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 Claims
This is a Non-Final Action on the merits in response to the claims filed on 11-14-2023.
Claims 1 – 20 are currently pending in this application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/14/2023 and 03-04-2026 have been acknowledged. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the Examiner. The initialed and dated copies of Applicant’s IDS forms, 1449, are attached to the instant Office Action.
Claim Rejections – 35 U.S.C. §101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or
composition of matter, or any new and useful improvement thereof, may obtain a patent
therefor, subject to the conditions and requirements of this title.
Signals Per Se – Claim 11 is not in one of the four statutory categories of invention. Claim 11 recites “a generation program that causes…” embodying various instructions. The broadest reasonable interpretation of a claim drawn to a computer software program, product, or medium typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of “a program”. There is no special definition, and as a result, Claim 11 encompasses within its scope signals per se and are thus not statutory. See In re. Nuijten, 500 F.3rd 1346, 1356-57.
Alice - Claims 1 – 11, are rejected under 35 U.S.C. §101 because the claimed invention is directed towards an abstract idea without significantly more.
wherein behavior information in which, for each of factors in a factor group, the factor is associated with a behavior taken when the factor is applicable,
process of acquiring, for each of samples, a predicted probability based on whether or not each factor in the factor group is applicable and an importance level of each factor in the factor group which level is a basis for the predicted probability,
an extraction process of extracting a specific factor from the factor group on a basis of the importance levels obtained by the acquisition process,
and a generation process of acquiring a specific behavior corresponding to the specific factor extracted by the extraction process, from the behavior information, and generating annotation information that presents the specific behavior to each sample to which the specific factor is applicable.
Claim 1 recites certain methods of organizing human activity, and particularly commercial interactions where the claim recites managing personal behavior. For example, claim 1 recites observing behavior information in which, for each of factors in a factor group, the factor is associated with a behavior taken when the factor is applicable; process of acquiring, for each of samples, a predicted probability based on whether or not each factor in the factor group is applicable and an importance level of each factor in the factor group which level is a basis for the predicted probability; evaluating an extraction process of extracting a specific factor from the factor group on a basis of the importance levels obtained by the acquisition process; and an evaluating process of acquiring a specific behavior corresponding to the specific factor extracted by the extraction process, from the behavior information, and generating annotation information that presents the specific behavior to each sample to which the specific factor is applicable. However all are merely commercial interactions where the claim involves analyzing collected data on a patient’s behavior and correlating the patient’s evaluated behavior to determine relationships and annotation information. Claims 10 and 11 are substantially similar to claim 1, and recite the same subject matter as claim 1. Accordingly, claims 1, 10, and 11 recite the abstract idea of certain methods of organizing human activity.
The dependent claims encompass the same abstract idea as well. For instance, claim 2 is directed towards observing the behavior information includes improvement behavior information in which each factor is associated with an improvement behavior recommended for the samples when the factor is applicable, in the extraction process, extracts a factor for which the importance level is greater than a threshold value, as the specific factor, and, in the generation process, acquires a specific improvement behavior corresponding to the specific factor from the improvement behavior information, and generates annotation information that presents the specific improvement behavior to each sample to which the specific factor is applicable; claim 3 is directed towards observing in the extraction process, extracts a factor for which the importance level is greater than the threshold value and is maximum, as the specific factor; claim 4 is directed towards observing the behavior information includes risk reduction behavior information in which each factor is associated with a risk reduction behavior recommended for the samples when the factor is applicable, in the extraction process, extracts a factor for which the importance level is smaller than a threshold value, as the specific factor, and, in the generation process, acquires a specific risk reduction behavior corresponding to the specific factor from the risk reduction behavior information, and generates annotation information that presents the specific risk reduction behavior to each sample to which the specific factor is applicable; claim 5 is directed towards observing in the extraction process, extracts a factor for which the importance level is smaller than the threshold value and is minimum, as the specific factor; claim 6 is directed towards observing in the generation process, generates the annotation information for a sample for which the predicted probability is equal to or higher than a predetermined probability or is at a predetermined ranking or higher; claim 7 is directed towards observing the behavior information includes, for each of second factors other than a first factor in the factor group, first-factor-applicable risk-reduction-behavior information in which the second factor is associated with a risk reduction behavior recommended for each sample when the first factor and the second factor are applicable to the sample, in the extraction process, extracts a second factor for which the importance level is smaller than a threshold value, as a specific second factor, and, in the generation process, acquires, for a specific sample to which the first factor is applicable, a specific risk reduction behavior corresponding to the specific second factor from the first-factor-applicable risk-reduction-behavior information, and generates annotation information that presents the specific risk reduction behavior to the specific sample to which the specific second factor is applicable; claim 8 is directed towards observing the behavior information includes, for each of second factors other than a first factor in the factor group, first-factor-inapplicable risk-reduction-behavior information in which the second factor is associated with a risk reduction behavior recommended for each sample when the first factor is not applicable to the sample but the second factor is applicable to the sample, in the extraction process, extracts a second factor for which the importance level is smaller than a threshold value, as a specific second factor, and, in the generation process, acquires, for a specific sample to which the first factor is not applicable, a specific risk reduction behavior corresponding to the specific second factor from the first-factor-inapplicable risk-reduction-behavior information, and generates annotation information that presents the specific risk reduction behavior to the specific sample to which the specific second factor is applicable; and claim 9 is directed towards observing the behavior information includes combined behavior information in which a combination of a risk amplification factor and a risk reduction factor in the factor group is associated with a behavior taken when the combination is applicable, in the extraction process, extracts, for each sample, a combination of a specific risk amplification factor and a specific risk reduction factor from the factor group, acquires a specific behavior corresponding to the combination of the specific risk amplification factor and the specific risk reduction factor extracted by the extraction process, from the combined behavior information, and generates annotation information that presents the specific behavior to each sample to which the combination of the specific risk amplification factor and the specific risk reduction factor is applicable. Thus, the dependent claims further limit the abstract idea.
