DETAILED ACTION
Status of Claims
This action is in reply to the communication filed on 6 February, 2026.
Claims 1, 11 - 13 and 20 have been amended.
Claims 1, 2, 7 – 14 and 17 – 22 are currently pending and have been examined.
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 .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6 February, 2026 has been entered.
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.
The following rejection is formatted in accordance with MPEP 2106.
Claim 1 is representative. Claim 1 recites:
A method comprising:
providing a health assessment program executed by a processor of a computing device, the computing device in communication with a cloud computing system having at least one cloud computing node, wherein at least one network adapter is connected between the at least one cloud computing node and the computing device;
obtaining, by the computing device, one or more data-sets for one or more physical or physiological parameters associated with an individual using at least one medical sensor carried by the individual, the at least one medical sensor in communication with the computing device, and capable of sensing in real time at least one of: a heart-rate; a respiratory rate; a blood pressure; an oxygen saturation level; a blood sugar level; a temperature; fertility; a ketone level; a physical activity level; a heart rate variability; a galvanic skin response; a cough; a change in a voice: or a behavior pattern:
obtaining, by the computing device, one or more environmental data-sets for one or more environmental parameters associated with a location of the individual, wherein the computing device uses the health assessment program and a location of the computing device to obtain, through the cloud computing system and the at least one cloud computing node, the one or more environmental data-sets associated with the location of the individual; wherein the one or more environmental data-sets is at least one of: a pollen count; an external temperature; an internal temperature; an oxygen level; humidity; barometric pressure; noise; air speed; acceleration; ambient light; altitude; traffic; or pollution;
storing the one or more data-sets and the one or more environmental data sets in a shared storage data hub;
training a machine learning algorithm to generate a health threat assessment wherein training comprises:
collecting the one or more data-sets and the one or more environmental data-sets from the shared storage data hub, wherein the one or more data-sets is modified overwritten, or destroyed by a learning model;
applying one or more pre-processes to each data-set to remove inconsistencies from the one or more data-sets and to assign a weight to each parameter of the one or more data-sets:
creating a training set comprising a partition of the one or more pre-processed data-sets,
wherein the partition of the one or more pre-processed data-sets comprises: a first processed data-set and a second processed data set,
wherein the first processed data-set and the second processed data-set are generated prior to generating the health threat assessment,
wherein the first processed data-set is used to generate the health threat assessment, and
wherein the second processed data-set is separately stored as a static data-set;
generating a learning model by dynamically selecting weights and parameters of the one or more data-sets:
training a neural network using the training set; and
establishing an accuracy level of the learning model using the training set;
communicating the one or more data-sets and the one or more environmental data-sets to the health assessment program executed by the processor of the computing device,
the health assessment program comprising instructions to determine one or more health threats to the individual; and wherein the instructions of the health assessment program use a determination from the neural network after training;
processing, with the processor, the one or more data-sets and the one or more environmental data-sets using the health assessment program to determine if one or more health threats to the individual are present in real time;
generating a health threat assessment related to the individual based on the one or more data-sets and the one or more environmental data-sets;
communicating one or more alerts of the health threat assessment to an electronic output device associated with the individual, the electronic output device configured to output the one or more alerts of the health threat assessment in readable format to the individual; and
displaying the one or more alerts of the health threat assessment on the output device; and
providing the individual with additional information related to or generated by the health threat assessment.
Claim 13 recites a system that executes the steps of the method recited in Claim 1.
Claims 1, 2, 4, 7 – 14 and 17 – 22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. a law of nature, a natural phenomenon, or an abstract idea), and does not include additional elements that either: 1) integrate the abstract idea into a practical application, or 2) that provide an inventive concept – i.e. element that amount to significantly more than the abstract idea. The Claims are directed to an abstract idea because, when considered as a whole, the plain focus of the claims is on an abstract idea.
STEP 1
The claims are directed to a system and a method, which are included in the statutory categories of invention.
