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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 26 December 2025 has been entered.
Formal Matters
Applicant's response, filed 26 December 2025, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Status of Claims
Claims 1, 4-7, 9-11, 13, 15-16, and 19-27 are currently pending and have been examined.
Claims 1, 4, 13, 16, 19-21 have been amended.
Claims 1, 4-7, 9-11, 13, 15-16, and 19-27 have been rejected.
Priority
The instant application does not claim the benefit of priority under 35 U.S.C 119(e) or under 35 U.S.C. § 120, 121, or 365(c) to any prior applications. Accordingly, the effective filing date for the instant application is 09 June 2022.
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, 4-7, 9-11, 13, 15-16, and 19-27 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 – Statutory Categories of Invention:
Claims 1, 4-7, 9-11, 13, 15-16, and 19-27 are drawn to a method, system, or manufacture, which are statutory categories of invention.
Step 2A – Judicial Exception Analysis, Prong 1:
Independent claim 1 recites a method, independent claim 13 recites a system, and independent claim 19 recites a non-transitory machine-readable medium each in part performing the steps of obtaining first medical data contained in medical records associated with a user identity; receiving second medical data associated with the user identity, the second medical data comprising data from sensors, medical devices, or user input devices determining a model relating to medical care and medical conditions associated with a plurality of user identities based on a result of an analysis, of the first medical data and the second medical data, and third medical data relating to the medical care and the medical conditions; generating a user-specific model associated with the user identity from the model wherein the user-specific model is generated by weighting or focusing on medical data specific to the user identity; utilizing the user-specific model to process only data relevant to the user identity for determining a proposed action relating to the medical care associated with the user identity, thereby reducing computational requirement and improving data processing efficiency; determining a device interface for communicating action information relating to the proposed action to a user; communicating the action information relating to the proposed action to the user.
These steps amount to methods of organizing human activity which includes functions relating to interpersonal and intrapersonal activities, such as managing relationships or transactions between people, social activities, and human behavior; satisfying or avoiding a legal obligation; advertising, marketing, and sales activities or behaviors; and managing human mental activity (MPEP § 2106.04(a)(2)(II)(C) citing the abstract idea grouping for methods of organizing human activity for managing personal behavior or relationships or interactions between people similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) – also note MPEP § 2106.04(a)(2)(II) stating certain activity between a person and a computer may fall within the “certain methods of organizing human activity” grouping).
Dependent claim 4 recites, in part, wherein the electronic medical records are first electronic medical records, and wherein the method further comprises: receiving updated medical data associated with the user identity from a user associated with the user identity, second electronic medical records associated with the user identity, or a sensor or a medical device associated with the user; and modifying, by the system, the user-specific model based on analysis, of the model, the updated medical data associated with the user identity, and historical medical data associated with the user identity, wherein the historical medical data comprises a portion of the first medical data, the second medical data, or the third medical data, and wherein the determining of the proposed action relating to the medical care associated with the user identity comprises determining the proposed action relating to the medical care associated with the user identity based on the user-specific model.
Dependent claim 5 recites, in part, wherein the portion is a first portion; wherein the receiving of the updated medical data comprises: prior to, and within a defined amount of time of, a medical care visit with a medical care provider by the user, receiving a second portion of the updated medical data from the user, the sensor, or the medical device and wherein the method further comprises: prior to the medical care visit with the medical care provider by the user, presenting the second portion of the updated medical data to the medical care provider in connection with the medical care visit; or prior to the medical care visit with the medical care provider by the user: communicating the second portion of the updated medical data to the medical care provider, and presenting the second portion of the updated medical data to the medical care provider in connection with the medical care visit.
Dependent claim 6 recites, in part, wherein the portion is a first portion, wherein the receiving of the updated medical data comprises: receiving a second portion of the electronic medical records from a first medical care provider; and receiving a third portion of the electronic medical records from a second medical care provider;
Dependent claim 7 recites, in part, perceiving a medical condition associated with a user associated with the user identity while the user is at a first location; based on the perceiving, generating medical condition information relating to the medical condition; and communicating the medical condition information to a medical care provider identity located at a second location.
Dependent claim 9 recites, in part, communicating a request for information relating to a medical condition associated with the user identity to a second [device] based on determining that the second [device] has access to medical field information relating to a medical field associated with the medical condition; and receiving the information relating to the medical condition, wherein the determining of the proposed action comprises determining the proposed action relating to the medical care based on the model and analysis of the information relating to the medical condition.
Dependent claim 10 recites, in part, access first medical treatment information relating to a first type of medical treatment to utilize to treat the medical condition, wherein the information relating to the medical condition comprises second medical treatment information relating to a second type of medical treatment to utilize to treat the medical condition, wherein the determining of the proposed action comprises determining which of the first type of medical treatment, the second type of medical treatment, or a third type of medical treatment is to be utilized or recommended to treat the medical condition based on the model and analysis of the first medical treatment information and the second medical treatment information, and wherein the third type of medical treatment is determined based on the first type of medical treatment and the second type of medical treatment.
Dependent claim 11 recites, receiving, from a medical care provider, a request for information relating to a medical or health history associated with the user identity; identifying the information relating to the medical or health history associated with the user identity in the model, a portion of the electronic medical records that is determined to be associated with the user identity, or sensor data associated with the user identity; presenting to the medical care provider the information relating to the medical or health history associated with the user identity.
