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 .
Claim Objections
Claims 1 and 12 are objected to because of the following informalities: the claims recite “one wearable device sensor and/or a health data aggregation platform.” Applicant is reminded that 37 CFR 1.71(a) requires the use of "full, clear, concise and exact terms". This requirement would be better met by amending "and/or" to "or" because the claim scope would not change, but the claim language would be more concise and exact. Appropriate correction is required.
Response to Arguments
Rejection Under 101
Applicant's arguments filed 02/20/2026 have been fully considered.
Applicant argues that the amended claims are not an abstract idea since they recite technical architecture and data flow improvements including server/network ingestion of wearable and laboratory data and AI based automated handling of recurring anomalies/incidents.
In response to Applicant’s argument, Examiner respectfully disagrees. The claims recite an abstract idea since they are directed at evaluating the life expectancy for a person and generating reports and recommendations for the individual. The technical features at issue in the argument amount to invoking a computer environment to carry out the abstract idea. See the updated rejection for further clarification.
Applicant argues that the amended claims do not fall under mental process or a mathematical concepts since the claims require specific computer/network operations and specific data processing architecture that cannot be practically performed in the human mind.
In response to the argument, the amended claims fall under organizing human activity, not mental process or mathematical concepts, since the claims recite limitations directed toward following rules or instructions to evaluate life expectancy for a person. See the updated rejection below. Additionally, the technical limitations amount to nothing more than invoking the use of a computer to carry out the abstract idea since the components are recited at a high level of generality and being used for their intended purposes (e.g., communication network for transmitting data, mass spectrometer for analyzing lab samples, etc.).
Applicant argues that the amended claims recite biodata being collected physically using a biomaterial portable container and is measured by a mass spectrometer prior to transmission to the server. This is a real world measurement process with laboratory instruments and physical sample analysis. The using of the real world measurement architecture demonstrates a practical application in a concrete technological environment rather than a mental process.
In response to the argument, the amended claims fall under organizing human activity, not mental process or mathematical concepts, since the claims recite limitations directed toward following rules or instructions to evaluate life expectancy for a person. See the updated rejection below. The use of a mass spectrometer to analyze the data amounts to invoking the use of a computer or tool to carry out the abstract idea since the technical component is recited at a high level of generality and being used for their intended purposes (e.g., mass spectrometer for analyzing lab samples). The invention is not directed to the mass spectrometer but rather the mass spectrometer is being used to carry out the abstract idea.
Applicant argues that the amended claims recite a practical application by reciting additional elements that limit the exception to a particular technology and technical outcome. The claims are not merely presenting information but recite concrete end to end pipeline that receives, stores, aligns/structures, and processes biodata to generate and transmit risk reports and recommendations. The claim goes beyond collecting data, it detects anomalies to maintain collection and perform self-healing.
In response to the argument, the amended claims recite additional elements that amount to invoking the use of computers/tools to carry out the abstract idea and insignificant extrasolution activity. Thus the additional elements cannot amount to a practical application. See the updated rejection for further clarification.
Applicant argues that the new claim 21 recites limitations that are not abstract but a specific operational mechanism for the technology stack to correlate the anomalies and self-heal the recurring incidents.
In response to the argument, the amended claims recite a technology stack to carry out this operational step for the recurring incidents. Its this operation step that falls under the abstract idea and the technology stack is the additional element to carry out the abstract idea. However, the technology stack is recited at a high level of generality and thus merely invoking the use of a computer. See the updated rejection for further clarification.
Applicant argues that the architecture/information flow improvements are recognized as eligible when the system changes the architecture itself – how the information flows. This is applicable here where the claims recite specific data ingestion, normalization, and anomaly-remediation governing how biodata flows from wearable/labs into the system.
In response to the argument, the amended claims recite evaluating life expectancy for a person and processing data in order to do so. This falls under the abstract idea for organizing human activity. The invention is not directed to managing workflows and data architecture changes similar to the USPTO memo.
