DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Instant application eligible to benefit from the Patent Prosecution Highway program. At the EPO the patent was filed under 21203197.5 on 10/18/2021. Acknowledgment is made of applicant's claim for foreign priority based on an application filed in EP on 10/18/2021. It is noted, however, that applicant has not filed a certified copy of the EP21203197.5 application as required by 37 CFR 1.55.
Therefore, the effective filing date is currently considered 10/17/2022.
Information Disclosure Statement
No IDS has been submitted.
Drawings
The drawings filed on 10/17/2022 are accepted
Specification (Abstract)
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Inclusion of reference numerals (e.g., "(600)", "(205)", "(610)") inside an abstract should be avoided by the applicants, as the abstract is supposed to be a summary, not a detailed description. Applicants are advised to remove the numbers (600, 205, 610, etc.) and convert the abstract into a narrative summary
The phrase "volatile composition obtained by using the volatile composition assembling method of the present invention" suggests a change in the invention's focus from "skin hydration" to "volatile composition," creating a lack of clarity. Applicants are advised to keep the subject matter (skin hydration) is consistent throughout.
The abstract is not double-spaced, applicants are advised to use double spacing for this section.
Specification
The specification filed on 10/17/2022 is accepted
Claim Status
Claims 1-13 are pending and examined on the merits.
Claims 1-13 are rejected.
Claim Objections
Claim 11 objected to because of the following informalities. The claim recites “a composition prediction method according to “claim land” in line 2. A space should be provided between “1” and “and”. Appropriate correction is required.
Claim Rejections - 35 U.S.C. § 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.
Claim 1-11, and 13rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 2A, Prong 1
In accordance with MPEP § 2106, the instant claims 1-10, 11 and 13, are drawn to a process (method), claims 12 is drawn to a product by process, and therefore are found to recite statutory subject matter (Step 1: YES). The instant claims are then analyzed to determine if the claims recite any concepts that equate to an abstract idea, law of nature or natural phenomenon (Step 2A, Prong 1). The instant claims recite the following limitations that equate to an abstract idea:
Claim 1 recites
A step of selecting at least one chemical compound digital identifier, to form a composition. (Mental process that can be performed in the human mind)
Retrieving from a database representative of a polarity value selected chemical compound identifier. (Mental process that can be performed in the human mind, for example, looking up a compound in a catalog based on its known property)
Predicting moisturizing factor value for chemical compound identifier of at least one retrieved polarity value. (mathematical concept (for example, as a function polarity value) or a mental process. (concepts that can be performed in the human mind, such as evaluation or analysis)
Claim 2 recites
Calculation of water evaporation rate on the measured evaporated or remaining water quantities. (Calculating a rate based on measured input and output data is a mathematical calculation.)
Moisturizing factor calculation as a function of the water evaporation rate calculated. (Mathematical concept-defines a mathematical formula)
Claim 3 recites
Computing a chemical compound polarity for a chemical compound associated to a stored moisturizing factor. (calculations to determine polarity falls under the mathematical concepts grouping of abstract ideas)
Modelling of a mathematical formula of moisturizing factor as a function of chemical compound polarity. (The formula mapping polarity to moisturizing factors is a mathematical formula or equation and falls under mathematical concept.)
Recording, in a database, the moisturizing factor formula modelled parameters. (Mental process- can be performed in the human mind)
Claim 4 recites the chemical compound polarity is computed as a function of at least one of the dispersions. (Mathematical concept/mathematical relationship or formula grouping to abstract ideas)
Claim 5 recites
A step of computing a mean moisturizing factor for at least one chemical compound identifier as a function of the moisturizing factor. (Mathematical concept- constitutes a mathematical formula)
Claim 6 recites a step of computing a composition mean moisturizing factor as a function of at least two moisturizing factors computed. (Mathematical concept-constitutes a mathematical formula)
Claim 7 recites a step of computing a composition moisturizing factor as a function of at least two mean moisturizing factors predicted. (Mathematical concept- constitutes a mathematical formula)
Claim 8 recites a step of computing a moisturizing factor linearity of a composition of at least two compound identifiers based on the predicted moisturizing factor. (Mathematical concept- the step involves calculations and mathematical relationships)
Claim 9 recites a step of defining a moisturizing factor threshold, at least one chemical compound digital identifier being removed from the selection as a function of the difference between the moisturizing factor being obtained for chemical compound digital identifier and the defined moisturizing factor threshold.
