of an impact on an activity level of a odorant receptor equal to the value representative of the impact on the activity level on said odorant receptor of the selected volatile molecule digital identifier" (claim 7);
• "a step of second selecting at least two volatile molecule digital identifiers of the formula, at least two said volatile molecules being associated with the activation of at least two distinct odorant receptors, comprising target receptors" (claim 8);
• "a step of fourth determining one volatile molecule digital identifier presenting a value representative of an impact on an activity level of each target receptor equal to the value representative of the impact on the activity level on said odorant receptor of the selected volatile molecule digital identifiers" (claim 8);
• " a step of estimating a quantity for at least one said volatile molecule digital identifier, said quantity being used in the downstream step of supplying" (claim 9);
• "a step of second determining a formula including a plurality of volatile molecule digital identifiers such that impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor" (independent claim 14);
• "a means of determining a value representative of an odorant receptor activation threshold, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold" (independent claim 15);
• "a means of determining by a computing system, for the formula to be determined and as a function of at least one value representative of at least one tonality of said composition, a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" (independent claim 16); and
• "a means of second determining a formula including a plurality of volatile molecule digital identifiers such that impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor" (independent claim 16).
The human mind is also sufficiently capable of determine/provide/select a property that identifies a molecule to be used digitally and estimate a quantity.
Dependent claims 5-6, 10, 12-13 and 17 recite further steps that limit the judicial exceptions in independent claim 1 and, as such, also are directed to those abstract ideas. For example, claim 5 further limit the value representative of the impact of the molecule; claim 6 further limit the step of second determining; claim 10 further limit the second determining or the third determining; claim 12 further limit the at least one volatile molecule digital identifier; claim 13 further limit the volatile molecule delivery capacity indicator and claim 17 further limit the step of modifying the value representative of the fragrance to a reduced value.
[Step 2A Prong One: claims 1-17: Yes ]
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application by an additional element (MPEP 2106.04(d))?
Background
MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application:
An improvement in the functioning of a computer or an improvement to other technology or another technical field, as discussed in MPEP §§ 2106.04(d)(1) and 2106.05(a);
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, as discussed in MPEP § 2106.04(d)(2);
Implementing a judicial exception with, or using a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, as discussed in MPEP § 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP § 2106.05(c); and
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e).
Analysis of instant claims
Instant claims 1, 3, 7-8, 11 and 14-16 recite additional elements that are not abstract ideas:
• "computer interface" (claims 1, 3, 7-8, 11, 14 and 16);
• "a step of inputting, upon a computer interface, a formula including a plurality of volatile molecule digital identifiers, said volatile molecule digital identifiers being representative of fragrant volatile molecules" (independent claim 1);
• "providing a first database cross-referencing volatile molecule digital identifiers, representative of real volatile molecules, and impact of each one of said volatile molecules on olfactory receptors, wherein each one of said olfactory receptors is represented by an olfactory receptor digital identifier and wherein each volatile molecule digital identifier in said first database is associated with at least one olfactory receptor digital identifier, said association being a many-to-many association" (independent claims 1 and 14);
• "providing a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association" (independent claims 1 and 14);
• "retrieving from said first database an impact of each one of said fragrant volatile molecules on said at least one olfactory receptor" (independent claims 1 and 14);
• "retrieving from said second database a tonality digital identifier associated with activation of said at least one olfactory receptor" (independent claim 1);
• "a step of supplying said set to a computer interface" (claim 3);
• "automatic" (claim 4);
• "supplying said set to the computer interface" (claim 7);
• "supplying the determined volatile molecule digital identifier to the computer interface" (claim 8);
• "a step of second inputting a quantity of at least one input volatile molecule, upon the computer interface, said quantity being used in the step of calculating, by the computing system, for at least one volatile molecule digital identifier of the formula, a value representative of an impact on an activity level of an odorant receptor" (claim 11);
• "a means of inputting a formula including a plurality of volatile molecule digital identifiers, said volatile molecule digital identifier being representative of fragrant volatile molecule" (independent claim 15);
• "a first database cross-referencing volatile molecule digital identifiers, representative of real volatile molecules, and impact of each one of said volatile molecules on olfactory receptors, wherein each one of said olfactory receptors is represented by an olfactory receptor digital identifier and wherein each volatile molecule digital identifier in said first database is associated with at least one olfactory receptor digital identifier, said association being a many-to-many association" (independent claims 15-16);
• "means for retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors" (independent claim 15);
• "a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association" (independent claim 15);
• "means for retrieving from said second database a tonality digital identifier associated with activation of said at least one olfactory receptor" (independent claim 15);
• "a means of inputting for at least one tonality digital identifier, upon a computer interface, a value representative of the tonality of a composition resulting from a formula to be determined, said formula including a plurality of volatile molecule" (independent claim 16);
• "a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor and said tonality digital identifiers, is a one-to-one association, means for retrieving from said second database an olfactory receptor digital identifier ,associated with said tonality digital identifier" (independent claim 16); and
• "a means for retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors" (independent claim 16).
Considerations under Step 2A, Prong Two
The recited limitations in claims 1, 3, 7-8, 11 and 14-16 are interpreted as requiring the use of a computer. Hence, the claims explicitly recite steps executed by computers and therefore can be described as computer functions or instructions to implement on a generic computer. Further steps directed to additional non-abstract elements of a computing device/computer do not describe any specific computational steps by which the "computer parts" perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. The instant claims state nothing more than that a generic computer performs the functions that constitute the abstract idea (MPEP 2106.05(f)).
Limitations of claims 1 and 14-16 are considered to perform the claimed abstract idea with a computer, which is not sufficient to integrate an abstract idea into a practical application (see MPEP 2106.05(f)); since steps that can be performed mentally and merely performing the mental process in a computer environment do not negate the fact that something that can be carried out in the human mind. See MPEP 2106.04(a)(2).III.C.
Claims directed to “inputting”, "retrieving" and "supplying … to a computer interface" read on transmitting data over a network -Symantec, 838 F.3d at 1321 - MPEP 2106.05(a) pertains; which constitutes just necessary data gathering and therefore correspond to insignificant extra-solution activity.
The recited "a first database cross-referencing volatile molecule digital identifiers …" and "a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers …" read on data gathering activities; not amounting to a practical application. The type of data doesn’t change that it is mere data gathering or conventional computer receiving means.
Hence, these are mere instructions to apply the abstract idea using a computer and insignificant extra-solution activity and therefore the claims do not integrate that abstract idea into a practical application (see MPEP 2106.04(d) § I; 2106.05(f); and 2106.05(g)).
