Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-4, 8-10, 12, 14, and 16-22 are currently pending. Claims 6-7 and 13 are canceled and Claims 21-22 are newly added in the Claims filed on November 5, 2025.
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 1-4, 8-10, 12, 14, and 16-22 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) without significantly more.
Step 1
Claims 1-4, 8-10, 12, 14, and 16-22 are within the four statutory categories. Claims 1-4, 8-9, and 21-22 are drawn to a method for determining and providing users with medical recommendations and diagnostics, which is within the four statutory categories (i.e. process). Claims 10, 12, 14, and 16-19 are drawn to a system for determining and providing users with medical recommendations and diagnostics, which is within the four statutory categories (i.e. machine). Claim 20 is drawn to a non-transitory medium for determining and providing users with medical recommendations and diagnostics, which is within the four statutory categories (i.e. manufacture).
Prong 1 of Step 2A
Claim 1, which is representative of the inventive concept, recites: A computer-implemented method comprising:
providing, with at least one application server communicably engaged with a mobile electronic device, a first instance of an end user application to a patient user, wherein the first instance of the end user application comprises a first graphical user interface configured for the patient user, the first graphical user interface comprising one or more interface elements associated with evaluation or management of an autoimmune condition of the patient user;
providing, with at least one application server communicably engaged with a client device, a second instance of the end user application to a provider user, wherein the second instance of the end user application comprises a second graphical user interface configured for the provider user, the second graphical user interface comprising one or more interface elements associated with the evaluation or management of the autoimmune condition of the patient user;
receiving, via an input device of the mobile electronic device, a first plurality of data comprising one or more user-generated inputs from the patient user corresponding to the autoimmune condition of the patient user;
receiving, with at least one wearable electronic device communicably engaged with the mobile electronic device, a second plurality of data comprising at least one physiological measurement from the patient user for a first time period,
wherein the at least one wearable electronic device comprises at least one physiological sensor configured to measure one or more physiological inputs from the patient user when the at least one wearable electronic device is worn by the patient user;
receiving, with the application server via at least one communications network, the first plurality of data and the second plurality of data from the mobile electronic device;
receiving, with the application server via at least one third-party server communicably engaged with the application server, a third plurality of data comprising electronic medical record data associated with the patient user;
aggregating, with a processor of the application server, the first plurality of data, the second plurality of data, and the third plurality of data to define an aggregated dataset;
analyzing, with an artificial intelligence engine operably engaged with the processor of the application server, the aggregated dataset according to at least one machine learning framework, wherein the at least one machine learning framework is configured to compute one or more variable for the aggregated dataset as a temporally scaled average of all variable values present in the aggregated dataset for the first time period,
wherein the at least one machine learning framework is executed by the artificial intelligence engine;
generating, with the artificial intelligence engine, an output comprising at least one diagnostic measure of a current or future state of the autoimmune condition according to the at least one machine learning framework based on the one or more computed variable for the first time period;
generating, with the processor of the application server, a diagnostic recommendation comprising a laboratory diagnostic procedure comprising an antibody test;
generating, with the processor of the application server, at least one activity recommendation in response to the at least one diagnostic measure, wherein the at least one activity recommendation corresponds to at least one patient outcome associated with the current or future state of the autoimmune condition;
providing, with the application server via the at least one communications network, the at least one activity recommendation to the patient user via the first instance of the end user application, wherein the at least one activity recommendation comprises at least one user prompt presented to the patient user via the first graphical user interface of the end user application; and
providing, with the application server via the at least one communications network, the at least one diagnostic measure and the diagnostic recommendation to the provider user via the second instance of the end user application;
receiving, via the second instance of the end user application, a provider confirmation of the diagnostic recommendation;
in response to the provider confirmation, automatically generating and transmitting, via an external application programming interface, an electronic laboratory test order for the antibody test to a laboratory information management system (LIMS), the electronic laboratory test order comprising structured fields including a lab test identifier and a laboratory facility identifier;
receiving, from the LIMS via the external application programming interface, laboratory test data associated with the antibody test; and
updating, with the artificial intelligence engine, the at least one diagnostic measure based at least in part on the laboratory test data,
wherein the second instance of the end user application is configured to receive an input comprising a recommended pharmacological intervention from the provider user via the second graphical user interface in response to providing the at least one diagnostic measure and/or the diagnostic recommendation and communicate the recommended pharmacological intervention to the patient user at the first instance of the end user application via the at least one communications network.