These judicial exceptions are not integrated into a practical application. Claim 1 recites the additional elements of a generation device comprising: a processor that executes a program, and a storage device that stores the program, and the processor executes. In addition to reciting the additional elements of claim 1, claim 10 recites the additional elements of a generation method executed by a generation device having a processor that executes a program and a storage device that stores the program, wherein the generation device stores; and in addition to reciting the additional elements of claim 1, claim 11 recites the additional element of a generation program that causes a processor capable of accessing behavior information to execute. However, a generation device comprising: a processor that executes a program, and a storage device that stores the program, the processor executes, a generation method executed by a generation device having a processor that executes a program and a storage device that stores the program, wherein the generation device stores, stored, a generation program that causes a processor capable of accessing behavior information to execute, and a processor are all generic computer components performing generic computer functions as per Applicant’s Specification shown below:
“[0018] Example of computer hardware configuration FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer. A computer 200 includes a processor 201, a storage device 202, an input device 203, an output device 204, and a communication interface (communication IF) 205. The processor 201, the storage device 202, the input device 203, the output device 204, and the communication IF 205 are connected to one another by a bus 206. The processor 201 controls the computer 200. The storage device 202 serves as a work area for the processor 201. Further, the storage device 202 is a non-temporary or temporary recording medium that stores various programs and data. Examples of the storage device 202 include a read only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), and a flash memory. The input device 203 inputs data. Examples of the input device 203 include a keyboard, a mouse, a touch panel, a numeric keypad, a scanner, a microphone, and a sensor. The output device 204 outputs data. Examples of the output device 204 include a display, a printer, and a speaker. The communication IF 205 connects to a network and transmits and receives data. [0019] The XAI 100 and the annotation information generating function 110 illustrated in FIG. 1 are implemented in the computer 200. The XAI 100 and the annotation information generating function 110 may be implemented in the same computer 200 or in different computers 200. The computer 200 in which at least the annotation information generating function 110 is implemented is referred to as a generation device. When the XAI 100 and the annotation information generating function 110 are implemented in different computers 200, the generation device receives the importance level matrix 103 from another computer in which the XAI 100 is implemented, via a network such as the Internet, a local area network (LAN), or a wide area network (WAN).”
and thus are not practically integrated nor significantly more.
Each of the additional limitations are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The combination of these additional elements are no more than mere instructions to apply the exception using generic computer components (e.g., processor). The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as the additional elements do not integrate the judicial exception into a practical application because the additional elements do not impose meaningful limits on practicing the idea, and amount to no more than mere instructions using generic computer components to implement the judicial exception. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Thus, the claims are directed to an abstract idea.
Dependent claims 2 – 9 when analyzed both individually and in combination are also held to be ineligible for the same reason above and the additional recited limitations fail to establish that the claims are not directed to an abstract idea. The additional limitations of the dependent claims when considered individually and as an ordered combination do not amount to significantly more than the abstract idea.
Looking at these limitations as ordered combination and individually add nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use generic computer components, to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claim as a whole amount to significantly more than the abstract idea itself. Therefore, claims 1 – 11, are not patent eligible under 35 U.S.C. § 101.
