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, 6-16, and 18-20 are currently pending.
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, 6-16, and 18-20 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, 6-16, and 18-20 are within the four statutory categories. Claims 1-4 and 6-9 are drawn to a method for clinical recommendations, which is within the four statutory categories (i.e. process). Claims 10-16 and 18-19 are drawn to a system for clinical recommendations, which is within the four statutory categories (i.e. machine). Claim 20 is drawn to a non-transitory medium for clinical recommendations, 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 method comprising:
providing, with at least one application server, a first instance of an end user application to a mobile electronic device associated with a first user, wherein the first instance of the end user application comprises a first graphical user interface configured for the first user, the first graphical user interface comprising one or more interface elements associated with evaluation or management of an autoimmune condition of the first user;
providing, with the at least one application server, a second instance of the end user application to a client device associated with a second user, wherein the second instance of the end user application comprises a second graphical user interface configured for the second user, the second graphical user interface comprising one or more interface elements associated with the evaluation or management of the autoimmune condition of the first 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 first user corresponding to the autoimmune condition of the first 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 first user, wherein the at least one wearable electronic device comprises at least one physiological sensor configured to measure one or more physiological inputs from the first user when the at least one wearable electronic device is worn by the first user;
receiving, via the first instance of the end user application or the second instance of the end user application, a third plurality of data via a live video feed between the first user and the second user;
wherein the first instance of the end user application or the second instance of the end user application is configured to automatically convert data from the live video feed to a raw data format to comprise the third plurality of data;
receiving, with the at least one application server via at least one communications network, the first plurality of data, the second plurality of data, and the third plurality of data;
receiving, with the at least one application server, a fourth plurality of data comprising at least one of electronic medical record (EMR) data, laboratory information management system (LIMS) data, or conversational data between the first user and a health coach;
for each record of the first, second, third, and fourth pluralities of data, associating the record with a patient specific identifier and time of capture metadata and storing the associated record in a data store;
synchronizing the stored records based on the time of capture metadata to create a time indexed analytical record spanning a first time period, wherein synchronizing the stored records comprises temporally aligning asynchronous records of the first, second, third, and fourth pluralities of data for the patient specific identifier into a common patient-specific temporal sequence;
deriving one or more digital phenotypes from at least the second and the third pluralities of data, the one or more digital phenotypes comprising features computed from sensor signals obtained from the at least one physiological sensor of the at least one wearable electronic device and features computed from the live video raw data;
wherein deriving the one or more digital phenotypes comprises extracting (i) one or more time-windowed physiological features from the second plurality of data and (ii) one or more live-video-derived features from the live video raw data, the one or more time-windowed physiological features comprising at least one of a step count trend, a sleep trend, a heart rate trend, a heart rate variability value, an exercise intensity value, or a vital-measure trend;
analyzing, with a processor of the at least one application server, the time indexed analytical record using at least one machine learning model comprising one or more artificial neural networks configured to jointly process the first, second, third, and fourth pluralities of data and the one or more digital phenotypes as ordered time-sequence input data to compute a flare prediction index corresponding to a current or future state of the autoimmune condition of the first user,
generating, with the processor of the at least one application server, an output comprising the flare prediction index as at least one diagnostic measure of the current or future state of the autoimmune condition of the first user;
generating, with the processor of the at least one application server, at least one activity recommendation in response to the flare prediction index, 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 of the first user, and wherein the at least one activity recommendation is generated based on the jointly processed ordered time-sequence input data;
providing, with the at least one application server via the at least one communications network, the at least one activity recommendation to the first user via the first instance of the end user application; and
providing, with the at least one application server via the at least one communications network, the flare prediction index and the at least one activity recommendation to the second user via the second instance of the end user application.
