DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 7/3/2025 has been entered.
Status of the Claims
The status of the claims as of the response filed 7/9/2025 is as follows: Claims 1, 6, 8, 10-15, 17, and 20 are currently amended. Claims 2-3, 5, 7, 9, 16, and 18-19 are as previously presented. Claim 4 is original. Claims 1-20 are currently pending in the application and have been considered below. Applicant’s arguments have been fully considered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/29/2025 is in compliance with the provisions of 37 CFR 1.97 and is being considered by the examiner.
Response to Amendment
Rejection Under 35 USC 101
The claims have been amended but the 35 USC 101 rejections are upheld.
Rejection Under 35 USC 103
The amendments made to the claims introduce limitations that are not fully addressed in the previous office action, and thus the corresponding 35 USC 103 rejections are withdrawn. However, Examiner will consider the amended claims in light of an updated prior art search and address their patentability with respect to prior art below.
Response to Arguments
Rejection Under 35 USC 101
On pages 17-18 of the response filed 7/9/2025 Applicant argues that the claims are similar to those found eligible in Example 39 because they describe the training and retraining of an AI model, which “do[es] not represent a mental process performed in the human mind or by pen and paper.” Applicant’s arguments are fully considered, but are not persuasive. The claim at issue in Example 39 was directed to a method of training a neural network for facial detection that involved the collection and transformation of digital facial images to create two training sets for the neural network trained in two stages. Though this claim was not found to recite an abstract idea at all, it differs from the claims of the instant application due to the nature of the collected and transformed data as well as the increased specificity of the resulting trained model. That is, the claim of Example 39 recited the collection and transformation of digital facial images to create training sets, which could not reasonably be accomplished by a human actor mentally or by following instructions for personal behavior, whereas the instant claim includes no details about how the training dataset is obtained or generated. Further, Example 39 is directed to the training of specifically a neural network using two distinct training sets in two separate training stages, whereas the instant claim merely recites “training a first artificial intelligence (AI) model based on a training dataset,” which could encompass a wide array of model architectures, training techniques, methods of iterating or tuning the system, etc. Finally, Example 39 was directed only to a method of training a neural network via the creation and use of training datasets, and did not include further steps that could be characterized as aspects of a certain method of organizing human activity. In contrast, the instant claims do include steps that can be reasonably characterized as “certain methods of organizing human activity” (e.g. collecting and analyzing health data, determining deviations, generating inference data, determining medical visit requirements, scheduling medical visits, determining user-related data, transmitting/sharing data, syncing up models, etc.). Accordingly, the claim of Example 39 does not recite an abstract idea, while the independent claims of the instant application do.
On pages 18-20 of the response Applicant argues that the independent claims include features that “amount to an improvement in the technology of medical assistance based on collected data, learned information, and learned neural parameter values” as well as provide “unconventional steps that confine the claim to a particular useful application,” specifically pointing to subject matter related to the AI models exchanging data. Applicant’s arguments are fully considered, but are not persuasive. Examiner notes that “medical assistance based on collected data, learned information, and learned neural parameter values” is not a technical field as Applicant asserts, and instead describes the business field of providing healthcare to patients. Examiner maintains that clinicians, researchers, or other professionals are able to share collected or determined information about their diagnostic procedures and models so that they may be appropriately updated or synced-up; because these functions are part of the abstract idea itself, they cannot provide “significantly more” than the abstract idea and thus do not confer eligibility (see MPEP 2106.05(a): “It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements.” See also 2106.05(a)(II): “it is important to keep in mind that an improvement in the abstract idea itself… is not an improvement in technology.”) The creation and use of encrypted sessions directly between the first and second AI models to achieve these functions is considered to merely digitize/automate such otherwise-abstract data sharing and model updating steps such that it amounts to instructions to “apply” the exception and does not provide integration into a practical application. In addition, the steps of creating/establishing encrypted communication sessions can be considered insignificant extra-solution activity because they nominally provide means for data output and receipt. These activities are also nothing more than those recognized as well-understood, routine, and conventional computer functions performed using generic computer components; for example, receiving or transmitting data over a network is recognized as a well-understood, routine, and conventional function previously known to the industry, as outlined in MPEP 2106.05(d)(II). Further, utilizing encrypted communication sessions to transfer health-related data is well-understood, routine, and conventional in the art, as evidenced by at least para. [0034] of Bitran et al. (US 20180144101 A1); para. [0119] of Jackson (US 20140297311 A1); paras. [0192]-[0194] of Chung et al. (US 20150106020 A1); and paras. [0068] & [0080] of Joshi (US 20210327582 A1). Accordingly, this subject matter does not integrate the abstract idea into a practical application or provide “significantly more” than the abstract idea itself.