Claim Rejections – 35 U.S.C. § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries 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(a) are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness
5. Claims 1 – 5 and 10-11 are rejected under 35 U.S.C. § 103 as being unpatentable over Tschulena, Ulrich et al. (U.S. Publication No. 2020/0258639) hereinafter “Tschulena” in view of Shapiro, Daniel Frederick Woolf (U.S. Publication No. 2023/0363439) hereinafter “Shapiro”.
Claims 1, 10, and 11 recite:
A generation device comprising: a processor that executes a program; Tschulena teaches in ¶ 0011, behavior information and factors such as BMI and HDL; Tschulena teaches in ¶ 0010, computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition arbitrarily selected from a total population described herein includes executing computer program instructions in a computer comprising one or more microprocessors.
and the processor executes an acquisition process of acquiring, for each of samples, a predicted probability based on whether or not each factor in the factor group is applicable and an importance level of each factor in the factor group which level is a basis for the predicted probability; Tschulena, Ulrich et al. (US Publication No. 2020/0258639) teaches in ¶ 0075, the present medical devices, systems and methods may provide efficient and accurate prediction of adverse health conditions for an arbitrarily selected subpopulation or individual based on health information obtained from publications, literature and the like that focus on a limited or selected subpopulation. Such technology may be used to predict and manage individual health risks as well as to analyze and manage health risks of a group or a population. Tschulena teaches in ¶ 0089, a flowchart diagram of an exemplary method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population. In step 102 literature or other publications, including medical records from hospitals or other health institutions, are systematically reviewed, and in step 104 the most reliable articles or meta analyses from the literature or publications are selected. From these selected documents characterizing features, e.g., adverse outcome incidence, risk factor prevalence and risk factor odds ratios, are extracted in step 106 and merged in step 108. The merged information is then used in step 110 to compute model parameters for the generalized model, which are stored in step 112. The model parameters may be computed using any appropriate type of probabilistic model, e.g., exponential family functions.
an extraction process of extracting a specific factor from the factor group on a basis of the importance levels obtained by the acquisition process; Tschulena teaches in ¶ 0091, once the search strategy and all required definitions are made the publications are screened and graded in steps 218 and 220, respectively, and a final selection of publications for analysis and extraction is made in step 222. The extraction of characterizing features etc. is then performed in step 224. Tschukena teaches in ¶ 0094, a flowchart diagram of one exemplary aspect of the method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population, in which the generalized model is updated by analyzing new publications. Periodical or event-triggered check for new publications is executed in step 502. The review may be performed in the same way as in step 102 of FIG. 1, and appropriate keywords may be determined in the same way as explained with reference to FIG. 2. Whenever a publication is found in step 504 that promises to provide new evidence or generally new data in respect of an adverse health condition, e.g. new subpopulations, new characteristic features, etc., the method follows the “yes”-path to step 508, in which the data extraction is executed, e.g. in the way discussed with reference to FIG. 1. The following steps 510 and 512 likewise may correspond to one or more of steps 106 to 110 discussed with reference to FIG. 1. Updating the generalized model using new publications allows for the model to become more accurate or effective in predicting without having to derive again the full model generation process that was initially required and thus saves time and effort.
and a generation process of acquiring a specific behavior corresponding to the specific factor extracted by the extraction process, from the behavior information, and generating annotation information that presents the specific behavior to each sample to which the specific factor is applicable; Tschulena teaches in ¶ 0010, In accordance with an aspect the computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition arbitrarily selected from a total population described herein includes executing computer program instructions in a computer comprising one or more microprocessors, volatile and/or non-volatile memory and one or more data and/or user interfaces, for extracting information about characterizing features of a plurality of second subpopulations, about occurrences and/or severity of the first adverse health condition found therein and/or about corresponding prognostic results, from a plurality of publications and/or primary clinical data recorded in electronic databases and elaborated according to probabilistic statistical models. One or more characterizing features, e.g. those known to be useful in the respective context, may be provided as further input for initializing the extraction, but this is not a strict requirement, since the extraction itself may identify other or further characterizing features as also or equally relevant, or even more relevant. The computer program instructions may configure the computer executing those instructions to provide an extraction module. The extraction module may control or cooperate with various interfaces for accessing publications and/or clinical data records and the like. The interfaces include one or more of data communication interfaces, cameras, scanners and the like. Tschulena teaches in ¶ 0011, patient properties include but are not limited to age, gender, height, weight, BMI, use of substances or alcohol, smoking, history of hypertension or hypotension, diabetes, COPD, lung cancer, CKD stage, history of cerebrovascular disease, coronary artery disease, peripheral artery disease, chronic heart failure, chronic obstructive pulmonary disease, autoimmune disorders, anxiety/depression, cancer, liver disease, BMI, albumin, glucose, HDL, LDL, Triglycerides, CRP, IL-6, serum uric acid, HsTNT, Phosphate, iPTH, proteinuria and albuminuria, other known chronic diseases, past cured diseases, behavior or lifestyle, psychological profiles, morphological characteristics assessed via any imaging technique, or functional characteristics assessed via any appropriate diagnostic testing. The list provided hereinbefore is non-exhaustive and may represent a subset of characterizing features cited further above. Tschulena teaches in ¶ 0012, The information about characterizing features extracted from the plurality of publications and/or primary clinical data recorded in electronic databases and elaborated according to probabilistic statistical models may comprise those patient's properties which show a positive or negative correlation with regard to the first adverse health condition.