STEP 2A PRONG ONE
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “mental processes” grouping – concepts performed in the human mind including observation, evaluation, judgment and opinion, including:
obtaining one or more data-sets for one or more physical or physiological parameters associated with an individual using at least one medical sensor carried by the individual, the at least one medical sensor capable of sensing at least one of: a heart-rate; a respiratory rate; a blood pressure; an oxygen saturation level; a blood sugar level; a temperature; fertility; a ketone level; a physical activity level; a heart rate variability; a galvanic skin response; a cough; a change in a voice: or a behavior pattern:
obtaining one or more environmental data-sets for one or more environmental parameters associated with a location of the individual, wherein the one or more environmental data-sets is at least one of: a pollen count; an external temperature; an internal temperature; an oxygen level; humidity; barometric pressure; noise; air speed; acceleration; ambient light; altitude; traffic; or pollution;
processing the one or more data-sets and the one or more environmental data-sets to determine if one or more health threats to the individual are present;
generating a health threat assessment related to the individual based on the one or more data-sets and the one or more environmental data-sets;
providing the individual with additional information related to or generated by the health threat assessment.
The claims recite obtaining physical or physiological parameters associated with an individual using medical sensors; environmental parameters associated with the location of the individual from a cloud computing system, and communicating the data to a health assessment program executed by a computing device. The health assessment program processes the data to determine if one or more health threats to the individual are present, and generates an assessment and communicates an alert to an electronic device of the individual for display.
The focus of the claims is on collecting (i.e. obtaining) information using sensors operating in their normal capacity, and from a source in the cloud; analyzing the information, and displaying results in the form of an alert and additional information. Obtaining data about individuals and their environment to determine health risks is a process that can be performed mentally. The specification discloses that the existence of “a high degree of correlation between environmental conditions and health conditions” is known. “For example, it is well understood that elevated heat and humidity can put individuals with high blood pressure at risk for cardiac issues, such as stroke or heart attack” (@ 0028). These are judgements that can be readily performed mentally.
Collecting information, including when limited to particular content, is within the realm of abstract ideas, and analyzing information by steps people go through in their minds, or by mathematical algorithms, without more, are mental processes within the abstract idea category (Electric Power Group v. Alstom S.A. (Fed Cir, 2015-1778, 8/1/2016).
Additionally, the specification discloses that a health threat is determined by a “health assessment program executed by a processor” that “processes” the data sets using a trained machine learning algorithm based on known correlations; for example knowing that elevated heat and/or humidity can put individuals with high blood pressure at risk for cardiac issues, and is a process that, except for generic computer implementation steps, can be performed in the human mind. As such, the claims recite an abstract idea within the mental process grouping.
The claims, as illustrated by Claim 1, recite limitations that encompass an abstract idea within the “certain methods of organizing human activity” grouping –
managing personal behavior or relationships or interactions between people including social activities, teaching, and following rules or instructions.
The claims require collecting data sets and generating a health threat assessment based on the data sets. Generating health threats assessments for an individual – in order to “notify individuals of risks to an individual’s health” is process that merely organizes this human activity. This type of activity, i.e. generating health threats assessments, includes conduct that would normally occur when managing threats to a patient’s particular medical condition or state. As such, the claims recite an abstract idea within the certain methods of organizing human activity grouping.
STEP 2A PRONG TWO
The claims recite limitations that include additional elements beyond those that encompass the abstract idea above. Some of the additional limitations are directed to insignificant data gathering steps including:
wherein the computing device uses the health assessment program and a location of the computing device to obtain, through the cloud computing system and the at least one cloud computing node, the one or more environmental data-sets associated with the location of the individual;
storing the one or more data-sets and the one or more environmental data sets in a shared storage data hub;
processing, with the processor, using the health assessment program.
Some of the additional limitations recite insignificant extra-solution activity including:
communicating the one or more data-sets and the one or more environmental data-sets to the health assessment program executed by the processor of the computing device, the health assessment program comprising instructions to determine one or more health threats to the individual;
communicating one or more alerts of the health threat assessment to an electronic output device associated with the individual, the electronic output device configured to output the one or more alerts of the health threat assessment in readable format to the individual;
displaying the one or more alerts of the health threat assessment on the output device.
Some of the additional limitations are directed to merely applying the abstract idea with a computer or generic computer elements including:
providing a health assessment program executed by a processor of a computing device, the computing device in communication with a cloud computing system having at least one cloud computing node, wherein at least one network adapter is connected between the at least one cloud computing node and the computing device;
at least one medical sensor carried by the individual, the at least one medical sensor in communication with the computing device and capable of sensing in real time.