The underlined limitations as shown above, given the broadest reasonable interpretation, recite the abstract idea of a certain method of organizing human activity because they recite managing personal behavior or relationships or interactions between people (i.e. social activities, teaching, and/or following rules or instructions – in this case, the receiving of the first, second, third, and fourth data, the conversion of the raw data to the third data, associating the first, second, third, and fourth data with a patient identifier and timing, the creation of the time indexed analytical record, deriving the one or more digital phenotypes, analyzing the time indexed analytical record, computing the flare prediction index, generating and outputting the flare prediction index as a diagnostic measure, generating the activity recommendation in response to the flare prediction index, providing of the activity recommendation to a first user, and the providing of the flare prediction index to a second user recite following rules or instructions in order to manage diagnosing and treating a 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 Claim 20 recites a non-transitory computer readable medium.
Dependent Claims 2-4, 6-9 and 11-16, and 18-19 include other limitations, for example Claim 2 recites receiving an input comprising a clinical recommendation from the second user, Claims 3 and 15 recite that the clinical recommendation comprises a dosage and timing of various types of medication, Claims 4 and 16 recite types of activity recommendations, Claims 5 and 17 recite types of diagnostic measures of the current or future state of the autoimmune condition, Claims 6-7 and 12-13 recite receiving conversational data between the patient and a health coach and utilizing the conversational data to generate the diagnostic measure, Claims 8-9 and 18-19 recite utilizing the first, second, third, and fourth data to determine the efficacy of the activity or clinical recommendation and communicating the efficacy to the provider, Claim 11 recites types of users, and Claim 14 recites receiving patient reported outcomes in response to activity recommendations, 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, and/or do not further narrow the abstract idea and instead only recite additional elements, which will be further addressed below. Hence dependent Claims 2-4, 6-9 and 11-16, and 18-19 nonetheless recite the same abstract idea as independent Claims 1 and 10.
Hence Claims 1-4, 6-16, and 18-20 recite the aforementioned abstract idea.
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 hardware elements including the server, the data store, the mobile electronic device, the wearable electronic device, and the communications network, the machine learning model, the steps of providing of the first and second end user applications, and the live video feed) amount to no more than limitations which:
amount to mere instructions to apply an exception – for example, the recitation of the aforementioned hardware elements, which amounts to merely invoking a computer as a tool to perform the abstract idea, e.g. see pg. 26, line 13 through pg. 27, line 6, and pg. 28, lines 5-12 of the present 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, and the limitations of the machine learning model, which amounts to limiting the abstract idea to the field of healthcare and machine learning, 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 and/or an insignificant application, and the recitation of receiving the third plurality of data via a live video feed between the first and second users, which amounts to mere data gathering, see MPEP 2106.05(g).
Additionally, dependent Claims 2-4, 6-9 and 11-16, and 18-19 include other limitations, but these limitations also amount to no more than generally linking the abstract idea to a particular technological environment or field of use (e.g. the types of data being processed recited in dependent Claims 2-4, 6-9, 11-16, and 18-19), 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, 6-16, and 18-20 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 hardware elements including the server, the data store, the mobile electronic device, the wearable electronic device, and the communications network, the machine learning model, the steps of providing of the first and second end user applications, and the live video feed), 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 additional elements comprise 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. 26, line 13 through pg. 27, line 6, and pg. 28, lines 5-12 of the Specification discloses that the additional elements (i.e. the aforementioned hardware elements) 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 first and second data from the mobile electronic device and the wearable electronic device, and transmits the first and second data to the server over a network, for example the Internet, e.g. see pg. 26, lines 6-12 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, on the server, and/or on the data store;
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 server, and retrieving the various patient data from storage of the server in order to generate the activity recommendation and the diagnostic measure;
Dependent Claims 2-4, 6-9 and 11-16, and 18-19 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 types of data being processed recited in dependent Claims 2-4, 6-9, 11-16, and 18-19), and/or the limitations recited by the dependent claims 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, 6-16, and 18-20 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, 6-16, and 18-20 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, 6-16, and 18-20 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, and/or synchronizing the stored records based on the time of capture to create a time indexed analytical record utilizing the gathered types of patient data. Additionally, Op Den Buijs does not teach using the neural network risk model to generate a flare prediction index for the autoimmune condition.