Tschulena teaches an extraction module, patient properties, subpopulation from a total population, subpopulation or individual based on health information and Tschulena and are similar where Tschulena and Shapiro teach behaviors and cessation of smoking and Shapiro further teaches the following:
and a storage device that stores the program, wherein behavior information in which, for each of factors in a factor group, the factor is associated with a behavior taken when the factor is applicable is stored; Shapiro teaches in claim 21, a non-transitory machine readable storage medium storing a set of instructions that, when executed by at least one microprocessor, causes the at least one microprocessor to perform operations, the operations comprising: receiving manually inputted information or data from the subject's smart device; identifying a baseline according to the subject's smoking or nicotine history, associated psychological oral habitual behaviors and preferences in the subject's profile; determining a schedule for providing communications to intervene and assist the subject with overcoming cravings associated with nicotine, smoking or psychological oral habits, based on the established baseline; sending communications to the subject's smart device based on the schedule; receiving use data of the smoking cessation or nicotine replacement device from the subject's smart device; aggregating the use data; identifying changes compared to the baseline; and modifying the schedule for providing communications to intervene and assist the subject with overcoming said cravings, in response to the aggregated use data. Shapiro further teaches in claim 22, the non-transitory machine readable storage medium according to claim 21, wherein the operations further comprise: receiving a response to a first communication sent to the subject's smart device; sending one or more additional communications to the subject's smart device, wherein the contents of the one or more additional communications is based on the subject's response to the first communication and the subject's smoking or nicotine history, associated psychological oral habitual behaviors and preferences in the subject's profile.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to combine a computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population of Tschulena with methods of preparing inhalable nicotine-free formulations, and to methods for treating a subject with a nicotine-free formulation for use in smoking cessation or nicotine replacement of Shapiro to assist businesses with implementing methods and devices for smoking cessation (Shapiro, Spec. ¶ 0011).
Claim 2:
Tschulena and Shapiro teach claims 1, 10, and 11. Tschulena further teaches the following:
wherein the behavior information includes improvement behavior information in which each factor is associated with an improvement behavior recommended for the samples when the factor is applicable, in the extraction process, the processor extracts a factor for which the importance level is greater than a threshold value, as the specific factor, and, in the generation process, the processor acquires a specific improvement behavior corresponding to the specific factor from the improvement behavior information, and generates annotation information that presents the specific improvement behavior to each sample to which the specific factor is applicable; Tschulean teaches in ¶ 0013, the method further includes executing computer program instructions in a computer for associating, based on data from each of the plurality of publications and/or primary clinical data recorded in electronic databases and elaborated according to probabilistic statistical models, one or more of the characterizing features identified therein with corresponding first factors indicating a relation with an adverse or beneficial contribution of the characterizing feature to the occurrence or progression of the first adverse health condition. The first factors may accordingly be represented, e.g., by effect size measures such as odds ratios, hazard ratios, relative risks. Tschulean teaches in ¶ 0014, the relation indicated by the first factor may express a risk increase or decrease for occurrence or progression of the first adverse health condition in a patient, e.g. expressed with respect to a reference population, either healthy or in an appropriately selected peer group, expressed as a value generated by comparing to a baseline model in which that particular characteristic feature is absent or at a “normal” level, or as an absolute risk. Tschulena teaches in ¶ 0068, the medical device may be configured to highlight those characterizing features having positive or negative effect on the risk or the probability of occurrence or progression of the adverse health condition which can be modified or influenced through one or more responsive actions from the non-exhaustive list comprising therapy, change of lifestyle, change of diet and medical intervention.
