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 .
DETAILED ACTION
This action is in reference to the communication filed on 11 JUNE 2025.
Claims 1-13 are present and have been examined.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 6 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Examiner finds it to be unclear what the standard error (SE) in claim 6 is a standard error of. A standard error calculation is known, but form the claim language Examiner finds it unclear of what value a standard error is taken of. The values themselves are not given in the claim nor in the specification, nor is there any information in the figures regarding the score itself.
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 1-13 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. As explained below, the claim(s) are directed to an abstract idea without significantly more.
Step One: Is the Claim directed to a process, machine, manufacture or composition of matter? YES
With respect to claim(s) 1-13 the independent claim(s) 1 recite(s) a method, i.e. a process, which is a statutory category of invention.
Step 2A – Prong One: Is the claim directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? YES
With respect to claim(s) 1, the independent claim(s) (claims 1) is/are directed, in part, to “a method for patient and site specific assessment of microbial imbalances in periodontitis building on clinical and demographic parameters comprising the steps of identification and quantification of bacterial species using genetic workflows in samples obtained from periodontal pockets or saliva; calculation of a microbial profile based on the quantity of one or several bacterial species…; using a reference, modeling the microbial profile as a function of a selection of relevant clinical and demographical parameters; using a set of clinical and demographical parameters from a patient, calculation of the predicted value of the microbial profile via the aforesaid model; comparison of the microbial profile between predicated value and the patient value obtained in the laboratory to assess the relative imbalance of the local microbiome and recommend a site and/or patient specific therapy, wherein the clinical information includes local factors…and systemic factors; and wherein the demographic information includes age, gender, race, ethnicity. These claim elements are considered to be abstract ideas because they are directed to a mental process including concepts performed in the human mind such as observation, evaluation, judgment, opinion). For example, calculating a value using a plurality of factors requires observation/identification of the factors, as well as evaluation/judgment in the determination. Further, the claims recite mathematical concepts – i.e. mathematical relationships, formulas, calculations, or equations. For example, the modeling process as claimed and executed is an example of mathematical concepts as identified. If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind (including an observation, evaluation, judgment, opinion), and/or mathematical relationships, formulas, calculations, or equations, then it falls within the Mental Process/Mathematical concepts abstract ideas respectively. Accordingly, the claim recites an abstract idea.
Step 2A – Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? NO.
This judicial exception is not integrated into a practical application. In particular, claim 1 recites a single additional element for consideration at this step: a “ reference database.” Examiner notes that this additional element is being considered in the interest of compact prosecution. This element is recited at a high-level of generality, such that it amounts no more than perhaps mere instructions to apply the exception using a generic computer component or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Examiner notes that storage of data is insignificant extra solution activity as currently claimed. Examiner finds no improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a). Accordingly, this/these additional element(s) do(es) not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? NO.
The independent claim(s) is/are additionally directed to claim elements such as a “reference database.” The “database” claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. Examiner looks to Applicant’s specification in:
[0027] The reference database compiles the following information about multiple samples from different sites and patients: [0028] Local and systemic clinical information, as outlined above [0029] Demographical information, as outlined above [0030] as well as microbial profile for a given genetic/genomic method and selection of bacterial species
These passages, as well as others, makes it clear that the invention is not directed to a technical improvement. The element is itself discussed only in functional terms/in relation to the data which is stored thereon. When the claims are considered individually and as a whole, the additional elements noted above, appear to merely apply the abstract concept to a technical environment in a very general sense . The most significant elements of the claims, that is the elements that really outline the inventive elements of the claims, are set forth in the elements identified as an abstract idea. The fact that the generic computing devices are facilitating the abstract concept is not enough to confer statutory subject matter eligibility.
As per dependent claims 2-13
Dependent claims 2-13 are not directed any additional abstract ideas and are also not directed to any additional non-abstract claim elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims and addressed above – such as additional information about the types of clinical/microbial information collected, the types of mathematical modeling conducted on the information, and further refining the types of information gleaned from the modeling (i.e. severity of disease, progression, prognosis, etc.). While these descriptive elements may provide further helpful context for the claimed invention these elements do not serve to confer subject matter eligibility to the invention since their individual and combined significance is still not heavier than the abstract concepts at the core of the claimed invention.
Claim Rejections - 35 USC § 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.
Claim(s) 1, 2, 4-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lindskog et al (US 20120116799 A1, hereinafter Lindskog), in view of Togawa et al (US 20210238659 A1, hereinafter Togawa) in view of Cho et al (“Analysis of periodontal data using mixed effects models” hereinafter Cho).
