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 . This is a Non-Final Office Action in response to application 16/165,370 entitled "LIFE INSURANCE SYSTEM WITH FULLY AUTOMATED UNDERWRITING PROCESS FOR REAL-TIME UNDERWRITING AND RISK ADJUSTMENT, AND CORRESPONDING METHOD THEREOF" with Claims 1, 8-24, 26, and 33-51 pending.
Status of Claims
Claims 1, 26, and 50 have been amended and are hereby entered.
Claims 1, 8-24, 26, and 33-51 are pending and have been examined.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
Response to Amendment
The amendment filed December 3, 2025, has been entered. Claims 1, 8-24, 26, and 33-51 remain pending in the application. Applicant’s amendments to the Specification, Drawings, and/or Claims have been noted in response to the Final Office Action mailed September 3, 2025.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on October 19, 2018 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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, 8-24, 26, and 33-51 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 8-24, 26, and 33-51 are directed to a system, method, or product program, which are/is one of the statutory categories of invention. (Step 1: YES).
The claimed invention is directed to an abstract idea without significantly more.
Independent Claim 1 recites:
“An …. smoker-detection and fraud-detection system providing signaling as a binary response of smokers and non-smokers by triggering on individual-specific parameters of individuals, and by using an automated selective multi-level triage process, the system providing automated detection of smokers, the system comprising:
…measuring smoking- related laboratory-scaled individual-specific parameters by the blood and/or urinalysis;
…configured to:
…including retrievable risk classes each comprising assigned risk class criteria, wherein the parameters of the individuals are captured relating to criteria of the stored risk classes and stored ...and identify and select a specific risk class associated with the risk of the individual out from the stored risk classes, based on the captured parameters, wherein the parameters of the individuals captured self- declaration of smoking or non-smoking of the individuals, wherein for the self-declaration of smoking or non-smoking, the system requests user inputs…, and wherein upon detecting parameters indicating a captured self-declaration of smoking of an individual,… is configured to automatically assign the individual to a first channel, in response to detecting parameters indicating captured self-declaration of non-smoking of an individual, the parameters of the individual are processed … using a …pattern recognition module for detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual, automatically assigning individuals with detected non-smoking patterns to a second channel as predicted non-smokers, and automatically assigning individuals with detected smoking patterns to a third channel as predicted smokers, capture the not smoking related input data exclusively provided by the individual for processing by the … pattern recognition module, the not smoking related input data comprising demographic data and risky avocation participation and risk- transfer benefits requested and employment-related information and applicant's medical condition and family history of impairments including at least one of cancer, heart attack, stroke, and residence location of the individual, wherein the …pattern recognition module, …is based on random forest processing …for classification, regression and prediction, wherein the …pattern recognition module, capturing the not smoking related input data exclusively provided by the individual by the user inputs…, operates by constructing a multitude of decision trees …and outputting a class that is the mode of the classes or mean prediction as regression of the individual trees thereby correcting by the random forest processing overfitting of the decision trees through their …set, and further a stochastic discrimination approach to the classification is implemented by the … pattern recognition module by selecting random subset of features …wherein for detecting and triaging non-smoking patterns from smoking pattern only the input data of the individual is used, the … pattern recognition module providing a dissimilarity measure between different input data enabling distinction of captured real input data from synthetic input data, and wherein pattern recognition applied to the parameters of the individuals is adapted to a range where the individuals processed to the first channel and second channel amount to more than 97% while the individuals assigned to the third channel requiring further …amount to fewer than 3%,
for detected individuals in the third channel, … is further configured to trigger the medical laboratory measuring devices for blood and/or urinalysis measuring smoking- related laboratory-scaled individual-specific parameters by the blood and/or urinalysis, … is configured to divert a definable percentage of individuals with detected non-smoking patterns to the third channel applying a stratified sampling process for the selection of the definable percentage of the individuals and requesting and capturing laboratory- scaled individual-specific parameters, and comparing the captured laboratory-scaled individual-specific parameters against predicted smoking or non-smoking patterns and …pattern recognition module and predictive model if comparison indicates error rates higher than a threshold, for the real-time risk assessment, a relative mortality factor is measured based on the generated binary response of smokers and non-smokers corresponding to the assigned channel, and based on the captured individual's specific parameter assignable to corresponding risk class criteria of the risk classes, the risk class criteria at least indicating smoking or non- smoking, for individuals in the first channel, the risk class criterion indicating smoking or non- smoking is automatically set to smoking, for individuals in the second channel, to non- smoking, and for individuals in the third channel, according to measured smoking or non-smoking parameters measured … is further configured to operate in an ongoing validation process diverting the definable percentage of the individuals with the detected non-smoking patterns to the third channel requesting and capturing the laboratory-scaled individual-specific parameters, and comparing the captured laboratory-scaled individual-specific parameters against the predicted smoking or non-smoking patterns and …pattern recognition module and the predictive model if the comparison indicates the error rates higher than the threshold, and … is further configured to …the automated real-time smoker-detection to a first system or a second system to automatically accept or reject an individual …the binary response of smokers and non-smokers.”