Claim 3:
Tschulena and Shapiro teach claims 1, 10, and 11. Tschulena further teaches the following
wherein, in the extraction process, the processor extracts a factor for which the importance level is greater than the threshold value and is maximum, as the specific factor; Tschulena teaches in ¶ 0043, in an aspect of the method the characterizing features and the associated factors are adjusted, ranked and/or selected differently for a specific first adverse health condition and/or a severity or current stage of the first adverse health condition. For example, when a first adverse health condition can assume different levels of severity or different stages, characterizing features may have different value or reliability for predicting the risk of progression of the adverse health condition into the next level of severity or stage. Tschulean further teaches in ¶ 0044, the method may include executing computer program instructions in a computer for providing a priming module that is adapted to receive input corresponding to selecting a prediction time period and/or a current level of severity or stage of the adverse health condition. The input from the priming module may be used for selecting one of a variety of statistical models for generating the generalized model in accordance with the priming input. For example, when an adverse health condition is generally divided into five different stages of severity the priming input may be used for generating a generalized model for progression of the adverse health condition from level 3 to level 4. This input may result in selecting the second subpopulation for data input accordingly, excluding those subpopulations from consideration that are not suitable, e.g., because the adverse health condition does not progress skipping levels.
Claim 4:
Tschulena and Shapiro teach claims 1, 10, and 11. Tschulena further teaches the following:
wherein the behavior information includes risk reduction behavior information in which each factor is associated with a risk reduction behavior recommended for the samples when the factor is applicable, in the extraction process, the processor extracts a factor for which the importance level is smaller than a threshold value, as the specific factor, and, in the generation process, the processor acquires a specific risk reduction behavior corresponding to the specific factor from the risk reduction behavior information, and generates annotation information that presents the specific risk reduction behavior to each sample to which the specific factor is applicable; Tschulena teaches in ¶ 0015, The method further includes associating, from each of the plurality of publications and/or primary clinical data recorded in electronic databases and elaborated according to probabilistic statistical models, one or more of the characterizing features identified therein with corresponding second factors indicating the relative frequency of occurrence, or prevalence, in the respective second subpopulation considered in the respective publication. The combination of data on characterizing features and associated first and second factors over a plurality of publications and/or primary clinical data recorded in electronic databases and elaborated according to probabilistic statistical models may provide an indication for a likelihood of occurrence or progression in the total population. The computer program instructions may configure the computer executing those instructions to provide an associator module adapted to execute the association steps. The associator module may implement various probabilistic statistical models that are selectable in accordance with the first adverse health condition, the type of data and/or characterizing features and the like. Tschulena teaches in ¶ 0016, the method further includes executing computer program instructions in a computer for combining the characterizing features and their first and second factors into a generalized model for the total population, wherein combining includes calculating a baseline risk of a virtual “general” member of the total population and estimating the conditional probability of a first adverse health condition for all the potential configurations of known health states contributing to the first adverse health event risk. A baseline risk may be considered a general risk for the virtual “general” member of the total population in which all identified and known risk factors are absent or at their lowest possible values. The computer program instructions may configure the computer executing those instructions to provide a combiner module adapted to execute the combining steps. The combiner module may implement various probabilistic statistical models that are selectable in accordance with the first adverse health condition, the type of data and/or characterizing features and the like.
Claim 5:
Tschulena and Shapiro teach claims 1, 10, and 11. Tschulena further teaches the following:
wherein, in the extraction process, the processor extracts a factor for which the importance level is smaller than the threshold value and is minimum, as the specific factor; Tschulena teaches in ¶ 0062, According to one aspect of the present disclosure the medical device implementing the model generator of the therapy control support system, when the processor executes the computer program instructions, may be configured to generate a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population. The generalized model may represent interrelationships between a plurality of medical risks and a plurality of health parameters. In accordance with this aspect, the medical device is configured to extract information about characterizing features of a plurality of second subpopulations, about occurrences and/or severity of the first adverse health condition and/or about corresponding prognostic results found in a plurality of publications. The publications may, e.g., be obtained through accessing one or more databases. The medical device may further be configured to associate, from each of the plurality of publications, one or more of the characterizing features identified therein with corresponding first factors indicating a relation with an adverse or beneficial contribution of the characterizing feature to the occurrence or progression of the first adverse health condition, and further to associate one or more of the characterizing features with corresponding second factors indicating the relative frequency of occurrence in the respective second subpopulation considered in the respective publication. The medical device may yet further be configured to combine the characterizing features and their first and second factors into a generalized model for the total population, wherein combining includes calculating a baseline risk of a virtual “general” member of the total population, and to store the generalized model and/or the baseline risk in a retrievable manner on a computer accessible and readable medium. The baseline risk may represent a risk of occurrence or progression of the adverse health condition for a subject that does not exhibit or is not exposed to any characterizing feature that had been identified as having a negative influence on the occurrence or progression of the adverse health condition, or exhibits or is exposed to such characterizing feature to the lowest possible extent.