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, obtaining patient data from wearable sensors, and/or generating a recommended activity based on test data. Additionally, Spurlock does not teach that the patient data includes video data.
Nguyen (US 2014/0276552) teaches receiving patient video data for a videoconference, patient responses to a questionnaire, and data from medical sensors, wherein the aforementioned data is analyzed to produce a patient report including a recommended course of treatment. However, Nguyen does not teach that the patient condition includes an autoimmune disease and/or using a machine learning model to perform any of the analysis.
Biswas (US) teaches analyzing video data obtained from a video conference to determine a patient’s mental state utilizing an artificial neural network. However, Biswas does not teach the configuration of the structural limitations regarding the devices/interfaces used by patients and providers, obtaining patient data from wearable sensors, evaluating patients for autoimmune diseases, and/or producing a flare up prediction index. Additionally, Biswas does not teach synchronizing the stored records to create a time indexed analytical record.
Hyde (US 2020/0121215) teaches determining an average value over time for a patient metric. However, Hyde does not teach the configuration of the structural limitations regarding the devices/interfaces used by patients and providers, evaluating patients for autoimmune diseases, and/or producing a flare up prediction index. Additionally Hyde does not teach synchronizing the stored records to create a time indexed analytical record.
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 the configuration 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 synchronizing the stored records to create a time indexed analytical record.
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, 6-16, and 18-20 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 May 13, 2026, regarding the objection of Claim 7 have been considered and, in combination with the amendments, are persuasive. The previous objection of Claim 7 is withdrawn.
Applicant’s arguments, see Remarks, filed May 13, 2026, with respect to the rejection of Claims 1-4, 6-16, and 18-20 under 35 U.S.C. 112(b) have been fully considered and, in combination with the amendments, are persuasive. The previous grounds of rejection of Claims 1-4, 6-16, and 18-20 under 35 U.S.C. 112(b) have been withdrawn.
Applicant’s arguments, see Remarks, filed May 13, 2026, with respect to the rejections of Claims 1-4, 6-16, and 18-20 under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicants first allege that the claimed invention is patent eligible because it is not directed towards the abstract idea of certain methods of organizing human activities, specifically because the claimed limitations cannot be practically performed in the human mind or with pen and paper, e.g. see pgs. 16-17 of Remarks – Examiner disagrees.
As shown above, the claimed invention recites a certain method of human activities because the identified claim limitations recite steps that are properly interpreted as following rules or instructions in order to manage diagnosing and treating a patient. This is further supported by the present Specification, which discloses that “the product described herein generates data-driven recommendations that can assist clinicians in determining the most optimal treatment faster, improving outcomes and reducing organ damage in patients resulting from autoimmunity or inflammation,” e.g. see lines 16-20 of pg. 11 of the as-filed Specification, and “The system can alert clinicians to an impending symptom flare and provide a treatment solution that reduces symptom severity, reduces or eliminates the onset,” e.g. see lines 16-18 of pg. 24 of the as-filed Specification. That is, the claimed invention is intended to assist with managing a treatment of a patient. Furthermore, the inquiry as to whether or not the claimed invention can be performed mentally and/or via pen and paper is immaterial to the determination of whether or not the claimed invention recites an abstract idea because, as stated above, Examiner does not assert that the claimed invention recites a mental process.
Applicants further allege that the claimed invention is patent eligible because it integrates any abstract idea into a practical application, specifically because it recites a particular sequence of data-processing operations that transform the data into a temporal patient analytical record, and further because it recites the technological improvement of multimodal patient data processing for autoimmune flare prediction, which is in accordance with the USPTO-issued 2024 AI Subject Matter Eligibility Guidance, and further because it recites limitations that are distinguishable from claims that merely apply generic machine learning to a new field, e.g. see pgs. 18-20 of Remarks – Examiner disagrees.