In Step 2A, Prong One above, claim steps and/or elements were identified as part of one or more judicial exceptions (JEs).
In this Step 2A, Prong Two immediately above claim steps and/or elements were identified as part of one or more additional elements. Additional elements are further discussed in Step 2B below.
Here in Step 2A, Prong Two, no additional step or element clearly demonstrates integration of the JE(s) into a practical application.
[Step 2A Prong Two: claims 1-17: No]
Step 2B: Do the claims recite a non-conventional arrangement of elements in addition to any identified judicial exception(s) (MPEP 2106.05)?
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during examination that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
Claims 1, 3, 7-8, 11 and 14-16 recite a computer or computer functions, interpreted as instructions to apply the abstract idea using a computer, where the computer does not impose meaningful limitations on the judicial exceptions; which can be performed without the use of a computer (MPEP 2106.04(d) § I; and MPEP 2106.05(f)).
Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
With respect to the instant claims, the prior art review to Zarzo ("The sense of smell: molecular basis of odorant recognition." Biological Reviews 82(3):455-479 (2007), newly cited) discloses that using databases to cross reference and retrieve information related to the impact of a ligand (i.e. fragrant molecules) in olfactory receptors is routine, well-understood and conventional in the art. Said portions of the prior art are, for example, pg. 459 col. 1 para. 4 to pg. 459 col. 2 para. 3.
When the claims are considered as a whole, they do not integrate the abstract idea into a practical application; they do not confine the use of the abstract idea to a particular technology; they do not solve a problem rooted in or arising from the use of a particular technology; they do not improve a technology by allowing the technology to perform a function that it previously was not capable of performing; and they do not provide any limitations beyond generally linking the use of the abstract idea to a broad technological environment. See MPEP 2106.05(a) and 2106.05(h).
The instant claims constitute insignificant extra solution activity, and when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(g)). Hence, these elements, when considered individually, are insufficient to constitute inventive concepts that would render the claims significantly more than an abstract idea (see MPEP 2106.05(d)).
[Step 2B: claims 1-17: No]
Conclusion: Instant claims are directed to non-statutory subject matter
For the reasons above, the claims in this instant application, when the limitations are considered individually and as a whole, are directed to an abstract idea and lack an inventive concept not clearly anything significantly more.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 101
The Remarks of 02/18/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 13 para. 2:
Accordingly, evaluation of this step must be with reference to the two databases and the manner in which they are integrated to provide the tonality, as defined in the present application. This principle was noted by the court in re Diamond v. Diehr and is explained in length in MPEP 2106.04(d). In the present case, the claim as a whole provides an inventive manner to evaluate the tonality produced by a formula accurately. This inventive concept includes determining the impact of each molecule in the formula on each OR. Since each molecule can impact multiple ORs, this step requires a database to retrieve the ORs impacted by each molecule and the value representing this impact. Next, the method calculates the total impact of the formula on each OR, i.e., the activity level. Lastly, it is required to obtain the overall tonality based on the activity level of each associated OR. The inventive concept includes calculating the activity level of each OR and determining which OR triggers the tonality … As noted by the Examiner, the step of calculating the activity level, under the broadest interpretation, encompasses a mental process carried out by the human mind. However, when evaluating the integration of the above mental process, the entire inventive concept, as claimed, must be considered.
It is respectfully submitted that this is not persuasive because the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than the abstract idea (i.e. calculating steps – which comprises the determination of a value representative of a tonality). The analysis at Step 2A, Prong 2, considers the claims as a whole, i.e., the additional elements in combination with the judicial exceptions (see MPEP 2106.05(a)), although the integration or improvement provided in the claim must flow from the additional elements and not the judicial exceptions to be considered persuasive. In this case, the identified additional elements do not provide sufficient evidence to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. The use of different databases reads on receiving and data gathering activities and does not amount into a practical application, neither the step of supplying the yielded value to a computer interface; which reads on data outputting. Further, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versa ta Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(Il)(i)).
Applicant asserts in pg. 14 para. 6:
In this context, it is noted that one of the considerations is the particularity or generality of the elements of the machine or apparatus. In other words, the degree to which the machine in the claim can be specifically identified (not any and all machines). When a particular device with specific requirements is required for the implementation of the method, the judicial exception is integrated into a practical application. This consideration aims to exclude merely adding a generic computer to apply the abstract idea. See re Alice Corp. Pty. Ltd. v. C.L.S. Bank Int'l. If a general-purpose computer were used to make the tonality determination, the integration of the computer would not have been specific or meaningful. Here, on the other hand, the integration of a first designated database to retrieve an impact of each one of the molecules on multiple olfactory receptors, and a second designated database to retrieve tonality associated with an activity level of each of the olfactory receptor, is specifically required for the determination of the tonality. These limitations apply the mental process of calculating activity level in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The claim clearly provides an inventive concept of an accurate evaluation of tonality of a formula with multiple molecules and does not aim to monopolize the mental conventional process of evaluating general effect of the formula. The use of the designated databases, specifically built with many-to-many association between molecules and ORs, and with one-to-one association between activity levels of ORs and tonality, is significantly more than just applying the known mental process on a generic computer. It is further noted that the court has found that additional elements are more than "apply it" or are not "mere instructions" when the claim recites a technological solution to a technological problem.
It is respectfully submitted that this is not persuasive because this application does not provide sufficient evidence to ensure that the claim as a whole amounts to significantly more than the judicial exception itself. MPEP 2106.04(d).I lists the following example considerations for evaluating whether a judicial exception is integrated into a practical application. It appears that the Applicant argues the 5th consideration - applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP § 2106.05(e). However, in this instant application, when analyzing "other meaningful limits" consideration, the Examiner still have to look at "do the additional elements provide that meaningful limitation?" Thus, the argument that the judicial exception provides the meaningful limit is not persuasive. Regarding the argued "particular device with specific requirements is required for the implementation of the method" is not persuasive because the claims only require the use of generic computer components and functions, with the added steps for storing data in the databases. Further, there is no indication that the manner in which the data is stored requires any special architecture neither changes the functioning of a computer.
Applicant asserts in pg. 16 para. 5:
It is safe to say that the steps of providing the first and second database as claim and determining the tonality in accordance with the activity level of olfactory receptors and tonality activation threshold of each OR, are neither well-known nor nominally or tangentially related to the invention.
It is respectfully submitted that this is not persuasive because step of tonality determination have been identified as a judicial exception and MPEP 2106.05(d) sets forth that, at Step 2B, it is the additional elements which are examined to determine whether they are well-understood, routine, conventional activities previously known to the industry; not the judicial elements.