The process involves calculating a difference between a value and a threshold, which is a mathematical formula or calculation, a key component of the mathematical concepts grouping. Also, the steps of “defining a threshold” and removing items from a list can be performed in the human mind. (Mental process or mathematical concept)
Claim 10 recites a step of replacing at least one chemical compound digital identifier as a function of the difference between the moisturizing factor being obtained for said chemical compound digital identifier and the moisturizing factor being obtained for an alternative chemical compound digital identifier candidate.
The step of replacing identifiers “as a function of the difference between the moisturizing factor” is a mathematical calculation/relationship (Mathematical concept). Identifying, comparing, and replacing data based on a calculated difference can be performed in the human mind (Mental process).
Claim 11 recites prediction method according to claim and for assembling the composition object.
The prediction relies on mathematical relationships, formulas, calculations, or algorithms to analyze (Mathematical concept/calculation)
As such, claims 1-11, and 13 recite an abstract idea (Step 2A, Prong 1: YES).
Step 2A, Prong 2
Claims found to recite a judicial exception under Step 2A, Prong 1 are then further analyzed to determine if the claims as a whole integrate the recited judicial exception into a practical application or not (Step 2A, Prong 2). Specifically, the claims recite the following additional elements:
Claims 1 recites
Computer implemented method to provide predictive, real time, skin hydration performance metrics for a composition.
Outputting at least one moisturizing factor value predicted.
Claim 2 recites
Storing the calculated moisturizing factor and the water evaporation rate in a database.
Measurement of a quantity of evaporated water from the skin replicating surface or of remaining water on the skin replicating surface after the deposition of the chemical compound at different measurement times.
Claim 5 recites the step of obtaining comprising a step of …… quantity input for said chemical compound identifier, … moisturizing factor computed. Claim 8 recites the step of outputting being configured to display the moisturizing factor linearity of the composition of said at least two chemical compound digital identifiers.
Claim 11 recites assembling the composition object.
Claim 12 recites composition is obtained according to a composition assembling method based on prediction method in claim 11.
Claim 13 recites that at least one chemical compound is a fragrant chemical compound.
The limitations about a computer implemented system serves as being merely an insignificant, routine, or conventional post-solution activity and used an input for the judicial exception. The gathering data and analysis of all steps (Claim 1-13) merely serve as calculation of mathematical calculations, mental process or human organizing activity and does not add any significant practical application. Storing the calculated moisturizing factor and the water evaporation rate in a database, outputting predicted moisturizing factor values are common and conventional practice of storing and displaying results using a computer implemented system and serve no practical application. Obtaining chemical composition from prediction model described in claim 11 is just data gathering from the prediction model and does not add any new practical application.
Therefore, these limitations are mere data gathering or analyzing activities and displaying the results using a conventional display system. As set forth in MPEP 2106.05(g), mere data gathering and analyzing activity has been identified by the courts as insignificant extra-solution activity that does not provide a practical application.
There are no limitations that indicate that the computing implemented method require anything other than a generic computing system. As such, these limitations equate to mere instructions to implement the abstract idea on a generic computer that the courts have stated does not render an abstract idea eligible in Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
The above recited additional elements do not provide a practical application of the recited judicial exception. As such, claims 1-13 are directed to an abstract idea (Step 2A, Prong 2: NO).
Step 2B
Claims found to be directed to a judicial exception are then further evaluated to determine if the claims recite an inventive concept that provides significantly more than the judicial exception itself (Step 2B). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims recite additional elements that equate to mere instructions to apply the recited exception in a generic computing environment or well-understood, routine and conventional activity. Also, remaining additional elements are routine and conventional in bioinformatic pipelines and merely serve extra solution activity.
As discussed above, there are no additional limitations to indicate that the claimed processor requires anything other than generic computer components in order to carry out the recited abstract idea in the claims. Claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984.
Furthermore, the additional elements recited in the claims amount to well-understood, routine and conventional activity.