The underlined limitations as shown above, given the broadest reasonable interpretation, recite the abstract idea of a certain method of organizing human activities because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and following rules or instructions – in this case, the steps of receiving first, second, and third data, aggregating the first, second, and third data to form an aggregate dataset, analyzing the aggregate dataset to compute a variable for a first time period, generating a diagnostic measure of an autoimmune condition based on the variable for the first time period, generating a diagnostic recommendation, generating an activity recommendation, providing the activity recommendation to the patient, providing the diagnostic measure and the diagnostic recommendation to the provider, receiving a provider confirmation of the diagnostic recommendation, generating and transmitting a lab test order in response to the provider confirmation, receiving lab test data, updating the diagnostic measure based on the lab test data, receiving a recommended pharmacological intervention from the provider, and communicating the recommended pharmacological intervention to the patient include following rules or instructions to analyze patient data in order to generate testing and treatment for the patient), e.g. see MPEP 2106.04(a)(2). Any limitations not identified above as part of the abstract idea are deemed “additional elements,” and will be discussed in further detail below.
Furthermore, the abstract idea for Claims 10 and 20 is identical as the abstract idea for Claim 1, because the only difference between Claims 1, 10, and 20 is that Claim 1 recites a method, whereas Claim 10 recites a system and its associated structural elements, and Claim 20 recites a non-transitory computer-readable medium. Additionally, Claims 10 and 20 further specify “receiving one or more external data inputs comprising at least one patient historical data input,” but this is also considered a certain method of organizing human activities, as receiving data is also proper interpreted as following rules or instructions to collect information.
Dependent Claims 2-4, 8-9, 12, 14, 16-19, and 21-22 include other limitations, for example Claims 2-3 recites receiving additional types of patient data, Claim 4 recites communicating the additional patient data to a provider, Claim 8 further defines the activity recommendation including a timing and type, Claim 9 recites communicating location data, Claim 12 recites types of inputs and updating the diagnostic measure based on the input, Claims 14 and 17-18 recite generating patient outcome metrics and communicating the patient outcome metrics to the provider and a payor, Claim 16 recites providing the diagnostic recommendation to the provider and receiving a confirmation from the provider, triggering the generation and transmission of the lab test order, Claim 19 recites types of recommended interventions and lab tests, Claim 21 recites updating the diagnostic measure and/or parameter based on the patient-reported outcome, and Claim 22 recites measuring efficacy as a measure of change in the diagnostic measure after receiving the lab test data, but these only serve to further narrow the abstract idea, and a claim may not preempt abstract ideas, even if the judicial exception is narrow, e.g. see MPEP 2106.04. Additionally, any limitations in dependent Claims 2-4, 8-9, 12, 14, 16-19, and 21-22 not addressed above are deemed additional elements to the abstract idea, and will be further addressed below. Hence dependent Claims 2-4, 8-9, 12, 14, 16-19, and 21-22 are nonetheless directed towards fundamentally the same abstract idea as independent Claims 1 and 10.
Prong 2 of Step 2A
Claims 1, 10, and 20 are not integrated into a practical application because the additional elements (i.e. the non-underlined limitations above – in this case, the application server, the mobile electronic device, the client device, the third-party server, the wearable electronic device, the providing of the first instance of the end user application and the second instance of the end user application, the artificial intelligence engine, the machine learning framework) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the application server, the mobile electronic device, the client device, the third-party server, the wearable electronic device, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see pg. 10, line 21 through pg. 11, line 8, and pg. 28, lines 3-18 of the as-filed Specification, see MPEP 2106.05(f);
generally link the abstract idea to a particular technological environment or field of use – for example, the claim language reciting that the interface elements are associated with evaluation or management of an autoimmune condition, which amounts to limiting the abstract idea to the field of healthcare, see MPEP 2106.05(h); and/or
add insignificant extra-solution activity to the abstract idea – for example, providing interface elements to a user via the first and second instances of the end user application, which amounts to mere data gathering, see MPEP 2106.05(g).
