DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Status
Claims 1-20 are pending.
Claims 1 and 11 are objected to.
Claims 1-20 are rejected.
Priority
This application is a CON of 16/890,748 filed Jun 2 2020 (now USP 11,526,555).
Accordingly, each of claims 1-20 are afforded the effective filing date of Jun 2 2020.
Information Disclosure Statement
The information disclosure statement (IDS) filed on Jan 2 2024 is in compliance with the provisions of 37 CFR 1.97 and has therefore been considered. A signed copy of the IDS document is included with this Office Action.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because: reference character “220” has been used to designate both a Heuristic Table in FIG. 2 and a Taste Index Table in FIG. 3-4; and reference character “128” has been used to describe Training Data in FIG. 1 and taste profiles and training data at [0025-0028].
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
The claims are objected to for the following informalities:
Claims 1 and 11 recite both “machine learning model” and “machine-learning model”. The claims should be amended to recite only one format, with or without the hyphen between “machine” and “learning”.
Claim 11, final limitations, recites “predicting, at the computing device, of”, which should be amended to remove “of”.
Claim Rejections - 35 USC § 112
35 U.S.C. 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, because the specification, while being enabling for predicting a taste of a user based on some biological extraction data, does not reasonably provide enablement for predicting any taste of a user based on data determined from any biological extraction data extracted from any physical biological sample, where the biological data is not required to be related to the user and the taste is predicted as a function of a genetic data table, as recited in claims 1 and 11, where the biological extraction data can comprise any genetic data, as recited in claims 3 and 13. The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to use the invention commensurate in scope with these claims.
In In re Wands (8 USPQ2d 1400 (CAFC 1988)), the CAFC considered the issue of enablement in molecular biology. The CAFC summarized eight factors to be considered in a determination of "undue experimentation”. These factors include: (a) the quantity of experimentation necessary; (b) the amount of direction or guidance presented; (c) the presence or absence of working examples; (d) the nature of the invention; (e) the state of the prior art; (f) the relative skill of those in the art; (g) the predictability of the art; and (h) the breadth of the claims.
In considering the factors for the instant claims:
(a) In order to practice the claimed invention, one of skill in the art must be able to predict any taste of a user based on data determined from any biological extraction data extracted from any physical biological sample, where the biological data is not required to be related to the user and the taste is predicted as a function of a genetic data table, as recited in claims 1 and 11, where the biological extraction data can comprise any genetic data, as recited in claims 3 and 13. For the reasons discussed below, there would be an unpredictable amount of experimentation required to practice the claimed invention.
(b) The specification as published provides guidance that “biological extraction” may refer to an element of user data corresponding to a category, including without limitation, microbiome analysis, blood test results, gut wall and food sensitivity analysis, toxicity report, medical history, biomarker, genetic or epigenetic indication, or any chemical, biological, or physiological markers of data of a user, with an embodiment where at least an element of biological extraction data corresponds to genetic data [0018].
The specification as published provides guidance that “genetic data” refers to at least an element of data that describes any genetic material including nucleic acids such as DNA and RNA, which may correspond to genetic elements of a user including coding regions (genes), non-coding regions such as promoters, enhancers, transposons, genome-integrated viral DNA, and the presence of structural RNA, such as tRNAs, miRNAs and other RNA types, and/or any analysis thereof; analysis may refer to detecting the presence of, enumeration of, and/or determining the sequence of a nucleic acid and/or stretch of nucleic acid [0019]. The specification as published provides guidance that genetic data may be obtained from at least a user physical sample, where “physical sample” is a biological user sample including blood, urine, feces, hair, saliva, skin, interstitial fluid, biopsy, or any other physical biological sample that genetic information may be obtained.
The specification as published provides guidance that a taste index refers to any mathematical, causative, correlated, proportional, heuristic, and/or any other relationship between two elements of data that is a measure of chemosensory phenomenon as it relates to the sense of taste [0020], and is used to determine a taste profile, which refers to a user's chemosensory ability as it relates to one or more elements of data of sensing a variety of flavors, such as sweet, sour, salty, bitter, savory (umami), fatty, alkaline, acidic, metallic, and water-like; and user preference for smell, texture, temperature, consistency in taste, and spiciness and/or hotness [0029]. Taste is therefore, under the BRI, considered to encompass a chemosensory phenomenon as it relates to the sense of taste and the sensations of sensing a variety of flavors, smell, texture, temperature, consistency in taste, and spiciness and/or hotness detection.