These limitations clearly relate to managing transactions/interactions between individuals, insurers, and/or re-insurers. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. For example, instructions for “fraud-detection” and “classification of individuals...automated detection of smokers...where risks associated with a plurality of individuals are ...transferable from an individual ...identify and select a specific risk class ...transferring a life risk... to a first electronically automated insurance system” recite a commercial or financial action, principle, or practice and managing interactions between people.
The Specification reads, [PG Pub 0003] “By grouping individuals' risk, the insurance systems are able to cover losses based on possibly future arising risks, out of a common pool of resources captured by the insurance systems from associated individuals for the transfer of their risks.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
Additionally, these limitations, under their broadest reasonable interpretation, covers performance of the limitation as mental processes but for the recitation of generic computer components. For example, detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual encompasses a reviewing a person’s purchase transaction history and determining if any smoking cessation products were acquired. “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea… The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation… Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’”, see MPEP 2106 – III. MENTAL PROCESSES. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a one that a person may perform by thinking then it falls within the “Mental Processes” grouping of abstract ideas. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[electronic automated] [a memory][and circuitry] [access a database stored in the memory, the database] [by the circuitry] [the circuitry] [implemented by the circuitry][ at remote computers ][ at the remote computers]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[machine learning-based] [as an ensemble learning structure] [during training] [training] [for the training][re- learning the machine learning-based] [re-learn the machine learning based]:
merely applying machine learning technology as a tool to perform an abstract idea
[medical laboratory measuring devices for blood and/or urinalysis] [laboratory tests] [by laboratory test at least comprising blood and/or urine analysis][by laboratory test at least comprising blood and/or urine analysis]:
merely applying physical laboratory testing as a tool to perform an abstract idea
merely applying laboratory measuring devices used to gather data for the abstract idea.
[electronically signal] [by signaling][real-time]:
insignificant extra-solution activity to the judicial exception of data gathering
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. The Applicant’s Specification merely requires
[PG Pub 0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.”
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 1 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
The “[electronically signal] [by signaling] [real-time]” that was considered extra-solution activity and determined to be well-understood, routine, conventional activity in the field. The background does not provide any indication that the network appliance is anything other than a generic, off-the-shelf computer component that is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). See MPEP 2106.05(d) states the court finds the following well-understood, routine, and conventional: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). For these reasons, there is no inventive concept. The claim is not patent eligible.
Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 8:
“electronic automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 9:
“electronic automated real-time”, “circuitry”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 10:
“electronic automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 11:
“electronic automated real-time”, “circuitry”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 12-15:
“electronic automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 16:
“electronic automated real-time”, “data store”, “circuitry”: merely applying computer processing, networking, and storage technologies as a tool to perform an abstract idea
Claim 17-24:
“electronic automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. The Applicant’s Specification merely requires [0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.” Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f).
The “medical laboratory measuring devices” are only described in the Specification as “system 1 measures and captures laboratory-scaled individual-specific parameters 915, 925, 935 via the automated laboratory unit 5, wherein the laboratory-scaled individual-specific parameters 915, 925, 935 are measured by means of laboratory measuring devices 914, 924, 934 of the automated laboratory unit 5”. It provides no description of the “medical laboratory measuring devices.”
Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. Thus, the claims not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Independent Claim 26 recites:
“An …smoker-detection and fraud-detection method for a system providing signaling as a binary response of smokers and non-smokers by triggering on individual-specific parameters of individuals, and by using an automated selective multi-level triage structure, the system providing automated detection of smokers, wherein the system comprises a table with retrievable stored risk classes each comprising assigned risk class criteria, wherein parameters of the individuals are captured relating to criteria of the stored risk classes …and wherein a specific risk class associated with the risk of the individual is identified out and selected from the stored risk classes based on the captured parameters, parameters of the individuals comprising at least parameters indicating a captured self-declaration of smoking or non-smoking of the individuals, wherein for the self-declaration of smoking or non-smoking, the system requests user inputs…the method comprising:
measuring …smoking-related laboratory-scaled individual-specific parameters by the blood and/or urinalysis;
upon detecting the parameters indicating a captured self-declaration of smoking of an individual based on first trigger parameters, automatically assigning the individual to a first channel;
upon detecting parameters indicating a captured self-declaration of non-smoking of an individual based on second trigger parameters, processing the parameters of the individual …pattern recognition module for detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual, automatically assigning individuals with detected non-smoking patterns to a second channel as predicted non-smokers, and automatically assigning individuals with detected smoking patterns to a third channel as predicted smokers;