Additionally, dependent Claims 2-4, 8-9, 12, 14, 16-19, and 21-22 include other limitations, but these limitations also amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the language further specifying the types of data being processed recited in dependent Claims 2-4, 8-9, 12, and 19), and/or do not include any additional elements beyond those already recited in independent Claims 1 and 10, and hence also do not integrate the aforementioned abstract idea into a practical application.
Hence, Claims 1-4, 8-10, 12, 14, and 16-22 do not include additional elements that integrate the judicial exception into a practical application.
Step 2B
Claims 1, 10, and 20 do not include additional elements that are sufficient to amount to “significantly more” than the judicial exception because the additional elements (i.e. the non-underlined limitations above – in this case, the application server, the mobile electronic device, the client device, the third-party server, the wearable electronic device, the providing of the first instance of the end user application and the second instance of the end user application, the artificial intelligence engine, the machine learning framework), as stated above, are directed towards no more than limitations that amount to mere instructions to apply the exception, generally link the abstract idea to a particular technological environment or field of use, and/or add insignificant extra-solution activity to the abstract idea, wherein the insignificant extra-solution activity comprises limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrated by:
The present Specification expressly disclosing that the structural additional elements are well-understood, routine, and conventional in nature:
Pg. 10, line 21 through pg. 11, line 8, and pg. 28, lines 3-18 of the as-filed Specification discloses that the additional elements (i.e. the application server, the mobile electronic device, the client device, the third-party server, the wearable electronic device, the first instance of the end user application and the second instance of the end user application, the artificial intelligence engine, the machine learning framework) comprise a plurality of different types of generic computing systems;
Relevant court decisions: The functional limitations interpreted as additional elements are analogized to the following examples of court decisions demonstrating well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II):
Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives various types of patient data, wherein the patient data is transmitted between the structural elements over a network, for example the Internet, e.g. see pg. 26, line 3 through pg. 27, line 3 of the present Specification;
Electronic recordkeeping, e.g. see Alice Corp v. CLS Bank – similarly, the current invention merely recites the storing of patient data at various locations, for example at least temporarily (such that it may be transmitted) on the wearable electronic device, the mobile electronic device, and/or on the third-party server;
Storing and retrieving information in memory, e.g. see Versata Dev. Group, Inc. v. SAP Am., Inc. – similarly, the current invention recites storing the various patient data in on the various structural elements, receiving and storing the data on the application server, and retrieving the various patient data from storage of the application server in order to generate the aggregated dataset and to ultimately utilize the aggregated dataset to generate the activity recommendation and the diagnostic measure;
Dependent Claims 2-4, 8-9, 12, 14, 16-19, and 21-22 include other limitations, but none of these limitations are deemed significantly more than the abstract idea because the additional elements recited in the aforementioned dependent claims similarly amount to generally linking the abstract idea to a particular technological environment or field of use (e.g. the language further specifying the types of data being processed recited in dependent Claims 2-4, 8-9, 12, and 19), and/or, as stated above, do not recite any additional elements not already recited in independent Claims 1 and 10, and hence do not amount to “significantly more” than the abstract idea.
Hence, Claims 1-4, 8-10, 12, 14, and 16-22 do not include any additional elements that amount to “significantly more” than the judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the abstract idea identified above. Furthermore, looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually, and there is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
Therefore, whether taken individually or as an ordered combination, Claims 1-4, 8-10, 12, 14, and 16-22 are nonetheless rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Subject Matter Free From Prior Art
Claims 1-4, 8-10, 12, 14, and 16-22 are not presently rejected under 35 U.S.C. 102 or 103, and hence would be in condition for allowance if amended to overcome the rejections presented under 35 U.S.C. 101. The following represents Examiner' s characterization of the most relevant prior art references and the differences between the present claim language and the prior art references in view of 35 U.S.C. 102 and/or 103:
With regards to 35 U.S.C. 102 and/or 103, the following represents the closest prior art to the claimed invention, as well as the differences between the prior art and the limitations of the presently claimed invention.