Dependent claim 15 recites, in part, wherein the operations further comprise: determining that first information relating to a medical condition associated with the user identity is subject to a higher privacy level based on a defined privacy criterion; determining that second information relating to the user identity is subject to a lower privacy level based on the defined privacy criterion; in response to determining that the first information is subject to the higher privacy level, presenting the first information to the user, wherein the first interface is determined to be able to provide privacy of the first information at the higher privacy level for the presenting of the first information to the user, and wherein the first information presented is only able to be perceived by the user; and in response to determining that the second information is subject to the lower privacy level, presenting the second information to the user, wherein the second interface is determined to be able to provide privacy of the second information at the lower privacy level for the presenting of the second information to the user, and wherein the second information presented is able to be perceived by the user and another user other than the user.
Dependent claim 16 recites, in part, determining that a user associated with the user identity has a medical condition based on the user specific model; receiving medical treatment information relating to a medical treatment for the medical condition, wherein the second [device] is not associated with a medical care provider that has provided medical care to the user; determining that the medical treatment has not been considered, by the medical care provider or the user, to treat the user for the medical condition based on analysis of the model, the user-specific model, or the first medical information, the second medical information, or the third medical information that pertains to the user; in response to determining that the medical treatment has not been considered by the medical care provider or the user, presenting the medical treatment information relating to the medical treatment for the medical condition to the user or to the medical care provider.
Dependent claim 21 recites, in part, receiving updated medical data associated with the user identity from a user associated with the user identity, second electronic medical records associated with the user identity, or a sensor or a medical device associated with the user; and modifying the user-specific model based on analysis, of the model, the updated medical data associated with the user identity, and historical medical data associated with the user identity, wherein the historical medical data comprises a portion of the first medical information, the second medical information, or the third medical information, and wherein the determining of the proposed action relating to the medical care associated with the user identity comprises determining the proposed action relating to the medical care associated with the user identity based on the user-specific model.
Dependent claim 22 recites, in part, perceiving a medical condition associated with a user associated with the user identity while the user is at a first location; based on the perceiving, generating medical condition information relating to the medical condition; and communicating the medical condition information to a medical care provider identity located at a second location.
Dependent claim 23 recites, in part, communicating a request for information relating to a medical condition associated with the user identity based on determining that the second [device] has access to medical field information relating to a medical field associated with the medical condition; and receiving the information relating to the medical condition, wherein the determining of the proposed action comprises determining the proposed action relating to the medical care based on the model and analysis of the information relating to the medical condition.
Dependent claim 20 recites, in part, wherein the electronic medical records are first electronic medical records, and wherein the operations further comprise: receiving, by the virtual assistant function, updated medical data associated with the user identity from a user associated with the user identity, second electronic medical records associated with the user identity, or a sensor or a medical device associated with the user; and modifying the user-specific model based on analysis, of the model, the updated medical data associated with the user identity, and historical medical data associated with the user identity, wherein the historical medical data comprises a portion of the first medical data, the second medical data, or the third medical data, and wherein the determining of the proposed action relating to the medical care associated with the user identity comprises determining the proposed action relating to the medical care associated with the user identity based on the user-specific model.
Dependent claim 24 recites, in part, wherein the virtual assistant function is a first virtual assistant function, wherein the portion is a first portion, wherein the receiving of the updated medical data comprises: prior to, and within a defined amount of time of, a medical care visit with a medical care provider by the user, receiving, by the virtual assistant function, a second portion of the updated medical data from the user, the sensor, or the medical device, and wherein the operations further comprise prior to the medical care visit with the medical care provider by the user, presenting, via the first virtual assistant function, the second portion of the updated medical data to the medical care provider in connection with the medical care visit; or prior to the medical care visit with the medical care provider by the user; communicating, by the first virtual assistant function, the second portion of the updated medical data to a second virtual assistant function associated with the medical care provider, and presenting, via the second virtual assistant function, the second portion of the updated medical data to the medical care provider in connection with the medical care visit.
Dependent claim 25 recites, in part, wherein the virtual assistant function is a first virtual assistant function, and wherein the receiving of the updated medical data comprises: receiving a second portion of the electronic medical records from a second virtual assistant function associated with a first medical care provider; and receiving a third portion of the electronic medical records from a third virtual assistant function associated with a second medical care provider.
Dependent claim 26 recites, in part, wherein the virtual assistant function is a first virtual assistant function, and wherein the operations further comprise: perceiving a medical condition associated with a user associated with the user identity while the user is at a first location; based on the perceiving, generating medical condition information relating to the medical condition; and communicating the medical condition information to a second virtual assistant function associated with a medical care provider identity, wherein the second virtual assistant function is located at a second location.
Dependent claim 27 recites, in part, receiving, from a medical care provider, a request for information relating to a medical or health history associated with the user identity; identifying the information relating to the medical or health history associated with the user identity in the model a portion of the electronic medical records that is determined to be associated with the user identity, or sensor data associated with the user identity; and presenting, via the virtual assistant function to the medical care provider, the information relating to the medical or health history associated with the user identity.
Each of these steps of the preceding dependent claims only serve to further limit or specify the features of independent claims 1, 13, or 19 accordingly, and hence are nonetheless directed towards fundamentally the same abstract idea as the independent claim and utilize the additional elements analyzed below in the expected manner.
Step 2A – Judicial Exception Analysis, Prong 2:
This judicial exception is not integrated into a practical application because the additional elements within the claims only amount to instructions to implement the judicial exception using a computer [MPEP 2106.05(f)].