The specification as published provides guidance for a taste index being a function that describes a relationship between the affinity for the level of sweetness a user may taste based on the expression of “sweetness-sensing genes” such as genes involved in the cellular signaling of molecules associated with “sweetness” such as monosaccharides such as glucose, fructose, galactose, disaccharides such as sucrose, lactose, maltose, polysaccharides such as glycogen, cellulose, chitin, artificial sweeteners such as saccharin, aspartame, sucralose, and/or their metabolites [0020]. The specification as published provides general guidance for “training data” which may be used to train a machine learning process, without providing any examples of training data as relates to biological data correlated to taste [0023-0024]. The specification as published provides guidance for using one or more tables of a user database, including a genetic data table, for predicting taste of a user and/or correlating biological extraction data, or a taste index table which may contain one or more inputs identifying one or more categories of data, for instance a palatable range of capsaicin [0027].
The specification as published provides guidance for calculating a taste index as a function of a user's expression level of a variety of taste-sensing genes that produce taste-sensing proteins in the tongue, for instance, T2Rs, T1Rs, ENaC, GLUT4, and SGLT1, and a database value that links the protein level from these genes to their functionality in the tongue, or a function that relates the expression level of the TAS2Rs and TAS1Rs G-protein coupled receptors in the tongue to a correlated amount of sweetness flavor that is palatable to a user for a variety of flavors, for instance vanilla, cinnamon, and wintergreen ([0028] see FIG. 3-4), but does not provide any data regarding these correlations. The specification as published provides guidance for a taste profile machine learning model that may input two taste indices, a first that describes a user's sensitivity to sweetness, and a second that describes a user's sensitivity to bitterness, and output a taste profile that summarizes the total user's taste sensitivity, which may for instance without limitation determine a user's palatability for cacao content in chocolate, or predict which types of wine a user may find palatable [0029], but does not provide any data regarding this process.
The specification does not provide guidance for predicting any taste of a user based on data determined from any biological extraction data extracted from any physical biological sample, where the biological data is not required to be related to the user and the taste is predicted as a function of a genetic data table, where the biological extraction data can comprise any genetic data. The specification also does not provide guidance for correlating any biological data to a genetic data table, or for how to measure the expression level of genes in a tongue, one of the potential embodiments discussed in the specification as hypothetical biological extraction data.
(c) The specification provides no working examples.
(d) The invention is drawn to a system and a method to predict a taste of the user as a function of the taste index and a genetic data table, wherein the prediction is generated by correlating genetic data to the at least a taste index.
(e) The state of the art of predicting taste of a user can be represented by Precone et al. (Eur. Rev. Med. and Pharmacol. Sci., 2019, 23:1305-1321; newly cited), Hwang et al. (Am J Clin Nutr, 219, 109:1724-1737; newly cited), Pirastu et al. (Plos One, 2014, 9(3):e92065; newly cited), Pirastu et al. (European Journal of Human Genetics, 2015, 23(12):1717-1722; newly cited), and Andrews et al. (US 20180057866; newly cited).
Precone makes clear that inter-individual variations in taste, olfactory and texture related genes drive food choice (abstract; entire document is relevant), and that while the biology and genetics of taste buds are complex, a handful of genes expressed in more than 50 coding regions such as ENaC, TRPVI, TRPML3, TAS1R1, TA1R2, TAS1R3, GPR120, and CD36 are responsible for different tastes (p. 1306, col. 2, par. 1 through p. 1307, col. 2, par. 2). Precone makes clear that certain genetic polymorphisms are responsible for the ability to taste, smell, or sense certain compounds and directly affect preferences and food choice (p. 1307, col. 2, par. 2 through p. 1314, col. 2, par. 1; Table 1).