For the reasons outlined above, the 35 USC 101 rejections are upheld for claims 1-20.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
In the instant case, claims 1-14 are directed to a method (i.e. a process), claims 15-19 are directed to a system (i.e. a machine), and claim 20 is directed to a non-transitory computer-readable storage medium (i.e. a manufacture). Thus, each of the claims falls within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A – Prong 1
Independent claims 1, 15, and 20 recite steps that, under their broadest reasonable interpretations, cover certain methods of organizing human activity, e.g. managing personal behavior, relationships, or interactions between people. Specifically, each claim recites:
training a first artificial intelligence (AI) model based on a training dataset;
collecting first data associated with a user, wherein the collected first data comprises historical health data and a set of sensor data, and the set of sensor data corresponds to a set of health-monitoring parameters;
applying the first AI model on the collected first data;
determining one or more first indicators based on the application of the first AI model, wherein the one or more first indicators correspond to a deviation in a health condition of the user with respect to reference values, and a confidence score associated with the first AI model;
determining the deviation in the health condition of the user based on the confidence score;
generating first inference data based on the determined one or more first indicators, wherein the first inference data comprises one or more labels associated with a cause of the deviation;
determining, based on the generated first inference data, a first requirement associated with a first visit by the user to a first healthcare center, wherein the determined first requirement comprises at least one of a medical emergency, a medical consultation, or a surgical intervention;
scheduling the first visit to the first healthcare center, based on the determined first requirement;
determining, based on the collected first data and the first inference data, a first set of user-related data associated with the determined first requirement;
transmitting, via a communication network, the determined first set of user-related data to a first electronic healthcare system, wherein the first electronic healthcare system is associated with the first healthcare center, and the transmission of the determined first set of user-related data is based on the scheduled first visit;
creating a first encrypted session between the first AI model and a second AI model, wherein the second AI model is associated with the first electronic healthcare system;
syncing-up the first AI model with the second AI model based on the first encrypted session;
transmitting, via the communication network, the first set of user-related data and weights associated with a plurality of nodes to the second AI model, wherein the first set of user-related data include real-time pulse rate measurements of the user and personal details of the user, the transmission of the first set of user-related data and the weights associated with the plurality of nodes is based on the sync-up of the first AI model with the second AI models, and the plurality of nodes is associated with the first AI model;
collecting, by the first AI model from the second AI model, medical data associated with a medical attention received at the first healthcare center, wherein the medical data is collected based on the sync-up of the second AI model with the first AI model; and re-training the first AI model based on the collected medical data.
But for the recitation of generic computer components like a processor, use of AI models, an electronic system, a communication network, etc., the italicized functions, when considered as a whole, describe a medical evaluation interaction that could take place between various human entities of a patient’s care team, e.g. the patient and one or more medical professionals. For example, a clinician could initialize or fit (i.e. train) a model (e.g. a scoring checklist, a decision tree, a regression model, etc.) based on a training dataset. The clinician could then collect historical data and sensor data values from a patient during an appointment or other interaction, and analyze the received information with the model to compute indicators that reflect a deviation in a health condition (e.g. higher than normal blood pressure) along with their confidence in the prediction and attribute a cause to the deviation (e.g. underlying cardiovascular conditions). The clinician could then determine a type of appointment (e.g. emergency, consult, surgical procedure) and appropriate schedule for a follow-up or referral appointment for the patient based on their medical expertise, facilitate the scheduling or booking of the desired appointment, and generate a summary of user-related data to review at the scheduled appointment and/or send to a referred clinician or colleague. Finally, the clinician could communicate with a colleague, researcher, or other entity with access to a second model to share or sync up features of the first model (e.g. including pulse rate measurements and personal details of a user) with the second model, thereby allowing the colleague to utilize the most up-to-date diagnostic model. Thereafter, the clinician could continue to communicate with their colleague at the first medical center to collect additional medical data about the user resulting from medical attention provided at the first medical center and use the additional data to update or retrain their local model. Thus, the steps recited in these claims describe the various interactions between a patient and one or more medical professionals, and accordingly claims 1, 15, and 20 recite an abstract idea in the form of a certain method of organizing human activity.