Applicant asserts in pg. 17 para. 5:
This inventive concept is clearly unique and, as a whole, cannot be considered as well understood, routine, and conventional functions…. one of the above examples even remotely resembles the inventive concept of the claim invention, i.e., providing a first and second databases as claimed and retrieving an impact of each molecule on olfactory receptors, from the first database, and retrieving tonality associated with the activation level of the olfactory receptors from the second database
It is respectfully submitted that this is not persuasive because the Examples of well understood, routine, and conventional functions do not negate, and in fact, support the instant steps identified as data gathering activities and outputting data; which does not amount into a practical application. See Claim Rejections above for detailed explanation of identified additional elements.
Claim Rejections - 35 USC § 103
The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under pre-AIA 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.
A. Claims 1-16 are rejected under 35 U.S.C. 103(a) as being unpatentable over Zhang (“A machine learning based computer-aided molecular design/screening methodology for fragrance molecules” Computers and Chemical Engineering 115:295–308 (2018)) in view of Saito (“Odor Coding by a Mammalian Receptor Repertoire” Sci Signal 2(60) (2009)) as evidenced by Zarzo ("The sense of smell: molecular basis of odorant recognition." Biological Reviews 82(3):455-479 (2007)), as cited on the 11/18/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 1 recites:
-a step of inputting, upon a computer interface, a formula including a plurality of volatile molecule digital identifiers, said volatile molecule digital identifiers being representative of fragrant volatile molecules
-providing a first database cross-referencing volatile molecule digital identifiers, representative of real volatile molecules, and impact of each one of said volatile molecules on olfactory receptors, wherein each one of said olfactory receptors is represented by an olfactory receptor digital identifier and wherein each volatile molecule digital identifier in said first database is associated with at least one olfactory receptor digital identifier, said association being a many-to-many association
-retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors sample
-a step of calculating by a computing system, a value representative of an activity level of at least one olfactory receptor as a function of impact of said volatile molecules represented in said formula,
-providing a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association,
-retrieving from said second database a tonality digital identifier associated with activation of said at least one olfactory receptor,
-a step of determining by the computing system, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold association
• Zhang teaches the computational design of fragrance molecules (pg. 302 col. 1 para. 2); wherein the first step is to identify product attributes wherein fragrance molecules evaporate depending on their volatility composition (i.e. fragrant volatile molecule) (pg. 302 col. 2 para. 1); wherein a representation of molecular structures is determined as the input of the machine learning model (i.e. computing system) (pg. 297 col. 2 para. 5); wherein a computer interface collects all the input data from the user including structure information such as group selection, group numbers, and all the property constraints (i.e. inputting a formula including a plurality of volatile molecule digital identifiers upon a computer interface) (pg. 304 col. 2 para. 2); wherein the convolutional neural network layer information for odor prediction model uses molecular groups as input (pg. 300 Fig. 3) defining a formula (pg. 306 Table 5); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. the classification of odor categories based on people’s perception reads on tonality selection) (pg. 297 col. 2 para. 2); wherein a radar chart shows the value representation of odor character for each molecule (i.e. a step of determining by the computing system, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold association) (pg. 298 Fig. 2) wherein a database and models to predict the perceived odor of a given molecule was developed using a large olfactory psychophysical dataset including 1026 odorants and corresponding odor descriptors (i.e. reading on the recited providing a first database cross-referencing volatile molecule digital identifiers… association being a many-to-many association) (pg. 296 col. 2 para. 2); wherein a predictive model of odor properties for the fragrance product was developed from a database using machine learning method (reading on the recited retrieving step) (pg. 296 col. 2 para. 2).
• Zhang teaches that the database and odor classification for odor pleasantness/odor characters (i.e. tonality ) are selected as the required key properties for a fragrance product reported as a scale from 0 to 100 with characters classified in terms of categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” reported as the representation of odor character for each molecule (i.e. providing a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association) (pg. 297 col. 2 para. 2).
• Zhang does not teach the "calculating", "retrieving" and "determining" steps as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties to odor coding (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. a step of calculating by a computing system, a value representative of an activity level of at least one olfactory receptor as a function of impact of said volatile molecules represented in said formula) (pg. 2 Fig. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed ( association between odorants and olfactory/odorant receptor identifiers ) (pg. 5 col. 1 para. 2); wherein using a machine-learning algorithm trained on the physicochemical descriptors, odorants that activated human odorant receptors were identified (pg. 7 col. 2 para. 4); wherein some odorants inhibit odorant receptors (pg. 8 col. 2 para. 2); whereas some odorants are agonists (i.e. enhance/activate odorant receptor) (pg. 3 Fig. 2); wherein sensitivity values were reported for several odorant receptors (i.e. reading on a value representative of an odorant receptor activation threshold) (pg. 8 Fig. 7F) since the increase in receptor sensitivity can be correlated with the increase of the olfactory receptor activation as evidenced by Zarzo (pg. 458 col. 2 para. 2 Zarzo); wherein the odorant space is reported by taking into account the sampled odorant variance in properties (i.e. reading on tonality digital identifier as it reports the digital representation of the properties/profile that define the odorant space) (pg. 3 Fig. 2); wherein distance in odorant space is correlated with receptor response (i.e. reading on olfactory/odorant receptor digital identifier being associated with one tonality/odorant property digital identifier in an one-to-one association as tonality is a property comprised in receptor response ) (pg. 4 Fig. 3B).
Claim 2 recites:
downstream of the step of calculating a step of computing by the computing system, for at least one odorant receptor, a total activity level as a function of at least one impact on an activity level calculated
• Zhang does not teach the recitation above. However, Saito teaches the computational prediction of odorant receptor activation (i.e. a step of calculating by a computing system, a value representative of an activity level of at least one olfactory receptor as a function of impact of said volatile molecules represented in said formula) from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. reading on a total/impact on activity level as a function of at least one impact on an activity level calculated) (pg. 2 Fig. 1).
Claim 3 recites:
-a step of modifying, upon the computer interface, a value representative of a fragrance tonality of the composition,
-a step of second calculation, by the computing system, a modification of total activity level of at least one odorant receptor,
-a step of second determination, by the computing system, a set of at least one volatile molecule digital identifier presenting a total activity level impact value equal to the total activity level modification calculated,
-a step of supplying said set to a computer interface
• Zhang teaches the generated molecules being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2) accounting for the inputted values for structure information such as group selection, group numbers, and all the property constraints (i.e. molecule digital identifiers) (pg. 304 col. 2 para. 2); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. reading on tonality selection) (pg. 297 col. 2 para. 2); wherein the results generated being displayed in the interface (i.e. step of supplying said set to a computer interface) (pg. 304 col. 2 pg. 2); wherein the user can also modify the code template to customize the optimization for fragrance design problem (i.e. a step of modifying, upon the computer interface, a value representative of a fragrance tonality of the composition) (pg. 304 col. 2 pg. 2).