Some of the additional limitations are directed to training a machine learning algorithm / neural network to determine if one or more health threats are present:
training a machine learning algorithm to generate a health threat assessment wherein training comprises:
collecting the one or more data-sets and the one or more environmental data-sets from the shared storage data hub, wherein the one or more data-sets is modified overwritten, or destroyed by a learning model;
applying one or more pre-processes to each data-set to remove inconsistencies from the one or more data-sets and to assign a weight to each parameter of the one or more data-sets:
creating a training set comprising a partition of the one or more pre-processed data-sets,
wherein the partition of the one or more pre-processed data-sets comprises: a first processed data-set and a second processed data set,
wherein the first processed data-set and the second processed data-set are generated prior to generating the health threat assessment,
wherein the first processed data-set is used to generate the health threat assessment, and
wherein the second processed data-set is separately stored as a static data-set;
generating a learning model by dynamically selecting weights and parameters of the one or more data-sets:
training the neural network using the training set; and
establishing an accuracy level of the learning model using the training set;
wherein the instructions of the health assessment program use a determination from the neural network after training.
However, these additional elements do not integrate the abstract idea into a practical application of that idea in accordance with the MPEP. (see MPEP 2106.05)
The health assessment program executed by a processor on a computer device and electronic output device are recited at a high level of generality such that it amounts to no more than instructions to apply the abstract idea using a generic computer component. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment. For example, the computing device is described in the specification as a general purpose computing system, and the electronic output device is described as a mobile phone or tablet, desktop, etc. (0033) Similarly, the cloud computing system having at least one cloud computing node is described in the specification as one example of many “well-known computing systems, environments, and/or configurations” (0033).
Obtaining data sets and communicating the data sets to a computer program for processing are insignificant extra-solution activities – i.e. a data gathering steps. In particular, obtaining physical or physiological parameters as recited in the claims – i.e. using at least one medical sensor . . . capable of sensing at least one of: a heart-rate; a respiratory rate; a blood pressure; an oxygen saturation level; a blood sugar level; a temperature; fertility; or a ketone level; is disclosed in the specification as using well known medical sensors in their ordinary capacity. For example, the medical sensor may be a pulse oximeter or blood pressure cuff (0042). The specification further discloses that data sets, including environmental data, may be obtained by retrieving the data stored in a cloud computing node – “one or more computer systems or server acting as a cloud computing node.” (0044).
Similarly, communicating and displaying the results of the abstract idea in the form of alerts and additional information is an insignificant post-solution activity that does not add a meaningful limitation to the abstract idea. Displaying the results of the abstract process does not improve the computer itself, or any other technology, nor does the display of results provide a meaningful limitation beyond generally linking the abstract idea to a particular technological environment.
The claims recite “training a machine learning algorithm to generate a health threat assessment . . . wherein . . . the health assessment program use a determination from the neural network after training” to determine a health threat to the individual. Training, as recited, includes; collecting data, pre-processing the data, creating a training set, dynamically selecting weights and parameters, partitioning the data sets, training the neural network, and finally establishing an accuracy level. As noted above, the specification broadly discloses these features, and Examiner has asserted that they are well-known techniques that generically describe training machine learning models of any type. Training a neural network, when disclosed and recited at a high level of generality, as it is here, amounts to no more than instructions to apply the abstract idea using a generic computer component. These elements merely add instructions to implement the abstract idea on a computer, and generally link the abstract idea to a particular technological environment.
The recitation of machine learning techniques in the claims apply established methods of machine learning to an abstract diagnostic process in a new data environment – i.e. applying a trained model to the physiological and environmental data sets. The specification teaches that the learning model may be trained to output a possible health risk using the physiological and environmental data sets; using any suitable model (@ 0038 - 0047). Machine learning limitations reciting broad, functionally described, well-known techniques executed by generic and conventional computing devices does not provide a practical application of the abstract diagnostic process. “Today we hold only that patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under §101.” (Recentive Analytics, Inc. v. Fox Corp. (Fed. Cir. 2025)).