6. Claims 6 – 9 are rejected under 35 U.S.C. § 103 as being unpatentable over Tschulena, Ulrich et al. (U.S. Publication No. 2020/0258639) hereinafter “Tschulena” in view of Shapiro, Daniel Frederick Woolf (U.S. Publication No. 2023/0363439) hereinafter “Shapiro” in view Jacobs, Cindy A. (U.S. Publication No. 2021/0077475) hereinafter “Jacobs”.
Claim 6:
Tschulena and Shapiro teach claims 1, 10, and 11; and Tschulena teaches an extraction module, patient properties, subpopulation from a total population, subpopulation or individual based on health information; and Shapiro teaches overcoming cravings associated with nicotine, smoking or psychological oral habits, based on the established baseline and sending communications to the subject's smart device and Tschulena, Shapiro, and Jacobs are similar where Tschulena, Shapiro, and Jacobs teach cessation and treatment for smoking; and Jacobs further teaches the following:
wherein, in the generation process, the processor generates the annotation information for a sample for which the predicted probability is equal to or higher than a predetermined probability or is at a predetermined ranking or higher; Jacobs teaches in ¶ 0060, The phrase “statistical significance,” as used herein refers to a result from data generated by testing or experimentation, is not likely to occur randomly or by chance, but is instead likely to be attributable to a specific cause. Statistical significance is evaluated from a calculated probability (p-value), where the p-value is a function of the means and standard deviations of the data samples and indicates the probability under which a statistical result occurred by chance or by sampling error. A result is considered statistically significant if the p-value is 0.05 or less, corresponding to a confidence level of 95%. Jacobs further teaches using the probability p-value in ¶ 0226, a collection of baseline attributes including age, race, sex, duration of smoking, and number of quit attempts, were analyzed for both the Cigarette Score primary outcome variable and Cess/W5-8/CO Success (FIGS. 26 and 27). Only the comparison between the cytisine 3.0 mg TID arm and the pooled placebo arm are presented. The same EMA models used for analyzing clinical sites were used. The forest graphs for these analyses follow. (Note: In these forest graphs a (M), (T), or (Q) at the end of a factor label indicates that the pooled data were split by the median, tertiles, or quartiles, respectively. Hx denotes “History”); and ¶ 0227, the only interaction P value of note was that for the duration of smoking history split by quartiles (“Smoke Hx Dur (y) (Q)”) for the Cigarette Score, with P=0.0800. Since the other smoking history duration variables did not have significant P values (P≥0.2566) this finding was regarded as unimportant. None of the other factors raised concern about heterogeneity of effect.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to combine a computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population of Tschulena and methods of preparing inhalable nicotine-free formulations, and to methods for treating a subject with a nicotine-free formulation for use in smoking cessation or nicotine replacement of Shapiro with methods of treatment of addiction and/or dependence, methods of promoting cessation of various addictions, such as smoking and/or vaping, and methods of promoting a reduction in various addictions, such as smoking and/or vaping, uses of cytisine as an addiction cessation treatment, and dosage regimens of Jacobs to assist businesses with implementing study design for treating nicotine addiction (Jacobs, Spec. ¶ 0169).
Claim 7:
Tschulena and Shapiro teach claims 1, 10, and 11; and Tschulena teaches an extraction module, patient properties, subpopulation from a total population, subpopulation or individual based on health information; and Shapiro teaches overcoming cravings associated with nicotine, smoking or psychological oral habits, based on the established baseline and sending communications to the subject's smart device and Tschulena, Shapiro, and Jacobs are similar where Tschulena, Shapiro, and Jacobs teach cessation and treatment for smoking; and Jacobs further teaches the following:
wherein the behavior information includes, for each of second factors other than a first factor in the factor group, first-factor-applicable risk-reduction-behavior information in which the second factor is associated with a risk reduction behavior recommended for each sample when the first factor and the second factor are applicable to the sample, in the extraction process, the processor extracts a second factor for which the importance level is smaller than a threshold value, as a specific second factor, and, in the generation process, the processor acquires, for a specific sample to which the first factor is applicable, a specific risk reduction behavior corresponding to the specific second factor from the first-factor-applicable risk-reduction-behavior information, and generates annotation information that presents the specific risk reduction behavior to the specific sample to which the specific second factor is applicable; Jacobs teaches in ¶ 0335, Serum samples will be collected for determining cotinine levels at Weeks 2, 4, 6, 8, 10, 12, 16, 20, and 24. Baseline cotinine testing will use frozen serum collected at the SV1 visit for subjects that are randomized. Cotinine levels will be determined at a central laboratory. Jacobs teaches in ¶ 0337, Systolic/diastolic blood pressure, pulse rate, and oral temperature measurements will be recorded in a seated position. Body weight will also be recorded. Height will be recorded at Screening Visit #1 for BMI calculation. The primary efficacy outcome (biochemically verified abstinence for the last 4 weeks of cytisinicline treatment) for each subject will be binary: success versus failure. Success will be defined for the subject as having reported smoking abstinence (no cigarettes since the last clinic visit) at each clinic assessment from Week 3 to Week 6 (Arm B) and Week 9 to Week 12 (Arm C) with biochemical verification at each assessment. Biochemical verification will be defined by a carbon monoxide concentration in exhaled breath of less than 10 ppm. Similar timeframe and analyses will occur for Arm A placebo subjects. Jacobs teaches in ¶ 0341, The secondary efficacy outcome 1 and 2 (continued biochemically verified abstinence to Week 24) for each subject will be binary: success versus failure. Success will be defined for the subject as having reported smoking abstinence since the last clinic visit at each clinic assessment from Week 6 (Arm B) or Week 12 (Arm C) to Week 24 with biochemical verification at each assessment. Biochemical verification will be defined by a carbon monoxide concentration in exhaled breath of less than 10 ppm. During the Follow-up Smoking Cessation Assessment Period between Weeks 12 to 24, self-report of smoking abstinence will be according to the Russell Standard.