Hwang makes clear that not all genetic polymorphisms in all genes contribute to differences in sweet perception, as genetic variants associated with the perception and intake of sweet substances have only been previously reported in a limited number of genes previously (Table 1), and out of >4,300,000, >5,800,000, and >10,300,000 polymorphisms identified from whole genome genotyping experiments and then examined for different sample sets (p. 1727, col. 1, par. 2 through col. 2, par. 1), far fewer of these polymorphisms, possibly even less than 100 (Figure 5), were found to be associated with the perceived intensity of different sugars, indicating that not all polymorphisms and not all genes are involved with taste perception. Pirastu 2014 and 2015 (entire documents relevant) similarly demonstrate that large scale genome-wide association studies are required to identify links between specific genes, their variants, and taste preferences for specific food items (e.g., coffee, wine).
Andrews makes clear that, in order to predict individual taste and/or scent preferences for gustative or olfactive products (abstract), the genotypes analyzed are those from a panel of variants identified as useful for identifying taste and/or scent preference (claim 1; entire document is relevant).
There is no direction as to how to predict any taste of a user based on data determined from any biological extraction data extracted from any physical biological sample, where the biological data is not required to be related to the user and the taste is predicted as a function of a genetic data table, or where the biological extraction data can comprise any genetic data.
(f) The skill of those in the art of molecular biology and bioinformatics is high.
(g) The art is unpredictable because taste sensory perception is influenced by many, but not all, biological and genetic features of a person.
(h) The claims are broad because they are drawn to predicting any taste of a user based on data determined from any biological extraction data extracted from any physical biological sample, where the biological data is not required to be related to the user and the taste is predicted as a function of a genetic data table, as recited in claims 1 and 11, where the biological extraction data can comprise any genetic data, as recited in claims 3 and 13. However, the instant specification does not provide specific guidance to practice these embodiments. As such, the skilled practitioner would turn to the prior art for such guidance. However, the prior art makes clear that not all biological data is correlated to taste preference. Said practitioner would turn to trial and error experimentation to determine how to predict a taste of a user, where taste is interpreted to broadly refer to any sense of taste and the sensations of sensing a variety of flavors, smell, texture, temperature, consistency in taste, and spiciness and/or hotness detection based on any biological extraction data, which is not necessarily related to the user, collected from any biological source, based on a correlation to genetic data, when the biological extraction data is not required to be genetic data, which represents undue experimentation.
Even if the biological extraction data is limited to genetic data, the prior art makes clear that not all genetic data is indicative of taste preferences, and the specification provides no data or evidence that specific genes are correlated to specific taste preferences, besides general examples of T2Rs, T1Rs, ENaC, GLUT4, and SGLT1 and a database value that links the protein level from these genes to their functionality in the tongue, and the expression level of the TAS2Rs and TAS1Rs G-protein coupled receptors in the tongue to a correlated amount of sweetness flavor that is palatable to a user for a variety of flavors, for instance vanilla, cinnamon, and wintergreen. Said practitioner would again turn to trial and error experimentation to determine how to predict a taste of a user based on any genetic data, which represents undue experimentation.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to one or more judicial exceptions without significantly more.
MPEP 2106 organizes judicial exception analysis into Steps 1, 2A (Prongs One and Two) and 2B as follows below. MPEP 2106 and the following USPTO website provide further explanation and case law citations: uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials.
Framework with which to Evaluate Subject Matter Eligibility:
Step 1: Are the claims directed to a process, machine, manufacture, or composition of matter;
Step 2A, Prong One: Do the claims recite a judicially recognized exception, i.e. a law of nature, a natural phenomenon, or an abstract idea;
Step 2A, Prong Two: If the claims recite a judicial exception under Prong One, then is the judicial exception integrated into a practical application (Prong Two); and
Step 2B: If the claims do not integrate the judicial exception, do the claims provide an inventive concept.
Framework Analysis as Pertains to the Instant Claims:
Step 1
With respect to Step 1: yes, the claims are directed to a system and a method, i.e., a process, machine, or manufacture within the above 101 categories [Step 1: YES; See MPEP § 2106.03].