As such, the combination of additional elements recited in the claims is well-understood, routine and conventional. The additional elements do not comprise an inventive concept when considered individually or as an ordered combination that transforms the claimed judicial exception into a patent-eligible application of the judicial exception. Therefore, the claims do not amount to significantly more than the judicial exception itself (Step 2B: No). As such, claims 1-22 are not patent eligible.
Claim Rejections - 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim 1, 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. (US 20190237194) in view of Naseem et al. (J. Cheminform (2021) 13:25).
Salvi et al. discloses a computer-implemented method for formulating skincare compositions. (Abstract) Specifically, it teaches:
A step of selecting, upon a computer interface, at least one chemical compound digital identifier (Figure 1; paragraph [0033]; claim 16) which suggests the limitation of chemical compound digital identifier. Salvi et al. also describes a “formulation system” (paragraph [0014]) and a user interface where specific skincare ingredients/formulations are selected and adjusted based on user skin data (e.g., hydration levels) (Abstract, Paragraph [0033]) suggesting the limitation of skin hydration performance metrics. Salvi et al. also teaches a machine learning framework to determine personalized skincare formulations by predicting how certain ingredients will impact skin health metrics, including hydration (Abstract, paragraph [0006], [0008], [0011]) which suggest limitation of use of predictive modeling for skincare formulations. Salvi et al. also teaches calculation of formulation instruction based on determining moistening indices. (paragraph [0051]) suggesting the limitation of predicting at least one moisturizing factor value. He also discloses computer-implemented system outputs a customized formulation to a storage device. (Figure 7; paragraph [0045], [0070]) suggesting the limitation of a step of outputting at least one moisturizing factor value predicted.
Salvi et al. does not teach explicitly the use of polarity values as the primary chemical identifier for predicting moisturizing factor.
However, Naseem et al. teaches how to utilizes a database of chemical properties for thousands of compounds. (pg. 5, column 1, bottom) suggesting the limitations of the use of polarity value in formulation. The database of chemical compounds contains polarity values of chemical compound and knowing polarity values ensures the which compound can dissolve and navigate through these specific barriers of skin to reach its target. Naseem identifies polarity as a critical feature in machine learning models to predict how chemicals interact with and permeate the skin barrier. (pg. 2, column 1, bottom; pg.7, column 2, top) and demonstrates that the predictive performance of a composition’s effect of skin (permeability/hydration) is a direct function of these retrieved physicochemical properties. (pg. 6, column 1, top; pg. 7, column 2, middle) which suggests the limitation of the use of polarity in predictive modeling for skincare formulation.
It would have been obvious to a person having ordinary skill in the art (PHOSITA) at the time the effective filing date to incorporate the specific chemical properties (polarity values) taught by Naseem into the predictive modelling system of Salvi.
The prior art includes (1) a computer-implemented, precise skincare formulation system taught by Sid Salvi and (2) specific chemical polarity values related to skin interaction taught by Naseem.
The motivation to combine these elements is to increase the accuracy of real-time performance metrics in the skincare formulation. The polarity of compounds is crucial because it directly dictates how efficiently an ingredient can penetrate the skin's protective barrier and reach its intended site of action. Skin absorption relies heavily on a compound's polarity, commonly measured by the octanol-water partition coefficient (log P). Since, Salvi seeks to provide precise formulations, utilizing Naseem’s quantified polarity values provides a predictable improvement in precision. Utilizing Naseem's polarity data to improve Salvi’s predictive system is a predictable application of known chemical modeling principles to an existing computer-implemented formulation system.
The resulting method, selecting a compound, retrieving its polarity, and predicting a moisturizing factor is the product of combining known elements according to known methods (predictive modeling) to yield a predictable result (increased accuracy of skin moisturizing factor)
Regarding claim 6, it further recites selecting at least two compounds and computing mean moisturizing factor. Salvi discloses multi-ingredient formulation, which suggests limitations of inclusion of more than one compound for skin formulation .
Regarding claim 7, it further recites “computing a composition moisturizing factor as a function of at least two mean moisturizing factors predicted”. Salvi discloses multi-ingredient formulation, (Abstract) and Naseem teaches constructing predictive models for skin interaction using chemical properties, including obtained polarity value(Abstract, pg. 7, c2, top, ).