Dependent claims 2-14 and 16-19 inherit the limitations that recite an abstract idea from their dependence on claims 1 and 15, respectively, and thus these claims also recite an abstract idea under the Step 2A – Prong 1 analysis. In addition, claims 2-8, 10-13, and 16-18 recite further limitations that merely further describe the abstract idea identified in the independent claims.
Specifically, claims 2-4 and 16 specify particular types of collected data, each of which a clinician would be capable of soliciting and analyzing during a medical appointment.
Claim 5 recites determining a current location of the user, determining one or more recommendations comprising one or more healthcare centers, wherein the one or more healthcare centers includes the first healthcare center, are associated with the first requirement, and are within a threshold distance from the current location. A clinician could achieve these steps by observing a patient’s location or asking them where they live and providing referral recommendations within a threshold distance of the location.
Claim 6 recites receiving a first input that comprises a first selection of the first healthcare center and a second selection of a specific schedule for an appointment with the first healthcare center, and scheduling the first visit to the first healthcare center based on the received first input, wherein the first set of user-related data is transferred to the first healthcare system based on the scheduling of the first visit to the first healthcare center. Such operations could be achieved as part of a certain method of organizing human activity by a clinician soliciting facility and appointment time selections from a patient, scheduling a follow-up or referral appointment based on the selections, and sending the patient summary to the selected healthcare system in accordance with the scheduled appointment (e.g. in advance of the appointment).
Claim 7 recites transmitting a specific request based on a determination that a current location of the user is different from a location of the first healthcare center and receiving an authorization, which a clinician could achieve by communicating with a colleague or other facility to request and authorize specific types of consultations based on a user’s known location with respect to a facility.
Claims 8 and 18 recite applying the second model on the first set of user-related data, generating a set of presentation data based on the application of the second model on the first set of user-related data, wherein the set of presentation data includes datapoints which are required by a medical practitioner associated with the first healthcare center, which a clinician could achieve by using the updated second model to summarize important/relevant information for referral to a colleague.
Claim 10 recites detecting a presence of the user at the first healthcare center, collecting the additional medical data based on the detection of the user, and updating the first model based on the collected medical data, which could be achieved as a certain method of organizing human activity by a clinician observing that a patient has arrived at a healthcare facility and collecting medical data from the patient during an appointment, which may then be used to update any diagnostic models the clinician uses.
Claim 11 recites applying the first model on the collected medical data and the collected first data to generate second inference data, determining, based on the generated second inference data, a second requirement associated with a second visit by the user to a second healthcare center different from the first healthcare center, determining a second set of user-related data based on the collected first data, the collected medical data, and the second inference data, which is associated with the determined second requirement and is required by a second healthcare center, and transferring the determined second set of user-related data to the second healthcare system. These operations could be achieved as part of a certain method of organizing human activity by a clinician collecting additional patient data at a second appointment, determining another appropriate referral, summarizing relevant data for the referral, and communicating the summary to the referral target facility.
Claim 12 recites determining that the determined first requirement corresponds to a medical emergency, determining a second healthcare center based on a current location of the user and the determination that the determined first requirement corresponds to the medical emergency, scheduling an emergency response service, and transferring, based on the scheduled ER service, the first set of user-related data to the second healthcare center. These operations could be achieved as part of a certain method of organizing human activity by a clinician determining that the current patient is experiencing a medical emergency, deciding that the current clinic location is not equipped to handle such an emergency, scheduling an ER appointment or transfer to a more well-equipped facility, and communicating a summary of patient data relevant to the emergency situation to the transfer facility.
Claim 13 recites transmitting an alert notification to one or more entities registered for receiving the alert notification, based on the determination that the first requirement corresponds to the medical emergency, which could be achieved by a clinician by contacting registered friends or family of the patient in the event of an emergency.
Claim 17 recites substantially similar subject matter as claims 5-6, and is considered to recite an abstract idea under the analysis for claims 5-6 above.
However, recitation of an abstract idea is not the end of the analysis. Each of the claims must be analyzed for additional elements that indicate the abstract idea is integrated into a practical application to determine whether the claim is considered to be “directed to” an abstract idea.