Claim 1 recites a system with a processor and a virtual assistant device. Claims 1 and 13 recite a user device. Claims 1, 13, and 19 recite a device interface of the user device comprising at least one of a display screen, audio speaker, or haptic device. Claim 13 recites a processor, a memory, and a virtual assistance device. Claim 19 recites non-transitory machine-readable medium. Claim 5, 6, 7, 9, 16, and 22-26 recite a second virtual assistant device (or function which notably includes an embodiment of an algorithm alone). Claims 15, 24, and 27 recite an interface of the virtual assistant device. Claims 6, 16, and 25 recite a third virtual assistant device (or function which notably includes an embodiment of an algorithm alone). The specification provides that these devices are “known by those of skill in the art” (see the Detailed Description in ¶ 00125) and can be practiced on any other computer system configurations with no specific hardware requirements (see the Detailed Description in ¶ 00136-139 and ¶ 00159). The use of the virtual assistant devices, interfaces, and corresponding hardware, in this case to communicate and process patient data, only recites virtual assistant devices, interfaces, and corresponding hardware as a tool to perform an existing process and only amounts to an instruction to implement the abstract idea using a computer (MPEP § 2106.05(f)(2) see case requiring the use of software to tailor information and provide it to the user on a generic computer within the “Other examples.. v.”).
Claims 1, 4, 13, and 19-21 recite an artificial intelligence functions. Claims 1, 13, and 19 recite applying the artificial intelligence function, including training of neural networks, Markov chains, or other machine learning algorithms, to historical medical data and updated medical data associated with the user identity. The specification discloses that that artificial intelligence functions may be any embodiment of AI/machine learning listing a plurality of different algorithm embodiments for performing a plurality of diverse tasks (see the Detailed Description in ¶ 0073-75. The use of an artificial intelligence function, in this case to perform any medical data related task, only recites the artificial intelligence function as a tool to apply data to an algorithm and report the results (MPEP § 2106.05(f)(2) see case involving a commonplace business method or mathematical algorithm being applied on a general purpose computer within the “Other examples.. i.”) amounting to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Claims 1, 13, and 19 recite wherein the action information includes a direction to the user device to use a haptic device to communicate the action information to the user, wherein the haptic device presents the action information in a haptic form to the user. The limitations are only recited as a tool which only serves as display/output of the data determined from the abstract idea (MPEP § 2106.05(g) - insignificant pre/post-solution activity that amounts to post-solution output on a well-known display device) and is therefore not a practical application of the recited judicial exception.
The above claims, as a whole, are therefore directed to an abstract idea.
Step 2B – Additional Elements that Amount to Significantly More:
The present claims do not include additional elements that are sufficient to amount to more than the abstract idea because the additional elements or combination of elements amount to no more than a recitation of instructions to implement the abstract idea on a computer.
Claim 1 recites a system with a processor and a virtual assistant device. Claims 1 and 13 recite a user device. Claims 1, 13, and 19 recite a device interface of the user device comprising at least one of a display screen, audio speaker, or haptic device. Claim 13 recites a processor, a memory, and a virtual assistance device. Claim 19 recites non-transitory machine-readable medium. Claim 5, 6, 7, 9, 16, and 22-26 recite a second virtual assistant device (or function which notably includes an embodiment of an algorithm alone). Claims 15, 24, and 27 recite an interface of the virtual assistant device. Claims 6, 16, and 25 recite a third virtual assistant device (or function which notably includes an embodiment of an algorithm alone). Claims 1, 4, 13, and 19-21 recite an artificial intelligence functions. Claims 1, 13, and 19 recite applying the artificial intelligence function, including training of neural networks, Markov chains, or other machine learning algorithms, to historical medical data and updated medical data associated with the user identity.
Each of these elements is only recited as a tool for performing steps of the abstract idea, such as the use of the storage mediums to store data, the computer and data processing devices to apply the algorithm, and the display device to display selected results of the algorithm. These additional elements therefore only amount to mere instructions to perform the abstract idea using a computer and are not sufficient to amount to significantly more than the abstract idea (MPEP 2016.05(f) see for additional guidance on the “mere instructions to apply an exception”).
Each additional element under Step 2A, Prong 2 is analyzed in light of the specification’s explanation of the additional element’s structure. The claimed invention’s additional elements do not have sufficient structure in the specification to be considered a not well-understood, routine, and conventional use of generic computer components. Note that the specification can support the conventionality of generic computer components if “the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a)” (Berkheimer in III. Impact on Examination Procedure, A. Formulating Rejections, 1. on p. 3).
Claims 1, 13, and 19 recite wherein the action information includes a direction to the user device to use a haptic device to communicate the action information to the user, wherein the haptic device presents the action information in a haptic form to the user. The use of haptic feedback to communicate information to a user is well understood, routine, and conventional activity. This position is supported by Alur et al., Haptic Technology :- A Comprehensive Review of its Applications and Future Prospects, 5(5) Int J of Computer Science and Information Technologies 6039-6043 (2014) teaching on the state of the art for haptic feedback design and function particularly in under interfaces in § 2. Design History on p. 6039-6040 and § 6.1 Use Of Haptic Feedbacks In Graphical User Interfaces on p. 6041 (treated as a review under MPEP § 2106.07(a)(III)(C) that describes the state of the art and discusses what is well-known and in common use in the relevant industry). Therefore, the use of a haptic device to communicate the action information to the user additional element is not sufficient to amount to significantly more than the recited judicial exception.
Thus, taken alone, the additional elements do not amount to significantly more than the above-identified judicial exception. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. Their collective functions merely provide conventional computer implementation.
Claims 1, 4-7, 9-11, 13, 15-16, and 19-27 are therefore rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 4-7, 9-11, 13, 15-16, and 19-27 are rejected under 35 U.S.C. 103 as being unpatentable over Rusak (US Patent Pub No 2021/0225495)[hereinafter Rusak] in view of Chen et al., FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare, 35(4) IEEE Intelligent Systems 83-93 (April 22, 2020)[hereinafter Chen] in further view of Herbst et al. (US Patent Pub No 20140184408([hereinafter Herbst].