Applicant argues that the Dejardins memo supports the claims being eligible since they reflect improvements in operation including ML operation associated with data structures/flows. Specifically, the claims recite ingestion, normalization, updating, anomaly remediation that improves the system and the underlying technology stack.
In response to the argument, the amended claims are unlike that of Dejardins since they are not directed toward in improvement in machine learning. Applicant’s amended claims recite evaluating life expectancy for a person and merely invoke the use of machine learning in order to process data. There is no improvement to the machine learning.
Applicant argues that the claims are not generic, not nominal features that improve the technical operations and therefore the claims recite an inventive concept. The additional elements are not extrasolution activity, they are essential to achieving the technical result.
In response to Applicants argument, the additional elements amount merely invoking a computer/tool to carry out the abstract idea and insignificant extrasolution activity. In the rejection below, evidence in the form of caselaw, specification sections, and the MPEP is provided for why the additional elements do not amount to an inventive concept. See the rejection below for further clarification.
Applicant argues that for claim 12 similar arguments apply.
In response to Applicants argument, the claim is similarly rejected and argued against based on the rejection and arguments for claim 1.
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 1-12, 14-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step 1 of the Alice/Mayo Test
Claims 1-11, 21 are drawn to a system, which is within the four statutory categories (i.e. apparatus). Claims 12, 14-20 are drawn to a method, which is within the four statutory categories (i.e. process).
Step 2A of the Alice/Mayo Test - Prong One
The independent claims recite an abstract idea. For example, claim 1 (and substantially similar with independent claim 12) recites:
A prediction system to assess life expectancy and a plurality of health parameter factors, said prediction system comprising:
one or more processors; and
a memory comprising instructions executable by the one or more processors to:
receive (i) online, real-time vital biodata from at least one wearable device sensor and/or a health data aggregation platform via a communication network, and (ii) offline biodata comprising laboratory results of biomaterial collected using an all-in-one portable biomaterial container and analyzed using a mass spectrometer, wherein the laboratory results are received via the communication network;
store the received biodata in a first database, and form network input data by aligning the received biodata to a stored list of required parameters for permanent tracking of at least one disease, including structuring unstructured medical database data into structured parameter fields corresponding to parameter names of the stored list and matching a user dataset to general-population datasets that use a same parameter list;
monitor said plurality of health parameter factors;
assess a particular health parameter factor of the plurality of health parameter factors using a neural network trained on data retrieved from a first database configured to store said plurality of health parameter factors from a group of individuals, wherein, said data retrieved from said first database is network input data comprising a set of parameters obtained from medical records, wearable devices, questionnaires and network output data of said neural network is a human health factor directly related to life expectancy of at least one individual of the group of individuals;
perform a multi-stage evaluation comprising: (i) instantly evaluating human data associated with the group of individuals obtained at a particular point in time using a training sample comprising a large number of people and summarizing characteristics of people from said training sample; (ii) developing a historical data neural network that analyzes historical data of an individual and a group of individuals with similar parameters, wherein said historical data neural network is trained with a large amount of data over a long period of time; and (iii) recognizing patterns from a plurality of input parameters using personalized network parameter(s) selected from genetic characteristics, current physical condition of a body, blood parameters, nutrition, and psycho emotional state to determine an accurate forecast for said individual;
determine a human health assessment factor from the personalized network parameter(s) and the accurate forecast;
in response to receiving subsequent historical data associated with the individual or detecting a significant change in at least one vital parameter that changes the accurate forecast, modify the historical data neural network and generate an updated human health assessment factor that is directly related to an updated life expectancy of the individual;
generate and transmit to a mobile application of a user device a machine-readable disease risk report and severe disease prevention recommendations based on the updated human health assessment factor; and
detect anomalies in incoming biodata transmissions affecting health and performance of a technology stack and automatically remediate recurring anomalies by performing self-healing through automation for recurring incidents to maintain collection of said plurality of health parameter factors.