Op Den Buijs (US 2014/0129247) teaches a clinical interface system in communication with a clinical decision support system, wherein the clinical interface system includes a plurality of devices and enables a user to input patient data and display data to a clinical specialist. Additionally, Op Den Buijs teaches obtaining patient data from wearable sensors, and the patient data is used to determine one or more suggested treatment options/orders. Furthermore, Op Den Buijs teaches utilizing a neural network risk model engine to generate patient prediction probabilities. However, Op Den Buijs does not teach that the patient data includes a patient autoimmune condition, a particular time period for the patient data, or that the neural network risk model generates a temporally scaled average of the patient values. Furthermore, Op Den Buijs does not teach receiving a provider selection of a recommended pharmacological intervention, automatically generating and transmitting a laboratory test order for an antibody test to a laboratory information management system, receiving test data, and/or updating the diagnostic measure generated by the artificial intelligence engine based on the test data.
Spurlock (US 2019/0108912) teaches a system that predicts patient health states and a future diagnosis for a specific disease including an autoimmune disease. However, Spurlock does not teach any of the structural limitations regarding the devices/interfaces used by patients and providers, generating a diagnostic recommendation for a lab test, ordering the lab test, and/or generating a recommended treatment based on the test data. Additionally, Spurlock does not teach that the lab test is an antibody test, or obtaining the patient data from wearable sensors.
Zhong (US 2018/0277246) teaches utilizing a neural network to generate an average value for a patient metric over time, wherein the patient metric is obtained from a patient-worn sensor. Zhong further teaches generating recommendations for the patient based on the patient metric. However, Zhong does not teach any of the structural limitations regarding the devices/interfaces used by patients and providers, generating a diagnostic recommendation for a lab test, ordering the lab test. Additionally, Zhong does not teach that the lab test comprises an antibody test.
Moturu (US 2017/0004260) teaches a care provider manually inputting and/or selecting a pharmacotherapeutic intervention, wherein the intervention may be generated by a therapeutic intervention predictive model utilizing one or more machine learning techniques. However, Moturu does not teach generating a diagnostic recommendation for a lab test, confirming the diagnostic recommendation, or ordering the lab test. Additionally, Moturu does not teach a laboratory test comprising an antibody test.
Van Der Zaag (US 2013/0226621) teaches a system including an interface enabling a clinician to choose options for diagnostic testing for a patient, and utilizing various machine learning techniques to classify patient data. However, Van Der Zaag does not teach any of the structural limitations regarding the devices/interfaces used by patients and providers, or that a patient condition includes an autoimmune condition. Furthermore, Van Der Zaag does not teach acquiring a temporal average of the patient data, or obtaining the patient data from wearable sensors.
The aforementioned references are understood to be the closest prior art. Various aspects of the present invention are known individually, but for the reasons disclosed above, the particular manner in which the elements of the present invention are claimed, when considered as an ordered combination, distinguishes from the aforementioned references and hence the invention recited in Claims 1-4, 8-10, 12, 14, and 16-22 is not considered to be disclosed by and/or obvious in view of the inventions of the closest prior art references.
Response to Arguments
Applicant’s arguments, see Remarks, filed November 5, 2025, with respect to the rejections of Claims 1-4, 8-10, 12, 14, and 16-22 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants allege that the claimed invention is patent eligible because it recites particular machine interactions and is a closed-loop, machine-to-machine workflow and hence does not recite an abstract idea, e.g. see pgs. 13-14 of Remarks – Examiner disagrees.