As per claim 1, Rusak discloses the following limitations:
a method, comprising: obtaining, by a system comprising a processor is taught in the Detailed Description in ¶ 0058 (teaching on a computer with a processor and memory for collecting and analyzing patient data for diagnostic and treatment recommendations via machine learning);
obtaining… first medical data contained in electronic medical records associated with a user identity is taught in the Detailed Description in ¶ 0083, ¶ 00149-150, ¶ 0177, ¶ 0181, ¶ 0203, and in the Figures at fig. 1 (teaching on receiving electronic medical record data for the current target patient);
receiving, by the system, second medical data associated with the user identity from a user device, the second medical data comprising data from sensors, medical devices, or user input devices of the user device is taught in the Detailed Description in ¶ 0161, ¶ 0181, and ¶ 0203 (teaching on receiving sensor device data for the current target patient from a plurality of patient and provider devices);
determining, by the system, a model relating to medical care and medical conditions associated with a plurality of user identities based on a result of an analysis utilizing an artificial intelligence function is taught in the Detailed Description in ¶ 0130, ¶ 0139, and in the Figures at fig. 1 (teaching on a machine learning model (treated as synonymous to a model utilizing an artificial intelligence function) for analyzing general data and current target patient specific data (treated as synonymous to "associated with a plurality of user identities"));
of the first medical data is taught in the Detailed Description in ¶ 0083, ¶ 00149-150, ¶ 0177, ¶ 0181, ¶ 0203, and in the Figures at fig. 1 (teaching on the machine learning model receiving electronic medical record data for the current target patient);
second medical data, and is taught in the Detailed Description in ¶ 0161, ¶ 0181, and ¶ 0203 (teaching on the machine learning model receiving sensor device data for the current target patient from a plurality of patient and provider devices);
third medical data relating to the medical care and the medical conditions is taught in the Detailed Description in ¶ 0162-163, ¶ 0181, and ¶ 0203 (teaching on the machine learning model receiving additional patient parameter data such as prior treatments and diagnosis, for the current target patient);
utilizing, by the system, the user-specific model to process only data relevant to the user identity for determining a proposed action relating to the medical care associated with the user identity, thereby reducing computational requirements and improving data processing efficiency is taught in the Detailed Description in ¶ 0194, ¶ 0181, and ¶ 0203 (teaching on the machine learning model determining a user action related to the medical care of the current target patient - Examiner notes that the intended use of reducing computational requirements and processing efficiency does not add meaningful limitations to the claim see MPEP § 2144.07 for more detail regarding art recognized suitability for an intended purpose );
determining, by the system, a device interface of the user device for communicating action information relating to the proposed action to a user of the user device, the device interface comprising at least one of a display screen, audio speaker, or haptic device; and is taught in the Detailed Description in ¶ 0203-205, ¶ 0217, and in the Summary in ¶ 0021 (teaching on determining a user interface for displaying the determined user action to a user device wherein the device comprises a display and speaker);
communicating, via a virtual assistant device of the system, the action information relating to the proposed action to the user device is taught in the Detailed Description in ¶ 0181 and ¶ 0203-205 (teaching on outputting the determined user action to a user interface of the one of the devices).
Rusak fails to teach the following; Chen, however, does disclose:
generating, by the system, a user-specific model associated with the user identity from the model by applying the artificial intelligence function, including training of neural networks, Markov chains, or other machine learning algorithms, to historical medical data and updated medical data associated with the user identity, wherein the user-specific model is generated by weighting or focusing on medical data specific to the user identity is taught in the § Overview of the Framework on p. 85-86, § Federated Learning on p. 86, and in Figure 2 on p. 86 (teaching on further refining a generalized model by training a personalized convolutional neural network model from a trained cloud/generalized model to optimize the cloud model parameters to the patient specific data).
One of ordinary skill in the art before the effective filing date would combine the generalized medical action machine learning model of Rusk with a personalized user model via transfer learning of Chen with the motivation of “aggregate[ing] the data from different organizations without compromising privacy security and achiev[ing] relatively personalized model learning through knowledge transfer” (Chen in the § Conclusions and Future Work on p. 91).
The combination of Rusak and Chen fails to teach the following; Herbst, however, does disclose:
wherein the action information includes a direction to the user device to use a haptic device to communicate the action information to the user, wherein the haptic device presents the action information in a haptic form to the user is taught in the Detailed Description in ¶ 0050-51 (teaching on sending an alert message to a user regarding a medical action via a haptic feedback).
One of ordinary skill in the art before the effective filing date would modify the sound and visual notification of Rusak and Chen with the haptic feedback of Herbst with the motivation of “interrupt[ing] or supersed[ing] any currently running applications on the mobile device” (Herbst in the Detailed Description in ¶ 0059).
Independent claims 13 and 19 are rejected under the same rational.
As per claim 4, the combination of Rusak and Herbst discloses the limitations of claim 1. Rusak also discloses the following:
the method of claim 1, wherein the electronic medical records are first electronic medical records, and wherein the method further comprises: receiving, by the virtual assistant device, updated medical data associated with the user identity from a user associated with the user identity, second electronic medical records associated with the user identity, or a sensor or a medical device associated with the user; and is taught in the Detailed Description in ¶ 0195-197 (teaching on receiving current iteration data of the patient parameters for the current target patient from a plurality of patient and provider devices including patient sensor data);
modifying, by the system, the user-specific model based on analysis, utilizing the artificial intelligence function, of the model, the updated medical data associated with the user identity, and historical medical data associated with the user identity wherein the historical medical data comprises a portion of the first medical data, the second medical data, or the third medical data, and is taught in the Detailed Description in ¶ 0137 and ¶ 0195-197 (teaching on updating the machine learning model with the new patient parameter data and the collected first (EHR data), second (historical patient sensor data), and third (additional patient parameter data) data sets);
wherein the determining of the proposed action relating to the medical care associated with the user identity comprises determining the proposed action relating to the medical care associated with the user identity based on the user-specific model is taught in the Detailed Description in ¶ 0137, ¶ 0197, and ¶ 0209 (teaching on the determined user action generated from the machine learning model being specific to the patient's real-time needs and training data).