These underlined elements recite an abstract idea that can be categorized, under its broadest reasonable interpretation, to cover the management of personal behavior or interactions (i.e., following rules or instructions), but for the recitation of generic computer components. For example, but for the prediction system, wearable device, a health data aggregation platform, a communication network, mass spectrometer, database, a trained historical data neural network, mobile app, user device, technology stack, the limitations in the context of this claim encompass an automation of organizing health information in order follow rules or steps to assess and evaluate the life expectancy of an individual and provide a report and prevention recommendations to the individual. If a claim limitation, under its broadest reasonable interpretation, covers management of personal behaviors or interactions but for the recitation of generic computer components, then the limitations fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. See MPEP § 2106.04(a).
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2-11 and 14-21 reciting particular aspects of the abstract idea).
Step 2A of the Alice/Mayo Test - Prong Two
For example, claim 1 (and substantially similar with independent claim 12) recites:
A prediction system to assess life expectancy and a plurality of health parameter factors, said prediction system comprising: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
one or more processors; and (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
a memory comprising instructions executable by the one or more processors to: (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
receive (i) online, real-time vital biodata from at least one wearable device sensor and/or a health data aggregation platform via a communication network, and (ii) offline biodata comprising laboratory results of biomaterial collected using an all-in-one portable biomaterial container and analyzed using a mass spectrometer, wherein the laboratory results are received via the communication network; (merely data-gathering steps as noted below, see MPEP 2106.05(g)) and (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f))
store the received biodata in a first database (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), and form network input data by aligning the received biodata to a stored list of required parameters for permanent tracking of at least one disease, including structuring unstructured medical database data into structured parameter fields corresponding to parameter names of the stored list and matching a user dataset to general-population datasets that use a same parameter list;
monitor said plurality of health parameter factors;
assess a particular health parameter factor of the plurality of health parameter factors using a neural network trained on data retrieved from a first database (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) configured to store said plurality of health parameter factors from a group of individuals, wherein, said data retrieved from said first database (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) is network input data comprising a set of parameters obtained from medical records, wearable devices (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)), questionnaires and network output data of said neural network is a human health factor directly related to life expectancy of at least one individual of the group of individuals;
perform a multi-stage evaluation comprising: (i) instantly evaluating human data associated with the group of individuals obtained at a particular point in time using a training sample comprising a large number of people and summarizing characteristics of people from said training sample; (ii) developing a historical data neural network that analyzes historical data of an individual and a group of individuals with similar parameters, wherein said historical data neural network is trained with a large amount of data over a long period of time; and (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) (iii) recognizing patterns from a plurality of input parameters using personalized network parameter(s) selected from genetic characteristics, current physical condition of a body, blood parameters, nutrition, and psycho emotional state to determine an accurate forecast for said individual;
determine a human health assessment factor from the personalized network parameter(s) and the accurate forecast;
in response to receiving subsequent historical data associated with the individual or detecting a significant change in at least one vital parameter that changes the accurate forecast, modify the historical data neural network (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and generate an updated human health assessment factor that is directly related to an updated life expectancy of the individual;
generate and transmit to a mobile application of a user device a machine-readable(merely insignificant extrasolution activity steps as noted below, see MPEP 2106.05(g)) and (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) disease risk report and severe disease prevention recommendations based on the updated human health assessment factor; and
detect anomalies in incoming biodata transmissions affecting health and performance of a technology stack (merely invokes use of computer and other machinery as a tool as noted below, see MPEP 2106.05(f)) and automatically remediate recurring anomalies by performing self-healing through automation for recurring incidents to maintain collection of said plurality of health parameter factors.
The judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations, which:
amount to mere instructions to apply an exception (such as recitations of the prediction system, wearable device, a health data aggregation platform, a communication network, mass spectrometer, database, a trained historical data neural network, mobile app, user device, technology stack, thereby invoking computers as a tool to perform the abstract idea, see applicant’s specification [0005]-[0009], [0020], [0036], [0041], [0046], [0056], [0058], [0075], see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of receiving online sensor data and offline lab data amounts to selecting a particular data source or type of data to be manipulated; transmitting a report and recommendation to an app on a user device amounts to insignificant extrasolution activity, see MPEP 2106.05(g))
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claim 2 recites taking into account time periods to train the neural network, which furthers the abstract idea; claim 3, 16 recites the assessment factor is a combination of values obtained from test data of the individual and a group individuals with the same parameters of functional characteristics, which furthers the abstract idea; claim 4, 17 recites the parameter factor is selected from one or in combination from height, weight, etc., which furthers the abstract idea; claim 5 recites the neural network is recurrent or convolutional, which furthers the abstract idea; claim 6, 15 recites to form a core network trained on a large data sample, which furthers the abstract idea; claim 7, 18 recites where using algorithms comprising stochastic gradient descent optimizer, etc., which furthers the abstract idea; claim 8 recites sending requests to the AI engine module and generating a disease risk report, which furthers the abstract idea; claim 9 recites splitting the parameters into offline and online tracking categories, which furthers the abstract idea; claim 10 recites the wearable contains at least one sensor from the list provided, which amounts to invoking computers as a tool to perform the abstract idea; claim 11, 20 recites a list of required parameters, which furthers the abstract idea; claim 14 recites evaluating human data training samples which furthers the abstract idea; claim 19 recites recording physical properties of the individual, which furthers the abstract idea; claim 21 recites detecting anomalies by identifying potential cause and an impact and performing self-healing through automation for recurring incidents, which furthers the abstract idea; and claims 2-11 and 14-21 additional limitations which generally link the abstract idea to a particular technological environment or field of use). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B of the Alice/Mayo Test for Claims
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and add insignificant extra-solution activity to the abstract idea. Additionally, the additional elements, other than the abstract idea per se, amount to no more than elements which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as using the prediction system, wearable device, a health data aggregation platform, a communication network, mass spectrometer, database, a trained historical data neural network, mobile app, user device, technology stack, e.g., Applicant’s spec describes the computer system with it being well-understood, routine, and conventional because it describes in a manner that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such elements to satisfy 112a. (See Applicant’s Spec. [0005]-[0009], [0020], [0036], [0041], [0046], [0056], [0058], [0075]); using a processor to perform the instructions, prediction system, a health data aggregation platform, a communication network, database, a trained historical data neural network, mobile app, user device, technology stack, e.g., merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions, Alice Corp. Pty. Ltd. v. CLS Bank Int’l, 134 S. Ct. 2347, 2358-59, 110 USPQ2d 1976, 1983-84 (2014).
adding insignificant extrasolution activity to the abstract idea, for example mere data gathering, selecting a particular data source or type of data to be manipulated, and/or insignificant application. The following represent examples that courts have identified as insignificant extrasolution activities (e.g. see MPEP 2106.05(g)): receiving online sensor data and offline lab data, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321 and MPEP 2106.05(g)(3); transmitting a report and recommendation to an app on a user device, e.g., outputting or providing access to the information, Symantec, 838 F.3d at 1321 and MPEP 2106.05(g)(3)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea, and are generally linking the abstract idea to a particular field of environment. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. Therefore, the claims are not patent eligible, and are rejected under 35 U.S.C. § 101.
Subject Matter Free of Prior Art
Claims 1-12, 14-21 are free of prior art over Klibanow (US 2010/0324943) in view of Jiao (US 2016/0140310), Lore (US 20180107662). The prior art references, or reasonable combination thereof, could not be found to disclose, or suggest all of the limitations found in the independent claims. The closest prior art is Klibanow (US 2010/0324943), which teaches evaluating a life expectancy. Jiao (US 2016/0140310) teaches predicting health outcomes using trained machine learning models. Lore (US 20180107662) teaches wearable sensors to monitor and obtain human data. The references, or reasonable combination thereof, could not be found to disclose, or suggest all of the limitations found in the independent claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to AMANDA R COVINGTON whose telephone number is (303)297-4604. The examiner can normally be reached Monday - Friday, 10 - 5 MT.
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/AMANDA R. COVINGTON/Examiner, Art Unit 3686
/RACHELLE L REICHERT/Primary Examiner, Art Unit 3686
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