In reference to claim 1:
Lindskog teaches: A method for the patient- and site-specific assessment of microbial imbalances in periodontitis building on clinical and demographic parameters, comprising the steps of
[identifying risk factors and calculating group risks for periodontitis as it pertains to demographic groups and given physical measurements] (at least [figs 1-5 and related text] modeling is done to determine a given profile or risk using a combination of factors);
using a set of clinical and demographical parameters from a patient, calculation of the predicted value of the microbial profile via the aforesaid model (at least [087] “The primary etiological predictor of periodontal disease that has been identified is an indigenous pathogenic bacterial plaque or biofilm. However, there are also host predictors (patient predictors), as well as a number of predictors that influence the patient's susceptibility to periodontal disease and modify disease progression. When predictors such as these accumulate and work in synergy, episodes of disease development or progression may occur.” See also [089-0125] for general discussion of the factors considered/scoring as provided in the model);
comparison of the microbial profile between the predicted value and the patient value obtained in the laboratory to assess the relative imbalance of the local microbiome and based on this, recommend a site and/or patient-specific therapy (at least [0106-0108] “a first set of numerical values may be produced, wherein each numerical value of the first set of numerical values is associated with a weight factor, and wherein the first risk score is calculated on the basis of both the thus produced numerical values of the first set of numerical values and the weight factors associated therewith. Each weight factor in turn corresponds to a measure of a predictor promoting periodontitis comprising host predictors, local predictors, and systemic predictors for periodontitis progression or for developing periodontitis for a patient, as has been previously described. In other words, each such predictor may be associated with a numerical value.” At [059-67] “prognosticating the outcome of a treatment for treating a patient…”).
wherein said clinical information includes local factors: pocket depth of the sampled site, bleeding-on-probing, tooth type, therapy stage selected from untreated, in active therapy and in supportive therapy (at least [fig 1, 3, 4 and related text, including generally 090-0109] Fig 1 identifies three groups of predictors, with additional discussion in figs 3, 4; factors listed include probe depth, bleeding on probe, tooth specific furcation involvements (see [0103] “It has been indicated that multi-rooted teeth, especially such teeth with furcation involvement, appear to be at a higher risk for periodontitis progression than molars and premolars without furcation involvement or single-rooted teeth.”), and patient’s cooperation/awareness, i.e. stage of treatment; see also [093] “The following systemic diseases and conditions represent the most important ones based on relative impact on the development and progression of periodontitis, as indicated by several earlier studies in the field: obesity, nutritional deficiencies, alcohol consumption, diabetes mellitus, aids, pregnancy, osteoporosis, blood disorders and immune deficiencies, Sjogren's syndrome, renal disease, granulomatous disease, monogenetic disease relevant to an impaired immune response or chromosomal aberrations, such as Down's syndrome, and medication which influence the gingival or saliva. It is to be understood that this list is not exhaustive.”)
systemic factors smoking status as well as diabetes status (at least [figs 1-2 and related text] smoking is shown as a systemic prediction in figure 1, and diabetes is elaborated as a systemic factor in figure 1).
said demographic information includes
age (at least [fig 1, 3 and related text] age related demographics) ,
gender (at least [fig 2 and related text] pregnancy and other gendered considerations, such as higher rates of osteoporosis in women) and
optionally race and ethnicity (at least [0162-0165] ethnicity considerations in the periodontitis diagnosis from genetic markers).
and wherein for modelling of the microbial profile, a linear model is used, which is based on molecular and clinical data, wherein the model includes the microbial profile as the response variable and clinical parameters including pocket depth as explanatory variables (at least [0152] “Multivariate linear regression was used to investigate the relationship between a numerical outcome variable (number of disease progression indicators) and explanatory variables (predictors). As known in the art, multi-variate linear regression is the extension of simple linear regression used when more than one explanatory variable is suspected to affect the response variable. Multivariate linear regression may tell how much an increase of one unit in each explanatory variable (or parameter thereof) affects progression of periodontitis under the assumption that all other explanatory variables are constant. The relationship between such variables can be modeled using regression or so-called ordinary least squares regression. As a supplement to the parameter value (estimator) .beta., the regression coefficient or explanatory value (or coefficient of determination) R.sub.2 is presented. The regression coefficient is a value that ranges from zero to one and which may tell how much of the variation in the outcome variable that is explained by variation of the explanatory variables or the variation that is "shared" by the variables.” See also [0211] for discussion of time series modeling).