Nothing in the claim recites specific limitations directed to an improved technology or technological process. Similarly, the specification is silent with respect to these kinds of improvements. A general purpose computer that applies a judicial exception by use of conventional computer functions, as is the case here, does not qualify as a particular machine, nor does the recitation of a generic computer impose meaningful limits in the claimed process. (see Ultramercial, Inc. v. Hulu, LLC, 772 F.3d 709, 716-17 (Fed. Cir. 2014)). As such, the additional elements recited in the claim do not integrate the abstract health assessment process into a practical application of that process.
STEP 2B
The additional elements identified above do not amount to significantly more than the abstract health assessment process. Obtaining and communicating information from a database, for example over a network, is a well-understood, routine and conventional computer function – i.e. receiving or transmitting data over a network as in Symantec, TLI, OIP and buySAFE. Displaying the results of the abstract process is an ancillary part of the abstract process itself as in Electric Power Group.
The additional structural elements or combination of elements in the claims, other than the abstract idea per se, amount to no more than a recitation of generic computer structure (i.e. a computing device/processor, a cloud computing system having at least one cloud computing node, electronic output device, memory, computer-readable storage medium). Each of the above components are disclosed in the specification as being purely conventional and/or known in the industry. Similarly, training a neural network in a generic way, using conventional techniques described in the specification is purely conventional. Because the specification describes these additional elements in general terms, without describing particulars, Examiner concludes that the claim limitations may be broadly, but reasonably construed, as reciting well-understood, routine and conventional computer components and techniques. The specification describes the elements in a manner that indicates that they are sufficiently well-known that the specification does not need to describe the particulars in order to satisfy U.S.C. 112. Considered as an ordered combination the limitations recited in the claims add nothing that is not already present when the steps are considered individually. As such, the additional elements recited in the claim do not provide significantly more than the abstract health assessment process, or an inventive concept.
The dependent claims add additional features including:
those that merely serve to further narrow the abstract idea above such as:
further limiting the type of data in the medical condition data set (Claim 10);
those that recite additional abstract ideas such as:
generating health threat assessment based on medical condition data; (Claim 9, 19);
generating population reports (12);
further limiting the type of additional information; (Claim 21, 22);
those that recite well-understood, routine and conventional activity or computer functions such as:
transmitting and playing alerts at a second device (Claim 8, 18);
obtaining and communicating medical condition data sets (Claim 9, 19);
pre-processing to remove inconsistencies, provide weights and redundant storage (Claim 11, 20);
communicating second data sets (Claim 12);
those that recite insignificant extra-solution activities such as:
obtaining environmental data from sensors (Claim 2, 14)
generating and displaying/playing audio or visual alerts (Claim 7, 17);
or those that are an ancillary part of the abstract idea.
The limitations recited in the dependent claims, in combination with those recited in the independent claims add nothing that integrates the abstract idea into a practical application, or that amounts to significantly more. These elements merely narrow the abstract idea, recite additional abstract ideas, or append conventional activity to the abstract process. As such, the additional element do not integrate the abstract idea into a practical application, or provide an inventive concept that transforms the claims into a patent eligible invention.
The apparatus claims are no different from the method claims in substance. “The equivalence of the method, system and media claims is readily apparent.” “The only difference between the claims is the form in which they were drafted.” (Bancorp). The method claims recite the abstract idea implemented on a generic computer, while the apparatus claims recite generic computer components configured to implement the same idea. Specifically, Claims 13, 14, 17 – 20 and 22 merely add the generic hardware noted above that nearly every computer will include. The apparatus claim’s requirement that the same method be performed with a programmed computer does not alter the method’s patentability under U.S.C. 101 (In re Grams). Therefore, the claims are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 7 – 14 and 17 – 22 are rejected under 35 U.S.C. 103 as being unpatentable over by Lee et al.: (US 10,885,759), in view of Krishna et al.: (US PGPUB 2020/0303075 A1).