Step 2A, Prong One
With respect to Step 2A, Prong One, the claims recite judicial exceptions in the form of abstract ideas. The MPEP at 2106.04(a)(2) further explains that abstract ideas are defined as:
mathematical concepts (mathematical formulas or equations, mathematical relationships and mathematical calculations);
certain methods of organizing human activity (fundamental economic practices or principles, managing personal behavior or relationships or interactions between people); and/or
mental processes (procedures for observing, evaluating, analyzing/ judging and organizing information).
The claims also recite a law of nature or a natural phenomenon. The MPEP at 2106.04(b) further explains that laws of nature and natural phenomena include naturally occurring principles/relations and nature-based products that are naturally occurring or that do not have markedly different characteristics compared to what occurs in nature.
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) as well as a law of nature or a natural phenomenon are as follows:
Independent claims 1 and 11: calculate at least a taste index of a user using a first machine learning model, wherein calculating comprises:
training the first machine-learning model using training data correlating the at least an element of biological extraction data with database taste indices;
determining at least a taste index of the user utilizing the first machine-learning model, wherein the first machine-learning model receives the at least an element of biological extraction data as input and outputs the taste index of the user.; and
predict a taste of the user as a function of the taste index and a genetic data table, wherein the prediction is generated by correlating genetic data to the at least a taste index.
Dependent claims 8 and 18: generate a taste profile as a function of the taste index.
Dependent claims 2, 4-7, 9-10, 12, 14-17, and 19-20 recite further steps that limit the judicial exceptions in independent claims 1 and 11 and, as such, also are directed to those abstract ideas. For example, claims 2 and 12 further limit the taste index to a relationship between two elements of data; claims 4 and 14 further limit the taste index to a plurality of numerical values; claims 5 and 15 further limit predicting the taste of the user to predicting the taste of the user as a function of a taste index table; claims 6-7 and 16-17 further limit the taste of a user to the user's preference of a flavor from a variety of flavors; claims 9 and 19 further limit generating the taste profile to using a second machine learning model; claims 10 and 20 further limit the prediction to being generated as a function of the taste profile of claims 8 and 18.
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 because the method only requires a user to manually predict a taste of a user. Without further detail as to the methodology involved in “calculate”, “train”, “determine”, “predict”, and “generate”, under the BRI, one may simply, for example, use pen and paper to calculate a taste index of a user based on biological extraction data, generate a taste profile of the user based on the taste index, and predict a taste of the user based on the generated profiles and indices. Some of these steps, including “calculating” a taste index in claims 1 and 11 which comprises numerical values, as in claims 4 and 14, and “utilizing/using” and “training” machine-learning models, as in claims 1, 9, 11, and 19, require mathematical techniques as the only supported embodiments, as is disclosed in the specification as published at: a taste index refers to any mathematical, causative, correlated, proportional, heuristic, and/or any other relationship between two elements of data that is a measure of chemosensory phenomenon as it relates to the sense of taste, which may be calculated via a mathematical operation [0020]; supervised machine-learning algorithms as defined by the specification include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function, and training supervised machine-learning models based on loss and error functions [0021; 0023]; see also FIG. 3 and 4, which describe the Taste Indexes and Profiles in mathematical terms.
The claims also recite a natural relationship between the biological data or genetics of a user and their taste preferences.
Therefore, claims 1 and 11 and those claims dependent therefrom recite an abstract idea and a law of nature/natural phenomenon [Step 2A, Prong 1: YES; See MPEP § 2106.04].
Step 2A, Prong Two
Because the claims do recite judicial exceptions, direction under Step 2A, Prong Two, provides that the claims must be examined further to determine whether they integrate the judicial exceptions into a practical application (MPEP 2106.04(d)). A claim can be said to integrate a judicial exception into a practical application when it applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. This is performed by analyzing the additional elements of the claim to determine if the judicial exceptions are integrated into a practical application (MPEP 2106.04(d).I.; MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the judicial exceptions, the claim is said to fail to integrate the judicial exceptions into a practical application (MPEP 2106.04(d).III).
Additional elements, Step 2A, Prong Two
With respect to the instant recitations, the claims recite the following additional elements:
Independent claims 1 and 11: receive at least an element of biological extraction data.
Dependent claims 3 and 13 recite steps that further limit the recited additional elements in the claims. For example, claims 3 and 13 further limit the received biological extraction data to genetic data.