Lindskog as cited teaches all the limitations above. While Lindskog does teach the use of microbial information for the prediction of periodontitis, and wherein said bacteria is extracted from the sites as claimed, it does not specifically disclose the quantification of the bacteria and the relationships between said bacteria.
Togawa however does teach: identification and quantification of bacterial species using genetic workflows in samples obtained from periodontal pockets or saliva (at least [fig 2 and related text] FIG. 2 is a scatter diagram of the bacterial group balance indexes (15 species of the “positively correlated bacteria” group and 13 species of the “negatively correlated bacteria” group) (vertical axis) and the periodontal pocket depth (Pd) (horizontal axis). The figure shows data for 220 subjects.” See also [figs 3, 4 and related text] discussion of the testing/measurement sites; “ at [0129-0137, see also table 3 and 0138] “The variable region of interest includes, but is not limited to, the 16S rRNA gene present in all bacteria in the genomic sequence. Of the 16S rRNA gene, it is desirable to target the full length or one or more regions of variable regions V1 to V9. More preferably, it is desirable to target the variable regions V1 to V6. Even more preferably, it is desirable to target the variable regions V3 to V6. It is known that the variable region of the 16S rRNA gene consists of the V1 to V9 regions, which have also been specified.” At [0138-139] applicability to probe depth, see also [0167] “The desired site to be amplified is preferably the ribosomal RNA (16S rRNA) gene in chromosomal DNA of an oral bacterium. Preferable examples of PCR primers that can be used for amplification of the region include those in Tables 2 (SEQ ID NOS: 37 and 38) and 3 (SEQ ID NOS: 41 to 53). Amplification of nucleic acids by the PCR method can be performed according to a standard method.”)
calculation of a microbial profile based on the quantity of one or several bacterial species associated with periodontitis or a ratio of the quantities of these bacteria (at least [0105-0112 ] “Bacterial species that increase as the periodontal pocket value increases (hereinafter sometimes referred to as “positively correlated bacteria”) are bacteria that increase with the deterioration of periodontal disease. Known examples of such species are Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola, which are used in existing periodontal disease bacterial tests…Of these, bacteria that increase when the periodontal pocket value is small, and then increases, the bacterial load of which is maintained when the periodontal pocket value becomes large are sometimes referred to as “progression index bacteria.” “Progression index bacteria” are thought to play a role in connecting the “bad bacteria” and “good bacteria” described below and serve as a pre-stage index of periodontal disease deterioration. Specific examples of “progression index bacteria” include Fusobacterium nucleatum species…Meanwhile, a bacterial group that increases as the periodontal pocket value increases may be hereinafter referred to as “bad bacteria.” Specific examples of “bad bacteria” include bacterial species other than the Fusobacterium nucleatum species among bacterial species that increases as the periodontal pocket value increases.” – i.e. given bacteria are grouped by the ratio to one another, as positive/negative/index, see also figs 2, 3, 6 and related text);
using a reference database, modelling of the microbial profile as a function of a selection of relevant clinical and demographical parameters (at least [0195, 0203-216] “In a first aspect of creating a discriminant model, the model can be a discriminant model based on the abundance ratio of “bacterial species that increases as the periodontal pocket value increases” and the abundance of “bacterial species that decreases as the periodontal pocket value increases” (balance index) – i.e. the modeling is done to determine where the balance/progression is; [Figs 1-1 through 1-7] reference male/female/ages of specimens, while [fig 6] references specifically progression index of the same 220 subjects);
and wherein for modeling of the microbial profile, a model is used based on molecular and clinical data from a reference database, wherein the model includes the microbial profile and [various clinical variables] (at least [036] “Further, the present inventors found that pathological conditions with the same periodontal pocket value can be further subdivided and classified by collectively measuring major oral bacterial groups (including a periodontal disease-related bacterial group and an indigenous bacterial group) in plaque and creating a model for determining deterioration of pathological conditions based on the abundance ratio of the periodontal disease-related bacterial group and the indigenous bacterial group.” At [095] “In a first aspect of creating a discriminant model, the model can be a discriminant model based on the abundance ratio of “bacterial species that increases as the periodontal pocket value increases” and the abundance of “bacterial species that decreases as the periodontal pocket value increases” (balance index)” at [0207] “In a second aspect of creating a discriminant model, the model can be a discriminant model based on the abundance ratio of “progression index bacteria (Fusobacterium nucleatum species)” and “bacterial species that decrease as the periodontal pocket value increases.” The “bacterial species that increases as the periodontal pocket value increases” in the first aspect is replaced by the Fusobacterium nucleatum species which is a “progression index bacterium.”).