capturing the not smoking related input data exclusively provided by the individual by the user inputs…, for processing …pattern recognition module, the not smoking related input data comprising demographic data and risky avocation participation and risk- transfer benefits requested and employment-related information and applicant's medical condition and history of impairments including at least one of cancer, heart attack, stroke, and residence location of the individual, wherein the …pattern recognition module is based on random forest processing as an …structure for classification, regression and prediction, wherein the … pattern recognition module, capturing the not smoking related input data exclusively provided by the individual by the user inputs…, operates by constructing a multitude of decision trees …and outputting a class that is the mode of the classes or mean prediction as regression of the individual trees thereby correcting by the random forest processing overfitting of the decision trees through their …set, and further a stochastic discrimination approach to the classification is implemented …pattern recognition module by selecting random subset of features …wherein for detecting and triaging non-smoking patterns from smoking pattern only the input data of the individual is used, the …pattern recognition module providing a dissimilarity measure between different input data enabling distinction of captured real input data from synthetic input data, and wherein pattern recognition applied to the parameters of the individuals is adapted to a range where the individuals processed to the first channel and second channel amount to more than 97% while the individuals assigned to the third channel requiring further …amount to fewer than 3%,
for detected individuals in the third channel, is further configured to trigger the medical laboratory measuring devices for blood and/or urinalysis measuring smoking- related laboratory-scaled individual-specific parameters by the blood and/or urinalysis;
diverting a definable percentage of individuals with detected non-smoking patterns to the third channel applying a stratified sampling process for the selection of the definable percentage of the individuals and requesting and capturing laboratory-scaled individual-specific parameters, and comparing the captured laboratory-scaled individual-specific parameters against predicted smoking or non-smoking patterns …pattern recognition module and predictive model if comparison indicates error rates higher than a threshold;
for the …risk assessment, measuring a relative mortality factor based on the generated binary response of smokers and non-smokers corresponding to the assigned channel, and based on the captured individual's specific parameter assignable to corresponding risk class criteria of the risk classes, wherein the risk class criteria at least indicating smoking or non- smoking, for individuals in the first channel, the risk class criterion indicating smoking or non- smoking is automatically set to smoking, for individuals in the second channel, to non- smoking, and for individuals in the third channel, according to measured smoking or non-smoking parameters measured … at least comprising blood and/or urine analysis, and operating in an ongoing validation process diverting the definable percentage of the individuals with the detected non-smoking patterns to the third channel requesting and capturing the laboratory-scaled individual-specific parameters, and comparing the captured laboratory-scaled individual-specific parameters against the predicted smoking or non-smoking patterns and …pattern recognition module and the predictive model if the comparison indicates the error rates higher than the threshold;
and …the automated real-time smoker-detection to a first system or a second system to … accept or reject an individual by signaling the binary response of smokers and non-smokers.”
These limitations clearly relate to managing transactions/interactions between individuals, insurers, and/or re-insurers. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. For example, instructions for “fraud-detection” and “classification of individuals...automated detection of smokers...where risks associated with a plurality of individuals are ...transferable from an individual ...identify and select a specific risk class ...transferring a life risk... to a first electronically automated insurance system” recite a commercial or financial action, principle, or practice and managing interactions between people.
The Specification reads, [PG Pub 0003] “By grouping individuals' risk, the insurance systems are able to cover losses based on possibly future arising risks, out of a common pool of resources captured by the insurance systems from associated individuals for the transfer of their risks.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
Additionally, these limitations, under their broadest reasonable interpretation, covers performance of the limitation as mental processes but for the recitation of generic computer components. For example, detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual encompasses a reviewing a person’s purchase transaction history and determining if any smoking cessation products were acquired. “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea… The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation… Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’”, see MPEP 2106 – III. MENTAL PROCESSES. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a one that a person may perform by thinking then it falls within the “Mental Processes” grouping of abstract ideas. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[and stored to a repository unit] [at remote computers][at the remote computers]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[by a machine learning-based] [by the machine learning-based] [ensemble learning] [during training] [training] [for the training][and re-learning the machine learning-based] [re-learn the machine learning-based]:
merely applying machine learning technology as a tool to perform an abstract idea
[by medical laboratory measuring devices for blood and/or urinalysis] [laboratory tests] [by laboratory test]:
merely applying physical laboratory testing as a tool to perform an abstract idea
merely applying laboratory measuring devices used to gather data for the abstract idea.
[automated real-time] [electronically signaling]:
insignificant extra-solution activity to the judicial exception of data gathering
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. The Applicant’s Specification merely requires
[PG Pub 0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.”
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 26 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
The “[electronically signal] [by signaling] [real-time]” that was considered extra-solution activity and determined to be well-understood, routine, conventional activity in the field. The background does not provide any indication that the network appliance is anything other than a generic, off-the-shelf computer component that is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). See MPEP 2106.05(d) states the court finds the following well-understood, routine, and conventional: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). For these reasons, there is no inventive concept. The claim is not patent eligible.
Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 33:
“automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 34:
“automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
“machine learning”: merely applying machine learning technologies as a tool to perform an abstract idea
Claims 35-40:
“automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
Claim 41:
“automated real-time”, “data store”: merely applying computer processing, networking, and storage technologies as a tool to perform an abstract idea
Claims 42-49:
“automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. The Applicant’s Specification merely requires
[PG Pub 0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.”
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f).
The “medical laboratory measuring devices” are only described in the Specification as “system 1 measures and captures laboratory-scaled individual-specific parameters 915, 925, 935 via the automated laboratory unit 5, wherein the laboratory-scaled individual-specific parameters 915, 925, 935 are measured by means of laboratory measuring devices 914, 924, 934 of the automated laboratory unit 5”. It provides no description of the “medical laboratory measuring devices.”
Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. Thus, the claims not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Independent Claim 50 recites:
“A …by an automated real-time smoker-detection and fraud-detection system providing signaling as a binary response of smokers and non-smokers triggering on individual-specific parameters of individuals, and by using an automated selective multi-level triage structure, the system providing automated detection of smokers, wherein the system comprises a table with retrievable stored risk classes each comprising assigned risk class criteria, wherein parameters of the individuals are captured relating to criteria of the stored risk classes …and wherein a specific risk class associated with the risk of the individual is identified out and selected from the stored risk classes based on the captured parameters, parameters of the individuals comprising at least parameters indicating a captured self-declaration of smoking or non-smoking of the individuals, the method comprising:
measuring …smoking-related laboratory-scaled individual-specific parameters by the blood and/or urinalysis;
upon detecting the parameters indicating a captured self-declaration of smoking of an individual based on first trigger parameters, automatically assigning the individual to a first channel;
upon detecting parameters indicating a captured self-declaration of non-smoking of an individual based on second trigger parameters, processing the parameters of the individual …pattern recognition module for detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual, automatically assigning individuals with detected non-smoking patterns to a second channel as predicted non-smokers, and automatically assigning individuals with detected smoking patterns to a third channel as predicted smokers;
capturing the not smoking related input data exclusively provided by the individual by the user inputs…, for processing …pattern recognition module, the not smoking related input data comprising demographic data and risky avocation participation and risk- transfer benefits requested and employment-related information and applicant's medical condition and history of impairments including at least one of cancer, heart attack, stroke, and residence location of the individual, wherein the …pattern recognition module is based on random forest processing as an …structure for classification, regression and prediction, wherein the … pattern recognition module, capturing the not smoking related input data exclusively provided by the individual by the user …., operates by constructing a multitude of decision trees …and outputting a class that is the mode of the classes or mean prediction as regression of the individual trees thereby correcting by the random forest processing overfitting of the decision trees through their …set, and further a stochastic discrimination approach to the classification is implemented …pattern recognition module by selecting random subset of features …wherein for detecting and triaging non-smoking patterns from smoking pattern only the input data of the individual is used, the …pattern recognition module providing a dissimilarity measure between different input data enabling distinction of captured real input data from synthetic input data, and wherein pattern recognition applied to the parameters of the individuals is adapted to a range where the individuals processed to the first channel and second channel amount to more than 97% while the individuals assigned to the third channel requiring further …amount to fewer than 3%, for individuals of the third channel, triggering the medical laboratory measuring devices for blood and/or urinalysis measuring smoking- related laboratory-scaled individual-specific parameters by the blood and/or urinalysis;
diverting a definable percentage of individuals with detected non-smoking patterns to the third channel applying a stratified sampling process for the selection of the definable percentage of the individuals and requesting and capturing laboratory-scaled individual-specific parameters, and comparing the captured laboratory-scaled individual-specific parameters against predicted smoking or non-smoking patterns …pattern recognition module and predictive model if comparison indicates error rates higher than a threshold;
for the …risk assessment, measuring a relative mortality factor based on the generated binary response of smokers and non-smokers corresponding to the assigned channel, and based on the captured individual's specific parameter assignable to corresponding risk class criteria of the risk classes, wherein the risk class criteria at least indicating smoking or non- smoking, for individuals in the first channel, the risk class criterion indicating smoking or non- smoking is automatically set to smoking, for individuals in the second channel, to non- smoking, and for individuals in the third channel, according to measured smoking or non-smoking parameters measured … at least comprising blood and/or urine analysis, and operating in an ongoing validation process diverting the definable percentage of the individuals with the detected non-smoking patterns to the third channel requesting and capturing the laboratory-scaled individual-specific parameters, and comparing the captured laboratory-scaled individual-specific parameters against the predicted smoking or non-smoking patterns and …pattern recognition module and the predictive model if the comparison indicates the error rates higher than the threshold;
and …the automated real-time smoker-detection to a first system or a second system to … accept or reject an individual by signaling the binary response of smokers and non-smokers.”