Claim 2 is rejected under 35 U.S.C 103 as being unpatentable over Salvi et al. in view of Naseem et al. as applied to claims 1, 6 and 7 above, view of Santos et al. (Current Protocols in Pharmacology e79, Volume 91).
Salvi et al. in view of Naseem et al. are applied to claims 1, 6 and 7.
In addition to limitation taught by Salvi et al. for claim 1,6, and 7 rejections, he also teaches hydration level measurement after disposition of the formulation (pg. 4, Figure 3) which is equivalent of measuring the quantity of water is evaporated (evaporated or retained water on the skin) and calculation of evaporation rate suggesting the limitation of “Measurement of a quantity of evaporated water or remaining water after deposition” and “calculation of water evaporation rate based on the measurements”. He also teaches calculation of moisturization index score from evaporation rate which equivalent to moisturizing factor calculation suggesting the claim limitation of “Moisturizing factor calculation based on the calculated water evaporation rate.”
Salvi et al. in view of Naseem et al. do not teach deposition of a volatile/non-volatile compound on a skin replicating surface.
Santos et al. establishes that using skin replicating surface to measure trans epidermal water loss (TEWL) is a standard technique for evaluating skin barrier function. (Abstract; pg. 3, middle; pg.24, column 2, bottom) suggesting the limitation of the use of the skin replicating surface to measure water evaporation.
He also, describes IVPT which involves dosing of the semi-solid product under evaluation on a biological membrane placed within a diffusion cell containing receptor solution (also known as receptor/
receiving fluid). The purpose of the test is to evaluate the drug permeation through the stratum corneum and subsequent skin layers (skin distribution), as well as the amount of compound collected in the receptor solution (skin penetration), allowing calculation of the compound’s skin flux (pg. 3, middle) which suggests the limitation of “Deposition of a volatile/non-volatile compound on a skin replicating surface.”
Under MPEP 2143 guidelines and KSR rationale, it would have been obvious to a PHOSITA at the time of the effective filing date to combine the teachings of these above cited references. Specifically, it was well known in the art to utilize skin-replicating surfaces to measure TEWL and evaluate skin barrier function as taught by Santos et al. A PHOSITA would have found it obvious to apply this standard TEWL measurements and skin replicating surfaces taught by Santos to calculate moisturization factors by integrating polarity values of chemical compound and evaporation testing (hydration measurement) to a predictive model taught by Salvi.
Modifying the Salvi’s method to include the skin-replicating surface of Santos simply applies known techniques to established prior art elements according to their standard functions. This combination yields predictable results with a reasonable expectation of success.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al. as applied to claims 1, 6 and 7 above, in view of Schiemann et al. (Physicochem. Eng. Aspects 331 (2008) 103–107) and further in view of Dragana Stojiljković et al. (Acta Media Medianae 2016, Vol. 55(2))
Salvi et al. in view of Naseem et al. are applied to claims 1, 6 and 7.
Salvi teaches storing model coefficients and parameters to enable efficient prediction of new compounds (Abstract, paragraph [0016, 0034, 0044], Claim 17) and teaches a model (Machine learning-equivalent to mathematical formula) that calculates moisturization factor (Figure 7, pg. suggesting the limitations of “a step of modelling of a mathematical formula of moisturizing factor as a function of chemical compound polarity “ and “the step of recording, in a database, the moisturizing factor formula modelled parameters.”
Salvi et al. in view of Naseem et al. in view of Schiemann et al. does not teach computing a chemical compound polarity for a chemical compound associated to a stored moisturizing factor.
Y. Schiemann et al. teach that chemical polarity affects the permeability of a compound into the skin (Abstract) and Stojiljković et al. teach the association of chemical polarity with skin moisturizing effects (pg. 25, col. 2; pg. 28, col. 2) both references teach the limitation of “computing a chemical compound polarity for a chemical compound associated to a stored moisturizing factor.”
Calculating a compound’s chemical polarity and modeling the factor as a function of polarity: It would have been obvious to a person having ordinary skill in the art (PHOSITA) to combine Salvi’s modeling system with the teachings of Naseem’s polarity and Schiemann et al. , Stojiljković. et al.’s teaching of permeability to skin barrier according to polarity of the compounds provide the known association between polarity and skin-moisturizing effects, suggesting the limitation of calculating a moisturizing factor as a function of polarity. Applying this to Salvi's prediction model is a use of known technique to improve chemical formulations for the skin product.(MPEP 2143, Example C).