• Zhang does not teach the "second calculation " and " second determination" steps. However, Saito teaches the computational prediction of odorant receptor activation (i.e. a step of calculating by a computing system, a value representative of an activity level of at least one olfactory receptor as a function of impact of said volatile molecules represented in said formula) from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. reading on a total/impact on activity level as a function of at least one impact on an activity level calculated) (pg. 2 Fig. 1).
Claim 4 recites:
in which each tonality is associated to a tonality digital identifier, said method further comprising a step of automatic tonality selection as a function of the result of comparing the tonality digital identifiers of the composition to a set of at least one predetermined tonality digital identifier, the step of modifying the value representative of the fragrance of said selected tonality identifiers of the composition being configured to reduce the value of said selected tonality identifiers to a reduced value
• Zhang teaches the fragrance design optimization model including an objective function, molecular structural constraints and property constraints; with the objective function being maximize/minimize one of the desired properties (i.e. reading on minimize/maximize a cost function for a digital identifier), such as odor pleasantness (i.e. selection and modification of the value representative of the fragrance of said selected tonality identifiers of the composition being configured to reduce the value of said selected tonality identifiers to a reduced value – hence "minimize") (pg. 303 col. 1para. 3).
Claim 5 recites:
in which the value representative of the impact of the molecule, represented by the corresponding volatile molecule digital identifier, on an activity level of an odorant receptor is representative of one or more of:
-the activation of said odorant receptor,
-the deactivation of said odorant receptor,
-the enhancement of said odorant receptor, or
-the inhibition of said odorant receptor
• Zhang does not teach the recitation above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed (olfactory/odorant receptor identifiers) (pg. 5 col. 1 para. 2); using a machine-learning algorithm trained on the physicochemical descriptors, odorants that activated human odorant receptors were identified (i.e. the activation of said odorant receptor) (pg. 7 col. 2 para. 4); wherein some odorants inhibit odorant receptors (i.e. the inhibition of said odorant receptor) pg. 8 col. 2 para. 2); whereas some odorants are agonists (i.e. enhance/activate odorant receptor) (pg. 3 Fig. 2).
Claim 6 recites:
in which the step of second determining is configured to provide at least one volatile molecule digital identifier presenting a value representative of an impact on the activity of an odorant receptor associated to a tonality modified during the step of modifying, said impact corresponding either to the enhancement of said odorant receptor or to the inhibition of said odorant receptor
• Zhang teaches the generated molecules being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2) accounting for the inputted values for structure information such as group selection, group numbers, and all the property constraints (i.e. molecule digital identifiers) (pg. 304 col. 2 para. 2); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. reading on tonality selection) (pg. 297 col. 2 para. 2); wherein the user can also modify the code template to customize the optimization for fragrance design problem (i.e. provide at least one volatile molecule digital identifier presenting a value representative of an impact on the activity of an odorant receptor associated to a tonality modified during the step of modifying) (pg. 304 col. 2 pg. 2).
• Zhang does not teach the "impact corresponding either to the enhancement of said odorant receptor or to the inhibition of said odorant receptor above" as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed (olfactory/odorant receptor identifiers) (pg. 5 col. 1 para. 2); using a machine-learning algorithm trained on the physicochemical descriptors, odorants that activated human odorant receptors were identified (i.e. the activation of said odorant receptor) (pg. 7 col. 2 para. 4); wherein some odorants inhibit odorant receptors (i.e. the inhibition of said odorant receptor) pg. 8 col. 2 para. 2); whereas some odorants are agonists (i.e. enhance/activate odorant receptor) (pg. 3 Fig. 2).
Claim 7 recites:
-a step of selecting a volatile molecule digital identifier of the formula, upon the computer interface, and
-a step of third determining, by the computing system, at least a set of at least one volatile molecule digital identifier presenting a value representative of an impact on an activity level of a odorant receptor equal to the value representative of the impact on the activity level on said odorant receptor of the selected volatile molecule digital identifier, and
-wherein the step of supplying further comprises supplying said set to the computer interface
• Zhang teaches the generated molecules being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2) accounting for the inputted values for structure information such as group selection, group numbers, and all the property constraints (i.e. molecule digital identifiers) (pg. 304 col. 2 para. 2); wherein the results generated being displayed in the interface (i.e. step of supplying said set to a computer interface) (pg. 304 col. 2 pg. 2); wherein the user can also modify the code template to customize the optimization for fragrance design problem (i.e. a step of selecting a volatile molecule digital identifier of the formula, upon the computer interface) (pg. 304 col. 2 pg. 2).
• Zhang does not teach the "calculating", "retrieving" and "determining" steps as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties to odor coding (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed ( association between odorants and olfactory/odorant receptor identifiers ) (pg. 5 col. 1 para. 2); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. a step of third determining, by the computing system, at least a set of at least one volatile molecule digital identifier presenting a value representative of an impact on an activity level of a odorant receptor equal to the value representative of the impact on the activity level on said odorant receptor of the selected volatile molecule digital identifier) (pg. 2 Fig. 1).
Claim 8 recites:
-a step of second selecting at least two volatile molecule digital identifiers of the formula, at least two said volatile molecules being associated with the activation of at least two distinct odorant receptors, comprising target receptor
-a step of fourth determining one volatile molecule digital identifier presenting a value representative of an impact on an activity level of each target receptor equal to the value representative of the impact on the activity level on said odorant receptor of the selected volatile molecule digital identifiers, and
-wherein the step of supplying further comprises supplying the determined volatile molecule digital identifier to the computer interface
• Zhang teaches the generated molecules being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2) accounting for the inputted values for structure information such as group selection, group numbers, and all the property constraints (i.e. molecule digital identifiers) (pg. 304 col. 2 para. 2); wherein the results generated being displayed in the interface (i.e. supplying the determined volatile molecule digital identifier to the computer interface) (pg. 304 col. 2 pg. 2);
• Zhang does not teach the "second selecting" and "fourth determining" steps as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties, odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed ( i.e. selecting at least two volatile molecule digital identifiers of the formula, at least two said volatile molecules being associated with the activation of at least two distinct odorant receptors, comprising target receptor) (pg. 5 col. 1 para. 2); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. determining one volatile molecule digital identifier presenting a value representative of an impact on an activity level of each target receptor equal to the value representative of the impact on the activity level on said odorant receptor of the selected volatile molecule digital identifiers) (pg. 2 Fig. 1).