The claims also include non-abstract computing elements. For example, independent claim 1 includes a system comprising at least a computing device, and independent claim 11 includes a computing device.
Considerations under Step 2A, Prong Two
With respect to Step 2A, Prong Two, the additional elements of the claims do not integrate the judicial exceptions into a practical application for the following reasons. Those steps directed to data gathering, such as “receiving” data, perform functions of collecting the data needed to carry out the judicial exceptions. Data gathering and outputting do not impose any meaningful limitation on the judicial exceptions, or on how the judicial exceptions are performed. Data gathering and outputting steps are not sufficient to integrate judicial exceptions into a practical application (MPEP 2106.05(g)).
Further steps directed to additional non-abstract computing elements do not describe any specific computational steps by which the “computer parts” perform or carry out the judicial exceptions, nor do they provide any details of how specific structures of the computer, such as the computer-readable recording media, are used to implement these functions. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, and therefore the claim does not integrate that judicial exceptions into a practical application. The courts have weighed in and consistently maintained that when, for example, a memory, display, processor, machine, etc.… are recited so generically (i.e., no details are provided) that they represent no more than mere instructions to apply the judicial exception on a computer, and these limitations may be viewed as nothing more than generally linking the use of the judicial exception to the technological environment of a computer (MPEP 2106.05(f)).
The specification does not provide a clear explanation for how the additional elements provide any improvements. Therefore, the additional elements do not clearly improve the functioning of a computer, or comprise an improvement to any other technical field. Further, the additional elements do not clearly affect a particular treatment; they do not clearly require or set forth a particular machine; they do not clearly effect a transformation of matter; nor do they clearly provide a nonconventional or unconventional step (MPEP2106.04(d)).
Thus, none of the claims recite additional elements which would integrate a judicial exception into a practical application, and the claims are directed to one or more judicial exceptions [Step 2A, Prong 2: NO; See MPEP § 2106.04(d)].
Step 2B (MPEP 2106.05.A i-vi)
According to analysis so far, the additional elements described above do not provide significantly more than the judicial exception. A determination of whether additional elements provide significantly more also rests on whether the additional elements or a combination of elements represents other than what is well-understood, routine, and conventional. Conventionality is a question of fact and may be evidenced as: a citation to an express statement in the specification or to a statement made by an applicant during prosecution that demonstrates a well-understood, routine or conventional nature of the additional element(s); a citation to one or more of the court decisions as discussed in MPEP 2106(d)(II) as noting the well-understood, routine, conventional nature of the additional element(s); a citation to a publication that demonstrates the well-understood, routine, conventional nature of the additional element(s); and/or a statement that the examiner is taking official notice with respect to the well-understood, routine, conventional nature of the additional element(s).
With respect to the instant claims, the courts have found that receiving and outputting data are well-understood, routine, and conventional functions of a computer when claimed in a merely generic manner or as insignificant extra-solution activity (see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information), buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network), Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015), and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93, as discussed in MPEP 2106.05(d)(II)(i)). As such, the claims simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (MPEP2106.05(d)). The data gathering steps as recited in the instant claims constitute a general link to a technological environment which is insufficient to constitute an inventive concept which would render the claims significantly more than the judicial exception (MPEP2106.05(g)&(h)).
With respect to claims 1 and 11 and those claims dependent therefrom, the computer-related elements or the general purpose computer do not rise to the level of significantly more than the judicial exception. The claims state nothing more than a generic computer which performs the functions that constitute the judicial exceptions. Hence, these are mere instructions to apply the judicial exceptions using a computer, which the courts have found to not provide significantly more when recited in a claim with a judicial exception (Alice Corp., 573 U.S. at 225-26, 110 USPQ2d at 1984; see MPEP 2106.05(A)). The specification also notes that computer processors and systems, as example, are commercially available or widely used at [0016]. The additional elements are set forth at such a high level of generality that they can be met by a general purpose computer. Therefore, the computer components constitute no more than a general link to a technological environment, which is insufficient to constitute an inventive concept that would render the claims significantly more than the judicial exceptions (see MPEP 2106.05(b)I-III).
Taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Even when viewed as a combination, the additional elements fail to transform the exception into a patent-eligible application of that exception. Thus, the claims as a whole do not amount to significantly more than the exception itself [Step 2B: NO; See MPEP § 2106.05].
Therefore, the instant claims are not drawn to eligible subject matter as they are directed to one or more judicial exceptions without significantly more. For additional guidance, applicant is directed generally to the MPEP § 2106.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1) and (a)(2) as being anticipated by Andrews et al. (US 2018/0057866; newly cited).
Claim 1 discloses a system for predicting the taste of a user, wherein the system comprises at least a computing device. Claim 11 discloses a method, performed on a computing system, for predicting the taste of a user.
The prior art to Andrews discloses methods of gustative or olfactive product selection by genetic profiling to predict individual taste and/or scent preferences for gustative or olfactive products (abstract). Andrews teaches that the invention includes a system for performing the computer implemented method for predicting taste and/or scent preferences of a subject [0020]. Andrews, indicated by the open circles, teaches the instant features, indicated by the closed circles, as follows. Instantly claimed elements which are considered to be equivalent to the prior art teachings are described in bold for all claims.
The steps performed by the computing device of claim 1 and the method of claim 11 comprise:
receiving at least an element of biological extraction data;
Andrews teaches a) obtaining a biological sample comprising nucleic acids from a subject; b) processing the sample to isolate or enrich the sample for the nucleic acids; and c) analyzing the genotype (i.e., biological extraction data) of the biological sample at a plurality of SNPs in a panel of SNPs identified as useful for identifying taste and/or scent preference [0007]. Andrews teaches receiving inputted data comprising: i) genotyping information for the subject regarding which alleles are present at one or more SNPs identified as useful for identifying taste and/or scent preference [0018].
calculating at least a taste index of a user using a first machine learning model, wherein calculating comprises: training the first machine-learning model using training data correlating the at least an element of biological extraction data with database taste indices; determining at least a taste index of the user utilizing the first machine-learning model, wherein the first machine-learning model receives the at least an element of biological extraction data as input and outputs the taste index of the user; and
Andrews teaches that one or more pattern recognition methods can be used in analyzing SNP genotyping data and information regarding a subject's personal characteristics and taste and/or scent preferences, including the use of a machine learning algorithm [0123]. Andrews teaches collecting data regarding >500 user’s preference to different wines and their genotypes at 41 single nucleotide variants (SNPs) chosen to identify a variety of alleles with likely association with differences in the ability to perceive certain tastes [0130]. Andrews teaches that the participants were assigned to a discovery (i.e., training data) and a validation cohort [0131]. Andrews teaches initial exploration of the associations (i.e., a taste index) between wine preferences (i.e., database taste indices) and genotype using hierarchical clustering [0132]. Andrews described hierarchical clustering, as a machine learning algorithm [0124].
predicting a taste of the user as a function of the taste index and a genetic data table, wherein the prediction is generated by correlating genetic data to the at least a taste index.
Andrews teaches a table of Genetic Variants (SNPs) for Taste Preference (Table 1). Andrews teaches further assessing the associations (i.e., a taste index) between wine preferences and genotype using ordered logistic regression of the genetic variants having the greatest size effect (i.e., genetic data in a genetic data table) in order to make models (i.e., a function) for predicting wine preference (i.e., predicting a taste of the user) [0132-0133].
Regarding claims 2 and 12, Andrews teaches claims 1 and 11. Claims 2 and 12 further add that the taste index comprises a relationship between two elements of data, wherein the relationship is a measure of a chemosensory phenomenon as it relates to a sense of taste.
Andrews teaches initial exploration of the associations (i.e., a taste index; relationship) between wine preferences and genotype (i.e., two elements of data) [0132]. Andrews teaches that the surveys regarding wine preference included questions about taste preferences [0131], which indicates that the associations are a measure of a chemosensory phenomenon as it relates to a sense of taste as instantly claimed.
Regarding claims 3 and 13, Andrews teaches claims 1 and 11. Claims 3 and 13 further add that the at least an element of biological extraction data comprises genetic data.