These limitations clearly relate to managing transactions/interactions between individuals, insurers, and/or re-insurers. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. For example, instructions for “fraud-detection” and “classification of individuals...automated detection of smokers...where risks associated with a plurality of individuals are ...transferable from an individual ...identify and select a specific risk class ...transferring a life risk... to a first electronically automated insurance system” recites a commercial or financial action, principle, or practice and managing interactions between people.
The Specification reads, [PG Pub 0003] “By grouping individuals' risk, the insurance systems are able to cover losses based on possibly future arising risks, out of a common pool of resources captured by the insurance systems from associated individuals for the transfer of their risks.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
Additionally, these limitations, under their broadest reasonable interpretation, covers performance of the limitation as mental processes but for the recitation of generic computer components. For example, detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual encompasses a reviewing a person’s purchase transaction history and determining if any smoking cessation products were acquired. “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea… The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation… Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’”, see MPEP 2106 – III. MENTAL PROCESSES. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a one that a person may perform by thinking then it falls within the “Mental Processes” grouping of abstract ideas. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of:
[non-transitory computer-readable storage medium including computer executable instructions, wherein the instructions, when executed] [and stored to a repository unit] [at remote computers][at the remote computers]:
merely applying computer processing, storage, and networking technology as tools to perform an abstract idea
[by a machine learning-based] [by the machine learning-based] [ensemble learning] [during training] [training] [for the training][and re-learning the machine learning-based] [re-learn the machine learning-based]:
merely applying machine learning technology as a tool to perform an abstract idea
[by medical laboratory measuring devices for blood and/or urinalysis] [laboratory tests] [by laboratory test]:
merely applying physical laboratory testing as a tool to perform an abstract idea
merely applying laboratory measuring devices used to gather data for the abstract idea.
[automated real-time] [electronically signaling]:
insignificant extra-solution activity to the judicial exception of data gathering
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. The Applicant’s Specification merely requires
[PG Pub 0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.”
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, Claim 50 is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f). Accordingly, the additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. The claim further defines the abstract idea and hence is abstract for the reasons presented above. The claim does not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination.
The “[electronically signal] [by signaling] [real-time]” that was considered extra-solution activity and determined to be well-understood, routine, conventional activity in the field. The background does not provide any indication that the network appliance is anything other than a generic, off-the-shelf computer component that is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here). See MPEP 2106.05(d) states the court finds the following well-understood, routine, and conventional: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). For these reasons, there is no inventive concept. The claim is not patent eligible.
Therefore, the claim is directed to an abstract idea. Thus, the claim is not patent eligible. (Step 2B: NO. The claim does not provide significantly more)
Dependent Claims recite additional elements.
This judicial exception is not integrated into a practical application. In particular, the recited additional elements of
Claim 51:
“automated real-time”: merely applying computer processing, networking, and display technologies as a tool to perform an abstract idea
are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function) such that it amounts no more than mere instructions to apply the exception using a generic computer components and/or electronic processes. The Applicant’s Specification merely requires
[PG Pub 0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.”
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
Accordingly, these additional elements, when considered separately and as an ordered combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, the claim is directed to an abstract idea without a practical application. (Step 2A-Prong 2: NO. The additional claimed elements are not integrated into a practical application)
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. The Applicant’s Specification merely requires
[PG Pub 0025] “a computer program product that includes computer program code means for controlling one or more processors of the control system in such a manner that the control system performs the proposed method; and it relates, in particular, to a computer program product that includes a computer-readable medium containing the computer program code means for the processors.”
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The additional elements merely add instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, see MPEP 2106.05(f).
The “medical laboratory measuring devices” are only described in the Specification as “system 1 measures and captures laboratory-scaled individual-specific parameters 915, 925, 935 via the automated laboratory unit 5, wherein the laboratory-scaled individual-specific parameters 915, 925, 935 are measured by means of laboratory measuring devices 914, 924, 934 of the automated laboratory unit 5”. It provides no description of the “medical laboratory measuring devices.”
Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Dependent claims further define the abstract idea that is present in their respective independent claims and hence are abstract for the reasons presented above. Thus, the claims not patent eligible. (Step 2B: NO. The claims do not provide significantly more)
Response to Remarks
Applicant's arguments filed on December 3, 2025, have been fully considered and Examiner’s remarks to Applicant’s amendments follow.
Response Remarks on Claim Rejections - 35 USC § 101
The Applicant states:
“As previously noted, Applicant respectfully submits that the Office Action does not establish that independent claims 1, 26, and 50 are directed to an abstract idea or at least an abstract idea without a practical application or significantly more. In fact, it is believed that, for instance, claim 1, as amended is not directed to an abstract idea…. Furthermore, the pending claims cannot fall into the "mental processes category" because the recited features cannot practically be performed in the human mind.”