Recording, in a database, the moisturizing factor formula modeled parameters: Salvi directly teaches storing model coefficients and parameters to enable efficient prediction (Abstract, paragraph [0034]). Furthermore, recording modeled parameters in a database is a routine and necessary step in computational modeling to support real-time prediction, which constitutes a design choice or common sense to a PHOSITA (MPEP 2143, Example F; KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 417 (2007)).
The combination of Salvi, Naseem, Schiemann, and Stojiljković renders the steps of claim 3 obvious. The modification represents a predictable result of combining known chemical modeling techniques with a known computerized skincare formulation system (MPEP 2143.01).
It would be an expectation of success of using all these cited arts together because they are all in the same technology (skin formulation system), and addressing the similar problem.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al. , Schiemann et al., Stojiljković et al. as applied to claims 1, 6, 7, and 3 above, and further in view of Hansen et al. (Hansen Solubility Parameters: A User’s Handbook)
Salvi et al. in view of Naseem et al., Schiemann et al., Stojiljković et al. are applied to claims 1, 6, 7, and 3 above.
Salvi et al. in view of Naseem et al., Santos et al. Schiemann et al., and Stojiljković et al. do not teach the limitation of : “computing polarity from dispersion, polar, and hydrogen bonding components of cohesive energy density.”
Hansen teaches the components define the Hansen Solubility Parameters (HSP), a standard framework for molecular interactions and solubility behavior (Hansen Solubility Parameters-A User’s Handbook) (pg. 29, 30), He also teaches dispersion interactions and permanent dipole–permanent dipole and hydrogen bonding (electron interchange) interactions in mixtures of unlike molecules (pg. 8, middle), also teaches cohesive energy density (Chapter 2, Equation 2.6 ) suggesting knowledge of these chemical parameter and properties described in Hansen Solubility Parameters book can be mapped to “polarity computation based on dispersion, polar, and hydrogen bonding components of cohesive energy density.”
A person of ordinary skill in the art (PHOSITA) at the time of the effective filing date would have been motivated to modify the skincare formulations of Salvi and Naseem by applying the Hansen Solubility Parameters [HSP] described in the prior art. Because optimizing ingredient compatibility and skin penetration is a universal challenge in formulating topical treatments, a skilled artisan would have utilized standard HSP methodologies to rationally predict miscibility and improve active delivery. This modification would involve nothing more than the predictable application of known chemical principles to existing skincare systems to ensure ingredient compatibility."
Hansen's method for determining polarity via three specific components is a known technique for quantifying molecular interaction, which could be applied to Salvi’s mathematical model to improve the system's accuracy, a predictable improvement. (MPEP 2143 (D)) Hansen provides a well-established and standard framework (HSP) for determining solubility and interaction. Utilizing a known, standard formula (dispersion, polar, hydrogen bonding components) in a computational model (Salvi) provides a reasonable expectation of success in improving the prediction of skin-chemical interactions.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al. , Schiemann et al., Stojiljković et al. as applied to claims 1, 6, 7, and 3 above, and further in view of Wortzman et al. (US Patent No. 4820508)
Salvi et al. in view of Naseem et al., Schiemann et al., Stojiljković et al. are applied to claims 1, 6, 7, and 3 above.
Salvi implicitly requires specific ingredient quantities for the formulation to function. The method of determining the optimal composition requires tracking these quantities, covering the imputing compound quantities aspect. (Abstract) suggesting the claim limitation of “A step of inputting, for each selected chemical compound, a quantity of said chemical compound.”. He also teaches outputs of one or more predicted ingredient combinations that increases in the skin health metric (pg. 22, c2, bottom) suggesting the limitation of “the quantity input for said chemical compound identifier, the step of outputting being configured to output at least one mean moisturizing factor computed.”
Salvi et al. in view of Naseem et al., Schiemann et al., and Stojiljković et al. does not teach the step of obtaining comprising a step of computing a mean moisturizing factor for at least one chemical compound identifier as a function of the moisturizing factor.