Lindskog/Togawa are analogous references as both references disclose a means of calculating a future risk of periodontitis using modeling and a variety of demographic considerations therein. One of ordinary skill the art would have found the inclusion of specific bacteria information as taught by Togawa obvious to include in the biofilm/host/systemic prediction of Lindskog, as Lindskog specifically identifies that biofilm observations are crucial to an effective diagnosis of periodontitis. Togawa specifically discloses the importance of the bacterial profile in a diagnosis by highlighting the presence of both beneficial/preventative bacteria and the bacteria more acutely responsible for a diagnosis of periodontitis. Togawa further discloses such ratios/profiles as an advancement of existing techniques of diagnosing periodontitis (see 005-0012), and as such one of ordinary skill would have included such strain specific information as taught by Togawa in the modeling and patient specific predictions as taught by Lindskog.
While both Lindskog and Togawa teach the use of a linear modeling process, in the interest of compact prosecution Examiner notes that linear modeling is not specifically taught. Cho however does teach:
wherein for modelling of the microbial profile, a linear mixed-effect model is used, which is based on molecular and clinical data from a reference database, wherein the model includes the microbial profile as the response variable and clinical parameters including pocket depth as explanatory variables.(at least [page 4] “The mixed effect models might represent associations between predictors and the binary outcome variable at the person level as well as at the teeth level. Specifically, the intercept and the effects of the predictors on the outcome variable at the teeth level are assumed to vary across the individual in a population [12,13]…. Generally, the covariance structure among deviation scores at the person level is assumed to be normally distributed. Those coefficients are referred to as fixed effects, which are interpreted as the population values. Additionally, u0j and u1j are the deviation scores from the average scores at the individual level.” See also generally, page 5 for discussion of modeling community vs. individual periodontitis risks). Cho is analogous to both Lindskog and Togawa as it discloses a means of periodontitis modeling. As shown above, both Lindskog/Togawa disclose linear modeling as a means of determining group/individual risk of periodontitis based on a plurality of factors. As such, one of ordinary skill in the art would have found the inclusion of a linear, mixed effect model as taught by Cho to be an obvious substitution for or variation of, the linear modeling common to both. Cho in particular teaches that “In order to handle multilevel-structured data appropriately, the mixed effects models should be used. In doing so, the bias resulting from the violation of the independency assumption might be avoided and the estimates might be provided accurately.” (see pages 1, 6). As such one of ordinary skill in the art would have found the substation to be obvious.
In reference to claim 2:
Lindskog/Togawa/Cho teaches all the limitations above. Togawa further teaches: wherein the microbial profile is defined as the quantity of one or several bacterial species identified as key markers of periodontitis, wherein several bacterial species mean a selection of two to 700 bacterial species, which represent the number of common phylotypes known in the human mouth (at least [fig 5 and related text] bacterium as prevalent, see [097-098] “However, bacteria belonging to the following can be detection target bacteria: the genera Porphyromonas, Tannerella, Treponema, Prevotella, Campylobacter, Fusobacterium, Streptococcus, Aggregatibacter, Capnocytophaga, Eikenella, Actinomyces, Veillonella, and Selenomonas, and further the genera Pseudomonas, Haemophilus, Klebsiella, Serratia, Moraxella, Eubacterium, Parvimonas, Filifactor, Alloprevotella, Solobacterium, Rothia, Peptostreptococcus, Gemella, Corynebacterium, Neisseria, Granulicatella, and Megasphaera; and the genera of the phylum SR1.”). The motivation to combine these references is the same as above and is therefore incorporated herein.
In reference to claim 4:
Lindskog/Togawa/Cho teaches all the limitations above. Cho further wherein a linear mixed-effect model is based on molecular and clinical data from a reference database, wherein the model includes the microbial profile as the response variable and clinical parameters including pocket depth as explanatory variables and wherein the model can be used to predict the microbial profile as well as the standard error for any combination of parameters used in the model (at least page 5: “The multilevel mixed effects model was applied, and the results revealed a linear relationship for tooth position and a significant effect for subgingival calculus and bleeding on probing with both LCAL and probing depth. Pereira et al. [17] explored the association between plasmic human immunodeficiency virus viral load and subgingival microbiota measured at 12 sites in each patient with chronic periodontitis (six sites from a tooth with the highest probing depth and six with a tooth in good periodontal health). A two-level model for an ordinal outcome variable was constructed with sites as the first level and persons as the second level.” The motivation to combine these references is the same as above and is therefore incorporated herein.