Examiner responds:
Examiner maintains that the limitations clearly relate to managing transactions/interactions between individuals, insurers, and/or re-insurers. These limitations, under their broadest reasonable interpretation, cover performance of the limitation as certain methods of organizing human activity. For example, instructions for “fraud-detection” and “classification of individuals...automated detection of smokers...where risks associated with a plurality of individuals are ...transferable from an individual ...identify and select a specific risk class ...transferring a life risk... to a first electronically automated insurance system” recites a commercial or financial action, principle, or practice and managing interactions between people.
The Specification reads, [PG Pub 0003] “By grouping individuals' risk, the insurance systems are able to cover losses based on possibly future arising risks, out of a common pool of resources captured by the insurance systems from associated individuals for the transfer of their risks.”
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
Additionally, these limitations, under their broadest reasonable interpretation, covers performance of the limitation as mental processes but for the recitation of generic computer components. For example, detecting and triaging non-smoking patterns and smoking patterns based on not smoking related input data of the parameters of the individual encompasses a reviewing a person’s purchase transaction history and determining if any smoking cessation products were acquired. “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea… The courts do not distinguish between mental processes that are performed entirely in the human mind and mental processes that require a human to use a physical aid (e.g., pen and paper or a slide rule) to perform the claim limitation… Nor do the courts distinguish between claims that recite mental processes performed by humans and claims that recite mental processes performed on a computer. As the Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’”, see MPEP 2106 – III. MENTAL PROCESSES. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a one that a person may perform by thinking then it falls within the “Mental Processes” grouping of abstract ideas. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
As claimed, it’s unclear whether the claimed self-reported “captured parameters” include data that any smartphone or tablet may discern, such as any smoking cessation app, see VeryWellMind article, entitled, “3 Best Free iPhone Apps to Help You Quit Smoking” that states “This section of the app lets you log cravings as they come. You can analyze all of the details, including severity, emotional state, when it happened, and who you were with... Once you've logged numerous smoking urges, you can display them on a graph to see patterns. The tool even lets you use a map option to log where the craving happened. This feature is useful in revealing your unique triggers to smoke, some of which might surprise you.” Or perhaps a smoking behavioral detection via a mobile device approach is used as in F. Joseph McClernon and Romit Roy Choudhury ( “I Am Your Smartphone, and I Know You Are About to Smoke: The Application of Mobile Sensing and Computing Approaches to Smoking Research and Treatment” - 2013 May 23) that suggests much is known about the immediate and predictive antecedents of smoking lapse, which include situations (e.g., presence of other smokers),activities (e.g., alcohol consumption), and contexts (e.g., outside).
Furthermore, certain biological information (i.e., heart rate) can be captured by a generic computing device, see CNET article, entitled, “How to track your heart rate with only your smartphone” or Saurabh Singh Thakur and Ram Babu Roy ( “A Mobile App based Smoking Cessation Assistance using Automated Detection of Smoking Activity” - CoDS-COMAD ’18, January 11–13, 2018, Goa, India) that proposes a sensor-based approach for automated recognition of smoking activity.
The focus of the invention is not to an improvement of a technological field such as machine learning, physical medical laboratory measuring devices for blood and/or urinalysis, databases, or ensemble learning as tools. Rather, the invention relies upon the use of those technological components, in an expected manner, as tools to perform the abstract idea.
The Applicant states:
“The data triage process in conjunction with the appropriate choice allows machine-learning to recognize structures and to classify them correctly and with a reproducible accuracy.”
Examiner responds:
To “recognize such structures … and to classify them correctly and with a reproducible accuracy”, again expresses gathering, sharing, and manipulation of data which is an Abstract Idea [Intellectual Ventures I v. Capital One Fin. Corp., 850 F.3d 1332, 121 USPQ2d 1940 (Fed. Cir. 2017) “collecting, displaying, and manipulating data” was considered part of the abstract idea], and Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]
The Applicant states:
“Claim 1, for example, is over four (4) pages in length and is directed to the technical field of automation - in particular, an automated detection system automatically detecting smoker individuals within a bulk of selected individuals. …. allows to minimize manual, time consuming, and expensive blood tests. The data triage process in conjunction with the appropriate choice allows machine-learning to recognize structures and to classify them correctly and with a reproducible accuracy.”
Examiner responds:
Examiner maintains that, as quoted from Applicant’s previous responses, “detection of smokers by minimizing the applications which have to undergo addition laboratory test… to minimize manual, time consuming, and expensive blood tests… the introduction and the selection of the form and size of the triage "channels" …of detecting fraudulent input information …. separation of the datasets of individuals to be underwritten …that only specifically selected datasets are selected” recites a commercial or financial action, principle, or practice and managing interactions between people. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a commercial or financial action, principle, or practice then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. (Step 2A-Prong 1: YES. The claims recite an abstract idea).
To “minimize manual, time consuming, and expensive blood tests” amounts to a business or financial enhancement, not a technological improvement.