Wortzman teaches that composition efficiency is based on the weighted contributions of components and calculates weighted averages to determine formulation efficacy. Wortzman teaches outputs predicted formulation effects based on this calculation (Wortzman pg. 2, Col 1; pg. 5, Col 14; Table B). Therefore, Wortzman provides a clear teaching to calculate a “mean” or “weighted” moisturizing factor based on the relative proportions of ingredients suggesting the claim limitation of “The step of obtaining comprising a step of computing a mean moisturizing factor for at least one chemical compound identifier as a function of the moisturizing factor.”
A PHOSITA seeking to predict the moisturizing efficacy of Salvi's ingredient cocktail would be motivated by the recognized need to optimize formulation performance. Under MPEP § 2143, Example E, this is an obvious to try scenario. Faced with a finite number of identifiable variables, the skilled artisan would apply routine problem-solving to: 1) identify quantities from Salvi; 2) apply known moisturizing values from Naseem; and 3) aggregate the results using standard weighted mean calculations from Wortzman.
Because Wortzman's mathematical techniques merely combine known elements in the same technological field, it would yield predictable results with a strong expectation of success. All these cited arts are all in the same technology (Skin formulation, chemical properties), and addressing the similar problem. So, it would be an expectation of success.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al., as applied to claims 1, 6, and 7 above, and further in view of Supratik Kar et al. (Toxics, 2019, 7, 15)
Salvi et al. in view of Naseem et al., are applied to claims 1, 6, and 7 above.
Salvi et al. in view of Naseem et al., does not teach of selecting at least two compounds, computing a “linearity” of a moisturizing factor for a composition based on individual predictions, and displaying the linearity.
Supratik Kar et al. teaches chemical mixtures including determining whether a property of a mixture follows a linear additive model or exhibits non-linear behavior (e.g., synergy or antagonism). (Abstract) suggesting the limitation of “computing a moisturizing factor linearity of a composition of said at least two compound identifiers based on the predicted moisturizing factor of at least two selected chemical compound digital identifiers” Supratik Kar et al. also discloses similarity between a target molecule and those in an existing database, the framework selects the most similar molecules, creating a tailored dataset for model training. (Figure 1; pg. 4, top/middle)
A person having ordinary skill in the art (PHOSITA) would have found it obvious to compute and display a linearity metric for a composition's moisturizing factor within the predictive framework of Salvi, in view of Naseem and Supratik.
This combination is supported by the following rationales:
Evaluating the additivity or interaction of combined ingredients is a routine and expected practice in formulation science. Supratik teaches a known linearity analysis technique that addresses the exact problem of modeling diagnostic outputs. A PHOSITA would have been motivated to incorporate Supratik’s linearity analysis into the predictive framework of Salvi (supplemented by Naseem) to establish whether combined ingredient effects behave additively or exhibit interactions.
Combining these teachings would yield predictable results. The computation of linearity metrics provides a standard diagnostic metric for interpreting model outputs. There is a reasonable expectation of success in combining these arts because they operate in the same technology (formulation science) and address identical predictive modeling problems.
Claim 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al., as applied to claims 1, 6, and 7 above, and further in view of David et al. (Regulatory Toxicology and Pharmacology 72 (2015) 683–693)
Salvi et al. in view of Naseem et al., are applied to claims 1, 6, and 7 above.
Salvi teaches iterative optimization of ingredient selection, (Abstract)
Salvi et al. in view of Naseem et al., does not teach a moisturization factor threshold and removing a chemical identifier based on its difference from that threshold.
David et al teaches threshold-based filtering (Dermal Sensitization Threshold (DST) approach) in which candidates failing to predefined performance criteria are excluded from selection. (Abstract; pg. 684, column 1, top; Figure 2; Table 1) suggesting the teaching of claim limitation “at least one chemical compound digital identifier being removed from the selection as a function of the difference between the moisturizing factor being obtained for said chemical compound digital identifier and the defined moisturizing factor threshold.”
A person having the ordinary skill in the art would have been motivated to incorporate David et al.’s threshold filtering into Salvi in view of Naseem to improve selection efficiency by retaining only compounds meeting desired performance levels. Comparing predicted values to a threshold and excluding non-compliant candidates is a routine decision-making operation.