As per claim 5, the combination of Rusak and Herbst discloses the limitations of claim 4. Rusak also discloses the following:
the method of claim 4, wherein the virtual assistant device is a first virtual assistant device, wherein the portion is a first portion; wherein the receiving of the updated medical data comprises: prior to, and within a defined amount of time of, a medical care visit with a medical care provider by the user, receiving, by the virtual assistant device, a second portion of the updated medical data from the user, the sensor, or the medical device is taught in the Detailed Description in ¶ 0203 and ¶ 0214 (teaching on collecting the patient parameter data from a patient sensor for a predetermined time interval rate prior to the physician utilizing the machine learning model in a follow up appointment to generate a determined user action);
and wherein the method further comprises: prior to the medical care visit with the medical care provider by the user, presenting, via a first interface associated with the first virtual assistant device, the second portion of the updated medical data to the medical care provider in connection with the medical care visit; or prior to the medical care visit with the medical care provider by the user: communicating, by the first virtual assistant device, the second portion of the updated medical data to a second virtual assistant device associated with the medical care provider, and presenting, via a second interface associated with the second virtual assistant device, the second portion of the updated medical data to the medical care provider in connection with the medical care visit is taught in the Detailed Description in ¶ 0065, ¶ 0203, and ¶ 0214-215 (teaching on sending an alert to the healthcare provider's user interface if, during the monitoring period of collecting patient parameter data, the data meets an alert threshold).
As per claim 6, the combination of Rusak and Herbst discloses the limitations of claim 4. Rusak also discloses the following:
the method of claim 4, wherein the virtual assistant device is a first virtual assistant device, wherein the portion is a first portion, wherein the receiving of the updated medical data comprises: receiving, by the first virtual assistant device, a second portion of the electronic medical records from a first device or a second virtual assistant device associated with a first medical care provider; and receiving, by the first virtual assistant device, a third portion of the electronic medical records from a second device or a third virtual assistant device associated with a second medical care provider is taught in the Detailed Description in ¶ 0212-215 (teaching on receiving additional second (historical patient sensor data) data from a secondary/plurality of other providers to be analysed by the machine learning model for generating a determined user action for the current target patient).
As per claim 7, the combination of Rusak and Herbst discloses the limitations of claim 1. Rusak also discloses the following:
the method of claim 1, wherein the virtual assistant device is a first virtual assistant device, and wherein the method further comprises: perceiving, by the first virtual assistant device, a medical condition associated with a user associated with the user identity while the user is at a first location; based on the perceiving, generating, by the first virtual assistant device, medical condition information relating to the medical condition; and is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on collecting a similar patient parameter data and corresponding first provider's interaction with said data related to the patient's condition (here the example is of a G6PD deficiency and diabetic diagnosis));
communicating, by the first virtual assistant device, the medical condition information to a second virtual assistant device associated with a medical care provider identity, wherein the second virtual assistant device is located at a second location is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on alerting on a second provider's user interface regarding a determined user action for the current target patient related to the similar patient's condition wherein the second provider is at a separate medical institute (treated as synonymous to located at a second location)).
As per claim 9, the combination of Rusak and Herbst discloses the limitations of claim 1. Rusak also discloses the following:
the method of claim 1, wherein the virtual assistant device is a first virtual assistant device, and wherein the method further comprises: communicating, by the first virtual assistant device, a request for information relating to a medical condition associated with the user identity to a second virtual assistant device based on determining that the second virtual assistant device has access to medical field information relating to a medical field associated with the medical condition; and is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on the system requesting the collection of similar patient parameter data and corresponding second provider's interaction with said data related to the patient's condition (here the example is of a G6PD deficiency and diabetic diagnosis));
receiving, by the first virtual assistant device, the information relating to the medical condition from the second virtual assistant device, wherein the determining of the proposed action comprises determining the proposed action relating to the medical care based on the model and analysis of the information relating to the medical condition is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on alerting on a first provider's user interface regarding a determined user action for the current target patient related to the similar patient's condition wherein the determined user action is determined via the machine learning model).
As per claim 10, the combination of Rusak and Herbst discloses the limitations of claim 9. Rusak also discloses the following:
the method of claim 9, wherein the first virtual assistant device accesses first medical treatment information relating to a first type of medical treatment to utilize to treat the medical condition, wherein the information relating to the medical condition comprises second medical treatment information relating to a second type of medical treatment to utilize to treat the medical condition, wherein the determining of the proposed action comprises determining which of the first type of medical treatment, the second type of medical treatment, or a third type of medical treatment is to be utilized or recommended to treat the medical condition based on the model and analysis of the first medical treatment information and the second medical treatment information, and wherein the third type of medical treatment is determined based on the first type of medical treatment and the second type of medical treatment is taught in the Detailed Description in ¶ 0211-214 and ¶ 0195-197 (teaching on utilizing the machine learning model to determine if an alternative (second or third) treatment would be better suited to treat the current target patient's condition and outputting the treatment as the determined user action to a user interface of the one of the devices).