Step 2A – Prong 2
The judicial exception is not integrated into a practical application. In particular, independent claims 1, 15, and 20 do not include additional elements that integrate the abstract idea into a practical application. Each of the independent claims include the additional elements of specifying that the first and second models are artificial intelligence (AI) models; transmitting data specifically via a communication network to a first electronic healthcare system; and creating a first encrypted session between the first AI model and a second AI model to sync-up the first and second AI models. Claim 15 further includes the additional element of a processor configured to achieve the various functions, while claim 20 includes the additional element of a non-transitory computer-readable storage medium having stored thereon computer-executable instructions which when executed by a computer, cause the computer to perform the various operations.
These additional elements, when considered in the context of each claim as a whole, merely serve to automate or digitize interactions that could occur between human actors (as described above), and thus amount to implementation of an abstract idea on generic computer components. For example, a clinician is capable of determining indicators corresponding to a deviation in a health condition for a user and a confidence score, and the training and use of a first artificial intelligence model recited at a high level of generality (i.e. with no specific architecture, training methods, description of the training dataset, inputs mapped to outputs, methods of updating, etc. claimed) to achieve this function merely automates an otherwise-abstract function. Similarly, members of a patient care team are capable of collecting, evaluating, and communicating or sharing data, and the use of processors or computer systems to achieve these functions and transfer data specifically to an electronic healthcare system via a communication network merely digitizes these otherwise-abstract operations. Finally, clinicians, researchers, or other professionals are able to share information about their diagnostic procedures and models so that they may be appropriately updated or synced-up, and the creation and use of encrypted communication sessions directly between the first and second AI models to achieve this function is considered to merely digitize/automate such otherwise-abstract data sharing and model updating steps. Accordingly, these generic computer elements are merely invoked as tools with which to digitize or automate medical interactions, and amount to mere instructions to “apply” the abstract idea in a computer environment (see MPEP 2106.05(f)). Accordingly, claims 1, 15, and 20 as a whole are each directed to an abstract idea without integration into a practical application.
The judicial exception recited in dependent claims 2-14 and 16-19 is also not integrated into a practical application under a similar analysis as above.
Claims 2-4, 10-12, and 16 are performed with the same additional elements identified in the independent claims without introducing new additional elements, and accordingly do not provide integration into a practical application.
Claims 5, 8, 17, and 18 recite the additional element of controlling a user device associated with the user to display data, which merely digitizes the output of recommendation or other patient information at a generic user device and thus amounts to the words “apply it” with a computer.
Claims 6 and 17 specify that data inputs are received at the user device, which similarly amounts to the words “apply it” with a computer because a generic user device is being utilized as a tool with which to digitize the input or solicitation of information.
Claim 7 recites establishing a virtual reality (VR)-based consultation session between a user device and a wearable electronic device worn by a medical practitioner and transferring the user-related data to the wearable electronic device while the VR-based consultation is active. These additional elements similarly amount to the words “apply it” with a computer because high-level computing elements like an unspecified user device and wearable electronic device are being utilized as tools with which to digitize otherwise-abstract user communication and data sharing operations.
Claims 8-9 and 18-19 include use of the second AI model to generate the set of presentation data, which amounts to the words “apply it” with a computer because a high-level artificial intelligence model (i.e. with no specific architecture, training methods, description of the training dataset, inputs mapped to outputs, methods of updating, etc. claimed) is merely being utilized to digitize and/or automate the otherwise-abstract step of summarizing pertinent healthcare information.
Claim 13 recites transmitting the alert notification specifically to one or more devices, which are recited at a high level of generality and merely digitize the otherwise-abstract step of data sharing such that they amount to the words “apply it” with a computer.
Claim 14 recites receiving, by a user device, a request to share a data portion of the collected first data with the first AI model, creating a third encrypted session between the first AI model and the user device based on the request, transferring, based on the third encrypted session is active, the data portion of the collected first data to first AI model, and storing the transferred data portion in an encrypted form on a datastore. These additional elements are considered insignificant extra-solution activity because they nominally describe methods for establishing communication between users and storing data that appear to be tangential to the crux of the invention.