Selectively evaluating portions of data rather than an entire universe of datasets whereby “only specifically selected datasets are selected for the technical pattern recognition by the appropriate detection structure, i.e., to save computational power and time” clearly results in “sped up pattern recognition.” This is well-understood, routine, and conventional, see MPEP 2106.05(d).
The machine-learning is “merely applied” as a tool to advance the abstract idea. Any generic machine learning tool may achieve the intended results. Thus it is well-understood, routine, and conventional, see MPEP 2106.05(d).
The claims’ invocation of computers and machine learning does not transform the claimed subject matter into patent-eligible applications. The claims at issue do not require any nonconventional computers or machine learning components, or even a “non-conventional and non-generic arrangement of known, conventional pieces,” but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computers and machine learning devices.
Consider the Specification that reads:
[PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
In the absence of unexpected results, changes or alteration of sequence do not make for a patentable invention, see Ex parte Rubin, 128 USPQ 440 (Bd. App. 1959) ; In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946); In re Gibson, 39 F.2d 975, 5 USPQ 230 (CCPA 1930)
The Applicant states:
“Furthermore, Applicant believes that the claims are directed to a "practical application" and also recite "significantly more" since the pending claims start automation by relying on self-disclosed data for a self-inflicted disadvantage. The problem of the conventional system is precisely that automation could not be achieved due to the high probability of fraud.
Typically, most people are not trustworthy when it comes to a self-inflicted disadvantage (in this case, smoking).”
Examiner responds:
The aspects of “self-disclosed data”, “high probability of fraud”, and “people are not trustworthy” are all abstract ideas and not technological components (additional elements). One abstract idea cannot integrate another abstract idea into a practical application.
The reliance upon “self-disclosed data” equivocates to determining and calculating differences between data elements which amounts to Mere Data Gathering [Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)], and Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]. Both of which are abstract ideas.
The Applicant states:
“Furthermore, human experts would not be able to deduct from not-smoking related input data the smoking status of an individual. Therefore, it needs technical considerations that applying a dedicated machine learning structure is able to perform such complex tasks, which typically need thousands of input data during the learning phase of the machine learning structure. A human expert would never be able to perform such a task, and the use of technical features to achieve this is not at all in an expected way.”
Examiner responds:
A human could analyze financial transactions and identify vape and tobacco shop purchases that indicate the smoking status of an individual.
Moreover, the Specification reads, (page 10, lines 19-23) “(4) an ongoing validation process that diverts a percentage of predicted non-smokers for lab testing in order to collect results about the risk-exposed individual's actual smoking or nonsmoking habit and compare them against predicted smoking or non-smoking habit and re-learn the predictive model if comparison indicates high error rates.” This indicates the potential fallibility of the system and the occurrence of error, just as in a completely human system.
The Applicant states:
“…using a pick-up truck for the transportation of good, instead of the time consuming and expensive use of horses or humans to carry the goods would obviously minimize manual, time consuming, and expensive transportation.
Thus, following the Office's assertion, any truck (which is clearly a technical device with a "practical application" and amounting to "significantly more") would not be patentable under § 101, since this technology amounts to a financial enhancement since it is cheaper to use trucks than horses or humans for transportation.”
Examiner responds:
Applicant’s invention is not directed towards trucks, but rather “real-time smoker-detection and fraud-detection system” which is clearly an abstract idea. Therefore, no analogy exists.
The Applicant states:
“Moreover, it is worth reiterating the claimed ensemble of machine learning structures. In the claimed invention, there is a well-defined sequence of machine learning structures being composed to achieve the overall goal. This cannot be achieved by a skilled person without a "practical application" or without "significantly more."
Examiner responds:
The focus of the claims is not on such an improvement in machine learning as a tool, but on certain independently abstract ideas that use machine learning as a tool. Nothing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional machine learning technology for gathering, synthesizing, sending, and presenting the desired information. See MPEP 2106.05(d) well-understood, routine, and conventional. For example, the Specification reads, [PG Pub 0049] It is important to note, that the machine-learning based pattern-recognition module operating based on random forest processing, gradient boosting (GBM), support vector machines (SVM) and/or logistic regression as learning structure for classification, regression and prediction, giving just examples. Other machine-learning based learning structures or combinations of machine-learning based learning structures are also imaginable...Explicitly, the operation of the system, i.e. the inventive system itself, is not restricted to risk-transfer related to life- or health-risks.
The “triple channel structure” are mere datasets which are abstract ideas. “[O]nly specifically selected datasets are selected” equivocates to determining and calculating differences between data elements which amounts to Mere Data Gathering [Obtaining information about transactions using the Internet to verify credit card transactions, CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011)], and Selecting A Particular Data Source or Type Of Data To Be Manipulated [Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)]. Both of which are abstract ideas.