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to combine a computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population of Tschulena and methods of preparing inhalable nicotine-free formulations, and to methods for treating a subject with a nicotine-free formulation for use in smoking cessation or nicotine replacement of Shapiro with methods of treatment of addiction and/or dependence, methods of promoting cessation of various addictions, such as smoking and/or vaping, and methods of promoting a reduction in various addictions, such as smoking and/or vaping, uses of cytisine as an addiction cessation treatment, and dosage regimens of Jacobs to assist businesses with implementing study design for treating nicotine addiction (Jacobs, Spec. ¶ 0169).
Claim 8:
Tschulena and Shapiro teach claims 1, 10, and 11; and Tschulena teaches an extraction module, patient properties, subpopulation from a total population, subpopulation or individual based on health information; and Shapiro teaches overcoming cravings associated with nicotine, smoking or psychological oral habits, based on the established baseline and sending communications to the subject's smart device and Tschulena, Shapiro, and Jacobs are similar where Tschulena, Shapiro, and Jacobs teach cessation and treatment for smoking; and Jacobs further teaches the following:
wherein the behavior information includes, for each of second factors other than a first factor in the factor group, first-factor-inapplicable risk-reduction-behavior information in which the second factor is associated with a risk reduction behavior recommended for each sample when the first factor is not applicable to the sample but the second factor is applicable to the sample, in the extraction process, the processor extracts a second factor for which the importance level is smaller than a threshold value, as a specific second factor, and, in the generation process, the processor acquires, for a specific sample to which the first factor is not applicable, a specific risk reduction behavior corresponding to the specific second factor from the first-factor-inapplicable risk-reduction-behavior information, and generates annotation information that presents the specific risk reduction behavior to the specific sample to which the specific second factor is applicable; Jacobs teaches in ¶ 0229, Prior anti-smoking intervention factors were analyzed for the Cigarette Score primary outcome variable and Cess/W5-8/CO Success comparing the cytisine 3.0 mg TID arm to the pooled placebo arm (FIGS. 28 and 29). The factors analyzed include: whether more than 2 anti-smoking intervention attempts, and if ever treated or recently treated with any of Chantix®, Zyban®, vaping, or nicotine replacement therapy. The same EMA models used for analyzing clinical sites were performed. The forest graphs for these analyses follow. Jacobs teaches in ¶ 0230, The only interaction P value of note was that for history of prior use of Chantix® (“Chantix® Hx”) for the Cigarette Score, with P=0.0508. However, the interaction P value for the factor variable indicating Chantix® as the most recent intervention (“Chantix® Most Recent”) did not indicate concern (P=0.3179). Since the interaction appeared to be quantitative and the finding for most recent use was discordant, the concern for Chantix® being an effect modifier was discounted. Baseline laboratory factors were analyzed for the Cigarette Score primary outcome variable and Cess/W5-8/CO Success comparing the cytisine 3.0 mg TID arm to the pooled placebo arm (FIGS. 30 and 31). These factors included nicotine metabolism ratio (NMR), expired CO, and serum cotinine, and were analyzed by median, tertiles, and quartiles. The same EMA models used for analyzing clinical sites were performed. The forest graphs for these analyses follow in FIG. 30 and FIG. 31. (Note: In these forest graphs a (M), (T), or (Q) at the end of a factor label indicates that the pooled data were split by the median, tertiles, or quartiles, respectively.) Only the interaction P values of 0.0434 and 0.0652 for the median and tertile splits of baseline cotinine, respectively, for the Cigarette Score met the criterion suggesting effect modification. However, the interaction P values for the continued abstinence secondary outcome were 0.2925, 0.3738, and 0.9732 for the median, tertile, and quartile splits, respectively, showing no effect modification regarding baseline cotinine levels. Since 4-week abstinence is planned to be the primary outcome for future Phase 3 studies, concern for baseline cotinine as an effect modifier is of less concern.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to combine a computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population of Tschulena and methods of preparing inhalable nicotine-free formulations, and to methods for treating a subject with a nicotine-free formulation for use in smoking cessation or nicotine replacement of Shapiro with methods of treatment of addiction and/or dependence, methods of promoting cessation of various addictions, such as smoking and/or vaping, and methods of promoting a reduction in various addictions, such as smoking and/or vaping, uses of cytisine as an addiction cessation treatment, and dosage regimens of Jacobs to assist businesses with implementing study design for treating nicotine addiction (Jacobs, Spec. ¶ 0169).