Accordingly, the additional elements of claims 1-20 do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Claims 1-20 are directed to an abstract idea.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of processor-based computer systems, trained artificial intelligence models, user devices, a communication network, electronic healthcare systems, and creation/establishment of encrypted communication sessions used for performing the collecting, applying, determining, generating, transmitting, syncing-up, etc. steps of the invention amount to mere instructions to apply the exception using generic computer components. As evidence of the generic nature of the above recited additional elements, Examiner notes the following portions of Applicant’s specification:
[0042]: “The user device 106 may include suitable logic, circuitry, interfaces, and/or code…. Examples of the user device 106 may include, but are not limited to, a wearable health device (such as a fitness band), a smartphone, a cellular phone, a mobile phone, a personal computer, a workstation, a kiosk device that may be associated with the set of sensors 1342, or a consumer electronic (CE) device that may be interfaced with or associated with the set of sensors 132”;
[0052]: “the server 128 may be implemented as a plurality of distributed cloud-based resources by use of several technologies that are well known to those ordinarily skilled in the art”;
[0177]: “The processor 1204 may be implemented based on a number of processor technologies known in the art”;
[0180]: “The network interface 1212 may be implemented by use of various known technologies to support wired or wireless communication of the system 102 with the communication network 134”;
[0205]: “A computer system or other apparatus adapted to carry out the methods described herein may be suited. A combination of hardware and software may be a general-purpose computer system with a computer program that, when loaded and executed, may control the computer system such that it carries out the methods described herein.”
These disclosures show that one of ordinary skill in the art would understand that known computer and electronic transmission technologies may be utilized to implement the invention. Further, Examiner notes at least paras. [0035]-[0041], which provide a high-level overview of known artificial intelligence and machine learning training techniques that may be utilized by the invention such that one of ordinary skill in the art would understand the AI models as encompassing a variety of generic artificial intelligence models trained via known techniques.
Regarding the functional additional elements, as noted above, the steps of creating/establishing encrypted communication sessions (as in claims 1, 14-15, and 20) and storing transferred data in an encrypted form on a datastore (as in claim 14) can be considered insignificant extra-solution activity. These activities are also nothing more than those recognized as well-understood, routine, and conventional computer functions performed using generic computer components; for example, receiving or transmitting data over a network and storing and retrieving information in memory are each recognized as well-understood, routine, and conventional functions previously known to the industry, as outlined in MPEP 2106.05(d)(II). Further, utilizing encrypted communication sessions to transfer health-related data and storing transferred health data in an encrypted form in a datastore is well-understood, routine, and conventional in the art, as evidenced by at least para. [0034] of Bitran et al. (US 20180144101 A1); para. [0119] of Jackson (US 20140297311 A1); paras. [0192]-[0194] of Chung et al. (US 20150106020 A1); and paras. [0068] & [0080] of Joshi (US 20210327582 A1).
Analyzing these additional elements as an ordered combination adds nothing that is not already present when considering the elements individually; the overall effect of the electronic implementation, nominally trained AI models, user devices, and encrypted communication and storage in combination is to digitize and/or automate a health evaluation, recommendation, and scheduling operation that could otherwise be achieved as a certain method of organizing human activity. Thus, when considered as a whole and in combination, claims 1-20 are not patent eligible.
Subject Matter Free from Prior Art
The following is a statement of reasons for the indication of subject matter free from prior art:
The prior art of record fails to expressly teach or suggest, either alone or in combination, each and every feature of the independent claims. Upon completion of an updated prior art search, Examiner submits that the closest related art includes:
- Bitran et al. (US 20180144101 A1), disclosing systems for using trained machine learning models to classify patient sensor data with respect to established reference data, determine when a patient is to see a medical professional, and identify relevant patient data for presentation to a medical professional at a scheduled appointment;
- Jackson et al. (US 20140297311 A1), disclosing systems for automated determination of medical events and scheduling of required health appointments based on the detected events;
- Ketchel, III et al. (US 20200334727 A1), disclosing systems for determining and scheduling appropriate medical services for a patient based on detected changes in monitored patient health parameters;
- Brisben et al. (US 20190008384 A1), disclosing systems for medical event detection with associated confidence scores;
- Xie et al. (Reference U on the PTO-892 mailed 12/23/2024), disclosing a system for peer-to-peer encrypted sessions between trained healthcare models to exchange parameters.
Though many aspects of the independent claims are disclosed in the prior art, it would not have been obvious to one of ordinary skill in the art to combine all of the disparate features into the invention of the instant claims. Accordingly, the prior art, either alone or in combination, does not disclose or render obvious all the features of the independent claims and they are found to recite subject matter free from prior art, as are the claims depending therefrom.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KAREN A HRANEK whose telephone number is (571)272-1679. The examiner can normally be reached M-F 8:00-4:00 ET.
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/KAREN A HRANEK/ Primary Examiner, Art Unit 3684