In reference to claim 5:
Lindskog/Togawa/Cho teaches all the limitations above. Lindskog further teaches: wherein the microbial profile of an additional patient with defined clinical parameters can be compared to the predicted value and the standard error based on the exact same of clinical parameters (at least [022, 0342-0347, inclusive of tables] standard error, see [0106-0108, 056] as cited above for the consideration of an additional patient when compared to existing model).
In reference to claim 7:
Lindskog/Togawa/Cho teaches all the limitations above. Togawa further teaches: wherein the bacterial species are identified by quantitative PCR, 16 S ribosomal RNA sequencing, shotgun sequencing or any other molecular techniques (at least [0113] “In the present invention, oligo DNA that can be used as a probe (a) can be hybridized with a base sequence of a bacterial-specific region (a region having a base sequence that changes depending on the bacterial type) of the base sequence of a nucleic acid from an oral bacterium. Here, the nucleic acid may be any of DNA and RNA including chromosomal DNA and plasmid DNA, and is not limited, but chromosomal DNA is preferable. Specifically, an oligonucleotide used as a probe in the present invention is capable of hybridizing with the base sequence of the 16S rRNA gene in the oral bacterial chromosomal DNA. “ at [0129-0137, see also table 3 and 0138] “The variable region of interest includes, but is not limited to, the 16S rRNA gene present in all bacteria in the genomic sequence. Of the 16S rRNA gene, it is desirable to target the full length or one or more regions of variable regions V1 to V9. More preferably, it is desirable to target the variable regions V1 to V6. Even more preferably, it is desirable to target the variable regions V3 to V6. It is known that the variable region of the 16S rRNA gene consists of the V1 to V9 regions, which have also been specified.” At [0138-139] applicability to probe depth, see also [0167] “The desired site to be amplified is preferably the ribosomal RNA (16S rRNA) gene in chromosomal DNA of an oral bacterium. Preferable examples of PCR primers that can be used for amplification of the region include those in Tables 2 (SEQ ID NOS: 37 and 38) and 3 (SEQ ID NOS: 41 to 53). Amplification of nucleic acids by the PCR method can be performed according to a standard method.”). The motivation to combine these references is the same as above and is therefore incorporated herein.
In reference to claim 8:
Lindskog/Togawa/Cho teaches all the limitations above. Lindskog further teaches wherein said method comprises determining the severity of periodontitis (at least [054] “For example, this enables implementation of a risk assessment scheme distinguishing between individuals suffering from periodontitis of varying severity.” At [0257] “Patients with severe forms of chronic periodontitis present with varying degrees of decreased inflammatory reactivity. Using the skin provocation test, it has been shown that an increasing number of negative reactions to increasingly lower doses of irritants was related significantly to an increased severity of chronic periodontitis.” At [0390] “Table 1.38 shows radiographic marginal bone loss (severity or history of chronic periodontitis at baseline) in patients with positive reactions to all three Lipid A concentrations in the skin provocation test and patients with one or more negative reactions to all the Lipid A concentrations.”).
In reference to claim 9:
Lindskog/Togawa/Cho teaches all the limitations above. Togawa further wherein said method comprises the monitoring of dysbiosis development in periodontitis over time. (at least [055-056, 255-0264] the ratio of good/bad bacterial species measured to determine the type of imbalance that would result in the higher chance of periodontitis, i.e. a dysbiosis of the biome). As noted above, Lindskog/Togawa are analogous references in that they both disclose a means of predicting periodontitis risk. Both references further discuss the balance/imbalance of bacterial presence in order to determine a diagnosis and progression. As such one of ordinary skill in the art would consider an ongoing monitoring of the ratio of the types of bacteria (or really, any contributing factor) to be an obvious inclusion when attempting to monitor a progression of a disease over time
In reference to claim 10:
Lindskog/Togawa/Cho teaches all the limitations above. While Lindskog as cited teaches selecting a treatment based on efficacy, Examiner notes Togawa further teaches: wherein said method comprises monitoring of the efficacy of periodontitis treatment (at least [060, 0222-0227] “The most basic idea is to utilize the clinical information of the specimen and clarify the bacteria that have increased or decreased before and after the treatment, thereby making it possible to objectively determine the therapeutic effects. In addition, the bacterial data after treatment make it possible to clarify the bacteria that were hard to be reduced by the treatment, and allow specific treatment.”) As noted above, Lindskog/Togawa are analogous references in that they both disclose a means of predicting periodontitis risk, but also both contemplate treatment of the diagnosis. One of ordinary skill in the art would recognize the importance of monitoring any administered treatment for efficacy in order to continue selecting the most appropriate treatment for future patients, and as such would have found the monitoring of Togawa obvious to incorporate.