Claim 9 recites:
downstream of the second determining, the third determining, or the fourth determining a step of estimating a quantity for at least one said volatile molecule digital identifier, said quantity being used in the downstream step of supplying
• Zhang teaches the generated molecules being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2) accounting for the inputted values for structure information such as group selection, group numbers, and all the property constraints (i.e. molecule digital identifiers) (pg. 304 col. 2 para. 2); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. a step of estimating a quantity for at least one said volatile molecule digital identifier) (pg. 297 col. 2 para. 2); wherein the results generated being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2).
Claim 10 recites:
in which the second determining or the third determining comprises executing an optimization rule which is at least one of the following:
-a first rule in which a determined set of at least one volatile molecule digital identifier is configured to present a minimal number of volatile molecule digital identifiers,
-a second rule in which a determined set of at least one volatile molecule digital identifier is configured to present a minimal total quantity indicator of corresponding volatile molecule digital identifiers,
-a third rule in which a determined set of at least one volatile molecule digital identifier is configured to present a minimal number of negative impact volatile molecule digital identifiers,
-a fourth rule in which at least one volatile molecule digital identifier is associated to a volatile molecule delivery capacity indicator, said rule being configured to adapt a determined set of at least one volatile molecule digital identifier as a function of said volatile molecule delivery capacity indicator for the volatile molecule digital identifiers in the set,
-a fifth rule in which a set of at least two volatile molecule digital identifier is configured to minimize the overlap of odorant receptors activated by said set of molecules,
-a sixth rule in which a set of at least two volatile molecule digital identifier is configured to minimize the modulation of the odorant receptors activated by said set of molecules,
-a seventh rule in which determined set of at least one volatile molecule digital identifier is configured to maximize the negative modulation or inhibition of odorant receptor linked to a predetermined undesired olfactory tonality,
-an eighth rule in which determined set of at least one volatile molecule digital identifier is configured to maximize the positive modulation or enhancement of odorant receptor linked to a predetermined desired olfactory tonality, or
-a ninth rule in which determined set of at least one volatile molecule digital identifier is configured to minimize or maximize at least one cost function, each volatile molecule being associated to at least one value representative of cost
• Zhang teaches the fragrance design optimization model including an objective function, molecular structural constraints and property constraints; with the objective function being maximize/minimize one of the desired properties (i.e. a ninth rule in which determined set of at least one volatile molecule digital identifier is configured to minimize or maximize at least one cost function, each volatile molecule being associated to at least one value representative of cost), such as odor pleasantness (pg. 303 col. 1para. 3).
Claim 11 recites:
which further comprises a step of second inputting a quantity of at least one input volatile molecule, upon the computer interface, said quantity being used in the step of calculating, by the computing system, for at least one volatile molecule digital identifier of the formula, a value representative of an impact on an activity level of an odorant receptor
• Zhang teaches the generated molecules being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2) accounting for the inputted values for structure information such as group selection, group numbers, and all the property constraints (i.e. inputting a quantity of at least one input volatile molecule, upon the computer interface, said quantity being used in the step of calculating, by the computing system, for at least one volatile molecule digital identifier) (pg. 304 col. 2 para. 2); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the results generated being displayed in the interface wherein he user can also modify the code template to customize the optimization for fragrance design problem (pg. 304 col. 2 pg. 2).
• Zhang does not teach "a value representative of an impact on an activity level of an odorant receptor" as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties, odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed (pg. 5 col. 1 para. 2); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. a value representative of an impact on an activity level of an odorant receptor) (pg. 2 Fig. 1).
Claim 12 recites:
in which at least one volatile molecule digital identifier is associated to a volatile molecule delivery capacity indicator, at least one of steps of calculating second determining, third determining, or fourth determining being performed as a function of the volatile molecule delivery capacity indicator associated to at least one input volatile molecule digital identifier
Claim 13 recites:
in which the volatile molecule delivery capacity indicator is representative of a substrate upon which the associated volatile molecule is located
• Zhang teaches that gas odorant molecules will diffuse (i.e. reading on volatile molecule delivery capacity indicator as in claims 12-13) through the surrounding air over time and distance (i.e. substrate upon which the associated volatile molecule is located as in claim 13), and finally, at a given time and distance some of the fragrance molecules will eventually reach the nose of the customer who perceive the odorants with a certain intensity and character; wherein odor properties include odor character and odor pleasantness, the physicochemical properties including diffusion, evaporation etc (pg. 302 col. 2 para. 1);l wherein the fragrance design optimization model including an objective function, molecular structural constraints and property constraints; with the objective function being maximize/minimize one of the desired properties, such as odor pleasantness (i.e. at least one of steps of calculating second determining, third determining, or fourth determining being performed as a function of the volatile molecule delivery capacity indicator associated to at least one input volatile molecule digital identifier) (pg. 303 col. 1para. 3).
Claim 14 recites:
-a step of inputting for at least one tonality digital identifier, upon a computer interface, a value representative of the tonality of a composition resulting from a formula to be determined, said formula including a plurality of volatile molecule
-providing a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor and said tonality digital identifiers, is a one-to-one association,-retrieving from said second database an olfactory receptor digital identifier, associated with said tonality digital identifier;
-a step of determining by a computing system, for the formula to be determined and as a function of at least one value representative of at least one tonality of said composition, a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor;
-providing a first database cross-referencing volatile molecule digital identifiers, representative of real volatile molecules, and impact of each one of said volatile molecules on olfactory receptors, wherein each one of said olfactory receptors is represented by an olfactory receptor digital identifier and wherein each volatile molecule digital identifier in said first database is associated with at least one olfactory receptor digital identifier, said association being a many-to-many association;
-retrieving from said first database an impact of each one of said fragrant volatile molecules on said at least one olfactory receptor,
-a step of second determining a formula including a plurality of volatile molecule digital identifiers such that impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor
• Zhang teaches the computational design of fragrance molecules (pg. 302 col. 1 para. 2); wherein the first step is to identify product attributes wherein fragrance molecules evaporate depending on their volatility composition (i.e. fragrant volatile molecule) (pg. 302 col. 2 para. 1); wherein a representation of molecular structures is determined as the input of the machine learning model (i.e. computing system) (pg. 297 col. 2 para. 5); wherein a computer interface collects all the input data from the user including structure information such as group selection, group numbers, and all the property constraints (i.e. inputting a formula including a plurality of volatile molecule digital identifiers upon a computer interface) (pg. 304 col. 2 para. 2); wherein the convolutional neural network layer information for odor prediction model uses molecular groups as input (pg. 300 Fig. 3) defining a formula (pg. 306 Table 5); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. the classification of odor categories based on people’s perception reads on tonality selection) (pg. 297 col. 2 para. 2); wherein a radar chart shows the value representation of odor character for each molecule (i.e. a step of determining by the computing system, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold association) (pg. 298 Fig. 2) wherein a database and models to predict the perceived odor of a given molecule was developed using a large olfactory psychophysical dataset including 1026 odorants and corresponding odor descriptors (i.e. reading on the recited providing a first database cross-referencing volatile molecule digital identifiers… association being a many-to-many association) (pg. 296 col. 2 para. 2); wherein a predictive model of odor properties for the fragrance product was developed from a database using machine learning method (i.e. reading on the recited retrieving from said first database step) (pg. 296 col. 2 para. 2).