Again, in the absence of unexpected results, changes or alteration of sequence do not make for a patentable invention, see Ex parte Rubin, 128 USPQ 440 (Bd. App. 1959) ; In re Burhans, 154 F.2d 690, 69 USPQ 330 (CCPA 1946); In re Gibson, 39 F.2d 975, 5 USPQ 230 (CCPA 1930)
The Applicant states:
“Since the selected input parameters of the machine learning-based pattern recognition module are not directly smoking-related parameters, the automated detection system is technically set up in a way which is very difficult to be compromised or influenced by a user lying in the first step about his/her smoking status.”
Examiner responds:
Examiner maintains that “very difficult to be compromised or influenced by a user lying in the first step about his/her smoking status” is not a technological constraint but that “a user lying” deals with Managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) which is also an abstract idea.
The rejection under 35 USC § 101 remains.
Response Remarks on Claim Rejections - 35 USC § 103
Applicant's amendments overcome the prior art according to the limitation, “wherein pattern recognition applied to the parameters of the individuals is adapted to a range where the individuals processed to the first channel and second channel amount to more than 97% while the individuals assigned to the third channel requiring further laboratory tests amount to fewer than 3%.”
The rejection under 35 USC § 103 remains lifted.
Prior Art Cited But Not Applied
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Cox (“DRIVING PATIENT CAMPAIGN BASED ON TREND PATTERNS IN PATIENT REGISTRY INFORMATION”, U.S. Publication Number: 20170235894 A1) proposes [0173] a fourth medical code from a fourth source may indicate that the patient has purchased or used a smoking habit suppression product, such as a gum or patch. The combination of these medical codes provide information from various sources that together give a picture or evidence as to the proper value for the variable associated with the variable listing, e.g., whether the patient is likely a smoker or not.
Eder (“AUTOMATED RISK TRANSFER SYSTEM”, U.S. Publication Number: 20120303408 A1) proposes a system for using artificial intelligence based cognitive learning methods to identify, measure and manage risks for a commercial enterprise on a continual basis.
Dorr (“SYSTEM AND METHOD FOR A LIFE SETTLEMENT AND/OR VIATICAL EXCHANGE”, U.S. Publication Number: 2006/0031151 A1) provides a life exchange system and method for life settlement and/or viaticals are provided where a plurality of life insurance policies can be sorted and matched between brokers and funders.
Hersch (“TRANSFERRING INSURANCE POLICIES”, U.S. Publication Number: 20090281840 A1) compares privacy related information to at least one predefined characteristic and determine that there is a match between the privacy related information and the characteristic. Based at least in part on there being a match, the server may filter-out the insurance policy such that the information on the insurance policy is not provided.
Wikipedia entry for "Random Forest" which teaches "The selection of a random subset of features is an example of the random subspace method, which, in Ho's formulation, is a way to implement the "stochastic discrimination" approach to classification proposed by Eugene Kleinberg"
VeryWellMind article, entitled, “3 Best Free iPhone Apps to Help You Quit Smoking” states “This section of the app lets you log cravings as they come. You can analyze all of the details, including severity, emotional state, when it happened, and who you were with... Once you've logged numerous smoking urges, you can display them on a graph to see patterns. The tool even lets you use a map option to log where the craving happened. This feature is useful in revealing your unique triggers to smoke, some of which might surprise you.”
CNET article, entitled, “How to track your heart rate with only your smartphone” teaches “You don't need a heart rate monitor or smartwatch to take your pulse -- these three apps can do it with your phone.”
Saurabh Singh Thakur and Ram Babu Roy (“A Mobile App based Smoking Cessation Assistance using Automated Detection of Smoking Activity” - CoDS-COMAD ’18, January 11–13, 2018, Goa, India) proposes a sensor-based approach for automated recognition of smoking activity. That may be used for providing interventions in near real-time via mobile app to promote smoking cessation.
F. Joseph McClernon and Romit Roy Choudhury (“I Am Your Smartphone, and I Know You Are About to Smoke: The Application of Mobile Sensing and Computing Approaches to Smoking Research and Treatment” - 2013 May 23) suggests much is known about the immediate and predictive antecedents of smoking lapse, which include situations (e.g., presence of other smokers),activities (e.g., alcohol consumption), and contexts (e.g., outside). This commentary suggests smartphone-based systems could be used to infer these predictive antecedents in real time and provide the smoker with just-in-time intervention. The smartphone of today is equipped with an array of sensors, including GPS, cameras, light sensors, barometers, accelerometers, and so forth, that provide information regarding physical location, human movement, ambient sounds, and visual imagery
MedicalExpo.com (“Medical tablet PCs”, 2021) suggests that a traditional tablet computer may serve as a medical device.
PNG
media_image1.png
743
1430
media_image1.png
Greyscale
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHINEDU EKECHUKWU whose telephone number is (571)272-4493. The examiner can normally be reached on Mon-Fri 9 AM ET to 3:30 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christine Behncke, can be reached on (571) 272-8103. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov.
Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/C.E./Examiner, Art Unit 3695
/CHRISTINE M Tran/Supervisory Patent Examiner, Art Unit 3695