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 .
Status of the Claims
The office action is in response to the claims filed on March 26, 2024 for the application filed March 26, 2024. Claims 1-22 are currently pending and have been examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a feature extraction module configured to collect and process…” and “a model building and optimization module configured to build a machine learning model…” in claim 1.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The corresponding structure of “a feature extraction module configured to collect and process” is determined to be the algorithms disclosed in paragraphs [0046] and [0054] and their equivalents. The corresponding structure of the “a model building and optimization module configured to build a machine learning model” is determined to be the algorithms disclosed in paragraphs [0044] and [0049] and their equivalents.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 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.
Claims 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Tsai et al. (Development and validation of an insulin resistance model for a population without diabetes mellitus and its clinical implication: a prospective cohort study) in view of Du (CN 113936803 A). Citations for Du are in reference to the EPO translation of CN 113936803 A.
Regarding claim 1, Tsai discloses
a database configured to provide a data set (Page 8, Discussion, a combined database of the NHANES and MJ databases. Also see page 23, Figure 1 and page 29, Figure 3.);
comprises age, gender, race, and body mass index of the subject (Page 4, Model building process, We randomly selected 55% of the patient population as the training group for model building, 15% as the in ternal validation group for hyperparameter optimization and independent 30% for the test group. We used the Synthetic Minority Over sampling Technique (SMOTE) preprocessing algorithm for sample-balance between the target and non-target populations in training group. The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender. Page 9, Statistical analysis, For unweighted data in the NHANES, MJ, and TWB databases, continuous variables were reported as means ± standard deviation (SD) and categorical data as numbers (percentages). Also see page 6 Table 1, page 23 Figure 1 and page 29 Figure 3.); and
Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender. First, a deep neural network (DNN) was used to estimate the first chosen method. Other traditional methods of machine learning algorithm, such as random forest (RF), eXtreme Gradient Boosting (XGboost), and logistic regression algorithms were also used. Their accuracies were then compared with the logistic regression model. After completing the model training, another group was applied for testing. In the training group, curves of ROC (receiver operating characteristic) from different algorithms were compared. Both the ROC curve and AUC (area under curve) were used to compare classification performances of different classifiers. The targeted value of AUC (>0.80) suggested that the model was adequate for predicting IR.).
Tsai does not appear to explicitly disclose predicting the pancreatic β-cell function of the subject or a system having modules for the feature extraction and model building and optimization.
Du teaches that it was old and well known in the art of medical diagnosis at the time of the filing to provide a system with modules for feature extraction and model building and optimization to predict pancreatic β-cell function of a subject (Du, page 3, an acquisition module, configured to acquire a plurality of sample data, in which test indicators corresponding to insulin resistance are recorded; a building module for constructing an evaluation model, the evaluation model includes a first initial model and a second initial model, and there are multiple first initial models; a first training module, configured to train each of the first initial models by using the plurality of sample data, and select a plurality of first models from the plurality of first initial models according to the training results; a second training module, configured to train the second initial model according to the first model and the plurality of sample data to obtain a second model; A determination module, configured to determine the insulin resistance index of the target user through the first model. Page 9, Determine the etiology type of the user according to the insulin resistance index and user data, where the user data includes at least one of basic data, disease data, exercise data and diet data. The insulin resistance index and user data are input into a disease identification model or a decision tree model based on machine learning or a preset rule, and the user's disease type is output. Page 10 according to the user's test indicators, it is determined that the user is suffering from pancreatic islet function decline caused by long-term lipotoxicity and glucotoxicity. Also see Du, page 4, a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is used to execute the determination method of the present invention.) to recommend an exercise plan or diet plan for the subject (Du, page 9).
Therefore, it would have been obvious to one of ordinary skill in the art of medical diagnosis at the time of the filing to modify the disclosure of Tsai to be performed by a system having modules and to predict the pancreatic β-cell function of the subject, as taught by Du, in order to facilitate recommending an exercise plan or diet plan for the subject.
Regarding claim 2, Tsai further discloses wherein the feature set further comprises fasting blood glucose, and/or glycohemoglobin of the subject (Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender.).
Regarding claim 3, Tsai further discloses wherein the feature set further comprises total cholesterol and/or high-density lipoprotein cholesterol of the subject (Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender.).
Regarding claim 4, Tsai further discloses wherein the feature set further comprises triglyceride of the subject (Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender.).
Regarding claim 5, Tsai further discloses wherein the feature set further comprises total cholesterol and/or high-density lipoprotein cholesterol of the subject (Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender.).
Regarding claim 6, Tsai further discloses wherein the feature set further comprises fasting blood glucose (Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender.)
Tsai does not appear to explicitly disclose wherein the feature set further comprises at least one selected from the group consisting of glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, total bilirubin, and albumin of the subject.
Du teaches that it was old and well known in the art of medical diagnosis at the time of the filing for the feature set to include at least one selected from the group consisting of glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, total bilirubin, and albumin of the subject (Du, page 9, The test index corresponding to the disease type is the index data representing the disease, and the test index corresponding to different disease types is different. For example, for diabetes, the test indicators include fasting blood glucose, fasting insulin, blood glucose mean, insulin mean, random blood glucose, visceral fat level, triglyceride, transaminase, glomerular filtration rate, blood uric acid, blood pressure, insulin, C-peptide , Glycated hemoglobin and other indicators. For example, for liver disease, the test indicators include transaminase, alanine transaminase, aspartate transaminase, albumin, globulin, white ball ratio, bilirubin, bile acid and other index data.) to determine a subject’s etiology type and recommend an exercise plan or diet plan for the subject (Du, page 9).