CLAIMS 1 and 13
Lee discloses a health and safety monitoring system that includes the following limitations:
providing a health assessment program executed by a processor of a computing device, the computing device in communication with a cloud computing system having at least one cloud computing node, wherein at least one network adapter is connected between the at least one cloud computing node and the computing device; (Lee col. 3 line 11 – 23, col. 9 line 51 to col. 10 line 11, col. 10 line 50 – 61);
Lee discloses an electronic device for a health and safety monitoring system that includes a computer processor executing program instructions (i.e. a health assessment program), that is in communication with a cloud computer system through a communication interface (i.e. network adapter). A cloud computer node is inherent to cloud computer systems. Lee further discloses:
obtaining, by the computing device, one or more data-sets for one or more physical or physiological parameters associated with an individual using at least one medical sensor carried by the individual, the at least one medical sensor in communication with the computing device, and capable of sensing in real time at least one of: a heart-rate; a respiratory rate; a blood pressure; an oxygen saturation level; a blood sugar level; a temperature; fertility; or ketone level; a physical activity level; a heart rate variability; a galvanic skin response; a cough; a change in a voice: or a behavior pattern; (Lee col. 3 line 11 – 66, col. 4 line 49 – 63, col. 5 line 23 - 47);
obtaining, by the computing device, one or more environmental data-sets for one or more environmental parameters associated with a location of the individual; wherein the computing device uses the health management program and a location of the computing device to obtain, through the cloud computing system and the at least one cloud computing node, the one or more environmental data-sets associated with the location of the individual; wherein the one or more environmental data-sets is at least one of: a pollen count; an external temperature; an internal temperature; an oxygen level; humidity; barometric pressure; noise; air speed; acceleration ambient light; altitude; traffic; or pollution; (Lee col. 3 line 11 – 17, col. 5 line 4 – 18, line 48 – 61, col. 15 line 1 – 19, 46 – 55, col. 27 line 50 - 58);
storing the one or more data-sets and the one or more environmental data sets in a shared storage data hub; (Lee col. 7 line 46 – 50; col. 10 line 3 – 28, Figure 10).
Lee discloses obtaining and storing, by an electronic device with processing logic (i.e. computing device), physiological parameters in data sets, including the physiological parameters recited in the claims, from medical sensors in communication with the processor. The sensors in Lee, and in the pending claims operate in their normal capacity to collect data in real time. Lee obtains environmental data, including the environmental parameters recited in the claims that are associated with the user’s location, from other sources including a cloud based system. Lee further discloses:
communicating the one or more data-sets and the one or more environmental data-sets to the health assessment program executed by the processor of the computing device; (Lee col. 3 line 11 – 22, col. 4 line 49 to col. 5 line 18, col. 9 line 64 - 67);
the health assessment program comprising instructions to determine one or more health threats to the individual; (Lee col. 49 line 20 – 63);
wherein the instructions of the health assessment program use a determination from the neural network after training; (Lee col. 17, line 54 – 67, col. 18 line 21 – 30, col. 22 line 4 – 25);
processing, with the processor, the one or more data-sets and the one or more environmental data-sets using the health assessment program to determine if one or more health threats to the individual are present in real time; generating a health threat assessment related to the individual based on the one or more data-sets and the one or more environmental data-sets; (Lee col. 3 line 22 – 41, col. 5 line 19 – 47, col. 7 line 4 – 50);
communicating one or more alerts of the health threat assessment to an electronic output device associated with the individual, the electronic output device configured to output the one or more alerts of the health threat assessment in readable format to the individual; and displaying the one or more alerts of the health threat assessment on the output device; and providing the individual with additional information related to or generated by the health threat assessment; (Lee col. 3 line 24 – 25, col. 5 line 19 – 47, col. 18 line 1 – 9, 21 - 30).
Lee discloses an electronic device for a safety monitoring system (i.e. a health assessment program) that includes a computer that receives physiological and environmental sensor data in real time, analyzes (i.e. processes) the sensor data in real time to determine that a safety event (i.e. a health threat to the individual) has occurred, or will occur in the future. Lee relies on a trained machine learning algorithm to determine that a safety event has occurred. A safety event may be a user who falls, becomes unconscious due to heatstroke, becomes confused or disoriented from low blood pressure or oxygenation levels, becomes fatigued or sick, etc. The electronic device may send alerts (i.e. a health threat assessment) as well as enable “timely and accurate” recommendations to the user or third party device for display when a safety event is identified or predicted (i.e. additional information).
With respect to the following:
training a machine learning algorithm to generate a health threat assessment, wherein training comprises:
collecting the one or more data-sets and the one or more environmental data-sets from the shared storage data hub, and assign a weight to each parameter of the one or more data-sets; generating a learning model by dynamically selecting weights and parameters of the one or more data-sets; and establishing an accuracy level of the learning model using the training set; (Lee col. 6 line 11 – 59, col. 14 line 29 – 41, col. 16 line 54 – 64, col. 17 line 54 – 67, col. 18 line 31 – 67, col. 19 line 17 to col. 23 line 34, col. 33 line 37 – 49).