• Zhang teaches that the database and odor classification for odor pleasantness/odor characters (i.e. tonality ) are selected as the required key properties for a fragrance product reported as a scale from 0 to 100 with characters classified in terms of categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” reported as the representation of odor character for each molecule (i.e. providing a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association) (pg. 297 col. 2 para. 2).
• Zhang does not teach the "a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" and "an impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor" as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties to odor coding (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. "a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" and "an impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor") (pg. 2 Fig. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed (association between odorants and olfactory/odorant receptor identifiers ) (pg. 5 col. 1 para. 2); wherein using a machine-learning algorithm trained on the physicochemical descriptors, odorants that activated human odorant receptors were identified (pg. 7 col. 2 para. 4); wherein some odorants inhibit odorant receptors (pg. 8 col. 2 para. 2); whereas some odorants are agonists (i.e. enhance/activate odorant receptor) (pg. 3 Fig. 2); wherein sensitivity values were reported for several odorant receptors (i.e. reading on a value representative of an odorant receptor activation threshold) (pg. 8 Fig. 7F) since the increase in receptor sensitivity can be correlated with the increase of the olfactory receptor activation as evidenced by Zarzo (pg. 458 col. 2 para. 2 Zarzo); wherein the odorant space is reported by taking into account the sampled odorant variance in properties (i.e. reading on tonality digital identifier as it reports the digital representation of the properties/profile that define the odorant space) (pg. 3 Fig. 2); wherein distance in odorant space is correlated with receptor response (i.e. reading on olfactory/odorant receptor digital identifier being associated with one tonality/odorant property digital identifier in an one-to-one association as tonality is a property comprised in receptor response ) (pg. 4 Fig. 3B).
Claim 15 recites:
-a means of inputting a formula including a plurality of volatile molecule digital identifiers, said volatile molecule digital identifier being representative of fragrant volatile molecule,
-a first database cross-referencing volatile molecule digital identifiers, representative of real volatile molecules, and impact of each one of said volatile molecules on olfactory receptors, wherein each one of said olfactory receptors is represented by an olfactory receptor digital identifier and wherein each volatile molecule digital identifier in said first database is associated with at least one olfactory receptor digital identifier, said association being a many-to-many association;
-means for retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors,
-a means of calculating a value representative of an activity level of each said molecule on an activity level of at least one olfactory receptor, as a function of impact of said volatile molecules represented in said formula,
a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association,
-means for retrieving from said second database a tonality digital identifier associated with activation of said at least one olfactory receptor,
- a means of determining a value representative of an odorant receptor activation threshold, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold receptor
• Zhang teaches the computational design of fragrance molecules (pg. 302 col. 1 para. 2); wherein the first step is to identify product attributes wherein fragrance molecules evaporate depending on their volatility composition (i.e. fragrant volatile molecule) (pg. 302 col. 2 para. 1); wherein a representation of molecular structures is determined as the input of the machine learning model (i.e. computing system) (pg. 297 col. 2 para. 5); wherein a computer interface collects all the input data from the user including structure information such as group selection, group numbers, and all the property constraints (i.e. inputting a formula including a plurality of volatile molecule digital identifiers upon a computer interface) (pg. 304 col. 2 para. 2); wherein the convolutional neural network layer information for odor prediction model uses molecular groups as input (pg. 300 Fig. 3) defining a formula (pg. 306 Table 5); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. the classification of odor categories based on people’s perception reads on tonality selection) (pg. 297 col. 2 para. 2); wherein a radar chart shows the value representation of odor character for each molecule (i.e. a step of determining by the computing system, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold association) (pg. 298 Fig. 2) wherein a database and models to predict the perceived odor of a given molecule was developed using a large olfactory psychophysical dataset including 1026 odorants and corresponding odor descriptors (i.e. reading on a first database cross-referencing volatile molecule digital identifiers… association being a many-to-many association) (pg. 296 col. 2 para. 2); wherein a predictive model of odor properties for the fragrance product was developed from a database using machine learning method (i.e. reading on the recited retrieving from any database) (pg. 296 col. 2 para. 2).
• Zhang teaches that the database and odor classification for odor pleasantness/odor characters (i.e. tonality ) are selected as the required key properties for a fragrance product reported as a scale from 0 to 100 with characters classified in terms of categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” reported as the representation of odor character for each molecule (i.e. a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association) (pg. 297 col. 2 para. 2).
• Zhang does not teach the "a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" and "an impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor" as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. "a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" and "an impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor") (pg. 2 Fig. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed (association between odorants and olfactory/odorant receptor identifiers ) (pg. 5 col. 1 para. 2); wherein using a machine-learning algorithm trained on the physicochemical descriptors, odorants that activated human odorant receptors were identified (pg. 7 col. 2 para. 4); wherein some odorants inhibit odorant receptors (pg. 8 col. 2 para. 2); whereas some odorants are agonists (i.e. enhance/activate odorant receptor) (pg. 3 Fig. 2); wherein sensitivity values were reported for several odorant receptors (i.e. reading on a value representative of an odorant receptor activation threshold) (pg. 8 Fig. 7F) since the increase in receptor sensitivity can be correlated with the increase of the olfactory receptor activation as evidenced by Zarzo (pg. 458 col. 2 para. 2 Zarzo); wherein the odorant space is reported by taking into account the sampled odorant variance in properties (i.e. reading on tonality digital identifier as it reports the digital representation of the properties/profile that define the odorant space) (pg. 3 Fig. 2); wherein distance in odorant space is correlated with receptor response (i.e. reading on olfactory/odorant receptor digital identifier being associated with one tonality/odorant property digital identifier in an one-to-one association as tonality is a property comprised in receptor response) (pg. 4 Fig. 3B).