Therefore, it would have been obvious to one of ordinary skill in the art of medical diagnosis at the time of the filing to modify the feature set of Tsai to include at least one selected from the group consisting of glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, total bilirubin, and albumin of the subject, as taught by Du, in order to facilitate determining a subject’s etiology type and recommending an exercise plan or diet plan for the subject.
Regarding claim 7, Tsai does not appear to explicitly disclose, but Du teaches that it was old and well known in the art of medical diagnosis at the time of the filing wherein the feature set further comprises glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, total bilirubin, and/or albumin of the subject (Du, page 9, The test index corresponding to the disease type is the index data representing the disease, and the test index corresponding to different disease types is different. For example, for diabetes, the test indicators include fasting blood glucose, fasting insulin, blood glucose mean, insulin mean, random blood glucose, visceral fat level, triglyceride, transaminase, glomerular filtration rate, blood uric acid, blood pressure, insulin, C-peptide , Glycated hemoglobin and other indicators. For example, for liver disease, the test indicators include transaminase, alanine transaminase, aspartate transaminase, albumin, globulin, white ball ratio, bilirubin, bile acid and other index data.) to determine a subject’s etiology type and recommend an exercise plan or diet plan for the subject (Du, page 9).
Therefore, it would have been obvious to one of ordinary skill in the art of medical diagnosis at the time of the filing to modify the feature set of Tsai to include at least one selected from the group consisting of glutamic oxaloacetic transaminase, glutamic pyruvic transaminase, total bilirubin, and albumin of the subject, as taught by Du, in order to facilitate determining a subject’s etiology type and recommending an exercise plan or diet plan for the subject.
Regarding claim 8, Tsai further discloses wherein the feature set further comprises fasting blood glucose and at least one selected from the group consisting of total cholesterol and high-density lipoprotein cholesterol of the subject (Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender.).
Regarding claim 9, Tsai further discloses wherein the machine learning model is trained by the features labeled with outcome related to the insulin resistance and/or a decline of β-cell function of the subject (Page 4, Definition of insulin resistance (IR), In this study, HOMA-IR values were calculated for participants in the combined the NHANES and MJ databases. We then separated participants into non-IR and IR groups according to their HOMA-IR values (≤ or >2.5). Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender. First, a deep neural network (DNN) was used to estimate the first chosen method. Other traditional methods of machine learning algorithm, such as random forest (RF) ,eXtreme Gradient Boosting (XGboost), and logistic regression algorithms were also used. Their accuracies were then compared with the logistic regression model. After completing the model training, another group was applied for testing. In the training group, curves of ROC (receiver operating characteristic) from different algorithms were compared. Both the ROC curve and AUC (area under curve) were used to compare classification performances of different classifiers. The targeted value of AUC (>0.80) suggested that the model was adequate for predicting IR. Also see page 4, Clinical implications of IR: CV mortality and call cause mortality.).
Regarding claim 10, Tsai further discloses wherein the database comprises: a first database derived from a first population of the subject; and a second database derived from a second population of the subject, wherein the race of the first population is different from the race of the second population (Page 8, Discussion, a combined database of the NHANES and MJ databases. Page 9, Discussion, Our predictive models were also trained from populations of two different ethnicities (Caucasians in the NHANES and Asians in the MJ database). Also see page 23, Figure 1 and page 29, Figure 3.).
Regarding claims 11-18 and 20-22: all limitations as recited have been analyzed and rejected with respect to claims 1-10. Claims 11-18 and 20-22 pertain to a method, corresponding to the system of claims 1-10. Claims 11-18 and 20-22 do not teach or define any new limitations beyond claims 1-10 (see page 4 of Du regarding the computer readable medium of claim 22 as cited with respect to claim 1); therefore claims 1-18 and 20-22 are rejected under the same rationale.
Regarding claim 19, Tsai further discloses wherein the model building and optimization module builds the machine learning model based on the feature set to predict the insulin resistance and/or the pancreatic β-cell function of the subject by classifying the subject into an insulin resistance group, a non-insulin resistance group, β-cell deficiency group, and a non-β-cell deficiency group, and generating a corresponding predictive value and a classification performance value thereof, wherein the classification performance value is an area under curve of a receiver operating characteristic curve of the machine learning model (Page 4, Definition of insulin resistance (IR), In this study, HOMA-IR values were calculated for participants in the combined the NHANES and MJ databases. We then separated participants into non-IR and IR groups according to their HOMA-IR values (≤ or >2.5). Page 4, Model building process, The features used to predict IR (HOMA-IR >2.5) were easily accessible variables, such as BMI, FPG, HbA1c, total cholesterol, HDL, triglyceride, race, age, and gender. First, a deep neural network (DNN) was used to estimate the first chosen method. Other traditional methods of machine learning algorithm, such as random forest (RF), eXtreme Gradient Boosting (XGboost), and logistic regression algorithms were also used. Their accuracies were then compared with the logistic regression model. After completing the model training, another group was applied for testing. In the training group, curves of ROC (receiver operating characteristic) from different algorithms were compared. Both the ROC curve and AUC (area under curve) were used to compare classification performances of different classifiers. The targeted value of AUC (>0.80) suggested that the model was adequate for predicting IR.).
Conclusion
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/DEVIN C HEIN/Examiner, Art Unit 3686
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