Lee discloses collecting, storing and analyzing current and historical physiological and environmental data (i.e. data sets) using a “correlator” to determine a correlation between physiological and environmental data. Lee discloses that the correlator may use various techniques for correlating data including Pearson correlation, multiple regression, regression, and machine learning algorithms. Lee collects current and historical physiological and environmental data for analysis, and pre-processes the data - detects and corrects data errors, adjust measurement information or measurement results, etc. Lee assigns initial weights to the data, and dynamically adjusts the weights as part of a training process for the machine learning algorithm. Lee continues training until a level of accuracy (i.e. a stable state) is obtained. These steps constitute training a machine learning algorithm. Lee then uses the correlations to predict a health threat for an individual
With respect to the following limitation:
applying one or more pre-processes to each data-set to remove inconsistencies from the one or more data-sets; (Krishna Abstract, 0007, 0015, 0033 – 0035);
creating a training set comprising a partition of the one or more pre-processed data-sets, wherein the partition of the one or more pre-processed data-sets comprises: a first processed data-set and a second processed data set, wherein the first processed data-set and the second processed data-set are generated prior to generating the health threat assessment, wherein the first processed data-set is used to generate the health threat assessment, wherein the one or more data-sets is modified overwritten, or destroyed by a learning model; and wherein the second processed data-set is separately stored as a static data-set; (Krishna 0022, 0033);
training the neural network using the training set; and establishing an accuracy level of the learning model using the training set; (Krishna Abstract, 0007, 0015, 0019, 0022
Lee discloses training a machine learning algorithm, but does not disclose partitioning training data. Krishna discloses a system and method for training a neural network to predict the occurrence of a chronic disease in a patient. A database stores a dataset of historical pathological test result data of a plurality of patient diagnosed with a disease. The neural network extracts data from the dataset and pre-processes the data, including: normalizing and scaling data. The dataset is then partitioned into a training dataset and a test dataset. The training data is used to train the neural network until a desired accuracy is achieved. The test dataset is then applied to the trained neural network for validation.
The claims require that one or more data-sets is modified overwritten, or destroyed by a learning model; and the second processed data-set is separately stored as a static data-set. The present specification discloses that the data sets are pre-processed and then partitioned into a first and second processed dataset. The first dataset is used to “generate the health risk assessment” – i.e. to train the neural network – and that such training is operable to “modify, overwrite or destroy” the dataset. The second dataset is used to test and validate the neural network. Here the specification expressly discloses that a dataset used for other purposes, including testing, is not modified, overwritten or destroyed. As such, Krishna’s training dataset and separate testing dataset meets this claim limitation.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the patient monitoring system of Lee so as to have included partitioning training data into a first and second dataset, in accordance with the teaching of Krishna, in order to allow for model testing and flexible training.
With respect to Claim 13, Lee also discloses the following limitations:
a system comprising a computing device having: a memory; one or more processing apparatuses in communication with the memory; a non-transitory computer readable storage medium; and one or more health assessment programs comprising program instructions stored on the non-transitory computer readable storage medium and executable by the one or more processing apparatus via the memory; (Lee col. 4 line 27 – 34, Figure 10).
Lee discloses an electronic device that includes a processor, memory, storage medium and program instructions.
CLAIMS 2, 7 – 10, 14 and 17 - 19
The combination of Lee/Krishna discloses the limitations above relative to Claims 1 and 13. Additionally, Lee discloses the following limitations:
obtaining, via one or more environmental sensors, the one or more environmental data-sets associated with the location of the individual; (Lee col. 3 line 11 – 17, col. 15 line 1 – 19, 46 – 55);
an alert generation program generating the one or more alerts of the health threat assessment responsive to generating the health threat assessment, the one or more alerts comprising at least one of an audio alert or visual alert; and wherein displaying the one or more alerts comprises playing or displaying the one or more alerts via the output device; transmitting at least one alert of the one or more alerts to a second output device, the second output device configured to receive the at least one alert; and playing or displaying the at least one alert via the second output device; (Lee col. 5 line 19 – 47);
obtaining one or more medical condition data-sets; communicating the one or more medical condition data-sets to the health assessment program; processing the one or more medical condition data-sets with the one or more data-sets and the one or more environmental data-sets using the health assessment program to determine if one or more health threats to the individual are present; and, generating the health threat assessment related to the individual based on the one or more data-sets, the one or more medical condition data-sets, and the one or more environmental data-sets; wherein the one or more medical condition data-sets includes data for one or more of the following: a medical history of the individual; medical records of the individual; a medical condition or disease pertaining to the individual; information regarding remedial care for a medical condition or disease pertaining to the individual; (Lee col. 9 line 51 – 64, col. 16 line 9 – 43, col. 23 line 35 – 63).