Claim 16 recites:
-a means of inputting for at least one tonality digital identifier, upon a computer interface, a value representative of the tonality of a composition resulting from a formula to be determined, said formula including a plurality of volatile molecule,
-a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor and said tonality digital identifiers, is a one-to-one association, means for retrieving from said second database an olfactory receptor digital identifier ,associated with said tonality digital identifier,
-a means of determining by a computing system, for the formula to be determined and as a function of at least one value representative of at least one tonality of said composition, a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor;
-a first database cross-referencing volatile molecule digital identifiers, representative of real volatile molecules, and impact of each one of said volatile molecules on olfactory receptors, wherein each one of said olfactory receptors is represented by an olfactory receptor digital identifier and wherein each volatile molecule digital identifier in said first database is associated with at least one olfactory receptor digital identifier, said association being a many-to-many association;
-a means for retrieving from said first database an impact of each one of said fragrant volatile molecules on olfactory receptors,
-a means of second determining a formula including a plurality of volatile molecule digital identifiers such that impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor
• Zhang teaches the computational design of fragrance molecules (pg. 302 col. 1 para. 2); wherein the first step is to identify product attributes wherein fragrance molecules evaporate depending on their volatility composition (i.e. fragrant volatile molecule) (pg. 302 col. 2 para. 1); wherein a representation of molecular structures is determined as the input of the machine learning model (i.e. computing system) (pg. 297 col. 2 para. 5); wherein a computer interface collects all the input data from the user including structure information such as group selection, group numbers, and all the property constraints (i.e. means of inputting a formula including a plurality of volatile molecule digital identifiers upon a computer interface) (pg. 304 col. 2 para. 2); wherein the convolutional neural network layer information for odor prediction model uses molecular groups as input (pg. 300 Fig. 3) defining a formula (pg. 306 Table 5); wherein to establish machine learning models, the required properties are selected first for the product design problem, which consist of the odor properties of the product and physicochemical properties (pg. 297 col. 1 para. 4); wherein the odor characters are classified in terms of the following twenty categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” (i.e. the classification of odor categories based on people’s perception reads on tonality selection) (pg. 297 col. 2 para. 2); wherein a radar chart shows the value representation of odor character for each molecule (i.e. means of determining by the computing system, a value representative of at least one tonality forming a composition, as a function of said activity level and an olfactory receptor tonality activation threshold association) (pg. 298 Fig. 2) wherein a database and models to predict the perceived odor of a given molecule was developed using a large olfactory psychophysical dataset including 1026 odorants and corresponding odor descriptors (i.e. reading on a first database cross-referencing volatile molecule digital identifiers… association being a many-to-many association) (pg. 296 col. 2 para. 2); wherein a predictive model of odor properties for the fragrance product was developed from a database using machine learning method (i.e. reading on the recited means of retrieving from any database) (pg. 296 col. 2 para. 2).
• Zhang teaches that the database and odor classification for odor pleasantness/odor characters (i.e. tonality ) are selected as the required key properties for a fragrance product reported as a scale from 0 to 100 with characters classified in terms of categories based on people’s perception namely “edible”, “bakery”, “sweet”, “fruit”, “fish”, “garlic”, “spices”, “cold”, “sour”, “burnt”, “acid”, “warm”, “musky”, “sweaty”, “ammonia/urinous”, “decayed”, “wood”, “grass”, “flower”, and “chemical” reported as the representation of odor character for each molecule (i.e. a second database cross referencing olfactory receptor digital identifiers and tonality digital identifiers, each of which being representative of a tonality associated with activation of one of said olfactory receptors, wherein association between said olfactory receptor digital identifiers and said tonality digital identifiers, is a one-to-one association) (pg. 297 col. 2 para. 2).
• Zhang does not teach the "a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" and " means of second determining … an impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor" as recited above. However, Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); wherein physiochemical properties captured were as molecular weight, carbon number, functional groups, and hydrophobicity (pg. 3 col. 1 para. 1); wherein EC50 values for each odorant/receptor pair was reported in a form of a heat map (i.e. "a value of olfactory receptor activity level for at least one olfactory receptor digital identifier representative of said olfactory receptor" and " means of second determining … an impact of said volatile molecules on olfactory receptor digital identifier is equal to the determined value representative of the activity level on said odorant receptor") (pg. 2 Fig. 1); wherein chemical descriptors were calculated for 2683 odorants (pg. 3 Fig. 2) and as with odorants, a set of descriptors for odorant receptors was constructed (association between odorants and olfactory/odorant receptor identifiers ) (pg. 5 col. 1 para. 2); wherein using a machine-learning algorithm trained on the physicochemical descriptors, odorants that activated human odorant receptors were identified (pg. 7 col. 2 para. 4); wherein some odorants inhibit odorant receptors (pg. 8 col. 2 para. 2); whereas some odorants are agonists (i.e. enhance/activate odorant receptor) (pg. 3 Fig. 2); wherein sensitivity values were reported for several odorant receptors (i.e. reading on a value representative of an odorant receptor activation threshold) (pg. 8 Fig. 7F) since the increase in receptor sensitivity can be correlated with the increase of the olfactory receptor activation as evidenced by Zarzo (pg. 458 col. 2 para. 2 Zarzo); wherein the odorant space is reported by taking into account the sampled odorant variance in properties (i.e. reading on tonality digital identifier as it reports the digital representation of the properties/profile that define the odorant space) (pg. 3 Fig. 2); wherein distance in odorant space is correlated with receptor response (i.e. reading on olfactory/odorant receptor digital identifier being associated with one tonality/odorant property digital identifier in an one-to-one association as tonality is a property comprised in receptor response) (pg. 4 Fig. 3B).
Rationale for combining (MPEP §2142-2143)
Regarding claims 1-16, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Zhang in view of Saito because all references disclose computational methods for odor coding. The motivation would have been to predict odorant receptor activation from molecular structure relating physicochemical odorant properties to decipher olfactory encoding by a thorough description of the ligands that activate each odorant receptor (pg. 1 para. 1 Saito).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the odor coding computational method of Zhang to the methods by Saito because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to computational methods for odor coding.