The specificity and/or narrowness of the claim limitations is not, by itself, dispositive as to whether or not the abstract idea is integrated into a practical application because 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. That is, even assuming, arguendo, that the claimed invention recites a particular configuration for an abstract idea, a narrow abstract idea nonetheless recites an abstract idea, and the broadness/narrowness of the abstract idea is not, by itself, dispositive of the eligibility of the claim. Furthermore, 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.
Additionally, regarding a technological improvement, even assuming, arguendo, that the claimed invention achieves the improvement of reduced symptom severity, reduced or eliminated the onset of symptoms (as is disclosed by the Specification), the aforementioned improvements are improvements to the abstract idea of a certain method of organizing human activities, namely diagnosing and treating a patient, and an improvement in the abstract idea itself is not an improvement in technology, e.g. see MPEP 2106.05(a)(II). Furthermore, as stated above, the additional elements of the AI framework and hardware limitations are recited merely to execute the abstract idea, and there is no disclosure in the Claims or Specification of an improvement to the computer itself and/or the AI. For example, unlike the invention of Desjardins, the claimed invention does not improve a machine learning algorithm, for example by providing reduced storage, reduced system complexity, and enabling the system to overcome the problem of catastrophic forgetting.
Similarly, the claimed limitations pertaining to the machine learning model are not patent eligible merely because the input data is narrowly and/or specifically claimed because defining the input data is merely part of the abstract idea, and, as stated above, a narrow abstract idea is still not eligible, e.g. see MPEP 2106.04. That is, the recited machine-learning limitations that compute the flare prediction index merely recite the specific inputs and outputs, without providing specific details regarding the machine learning model itself beyond it comprising an artificial neural network. For example, there is no recitation of any limitations that define the artificial neural network in a way that achieves any particular technological improvement. Moreover, the additional element of the machine learning model comprising an artificial neural network may result in “determining the most optimal treatment faster, improving outcomes and reducing organ damage in patients,” e.g. see lines 18-19 of the as-filed Specification, but these improvements are improvements to the abstract idea of a certain method of organizing human activities and an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology, e.g. see MPEP 2106.05(a)(II).
Applicants further allege that the claimed invention is patent eligible because it recites significantly more than the abstract idea, specifically because the cited prior art references are distinguished from the claimed limitations and hence the claimed limitations are not well-understood, routine, or conventional, and because the limitations need to be considered as an ordered combination, e.g. see pgs. 20-22 of Remarks – Examiner disagrees.
Examiner acknowledges that the Claims are not rejected under 35 U.S.C. 102 and/or 103 for the reasons disclosed above. However, 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). Hence, the claimed limitations are not properly interpreted as “significantly more” than the abstract idea merely because they are not anticipated by and/or are non-obvious in view of the previously cited prior art references.
Additionally, Applicant has provided no rationale or explanation as to how the analysis of the claimed limitations would change when considered as an ordered combination versus individually and/or separately, and the claimed invention instead recites a data processing process wherein the additional elements add nothing that is not already present when considered separately, e.g. see MPEP 2106.05(I)(B). As shown above, Examiner has considered the invention as a whole and the limitations as an ordered combination without ignoring the requirements of individual steps, but neither the Claims nor the Specification disclose how the additional elements, when considered as an ordered combination would amount to significantly more than the abstract idea.
For the aforementioned reasons, Claims 1-4, 6-16, and 18-20 are rejected under 35 U.S.C. 101.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is as follows:
Vodencarevic (US 2019/0362846) – teaches a system that creates predictive models for predicting clinical events including disease activity for autoimmune diseases, such as a flare. The system utilizes deep learning neural network to construct the predictions.
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 JOHN P GO whose telephone number is (703)756-1965. The examiner can normally be reached Monday-Friday 9am-6pm Pacific.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PETER H CHOI can be reached at (469)295-9171. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/JOHN P GO/Primary Examiner, Art Unit 3681
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