Claim 9:
Tschulena and Shapiro teach claims 1, 10, and 11; and Tschulena teaches an extraction module, patient properties, subpopulation from a total population, subpopulation or individual based on health information; and Shapiro teaches overcoming cravings associated with nicotine, smoking or psychological oral habits, based on the established baseline and sending communications to the subject's smart device and Tschulena, Shapiro, and Jacobs are similar where Tschulena, Shapiro, and Jacobs teach cessation and treatment for smoking; and Jacobs further teaches the following:
wherein the behavior information includes combined behavior information in which a combination of a risk amplification factor and a risk reduction factor in the factor group is associated with a behavior taken when the combination is applicable; Jacobs teaches in ¶ 0242, The results from the study indicated that cytisine benefit occurred all baseline characteristics and attributes. In specific, the cytisine benefit occurred across subject demographics, baseline CO levels and the number of cigarettes smoked daily, as well as based on smoking history. In terms of subject demographics, the benefit was consistent across the subject population regardless of race, gender, age, and BMI.
in the extraction process, the processor extracts, for each sample, a combination of a specific risk amplification factor and a specific risk reduction factor from the factor group, on the basis of the importance levels acquired by the acquisition process; Jacobs teaches in ¶ 0211, additional assessments for BMI and baseline mean number of cigarettes interactions specifically for the 3.0 mg TID arm compared to the pooled placebo arm.
and, in the generation process, the processor acquires a specific behavior corresponding to the combination of the specific risk amplification factor and the specific risk reduction factor extracted by the extraction process, from the combined behavior information, and generates annotation information that presents the specific behavior to each sample to which the combination of the specific risk amplification factor and the specific risk reduction factor is applicable; Jacobs teaches in ¶ 0212, For BMI, this assessment for the existence of an interaction with arm was evaluated as an EMA using BMI as the factor, where the size of the interaction P would be informative of the existence of this interaction (FIG. 16). The EMA interaction P value for BMI was 0.1303, not small enough to induce concern, but based on the stratum-specific effect size estimates there was a suggestion of possibly more efficacy for high BMI patients as compared to lower BMI subjects.
Before the effective filing date of the claimed invention it would have been obvious to one of ordinary skill in the art to combine a computer-implemented method of generating a generalized model for adaptively predicting occurrence or progression of a first adverse health condition for a first subpopulation arbitrarily selected from a total population of Tschulena and methods of preparing inhalable nicotine-free formulations, and to methods for treating a subject with a nicotine-free formulation for use in smoking cessation or nicotine replacement of Shapiro with methods of treatment of addiction and/or dependence, methods of promoting cessation of various addictions, such as smoking and/or vaping, and methods of promoting a reduction in various addictions, such as smoking and/or vaping, uses of cytisine as an addiction cessation treatment, and dosage regimens of Jacobs to assist businesses with implementing study design for treating nicotine addiction (Jacobs, Spec. ¶ 0169).
Conclusion
The prior art made of record and not relied upon is considered relevant but not applied:
Note: these are additional references found but not used.
- Reference Sachs, David P.L (U.S. Publication No. 2004/0006113) discloses method for predicting nicotine replacement dosage to achieve a target nicotine serum concentration relies on measuring blood nicotine concentration prior to smoking cessation.
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/FRANK MAURICE ALSTON/
Examiner, Art Unit 3625
04/04/2026
/JOSEPH M WAESCO/Primary Examiner, Art Unit 3625