In reference to claim 11:
Lindskog/Togawa/Cho teaches all the limitations above. Lindskog further teaches wherein said method comprises the recommending and selecting a strategy for the treatment of periodontitis (at least [023-24, 098] “ The prognosis thus obtained by means of the method for prognosticating the outcome of a treatment procedure for treating a patient suffering from periodontitis according to the invention may subsequently be used as data on which a decision for choice of a treatment plan for the current disease state may be based. The method for prognosticating the outcome of a treatment procedure for treating a patient suffering from periodontitis according to the invention may hence be used to simulate the outcome of a treatment procedure to be applied to a patient, by estimating the impact the treatment procedure may have on one or more risk predictors promoting periodontitis progression comprising host predictors, local predictors, and systemic predictors for periodontitis progression for the patient.
In reference to claim 12:
Lindskog/Togawa/Cho teaches all the limitations above. Lindskog further teaches wherein said method comprises the analysis of samples that are taken from a periodontal pocket in a predetermined time interval, such as every 1 day, every 2 days, every 3 days, every 4 days, every 5 days, weekly, 2-weekly or 3-weekly or monthly up to 5 months (at least [0277] “The patients were selected by the investigators on a consecutive referral or treatment basis during a period of four months.”)
In reference to claim 13:
Lindskog/Togawa/Cho teaches all the limitations above. Lindskog further teaches wherein said method comprises the analysis of samples after or during the treatment that are taken from a periodontal pocket in a predetermined time interval, such as every | day, every 2 days, every 3 days, every 4 days, every 5 days, weekly, 2-weekly or 3-weekly or monthly up to 5 months (at least [0277] “The patients were selected by the investigators on a consecutive referral or treatment basis during a period of four months.”).
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Lindskog et al (US 20120116799 A1, hereinafter Lindskog), in view of Togawa et al (US 20210238659 A1, hereinafter Togawa) in view of Cho et al (“Analysis of periodontal data using mixed effects models” hereinafter Cho), further in view of Meuric et al (Signature of Microbial Dysbiosis in Periodontitis, hereinafter Meuric).
In reference to claim 3:
Lindskog/Togawa/Cho teaches all the limitations above. While each reference discloses a modeling process and/or indexed values, a specific dysbiosis index is not taught. Meuric however does teach: Wherein the microbial profile is selected from ecological measures of microbial imbalances, such as indices quantifying microbial dysbiosis, specifically a Microbial Dysbiosis Index or a Subgingival Microbial Dysbiosis Index (at least [pages 5-7] discuss determining a dysbiosis ratio for the purpose of a scaled index for future use). Meuric is analogous to the aforementioned references as each disclose a means of modeling to determine a future risk of periodontitis. Meuric discloses that a standardized scale or ratio for a determination of dysbiosis in a sample is a long felt need specifically in the field of periodontitis modeling, as the role of the bacteria involved becomes more clear through research (see abstract). Meuric specifically notes “Defining microbiota typical of oral health or chronic periodontitis is difficult. The evaluation of periodontal disease is currently based on probing of the periodontal pocket. However, the status of pockets “on the mend” or sulci at risk of periodontitis cannot be addressed solely through pocket depth measurements or current microbiological tests available for practitioners. Thus, a more specific microbiological measure of dysbiosis could help in future diagnoses of periodontitis.” As such one of ordinary skill in the art would have found the inclusion of such a value set to be obvious for future research.
Non-Obvious Subject Matter
Examiner believes claim 6 to be free of the prior art; Examiner will reassess the conclusion if amendments to claim 6 in view of the 112b rejection above necessitate.
Additional Prior Art
The following reference(s) while not cited are made a part of the record:
US 20180010171 A1 to Mougeot discloses collection and modeling of oral microbiome signatures.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KATHERINE KOLOSOWSKI-GAGER whose telephone number is (571)270-5920. The examiner can normally be reached Monday - Friday.
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/KATHERINE . KOLOSOWSKI-GAGER/
Primary Examiner
Art Unit 3687
/KATHERINE KOLOSOWSKI-GAGER/Primary Examiner, Art Unit 3687