Andrews teaches analyzing the genotype (i.e., genetic data) of the biological sample at a plurality of SNPs in a panel of SNPs identified as useful for identifying taste and/or scent preference ([0007]; see also the title and abstract; entire document is relevant).
Regarding claims 4 and 14, Andrews teaches claims 1 and 11. Claims 4 and 14 further add that the taste index comprises a plurality of numerical values, wherein the numerical values represent at least an aspect of taste.
Andrews teaches the surveys regarding wine preference included questions about taste preferences (i.e., an aspect of taste), where taste preference was encoded as -1 to +1 (i.e., numerical values) [0131]. Andrews teaches clustering the wine preferences and survey responses and genotype to determine associations (i.e., taste index), it is considered that such a process is inherently mathematical and would rely on numerical values.
Regarding claims 5 and 15, Andrews teaches claims 1 and 11. Claims 5 and 15 further add that predicting the taste of the user comprises predicting the taste of the user as a function of a taste index table.
Andrews teaches further assessing the associations (i.e., a taste index) between wine preferences and genotype (FIG. 3B). using ordered logistic regression of the genetic variants having the greatest size effect (i.e., genetic data in a genetic data table) in order to make models (i.e., a function) for predicting wine preference (i.e., predicting a taste of the user) [0132-0133]. FIG. 3B shows associations between examined genetic variants and wine preference, and reads on a taste index table as instantly claimed. Such an interpretation is supported by the instant specification as published, which discloses that a taste index table may contain a plurality of entries associating at least an element of biological extraction data with a relationship to taste [0027].
Regarding claims 6 and 16, Andrews teaches claims 1 and 11. Claims 6 and 16 further add that the taste of a user comprises the user's preference of a flavor from a variety of flavors.
Andrews teaches questioning participants on their preferences for 12 wines and their ability to detect defined flavors in them [0129].
Regarding claims 7 and 17, Andrews teaches claims 1, 6, 11, and 16. Claims 7 and 17 further add that the flavor comprises the user's preference for a texture.
Andrews teaches including SNPs for the gene CD36 in their panel, which contributes to the phenotype of fat perception, where salad dressings tasted creamier (Table 1). CD36 is therefore considered to read on a gene that contributes to preference for a texture as instantly claimed.
Regarding claims 8 and 18, Andrews teaches claims 1 and 11. Claims 8 and 18 further add generating a taste profile as a function of the taste index.
Andrews teaches creating a user taste profile based on SNP genotyping and analyzing responses to the survey on personal characteristics and taste and/or scent preferences [0011; 0059; 0099]. Andrews teaches developing a model to predict preferences for 12 wines (i.e., a taste profile) based on the associations (i.e., taste index) between wine preferences and survey responses and genotype using ordered logistic regression [0132-0133].
Regarding claims 9 and 19, Andrews teaches claims 1 and 11. Claims 9 and 19 further add that the taste profile is generated using a second machine learning model.
Andrews teaches developing a model to predict preferences for 12 wines (i.e., a taste profile) based on the associations (i.e., taste index) between wine preferences and survey responses and genotype using ordered logistic regression (i.e., a second machine learning model) [0101; 0132-0133]. Andrews teaches logistic regression is a type of machine learning algorithm [0123].
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 8-13, and 18-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 9, and 11 of U.S. Patent No. 11,526,555. Although the claims at issue are not identical, they are not patentably distinct from each other because for the following reasons. Claims 4-7 and 14-17 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims , 3, 9, and 11 of U.S. Patent No. 11,526,555, as applied to claims 1 and 11, and in view of Andrews et al. (US 2018/0057866; newly cited).
Regarding instant claims 1 and 11, reference claims 1 and 9 (receive, at least a first element of biological extraction data; train, iteratively, a first machine-learning model using first training data correlating biological extraction data with database taste indices; calculate at least a first taste index of a user utilizing the first machine-learning model, wherein the first machine-learning model receives the at least a first element of biological extraction data as input and outputs the first taste index of the user; generate a taste profile using the first taste index, wherein generating a taste profile further comprises: training a second machine-learning model using second training data correlating database taste indices with taste profile data; and generating, using the second machine-learning model, the taste profile, wherein the second machine-learning model receives the first taste index as input and outputs the taste profile of the user) and reference claims 3 and 11 (determining a change in user taste profile further comprises generating a second taste profile and determining the change in user taste profile as a function of the first taste profile and the second taste profile) disclose the instant limitations. Correlating database taste indices with taste profile data reads on predicting a taste of the user as a function of the taste index and a genetic data table as supported by the instant specification at [0020].