Lee discloses collecting environmental parameters using sensors relative to a location of the user and physical or physiological parameters of the user. Lee generates and displays health alerts to users and third parties, including alerts based on data related to the medical condition of the user.
CLAIMS 11 and 20
The combination of Lee/Krishna discloses the limitations above relative to Claims 9 and 19. Additionally, Lee discloses the following limitations:
prior to processing the one or more medical condition data-sets with the one or more data-sets: pre-processing the one or more data-sets and the one or more medical condition data-sets to assign one or more weight factors to one or more parameters of the one or more data-sets or the one or more medical condition data-sets; (Lee col. 22 line 33 to col. 34 line 34).
Lee discloses pre-processing data sets by assigning weights to the parameters in the data elements. With respect to the following limitations:
pre-processing the one or more data-sets and the one or more medical condition data-sets to remove inconsistencies from the data of the one or more data-sets and the data of the one or more medical condition data-sets; (Krishna Abstract, 0007, 0015, 0033 – 0035);
prior to generating the health threat assessment, generating the first processed data-set and the second processed data-set; (Krishna 0022, 0033).
Lee discloses training a machine learning algorithm, but does not disclose partitioning training data. Krishna discloses a system and method for training a neural network to predict the occurrence of a chronic disease in a patient. A database stores a dataset of historical pathological test result data of a plurality of patient diagnosed with a disease. The neural network extracts data from the dataset and pre-processes the data, including: normalizing and scaling data. The dataset is then partitioned into a training dataset and a test dataset. The training data is used to train the neural network until a desired accuracy is achieved. The test dataset is then applied to the trained neural network for validation.
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing data of the claimed invention, to have modified the patient monitoring system of Lee so as to have included partitioning training data into a first and second dataset, in accordance with the teaching of Krishna, in order to allow for model testing and flexible training.
CLAIM 12
The combination of Lee/Krishna discloses the limitations above relative to Claim 1. Additionally, Lee discloses the following limitations:
communicating the second processed data-set to a computer program, the computer program configured to store the second processed data-set with one or more additional processed data-sets for other individuals; (Lee col. 25 line 27 to col. 26 line 21); - disclosing storing information at a group level;
generating a report, based on the second processed data-set and the one or more additional processed data-sets, pertaining to one or more health threats to a population of individuals; (Lee col. 31 line 20 – 45); - disclosing generating alerts (i.e. reports) for groups of individuals.
CLAIMS 21 and 22
The combination of Lee/Krishna discloses the limitations above relative to Claims 1 and 13. Additionally, Lee discloses the following limitations:
wherein additional information related to or generated by the health threat assessment is at least one of: a lifestyle plan; a remedial plan adapted to reduce chances of deteriorating condition of the individual; or recommendations to overcome impacts of infection; (Lee col. 5 line 19 – 44, col. 18 line 1 – 9, 21 – 30).
Lee discloses providing recommendations to reduce the chances of a deteriorating condition.
Response to Arguments
Applicant's arguments filed 6 February, 2026 have been fully considered but they are not persuasive.
The U.S.C. §101 Rejection
Applicant asserts that the claimed invention is not directed to a mental process because the claims are “directed to concepts which are not merely mental processes, and which include requirements that cannot be practically performed in the human mind”. Applicant relies on “the ‘Kim Memo’” indicating that “Claim limitations that encompass AI in a way that cannot be practically performed in the human mind do not fall within this [mental process] grouping”.