Initially, Examiner notes that, as shown above, the Claims are now shown to recite the abstract idea of a certain method of organizing human activities and hence any arguments pertaining to a mental process are now moot. Furthermore, Examiner notes that “certain methods of organizing human activity” encompasses certain activity between a person and a computer, e.g. see MPEP 2106.04(a)(2)(II), and hence the as-filed Claims do not automatically fall outside this category merely because they recite limitations involving machine-to-machine communication. Moreover, the machine-to-machine communication recited in the Claims nonetheless is performed based on human interactions – for example, Claim 1 recites receiving provider confirmation of the diagnostic recommendation and providing interfaces to patients and providers to receive input. That is, the present invention is not, for example, only machines communicating instructions to other machines in order to perform a function that controls another machine such as a drug dispenser, but instead recites a plurality of computing devices that receive inputs from human users, execute rules or instructions to perform processing operations on the input data, produces recommendations from the processing operations, and provides the recommendations to the human users. Additionally, as shown above, the structural elements (i.e. the LIMS, the various devices, etc.) are not considered to be abstract, and are evaluated under prong 2 of Step 2A, and step 2B. Hence Claims 1-4, 8-10, 12, 14, and 16-22 are properly characterized as reciting an abstract idea, specifically a certain method of organizing human activity.
Applicants further allege that the claimed invention is patent eligible because the additional elements integrate any abstract idea into a practical application, specifically because the steps of generating and transmitting the lab order and receiving the lab results updates the system’s diagnostic measure and model, which improves the functioning of the overall healthcare information system, e.g. see pg. 14 of Remarks – Examiner disagrees.
Even assuming, arguendo, that the aforementioned limitations result in an improved healthcare information system, the improvements achieved are improvements in patient outcomes and/or healthcare workflow, e.g. see pg. 11, lines 9-19 of the as-filed Specification. That is, the claimed limitations do not recite technological improvements to the computer itself and/or another technology, but instead merely utilize computer structure and processing power to improve a healthcare process that has existed since long before the advent of any sort of computer technology. For example, there is no limitations in the claims that result in an improved machine learning/artificial intelligence algorithm and/or training process.
Applicants also allege that the claimed invention is patent eligible because the claimed invention amounts to significantly more than well-understood, routine, or conventional activity, as demonstrated by the lack of prior art references cited under 35 U.S.C. 102 and/or 103, and because it is narrowly tailored such that there is no risk of pre-empting the field of computerized diagnostics, e.g. see pg. 15 of Remarks – Examiner disagrees.
Firstly, the “novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the 101 categories of possibly patentable subject matter,” and specifically, lack of novelty under 35 U.S.C. 102 or obviousness under 35 U.S.C. 103 of a claimed invention does not necessarily indicate that additional elements are well-understood, routine, conventional elements. Because they are separate and distinct requirements from eligibility, patentability of the claimed invention under 35 U.S.C. 102 and 103 with respect to the prior art is neither required for, nor a guarantee of, patent eligibility under 35 U.S.C. 101, e.g. see MPEP 2106.05I(I).
Additionally, Examiner notes that a claim reciting a narrow abstract idea nonetheless recites an abstract idea. That is, the absence of complete preemption does not guarantee that a claim will be eligible, and further notes that preemption is not a stand-alone test for patentability, but rather is inherent in the two-part Alice/Mayo framework, e.g. see MPEP 2106.04. As shown above, Examiner has provided evidence demonstrating that the present invention is directed towards at least one court-identified abstract idea that is not integrated into a practical application, and further that the additional elements of the present invention (i.e. any elements not identified as part of the abstract idea) do not represent significantly more than the abstract idea, and hence has addressed any concerns arising from preemption.
For the aforementioned reasons, Claims 1-4, 8-10, 12, 14, and 16-22 are rejected under 35 U.S.C. 101.
Applicant’s arguments, see Remarks, filed November 5, 2025, regarding the rejections of Claims 1-4, 8-10, 12, 14, and 16-22 under 35 U.S.C. 103 have been considered and are persuasive for the reasons disclosed above. The previous grounds of rejection of Claims 1-4, 8-10, 12, 14, and 16-22 under 35 U.S.C. 103 have been withdrawn.
Conclusion
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/JOHN P GO/Examiner, Art Unit 3681