It would have been obvious to apply threshold-based removal of chemical identifiers based on predicted moisturizing factors in Salvi in view of Naseem, Santos, and David W. Roberts et al. and would have been expectation of success of using all these cited arts together because they are all in the same technology (formulation science), and addressing the similar problem.
Regarding claim 10, it further recites replacing a selected chemical compound identifier with an alternative candidate based on a difference between their respective moisturizing factors.
David et al. teaches threshold-based filtering (Dermal Sensitization Threshold (DST) approach) in which candidates failing to predefined performance criteria are excluded from selection and replacing with alternative to determine the applicability (Abstract; pg. 684, column 1, top; Figure 2; Table 1) suggesting the claim limitation of “replacing at least one chemical compound digital identifier as a …. moisturizing factor being obtained for an alternative chemical compound digital identifier candidate.”
Claim 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al., as applied to claims 1, 6, and 7 above and further in view of Cao, L. (Combining artificial intelligence and robotic system in chemical product/process design)
Salvi et al. in view of Naseem et al., are applied to claims 1, 6, and 7 above.
Regarding claim 11:
Salvi outputs digital formulations for production (Abstract)
Salvi et al. in view of Naseem et al., does not teach method of chemical compound composition assembly, comprising: a composition prediction method according to claim and - a step of assembling the composition object of the prediction method.
Cao, L. in his thesis teaches that machine learning methods, together with automated experimental system, can speed-up the R&D process of formulated product design as well as gain new physical knowledge of the complex systems. (Abstract).
A person of ordinary skill in the art would have been motivated to combine the predicted formulations of Salvi and Naseem with Cao, L. because doing so requires nothing more than routine formulation workflows. Translating computational outputs into a physical compound is a predictable step designed to verify predictive data. Because the elements were combined according to known methods to yield predictable results, the physical assembly was obvious.
Regarding claim 12, it is directed to a composition defined by the process of its assembly in claim 11. As a product-by process claim, patentability is based on the structure and properties of the product, not the method of making it.
Salvi discloses skincare compositions with selected moisturizing agents for hydration (e.g., hydration levels) (Abstract, Paragraph [0033]).
Naseem provides predictive selection of compounds based on properties such as polarity and moisturizing effect. (pg. 2, column 1, bottom; pg.7, column 2, top)
Cao, L. teaches automated assembly of chemical compositions using digitally generated ingredient selections. (Abstract)
The claimed composition is formed from known cosmetic ingredients used for skin hydration, and the use of a computational selection method does not impart any discernible structural or functional difference over the prior art.
Claim 12 does not demonstrate any structural, chemical, or functional differences from compositions of Salvi or those routinely produced using Cao, L.’s automated formulation techniques. Any moisturizing properties are inherent to the known ingredients and do not confer patentable distinction.
A Person having ordinary skill in the art would have been arrived at a substantially identical composition by applying the teaching of Salvi, Naseem, and Cao, L.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Salvi et al. in view of Naseem et al. as applied to claims 1, 6, and 7 above, and further in view of Nieto et al. (Curr Treat Options Allergy (2021) 8:21–41)
Salvi et al. in view of Naseem et al., are applied to claims 1, 6, and 7 above.
Salvi et al. in view of Naseem et al., do not teach: Inclusion of fragrant chemical compound in the formulation.
Nieto et al. describes the benefit of skincare compositions comprising fragrance components for therapeutic benefits related to stress reduction and memory. (Abstract) suggesting the claim limitation of “necessity to include of fragrance in formulation of skin product”. Fragrant compounds are well-known cosmetic ingredients with measurable physicochemical properties and fall within the predictive frameworks of Naseem.
It would have been obvious to a person of ordinary skill in the art to add fragrance compounds to Salvi’s formulation system in view of Naseem, and Nieto et al. Under KSR rationale, incorporating fragrance into a cosmetic product is nothing more than the predictable application of a known element to improve a composition in a standard way. Because fragrance is a recognized market standard directly affecting user acceptability, combining it with Salvi's base system is merely the use of common sense and routine skill to adapt known elements to address a predictable need.
Conclusion
No claims are allowed.
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/AK/Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686
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