Nonetheless, while specific limitations in a claim that encompass training machine learning algorithms are not directed to a mental process, that does not exempt any other limitation or combination of limitations in the claim from being so directed. Examiner does not allege anywhere that training an algorithm is a mental process, or a method of organizing human activity.
For example, the Kim Memo references Example 39, as does the Applicant, indicating that a claim directed to training and retraining a machine learning algorithm is not abstract. However, Example 39 includes limitations that are ONLY directed to this concept. Applicant’s contention that a claim is not directed to a mental process if at least one claim requirement cannot be practically performed in the human mind, is misplaced. This interpretation would make a claim that is otherwise directed to an abstract idea patent eligible if the claim merely includes a transmitting, storing or displaying step, or any other conventional computer function. Clearly this would be an incorrect interpretation.
Here, the pending claims recite limitations beyond the machine learning features, and these limitation are subject to evaluation using the Mayo framework. It is notable that the Kim Memo further reminds Examiner’s that the same AI features that cannot be performed mentally, must be evaluated under Step 2A Prong Two and Step 2B; and in particular to “Improvements” consideration and “Apply It” consideration. Applicant does not propose to improve the machine learning algorithm, rather the algorithm is trained using merely apply the algorithm to improve a health risk assessment – i.e. an improvement to the abstract idea itself.
It is notable that Applicant further relies on Ex Parte Desjardins, where the review panel vacated the Board’s 101 rejection because the claimed invention provides technical improvements over the prior art – i.e. reducing storage requirements and preserving task performance across sequential training – which were expressly disclosed in the specification. But the Applicant does not assert an improvement to a trained machine learning algorithm, only that it be applied to the both physiological parameters AND environmental data. The claims rely on the use of generic machine learning technology, which is conventional. Further iterative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning. Using a machine learning technique necessarily includes an iterative training step. (Recentive Analytics)
Applicant further relies on Example 42; however, Applicant indicates that Example 42 includes training a neural network. Example 42 has nothing to do with training neural network or any other algorithm. With respect to Example 42’s “converting from a non-standard format to a standard format, the pending claims do not recite any similar features.
Applicant asserts that “it would not be practical for a human mind to use the one or more data sets and the one or more environmental sets and the health assessment program to determine if one or more health threats were present to the individual in real time.” Using the health assessment program to process data in real time is an “apply it” limitation – using a generic computer to improve speed or efficiency. The specification discloses that it is known that patients with high blood pressure are at risk for cardiac issues in the presence of elevated heat or humidity. One of ordinary skill would know that a person under heavy exertion in extreme temperature and humidity is at risk for heat exhaustion or heat stroke. The claims encompass collecting a limited amount of data for processing – i.e. ONE data-set with ONE physiological parameter, and ONE environmental data-set with ONE environmental parameter. The amount of data is easily analyzed mentally.
The U.S.C. §103 Rejection
Applicant argues that Lee and Subbu do not teach “partitioning”. Examiner agrees. However, on further search and consideration a new grounds of rejection in view of Krishna is made herein. Subbu is no longer relied on.
Applicant does not separately argue the dependent claims.
CONCLUSION
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 6,135,966 A to Ko discloses a system and method for diagnosis of cardiac disorders that includes partitioning training data into a training set and a testing set for training and testing a neural network.
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to John A. Pauls whose telephone number is (571) 270-5557. The Examiner can normally be reached on Mon. - Fri. 8:00 - 5:00 Eastern. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Robert Morgan can be reached at (571) 272-6773.
Official replies to this Office action may now be submitted electronically by registered users of the EFS-Web system. Information on EFS-Web tools is available on the Internet at: http://www.uspto.gov/patents/process/file/efs/guidance/index.jsp. An EFS-Web Quick-Start Guide is available at: http://www.uspto.gov/ebc/portal/efs/quick-start.pdf.
Alternatively, official replies to this Office action may still be submitted by any one of fax, mail, or hand delivery. Faxed replies should be directed to the central fax at (571) 273-8300. Mailed replies should be addressed to “Commissioner for Patents, PO Box 1450, Alexandria, VA 22313-1450.” Hand delivered replies should be delivered to the “Customer Service Window, Randolph Building, 401 Dulany Street, Alexandria, VA 22314.”
/JOHN A PAULS/Primary Examiner, Art Unit 3683
Date: 27 March, 2026