As per claim 11, the combination of Rusak and Herbst discloses the limitations of claim 1. Rusak also discloses the following:
the method of claim 1, further comprising: receiving, by the virtual assistant device from a medical care provider, a request for information relating to a medical or health history associated with the user identity is taught in the Detailed Description in ¶ 0201 and in the Figures in fig. 5 reference character 212 (teaching on the provider requesting a "battle plan" for a set of proposed determined user actions wherein requesting a plan necessarily requires the use of collecting patient historical and current data for processing by the model);
identifying, by the virtual assistant device, the information relating to the medical or health history associated with the user identity in the model, a portion of the electronic medical records that is determined to be associated with the user identity, or sensor data associated with the user identity is taught in the Detailed Description in ¶ 0194, ¶ 0181, ¶ 0203, and in the Figures in fig. 5 reference characters 213 and 209 (teaching on processing via the machine learning model the collected historical and current patient data wherein the patient data includes electronic health record data);
presenting, via an interface of the virtual assistant device to the medical care provider, the information relating to the medical or health history associated with the user identity is taught in the Detailed Description in ¶ 0200-201 and Figures in fig. 5 reference character 218 (teaching on displaying on the provider's user interface the "battle plan" with corresponding proposed determined user actions and real time current patient information concurrently).
As per claim 16, the combination of Rusak and Herbst discloses the limitations of claim 13. Rusak also discloses the following:
the system of claim 13, wherein the virtual assistant device is a first virtual assistant device, and wherein the operations further comprise: determining that a user associated with the user identity has a medical condition based on the user-specific model is taught in the Detailed Description in ¶ 0135, ¶ 0137, and ¶ 0195-197 (teaching on determining via the machine learning model with the new patient parameter data and the collected first (EHR data), second (historical patient sensor data), and third (additional patient parameter data) data sets, a target patient outcome which may be the target has or does not have a particular condition);
receiving, by the first virtual assistant device from a second virtual assistant device, medical treatment information relating to a medical treatment for the medical condition, wherein the second virtual assistant device is not associated with a medical care provider that has provided medical care to the user is taught in the Detailed Description in ¶ 0178, ¶ 0211-212, and in the Figures at fig. 12B (teaching on receiving at the first provider's user interface a second provider's treatment for the same condition for a different patient (treated as synonymous to a provider not associated with the user));
determining that the medical treatment has not been considered, by the medical care provider or the user, to treat the user for the medical condition based on analysis of the model, the user-specific model, or the first medical information, the second medical information, or the third medical information that pertains to the user; in response to determining that the medical treatment has not been considered by the medical care provider or the user, presenting, by the first virtual assistant device, the medical treatment information relating to the medical treatment for the medical condition to the user or to the medical care provider via a third virtual assistant device associated with the medical care provider is taught in the Detailed Description in ¶ 0210-212 and in the Figures at fig. 12B (teaching on presenting of the first provider via the user interface the additional treatment option that has not been utilized by the first provider to treat the current patient - Examiner notes that the use of a third virtual assistant device is a duplication of parts as displaying the additional treatment information on device, whether the first or a third device, has no new or unexpected results see MPEP 2144.04(VI)(B)).
As per claim 21, the combination of Rusak and Herbst discloses the limitations of claim 13. Rusak also discloses the following:
the system of claim 13, wherein the operations further comprise: receiving, by the virtual assistant device, updated medical data associated with the user identity from a user associated with the user identity, second electronic medical records associated with the user identity, or a sensor or a medical device associated with the user; and is taught in the Detailed Description in ¶ 0195-197 (teaching on receiving current iteration data of the patient parameters for the current target patient from a plurality of patient and provider devices including patient sensor data);
modifying the user-specific model based on analysis, utilizing the artificial intelligence function, of the model, the updated medical data associated with the user identity, and historical medical data associated with the user identity, wherein the historical medical data comprises a portion of the first medical information, the second medical information, or the third medical information, and is taught in the Detailed Description in ¶ 0137 and ¶ 0195-197 (teaching on updating the machine learning model with the new patient parameter data and the collected first (EHR data), second (historical patient sensor data), and third (additional patient parameter data) data sets);
wherein the determining of the proposed action relating to the medical care associated with the user identity comprises determining the proposed action relating to the medical care associated with the user identity based on the user-specific model is taught in the Detailed Description in ¶ 0137, ¶ 0197, and ¶ 0209 (teaching on the determined user action generated from the machine learning model being specific to the patient's real-time needs and training data).
As per claim 22, the combination of Rusak and Herbst discloses the limitations of claim 13. Rusak also discloses the following:
the system of claim 13, wherein the virtual assistant device is a first virtual assistant device, and wherein the operations further comprise: perceiving, by the first virtual assistant device, a medical condition associated with a user associated with the user identity while the user is at a first location; based on the perceiving, generating, by the first virtual assistant device, medical condition information relating to the medical condition; and is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on collecting a similar patient parameter data and corresponding first provider's interaction with said data related to the patient's condition (here the example is of a G6PD deficiency and diabetic diagnosis));
communicating, by the first virtual assistant device, the medical condition information to a second virtual assistant device associated with a medical care provider identity, wherein the second virtual assistant device is located at a second location is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on alerting on a second provider's user interface regarding a determined user action for the current target patient related to the similar patient's condition wherein the second provider is at a separate medical institute (treated as synonymous to located at a second location)).
As per claim 23, the combination of Rusak and Herbst discloses the limitations of claim 13. Rusak also discloses the following:
the system of claim 13, wherein the virtual assistant device is a first virtual assistant device, and wherein the operations further comprise: communicating, by the first virtual assistant device, a request for information relating to a medical condition associated with the user identity to a second virtual assistant device based on determining that the second virtual assistant device has access to medical field information relating to a medical field associated with the medical condition; and is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on the system requesting the collection of similar patient parameter data and corresponding second provider's interaction with said data related to the patient's condition (here the example is of a G6PD deficiency and diabetic diagnosis));
receiving, by the first virtual assistant device, the information relating to the medical condition from the second virtual assistant device, wherein the determining of the proposed action comprises determining the proposed action relating to the medical care based on the model and analysis of the information relating to the medical condition is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on alerting on a first provider's user interface regarding a determined user action for the current target patient related to the similar patient's condition wherein the determined user action is determined via the machine learning model).