Regarding claims 3-4, 6-11, 14 and 16; one of ordinary skill in the art would be motivated to modify steps (claims 3-4 and 6) or repeat steps (i.e. second determining, third determining, or fourth determining; second inputting; second selecting – in claims 3, 6-11, 14 and 16) to achieve routine optimization. See MPEP 2144.05 II (A) Routine optimization … (a change in form, proportions, or degree "will not sustain a patent"); In re Williams, 36 F.2d 436, 438, 4 USPQ 237 (CCPA 1929) ("It is a settled principle of law that a mere carrying forward of an original patented conception involving only change of form, proportions, or degree, or the substitution of equivalents doing the same thing as the original invention, by substantially the same means, is not such an invention as will sustain a patent, even though the changes of the kind may produce better results than prior inventions."). See also KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 416, 82 USPQ2d 1385, 1395 (2007) (identifying "the need for caution in granting a patent based on the combination of elements found in the prior art").
Regarding claim 4; one of ordinary skill in the art would be motivated to automate a tonality selection step. See MPEP 2144.04 (III) – obvious to automate rationale. In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958) (Appellant argued that claims to a permanent mold casting apparatus for molding trunk pistons were allowable over the prior art because the claimed invention combined "old permanent-mold structures together with a timer and solenoid which automatically actuates the known pressure valve system to release the inner core after a predetermined time has elapsed." The court held that broadly providing an automatic or mechanical means to replace a manual activity which accomplished the same result is not sufficient to distinguish over the prior art.).
B. Claim 17 is rejected under 35 U.S.C. 103(a) as being unpatentable over Zhang and Saito as applied to claims 1 and 3-4 above further in view of Daskalakis ("The limit points of (optimistic) gradient descent in min-max optimization." Advances in neural information processing systems 31 (2018)), as cited on the 11/18/2025 Form PTO-892. Any newly recited portions are necessitated by claim amendment.
Claim 17 recites:
wherein the reduced value is zero
• Neither Zhang or Saito teach the recited limitation above. However, Daskalakis teaches the min/max optimization of objective functions (pg. 2); wherein gradient descent/ascent dynamics converges to zero (pg. 6 Fig. 6).
Rationale for combining (MPEP §2142-2143)
Regarding claim 17, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine, in the course of routine experimentation and with a reasonable expectation of success, the methods of Zhang and Saito in view of Daskalakis because all references disclose computational methods for modifying variables towards optimization. The motivation would have been to incorporate the analysis of Gradient Descent/Ascent dynamics being applied to min-max optimization problems (pg. 2 para. 3 Daskalakis).
Therefore it would have been obvious to one of ordinary skill in the art to substitute the optimization computational method of Zhang and Saito to the methods by Daskalakis because such a substitution is no more than the simple substitution of one known element for another. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to computational methods for modifying variables towards optimization.
Response to applicant's remarks in regard to Claim Rejection 35 U.S.C. ~ 103
The Remarks of 02/18/2026 have been fully considered but are not persuasive for the reasons below:
Applicant asserts in pg. 19 para. 4:
Initially, it is submitted that according to the present invention, a one-to-one relationship is established between odorant receptor and tonality. As explained in the description, a model is provided that not only links a molecule to an olfactory receptor or a molecule to a perception. Rather, the claimed invention allows detecting an odorant receptor activated by a formula, and therefore, determines the tonality associated with the activated odorant receptor. As indicated above, the term tonality, as defined in the claims, refers to a dominant note or theme of a fragrance or an organoleptic property that produces a specific experience in the subject. Tonality is not a combination of general properties of odorant, as suggested by the Examiner, but rather a specific organoleptic property that is the dominant note experienced by the subject, which is produced by the formula
It is respectfully submitted that this is not persuasive because a combination of properties that defines an odorant comprises organic properties such as the smell characteristics (See Frater ("Fragrance chemistry." Tetrahedron 54.27 (1998): 7633-7703) in pg. 7665 para. 4) – including the dominant property or an "organoleptic property that is the dominant note" as described by the Applicant to define tonality. Therefore, the instant specification is correctly applied and supports the interpretation of the claim language.
Applicant asserts in pg. 20 para. 2:
According to Zhang, a model is developed in order to directly link odor perception or pleasantness to a molecule. … Therefore, Zhang tries to model a direct relationship between a molecule and its tonality. Zhang teaches away from adding a step linked to the use of olfactory receptors. Zhang is also completely silent with regard to a direct one-to-one association between an olfactory receptor and a tonality. Accordingly, Zhang does not teach at least the following features …
It is respectfully submitted that this is not persuasive because "One cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references" (MPEP 2145 § IV). This argument is unpersuasive, because it analyzes the teachings of the references separately and independently, whereas the rejection is based on the combined teachings of the references. While none of the references teach all claim limitations, and the examiner does not dispute Appellant's identification of material missing from each one, all the claim limitations are taught by the combination of references. All aspects regarding OR activation as well the one to one association between an olfactory receptor and a tonality are taught by Saito as described in the Claim Rejections in detail; as well as the motivation to combine the teachings by Zhang and Saito.
Applicant asserts in pg. 22 para. 3:
In view of the above, it is respectfully submitted that Saito corresponds to the second type of prior art mentioned in the present application: "Conversely, because each OR may interact with several odorant and aroma molecules possessing different chemical structures and evoke different sets of tonalities, it has often been difficult to infer the nature of the information encoded by an OR." Moreover, according to the Examiner, one of ordinary skill in the art would have been motivated to modify the method of Zhang to include computational prediction of odorant receptor activation from molecular structure relating physicochemical odorant properties of Saito. The Examiner explains that one of ordinary skill at the time of the invention would want to take into account olfactory receptor response in addition to structure. One of ordinary skill in the art would be able to motivated to combine the teachings in these references with a reasonable expectation of success since the described teachings pertain to computational methods for odor coding. It is submitted, however, that given that Zhang teaches directly modeling a sensory perception based on physicochemical properties of molecules, one skilled in the art would not have been prompted to add other steps linked to OR activation without having any hint of a direct relationship between the OR activation and tonality …
It is respectfully submitted that this is not persuasive because Saito teaches the computational prediction of odorant receptor activation from molecular structure (pg. 1 col. 1 para. 1) relating physicochemical odorant properties to odor coding (i.e. reading on molecule tonality), odorant receptor sequences, and their interactions (pg. 1 para. 1); which reads on the association between OR activation and tonality. a combination of properties that defines an odorant comprises organic properties such as the smell characteristics (See Frater ("Fragrance chemistry." Tetrahedron 54.27 (1998): 7633-7703) in pg. 7665 para. 4) – including the dominant property or an "organoleptic property that is the dominant note" as described by the Applicant to define tonality. Therefore, odor coding as taught by Saito, which involved the categorization of odors for each analyzed molecule reads on tonality analysis. Furthermore, the rejection as explained above does establish a prima facie case of obviousness under the applicable legal standards.
Conclusion
No claims are allowed.
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.
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/F.F.L./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
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