Regarding instant claims 2 and 12, reference claims 1 and 9 (wherein the first taste index comprises a relationship between two elements of data that is a measure of chemosensory phenomenon as it relates to a sense of taste) disclose the instant limitations.
Regarding instant claims 3 and 13, reference claims 1 and 9 (wherein the at least a first element of biological extraction data comprises genetic data) disclose the instant limitations.
Regarding instant claims 4 and 14, the reference claims do not disclose the instant limitations.
However, the prior art to Andrews discloses methods of gustative or olfactive product selection by genetic profiling to predict individual taste and/or scent preferences for gustative or olfactive products (abstract). Andrews teaches a) obtaining a biological sample comprising nucleic acids from a subject; b) processing the sample to isolate or enrich the sample for the nucleic acids; and c) analyzing the genotype (i.e., biological extraction data) of the biological sample at a plurality of SNPs in a panel of SNPs identified as useful for identifying taste and/or scent preference [0007]. Andrews teaches that one or more pattern recognition methods can be used in analyzing SNP genotyping data and information regarding a subject's personal characteristics and taste and/or scent preferences, including the use of a machine learning algorithm [0123]. Andrews teaches the surveys regarding wine preference included questions about taste preferences (i.e., an aspect of taste), where taste preference was encoded as -1 to +1 (i.e., numerical values) [0131]. Andrews teaches clustering the wine preferences and survey responses and genotype to determine associations (i.e., taste index), it is considered that such a process is inherently mathematical and would rely on numerical values.
Regarding instant claims 5 and 15, the reference claims do not disclose the instant limitations.
However, Andrews teaches further assessing the associations (i.e., a taste index) between wine preferences and genotype (FIG. 3B). using ordered logistic regression of the genetic variants having the greatest size effect (i.e., genetic data in a genetic data table) in order to make models (i.e., a function) for predicting wine preference (i.e., predicting a taste of the user) [0132-0133]. FIG. 3B shows associations between examined genetic variants and wine preference, and reads on a taste index table as instantly claimed. Such an interpretation is supported by the instant specification as published, which discloses that a taste index table may contain a plurality of entries associating at least an element of biological extraction data with a relationship to taste [0027].
Regarding instant claims 6 and 16, the reference claims do not disclose the instant limitations.
However, Andrews teaches questioning participants on their preferences for 12 wines and their ability to detect defined flavors in them [0129].
Regarding instant claims 7 and 17, the reference claims do not disclose the instant limitations.
However, Andrews teaches including SNPs for the gene CD36 in their panel, which contributes to the phenotype of fat perception, where salad dressings tasted creamier (Table 1). CD36 is therefore considered to read on a gene that contributes to preference for a texture as instantly claimed.
Regarding instant claims 8 and 18, reference claims 1 and 9 (generate a taste profile using the first taste index) disclose the instant limitations.
Regarding instant claims 9 and 19, reference claims 1 and 9 (wherein generating a taste profile further comprises: training a second machine-learning model using second training data correlating database taste indices with taste profile data; and generating, using the second machine-learning model, the taste profile) disclose the instant limitations.
Regarding instant claims 10 and 20, reference claims 3 and 11 (determining a change in user taste profile further comprises generating a second taste profile and determining the change in user taste profile as a function of the first taste profile and the second taste profile) disclose the instant limitations.
Regarding claims 4-7 and 14-17, 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 reference patent and Andrews because both references disclose methods for predicting the taste of a user based on genetics. The motivation to combine the methods would have been to provide a method for selection of gustative or olfactive products for an individual based on such genetic profiling, as taught by Andrews [0002].
Conclusion
No claims are allowed.
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/J.N.S./Examiner, Art Unit 1685
/OLIVIA M. WISE/Supervisory Patent Examiner, Art Unit 1685
/YVONNE L EYLER/Director, Art Unit 1600