As per claim 20, the combination of Rusak and Herbst discloses the limitations of claim 19. Rusak also discloses the following:
the non-transitory machine-readable medium of claim 19, wherein the electronic medical records are first electronic medical records, and wherein the operations further comprise: receiving, by the virtual assistant function, updated medical data associated with the user identity from a user associated with the user identity, second electronic medical records associated with the user identity, or a sensor or a medical device associated with the user; and is taught in the Detailed Description in ¶ 0195-197 (teaching on receiving current iteration data of the patient parameters for the current target patient from a plurality of patient and provider devices including patient sensor data);
modifying the user-specific model based on analysis, utilizing the artificial intelligence function, of the model, the updated medical data associated with the user identity, and historical medical data associated with the user identity, wherein the historical medical data comprises a portion of the first medical data, the second medical data, or the third medical data, and is taught in the Detailed Description in ¶ 0137 and ¶ 0195-197 (teaching on updating the machine learning model with the new patient parameter data and the collected first (EHR data), second (historical patient sensor data), and third (additional patient parameter data) data sets);
wherein the determining of the proposed action relating to the medical care associated with the user identity comprises determining the proposed action relating to the medical care associated with the user identity based on the user-specific model is taught in the Detailed Description in ¶ 0137, ¶ 0197, and ¶ 0209 (teaching on the determined user action generated from the machine learning model being specific to the patient's real-time needs and training data).
As per claim 24, the combination of Rusak and Herbst discloses the limitations of claim 20. Rusak also discloses the following:
the non-transitory machine-readable medium of claim 20, wherein the virtual assistant function is a first virtual assistant function, wherein the portion is a first portion, wherein the receiving of the updated medical data comprises: prior to, and within a defined amount of time of, a medical care visit with a medical care provider by the user, receiving, by the virtual assistant function, a second portion of the updated medical data from the user, the sensor, or the medical device, and is taught in the Detailed Description in ¶ 0203 and ¶ 0214 (teaching on collecting the patient parameter data from a patient sensor for a predetermined time interval rate prior to the physician utilizing the machine learning model in a follow up appointment to generate a determined user action);
wherein the operations further comprise prior to the medical care visit with the medical care provider by the user, presenting, via a first interface associated with the first virtual assistant function, the second portion of the updated medical data to the medical care provider in connection with the medical care visit; or prior to the medical care visit with the medical care provider by the user; communicating, by the first virtual assistant function, the second portion of the updated medical data to a second virtual assistant function associated with the medical care provider, and presenting, via a second interface associated with the second virtual assistant function, the second portion of the updated medical data to the medical care provider in connection with the medical care visit is taught in the Detailed Description in ¶ 0065, ¶ 0203, and ¶ 0214-215 (teaching on sending an alert to the healthcare provider's user interface if, during the monitoring period of collecting patient parameter data, the data meets an alert threshold).
As per claim 25, the combination of Rusak and Herbst discloses the limitations of claim 20. Rusak also discloses the following:
the non-transitory machine-readable medium of claim 20, wherein the virtual assistant function is a first virtual assistant function, and wherein the receiving of the updated medical data comprises: receiving a second portion of the electronic medical records from a first device or a second virtual assistant function associated with a first medical care provider; and receiving a third portion of the electronic medical records from a second device or a third virtual assistant function associated with a second medical care provider is taught in the Detailed Description in ¶ 0212-215 (teaching on receiving additional second (historical patient sensor data) data from a secondary/plurality of other providers to be analysed by the machine learning model for generating a determined user action for the current target patient).
As per claim 26, the combination of Rusak and Herbst discloses the limitations of claim 19. Rusak also discloses the following:
the non-transitory machine-readable medium of claim 19, wherein the virtual assistant function is a first virtual assistant function, and wherein the operations further comprise: perceiving a medical condition associated with a user associated with the user identity while the user is at a first location; based on the perceiving, generating medical condition information relating to the medical condition; and is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on collecting a similar patient parameter data and corresponding first provider's interaction with said data related to the patient's condition (here the example is of a G6PD deficiency and diabetic diagnosis));
communicating the medical condition information to a second virtual assistant function associated with a medical care provider identity, wherein the second virtual assistant function is located at a second location is taught in the Detailed Description in ¶ 0116, ¶ 0130, and ¶ 0214-215 (teaching on alerting on a second provider's user interface regarding a determined user action for the current target patient related to the similar patient's condition wherein the second provider is at a separate medical institute (treated as synonymous to located at a second location)).
As per claim 27, the combination of Rusak and Herbst discloses the limitations of claim 19. Rusak also discloses the following:
the non-transitory machine-readable medium of claim 19, wherein the operations further comprise: receiving, from a medical care provider, a request for information relating to a medical or health history associated with the user identity is taught in the Detailed Description in ¶ 0201 and in the Figures in fig. 5 reference character 212 (teaching on the provider requesting a "battle plan" for a set of proposed determined user actions wherein requesting a plan necessarily requires the use of collecting patient historical and current data for processing by the model);
identifying the information relating to the medical or health history associated with the user identity in the model a portion of the electronic medical records that is determined to be associated with the user identity, or sensor data associated with the user identity; and is taught in the Detailed Description in ¶ 0194, ¶ 0181, ¶ 0203, and in the Figures in fig. 5 reference characters 213 and 209 (teaching on processing via the machine learning model the collected historical and current patient data wherein the patient data includes electronic health record data);
presenting, via an interface of the virtual assistant function to the medical care provider, the information relating to the medical or health history associated with the user identity is taught in the Detailed Description in ¶ 0200-201 and Figures in fig. 5 reference character 218 (teaching on displaying on the provider's user interface the "battle plan" with corresponding proposed determined user actions and real time current patient information concurrently).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Rusak (US Patent Pub No 2021/0225495)[hereinafter Rusak] in view of Chen et al., FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare, 35(4) IEEE INTELLIGENT SYSTEMS 83-93 (April 22, 2020)[hereinafter Chen] in further view of Herbst et al. (US Patent Pub No 2014/0184408)[hereinafter Herbst] in further view of Shan et al. (US Patent No. 11636955)[hereinafter Shan].
As per claim 15, The combination of Rusak, Chen, and Herbst discloses the limitations of claim 13. Rusak fails to teach the following; Shan, however, does disclose:
the system of claim 13, wherein the operations further comprise: determining, by the virtual assistant device, that first information relating to a medical condition associated with the user identity is subject to a higher privacy level based on a defined privacy criterion is taught in the Detailed Description in col 13 lines 4-13, col 13 lines 27-62, and in the Figures at fig. 7 reference character 704 (teaching on assigning patient data items to an access right level including secure data items (treated as synonymous to a higher privacy level));
determining, by the virtual assistant device, that second information relating to the user identity is subject to a lower privacy level based on the defined privacy criterion is taught in the Detailed Description in col 13 lines 4-13, col 13 lines 27-62, and in the Figures at fig. 7 reference character 704 (teaching on assigning patient data items to an access right level including unsecure data items (treated as synonymous to a lower privacy level));
in response to determining that the first information is subject to the higher privacy level, presenting, via a first interface of the virtual assistant device, the first information to the user, wherein the first interface is determined to be able to provide privacy of the first information at the higher privacy level for the presenting of the first information to the user, and wherein the first information presented via the first interface is only able to be perceived by the user; and is taught in the Detailed Description in col 14 lines 61-67, col 15 lines 44-51, and in the Figures at fig. 7 reference character 710 and 718 (teaching on displaying to a first provider and user the patient data determined to be a secure data item);
in response to determining that the second information is subject to the lower privacy level, presenting, via a second interface of the virtual assistant device, the second information to the user, wherein the second interface is determined to be able to provide privacy of the second information at the lower privacy level for the presenting of the second information to the user, and wherein the second information presented via the second interface is able to be perceived by the user and another user other than the user is taught in the Detailed Description in col 15 lines 26-31, col 15 lines 44-51, and in the Figures at fig. 7 reference character 714 and 718 (teaching on displaying to a second provider on a user interface only the data determined to be an unsecure data item).
One of ordinary skill in the art before the effective filing date of the invention would combine the machine learning clinical decision support system of Rusak with the data privacy controls for data access on different user interfaces of Shan with the motivation of “keep[ing] other users informed, engaged and participating in the patient’s disease management” (Shan in the Detailed Description in col 4 lines 40-43).
Response to Arguments
Applicant's arguments filed 26 December 2025 with respect to 35 USC § 101 have been fully considered but they are not persuasive. Applicant first asserts that the generating and utilizing the user specific model are not abstract under Step 2A Prong 1. Examiner disagrees – while the use of a machine learning/AI function to train a model is treated as an additional element under Step 2A Prong 2 and Step 2B, the personal model is included in the abstract idea as an algorithm. Inputting data and determining a proposed medical action output to “any learning algorithm” is similar to iii. a mental process that a neurologist should follow when testing a patient for nervous system malfunctions, In re Meyer, 688 F.2d 789, 791-93, 215 USPQ 193, 194-96 (CCPA 1982) and therefore considered a method of organizing human activity under Step 2A Prong 1. Next, Applicant asserts that a generating a user specific model by weighting or focusing on medical data specific to the user identity amounts to an improvement to technology under Step 2A Prong 2. Considering information relevant to a patient over irrelevant data unrelated to the patient condition is an improvement to the abstract idea. An improvement to the abstract idea of tailoring a medical action to patient specific data does not amount to an improvement to technology or a technical field (see MPEP § 2106.05(a)(III) stating “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.”)
Furthermore, Examiner notes that transfer learning to generate a patient specific model is well known in the art and is not an unconventional solution to a technological problem. For example, when claims merely recite a description of a problem to be solved or a function or result achieved by the invention, the boundaries of the claim scope may be unclear (Halliburton Energy Servs., Inc. v. M-I LLC, 514 F.3d 1244, 1255, 85 USPQ2d 1654, 1663 (Fed. Cir. 2008) noting that the Supreme Court explained that a vice of functional claiming occurs "when the inventor is painstaking when he recites what has already been seen, and then uses conveniently functional language at the exact point of novelty". This position is further supported by Zhuang et al., A Comprehensive Survey on Transfer Learning, 109(1) Proceedings of the IEEE (Jan. 2021) teaching on the state of the art of transfer learning for patient specific models in different healthcare applications in the § A. Medical Application on p. 65 and § B. Bioinformatics Application on p. 65-66.
Finally, Applicant asserts that under step 2B, the recited subject matter amounts to significantly more than the abstract idea. The consideration under Step 2B is if the additional elements, alone or in combination, are well-understood, routine and conventional in the field – the novelty of the abstract idea is not considered relevant under the Step 2B analysis. Here, the additional elements, alone or in combination, amount to instruction to implement the abstract idea using a general purpose computer. Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 1357 (2014).
Applicant’s arguments filed 26 December 2025 with respect to 35 USC § 103 have been considered and are persuasive regarding the newly added limitations. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in view of Chen, as per the rejection above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gupta et al., Transfer Learning for Clinical Time Series Analysis Using Deep Neural Networks, 4 J of Healthcare Informatics Research 112-137 (Dec. 13, 2019) teaching on a domain specific transfer learning recurrent neural network model in the § 11 Conclusion on p. 131-132.
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/JORDAN L JACKSON/Primary Examiner, Art Unit 2857